[From the U.S. Government Printing Office, www.gpo.gov]







       NOAA Technical Report NMFS                   123                               April 1995


                     NOAA Coastal Change
                     Analysis Pr         'ogram (C-CAP):
                     Guidance for
                     Regional Implementation


                     J. E. Dobson
                     E. A. Bright
                     R. L. Ferguson
                     D. W. Field
                     L. L. Wood
                     K. D. Haddad
                     H. Iredale III
                     J. R. Jensen
                     V. V. Klemas
                     R J. Orth
                     J. P. Thomas
















   SH1 1             U.S. Department of Commerce
   .A44672
   no. 123
   c.2









                    U.S. Department
                    of Commerce                                      NOAA
                    Ronald H. Brown
                    Secretary

                    National Oceanic and
                    Atmospheric AdministrationTechni*cal
                    D. James Baker
                    Under Secretary for Oceans and
                    Atmosphere                                       Reports NMFS
                    National Marine
                    Fisheries Service                                Technical Reports of the Fisheg Bulletin

                    Rolland A. Schmitten
                    Assistant Administrator for                      Scientific Editor
                    Fisheries                                        Dr. Ronald W. Hardy
                                                                     Northwest Fisheries Science Center
                                                                     National Marine Fisheries Service, NOAA
                                                                     2725 Mondake Boulevard East
                                                                     Seattle, Washington 98112-2097

                                  oxgv Or FQS,                       Editorial Conunittee
                                                                     Dr. Andrew E. Dizon National Marine Fisheries Service
                                                                     Dr. Linda L. Jones National Marine Fisheries Service
                                              15
                                                                     Dr. Richard D. Methot National Marine Fisheries Service
                                     r s o                           Dr. Theodore W. Pietsch University of Washington
                                                                     Dr. Joseph E. Powers National Marine Fisheries Service
                                                                     Dr. Tim D. Smith National Marine Fisheries Service

                                                                     Managing Editor
                                                                     James W. Orr
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                                           NOAA Technical Report NMFS 123
                                           A Technical Report of the Fishe?y Bulletin

                                           NOAA Coastal Change
                                           Analysis Program (C-CAP):
                                           Guidance for
                                           Regional Implementation

                                           J. E. Dobson and E. A. Bright
                                           Oak Ridge National Laborato?y
                                           Oak Ridge, TN 3 7831

                                           R. L. Fe'rguson, D. W. Field, and L. L. Wood
                                           NOAAINAYS Beaufort Laboratoiy
                                           Beaufort, NC 28516

                                           K. D. Haddad
                                           Florida Dept. of Environmental Protection, Marine Research Institute
                                           St. Petersburg, FL 33701

                                           H. Iredale III
                                           NOAAINESDIS National Oceanog7-aphic Data Center
                                           Washington, DC 20235

                                           J. R. Jensen
                                           Dept. of Geography
                                           University of South Carolina
                                           Columbia, SC 29208


                                           V. V. Klemas
                                           College of Marine Studies, University of Delaware
                                           Newark, DE 19716

                                           R. J. Orth
                                           Virginia Institute of Marine Science
                                           College of William and Ma7y
                                           Gloucester Point, VA 23062

                                           J. P. Thomas
                                           NOAAINMFS Office of Protected Resources
                                           Silver Spring, MD 20910

                                           April 1995





                                           U.S. Department of Commerce
                                           Seattle, Washington








                                                        Contents

             Preface  ........................................................................                 vii

             Executive Summary  ...............................................................                ix

             Chapter 1. Introduction

             The Coastal Region Management Problem        ..........................................           I
             The NOAA Coastal Change Analysis Program (C-CAP) Solution          ........................       I
             C-CAP's National Scope and Regional Implementation        ................................        3
             Change Detection Every One to Five Years     ..........................................           4
             The Need for Standardization and Guidelines      .......................................          4
             General Steps Required to Conduct Regional C-CAP Projects       ............................      4

             Chapter 2. 7`he C-C4P Coastal Land-Cover Classification System

             Introduction  ...................................................................                 7
             Superclasses of the C-CAP System     .................................................            7
               Uplands    ....................................................................                 7
               Wetlands   ....................................................................                 9
               Water and Submerged Land       ....................................................             10

             Chapter 3. Monitoring Uplands and Wetlands Using Satellite Remote Sensor Data

             Remote Sensing System Considerations      .............................................           I I
               Temporal Resolution    ..........................................................               I I
               Spatial Resolution and Look Angle     ..............................................            11
               Spectral Resolution   ...........................................................               11
               Radiometric Resolution    ........................................................              12
               The Preferred C-CAP Satellite Sensor System    ......................................           12
             Important Environmental Characteristics    ...........................................            13
               Atmospheric Conditions     .......................................................              13
               Soil Moisture Conditions   .......................................................              13
               Vegetation Phenological Cycle Characteristics     .....................................         14
               Effects of Tidal Stage on Image Classification ......................................           14
             Image Processing Data to Inventory Upland and Wetland Change         .......................      14
               Rectification of Multiple-Date Remote Sensor Data     .................................         14
               Radiometric Normalization of Multiple-Date Images       ................................        15
             Selecting the Appropriate Change Detection Algorithm       ...............................        16
               Change Detection Using Write Function Memory Insertion         ..........................       16
               Multiple-Date Composite Image Change Detection        .................................         17
               image Algebra Change Detection       ...............................................            17
               Postclassification Comparison Change Detection      ...................................         19


                                                              iii








           Multiple-Date Change Detection Using a Binary Change Mask Applied to Tb_1 or Tbl             ..... 20
           Multiple-Date Change Detection Using Ancillary Data Source as Tb          ....................     25
           Manual On-screen Digitization of Change        .........................................           26
         Selecting Appropriate Classification Algorithms     ......................................           28
           Supervised and Unsupervised Image Classification Logic         ............................        29
           Selection of Training and Verification Samples for Supervised and Unsupervised
              Classification   ..............................................................                 31
           Use of Collateral Data in Image Classification    ......................................           32
         Cartographic Portrayal of Classification and Change Detection Maps          ....................     33
           The Concept of the Minimum Measurement Unit             ..................................         33
           Analog (Hardcopy) Cartographic Products         ........................................           33

         CWapter 4. Monitoring Submerged Land Using Aerial Photography

         C-Cap Focus on Aerial Photography of Submersed Rooted Vascular Plants (SRV)             ..........   35
           Ancillary Categories of Submersed Habitat       ........................................           35
           Ancillary Technologies for Collection of Submersed Habitat Data         .....................      35
         Aerial Photography of SRV      .......................................................               36
           Film   ........................................................................                    36
           Metric Photography and Photographic Scale         ......................................           36
           Flightlines, Reconnaissance Flights, and Photographic Overlap         .......................      37
           Environmental Considerations      ...................................................              37
         Photointerpretation of SRV      ........................................................             38
         Field Surveys  ...................................................................                   39
           Species and Habitat at Randomly Selected Stations        .................................         39
           Signature Verification and Supplemental Spatial Data       ...............................         40
         Base Maps and Registration of Habitat Polygons        .....................................          40
           Planimetric Base Maps     .........................................................                40
           Transfer of Polygons to the Map Coordinate Projection System          .......................      41
         Digitization of Habitat Polygons    ...................................................              41
         Change Detection with Aerial Photographic Data         ....................................          42
           Recent Photography       ..........................................................                42
           Historical Photography     ........................................................                42
           Change Detection of Seagrass Habitat in North Carolina         ............................        44

         Chapter 5. Spacial Data Quality Assurance and Control

         Lineage   .......................................................................                    45
         Positional Accuracy and Precision     .................................................              45
         Generalization Versus Error     ......................................................               46
         Attribute Accuracy and Precision    ..................................................               47
         Logical Consistency    .............................................................                 47
         Completeness    ..................................................................                   47
         Temporal Accuracy and Precision       .................................................              47


                                                             iv









               Fitness for Use  ...............................................................              47
               Recommended Accuracy Assessment Test       .........................................          48
               Sample Selection and Field Mapping     .............................................          48
               Accuracy Assessment for Individual Date Classification of Upland and
                 Wetland Habitat Data    .......................................................             48
               Accuracy Assessment for Individual Date Classification of Water and
                 Submerged Land Data     .......................................................             49
               Accuracy Assessment for Land-Cover Change Data       .................................        49
               Comparison and Statistical Analysis   ..............................................          49


            Chapter 6. P@-oduct Availability

            Digital Product  .................................................................               51
               Description and Availability  ....................................................            51
               Digital Product Redistribution Restrictions  ........................................         51
               Digital Product Liability Disclaimer  ..............................................          51
               Digital Product Format and Contents     ............................................          51
               Digital Product Medium    ........................................................            52
               Digital Product Cost   ..........................................................             52
               Digital Product Ancillary Documentation    .........................................          52
            Hardcopy Products     .............................................................              52
               Upland and Wetland Habitats      ..................................................           52
               Submersed Habitats    ...........................................................             52


            Chapter 7. Users and Information Needs


            Chapter 8. Regional Participation

            Purpose   .......................................................................                55
            Regional Project Summaries     ......................................................            55
               St. Croix River Estuary (Border of Maine and New Brunswick, Canada)       ................    55
               Coastal Massachusetts   .........................................................             55
               Universities of Connecticut and Rhode Island     .....................................        55
               University of Delaware  .........................................................             55
               Oak Ridge National Laboratory     .................................................           56
               Virginia Institute of Marine Sciences  .............................................          56
               North Carolina State University   .................................................           56
               Beaufort Laboratory, National Marine Fisheries Service    .............................       57
               University of South Carolina  ....................................................            57
               State of Florida, Department of Environmental Protection     ...........................      57
               Texas Parks and Wildlife Department     ............................................          58
               Columbia River, Tillamook Bay, and Willapa Bay (Oregon and Washington)         ............   58
               Hubbard Glacier and Russell Fjord, Alaska    ........................................         58



                                                             V









        Acknowledgments   .................................................................         59

        Literature Cited  .................................................................         61


        Appendices

        Appendix Table 1 - U.S. Geological Survey Land-Cover Classification Scheme for Remote
                             Sensor Data   .................................................        65
        Appendix Table 2 - U.S. Fish and Wildlife Service Wetland Classification System  .......... 67
        Appendix 3 - C-CAP Wetland Classification Scheme Definitions     .......................    69
        Appendix 4 - C-CAP Workshops       .................................................        79
        Appendix 5 - C-CAP Protocol Development Research       ................................     81
        Appendix 6 - Workshop Participants     ..............................................       83



































                                                       Vi







                                                                      Preface

               This unique document represents a first attempt to              edly will occur, we intend to update this document
               develop guidelines that will allow researchers and re-          periodically. These updates, however, require time to
               source managers alike to quantitatively monitor changes         publish, so anyone planning to use these guidelines
               that are occurring in the abundance of emergent and             should contact either the senior author or program
               submergent wetlands'and adjacent uplands in coastal             manager to obtain drafts of any revised chapters that
               regions. Such information is essential in order to effec-       have not yet been published.
               tively relate changes in coastal land use to changes in           Finally, I would like to express my appreciation to the
               the productivity of estuaries and coastal waters on a           authors for their fine effort and to Dr. Don Scavia,
               regional scale.                                                 Director of NOAA's Coastal Ocean Program, for his
                  This is a document that was developed from the               support, both financial and moral, during the develop-
               input of approximately 200 research scientists and re-          ment of this document. I believe we have made a signifi-
               source managers that attended five regional workshops           cant step in addressing an important coastal issue.
               and several topical interagency meetings. Thus, we be-
               lieve it represents a general consensus of how to ap-                       Ford A Cross
               proach the issue of quantifying land-cover and wetland                      Manager, C-CAP
               change in coastal regions. Because improvement in                           National Marine Fisheries Service/NOAA
               existing technologies and in our understanding of how                       Beaufort Laboratory
               to measure habitat change on a regional scale undoubt-                      Beaufort, NC 28516



































                                                                           Vii






                                                           Executive Summary

               The Coastal Change Analysis Program      I (C-CAP) is de-       Aggregations to larger areas (representing habitats, wild-
               veloping a nationally standardized database on land-            life refuges, or management districts) will be provided
               cover and habitat change in the coastal regions of the          on a case-by@case basis. Ongoing C-CAP research will
               United States. C-CAP is part of the Estuarine Habitat           continue to explore techniques for remote determina-
               Program (EHP) of NOAA's Coastal Ocean Program                   tion of biomass, productivity, and functional status of
               (COP). C-CAP inventories coastal submersed habitats,            wetlands and will evaluate new technologies (e.g. re-
               wetland habitats, and adjacent uplands and monitors             mote sensor systems, global positioning systems, image
               changes in these habitats on a one- to five-year cycle.         processing algorithms) as they become available. Se-
               This type of information and frequency of detection             lected hardcopy land-cover change maps will be pro-
               are required to improve scientific understanding of the         duced at local (1:24,000) to regional scales (1:500,000)
               linkages of coastal and submersed wetland habitats with         for distribution. Digital land-cover change data will be
               adjacent uplands and with the distribution, abundance,          provided to users for the cost of reproduction.
               and health of living marine resources. The monitoring             Much of the guidance contained in this document
               cycle will vary according to the rate and magnitude of          was developed through a series of professional work-
               change in each geographic region. Satellite imagery             shops and interagency meetings that focused on a)
               (primarily Landsat Thematic Mapper), aerial photo-              coastal wetlands and uplands; b) coastal submersed
               graphy, and field data are interpreted, classified, ana-        habitat including aquatic beds; c) user needs; d) re-
               lyzed, and integrated with other digital data in a geo-         gional issues; e) classification schemes; f) change detec-
               graphic information system (GIS). The resulting land-           tion techniques; and g) data quality. Invited partici-
               cover change databases are disseminated in digital form         pants included technical and regional experts and rep-
               for use by anyone wishing to conduct geographic analy-          resentatives of key State and Federal organizations.
               sis in the completed regions.                                   Coastal habitat managers and researchers were given
                  C-CAP spatial information on coastal change will be          an opportunity for review and comment.
               input to EHP conceptual and predictive models to sup-             This document summarizes C-CAP protocols and pro-
               port coastal resource policy planning and analysis. G           cedures that are to be used by scientists throughout the
               CAP products will include 1) spatially registered digital       United States to develop consistent and reliable coastal
               databases and images, 2) tabular summaries by state,            change information for input to the GCAP nationwide
               county, and hydrologic unit, and 3) documentation.              database. It also provides useful guidelines for contribu-
                                                                               tors working on related projects. It is considered a work-
                 Formerly known as the "Coast Watch Change Analysis Project."  ing document subject to periodic review and revision.























                                                                           ix







                                                                              aiapter I
                                                                          Introduction



                 The Coastal Region Management                                           submersed habitat may be a sensitive integrator of over-
                 Problem                                                                 all water quality and potential for change in fisheries
                                                                                         productivity. Submersed rooted vascular aquatic beds
                 The conterminous United States lost 53 percent of its                   define habitat critical for the support of many recre-
                 wetlands to agricultural, residential, and commercial                   ational and sport fisheries (Ferguson et al., 1980; Zieman,
                 land use from the 1780's to 1980's (Dahl, 1990). Oil                    1982; Phillips, 1984; Thayer et al., 1984; Zieman and
                 spills occurring throughout the world continue to dev-                  Zieman, 1989; Klemas et al., 1993). Changes in up-
                 astate coastal wetlands Uensen et al., 1990; Narumalani                 lands, wetlands, and submersed habitats can be rapid and
                 et al., 1993). Sea level has risen approximately 130 m in               pervasive. Hence, effective management requires frequent
                 the past 17,500 years. More abundant "greenhouse"                       monitoring of coastal regions (at least twice per decade).
                 gases in the atmosphere may be increasing the Earth's                      It has long been suspected that a crucial factor in the
                 average temperature (Clarke and Primus, 1990) and                       observed decline of fisheries in most coastal regions is
                 may, yet again, accelerate the global sea level rise, even-             the declining quantity and quality of habitat. Land-
                 tually inundating much of today's coastal wetlands (Lee                 cover change is a direct measure of quantitative habitat
                 et al., 1992). Unfortunately, current projections for                   loss or gain. For many marine fisheries the habitats (i.e.
                 U.S. population growth in coastal regions suggest accel-                land covers) of greatest importance are saltmarsh and
                 erating losses of wetlands and adjacent habitats, as waste              scagrass. Other fisheries, such as those for salmon, de-
                 loads and competition for limited space and resources                   pend on a variety of habitats that may include upland as
                 increase (U.S. Congress, 1989). Coastal wetlands and                    well. Land-cover change is also a direct measure of
                 submersed habitats are being destroyed by erosion,                      increases or decreases in sources of pollution, sedimen-
                 dredge and fill, impoundments, toxic pollutants,                        tation, and other factors that determine habitat quality.
                 eutrophication, and (for submersed habitats) excessive                  Increases in developed land, for example, are accompa-
                 turbidity and sedimentation. Many marine finfish and                    nied by land disturbance that increases erosion and
                 shellfish depend on these coastal habitats for their sur-               sedimentation and by hydrologic alteration that in-
                 vival. Salt marsh grasses, mangroves, macroalgae, and                   creases runoff. Similarly, cultivated land is associated
                 submersed grasses and forbs are essential as nourish-                   with fertilizer and pesticide use that ultimately affects
                 ment and animal habitat. Continued loss of these wet-                   the marine environment. Hence, land-cover change is
                 lands may lead to the collapse of coastal ecosystems and                linked to habitat quantity and quality.
                 associated fisheries. Documentation of the loss or gain
                 of coastal wetlands is needed for their conservation and
                 effective managment of marine fisheries (Haddad and                     The NOAA Coastal Change Analysis Program
                 Ekberg, 1987; Haddad and McGarry, 1989; Kiraly et al.,                  (C-CAP) Solution
                 1990; Kean et al.').
                    Submersed grasses and forbs include seagrasses that                  For these reasons, the National Oceanic and Atmo-
                 require high salinity and other species of submersed                    spheric Administration (NOAA) Coastal Ocean Pro-
                 rooted vascular plants (SRV) that tolerate or require                   gram initiated the Coastal Change Analysis Program
                 low salinity water. Submersed grasses and forbs may be                  (C-CAP), a cooperative interagency, State, and Federal
                 crucial indicators of water quality and overall health of               effort to detect coastal upland and wetland land cover
                 coastal ecosystems (Dennison et al., 1993). Submersed                   and submersed vegetation and to monitor change in
                 vegetation has the additional requirement of living at                  the coastal region of the United States (Cross and Tho-
                 photic depths and therefore is particularly sensitive to                mas, 1992; Haddad, 1992). The project uses digital
                 water clarity (Kenworthy and Haunert, 1991). Change                     remote sensor data, in situ measurement in conjunc-
                 (increase or decrease in areal extent, movement, con-                   tion with global positioning systems (GPS), and geo-
                 solidation or fragmentation, or qualitative change) in                  graphic information system (GIS) technology to moni-
                   Kean, T. H., C. Campbell, B. Gardner, and W. K. Reilly. 1988.         tor changes in coastal wetland habitats and adjacent
                   Protecting America's wetlands: an action agenda. Final Report of      uplands. Landsat multispectral scanner (MSS) data,
                   the National Wetlands Policy Forum. The Conservation Founda-          Landsat Thematic Mapper (TM) data, and SPOT high
                   tion, Washington, D.C.                                                resolution visible (HRV) data have been used success-







            2        NOAA Technical Report NMFS 123: Dobson et aL: Coastal Change Analysis Program


            fully to detect major categories of wetlands (Haddad             local, State, and Federal wetland management strate-
            and Harris, 1985; Jensen et al., 1993b; Lade et al.2).           gies, and construction of predictive models. C-CAP di-
            However, they have not been used previously to map or            rectly supports NOAA's responsibilities in estuarine and
            monitor wetlands for regional or national coverage.              marine science, monitoring, and management as legis-
            The use of satellite imagery for mapping wetlands pro-           lated in the Fish and Wildlife Coordination Act; the
            vides a number of advantages over conventional aerial            Magnuson Fishery Conservation and Management Act;
            photographs including timeliness, synopticity, and re-           the Coastal Zone Management Act; the Clean Water
            duced costs. While aerial photography may be appro-              Act; the Marine Protection, Research, and Sanctuaries
            priate for high resolution cartography, satellite imagery        Act; and the National Environmental Policy Act. Land-
            is better suited and less costly for rapid, repeated obser-      cover change data are essential to the implementation
            vations over broad regions (Haddad and Harris, 1985;             of a "No Net Loss" wetlands policy.
            Bartlett, 1987; Klemas and Hardisky, 1987; Ferguson et            A large community of managers, scientists, and users
            al., 1993). Although the program will stress the use of          were involved in developing a C-CAP protocol at the
            satellite imagery, particularly for coastal wetlands and         national level. Guidance in this document was derived
            adjacent uplands, aerial photography or a combination            from a series of professional workshops and interagency
            of photography and satellite imagery (TM or SPOT)                working group meetings which focused on
            will be used for mapping SRV (Orth and Moore, 1983)
            and certain other habitats, as suggested by Patterson            0user needs
            (1986) and Lade et al. (1988). A methodology to pho-             0upland, wetland, and water classification schemes
            tographically observe, analyze, and display spatial change       eregional boundary issues
            in habitat defined by the presence of SRV was a prereq-          0cartographic datum and data structures
            uisite to a nationwide change detection effort (Thomas           0selection of appropriate satellite imagery and aerial
            and Ferguson, 1990).                                              photography
             The C-CAP nationally standardized database will be              *field work and field verification methods
            used to monitor land-cover and habitat change in the             esatellite remote sensing of coastal wetlands and
            coastal regions of the United States (Thomas and                  uplands
            Ferguson, 1990; Thomas et al. 1991) and to improve               ephoto interpretation of coastal submersed habitat,
            understanding of coastal uplands, wetlands (e.g. salt             including seagrasses
            marshes), and submersed habitats (e.g. seagrass) and             0calibration among regions and scenes
            their linkages with the distribution, abundance, and             0classification and change detection algorithms
            health of living marine resources. Coastal regions of            9geographic information processing and analysis
            the U.S. will be monitored every one to five years de-           0regional ecological modeling
            pending on the anticipated rate and magnitude of                 *quality assurance and control
            change in each region and the availability of suitable           *product availability and distribution
            remote sensing and in situ measurements. This moni-              *research issues
            toring cycle will provide feedback to habitat managers
            on the success or failure of habitat management poli-             Approximately 40 scientists and environmental man-
            cies and programs. Frequent feedback to managers will            agers attended each major regional workshop held in
            enhance the continued integrity or recovery of coastal           the Southeast, Northeast, Pacific Coast, and Great Lakes
            ecosystems and the attendant productivity and health             regions; about 200 individuals participated in all work-
            of fish and other living marine resources at minimal             shops and special meetings. The community of users
            cost. In addition, the geographical database will allow          and providers of coastal habitat information were given
            managers and scientists to evaluate and, ultimately, to          an opportunity for review and comment. A detailed list
            predict cumulative direct and indirect effects of coastal        of workshops is provided in Appendix 4.
            development on wetland habitats and living marine                 Although C-CAP is national in scope, it is based on
            resources. Initially, C-CAP products will document cur-          procedures also applicable at local and regional levels.
            rent land-cover distribution and change that have oc-            Much of the content of this document is based on
            curred in the recent past. The database, as it increases         C-CAP sponsored. research conducted at the regional
            with each subsequent monitoring cycle, will be an in-            level. For example, Klemas et al. (1993) of the College
            valuable baseline resource for research, evaluation of           of Marine Studies at the University of Delaware devel-
                                                                             oped the "C-CAP Coastal Land Cover Classification Sys-
            2 Lade, P. K., D. Case, J. French, and H. Reed. 1988. Delineation and tem" by investigating existing upland and wetland clas-
             classification of submerged aquatic vegetation using SPOT satellite sification systems and then synthesizing a new system
             multispectral digital data. Final report to the Maryland Dept. Natu-
             ral Resources, Tidewater Administration, Coastal Resources Div., that is practical at the regional level. Dobson and Bright
             Annapolis, MD.                                                  (1991, 1992, and 1993) of the Oak Ridge National







                                                                                                          Chapter 1: Introduction          3


                Laboratory (ORNL) developed a regional prototype to              proach is to designate 1) standard coverage limits for
                inventory uplands and wetlands in the Chesapeake Bay             general application and 2) extended coverage limits
                region. Jensen et al. (1993a) evaluated various change           for regions with special needs. Standard coverage will
                detection algorithms for inland and coastal wetland              utilize biological and other geographical boundaries
                environments near Charleston, S. C. Ferguson et al.              appropriate to the needs of specified C-GAP users iden-
                (1993) developed a regional prototype to inventory               tified through the protocol workshops. Extended cov-
                SRV in North Carolina based on protocols developed               erage will be defined for each regional project in col-
                by the Beaufort Laboratory, Southeast Fisheries Sci-             laboration with states and other regional organiza-
                ence Center, National Marine Fisheries Service (NMFS).           tions. NOAA will make every effort to identify and
                Khorram et al. (1992) investigated methods of seamlessly         accommodate research, conservation, management, and
                integrating multiple-region C-CAP databases.                     the needs of other interests that rely on wetland maps
                  The C-CAP protocol continues to evolve and im-                 and data. Regional projects will be designed to identify
                prove. For example, projects underway in 1993 include            special needs that may require extended coverage and
                analysis of the effects of tidal stage on remote-sensing         to suggest sources of funds to support the additional
                classification, change detection accuracy assessment,            cost of extended coverage.
                refined techniques for classification of forested wet-             The estuarine drainage area (EDA), defined by
                lands, and advanced change detection techniques (Ap-             NOAA's National Ocean Service (NOS) as the "land
                pendix 5). Research continues on functional health               and water component of an entire watershed that most
                indicators (e.g. biomass, productivity), plant stress (e.g.      directly affects an estuary," is an appropriate standard
                mangrove freeze), new data-collection instruments, and           coverage area for C-CAP. For the purposes of this pro-
                regional ecological modeling. Thus, C-CAP will con-              gram, all U.S. coasts are or will be defined as part of an
                tinue to have a strong research and development com-             EDA. The boundary of each EDA basin is defined to be
                ponent to improve and refine its operational techniques.         consistent with U.S. Geological Survey (USGS) hydro-
                                                                                 logic units and codes.
                                                                                   The estuarine drainage boundary as defined by NOS
                National Scope and Regional Implementation                       is considered a standard inland boundary for
                of C-CAP                                                         C-CAP regional projects. Regional analysts may employ
                                                                                 C-CAP protocols upstream, but C-CAP funding is not
                No single Federal or State organization will collect all         intended for coverage beyond the EDA. However,
                the information residing in the C-CAP database. In-              C-CAP funding may be used to purchase satellite scenes
                stead, regional inventories will be completed by re-             that extend beyond the EDA if they are necessary to
                gional experts following C-CAP guidelines. Therefore,            cover the coastal region. Functional definitions, such as
                it is important to define the logic used to specify a C-         "limits of tidal influence," may be employed in response
                CAP region. First, regional boundaries must coincide             to local situations justified by local user communities
                with the following NOAA/NMFS regions:                            and local or regional experts on a coastal region-by-
                                                                                 region or estuary-by-estuary basis. Regional analysts
                Northeast -Virginia through Maine, including the                 should be aware of local, State, and Federal rights and
                              Great Lakes                                        responsibilities and should seek intergovernmental and
                Southeast - Texas through North Carolina, U.S. Vir-              interagency cooperation. Because C-CAP interests in-
                              gin Islands, and Puerto Rico                       clude the effects of eutrophication due to development
                Northwest - Oregon, Washington, and Alaska                       of uplands, information from outside the EDA may be
                Southwest - California, Hawaii, Midway Islands, Wake,            justified in high order streams that extend beyond the
                              Guam, Mariana Islands, American Samoa,             coastal region. In this case, the point where the river
                              Johnston Atoll, Trust Territory of the Pa-         enters the region will be defined as a point source for
                              cific Islands, Baker and Howland Islands,          inputs.
                              and Jarvis Island.                                   The offshore boundary of each region is defined as
                                                                                 the seaward extent of wetlands, seagrass, coral, or other
                Coastal regions may be further subdivided, as appropri-          submersed habitat detectable using remote sensing sys-
                ate, on the basis of State and other administrative bound-       tems. The functional definition of limits of detection
                aries or ecoregions as defined, for example, by Omernik          normally will be based on satellite and aerial sensors
                (1987).                                                          and will vary within and among regions. Both the limits
                  The boundary should encompass coastal watersheds               of detection and the actual bathymetric range of SRV
                plus offshore coral reefs, algae, and seagrass beds in the       are based on light attenuation and, thus, will not be a
                photic zone. In keeping with the goals of C-CAP and              consistent bathymetric contour even within a single
                anticipated funding constraints, the recommended ap-             region.







            4         NOAA Technical Report NMFS 123: Dobson et a].: Coastal Change Analysis Program


              Overlap of regions, consistent with TM scene bound-             (e.g. urban analysis, forest inventory) often are not
            ary overlap, is preferred so that analysts may calibrate          suitable for use in C-CAP. Aquatic beds, and even coastal
            results from neighboring regions. A healthy exchange              wetlands, may not be identifiable on aerial photographs
            between neighboring regional analysts could reconcile             obtained for other purposes.
            differences, not only in the area of overlap, but also in
            signature identification across both regions. Each re-
            gional project team will be responsible for calibrating           The Need for Standardization and
            the relationship between remotely sensed spectral in-             Guidelines
            formation and other information such as field mea-
            surements of biomass and photosynthetic rates. Histori-           C-CAP desires to create a synoptic, digital database of
            cally, such measurements have focused on relatively few           coastal wetland and upland land cover by class for a
            of the many species, habitats, and land-cover types of            base time period and to identify change between the
            significance in the coastal region. Analysts should also          base period and other time periods. The use of satellite
            ensure that protocols originally developed for north-             remote sensing to inventory uplands and wetlands, con-
            ern temperate latitudes are modified sufficiently to serve        ventional aerial photography to inventory submerged
            well in tropical areas of the southern United States,             lands, and GIS to analyze the data are important ele-
            Caribbean, and Pacific Ocean, and in the Arctic areas             ments of the C-CAP methodology. However, the goal of
            of Alaska. It will be necessary, for example, to use differ-      completing an accurate change detection product over-
            ent methods and sensors for coral reefs than for wet-             rides any given technical consideration. Therefore,
            lands. Similarly, the identification of Arctic muskeg             timely high-quality information from aerial photographs,
            may require different methods and sensors from those              topographic maps, field experience, or other sources
            used to identify temperate, herbaceous wetland.                   may be used to prepare C-CAP products if appropriate
                                                                              guidelines are followed.
                                                                                By standardizing procedures at the national level,
            Change Detection Every One to Five Years                          this document will benefit not only C-CAP but also
                                                                              coastal management research conducted by other State
            The frequency of change detection is a crucial issue.             and Federal agencies. C-CAP desires to facilitate the
            For most regions in the United States, the base year              exchange of standardized data among programs, de-
            (referred to as Tb or Date I in the diagrams) should be           crease duplication, and improve the quality and utility
            the most recent year for which acceptable satellite im-           of decision support for wetlands policy, management,
            agery for uplands and wetlands or aerial photographs              and research activities. All data accepted for inclusion
            for submersed habitat can be obtained, and for which              and eventual distribution in the C-CAP database must
            sufficient in situ information is available to conduct an         adhere to the protocol described in this manual. The
            error evaluation. Exceptions may occur in regions where           protocol is designed to allow flexibility in the use of
            cloud cover is a perennial problem or where other                 elements of the classification scheme and in the choice
            considerations favor aerial photographs over satellite            of remote sensor data, classification and change detec-
            imagery. The choice of the second date of imagery                 tion procedures, and other key elements that vary re-
            (Date b-I or b+I) may be more flexible. It may be                 gionally. However, potential users must adhere to the
            desirable to choose a date one to five years earlier than         protocol in order to maintain high quality information
            the base period to capture recent changes in coastal              in the C-CAP database. Coastal land-cover change data-
            habitats. Plans should then be made for another change            bases derived independently from C-CAP will be con-
            analysis no later than five years after the base time.            sidered for dissemination as C-CAP products if originat-
            However, plans may be altered abruptly when natural               ing organizations can document compliance with
            or human-induced events, such as hurricanes and oil               C-CAP protocol and data quality standards.
            spills, occur.
              Five years is the recommended frequency of change
            detection for most regions, but shorter periods may be            General Steps Required to Conduct Regional
            necessary in regions undergoing rapid economic devel-             C-CAP Projects
            opment or affected by catastrophic events. Longer peri-
            ods may be necessary where funds are limited or where             The general steps required to conduct regional C-CAP
            change is exceptionally slow. Regional analysts are ad-           change detection projects using satellite remotely sensed
            vised to evaluate rates of change and explicitly recom-           data are summarized in Table 1. This document is
            mend the base year and change period as a part of each            organized according to these specific requirements and,
            regional project proposal. Unfortunately, remotely                in certain instances, provides step-by-step instructions
            sensed data obtained specifically for other purposes              to be used when conducting regional projects. One of







                                                                                                                               Chapter 1: Introduction                5


                   the first requirements of regional participants is to                        scheme was suitable for all C-CAP requirements. There-
                   precisely identify land-cover classes of interest to be                      fore, great effort went into the development of the G
                   monitored and eventually placed in the C-CAP change                          CAP Coastal Land-Cover Classification System, which
                   detection database. This must be performed in con-                           can be used to inventory uplands and wetlands by using
                   junction with an appropriate classification scheme. Un-                      satellite remote sensor data and to inventory SRV by
                   fortunately, no existing standardized classification                         using metric aerial photography.




                                                                                         Table I
                      General steps required to conduct regional G-CAP change detection projects to extract upland and wetland information
                      using satellite remote sensing systems. Each major step is listed in the order to be accomplished.


                      1.  State the regional change detection problem                                  b.  Preprocess the multiple-date remotely sensed data
                          a. Define the region                                                             1) Geometric rectification
                          b. Specify frequency of change detection (I to 5 yr)                             2) Rachometric correction (or normalization)
                          c. Identify classes of the C-CAP Coastal Land-Cover                          c.  Select appropriate change detection algorithm from
                             Classification System                                                         the three C-CAP alternatives
                                                                                                       d.  Apply appropriate image classification logic if necessary
                      2. Consider significant factors when performing change                               1) Supervised
                          detection                                                                        2) Unsupervised
                          a. Remote sensing system considerations                                          3) Hybrid
                             1) Temporal resolution                                                    e.  Perform change detection using CIS algorithms
                             2) Spatial resolution                                                         1) Highlight selected classes using change detection
                             3) Spectral resolution                                                           matrix
                             4) Radiometric resolution                                                     2) Generate change map products
                             5) The preferred C-CAP remote sensing system                                  3) Compute change statistics
                          b. Environmental considerations
                             1) Atmospheric conditions                                              4. Conduct quality assurance and control
                             2) Soil moisture conditions                                               a. Assess spatial data quality
                             3) Vegetation pherrological cycle characteristics                         b. Assess statistical accuracy of
                             4) Tidal stage                                                                1) Individual date classification
                                                                                                           2) Change detection products
                      3. Conduct image      processing of remote sensor data to
                          extract upland and wetland information                                    5. Distribute C-CAP Results
                          a. Acquire appropriate change detection data                                 a. Digital products
                             1) In situ and collateral data                                            b. Analog (hardcopy) products
                             2) Remotely sensed data
                                 a) Base year (Time b)
                                 b) Subsequent year(s) (Time b-1 or b+I)







                                                                     Chapter 2
                                The C-CAP Coastal Land-Cover Classification System

               Introduction                                                     categories (Dobson, 1993a). C-GAP focuses on land
                                                                                cover and its relationship to other functional compo-
               It is essential that the coastal land-cover information          nents of landscape (Dobson, 1993b). Definitions of the
               stored in the G-GAP database be taxonomically correct            pertinent terms are as follows:
               and consistent with coastal wetland information de-
               rived from other agencies. The G-GAP Coastal Land-               *land cover-vegetation, soils, rocks, water (in its vari-
               Cover Classification System (Table 2) includes three              ous forms), and constructed materials covering the
               Level I superclasses (Klemas et al., 1993):                       land surface, physically present and visible.
                                                                                aland use-economic and cultural activities, permit-
                  LO-Upland,                                                     ted or not, that are practiced at a place which may or
                  2.0-Wetland, and                                               may not be manifested as visible land-cover features.
                  3.0-Water and Submerged Land.                                  For example, forestry land use may be visibly mani-
                                                                                 fested as forest land cover, but recreational land use
               These superclasses are subdivided into classes and sub-           may occur in many different types of land cover,
               classes at Levels II and III, respectively. While the cat-        often without visible evidence of recreational use.
               egories Wetland and Water and Submerged Land con-                 landscape-the zone of interaction and convergence
               stitute the primary habitats of interest to NOAA, Up-             of the atmosphere, the hydrosphere, and the solid
               lands are also included because they influence adjacent           earth. Its vertical bounds are determined by the fre-
               wetlands and water bodies. The classification system is           quency and extent of interactions pertinent to a given
               hierarchical, reflects ecological relationships, and fo-          field of inquiry. Horizontally, landscape may be di-
               cuses on land-cover classes that can be discriminated             vided into areal units defined by physical or cultural
               primarily from satellite remote sensor data. It was               features pertinent to a field of inquiry.
               adapted and designed to be compatible with other
               nationally standardized classification systems, especially        While all categories of the CCAP classification system
                                                                                can be represented as two-dimensional features at the
               ï¿½  the U.S. Geological Survey (USGS) "Land Use and               mapping scale of 1:24,000, some features may be mapped
                  Land Cover Classification System For Use with Re-             as lines (e.g. a Marine/Estuarine Rocky Shore) or points
                  mote Sensor Data" (Anderson et al., 1976; USGS,               (e.g. unique landmarks). Most linear and point features
                  1992; Appendix Table 1),                                      will be obtained from nonsatellite sources of information
               ï¿½  the U.S. Fish and Wildlife Service (USFWS) "Classifi-         (e.g. aerial photography or in situ measurement using
                  cation of Wetlands and Deepwater Habitats of the              GPS). Those classes and subclasses that are required by C-
                  United States" (Cowardin et al., 1979; Wilen, 1990;           CAP and which each regional C-CAP project will include
                  Appendix Table 2), and                                        in its database are underlined in Table 2. The underlined
               ï¿½  the U.S. Environmental Protection Agency (USEPA)              classes, with the exception of aquatic beds, can generally
                  Environmental Monitoring and Assessment Program               be detected by satellite remote sensors, particularly when
                  (EMAP) classification system.                                 supported by surface in situ measurement

               Dedicated workshops on the C-CAP classification sys-
               tem and productive discussions and reviews with repre-           Superclasses of the C-CAP System
               sentatives from each of these major agencies resulted in
               a classification system that is in harmony with other            Uplands
               major U.S. land-cover databases. The C-CAP Coastal
               Land-Cover Classification System includes upland, wet-           The Upland superclass consists of seven subclasses
               land, submerged land, and water in a single, compre-             (Table 2): Developed Land, Cultivated Land, Grass-
               hensive scheme. An attempt has been made to identify             land, Woody Land, Bare Land, Tundra, and Snow/Ice.
               land-cover classes that can be derived primarily through         Upland classes are adapted from Level I classes in the
               remote sensing and that are important indicators of              USGS Land-Use and Land-Cover Classification System
               ecosystem change. Modifications were necessary to rec-           (Anderson et al., 1976; USGS, 1992; Appendix Table
               oncile inconsistencies between Anderson et al. (1976)            1). Detailed definitions of all C-CAP classes and sub-
               and Cowardin et al. (1979) and to remove all land-use            classes in Table 1 are found in Appendix 3.

                                                                                                                                         7







             8           NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program



                                                                                  Table 2
                 C-CAP Coastal Land-Cover Classification System (Modified from Klemas et al., 1993). C-CAP is committed to include the
                 underlined classes in the land cover change databases.


                 1.0 Upland                                                                          2.41 Deciduous
                                                                                                        2.411 Forest
                    1.1 Developed Land                                                                  2.412 Scrub/shrub
                                                                                                        2.413 Dead
                        1.11 High Intensity                                                          2.42 Evergreen
                        1.12 Low Intensity                                                              2.421 Forest
                                                                                                        2.422 Scrub/Shrub
                    1.2 Cultivated Land                                                                 2.423 Dead
                                                                                                     2.43 Mixed
                        1.21 Orchards/ Groves/Nurseries                                                 2.431 Forest
                        1.22 Vines/Bushes                                                               2.432 Scrub/shrub
                        1.23 Cropland                                                                   2/433 Dead

                    1.3Grassland                                                                 2.5 Riverine Unconsolidated Shore (Beach, Flat, Bar)


                        1.31 Unmanaged                                                               2.51 Cobble-Gravel
                        1.32 Managed                                                                 2.52 Sand
                                                                                                     2.53 Mud/Organic
                    1.4 Woody Land
                                                                                                 2.6 LacusLrine Unconsolidated Shore (Beach, Flat, Bar)
                        1.41 Deciduous
                            1.411 Forest                                                             2.61 Cobble-Gravel
                            1.412 Scrub/Shrub                                                        2.62 Sand
                        1.42 Evergreen                                                               2.63 Mud/Organic
                            1.421 Forest
                            1.422 Scrub/Shrub                                                    2.7 Palustrine Unconsolidated Shore (Beach, Flat, Bar)
                        1.43 Mixed
                            1.431 Forest                                                             2.71 Cobble-Gravel
                            1.432 Scrub/Shrub                                                        2.72 Sand
                                                                                                     2.73 Mud/Organic
                    1.5 Bare Land
                                                                                                 2.8 Palustrine Emergent Wetland (Persistent)
                    1.6Tundra
                    1.7 Snow/Ice                                                                 2.9 Palustrine Woody Wetland
                        1.71 Perennial Snow/Ice                                                      2.91 Deciduous
                        1.72 Glaciers                                                                   2.911 Forest
                                                                                                        2.912 Scrub/shrub
                                                                                                        2.913 Dead
                 2.0 Wetland                                                                         2.92 Evergreen
                                                                                                        2.921 Forest
                    2.1 Marine/Estuarine Rocky Shore                                                    2.922 Scrub/shrub
                        2.11 Bedrock                                                                    2.923 Dead
                        2.12 Rubble                                                                  2.93 Mixed
                                                                                                        2.931 Forest
                    2.2 Marine/Estuarine Unconsolidated Shore                                           2.932 Scrub/shrub
                        (Beach, Flat, Bar)                                                              2.933 Dead
                        2.21 Cobble-gravel                                                    3.0 Water and Submerged Land
                        2.22 Sand
                        2.23 Mud/Organic                                                         3.1 Water
                    2.3 Marine /Estuarine Emergent Wetland
                                                                                                     3.11 Marine/Estuarine
                        2.31 Haline (Salt Marsh)                                                     3.12 Riverine
                        2.32 Mixohaline (Brackish Marsh)                                             3.13 Lacustrine (Basin > 20 acres)
                                                                                                     3.14 Palustrine (Basin < 20 acres)
                    2.4 Estuarine Wooft Wetland






                                                                                 Chapter 2: C-CAP Coastal Land-Cover Classification System                        9



                                                                               Table 2 (continued)

                         3.2 Marine/Estuarine Reef                                                      3.41 Rooted Vascular/Algal/Aquatic Moss
                                                                                                        3.42 Floating Vascular
                         3.3 Marine /Estuarine Aquatic Bed
                                                                                                     3.5 Lacustrine Aquatic Bed (Basin > 20 acres)
                            3.31 Algal (e.g., kelp)
                            3.32 Rooted Vascular (e.g., seagrass)                                       3.51 Rooted Vascular/Algal/Aquatic Moss
                                3.321 (High Salinity (2!5 ppt; Mesohaline,                              3.52 Floating Vascular
                                      Polyhaline, Euhaline, Hyperhaline)
                                3.322 Low Salinity (< 5 ppt; Oligohaline, Fresh)                     3.6 Palustrine Aquatic Bed (Basin < 20 acres)
                         3.4 Riverine Aquatic Bed                                                       3.61 Rooted Vascular/Algal/Aquatic Moss
                                                                                                        3.62 Floating Vascular



                     Developed Land (derived from the Anderson et al.                          ing or for growing and harvesting hay and straw for
                  [1976] Urban or Built-Up class) characterizes con-                           animal feed.
                  structed surfaces composed of concrete, asphalt, roof-                         Woody Land includes nonagricultural trees and
                  ing, and other building materials with or without veg-                       shrubs. The category alleviates the problem of separat-
                  etation. This class has been divided into two subclasses                     ing various sizes of trees and shrubs using satellite remote
                  based on the amount of constructed surface relative to                       sensor data but allows a height-based separation if high
                  the amount of vegetated surface present. High-Inten-                         resolution aerial photographs are available. The class may
                  sity Developed Land contains little or no vegetation.                        be partitioned into three subclasses: Deciduous, Evergreen,
                  This subclass includes heavily built-up urban centers as                     and Mixed. These three subclasses generally can be dis-
                  well as large constructed surfaces in suburban and rural                     criminated with satellite remote-sensing systems.
                  areas. Large buildings (such as multiple-family hous-                          Bare Land (derived from Barren Land of Anderson
                  ing, hangars, and large barns), interstate highways, and                     et al. [1976]) is composed of bare soil, rock, sand, silt,
                  runways typically fall into this subclass. Low-Intensity                     gravel, or other earthen material with little or no veg-
                  Developed Land contains substantial amounts of con-                          etation. Anderson et al.'s Barren Land was defined as
                  structed surface mixed with substantial amounts of veg-                      having limited ability to support life; C-CAP's Bare Land
                  etated surface. Small buildings (such as single family                       is defined by the absence of vegetation without regard
                  housing, farm outbuildings, and sheds), streets, roads,                      to inherent ability to support life. Vegetation, if present,
                  and cemeteries with associated grasses and trees typi-                       is more widely spaced and scrubby than that in the
                  cally fall into this subclass.                                               vegetated classes. Unusual conditions such as a heavy
                     Cultivated Land ("Agricultural Land" of Anderson et                       rainfall may occasionally result in growth of a short-
                  al. [1976]) includes herbaceous (cropland) and woody                         lived, luxuriant plant cover. Wet, nonvegetated, exposed
                  (orchards, nurseries, vineyards, etc.) cultivated lands. Sea-                lands are included in the Wetland categories. Bare Land
                  sonal spectral signatures, geometric field patterns, and                     may be bare temporarily because of human activities. The
                  road network patterns may help identify this land-cover                      transition from Woody Land, Grassland, or Cultivated
                  type. Always associated with agricultural land use, culti-                   Land to Developed Land, for example, usually involves
                  vated land is used for the production of food and fiber.                     a Bare Land phase. Developed Land also may have tempo-
                     Grassland differs from "Rangeland" of Anderson et                         rary waste and tailing piles. Woody Land may be clearcut,
                  al. (1976) by excluding shrub-brushlands. Unmanaged                          producing a temporary Bare Land phase. When it may be
                  Grasslands are dominated by naturally occurring grasses                      inferred from the data that the lack of vegetation is due to
                  and forbs which are not fertilized, cut, tilled, or planted                  an annual cycle of cultivation (e.g. plowing), the land is
                  regularly. Managed Grasslands are maintained by hu-                          not included in the Bare Land class. Land temporarily
                  man activity such as fertilization and irrigation, are                       without vegetative cover because of cropping or tillage is
                  distinguished by enhanced biomass productivity, and                          classified as Cultivated Land, not Bare Land.
                  can be recognized through vegetative indices based on
                  spectral characteristics. Examples of such areas include
                  lawns, golf courses, forest or shrub areas converted to                      Wedands
                  grassland, or areas of permanent grassland with altered
                  species composition. This category includes managed                          Wetlands are lands where saturation with water is the
                  pastures and pastures with vegetation that grows vigor-                      dominant factor determining soil development and the
                  ously as fallow. Managed Grasslands are used for graz-                       types of plant and animal communities living in the soil







            10         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            and on its surface (Cowardin et al., 1979). A character-              sensors. These wetlands are classified as Riverine Water
            istic feature shared by all wetlands is soil or substrate             and Lacustrine Water.
            that is at least periodically saturated with or covered by
            water. The upland limit of wetlands is designated as 1)
            the boundary between land with predominantly hydro-                   Water and Submerged Land
            phytic cover and land with predominantly mesophytic
            or xerophytic cover; 2) the boundary between soil that                All areas of open water with <30% cover of trees, shrubs,
            is predominantly hydric and soil that is predominantly                persistent emergent plants, emergent mosses, or lichens
            nonhydric; or 3) in the case of wetlands without vegeta-              are assigned to the superclass Water and Submerged I-and,
            tion or soil, the boundary between land that is flooded               whether the area is considered wetland or deepwater
            or saturated at some time during the growing season                   habitat under the Cowardin et al. (1979) classification.
            each year and land that is not (Cowardin et al., 1979).                 The Water class includes Cowardin et al.'s (1979)
            Most wetlands are vegetated and found on soil.                        classes Rock Bottom and Unconsolidated Bottom, and
               Wetland in the C-CAP Coastal Land-Cover Classifica-                Nonpersistent Emergent Wetlands, as well as Reefs and
            tion System (Table 2) includes all areas considered                   Aquatic Beds that are not identified as such. Most
            wetland by Cowardin et al. (1979) except for bottoms,                 C-CAP products will display water as a single class. How-
            reefs, aquatic beds, and nonpersistent emergent wet-                  ever, the major systems (Marine/ Estuarine, Riverine,
            lands. The class subdivision was adopted primarily from               Lacustrine, Palustrine) are ecologically different from
            the Cowardin et al. system, shown in Appendix Table 2.                one another, and for this reason, the C-CAP system
            At Level 11, C-CAP incorporates certain Cowardin et al.               identifies the four systems as Level III subclasses: 3.11-
            classes (e.g. Rocky Shore, Unconsolidated Shore, Emer-                Marine/Estuarine Water, 3.12-Riverine Water, 3.13-
            gent Wedand) or grouped Cowardin et al. classes (e.g.                 Lacustrine Water, and 3.14-Palustrine Water. While
            Woody Wetland may be further divided into Scrub-                      C-CAP does not require these subclasses, the option is
            Shrub and Forested categories) in combination with Cow-               provided to participants who may have such data avail-
            ardin et al. systems (i.e. Marine, Estuarine, Riverine, Lacus-        able from ancillary sources. Having the water subclasses
            trine, Palustrine). Thus, a typical Level II class in the             also makes the C-CA-P scheme more compatible with
            C-CAP system might be Palusuine Woody Wetland.                        the Cowardin et al. (1979) system. The subclass 3.11-
               Marine and Estuarine Rocky Shores (Cowardin et al.,                Marine/Estuarine Water includes Bottoms and unde-
            1979) were combined into a single class, Marine/Estua-                tected Reefs and Aquatic Beds. The subclasses 3.12-
            rine Rocky Shore. The same logic was used to produce                  Riverine Water, 3.13-Lacustrine Water, and 3.14-
            Marine/Estuarine Unconsolidated Shore.                                Palustrine Water include Bottoms and undetected
               Salinity exhibits a horizontal gradient in coastal es-             Aquatic Beds as well as Nonpersistent Emergent Wet-
            tuarine marshes. This is evident not only through the                 lands. Palustrine waterbodies, defined as covering <20
            direct measurement of salinity but in the horizontal                  acres, are smaller than Lacustrine waterbodies.
            distribution of marsh plants (Daiber, 1986). Therefore,                 C-CAP combined Marine and Estuarine Reefs and
            the Estuarine Emergent Wetland class is partitioned                   Aquatic Beds into two classes: Marine/Estuarine Reefs
            into Haline (Salt) and Mixohaline (Brackish) Marshes.                 and Marine/Estuarine Aquatic Beds. Marine/ Estuarine
            For both subclasses, the C-CAP classification system                  Aquatic Beds includes the subclass Rooted Vascular,
            uses the Cowardin et al. (1979) definitions. Mixohaline               which is subdivided into High Salinity Q!5 ppt) and
            salinity ranges from 0.5 ppt to 30 ppt, and Haline salin-             Low Salinity (<5 ppt). The @!5 ppt salinity ievel separates
            ity is @!30 ppt. Within a marsh, plant zonation is usually            seagrasses from submersed grasses and forbs that toler-
            quite evident. Along the Atlantic coast of North America              ate or require low salinity. Both types of plants define
            the pioneer plant on regularly flooded mudflats is                    aquatic beds, submersed habitats that are important to
            saltmarsh cordgrass, Spa7tina alternijlora, which often               the C-CAP project. High Salinity includes mesohaline,
            appears in pure stands. In more elevated areas that are               polyhaline, euhaline, and hyperhaline salinity catego-
            flooded less frequently, saltmeadow hay, Spailina Pat-                ries of Cowardin et al. (1979). Low Salinity includes
            ens, often dominates. The upland interfaces are bor-                  oligohaline and fresh categories (<5 ppt salinity),
            dered by marsh elder, Ivafrutescens, and groundsel tree,                With the noted exceptions, most of the Wetland and
            Bacchafis halimifolia. Thus, salt marshes may be subdi-               Water classes have definitions similar to those contained
            vided further into High Marsh and Low Marsh, but this                 in Cowardin et al. (1979) so that data can be inter-
            distinction is not required in C-CAP regional projects.               changed with other programs, such as the USFWS Na-
               The C-CAP Coastal Land-Cover Classification System                 tional Wetlands Inventory (NWI) program, which is based
            does not attempt to identify freshwater nonpersistent                 on the Cowardin et al. (1979) classification system. De-
            emergent wetlands because they are invisible during                   tailed definitions of all superclasses, classes, and subclasses
            much of the year and are difficult to detect by remote                shown in Table 2 are provided in Appendix 3.







                                                                       Chapter 3
                 Monitoting Uplands and Wetlands Using Satellite Remote Semor Data

                Successful remote-sensing change detection of uplands             Geometric rectification algorithms Uensen, 1986; Novak,
                and wetlands in coastal regions requires careful atten-           1992) are used to register the images to a standard map
                tion to 1) sensor systems, 2) environmental characteris-          projection (Universal Transverse Mercator [UTM] for
                tics, and 3) geodetic control. Failure to understand the          most U.S. projects). Rectification should result in the
                impact of the various parameters on the change detec-             two images having a root mean square error (RMSE) of
                tion process can lead to inaccurate results. Ideally, the         !!@ ï¿½0.5 pixel. RMSE @! ï¿½0.5 pixel may result in the identi-
                remotely sensed data used to perform C-CAP change                 fication of spurious areas of change between the two
                detection are acquired by a remote sensor system that             datasets. See "Rectification of Multiple-date Remote
                holds the following factors constant: temporal, spatial           Sensor Data" for a summary of C-CAP image rectifica-
                (and look angle), spectral, and radiometric. It is in-            tion requirements.
                structive to review each of these parameters and iden-              It is possible to perform change detection using data
                tify why they have a significant impact on the success of         collected by two different sensor systems with different
                C-CAP remote-sensing change detection projects. Table             IFOV's, e.g. Landsat TM data (30 x 30 in) for date I and
                3 summarizes the characteristics of some of the most              SPOT HRV data (20 x 20 in) for date 2. In such cases, it
                important satellite remote-sensing systems.                       is necessary to decide upon a representative minimum
                                                                                  mapping unit (e.g. 20 x 20 in) and then resample both
                                                                                  datasets to this uniform pixel size. This does not present
                Remote-Sensing System Considerations                              2, significant problem as long as one remembers that
                                                                                  the information content of the resampled data can
                Temporal Resolution                                               never be greater than the IFOV of the original sensor
                Two important temporal resolutions should be held                 system (i.e. even though the Landsat TM data are
                constant when performing coastal change detection                 resampled to 20 x 20 in pixels, the information was still
                using multiple dates of remotely sensed data. First, the          acquired at 30 x 30 in resolution, and one should not
                data should be obtained from a sensor system which                expect to be able to extract additional spatial detail in
                acquires data at approximately the same time of day               the dataset).
                (e.g. Landsat TM data are acquired before 0945 h for                Some remote-sensing systems like SPOT collect data
                most of the conterminous United States). This elimi-              at off-nadir look angles as much as ï¿½20' (Table 3), i.e.
                nates diurnal sun angle effects which can cause anoma-            the sensors obtain data of an area on the ground from
                lous differences in the reflectance properties of re-             an "oblique" vantage point. Two images with signifi-
                motely sensed objects. Second, whenever possible it is            cantly different look angles can cause problems when
                desirable to use remotely sensed data acquired on anni-           used for change detection purposes. For example, con-
                versary dates (e.g. 1 October 1988 versus I October               sider a maple forest consisting of very large, randomly
                1993). Using anniversary date imagery removes sea-                spaced trees. A SPOT image acquired at 0* off-nadir will
                sonal sun angle differences that can make change de-              look directly down upon the "top" of the canopy. Con-
                tection difficult and unreliable Uensen et al., 1993a).           versely, a SPOT image acquired at 20' off-nadir will
                Usually, precise anniversary date imagery is not avail-           record reflectance information from the "side" of the
                able. The determination of acceptable near-anniversary            canopy. Differences in reflectance from the two datasets
                dates then depends on local and regional factors such as          can cause spurious change detection results. There-
                phenological cycles and annual climatic regimes.                  fore, the data used in a remote-sensing digital change
                                                                                  detection should be acquired with approximately the
                                                                                  same look angle whenever possible.
                Spatial Resolution and Look Angle

                Accurate spatial registration of at least two images is           Spectral Resolution
                essential for digital change detection. Ideally, the re-
                motely sensed data are acquired by a sensor system that           A fundamental assumption of digital change detection
                collects data with the same instantaneous-field-of-view           is that there should exist a difference in the spectral
                (IFOV) on each date. For example, Landsat TM data                 response of a pixel on two dates if the biophysical
                collected at 30 x 30 in spatial resolution (Table 3) on           materials within the IFOV have changed between dates.
                two dates are relatively easy to register to one another.         Ideally, the spectral resolution of the remote sensor







              12         NOAA Technical Report NMFS 123: Dobson et at.: Coastal Change Analysis Program



                                                                                   Table 3
                 Selected satellite remote-sensing system characteristics; abbreviations: MSS=multispectral scanner; TM=thematic mapper.

                                                   Spectral resolution               Spatial resolution              Temporal                Radiometric
                 Remote sensor system                     (gm)                              (m)                   resolution (d)           resolution (bits)

                 Landsat MSS 1-5                   Band 1 (0.50-0.60)                      80 x 80                      18                         81
                                                   Band 2   (0.60-0.70)                    80 x 80                      18                         8
                                                   Band 3   (0.70-0.80)                    80 x 80                      18                         8
                                                   Band 4 (0.80 -1.1)                      80 x 80                      18                         8


                 Landsat TM 4-6                    Band 1 (0.45-0.52)                      30 x 30                      16                         8
                                                   Band 2 (0.52-0.60)                      30 x 30                      16                         8
                                                   Band 3 (0.63-0.69)                      30 x 30                      16                         8
                                                   Band 4 (0.76-0.90)                      30 x 30                      16                         8
                                                   Band 5 (1.55-1.75)                      30 x 30                      16                         8
                                                   Band 7 (2.08-2.35)                      30 x 30                      16                         8
                                                   Band 6 (10.4-12.5)                    120 x 120                      16                         8

                 Landsat TM ro, pAN2               Band 8 (0.5-0.90)                       15 x 15                      16                         8

                 SPOT HRV, XS                      Band 1 (0.50-0.59)                      20 x 20                   pointable                     8
                                                   Band 2 (0.61-0.68)                      20 x 20                   pointable                     8
                                                   Band 3 (0.79-0.89)                      20 x 20                   pointable                     8

                 SPOT HRV, PAN                     Pan (0.51-0.73)                         lox 10                    pointable                     8

                 1 Landsat MSS I and 2 collected data in 7 bits.
                 2 The panchromatic (PAN) band was found on Landsat 6, which was lost during a launch mishap.


              system is sufficient to record reflected radiant flux in                     values ranging from 0 to 255 (Table 3). Ideally, the
              spectral regions that best capture the most descriptive                      sensor systems collect the data at the same radiometric
              spectral attributes of the object. Unfortunately, differ-                    precision on both dates. When the radiometric resolu-
              ent sensor systems do not record energy in exactly the                       tion of data acquired by one system (e.g. MSS I with 7-
              same portions of the electromagnetic spectrum, i.e.                          bit data) are compared with data acquired by a higher
              bandwidths (Table 3). For example, Landsat MSS                               radiometric resolution instrument (e.g. TM with 8-bit
              records energy in four relatively broad bands, SPOT                          data) then the lower resolution data (e.g. 7-bit) should
              HRV sensors record in three relatively coarse multi-                         be "decompressed" to 8-bit data for change detection
              spectral bands and one panchromatic band, and TM                             purposes. However, the precision of decompressed
              records in six relatively narrow optical bands and one                       brightness values can never be better than the original,
              broad thermal band (Table 3). Ideally, the same sensor                       uncompressed. data.
              system is used to acquire imagery on multiple dates.
              When this is not possible, the analyst should select
              bands which approximate one another. For example,                            The Preferred C-CAP Satellite Sensor System
              SPOT bands I (green), 2 (red), and 3 (near-infrared)
              can be used successfully with TM bands 2 (green), 3                          TM is currently the primary sensor recommended for
              (red), and 4 (near-infrared) or MSS bands I (green), 2                       GCAP image acquisition and change analysis for all
              (red), and 4 (near-infrared). Many of the change detec-                      land cover except aquatic beds. Although its spatial reso-
              tion algorithms to be discussed do not function well                         lution is not as good as that of a SPOT satellite or aircraft
              when bands from one sensor system do not match                               MSS image, a TM image is generally less expensive to
              those of another sensor system. For example, using TM                        acquire and process for large-area coverage. Compared
              band I (blue) with either SPOT or MSS data is not wise.                      with SPOT imagery, TM has better spectral resolution and
                                                                                           specific spectral bands that are more applicable to wet-
                                                                                           lands delineation (bands 5 and 7). In addition, TM is
              Radiometric Resolution                                                       preferred over SPOT because TM has collected data for a
                                                                                           longer time (since 1982, as opposed to SPOT since 1986)
              Converting satellite remote sensor data from analog to                       and because many TM scenes of U.S. coastal regions were
              digital usually results in 8-bit brightness values with                      systematically collected on a routine basis.







                                                                                      Chapter 3: Monitoring Uplands and Wetlands               13


                   There are advantages and disadvantages to using other           thin layer of haze can alter spectral signatures in satel-
                sensors. Aircraft multispectral scanners are more ex-              lite images enough to create the false impression of
                pensive and complex to use over large regions Uensen               spectral change between two dates. Obviously, 0% cloud
                et al., 1987). However, good algorithms are now avail-             cover is preferred for satellite imagery and aerial pho-
                able for georeferencing, and in certain cases (e.g. when           tography. At the upper limit, cloud cover >20% is usu-
                higher spectral or spatial resolution is needed and when           ally unacceptable. In addition, clouds not only obscure
                unfavorable climatic conditions for satellite sensors ex-          terrain but the cloud shadow also causes major image
                ist) aircraft sensors may be optimum. The SPOT sensor              classification problems. Any area obscured by clouds or
                has a greater temporal coverage because the satellite              affected by cloud shadow will filter through the entire
                can collect data off-nadir. However, if off-nadir SPOT             change detection process, severely limiting the utility of
                imagery is used for C-CAP change analyses, the data                the final change detection product. Therefore, regional
                must be normalized to compensate for different look                analysts must use good professional judgment to evalu-
                angles that may preclude pixel-to-pixel spectral-change            ate such factors as the criticality of the specific locations
                analysis. Nevertheless, SPOT imagery may be a reason-              affected by cloud cover and shadow and the availability
                able alternative in certain areas because of cloud cover           of timely surrogate data for those areas obscured (e.g.
                or other impediments to TM data availability.                      perhaps substituting aerial photography interpretation
                   C-CAP remains flexible to. take. advantage of new               for a critical area). Even when the stated cloud cover is
                sensors and other technologies that become operational             0%, it is advisable to "browse" the proposed image on
                during the lifetime of the program. Regional partici-              microfiche at the National Cartographic Information
                pants should work with the C-CAP program coordina-                 Center in each State to confirm that the cloud cover
                tors to ensure that the sensor selection meets the fol-            estimate is correct.
                lowing C-CAP requirements:                                           Assuming no cloud cover, the use of anniversary dates
                                                                                   helps to ensure general, seasonal agreement between
                ï¿½  Standard radiometrically corrected TM data are re-              the atmospheric conditions on the two dates. However,
                   quired, and geocoded (georeferenced) data are op-               if dramatic differences exist in the atmospheric condi-
                   tional. If geocoded data are selected, the coordinate           tions present on the n dates of imagery to be used in the
                   system should be UTM.                                           change detection process, it may be necessary to re-
                ï¿½  Regional participants must collaborate with C-CAP               move the atmospheric attenuation in the imagery. Two
                   managers to ensure that the exchange medium and                 alternatives are available. First, sophisticated atmo-
                   its format will be amenable to the processing capabili-         spheric transmission models can be used to correct the
                   ties of the participants.                                       remote-sensor data if substantial in situ data are avail-
                ï¿½  C-CAP normally will purchase and archive the raw                able on the day of the overflights. Second, an alterna-
                   data in collaboration with the regional image pro-              tive empirical method may be used to remove atmo-
                   cessing center. In cases where the regional partici-            spheric effects. A detailed description of one empirical
                   pants already have usable raw imagery or are making             method of image-to-image normalization is found in
                   their own purchases, formal agreements between                  "Radiometric Normalization of Multiple-Date Images.
                   C-CAP managers and participants must address ven-
                   dor licensing and other legal requirements as well as
                   C-CAP archiving and quality-control protocol.                   Soil Moisture Conditions
                Important Environmental Characteristics                            Ideally, the soil moisture conditions should be identical
                                                                                   for the n dates of imagery used in a change detection
                Failure to understand the impact of various environ-               project. Extremely wet or dry conditions on one date
                mental characteristics on the remote-sensing change                can cause serious change detection problems. There-
                detection process can also lead to inaccurate C-CAP                fore, when selecting the remotely sensed data to be
                results. When performing change detection it is desir-             used for change detection it is very important not only
                able to hold environmental variables as constant as                to look for anniversary dates but also to review precipi-
                possible. Specific environmental variables and their               tation records to determine how much rain or snow fell
                potential impacts are described below.                             in the days and weeks prior to data collection. When
                                                                                   soil moisture differences between dates are significant
                                                                                   for only certain parts of the study area (perhaps due to
                Atinospheric Conditions                                            a local thunderstorm), it may be necessary to stratify
                                                                                   (eliminate) those affected areas and perform a sepa-
                There should be no clouds, haze, or extreme humidity               rate analysis that can be added back in the final stages
                on the days remote-sensing data are collected. Even a              of the project.







           14        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

           Vegetation Phenological Cycle Characteristics                     acceptable scenes may be quite   small. In some regions
                                                                             it may be necessary to seek alternative data such as
           Vegetation grows according to seasonal and annual                 SPOT satellite data, aerial photographs, or other land-
           phenological cycles. Obtaining near-anniversary images            cover databases. For most regions, mean low tide (MLT)
           greatly minimizes the effects of wetland seasonal phe-            or lower will be preferred, one or two feet above MLT
           nological differences that may cause spurious change              will be acceptable, and three feet or more will be unac-
           to be detected in the imagery. One must also be careful           ceptable (Jensen et al., 1993a). Ideally, tides for aerial
           about two other factors when dealing with upland sea-             photographic surveys of submersed habitat should ap-
           sonal agricultural crops. First, many monoculture crops           proach low tide as predicted in NOS tide tables, but
           (e.g. corn) normally are planted at approximately the             optimal visualization of the subtidal bottom depends
           same time of year. A month lag in planting date be-               on water clarity as well as depth. Two of the 1993 C-CAP
           tween fields having the same crop can cause serious               protocol development projects focus on improving the
           change detection error. Second, many monoculture                  C-CAP protocol for tidal effects (see Appendix 5).
           crops are composed of different species (or strains) of
           the same crop, which can cause the crop to reflect
           energy differently on multiple dates of anniversary im-           Image Processing Data to Inventory Upland
           agery. These observations suggest that the analyst must           and Wetland Change
           know the biophysical characteristics of the vegetation
           as well as the cultural land-tenure practices in the study        With the classification scheme developed and the ap-
           area so that imagery which meets most of these charac-            propriate remote-sensor data selected, it is possible to
           teristics can be selected for change detection.                   process the data to extract upland and wetland change
             The choice of image date is best determined by mu-              information. This involves geometric and radiometric
           tual agreement among remote-sensing specialists, bi-              correction, selection of an appropriate change detec-
           ologists, ecologists, and local experts. The selection of         tion algorithm, classification if necessary, creation of
           the acceptable window of acquisition will be made inde-           change detection products, and error evaluation (Table
           pendently by participants in each region. No single               1). A separate section (Chapter 4) describes the extrac-
           season will serve for all areas because of substantial            tion of information on SRV because aerial photography
           latitudinal variation extending from temperate to tropi-          and significantly different photogrammetric techniques
           cal regions. For example, coastal marshes in the mid-             must be utilized.
           Atlantic region are best inventoried fromJune through
           October while submersed habitats in southern Florida
           may be best inventoried in November. Even within                  Rectification of Multiple-Date Remote Sensor
           regions, some cover types will be more easily distin-             Data
           guished in different seasons. For example, in the Carib-
           bean, estuarine seagrasses can be best detected in early          Georeferencing (spatial registration of a remotely sensed
           January, yet marine seagrasses can be best detected in            image to a standard map projection) is a necessary step
           May or June. Technically, these vegetation patterns               in digital change detection and cartographic represen-
           should be monitored at optimal times throughout the               tation. The following C-CAP recommendations should
           year, but cost limitations usually limit the analyst to a         be followed when rectifying the base image to a stan-
           single date.                                                      dard basemap:

                                                                             eCreocoded base TM images can be purchased if pre-
           Effects of Tidal Stage on Image Classification                     ferred by regional analysts. However, participants
                                                                              should be aware that some analysts have reported
           Tidal stage is a crucial factor in satellite image scene           undocumented variations in commercial products that
           selection and the timing of aerial surveys. Ideally, tides         can lead to poor registration in certain regions, espe-
           should be constant between time periods, but this would            cially where local relief requires substantial terrain
           rule out synoptic satellite sensors since tidal stages are         correction. Additional registration may be necessary
           not synchronized within a region or even within a single           to achieve the C-CAP standard precision of RMSE
           image. Alternatively, analysts should avoid selecting the          ï¿½0.5 pixel. Therefore, it is recommended that each
           highest tides and should take into account the tide                regional project perform its own base image-to-map
           stages occurring throughout each scene. Tidal effect               rectification by using data that is radiometrically cor-
           varies greatly among regions. In the Northwest, for                rected but not geocoded.
           example, when all of the temporal, atmospheric, and               *Ground control points (GCP's) used to compute rec-
           tidal criteria are taken into account, the number of               tification transformation coefficients should be rela-







                                                                                      Chapter 3: Monitoring Uplands and Wetlands               15


                  tively static features in the landscape (e.g. road inter-           of reducing or better controlling co-registration er-
                  sections) or should be based on new GPS measure-                    ror among images. Selection and consistency of con-
                  ments taken in the field. When GCP's are digitized                  trol points and rectification algorithms are important
                  from USGS 7.5' (1:24,000) maps, analysts should use                 to the success of this technique. Cubic convolution
                  the marginal information and available updates to                   algorithms normally yield the most precise spatial fit,
                  improve the location of control points. GCP's should                but cubic convolution and bilinear interpolation al-
                  be extracted from mylar copies of the USGS maps                     gorithms suffer from the disadvantage of averaging
                  whenever possible to minimize system-produced digi-                 pixel brightness values. Nearest-neighbor algorithms
                  tizing error. Traditional paper maps expand and con-                are spatially less precise, but they offer the advantage
                  tract with changes in relative humidity and should                  of retaining pixel brightness values through the pro-
                  not be used for digitizing GCP's.                                   cesses of rectification and registration.
                 ï¿½C-CAP recommends the use of the current NAD '83
                  national datum. Unfortunately, most existing map
                  series are based on the NAD '27 datum. NAD '27 will be            Radiometric Normalization of Multiple-Date
                  acceptable on a region-by@region basis until published            Images
                  maps based on NAD '83 are universally available.
                 ï¿½In all but the flattest coastal regions, terrain correc-          The use of remotely sensed data to classify coastal and
                  tion of imagery may be necessary to reduce image                  upland land cover on individual dates is contingent
                  distortion caused by local relief.                                upon there being a robust relationship between re-
                 ï¿½The required coordinate system is UTM. If another                 motely sensing brightness values (BV's) and actual sur-
                  coordinate system is used (e.g. state plane), it is the           face conditions. However, factors such as sun angle,
                  responsibility of the regional analyst to provide com-            Earth/Sun distance, detector calibration differences
                  plete documentation and conversion equations.                     between the various sensor systems, atmospheric condi-
                 ï¿½It is the responsibility of the regional analyst to un-           tion, and sun/target/ sensor geometry (phase angle)
                  derstand (or seek advice concerning) the variety of               will also affect pixel brightness value. Differences in
                  rectification-resampling algorithms (e.g. bilinear in-            direct beam solar radiation due to variation in sun
                  terpolation, nearest neighbor, cubic convolution) and             angle and Earth/sun distance can be calculated accu-
                  their impact on the data. Nearest-neighbor resampling             rately, as can variation in pixel BV's due to detector
                  is recommended.                                                   calibration differences between sensor systems. Remov-
                                                                                    ing atmospheric and phase-angle effects requires infor-
                  Rectification of an earlier date (Tb._,) or later date            mation about the gaseous and aerosol composition of
                 (Tb,j) to the base image (Tb) can be accomplished in               the atmosphere and the bidirectional reflectance char-
                 several ways. The primary concern is to accomplish the             acteristics of elements within the scene. However, at-
                 most exact co-registration of pixels from each time                mospheric and bidirectional reflectance information
                 period and thus reduce a potentially significant source of         are rarely available for historical remotely sensed data.
                 error in change analysis (Lunetta et al., 1991). The follow-       Also, some analysts may not have the necessary exper-
                 ing are minimum recommendations and requirements:                  tise to perform a theoretically based atmospheric path
                                                                                    radiance correction on remotely sensed data. Hence, it
                 ï¿½Geocoded and terrain-corrected TM data can be or-                 is suggested that a relatively straightforward "empirical
                  dered from commercial vendors. Two separate im-                   scene normalization" be employed to match the detec-
                  ages can be overlaid according to like coordinates,               tor calibration, astronomic, atmospheric, and phase-
                  but this technique may introduce error if prior                   angle conditions present in a reference scene.
                  geocoding was not precisely the same in both images.                Image normalization reduces pixel BV variation
                  The regional analyst has no control in this process,              caused by nonsurface factors, so variations in pixel BV's
                  but if high precision is accomplished by the vendor,              between dates can be related to actual changes in sur-
                  the analyst can significantly reduce image processing             face conditions. Normalization enables the use of im-
                  effort at the regional facility.                                  age analysis logic developed for a base-year scene to be
                 ï¿½The regional analyst can geocode the image to UTM                 applied to other scenes. This can be accomplished us-
                  coordinates as was done with the base image. If this              ing techniques pioneered by the U.S. Bureau of Land
                  technique is adopted, it is important to use the iden-            Management (Eckhardt et al., 1990). Image normaliza-
                  tical GCP's and resampling algorithm that were used               tion is achieved by developing simple regression equa-
                  to rectify the base image.                                        tions between the brightness values of "normalization
                 ï¿½For multiple images, the preferred technique is to                targets" present in Tb and the scene to be normalized
                  rectify nongeocoded images directly to the geocoded               (e.g. T,._j or T,,,j). Normalization targets are assumed to
                  base image. This technique may have the advantage                 be constant reflectors, therefore any changes in their







            16        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Prograin

            brightness values are attributed to detector calibration,        Selecting the Appropriate Change Detection
            astronomic, atmospheric, and phase-angle differences.            Algorithm
            Once these variations are removed, changes in BV may
            be related to changes in surface conditions.                     C-CAP is the first Federal program to state as a primary
              Acceptance criteria for potential "normalization tar-          goal the monitoring of coastal habitat change using
            gets" (Eckhardt et al., 1990) are as follows:                    satellite technology (Cross and Thomas, 1992). The
                                                                             implementation and continuing evolution of the pro-
            ï¿½ Targets must be at approximately the same elevation            gram is based on the fact that improved cartographic,
              as the land cover of primary interest within the scene.        digital image processing, and photointerpretation meth-
              Most aerosols in the atmosphere occur <1000 m above            ods must be developed for a program of this geographic
              ground level (AGL). Selecting a mountain-top nor-              coverage, spatial resolution, and temporal frequency
              malization target, thus, would be of little use in esti-       (nationwide, 30 x 30 m pixel, every one to five years).
              mating atmospheric conditions near sea level. Al-              Initial implementation of C-CAP will require a blend of
              though C-CAP projects are on the coast, many re-               traditional and innovative approaches to change analysis.
              gions include areas of substantial local relief.               Because the program has adopted a digital format, with
            ï¿½ Targets should contain only minimal amounts of veg-            TM as a primary sensor, new techniques in processing can
              etation. Vegetation spectral reflectance can change            be easily incorporated into future iterations.
              over time because of environmental stresses and plant            The selection of an appropriate change detection
              phenology. Good targets include bare soil fields and           algorithm is very important Uensen, 1986; Dobson and
              deep, nonturbid water bodies.                                  Bright, 1991, 1992, and 1993; Jensen et al., 1993a).
            ï¿½ Targets must be on relatively flat terrain so that incre-      First, it will have a direct impact on the type of image
              mental changes in sun angle between dates will have            classification to be performed (if any). Second, it will
              the same proportional increase or decrease in direct           dictate whether important "from-to" information can be
              beam sunlight for all normalization targets.                   extracted from the imagery. C-CAP requires that from-to
            ï¿½ Normalization targets should have approximately the            information be readily available in digital form suitable
              same texture over time. Changing textural patterns             for geographic analysis and for producing maps and tabu-
              indicate variability within the target, which could mean       lar summaries. At least seven change detection algorithms
              that the reflectance of the target as a whole may not          are commonly used by the remote-sensing community:
              be constant over time. For example, a mottled pat-
              tern on what had previously been a uniformly gray,             1. Change Detection Using Write Function Memory In-
              dry lake bed indicates changing surface moisture con-            sertion-Example: Kittredge and Fort Moultrie, S.C.
              ditions, which would eliminate the dry lake bed from           2. Multiple-Date Composite Image Change Detection-
              consideration as a normalization target.                         No example provided.
                                                                             3. Image Algebra Change Detection (Band Differencing
              The mean BV's of the Tb targets are regressed against            or Band Ratioing) -No example provided.
            the mean BV's of the T,,_i or Tb.,, targets for the n bands      4.Postclassification Comparison Change Detection-
            used in the classification of the remote sensor data (e.g.         Example: Fort Moultrie, S.C.
            TM bands 2, 3, and 4). The slope and yintercept of the           5. Multiple-Date Change Detection Using a Binary Mask
            n equations are then used to normalize the Tb+1 or Tb._1           Applied to Tb.-l-Example: Chesapeake Bay, Md.
            Landsat TM data to the Tb Landsat TM data. Each regres-          6. Multiple-Date Change Detection Using Ancillary Data
            sion model contains an additive component (y-inter-                Source as TC---No example provided.
            cept) that corrects for the difference in atmospheric            7. Manual On-Screen Digitization of Change-No ex-
            path radiance between dates and contains a multiplica-             ample provided.
            tive term (slope) that corrects for the difference in
            detector calibration, sun angle, Earth/Sun distance,             It is instructive to review these alternatives, identify
            atmospheric attenuation, and phase angle between dates.          those acceptable to C-CAP, and provide specific ex-
              It is customary first to normalize the remote-sensor           amples where appropriate.
            data and then perform image rectification (using near-
            est-neighbor resampling if image classification is to take
            place). These data are then ready for individual date            Change Detection Using Write Function Memory
            classification or the application of various multi-image         Insertion
            change detection algorithms. Most studies that attempt
            to monitor biophysical properties such as vegetation             It is possible to insert individual bands of remotely
            biomass, chlorophyll absorption, and health require              sensed data into specific write function memory banks
            atmospheric correction.                                          (red, green, and/or blue) in the digital image process-






                                                                                     Chapter 3: Monitoring Uplands and Wetlands               17

                ing system (Fig. 1) to visually identify change
                in the imagery Uensen et al., 1993b). For                       Multi-Date Visual Change Detection
                example, consider two Landsat TM scenes of                   Using Write-Function Memory Insertion
                the Fort Moultrie quadrangle near Charles-
                ton, SC, obtained on I I November 1982 and                   Date 1 band n                           Red image plane
                19 December 1988. Band I of the 1982 image                                                            Green image plane
                was placed in the green image plane; band I                  Date 2 band n                           Blue image plane
                of the 1988 image, in the red image plane;                   Date 3 band n
                and no image, in the blue image plane (Fig.
                2). All areas that did not change between the            Advantages:                     Disadvantages:
                two dates are depicted in shades of yellow                visual examination of 2 or 3   , non-quantitative
                                                               les
                (i.e. in additive color theory, equal intensiti           years of non-specific change   - no 'from-to'change class information
                of green and red make yellow). The graphic
                depicts numerous changes, including                                                   Figure I
                ï¿½ beach and sand bar accretion (red) and              Diagram of Multiple-Date Change Detection using Write Function Memory
                  erosion (green),                                    insertion Uensen, 1994).
                ï¿½ new urban development (red), and
                ï¿½ changes in tidal stage between dates (green                       1987 and 1988; Eastman and Fulk, 1993). This results
                  and red).                                                         in the computation of eigenvalues and factor loadings
                                                                                    that are used to produce a new, uncorrelated PCA
                  Advantages of this technique include the possibility              image dataset. Usually, several of the new bands of
                of looking at two and even three dates of remotely                  information are directly related to change. The diffi-
                sensed imagery at one time, as demonstrated byjensen                culty arises when trying to interpret and label each
                et al. (1993b). Unfortunately, the technique does not               component image. Nevertheless, the method is valu-
                produce a classified land-cover database for either date            able and is used frequently.
                and, thus, does not provide quantitative information                 The advantage of the techniques is that only a single
                on the amount of area changing from one land-cover                  classification is required. Unfortunately, it is often diffi-
                category to another. Nevertheless, it is an excellent               cult to label the change classes, and no from-to change
                analog method for quickly and qualitatively assessing               class information is available,
                the amount of change in a region, which might help to
                select one of the more rigorous change detection tech-
                niques to be discussed.                                             Image Algebra Change Detection

                                                                                    It is possible to simply identify the amount of change
                Multiple-Date Composite Image Change Detection                      between two images by band ratioing or image
                                                                                    differencing the same band in two images that have
                Numerous researchers have rectified multiple dates of               previously been rectified to a common basemap. Image
                remotely sensed imagery (e.g. selected bands of two                 differencing involves subtracting the imagery of one
                TM scenes of the same region) and placed them in a                  date from that of another (Fig. 4). The subtraction
                single dataset (Fig. 3). This composite dataset can be              results in positive and negative values in areas of radi-
                analyzed in a number of ways to extract change infor-               ance change and zero values in areas of no-change in a
                mation. First, a traditional classification using all n bands       new "change image." In an 8-bit (28) analysis with pixel
                (six in the example in Fig. 3) may be performed. Unsu-              values ranging from 0 to 255, the potential range of
                pervised classification techniques will result in the cre-          difference values is -255 to 255. The results are nor-
                ation of "change" and "no-change" clusters. The analyst             mally transformed into positive values by adding a con-
                must then label the clusters accordingly.                           stant, c (usually 255). The operation is expressed math-
                   Other researchers have used principle component                  ematically as
                analysis (PCA) to detect change Uensen, 1986). Again,
                the method involves registering two (or more) dates of                             Dijk @ BV (1) - BV..  k(2) + c
                remotely sensed data to the same planimetric basemap                                         ijk
                as described earlier and then placing them in the same              where
                dataset. A PCA based on variance-covariance matrices                D-h     = change pixel value,
                or a standardized PCA based on an analysis of correla-              Br.-k(l) = brightness value at Tb,
                tion matrices is then performed (Fung and LeDrew,                   B@-k(2) = brightness value at Tb-, or Tbl,







           18        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


                  c = a constant (e.g. 255),                                distributed around the mean and pixels of change are
                  i = line number,                                          found in the tails of the distribution. Band ratioing
                  j = column number, and                                    involves exactly the same logic except a ratio is com-
                  k = a single band (e.g. TM band 4).                       puted between Tb and Tb.,, or Tb.-jand the pixels that
                                                                            did not change have a value of "l " in the change image.
              The "change image" produced using image differ-                 A critical element of both image-differencing and
           encing usually yields a BV distribution approximately            band-ratioing change detection is deciding where to
           Gaussian in nature, where pixels of no BV change are             place the threshold boundaries between "change" and










                    A"T
                                              A
                                                                    At-
                    ?Mv                           lot
                                                                                      4k







                                                                                                       Y


                                                                                                          JP1

                                                    v
                                                              -1A
                                      7                                                       oil













                                          40





                                                                    Figure 2
                    Example of Multiple-Date Change Detection using Write Function Memory Insertion using two dates of
                    Landsat Thematic Mapper imagery of Fort Moultrie, S. C. Red image plane = TM band 3, 19 Dec 1988; green
                    image plane = TM band 3, 9 Nov 1982; blue image plane = blank.







                                                                                            Chapter 3: Monitoring Uplands and Wetlands                  19

                  no-change" pixels displayed in the histogram                        Multi-Date Composite Change Detection
                  of the change image Uensen, 1986). Often, a
                  standard deviation from the mean is selected                                                                 2
                                                                                                                         :1    3Rectified Thematic
                  and tested empirically. Conversely, most ana-                             Date 1                             4 Mapper bands
                  lysts prefer to experiment empirically, plac-                                                          0
                  ing the threshold at various locations in the                                                                3
                  tails of the distribution until a realistic amount                        Date 2    -its;                    4
                  of change is encountered. Thus, the amount                                          _9@1
                  of change selected and eventually "recoded"                                                 or
                  for display is often subjective and must be
                  based on familiarity with the study area. There                                                                   6 Principal
                  are also analytical methods that can be used                        Traditional                                   Components
                  to select the most appropriate thresholds.                          Classification
                  Unfortunately, image differencing simply
                  identifies those areas that may have changed                  Advantages:                        Disadvantages:
                  and provides no information on the nature of                   requires single classification    - difficult to label change classes
                  the change, i.e. no from-to information. Nev-                                                    - no 'from-to' change classes available
                  ertheless, the technique is valuable when used
                  in conjunction with other techniques such as                                                 Figure 3
                  the multiple-date change detection using a
                                                                 4 1_       Diagram of Multiple-Date Composite Image Change Detection (Jensen, 1994).
                  binary change mask to be discussed in " u
                  tiple-Date Change Detection Using a Binary
                  Change Mask Applied to Tb_1 or Tb,,."                                  Image Algebra Change Detection

                                                                                                                         2
                  Postdassification Comparison Change                                                                    3- Rectified Thematic
                  Detection                                                          Date 1                              4     Mapper bands
                  The most commonly used quantitative method
                  of change detection is postclassification com-                                                         2     Rectified Thematic
                  parison Uensen, 1986; Jensen et al., 1993a)                                                            3 -
                  and may be used in regional C-CAP projects                          Date 2                             4     Mapper bands
                  under certain conditions. It requires rectifi-
                  cation and classification of each of the re-                                                                  Composite
                  motely sensed images (Fig. 5). These two maps                                                                   Dataset
                  are then compared on a pixel-by@pixel basis
                  by using a "change detection matrix" to be
                  discussed. Unfortunately, every error in the                                                           Image differenced or
                  individual date classification map will also be                                                        band ratioed image
                  present in the final change detection map
                  (Rutchey and Velcheck, 1993). Therefore, it
                  is imperative that the individual classification                                                       Recoded to produce binary
                  maps used in the postclassification change
                                                                                                                         'Change/No-change' Mask
                  detection method be extremely accurate
                  (Augenstein et al., 1991; Price et al., 1992).
                    To demonstrate the postclassification com-
                  parison change detection method, consider                   Advantages:                          Disadvantages:
                  the Kittredge (40 river miles inland from                     efficient method of identifying    - no 'from-to'change classes available
                                                                                pixels which have changed          - requires careful selection of the
                  Charleston, S.C.) and Fort Moultrie, S.C. study               in brightness value between dates 'change/no-change' threshold
                  areas (Fig. 6) Uensen et al., 1993a). Nine
                  classes of land cover were inventoried on each                                               Figure 4
                  date (Fig. 7). The 1982 and 1988 classifica-                    Diagram of Image Algebra Change Detection Uensen, 1994).
                  tion maps were then compared on a pixel-by-
                  pixel basis using an n x n GIS "matrix" algorithm whose                ues from 1 to 81. The analyst then selected specific
                  logic is shown in Figure 8. This resulted in the creation              from-to classes for emphasis. Only a select number of
                                                                                                                 V-























                  of "change images maps" consisting of brightness val-                  the 72 (W-n) possible off-diagonal from-to land-cover







             20          NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


             change classes summarized in the change matrix (Fig.                           Postclassification comparison change detection is
             8) were selected to produce the change detection maps                       widely used and easy to understand. When conducted
             (Fig. 9). For example, all pixels which changed from                        by skilled image analysts it represents a viable tech-
             any land cover in 1982 to Developed Land in 1988 were                       nique for the creation of C-CAP change detection prod-
             color coded red (RGB=255, 0, 0) by selecting the ap-                        ucts. Advantages include the detailed from-to informa-
             propriate from-to cells in the change detection matrix                      tion and the classification map for each year. Unfortu-
             (10, 19, 28, 37, 46, 55, 64, and 73). Note that the change                  nately, the accuracy of change detection is heavily de-
             classes are draped over a TM band-4 image of the study                      pendent on the accuracy of the two separate classifica-
             area to facilitate orientation. Similarly, all pixels in                    tions. Postclassification comparison is not recommended
             1982 that changed to Estuarine Unconsolidated Shore                         for C-CAP regional projects except under special cir-
             by 19 December 1988 (cells 9,18, 27, 36,45, 54,63, and                      cumstances, such as when different sensors are involved
             72) were depicted in yellow (RGB=255, 255, 0). If de-                       or when two separate organizations are classifying the
             sired, the analyst could highlight very specific changes,                   same region at different times.
             such as all pixels that changed from Developed Land to
             Estuarine Emergent Wetland (cell 5 in the matrix), by
             assigning a unique color look-up table value (not                           Multiple-date Change Detection Using a Binary
             shown). A color-coded version of the change detection                       Change Mask Applied to Tb--i or Tb-,,
             matrix can be used as an effective from-to change de-
             tection map legend Uensen and Narumalani, 1992).                            This method of change detection is highly recom-
                                                                                                     mended for C-CAP regional projects. First,
                                                                                                     the analyst selects the base image, T.. Date 2
                                                                                                     may be an earlier image Tb._1 or a later image
                       Multi-Date Change Detection Using                                             Tlw* A traditional classification of Tb is per-
                           Post-Classification Comparison                                            formed by using rectified remote sensor data.
                                                                                                     Next, one of the bands (e.g. band 3 in Figure
                                                  2 Rectified Thematic                               10) from both dates of imagery are placed in a
                                                  " Ma
                  Date                            3                                                  new dataset. The two band dataset is then ana-
                                                  4   pper bands
                                                                                                     lyzed by using various image algebra functions
                                                                                                     (e.g. band ratioing, image differencing, princi-
                                                  classification map of Date 1                       pal components analysis) to produce a new
                                                                                                     image file. The analyst usually selects a thresh-
                                                                                                     old value to identify spectral change and no-
                                                                                                     change pixels in the new image as discussed in
                                                  2                                                  "Image Algebra Change Detection." The spec-
                                                     e
                  Date 2                          3  ctified Thematic
                                                                                                     tral change image is then recoded into a binary
                                                  4 Mapper bands
                                                                                                     mask file, consisting of pixels with spectral change
                                                                                                     between the two dates, and these are viewed as
                                                  Classification map of Date 2                       candidate pixels for categorical change. Great
                                                                                                     care must be exercised when creating the
                                                                                                     change/no-change binary mask (Dobson and
                                                  Classification map of Date 1                       Bright, 1993; Jensen et al., 1993a). The change
                                                                                                     mask is then overlaid onto Tb-i or Tb,., of the
                                                                                                     analysis and only those pixels which were de-
                                                  Change map produced using                          tected as having changed are classified in Tb._1 or
                                                  'change detection matrix' logic                    Tbl* A traditional postclassification comparison
                                                  applied to Date 1 and Date 2                       can then be applied to yield from-to change
                                                  classification maps                                information. Hence, many pixels wi th sufficient
                                                                                                     change to be included in the mask of candidate
                 Advantages:                         Disadvantages:                                  pixels may not qualify as categorical land-cover
                 - provides 'from- to' change        * dependent on accuracy of individual           change.
                  class information                   date classifications                              Dobson and Bright (1991, 1992, and 1993)
                 * next base year is already         # requires two separate classifications
                  completed                                                                          used this change detection methodology to
                                                                                                     inventory change in the area surrounding the
                                                  Figure 5                                           Chesapeake Bay using TM imagery obtained
                                    ;S7









             Diagr-am of Postclassification Comparison Change Detection (Jensen, 1994).              on 9 September 1984 and 3 November 1988







                                                                                            Chapter 3: Monitoring Uplands and Wetlands                 21



                                             7










                                                   ... .......




                                %










                                                                             %









                                                                                  a

                        Kittredge, S.C.                                 11/09/82                                                      12/19/88


                                                                 XA





                                                                                                                  11, '411
                                                                                                                  W0.









                                      4e@




                                                                                   C                                                             d
                        Fort Moultrie, S.C.                             11/09/82                                Scale                 12/19/88
                        Landsat Thematic        Mapper Data                                                                       22@       Meters
                        Bands 4,3,2 = RGB                                                5000                  0                  5000

                                                                                 Figure 6
                        Rectified Landsat Thematic Mapper data: (a and b) obtained for the Kittredge, S. C., 75 quadrangle C-CAP study
                        area, 9 Nov 1982 and 19 Dec 1988 (Jensen et al., 1993a) (c and d) Obtained for the Fort Moultrie, S. C., 75
                        quadrangle study area, 9 Nov 1982 and 12 Dec 1988.







          22        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program














                                             40



                                                               %


                            2%




                      Kittredge, S. C.                  11/09/82                                      12/19/88
                                                                                                             _JEw
                                                           41










                                                                C                                             d


                      Fort Moultrie, S. C.              11/09/82                                      12/19/88
                                                                Legend

                                Developed Land                     r___j   Estuarine Unconsolidated Shore

                                Grassland                                  Cultivated Land

                                Woody Land                                 Riverine Aquatic Bed

                                Palustrine Woody Wetland                   Water

                                Estuarine Emergent Wetland

                                                                Figure 7
                      Multiple-date land-cover classification maps: (a and b) Kittredge, S. C., study area, produced from
                      9 Nov 1982 and 19 Dec 1988 Landsat TM data. (c and d) Fort Moultrie, S. C., study area, produced
                      from 9 Nov 1982 and 19 Dec 1988 Landsat TM data Uensen et al., 1993a).







                                                                                        Chapter 3: Monitoring Uplands and Wetlands              23







                                                                                                                             V)
                                                                                                                             "a
                                                                                         1988

                                                                                 "ZI   a               CIO
                                                                                  CI                   E
                                                                                                 r_
                                            "From -To"                                           ca
                                   Change Detection Legend              TO:                 S@                    r_
                                                                                                             >
                                                                                                            ii
                                                            From:                      U   0           W          a.         W

                                                          Developed Land
                                                           Cultivated Land             MEESE:
                                                  1982 Grassland                    E MESS                               -m 27
                                                                                                                               36
                                                              Woody Land            ME MEE
                                                                                                                           1 45
                                              Estuarine Emergent Wetland          VMS ME`
                                                    Riverine Aquatic Beds                                     M                14
                                                                                                                               63
                                                Palustrine Woody Wetland                              no
                                                                      Water         MEESE
                                         Estuarine Uncolsolidated Bottom           NEESE.                                      @3'1
                                   Color look-up table values in        Red     255    255 255 255 255 255          0     0  255
                                       change detection map        -iw-Green      0    255 255 255 163          0255      0  255
                                                                        Blue      0    255 255 255       0   255 255    255    0

                                                                                       No change in landcover between dates, and
                                                                                       not selected for display

                                                                                       Change in land cover between dates,
                                                                               F@      but not selected for display
                                                                                       New Developed Land (cells 10, 19, 28,37,
                                                                                       46,55,64,73) shown in red (RGB=255,0,0)

                                                                                       New Estuarine Unconsolidated Shore (cells
                                                                                       9,18,27,36,45,54,63,72) shown in yellow
                                                                                       (RGB=255,255,O)

                                                                            Figure 8
                                 Change detection matrix. The basic elements of a change detection matrix may be used to
                                 select specific "from-to" classes for display in a "postclassification comparison" change detec-
                                 tion map. There arc (n2 - n) off-diagonal possible change classes which may be displayed in the
                                 change detection map (72 in this example) although some may be highly unlikely. The
                                 colored off-diagonal cells in this diagram were used to produce the change maps in Figure 9.
                                 For example, any pixel in the 1982 map that changed to Developed Land by 1988 is red
                                 (RGB=255,0,0). Any pixel that changed into Estuarine Unconsolidated Shore by 1988 is yellow
                                 (RGB=255,255,O). Individual cells can be color coded in the change map to identify very
                                 specific "from-to" changes (Jensen et al., 1993a).











                                _7@



                                                              4
                                                                                                      lip


                                                              j



                                                                                                      1     dr












                                                                  A
                                                                 0





                                          A




                                                      Kittredge, S.C.                                      Fort Moul
               Changes in Land Cover 1982 to 1988                                            Scale

                      Developed Land                                                            =5MMEwe
                      Palustrine Woody Wetland                         5 60@0                0                  50
                      Estuarine Emergent Wetland
                      Estuarine Unconsolidated Shore
                      Riverine Aquatic Bed

                      water


                                                                  Figure 9
               Change detection maps of the Kittredge and Fort Moultrie, S. C., study areas derived from analysis of 11 Nov 1982 and 19 Dec 1988 Lands
               The nature of the change classes selected for display are summarized in Figure 8. The change information is overlaid onto the Landsat
               image of each date for orientation purposes Uensen et al., 1993a).







                                                                                            Chapter 3: Monitoring Uplands and Wetlands                   25


                 imagery and only those pixels which were detected as                    Tb-,, image is required. It may also be possible to update
                 having changed were classified in the earlier image. A                  the NWI map (Tb) with more current wetland informa-
                 from-to matrix similar to the one shown in Figure 9 was                 tion (this would be done using a GIS "dominate" func-
                 then used to produce a change map of the region (Fig.                   tion and the new wetland information found in the Tb_1
                 15). Summary statistics for the region are found in                     or Tbl classification). The disadvantage is that the NWI
                 Table 4. This process may be repeated with a later scene                data must be digitized and generalized to be compat-
                 to determine successive change.                                         ible with the C-CAP Coastal Land-Cover Classification
                    This method may reduce change detection errors                       System, then converted from vector to raster format to
                 (omission and commission) and provides detailed                         be compatible with the raster remote-sensor data. Any
                 from-to change class information. The technique re-                     manual digitization and subsequent conversion intro-
                 duces effort by allowing analysts to focus on the small                 duces error into the database which may not be accept-
                 amount of area that has changed between               dates. In         able (Lunettaetal., 1991).
                 most regional projects, the amount of actual
                 change over one to five years is probably no
                 greater than 10% of the total area. The method                        Multi-Date Change Detection Using A
                 is complex, requiring a number of steps, and                          Binary Change Mask Applied to Date 2
                 the final outcome is dependent on the quality
                 of the change/no-change binary mask used in                                                       2
                 the analysis. A conservative threshold may ex-                      Date 1                        3 Rectified Thematic
                                                                                                                   4 Map
                 clude real change while a liberal threshold may                                                          per bands
                 create problems similar to those of the post-
                 classification comparison technique (See                                                           Traditional classification
                 "Postclassification Comparison Change Detec-                                                       of Date 1
                 tion.")
                                                                                                                   3 Date 1 band 3
                 Multiple-date Change Detection Using                                               @F`_ @3 Date2ba,d3
                 AncWary Data Source as Tb
                                                                                                                    Image algebra to identify
                 Sometimes a land-cover data source may be                                                          change pixels, e.g. ratio of
                                                                                         A"- "!
                 used in place of a traditional remote-sensing                                                      multidate band 3 data.
                 image in the change detection process. For                                                         Create change pixel mask
                 example, the NWI is inventorying all wetlands                                       I
                 in the United States at the 1:24,000 scale.                         Date 2                         Mask out change pixels
                                                                                                                   2
                 Some of these data have been digitized. In-                                                       3in Date 2 imagery and
                 stead of using a remotely sensed image as Tb                                                       classify
                 in the analysis, it is possible to substitute the                                                  Classification map of Date 2
                 digital NWI map of the region (Fig. 16). In
                 this case, the NWI map would be "recoded"
                 to be compatible with the C-CAP Coastal Land-                                                      Classification map of Date I
                 Cover Classification System (Table 2). This
                 should not be difficult since the two systems
                 are highly compatible. Next, Tb.-i        or Tb-,, of                                                    rm Post-Classification
                                                                                                                    Comparison Change Detection
                 the analysis is classified and then compared
                 on a pixel-by-pixel basis with Tb information.                                                     or Update Date 1 map with
                 Traditional from-to information can then be                                                        Date 2 change information
                 derived. As with any other postclassification                                                      using GIS dominate function
                 comparison, the accuracy of the change data-
                 base is dependent on the accuracy of both                     Advantages:                          Disadvantages:
                 input databases (C-CAP and NWI)                                 may reduce change detection        - requires a number of steps
                   Advantages of the method include the use                      errors (omission and comission)    - dependent on quality of'change/
                                                                               - provides 'from- to' change          no-change' binary mask
                 of a well-known, trusted data source (NWI)                      class information
                 and the possible reduction of errors of omis-
                 sion and commission. Detailed from-to infor-                                                 Figure 10
                 mation may be obtained by using this method.               Diagram of Multiple-Date Change Detection Using a Binary Change
                 Also, only a single classification of the Tb_1 or          Mask Applied to Date 2 Uensen, 1994).







            26       NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program











                                                                                                   el




                                                               '_4       Ap
                                        4r


                                                                                          !L






                                                 4@









                                                  P,
                                             'A



                                   V
                                 -4,
              V,
                                                                                                M Path 14 Row 34
                                                                                                          9121184
                                                                                             Metomkin Inlet AM


                                  tw







                                                                    Figure I I
                Rectified Thematic Mapper imagery of the Metomkin Inlet area obtained on 21 Sep 1984 (Dobson and Bright, 1992).

            Manual On-Screen Digitization of Change                         basemap and compared to identify change. Digitized
                                                                            high resolution aerial photographs displayed on a CRT
            Considerable amounts of high resolution remote sen-             screen can be interpreted easily using standard photo
            sor data are now available (e.g. SPOT 10 X 10 m, the            interpretation techniques based on size, shape, shadow,
            aircraft mounted Calibrated Airborne Spectrographic             texture, etc. (Ryerson, 1989). Therefore, it is becoming
            Imager [CASI] of the National Aerial Photography Pro-           increasingly common for analysts to interpret visually
            gram [NAPP]). These data can be rectified and used as           both dates of aerial photographs (or other type of
            planimetric basemaps or orthophotomaps. Often aerial            remote-sensor data) on the screen, annotate the impor-
            photographs are scanned (digitized) at high resolu-             tant features using heads-up on-screen digitizing, and
            tions into digital image files (Light, 1993). These pho-        compare the various images to detect change (Cowen
            tographic datasets can then be registered to a common           et al., 1991; Cheng et al., 1992; Lacy, 1992; Wang et al.,






                                                                                 Chapter 3:  Monitoring Uplands and Wetlands          27



















                                                                                              IVA


                                               AW-                                      @41







                                                                                                   TM Path 14 Row                   1.
                                                                                                               11/03188
                                                                                                    Metomkin Inlet .-Tn-




                                         -A





                                                                        Figure 12
                    Rectified Thematic Mapper imagery of the Metomkin Inlet area obtained on 3 Nov 1988 (Dobson and Bright, 1992).



                1992; Westmoreland and Stow, 1992). The process is              to interferences from aquatic as well as atmospheric
                especially easy when 1) both digitized photographs (or          sources. As with other new technologies, demonstra-
                images) are displayed on the CRT side by side, and 2)           tion of the appropriateness of interpretation of scanned
                they are topologically linked through object-oriented           photographs will be a critical step in expanding the G
                programming so that a polygon drawn around a feature            CAP Protocol (Also see "Accuracy Assessment for Indi-
                on one photograph will also be drawn around the same            vidual Date Classification of Water and Submersed Habi-
                feature on the other photograph. Scanning aerial pho-           tat Data"). The manual on-screen approach is recom-
                tographs unavoidably reduces the spatial and spectral           mended as a useful adjunct to other change detection
                resolution of source data. This loss may be significant in      methods. Its principle drawback is the time required to
                photographs of submerged features, which are subject            cover large regions in such a labor-intensive fashion.






          28       NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program



                                                                                 E


                                                                                          4**






                                                       c,
                                                                                         V






                                                                           V


                           N

                                                     x
                                              e@,    i
                                                                            >



                V
                V,_

                                                                       44
                     N,
                                                                                            C-CAP 63 MrTMM
                 - - @7
                                                                          g                       11/03/88

                        -v-
                                                                                             Developed-High
                                                                                     d         eveloped-Low 5=1, -
                                           Al
                                                                                             Cropland
                                                                                             Grassland
                                                                                             Deciduous Forest
                     A                               S nI
                                                     1@ I '-                                 Evergreen Forest
                           J
                            J
                             @ J           ,           I  ;   ,                              Mixea Forest
                                                                                             Scrub/Shrub
                                                                                             Palustrine Forest
                                                                                             Estuarine Marsh
                                                                                             Palus rine Marsh
                                                                                                  t
                                                                                             r1dal Flats
                                                   _14                                       Barren Land
                                                                                             Water
                                                   4

                                              7-1  J_rq       \-71






                                                            Figure 13
             Classification map of 3 Nov 1988 Landsat Thematic Mapper imagery of the Metomkin Inlet area (Dobson and Bright, 1992).


          Selecting Appropriate Classification                       The previous section indicated that these three of
          Algorithms                                               the seven most commonly used change detection algo-
                                                                   rithms are acceptable for C-CAP regional projects:
          C-CAP requires that the classification procedures used
          as part of the change detection process be approved      6 Postclassification Comparison
          and documented. Classification algorithms used in each   e Change Detection Using a Binary Change Mask Ap-
          region will be selected based on the capabilities and needs plied to Tb._1 or T.,
          of the regional participants. C-CAP assumes that the re- e Change Detection Using Ancillary Data Source as Tb.
          gional participants are experienced in image processing
          and mapping. If not, C-CAP will attempt to provide funda- Each of these requires a complete pixel-by-pixel classifi-
          mental technical assistance on a case-by-case basis.     cation of one date of imagery and, at least, a partial







                                                                                      Chapter 3: Monitoring Uplands and Wetlands              29
















                                                                                _41.4
                                                         - 410.4c



                                                           V4
                                                                                 1b

                                                                                                             .0e




                                                                                  11b
                                 4@

                                    41
                                    41
                                                     V
                                    It
                                                                    W.
                                                                                                                 Spectral Change
                                                                                                                     Mask File


                         'AW
                                                       P4








                        01.




                                                                           Figure 14
                     Binary "change/no-change mask" produced by image differencing TM bands 3, 4, and 5 of each date (Dobson and Bright, 1992).


                classification of an additional date. Hence, it is instruc-        will have sufficient expertise to assess the advantages of
                tive to review the C-CAP-approved image classification             alternative classification algorithms and to recognize
                logic which may be used in the regional projects.                  when human pattern recognition and other types of
                                                                                   intervention are necessary. In practice, it may be neces-
                                                                                   sary to employ a suite of algorithms including both
                Supervised and Unsupervised Image                                  supervised and unsupervised statistical pattern recogni-
                Classification Logic                                               tion approaches. Currently, maximurn-likelihood clas-
                                                                                   sifiers often serve as a good first step, but new statistical
                The primary reason for employing digital image classi-             approaches are being developed and implemented on a
                fication algorithms is to reduce human labor and im-               routine basis Uensen et al., 1987; Hodgson and Plews,
                prove consistency. It is expected that regional analysts           1989; Foody et al., 1992). It is important for analysts to







           30        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           remain flexible with regard to procedures and algorithms.       pixels are assigned to the nearest class in n-dimensional
              Standard supervised and unsupervised classification          feature space, or passed to a maximum-likelihood clas-
           techniques have been available for more than 20 years           sification algorithm that assigns an unknown pixel to
           and are well documented in texts byjensen (1986) and            the class in which it has the highest probability of being
           Campbell (1987). In a supervised classification, the            a member. Great care must be exercised when selecting
           analyst "trains" the classifier by extracting mean and          training samples (Mausel et al., 1990).
           covariance statistics for known phenomena in a single              In an unsupervised classification, the computer is
           date of remotely sensed data (Gong and Howarth, 1990).          allowed to query the multispectral properties of the
           These statistical patterns are then passed to a mini-           scene by using user-specified criteria and to identify x
           mum-distance-to-means algorithm in which unknown                mutually exclusive    clusters in n-dimensional feature

                                                1-4 -1-4













                        41






                                                                                W    1%

                                                       2tAI







                                              V


                                              to
                                                                  Y





                                                                     Fi gure 15
                                                                                               WIA6
                                                                                                      44
                                                                                                                      7
                                                                                                                      %,.a
                                                                                                                   _.Rat








                 Lcj@@



                                                                             MAP








              A map showing selected C-CAP change classes derived from analysis of the 21 Sep 1984 and 3 Nov 1988 Landsat TM data
              of the Metomkin Inlet area (Dobson and Bright, 1992).






                                                                                           Chapter 3: Monitoring Uplands and Wetlands                 31


                                                          Multi-Date Change Detection Using
                                                          An Ancillary Data Source as Date 1

                                                                                    Ancillary data source e.g.,
                                                      Dat 1                         National Wetlands inventory
                                                                                    Map


                                                                         ...............2Rectified Thematic
                                                      Date 2                      -3
                                                                                  4 Mapper bands


                                                                                    Classification map of Date 2


                                                                                    Classification map of Date 1


                                                                                    Perform Post-Classification
                                                                                    Comparison Change Detection
                                                                                    or Update Date 1 NWI map with
                                                                                    Date 2 change information using
                                                                                    GIS dominate function


                                                  Advantages:                        Disadvantages:
                                                  ï¿½ may reduce change detection       dependent on quality of ancillary
                                                   errors (omission and cornission)    information
                                                  ï¿½ provides 'from-to'change
                                                   class information
                                                  ï¿½ requires a single classification



                                                                                  Figure 16
                                              Diagram of Multiple-Date Change Detection Using Ancillary Data Source
                                              as Date I Uensen, 1994).



                 space (Chuvieco and Congalton, 1988). The analyst                      of five training sites per land-cover class be collected.
                 must then convert (label) the x spectral clusters into                 This creates a representative training set when per-
                 information classes such as those found in the C-CAP                   forming supervised classification and makes labeling
                 Coastal Land-Cover Classification System. Training sites               clusters much easier in an unsupervised classification.
                 visited in the field and identifiable in the digital imag-             In addition to the image analysts, the field team should
                 ery are also indispensable when labeling clusters in an                contain specialists in ecology, biology, forestry, geogra-
                 unsupervised classification. The following sections dis-               phy, statistics, and other pertinent fields, such as agronomy.
                 cuss C-CAP guidelines for collecting training and verifi-              Field samples should be stratified by land-cover type and
                 cation samples.                                                        by various physical factors such as slope, elevation, vegeta-
                                                                                        tion density, species mix, season, and latitude. The po-
                                                                                        lygonal boundary of all field sites should be measured
                 Selection of Training and Verification Samples                         using GPS whenever possible, and the locational, tempo-
                 for Supervised and Unsupervised Classification                         ral, and categorical information should be archived.
                                                                                           The collection of field training sites often requires
                 Only training sites that were actually visited on the                  multiple visits to the field. Some of the field sites may be
                 ground by experienced professionals should be selected                 used to train a classifier or label a cluster while a certain
                 for extracting the multispectral statistical "signature" of            proportion of the field sample sites should be held back
                 a specific class when performing a supervised or unsu-                 to be used for classification error assessment, which will
                 pervised classification. It is suggested that a minimum                be discussed.







              32          NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program



                                                                                    Table 4
                 Statistical summary of areal change (in ha) by land-cover class for the Metomkin Inlet area shown in Figures 12-16. Read
                 across each row to find which categories the 1988 totals came from. Read down each column to find which categories the
                 1984 totals changed to. Bold numbers along the diagonal indicate the area that did not change from 1985 to 1988.
                 uncon. = unconsolidated.


                                                                                             1984 Classification


                                          Developed Grassland/ Forest          Scrub/ Palustrine Estuarine        Palustrine     Water/         Bare
                                             land       cultivated     land      shrub     forest    emergent     emergent uncon. shore         land    TOTAL


                 1988 Classification

                    Developed land           1,158            85           8       0          0              4         0                1         0       1,256

                    Grassland/ cultivated          0       219341        562       0          0              2         0                0         17    21,922

                    Forest land                    0          165     189915       0          0              1         0                0         0     19,081

                    Scrub/shrub                    0          240        562      854         0              2         0                0         0       1,658

                    Palusuine forest               0          20           9       0         787,            0         0                0         0         816

                    Estuarine emergent             0          26           9       0          0        11,587          0               13         8     11,643
                    Palustrine emergent            0            0          0       0          0              0         0                0         0             0
                    Water/uncon. shore             0             4         0       0          0              2         0          37,172        144     37,322

                    Bare land                      0          19           0       0          0             23         0              124       507         673

                    TOTAL                    1,158         21,900     20,065      854        787        11,621         0          37,310        676     94,371




                 The following materials are indispensable to a suc-                         is preferred rather than transparent-overlay techniques,
              cessful field exercise:                                                        which are cumbersome and difficult to use under field
                                                                                             conditions.
              ï¿½  Imagery geocorrected to a standard map projection                              C-CAP investigators have assembled and tested a field
              ï¿½  Topographic maps at 1:24,000 or the largest available                       station based on a color laptop computer with commer-
                 scale                                                                       cial software. At present the software supports visualiza-
              ï¿½  Global Positioning System (GPS)                                             tion of raster imagery (e.g. satellite data, digital
              ï¿½  Aerial photographs                                                          orthophotographs, scanned aerial photographs) and
                                                                                             vector databases (e.g. TIGER road networks, NWI wet-
                 it is advisable to perform, at least, a cursory classifica-                 lands). A version of the software soon to be available
              tion before initiating fieldwork. In this case, both raw                       from commercial vendors will allow realtime input of
              and classified data should be taken to the field. The                          GPS coordinates. It will then be possible to follow field
              primary function of the cursory classification is to guide                     movements directly on the image and map data. The
              field workers in targeting the covers and signatures that                      software also allows for completion of field forms on
              are most difficult and confusing. Keep in mind that the                        screen in the field. Preliminary tests are encouraging,
              vast majority of all cover will be easy to identify on the                     but the field station is not fully operational at this time.
              ground and on the imagery. Efficient use of field time                         One shortcoming, for example, is the poor performance
              provides for quick verification of easy cover types and                        of active matrix color screens in sunlight.
              maximum attention to difficult, unusual, and ecologi-
              cally critical cover types.
                 Field investigators should anticipate the need to know                      Use of Collateral Data in Image Classification
              not only the geodetic coordinates of training sites but
              also the layout of the road network that will provide                          The overriding goal is to produce accurate individual
              access. It is advisable to imbed roadway information                           date classifications and accurate change detection data-
              into the raw imagery. This can be done using the Bu-                           bases. Any information or operation that enhances data
              reau of the Census Topologically Integrated Geographic                         quality is generally encouraged. C-CAP does not en-
              Encoding and Referencing (TIGER) files. Imbedding                              dorse the notion that the use of collateral data in a






                                                                                   Chapter 3: Monitoring Uplands and Wetlands             33

                remote-sensing project is "hedging." Instead, the objec-         CAP regional projects, the input data will be 30 x 30 in
                tive is to use collateral data innovatively to improve the       pixel data recorded by a Landsat TM sensor. The mini-
                accuracy of the C-CAP database.                                  mum measurement unit, however, combines the ability
                  There are many potential sources of collateral data            (e.g. sensor limitations) and effort (e.g. field verifica-
                including soil maps, NOAA coastlines (T-sheets), tim-            tion) required to measure a category with the spatial
                ber surveys, USGS digital line graphs, and digital eleva-        precision and accuracy necessary to accomplish the
                tion models (for elevation, slope, and aspect). These            intended use of the data. Each land-cover category
                can be incorporated by masking, filtering, probability           could potentially have a different minimum measure-
                weighting, or including in the signature file (Ryerson,          ment unit based on the size of individual parcels and
                1989; Baker et al., 1991). Depending on the impor-               the distinctiveness of the signature. Thus, the mini-
                tance of each category, analysts may use certain catego-         mum measurement unit differs from a traditional mini-
                ries to overrule others Uensen et al., 1993a).                   mum mapping unit, which by definition imposes a pre-
                  The NWI is an especially valuable collateral database          determined polygon (or pixel) size for all land-cover
                that may be of value when classifying wetlands. Re-              categories (for example, a rule that all parcels of one
                gional analysts should incorporate NWI data to the               hectare or larger will be mapped). This traditional ap-
                maximum extent possible. NWI data are recognized as              proach is acceptable for manual mapping using analog
                the most authoritative and complete source of wetlands           aerial photographs but is difficult to apply to raster
                land-cover data (Wilen, 1990). However, NWI maps are             imagery. Regional analysts will be responsible for defin-
                not temporally synchronized in each region and are               ing minimum measurement units, which will generally
                not in a digital format for many regions. An approach            be larger than a single pixel but no larger than three
                based on complementary use of NWI and imagery will               pixel dimensions on the short axis.
                be an asset to both C-CAP and NWI. At a minimum,                   Regardless of the minimum measurement unit,
                NWI maps, digital data, or both should be used to                change analysis will be conducted pixel by pixel. C-CAP
                define training samples, to check intermediate results,          protocol requires that the inherent resolution of the
                and to aid in the final verification of the wetlands             raw data must be retained throughout the classification
                portion of the G-GAP maps. NWI digital data may be               and change-analysis processes. Aggregation and filter-
                used as a probability filter in the classification process.      ing of pixels should occur only in regard to carto-
                In this approach, C-CAP recommends an "innocent                  graphic presentation of the completed change detec-
                until proven guilty" attitude toward the NWI data. In            tion database.
                other words, the NWI category is considered correct for            Regardless of the techniques employed, the final da-
                a given pixel area for each time period, unless spectral         tabase should be capable of representing land-cover by
                signatures or collateral data suggest that the NWI cat-          class for the base time, land cover by class for each
                egory is incorrect or a land-cover change has occurred.          earlier or later time, and land-cover change by class for
                Even if the NWI data were 100% correct at the time of            each change period. The final database should contain
                NWI mapping, overriding by spectral data would be                the full change matrix (all "from" and "to" categories)
                necessary to detect change over time. Ultimately in              for each change period.
                turn, the G-CAP change detection database can assist
                NWI managers in determining the need for NWI updates.            Analog (Hardcopy) Cartographic Products

                                                                                 Hardcopy maps of the final database are not specifically
                Cartographic Portrayal of Classification and                     required by C-CAP, but they are certain to be useful
                Change Detection Maps                                            when presenting results. Often it is useful to produce a
                                                                                 smaller scale regional map (usually requiring some pixel
                C-CAP products must meet stringent cartographic stan-            aggregation) that gives an impression of the scope of
                dards. The following sections discuss the minimum                the effort and to produce several larger scale maps at
                measurement unit and its proper use when aggregating             full resolution that demonstrate the level of detail and
                change information. Formats of classification maps and           highlight notable findings. All maps should come directly
                change maps must satisfy C-CAP criteria whenever                 from the final database complying with CCAP protocols,
                hardcopy maps are produced.                                      but overlaying or imbedding ancillary data, such as DLG
                                                                                 and TIGER data, is encouraged with proper notation.
                                                                                   If the statistical summary of changes is present on a
                The Concept of the Minimum Measurement Unit                      map, C-CAP recommends that the numbers included
                                                                                 in it always be calculated for the area shown on the
                The minimum measurement unit is a measure of both                map. It is not acceptable to associate the summary of
                the precision and accuracy of input data. For most C-            changes for one area (larger or smaller) with a map of







           34        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           another. The statistical summaries of the change detec-           trix numbers change with aggregation for the same
           tion matrix must always be calculated from the data-              territory.
           base at full resolution, rather than from the aggregated            Technically, the minimum cartographic presentation
           data of the plot file. It is not advisable to allow the           is 1) a map for the base time, 2) a map showing gains by
           numerical count of class area to float with the level of          class, and 3) a map showing losses by class. A full classi-
           cartographic aggregation. Unless all counts are based             fication for the earlier or later (nonbase) time may be
           on the full resolution database, some classes composed            useful, but it is not essential to present the matrix of
           of small features may disappear at higher levels of               possible changes. Examples of some of these products
           aggregation. Map readers may become confused if ma-               are found in Figures 6-13.







                                                                             Chapter 4
                                                          Monitoring Submerged Land
                                                             Using Aerial Photography

                 GCAP Focus on Aerial Photography of                                    Ancillary Technologies for Collecting
                 Submersed Rooted Vascular Plants (SRV)                                 Submersed Habitat Data

                 Photic submerged land can support submersed rooted                     Some successes have been reported with satellite imag-
                 vascular plants (SRV) (including salt-requiring sea-                   ery and a number of other technologies in monitoring
                 grasses and oligolialine and freshwater tolerant grasses               photic submerged land. Presently, these technologies
                 and forbs), macroalgae, and coral reefs (see "Water                    supplement, and eventually may replace, aerial photog-
                 and Submerged Land" and Appendix 3). The G-CAP                         raphy for change detection in SRV. Some of them are
                 Coastal Land-Gover Classification (see Table 1) identi-                briefly mentioned here.
                 fies Marine/Estuarine Aquatic Beds, specifically SRV,                    Satellite imagery has some advantages and disadvan-
                 of primary importance to be inventoried and placed in                  tages compared with photography. Satellite data gener-
                 the GCAP database (Klemas et a]., 1993). Many of the                   ally have greater spectral resolution than aerial photog-
                 steps discussed in Chapter 3 to monitor uplands and                    raphy but lesser spatial resolution. Satellite imagery is
                 wetlands are pertinent to monitor SRV. However, there                  already in a digital format whereas information derived
                 are significant differences which cannot be ignored (Table             from aerial photography must eventually be digitized
                 5). Important considerations include the following:                    to be quantitatively analyzed. Landsat and SPOT data
                                                                                        have been successfully used to inventory some
                 ï¿½  mapping SRV is primarily a photogrammetric               task,      macroalgae such as the giant kelp, Macrocystis pyiifera,
                    rather than a satellite task, requiring an entirely dif-            along southern California shorelines Uensen et al., 1980;
                    ferent sensor system (aircraft, camera filter, and film);           Augenstein et al., 1991). In clear shallow tropical waters
                 ï¿½  aerial photography is normally not radiometrically                  with highly reflective substrate, Landsat imagery may
                    (except for color balance between photographs) or                   discriminate sandy from coral reef or seagrass areas
                    geometrically corrected;                                            (Luczkovich et al., 1993) or provide an estimate of
                 ï¿½  time of day, sensor altitude, and flightline placement              biomass for unispecific beds of Thalassia testudinum
                    are very flexible, unlike fixed orbit satellite sensor              (Armstrong, 1993). In the turbid estuaries of the east-
                    systems;                                                            ern United States, Landsat and SPOT imagery can be
                 ï¿½  numerous environmental conditions must be consid-                   used to detect some (e.g. large, dense, shallow) but not
                    ered (sea state, water clarity, water depth, low altitude           all of the SRV that is visible in the best aerial photogra-
                    atmospheric conditions) to optimize photography;                    phy. Aerial photography is, in fact, often used as "ground
                    and                                                                 truth" when interpreting satellite imagery. Because of
                 ï¿½ aerial photographs are in analog format.                             the fixed orbital paths of satellites, it is only fortuitous
                                                                                        when a satellite image is acquired under optimum con-
                 These differences are so significant that it is instructive            ditions to inventory SRV (see "Environmental Consid-
                 to focus on aerial photography of SRV.                                 erations"). For these reasons, aerial photography is the
                                                                                        C-CAP imagery of choice for comprehensive mapping
                                                                                        and change detection (Ferguson and Wood, 1990; Tho-
                 Ancillary Categories of Submersed Habitat                              mas and Ferguson, 1990; Orth et al., 1991; Ferguson et
                                                                                        al., 1992 and 1993). Photo interpretation supported by
                 Other types of submersed habitat classified by C-CAP                   surface-level signature verification and species identifi-
                 can be monitored with guidelines similar to those pre-                 cation is qualitatively and spatially more reliable for
                 sented here for SRV. At a minimum, regional coopera-                   SRV than are satellite-based methods.
                 tors are requested to map and conduct change analysis                    Several technologies may provide valuable supple-
                 for SRV. Increasing the number of habitat types to be                  mental data to aerial photographic detection of habitat
                 included in the study will be based on local or regional               change. These include closed circuit television (CCTV)
                 interest and support for the effort. For example, a                    on an airplane, small boat, or remotely operated ve-
                 comprehensive mapping of SRV, macroalgae, and coral                    hicle (ROV), side-scan and down-looking sonar, new
                 reefs is underway in the Florida Keys (see "State of                   satellite sensors, airborne spectral scanners and digital
                 Florida, Department of Environmental Protection").                     video scanners, and digitized photography. Such new

                                                                                                                                                       35







              36         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

                                            Table 5                                         technology will be incorporated into the C-CAP guide-
                 General steps required to conduct regional C-CAP                           lines as it is demonstrated to meet qualitative, quantita-
                 change detection projects to extract water and sub-                        tive, resolution, and geographic positioning standards.
                 merged land information using aerial photography.                          At present, CCTV is effective for surveillance applica-
                 Each major step is listed in the order to be accomplished.                 tions but georeferencing and rectification fall short of
                                                                                            metric quality photography. Airborne multispectral scan-
                 1. State the regional change detection problem                             ners and digital cameras are technologies with applica-
                    a. Define the region                                                    tions in the demonstration stage of development.
                    b. Specify frequency of change detection (I to 5 yr)                       Direct mapping of habitat borders can be performed
                    c. Identify classes of the C-CAP Coastal Land-Cover                     with differentially correctable GPS instrumentation
                        Classification System                                               when the perimeter of that habitat can be visually ob-
                                                                                            served or detected with the aid of instruments in the
                 2. Consider significant factors when performing change                     field. Differentially corrected GPS can provide posi-
                    detection                                                               tions of surface level data at an accuracy suitable to
                    a.  Remote sensing system considerations                                supplement or assess the accuracy of aerial photographic
                        1) Spatial resolution and scale
                        2) Flightline considerations                                        data. With differential correction, single position fixes
                        3) Spectral resolution and film/filter combination                  with GPS are accurate to a circular error probable
                        4) Temporal resolution and diurnal sun angle                        (CEP)of ï¿½5 m 50% of the time. The methodology for
                        5) The preferred C-CAP aerial photography system                    using GPS in accuracy assessment and monitoring of
                    b.  Environmental considerations                                        SRV is a current research topic funded by C-CAP.
                        1) Atmospheric conditions
                        2) Turbidity conditions
                        3) Vegetation phenological cycle characteristics
                        4) Tidal stage                                                      Aerial Photography of SRV
                        5) Surface roughness and sun glint conditions
                                                                                            Film

                 3. Interpret aerial photographs to extract water and
                    submerged land information                                              The recommended film for aerial photography of SRV
                    a.  Acquire appropriate change detection data                           is Aerocolor 2445 color-negative film. Second choices
                        1) in situ surface level verification and basemaps                  are Aerochrome 2448 color-reversal and Aerographic
                        2) Aerial photography                                               2405 black-and-white negative film. A haze filter should
                           a) Base year (Td                                                 always be used to minimize the degrading effect of haze
                           b) Subsequent year(s) (T., or T,,)                               on photographic images. We do not recommend infra-
                    b.  Preprocess the multiple-date photography
                        1) Radiometric correction (color balance)                           red film for delineating SRV. In our experience in
                        2) Optically register photography to planimetric                    North Carolina with tandem cameras, Aerochrome 2443
                           basemap                                                          false-color infrared film was much less effective than
                    c.  Select appropriate change detection algorithm                       color film at recording benthic features in shallow,
                        (usually postclassification comparison)                             moderately turbid water. True color film gives more
                    d.  Image analysis
                        1) Monoscopic (interpretation of single photos or                   information than black-and-white or infrared film, is
                           orthophotographs)                                                critical for initial mapping attempts in new or unfamil-
                        2) Stereoscopic (analog or analytical)                              iar areas, and may permit identification of species in
                    e.  Transfer polygons to planimetric basemap                            some tropical areas. Color negative film also appears to
                    f.  Digitize polygons                                                   be better than color reversal or black-and-white film for
                    g.  Perform change detection using GIS algorithms
                        1) Highlight selected classes using change detection                identification of habitat under moderately turbid or
                           matrix                                                           hazy conditions. Color transparency prints are dimen-
                        2) Generate change map products                                     sionally stable and are most amenable to illuminating
                        3) Compute change statistics                                        dark areas of the photograph under magnification.
                 4. Conduct quality assurance and control                                   Paper prints are not as dimensionally stable as transpar-
                    a. Assess spatial data quality                                          encies (i.e. paper prints are subject to stretching and
                    b. Assess statistical accuracy of                                       shrinking) but they are more resistant than transparen-
                        1) Individual date classification                                   cies to damage from handling when used for field work.
                        2) Change detection products

                 5. Distribute C-CAP results                                                Metric Photography and Photographic Scale
                    a. Digital products
                    b. Analog (hardcopy) products                                           Metric-quality aerial photographs (:53' of tilt off-nadir
                                                                                            and including camera calibration data) are essential






                                                                                       Chapter 4: Monitoring Submerged Lands            37


                and should be acquired with a protocol similar to that          1:24,000 (1 inch = 2,000 ft), a standard 9 X 9 inch aerial
                employed by NOAA's Photogrammetry Branch (1980)                 photograph has a coverage of 18,000 x 18,000 ft. Large
                to produce the highest quality data possible. The need          areas (relative to coverage of a single photograph) of
                for rectification of photography is minimized by pre-           open water require parallel flightlines and bridging of
                cise control of aircraft altitude and orientation relative      the large-scale photography to control points with the
                to the vertical during photography and by interpreta-           small-scale photography, construction of towers, etc.,
                tion in stereo. Photography should be obtained at a             to supplement horizontal control features or inflight
                scale appropriate to the areal extent of habitat, local         GPS positioning of photographic centers.
                water conditions, type of habitat being studied, and              Overlap of photographs includes endlap of adjacent
                resolution requirements for the resultant data. Scale is        photographs along a flightline and sidelap of photo-
                a compromise among resolution of signatures, cover-             graphs along parallel flightlines. Sixty percent endlap
                age of habitat, inclusion of land features sufficient for       allows stereoscopic interpretation, facilitates interpre-
                horizontal control, and cost. Photographic scale should         tation from the most central region of the photographs,
                normally range from 1:12,000 to 1:24,000. For exten-            and compensates for loss of coverage due to sun glint in
                sive areas of high and variable turbidity such as Chesa-        the photographs. (Sun glint is the image of the sun
                peake Bay and eastern North Carolina, 1:24,000 or               reflected off the surface of the water. See "Sun Angle.")
                1:20,000 scale photographs may be adequate when the             Sidelap of 30% ensures contiguous coverage of adja-
                water is clear. For chronically turbid estuarine or brack-      cent flightlines and produces a block of aerial photo-
                ish water areas, 1:12,000 or larger scale photographs           graphs that may be subjected to photogrammetric
                obtained at times of minimal turbidity may be required          bundle adjustment if necessary.
                for acceptable visualization of submerged features.
                Small-scale photography may be necessary to bridge
                habitat delineated in larger scale photographs to local         Environmental Considerations
                horizontal control points on adjacent land features that
                are not included in the larger scale photographs. GPS           Knowledge of the study area that is important to a
                onboard the airplane for positioning photographic cen-          successful project includes the plant species compris-
                ters during exposure may reduce this limitation of larger       ing SRV; morphology and phenology of these plants;
                scale photography. For extensive areas of relatively clear      depth range and location of known habitat; locations
                water, such as the Florida Keys, a scale of 1:48,000 may        with water depth potentially suitable for habitat, types
                be sufficient and cost effective. This is a current C-CAP       and locations of benthic features that may confuse photo
                research topic (see "State of Florida, Department of            interpretation of SRV; seasonality of turbidity, weather,
                Environmental Protection").                                     and haze; daily patterns in wind speed and direction;
                                                                                and progression of sun angle through the day. Primary
                                                                                and secondary seasonal windows and the day and time
                Flightlines, Reconnaissance Flights, and Photo-                 to conduct photography are selected to optimize the
                graphic Overlap                                                 visibility of habitat in the photography. Surface waters
                                                                                in different locations and at different times of the year
                Flightlines are planned with reference to aeronautical          will be more or less sensitive to turbidity from local
                and nautical charts to include all areas known to have,         runoff, plankton blooms, local resuspension of sedi-
                or which potentially could have, SRV. The efficiency of         ment, and surface waves, Seasonal and daily trends for
                photographic missions can be optimized by minimizing            haze, cloud cover, wind direction, wind duration, and
                the number of flightlines and by contingency planning.          wind velocity should be included in planning for pho-
                Some airspace is restricted for military or other use, for      tography. The decisions of when to have the aircraft
                example, and is indicated on aeronautical charts. Nau-          arrive at the study area (within the seasonal window)
                tical charts provide bathymetric data useful for desig-         and when to collect photography are based on NOS
                nating potential habitat areas when combined with lo-           tide tables, local knowledge of factors affecting water
                cal knowledge of the depth of vegetated bottoms. Re-            clarity and depth, observation of recent weather pat-
                connaissance flights can provide valuable perspective           terns (precipitation, wind direction, and wind speed),
                on SRV distribution if timed to optimize visualization of       and water clarity. The final decision to photograph
                shallow bottoms (see "Environmental Considerations").           includes observations from the air based on the pilot's
                Ideally, each photograph in a flightline records cul-           estimate of haze, cloud cover, and overall visibility.
                tural and shoreline features required to register the             Primary and secondary photographic windows should
                image to the base map, about 1/3 of the exposure. This          be one or two months duration to ensure optimal con-
                permits correction of photographic scale and orienta-           ditions for photography. For single day missions it may
                tion to the external reference system. At a scale of            be possible to have the plane and flightcrew fly to the







           38         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           study area on the day of photography. In our experi-                 floating debris should not be visible from the air or in the
           ence in North Carolina, staging of the plane and flight              photographs. For some areas, ocean swell can be an im-
           crews to the study area several times for several days was           portant consideration and should not exceed 3 ft.
           required to complete missions involving more than one
           day of actual photography.                                           SunAngle Sun angle affects the illumination of benthic
                                                                                features, sun glint, and shadows from tall shoreline
           Phenology-The best time of year to acquire photogra-                 features in the photographs. A sun angle of 20-25* is
           phy is during the season of maximum biomass or flow-                 optimal to record benthic features (Keller, 1978). A
           ering of dominant species, considering the phenologic                sun angle of 15-30' is recommended by C-CAP. This
           overlap for the entire community. This is June for the               interval maximizes the time for photography consider-
           SRV of the Pacific Northwest and Atlantic Northeast,                 ing both the illumination of submerged features and
           April and May for eelgrass in eastern North Carolina,                sun glint. Sun angles above 15* illuminate the bottom
           and September for most of the other species of SRV in                sufficiently for photographic purposes. Sun glint also
           the eastern United States.                                           increases with sun angle but precludes visualization of
                                                                                benthic features where it occurs in the photograph. As
           Clouds and Haze It is best to have no clouds and                     sun angle increases, sun glint also increases and moves
           minimal haze. Thin broken clouds or thin overcast                    from the edge toward the center of the photograph.
           above the plane may be acceptable when these are                     Loss of coverage due to sun glint at sun angles of up to
           determined by visualization from the air neither to cast             about 30' is compensated (to ensure monoscopic cov-
           shadows nor adversely affect illumination of the study               erage, at a minimum) by the recommended endlap of
           area. Haze reduces illumination and clarity of the im-               60% (see "Flightlines, Reconnaissance Flights, and Pho-
           age of benthic features being recorded in the photo-                 tographic Overlap"). Eighty percent endlap will im-
           graph. Cooperators are referred to the "Aerial Photog-               prove coverage when high sun angles cannot be avoided.
           raphers Clear Day Map," U.S. Department of Com-                      Photography at sun angles above 30' is not recom-
           merce, Environmental Data Service.                                   mended. Sun glint is minimized when the sun and land
                                                                                are on the same side of the plane because sun glint
           Turbidity-Aerial photography should be conducted                     does not occur on land. Shadows from tall objects on
           when turbidity is low. Care should be exercised in areas             shore such as trees, however, can preclude visualization
           adjacent to sources of suspended sediment and nutri-                 of benthic features and may be a factor when the land
           ents. Data collection should be avoided during seasonal              and sun are on the same side of the plane.
           phytoplankton blooms or immediately following heavy
           rains or persistent strong winds. Potential days for pho-
           tography are those during the photographic window                    Photointerpretation of SRV
           when high water clarity is expected, based on local
           experience, recent weather patterns, and surface level               Habitat defined by the presence of SRV can be inter-
           observation. The flightcrew should confirm water clar-               preted from metric-quality aerial photographs exposed
           ity from the air on the day of photography.                          as recommended in the previous sections. The accurate
                                                                                identification of SRV in aerial photographs requires
           Tides-Generally, aerial photography should be col-                   visual evaluation of the fundamental elements of image
           lected within ï¿½2 hours of the lowest tide predicted by               interpretation (tone, color, contrast, texture, shadow, etc.).
           NOS tide tables, although factors affecting water depth              It also requires extensive experience at ground level in the
           and water clarity should be considered simultaneously.               study area; the photographic images of habitat and
           In general, extreme low tide, which may be -0.5 to -1.0 m            nonhabitat features vary in ways which cannot readily be
           or more around the U.S. coast is preferred, if compat-               modeled, described, or communicated. Training for a
           ible with other constraints. The significant "lag" in the            habitat change analysis effort includes literature research;
           tidal stage of some estuaries should be considered for               discussions with local ecologists and biologists; site visits
           data acquisition.                                                    on foot, swimming (snorkel or scuba), or small boat;
                                                                                overflights in a small plane; and examination of historical
                                                                                aerial photographs of the area. Training of photo inter-
           Wind and Surface Waves-No wind and no waves is best                  preters is active throughout the life of the project.
           for aerial photography. Low wind (<10 mph) may be                       SRV are best observed by using stereo pairs of photo-
           acceptable. The direction, persistence, fetch (the distance          graphs and high quality stereoscopic instruments (e.g.
           that wind can blow unobstructed over water), and recent              Wild, AVIOPRET, APT2, stereoscopes) - Polygons are
           wind events should be taken into account. Breaking waves             traced on overlays fixed to each photograph. To be
           and associated turbidity, white caps, lines of bubbles, and          delineated as habitat, recognizable and verified signa-







                                                                                           Chapter 4: Monitoring Submerged Lands               39


                 tures of SRV must be present in the photographs. SRV                 SRV with unrecognized signatures due to poor pho-
                 (and other benthic features) in a given area will present          tographic conditions cannot be mapped as habitat un-
                 a variety of signatures depending upon the species                 less the area is rephotographed or additional sources of
                 present, bottom sediment, depth, season, haze, clouds,             data are incorporated into the database. When photo
                 water clarity and surface disturbances, and sun angle at           interpretation is difficult or not possible, the preferred
                 the time of photography.                                           option is to rephotograph the area under better condi-
                   The designation of a given area as SRV is a function             tions. Although desirable, this may not be possible.
                 of the minimum detection unit, the minimum map-                    Even under the best photographic conditions, delinea-
                 ping unit, and the proximity of the area to other SRV.             tion of all or part of some habitat polygons may require
                 Assuming a photographic scale of 1:24,000, high qual-              additional effort in regard to surface level verification or
                 ity optics, high resolution film, and ideal conditions             direct inclusion of surface level data. Polygon borders
                 (e.g. dense clusters of large vigorous shoots growing on           derived from surface level data must be so designated in
                 light-colored sediment in shallow, clear, calm water), it          the lineage database for "truth in labeling" requirements
                 is usually possible to have a minimum detection unit of            (see "Digital Product"). Suitable surface level positioning
                 approximately I in. All detected SRV that appear to be             techniques include GPS or more traditional survey posi-
                 in a continuum with adjacent SRV in an area exceeding              tioning techniques that can be demonstrated to provide
                 0.03 ha will be mapped as a single. polygon. The mini-             the positional accuracy required by C-CAP.
                 mum mapping unit is the smallest area to be mapped as                Within a polygon of SRV, the extent of bottom cover-
                 habitat. At the C-CAP map scale of 1:24,000, the mini-             age by shoots of SRV and the pattern of distribution of
                 mum mapping unit is 0.03 ha for SRV (i.e. a diameter               the shoots or bed form (e.g. circular, doughnut-shaped,
                 of about 0.8 mm on the map represents a diameter of                irregular patches, or continuous cover of SRV) reflects
                 about 20 in or an area of about 0.03 ha on the ground).            the interaction of biotic, physical, and anthropogenic
                 Therefore, isolated groups of shoots with a diameter of            factors. Coverage and bed form can be estimated from
                 less than 20 in may be detected but not mapped as                  aerial photographs but is not a requirement of C-CAP.
                 habitat. The presence of SRV signature in the photo-               An example of a coverage index is an adaptation of the
                 graph defines habitat if 1) the total area exceeds 0.03 ha;        crown density scale originally developed to categorize
                 2) no unvegetated discontinuities, such as dredged or              coverage by trees crowns in forests (Orth et al., 1991).
                 natural channels, partition the distribution into spatial          However, coverage indices and bed-form identifications
                 units less than 0.03 ha; and 3) unvegetated areas be-              are affected by factors such as water depth and brightness
                 tween plants are not large relative to the minimum                 of bottom sediments. The degree of contrast between
                 mapping unit. Unfortunately, not all areas of SRV can              shoots and exposed sediment and the clarity of the photo-
                 be detected when photographic conditions are less than             graphic image determines the minimum detection unit of
                 ideal. Because of the constraint of the minimum map-               features within SRV. Comparison of habitats with differ-
                 ping unit and the possibility of suboptimal photogra-              ent depths, water clarity, or substrate brightness, there-
                 phy, delineations of SRV will tend to be conservative.             fore, is problematic. Analysis of change over time at a
                 The degree of underestimation depends upon the at-                 given location may be useful but requires consistent pho-
                 mospheric and hydrographic conditions at the time of               tographic conditions and field verification. Changes in
                 photography, the experience of the photo interpreter,              coverage or bed form over time in a given location may
                 and the nature of the subject area.                                indicate changing conditions in that habitat polygon or
                   Optimizing conditions for photography will mini-                 disturbances, such as scarring by boat propellers.
                 mize underestimation of SRV, particularly in areas that              Some data including species, biomass, productivity,
                 are intrinsically more difficult to interpret. Where habi-         functional status, and health of SRV may not be inter-
                 tat edges are clearly distinct in superior-quality photog-         pretable from the aerial photographs. Species identifi-
                 raphy, they may also be detected in inferior-quality               cation is not possible from aerial photography in tem-
                 photography (e.g. high biomass of SRV along a clear                perate areas such as North Carolina and the Chesa-
                 water channel with a steep bank of light-colored sedi-             peake Bay. In some tropical areas, species distributions
                 ment). In other cases where the edges are not clearly              and photographic signatures may be sufficiently dis-
                 distinct in superior-quality photography, they are likely          tinct to discriminate by species.
                 to remain undetected in inferior photography (e.g. low
                 biomass of SRV growing on a shallow depth gradient of              Field Surveys
                 deep, turbid water over dark-colored sediment). The
                 deep-water edge of habitat often will be difficult to              Species and Habitat at Randomly Selected Stations
                 delineate. This edge may also be at high risk for loss due to
                 degradation in water quality that limits the illumination of       Once selected by stratified random sampling of poten-
                 the bottom with photosynthetically active radiation.               tial habitat, stations are observed for SRV species and






           40         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

           the presence or absence of aquatic beds during the                  sary, to collect surface level data for inclusion in the
           same season and preferably within one year of the                   spatial database. Surface level data intended to aug-
           photography. Stations are stratified by water depth and             ment photo interpreted data require differentially cor-
           water body. Water depth determines if sampling can be               rected GPS positioning to a CEP of ï¿½5 m.
           accomplished by wading, snorkeling, or scuba diving.
           Clear water with a bottom depth of @!2.5 m or somewhat
           shallower turbid water may require scuba. Stratification            Base Maps and Registration of Habitat
           permits flexibility in sampling intensity and effort (sam-          Polygons
           pling by scuba requires special training and resources and
           takes about twice the time per station). Bathymetry and             Accurate and up-to-date planimetric base maps of coastal
           reference coordinates in NOAA nautical charts of the                land features are essential for georeferencing (estab-
           study area facilitate selection and positioning of stations.        lishing of geographic location) and scaling polygons of
           Navigation to stations is with GPS. The spatial density of          habitat interpreted from aerial photographs. C-CAP
           points is adjusted according to the resources and scale of          recommends 1) use of the most accurate and up-to-
           the project (e.g. an average of 1.5 to 2.5 nmi from station         date base map available for the study area and 2) use of
           to station in North Carolina). Great care is taken to in-           the most cost-effective technology to apply local hori-
           clude all locations of potential habitat in the surface level       zontal control to interpreted data by registration of the
           survey. SRV are limited to water depths less than about 2           photographs to base maps. The base map and the regis-
           m at mean lower low water (MLLW) for Chesapeake Bay.                tration technology may vary regionally.
           A similar depth limit was determined for that habitat in
           eastern North Carolina. To determine that depth in North
           Carolina, potential habitat was sampled to water depths of          Planimetric Base Maps
           10 ft MLLW (Ferguson and Wood, 1990,1994). SRV are
           not known to occur seaward of the barrier islands in                The accuracy of the base map used for local horizontal
           North Carolina. In sharp contrast, the maximum depth                control places a limit on the accuracy of the C-CAP
           for SRV is 9 m off the northwest coast of Florida.                  product. The two base maps broadly available are NOAA
              The presence or absence of aquatic beds and species              shoreline and USGS 7.5' topographic maps. NOS pro-
           of SRV are determined within an area equal to the                   duces highly accurate shoreline maps based on tide-
           minimum mapping unit and centered around the nomi-                  coordinated and fully rectified photography (Swanson,
           nal station location. If SRV are present, visual observa-           1949; Ellis, 1978; Slamma, 1980; NOAA Photogramme-
           tions of the number, size, and distribution of groups of            try Branch, 1989; Crowell et al., 1991). When available
           plant shoots are recorded. These data are translated                and current, NOAA shoreline and coastal data should
           into an assessment of the presence or absence of an                 be used for C-CAP projects (e.g. Ferguson et al., 1991).
           aquatic bed at the station considering the spatial distri-          These data, available in graphic and digital form, are
           bution of SRV relative to the minimum detection and                 products of the NOAA Coastal Mapping Program and
           mapping units. The goal is to assess presence data in a             are available from NOS. Shoreline data are produced
           manner relevant to photo interpretation (see "Photo                 from tide-coordinated photographic data and ground
           interpretation of SRV"). Ancillary data recorded are                level survey data by the Photogrammetry Branch of
           water depth, salinity, water clarity, latitude and longi-           NOS and meet or exceed national map accuracy stan-
           tude, and descriptions of benthic sediment       'algae, ani-       dards. Horizontal ground control meets or exceeds
           mals or animal shells, boulders, etc. A GPS position fix            third-order class I specifications found in the geodetic
           is taken to be differentially corrected (postprocessing)            control standards (Federal Geodetic Control Commit-
           to a CEP of ï¿½5 m. If the station data are not required to           tee, 1984). The Coastal Mapping Project of the Photo-
           verify photo interpretation (see below), they can be                grammetry Branch provides data that depict the delin-
           used to estimate the accuracy of the habitat data (see              eation of the mean high water line, the limit of emer-
           "Recommended Accuracy Assessment Test").                            gent vegetation (apparent shoreline) and/or cultural
                                                                               shoreline, and in some areas, e.g. North Carolina, the
                                                                               approximate MLLW line. NOS shoreline data are a
           Signature Verification and Supplemental Spatial                     data source for NOAA nautical charts and USGS topo-
           Data                                                                graphic maps. Coverage of the U.S. coastline is not com-
                                                                               plete, however, and for some areas the data may be dated.
           Locations selected from the photographs are observed                  In some locations, USGS 75 topographic maps may be
           during the same season and within one year of the                   the only base maps available at a scale of 1:24,000. These
           photographic mission. The purpose of this survey is to              maps delineate the high tide line and cultural features
           resolve uncertainties in the photographs and, if neces-             and may meet CCAP requirements. In many instances,







                                                                                         Chapter 4: Monitoring Submerged Lands              41


                however, these maps are out of date and temporal changes          The three-dimensional stereo model of the aerial pho-
                in shorelines may cause problems in the application of            tographs is leveled and scaled in the analytical plotter
                local horizontal control to compile the habitat polygons          (AP) and the interpreter views a three-dimensional land-
                (Ferguson et al., 1989; Ferguson and Wood, 1990). This            scape during photo interpretation. All polygonal inter-
                can reduce the positional and scaling accuracy of habitat         pretations are automatically stored in digital xy coordi-
                data which is critical for change analysis (see "Recent           nates in their proper planimetric position during photo
                Photography"). Care should be taken to determine the              interpretation (Welch et al., 1992), avoiding any error
                effective date of coastal features in these maps. Updates of      that might arise during information transfer in meth-
                these maps generally include cultural but not natural             ods I and 2 discussed above. The polygon data are
                changes in shoreline. Coverage of the coastal United States       registered and digitized without the errors that are
                is almost comprehensive, but dated. In some coastal areas,        associated with transfer in a zoom transfer scope or by
                1:24,000 scale orthophotoquads, have been published as            hand digitization. Unfortunately, analytical stereo-
                an alternative to topographic maps. Orthophotoquads at            plotters are expensive and their use requires special
                a scale of 1:24,000 are unsatisfactory for compilation from       training. Some additional expense to locate x, y, and z
                aerial photography in remote areas. Orthophotoquads do            control points may be necessary to successfully level the
                not have delineated shorelines, which may be needed               block of aerial photography. Recent advances in soft-
                when the preferred cultural features are insufficient to          copy photogrammetry allow analytical stereoplotter
                register the photograph to the map base.                          functions to be accomplished using UNIX type worksta-
                                                                                  tions and image processing software (e.g. ERDAS
                                                                                  ORTHO-max). Therefore, this alternative will become
                Transfer of Polygons to the Map Coordinate                        more affordable and attractive in the future.
                Projection System                                                   An adaptation of the third approach is being tested
                                                                                  by NOAA and the State of Florida. Photo interpretation
                Polygons of habitat interpreted from aerial photographs           is done as in approach 1. Registration and digitization
                are mapped into a standard map projection coordinate              of the interpreted habitat polygons is completed in the
                system. The UTM projection is recommended. C-CAP                  AP. Due to the high expense of AP and the specializa-
                protocol allows the polygons interpreted from aerial              tion of AP technicians, this option may be feasible for
                photography to be transferred onto planimetrically ac-            processing data from SRV interpreters who do not have
                curate basemaps using three approaches:                           direct access to or training on an AP.
                   1) Stereoscopically interpret the photographs and
                optically scale the polygons and photographic image to
                fit planimetric horizontal control in the basemap with a          Digitization of Habitat Polygons
                zoom transfer scope. This is the least expensive and
                often the most reliable approach. Habitat delineations            Habitat polygons that have been transferred to the
                drawn at the photographic scale through stereo view-              planimetric base map according to procedure I or 2
                ing under magnification are transferred using camera              above require digitization to be incorporated into the
                lucida principles from the photographic overlay di-               GCAP spatial data base. Normally, digitization is ac-
                rectly onto the planimetric basemap.                              complished using a digitizing tablet. Polygons are digi-
                   2) Process the aerial photographs into planimetrically         tized with a digitizing table in point mode. The overlays
                accurate orthophotographs, and interpret and directly             are labeled according to the base map. Compilations
                trace habitat polygons onto the planimetric base map.             are checked for clear delineation and cartographic ac-
                interpretation of the orthophotographs is performed               ceptability of line work, existence of and consistency in
                using monoscopic airphoto interpretation techniques.              feature attributes, and adequacy of horizontal control
                The orthophotographs must be at the same scale as the             points. Compilations are checked along neat lines to
                base map or the images must be enlarged or reduced to             confirm edgeline match and label match for polygons
                the map scale. This approach applies orthophotographic            extending over adjoining maps. Any inconsistencies
                rectification (Thrower and Jensen, 1976), which cor-              are brought to the attention of the map author.
                rects relief displacement in the original photographs                Compilations are affixed to a digitizing table for
                and ensures planimetric mapping results in the data-              georeferencing and data entry. The accuracy of the
                base. Some loss of detail may occur since the ortho-              reference points, the four corners of the neat line, and
                photography is a generation away from the original                no less than four internal tick marks on the overlays are
                aerial photography. The process is expensive but accu-            checked to ensure that control points are within ï¿½0.02
                racy is improved in areas with substantial vertical relief.       inches. This translates to ï¿½40 ft or ï¿½12.2 in from its
                   3) Delineate and simultaneously rectify and digitize           stated location. If tolerance is exceeded on any one
                habitat polygons by using an analytical stereo plotter.           point, new control points are selected, digitized, and







            42        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            reevaluated until all points test within tolerance. Infor-         different years are independently interpreted, verified,
            mation regarding the georeferencing error for each                 and compiled to the base map. In this case indepen-
            control point is recorded on a documentation form. In              dence does not mean different photo interpreters, com-
            addition the technician records other information about            pilers, and field personnel but rather an avoidance of
            the overlay manuscript such as scale, size, media type,            side-by-side comparison of the data until after classifica-
            source map information, and author.                                tion is complete. Postclassification change detection
              Polygons are digitized with the cartographic style and           can be accomplished graphically, or polygons may be
            accuracy that is represented on the source manuscript. A           digitized and compared by using a geographic informa-
            technician performs digitizing and data processing to map          tion system to detect spatial displacement and to quan-
            completion, including matching edgelines, preparing ini-           tify change. Although simple in concept, the statistics
            tial check plots, and reviewing, editing, and preparing            of change analysis are not well understood. Development
            final check plots. All linework and labeling are reviewed          of consensus for statistical evaluation of qualitative or
            using check plots produced at the source map scale. Each           spatial change is a subject of ongoing C-CAP research.
            arc is checked for acceptance on a light table with the final         As an expedient to postclassification change detec-
            check plot overlaid on the source map. Digitized linework          tion, photographs from different years are compared
            should conceal original linework with exceptions for dif-          directly or with mapped polygons. By using such com-
            ference in line thickness, differences in media, and subtle        parisons, areas where change may have occurred can be
            differences of horizontal control on the source map and            identified rapidly but subjectively. Determining what con-
            in digital files. Unacceptable data is flagged, edited, and        stitutes significant change and how to objectively quantify
            reviewed prior to acceptance into the digital database. A          the degree of change remain to be accomplished.
            data layer specification form is completed for formal docu-
            mentation at the conclusion of all digitizing.
              Scan digitizing may be an acceptable alternative to              Historical Photography
            hand digitizing and could be applied at one of two
            stages: 1) when polygons are positioned on overlays of             The earliest metric-quality aerial photographs were ac-
            base maps or 2) when polygons are interpreted from                 quired in about 1939. Prior to 1960, virtually all aerial
            individual photographs. Large format scanners would                photographs were black and white. Incomplete cover-
            be required to scan an entire map, approximately                   age, lack of coordination with tide, lack of camera
            19 x 23 inch, in one pass. A standard desktop scanner,             calibration data, inappropriate scale, sun angle, and
            8.5 X 11 inch, could scan the overlay from a single 9 x 9          inappropriate time of year, or poor quality for visualiza-
            inch photograph. In the latter case, geopositioning                tion of benthic features often make these photographs
            might be accomplished digitally without the use of the             unacceptable for a C-CAP change analysis. Interpreta-
            zoom transfer scope. In either case, the digital product           tion of historical photographs is likely to proceed with
            would have to meet the same positional tolerances de-              limited or no concurrent surface level information for
            scribed above for data entered with the digitizing tablet.         signature verification and should be attempted only by
                                                                               interpreters with extensive experience in the study area.
                                                                               Unless historical photography meets the C-CAP require-
            Change Detection WiLdi Aerial Photographic                         ments listed in "Aerial Photography of SRW and is sup-
            Data                                                               ported by surface level data as discussed in "Field Surveys,"
                                                                               the historical presence or absence of SRV at a given loca-
            The C-CAP objective of site-specific change detection              tion may remain an open question. Some but not all SRV
            places greater emphasis on accuracy and precision of               can be identified in less than optimal photography and be
            spatial data than required in one-time inventories or              confirmed in the literature or in the memory of local
            regional summaries of change. Methodology for moni-                residents. A visible signature for "bare bottom" or another
            toring site-specific change on a statewide or regional             nonhabitat signature is required to interpret absence of
            scale is a recent development (Ferguson and Wood,                  habitat at the time of photography. As a result, documen-
            1990; Orth ct al., 1991; Ferguson et al., 1993). Quantita-         tation of loss may be more likely than documentation of
            tive historical data, with possible exceptions in Chesa-           gain of SRV with historical photography.
            peake Bay or spatially limited study sites, does not exist.           Historical photographs may contain limited but valu-
                                                                               able information on presence of submersed habitat
                                                                               other than SRV. Canopies of the giant kelp, Macrocystis
            Recent Photography                                                 pyfifera, for example, are readily discernible in color
                                                                               infrared (IR) photography because they have very high
            C-CAP recommends post-classification change detec-                 IR reflectance against a background of water that has
            tion for SRV. Photographs taken in the same season of              no reflectance. The case of photo interpretation of







                                                                                             Chapter 4: Monitoring Submerged Lands                43


                some macroalgae allows historical photography to be                  names of USGS 7.5' quadrangles that locate the area of
                used to identify this and perhaps other types of habitat.            interest. The ESIC office conducts a microfiche or com-
                   The most complete and general. (but not compre-                   puter-based search. Information produced includes lati-
                hensive) source for historical photography is the Earth              tude and longitude, emulsion, scale, month, year, source
                Resources Observation Systems (EROS) Data Center in                  of the photography, cloud cover, camera, and frame
                Sioux Falls, South Dakota EROS records can be searched               numbers. Sources of this photography are USGS, Na-
                through the Earth Sciences Information Center (ESIC) of              tional Aeronautics and Space Administration (NASA),
                the USGS. The searcher must supply coordinates or the                USFWS, Agricultural Stabilization and Conservation Ser-



                                                                                              I.b
                                                                                              +

                                                                                                    Aw
                                                @THROLINA                                                               lk@
                                                                                                                              +34
                                                                  YAREA










                                                           ig
                                                           ?
                                                           I.
                                                      34'474                                                        N


                                                                                41



                                                                       0                              ATLANTIC
                                                                                                        0 C E A N
                                                                   G                  41 + 34-43'

                                                      BROWNS
                                                       ISLAND



                                            1166





                                             0                                                          MARCH      APRIL

                                                    'V                                     sAv HABrrAT


                                                                                         SPA-nAL SCALE
                                                                                                       100ACRE@ 100HECTARES
                                          34* 37 +
                                                                                          3@ 0    1     2    3     4    5
                                                                                                      MILES
                                                                                               0   1 2 3 4      5
                                                                                                   KILOMETERS

                                                                             Figure 17
                                    Seagrass habitat in Back Sound and southern Core Sound in 1985, gray, and in 1988, green.
                                    Pure gray indicates habitat present in 1985 but not in 1988. Light green indicates habitat in
                                    1988 but not in 1985. The overlap of gray and green indicates the presence of habitat in
                                    both 1985 and 1988. (A) Head of the Hole, an area where seagrass habitat decreased due to
                                    mechanical harvesting of clams. (B) Spoil deposition island from which uncontained spoil
                                    was released into the water and buried seagrass habitat (Ferguson et al., 1992, 1993).







           44        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           vice (ASCS), Soil Conservation Service (SCS), U.S. Forest         ter surface. The aerial photographs were interpreted ste-
           Service (USES), and some private companies. All histori-          reoscopically and the polygons were transferred to plani-
           cal photography identified in an ESIC search is reproduc-         metric NOS shoreline maps with a zoom transfer scope. A
           ible. Substantial collections of historical photography may       graphical postclassification overlay approach was used to
           also be found in other Federal or State agencies, universi-       visually identify changes between years (Fig. 17). A gray
           ties, or private companies. These collections of photogra-        tone in the chart indicates habitat present in 1985 but not
           phy may be available for reproduction, distribution, loan,        in 1988. Light green indicates habitat in 1988 but not in
           or examination. Federal sources include U.S. Army Corps           1985. The overlay of gray and green indicates the pres-
           of Engineers (COE), NOAA, National Archives for pre-              ence of seagrass habitat in both 1985 and 1988.
           1956 photos, and the Smithsonian Institution.                       Summary statistics, obtained via automated geomet-
                                                                             ric analysis of digitized video images of individual poly-
                                                                             gons (pixel size <0.03 ha) revealed that seagrass habitat
           Change Detection of Seagrass Habitat in North                     is a major resource in the study area, comprising about
           Carolina                                                          35% of the subtidal land. Total area of habitat changed
                                                                             less than 6%, from 7,030 ha in 1985 to 6,637 ha in 1988.
           Ferguson et al. (1993) followed the C-CAP guidelines              Polygons along the mainland and Harkers Island tended
           in Back Sound and southern Core Sound in North                    to be linear and close to shore. Large broad areas of
           Carolina. That study demonstrated the feasibility of              seagrass habitat were present in the subtidal shallows
           monitoring spatial change in SRV using C-CAP guide-               east of Browns Island, north of Shackleford Banks, and
           lines for large-scale metric aerial photography, photo            west of Core Banks. The total number of habitat poly-
           interpretation, geographic positioning, and postclass-            gons was similar in the two years, 151 in 1985 and 149 in
           ification change detection techniques. Aerochrome MS              1988. Reliability of detected change was conducted by
           2448 color-reversal film was exposed in March 1985 at             reinspection of the photography and is summarized in
           1:20,000 and 1: 12,000 scales. Aerocolor 2445 color-nega-         Ferguson et al. (1993). Some areas of detected change
           tive film was exposed at 1:24,000 scale in April 1988. All        were confirmed by surface-level observations and two of
           aerial photography was obtained by the NOS Photo-                 these were associated with known anthropogenic dis-
           grammetry Branch. The photography was coordinated                 turbances. Some areas of detected change were con-
           with low tide and sun angle and was collected with                firmed but could not be associated with potential causes.
           minimal haze, no clouds below the aircraft, and no                Still others could not be confirmed, which may have
           visible shadows from high clouds. Water was essentially           been the result of variable quality in the photography.
           free of white caps and clear enough for identification            In a continuation of this study, the study area was re-
           of vegetated and shallow unvegetated bottoms. Epi-                photographed in 1992, selected polygons were mapped
           sodic wind, haze, local turbidity, and airborne pollen            with GPS at surface level during the 1992 photographic
           often precluded photography for one or more days.                 window, and surface-level verification of signature was
           The sun angle during photography ranged from 15 to                completed in 1993. Data for all three years, 1985, 1988,
           30'. This sun angle localized sun glare to one edge of the        and 1992, will be digitized and change and positional
           photography while presenting illumination below the wa-           accuracy will be assessed in a GIS.







                                                                      Chapter 5
                                       Spatial Data Quality Assurance and Control

               Quality assurance and control (QA/QC) from data ac-              measures of accuracy, field-based reference data are
               quisition through final database compilation are the             exclusively preferred over other data sources, including
               responsibility of each regional project team. Accep-             aerial photographs.
               tance of the final database into the C-CA-P archive and
               dissemination system are contingent upon the demon-
               stration that the project has complied with the manda-           Lineage
               tory requirements stated in this document.
                 C-CAP standards of data quality are based on authori-          The sources, scales, or resolutions, and dates of materi-
               tative references (Goodchild and Kemp, 1990; Chris-              als involved in the preparation of all regional C-CAP
               man, 1991; Congalton, 1991; Lunetta et al., 1991; NIST,          databases must be documented (Lunetta et al., 1991),
               1992). These documents recommend that producers of               including
               data document
                                                                                e satellite images or aerial photographs used in the
               ï¿½ Lineage-A record of the type of data sources and                 analysis,
                 the operations involved in the creation of a database.         0 aerial photographs (including oblique photographs)
               ï¿½ Positional accuracy and precision-The closeness of               used as an aid in training or field verification if the
                 locational information (in xy coordinates) to the                photographs directly influenced the identification of
                 true position.                                                   land-cover types for significant portions of a given
               ï¿½ Attribute accuracy and precision-The closeness of                area,
                 attribute values to their true values.                         0 collateral information such as NWI data or soils maps
               ï¿½ Logical consistency-The adherence of internal data               if the information directly influenced the identifica-
                 structures to established conventions or stated rules.           tion for significant portions of a given area,
               ï¿½ Completeness-The degree to which the data ex-                  0 planimetric basemaps,
                 haust the universe of possible items.                          0 state and county land-cover inventories or other sur-
                                                                                  face level data, and
               C-CAP has added to this list                                     0 sources and techniques of georeferencing, especially
                                                                                  for submerged land and other land where identifi-
               *Temporal accuracy and precision-The time over                     able features are sparse.
                 which source materials were acquired and observa-
                 tions were made.
                                                                                Positional Accuracy and Precision
               Users are responsible for determining
                                                                                Positional accuracy is concerned with the accuracy of
                 Fitness for use-The degree to which the data quality           the geometric placement of points, lines, and polygon
                 characteristics of each database and its components            boundaries. In land-cover databases, polygons are de-
                 collectively suit an intended application.                     rived either from raster spectral data representing dis-
                                                                                crete pixels or from closed polygons delineating the
               The GCAP protocol also distinguishes between                     edges of spectral signatures in photographs. In the first
                                                                                case, the placement of polygon boundaries depends on
               ï¿½ Accuracy-The closeness of results, computations, or            a) selection of spectral signatures for class boundaries
                 estimates to true values (or values accepted to be             and b) registration of pixel locations. The second case
                 true), and                                                     generally applies to C-CAP SRV projects in which the
               ï¿½ Precision-The number of decimal places or signifi-             primary intent is to delineate limits between presence
                 cant digits in a measurement.                                  and absence of habitat classes. In this application, poly-
                                                                                gons of class I tend to occur as discrete objects in a
               The accuracy of the resulting land-cover database for            large polygon of class 0 that has specified boundaries
               each time period and for change between time periods             landward and unspecified boundaries seaward. In addi-
               is a crucial measure of the success of C-CAP. Several            tion, one or more polygons of class 0 may be included
               different types of accuracy are involved, and some of            within a polygon of class 1. The placement of polygon
               them are difficult to measure. For rigorous statistical          boundaries depends on a) limits of signatures attrib-

                                                                                                                                          45







            46        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            uted to the habitat class and b) registration of horizon-         tween aerial photography for the base map and for the
            tal control points present in the base map and visible in         submersed habitat. A positional accuracy that meets
            the photography. In both cases, signature selection di-           national map accuracy standards is assumed for sub-
            rectly affects attribute distribution which, in turn, af-         mersed habitat data. At the compilation scale of 1:24,000
            fects the size and shape of polygons. This effect is most         this amounts to ï¿½13.3 m on the ground, close to the ï¿½15
            common at polygon edges but may occur throughout                  meter precision of Landsat TM data for uplands and
            the polygon, for example, as internal voids or as cir-            wetlands.
            cumscribed polygons of different classes. The selection
            of spectral signatures for class boundaries is similar to
            the task of generalization that cartographers have tradi-         Generalization Versus Error
            tionally faced in deciding where to draw boundaries
            between land-cover features.                                      It is a tribute to the power of modern information
              For most remote-sensing applications, positional ac-            technologies that what we used to call generalization,
            curacy on the order of ï¿½1-2 pixels has not been a major           we now call error. With analog maps it has always been
            concern. Neither has positional accuracy for photo-               necessary to use human judgment in deciding, for ex-
            graphic delineations of submersed habitat been a ma-              ample, precisely where a forest becomes a field. In
            jor concern or a subject of independent verification.             reality most forests have some grass, and most fields
            For a single time period, positional errors may not               have some trees or shrubs. In natural circumstances the
            greatly affect the aggregate area of each land-cover              boundary is not a precise line but rather a "fuzzy" zone
            type. Positional errors may be difficult to detect even           of highly variable width in which the predominant land
            when a specific polygon in the field is visited. For G            cover grades from one class to another. Scale and reso-
            CAP, however, positional accuracy is a crucial concern            lution are crucial determinants of such boundaries. In
            (Ferguson et al., 1992 and 1993). The change database             an analog map, scale limits the feasibility of drawing the
            amounts to a comparison that will conspicuously record            densities and convolutions of lines that would be neces-
            positional errors of one or more pixel dimensions in              sary to represent each patch of forest or each individual
            the satellite imagery and errors in excess of about 10 m          tree. Conceptually there will always be unrepresented
            in the photographic images used to delineate submersed            boundaries because, in the modern sense of fractals, a
            habitat. This compounds the problem of recognizing                nearly infinite number of convolutions are possible.
            real changes, which also tend to concentrate at polygon           Digital systems are capable of representing a much
            edges and class boundaries.                                       larger portion of all possible boundaries, but there are
              The registration of pixel locations is a purely geomet-         practical limitations affecting digital systems as well. In
            ric problem which has been greatly improved with re-              current technology the most often encountered limita-
            cent advances in sensors, GPS, and image processing               tion is the established resolution of satellite sensors.
            systems. Many vendors claim a positional accuracy of                @Vhile the terms "error" and "accuracy" are frequently
            +0.5 pixel root mean square error (RMSE) for commer-              used in regard to generalized boundaries, conceptually
            cial image processing systems and a CEP of 3-5 rn for             the "accurate" boundary can only be determined on
            GPS. Selective availability (SA, the intentional distor-          the basis of a highly specific set of criteria that goes far
            tion of GPS signals for military security purposes) re-           beyond what can actually be implemented for large
            duces CPS precision to a CEP of 40 m when SA is in                areas. Land-cover phenomena are prime examples of
            operation. Differential readings by multiple receivers            fuzzy sets. This fuzzy characteristic is explicitly recog-
            can improve the quality of positional data, even when             nized in the procedures of image processing (for ex-
            SA is active, to a CEP of <5 m. C-CAP regional analysts           ample, the use of maximum-likelihood statistics), but
            should verify vendor claims to their own satisfaction             the remote-sensing community traditionally has pre-
            based on sources of higher precision. Unless stated oth-          sumed that a "right" answer or "ground truth" can be
            erwise, a geometric registration of ï¿½0.5 pixel RMSE will be       determined if the analyst can get close enough to see
            assumed for all GGAP regional databases (ï¿½15 m if Landsat         the polygon and its boundary in the field or on the
            TM data are used).                                                photograph, Yet different investigators "see" different
              For submerged land, the registration of polygon edges           land covers, a problem that is especially troublesome
            is a function of the metric quality of photographs, meth-         when the area is large enough to require multiple teams.
            ods used to transfer the information to a planimetric             In reality, land-cover phenomena are fuzzy sets whether
            map projection, and quality of the digitization per-              viewed directly in the field or through remote sensors.
            formed. Positional accuracy is therefore subject to the           Fuzziness persists because each class is defined, not by a
            accuracy of the base map including deviations not only            discrete boundary, but by factors that grade from one
            between the source photography and the base map but               class to another-spatially, temporally, categorically, and
            also actual changes in the study area in the time be-             observationally. Also, classification and accuracy assess-







                                                                                                 Chapter 5: Spatial Data Quality          47


               ment procedures are not always implemented in a timely           (Congalton, 1991). Generally, these procedures serve
               manner but often months or years after the image is              well for current time periods and for relatively small
               collected or analyzed.                                           study areas. Past time periods, however, cannot be field
                  Generalization also occurs within delineated poly-            verified. Conventional procedures also are difficult to
               gons whether derived from satellite or photographic              apply to large areas. Accuracy assessment of large change
               images. In both cases a finite limit for signature detec-        databases is currently infeasible due to the combina-
               tion and mapping exists. Minimum detection units are             tion of past time period, large area, and the excessive
               one pixel for spectral scanner data and about one meter          number of "from" and "to" classes.
               for high altitude photographic images. Elimination of
               "salt and pepper" and preservation of reasonable accu-
               racy for perimeter or areal estimates requires a mini-           Logical Consistency
               mum mapping unit of 4 pixels or about 0.4 ha. At a
               compilation scale of 1:24,000 the smallest polygon that          Tests for logical consistency should indicate that all row
               can be traced from a photograph is about 20 m in                 and column positions in the selected latitude/longi-
               diameter or an effective area on the ground of about             tude window contain data. Conversion, integration, and
               0.03 ha. Realistically, the goal for improving generaliza-       registration with vector files should indicate that all
               tion should be to strive for consistency more than               positions are consistent with earth coordinates. Attribute
               "accuracy."                                                      files must be logically consistent. For example, when
                 Reference data for accuracy assessment must have a             examining the change matrix for logical consistency,
               resolution and reliability that meet or exceed those of          very few pixels should change from the urban category
               the C-CAP remotely sensed data. The reliability, incltid-        to any other category or from water to any category
               ing attribute and positional accuracy, must be demon-            other than bare ground or marsh. The range of appro-
               strated prior to its qualification as reference data for C-      priate tests is left to the judgment and experience of
               CAP. Reference data, including surface level observa-            regional analysts. All attribute classes should be mutu-
               tions, must be evaluated in accordance with C-CAP's              ally exclusive. The criteria cannot be met if land-use
               minimum detection unit and minimum mapping unit                  classes are included along with land-cover classes.
               for the remote data and with the classification system
               used to categorize the habitat. The presence of a char-
               acteristic species or natural or cultural feature may or         Completeness
               may not, in itself, establish an area as a particular type
               of habitat. A number of questions need to be answered            The classification scheme should be comprehensive,
               to conclude the appropriate category of land cover to            containing all anticipated land covers. The C-CAP
               assign based on the reference data: Does a characteris-          Coastal Land-Cover Classification System is intended to
               tic species or feature meet the minimum detection unit           provide complete coverage, but regional analysts may
               of the remote sensor? What other characteristic species          find special land covers that are not included. It is the
               or features also are present within the minimum map-             responsibility of regional project personnel to ensure
               ping unit? and What conclusion can be drawn from the             that all categories are included and that all pixels are
               reference data as to the C-CAP category for a given              assigned a category. Regional analysts may use their
               location based upon data generalized to the minimum              discretion in deciding at what classification system level
               detection and minimum mapping units?                             (0 to 3) they wish to classify. The level need not be the
                                                                                same for all branches of the classification scheme.

               Attribute Accuracy and Precision
                                                                                Temporal Accuracy and Precision
               Attribute accuracy is a measure of the probability that
               the land-cover type for any given polygon is properly            Regional analysts should document the time of data
               identified according to the land-cover scheme. For ex-           collection for the primary input data to at least the
               ample, the identification of a substantial polygon of            precision of year, day, and hour.
               "High-Intensity Developed" land as "Deciduous Woody
               Wetland" is a clear instance of categorical error. If 15%
               of all sample polygons for this class are misclassified as       Fitness for Use
               "Deciduous Woody Wetland" and other categories, the
               categorical accuracy for the "High-Intensity Developed"          C-CAP workshops have involved many discussions with
               class is 85%. The remote-sensing literature is replete           potential users and have devoted a great deal of effort
               with procedures for measuring attribute accuracy                 to field verification and other types of verification.







           48        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           C-CAP is confident that the databases resulting from             cedures, and manage the data production process to
           compliance with this document will be of sufficient              meet DQO's. C-CAP has conducted three workshops
           quality to support most policy and management activi-            on accuracy assessment and sponsored two protocol
           ties as well as some regulatory, enforcement, and re-            development projects in the hope of devising new pro-
           search activities. The spatial precision and attribute           cedures that will work for accuracy assessment of large
           accuracy are not sufficient for enforcement of indi-             land-cover change databases.
           vidual small permits, but they may be useful in evaluat-            Nevertheless, the following material identifies sound
           ing cumulative impacts in the vicinity of a permit site or       procedures that may be used to obtain unbiased field
           for evaluating individual sites larger than the minimum          information which, in turn, may be statistically evalu-
           mapping unit. In the southeast region of the United              ated to perform an accuracy assessment for a single
           States, a vast majority of the total area of coastal salt        time period. This is a blind field test in which the field
           marsh or seagrass habitat that is potentially subject to         mapping personnel will not see the C-CAP Land-Cover
           direct loss, according to permits submitted, would be            and Land-Cover-Change Maps until all mapping has
           detectable in the C-CAP data (Rivera et al., 1992). Data-        been completed.
           bases will also be of value in many applications, such as           Since the field mapping personnel may be unfamil-
           land-use planning, unrelated to the C-CAP mission.               iar with C-CAP, it is advised that they be required to
           Ultimately, however, only the user can make the deci-            submit a memorandum stating the design of the field
           sion regarding fitness for use.                                  mapping implementation. Early in the effort, regional
                                                                            analysts should review the design, in collaboration with
                                                                            NOAA, and reach agreement with the field mapping
           Recommended Accuracy Assessment Test                             personnel regarding final implementation. Field per-
                                                                            sonnel should be provided copies of the land-cover
           The recommended accuracy assessment for C-CAP re-                classification scheme and should be trained in its use.
           gional databases is a test based on comparison with                 The field personnel will be responsible for ensuring
           independent field samples. Independence should be                the positional accuracy and precision of each sample
           guaranteed through the use of personnel who are not              site and each land-cover class boundary within each
           familiar with and do not have access to the results of the       site. Field personnel will be responsible for determin-
           land-cover classification (Congalton, 1991).                     ing physical accessibility and obtaining permission for
                                                                            legal access to the sample sites.
                                                                               An early determination will be made regarding who
           Sample Selection and Field Mapping                               is responsible for acquiring the best available aerial
                                                                            photographs, topographic maps, and other collateral
           Regional analysts are responsible for selecting unbi-            data to ensure an accurate mapping of each sample site
           ased, statistically meaningful area samples for field veri-      for each time period. These materials will assist in map-
           fication in the accuracy assessment process.                     ping land cover and land-cover change for each site at
                                                                            1:24,000 scale. Positional accuracy shall comply with
                                                                            national map accuracy standards. The determination
           Accuracy Assessment for Individual Date                          of class type will be based primarily on field observa-
           Classification of Upland and Wetland Habitat                     tion. The determination of class areas and boundaries
           Data                                                             will be based primarily on aerial photographs. The final
                                                                            results for each sample polygon will be provided in a
           Accepted procedures in the remote-sensing, carto-                digital form.
           graphic, and geographic literature assess 1) the posi-              After completing field mapping, regional analysts
           tional accuracy of identifiable, stable features and 2)          will compare the generated map for each sample site
           the categorical accuracy at the interior of class poly-          with the C-CAP map for the same site. All discrepancies
           gons. Unfortunately, the methods often neglect the               will be referred back to the field mapping personnel for
           fuzzy nature of land cover-categorically (e.g. the class         a final check. The regional analyst may request a special
           boundary between grass and marsh), spatially (e.g. the           examination and may accompany the field mapping
           polygon boundary between water and marsh), tempo-                personnel for a final reconciliation of any discrepan-
           rally, and observationally. Given these limitations, it is       cies for which field error is suspected.
           not feasible at this time to provide a quantitative esti-           The regional analysts will compile the results of all
           mate of accuracy with every C-CAP regional database. A           sample polygon comparisons and conduct a statistical
           reasonable alternative is to establish data quality objec-       analysis. The results of this analysis will be provided to
           tives (DQO) designed to serve expected uses, establish           the field mapping personnel for review and comment.
           and consistently implement a set of protocols and pro-           At this point the field mapping personnel may also see







                                                                                                      Chapter 5: Spatial Data Quality           49

                the C-CAP land-cover and land-cover-change maps for                 Accuracy Assessment for Land-Cover Change
                the sample quadrangles.                                             Data
                  The field mapping personnel will provide a brief
                documentation of the field mapping task for inclusion               The methodological difficulties of accuracy assessment
                in the final accuracy assessment report to be prepared              for the final change map are significantly greater than
                by the regional analysts. The field mapping personnel               those for a single, current database. Remote-sensing,
                must be given an opportunity to comment on the re-                  cartographic, and geographic literature provide no guid-
                sults of the final statistical analysis if they so choose.          ance on techniques for assessing the accuracy of a
                                                                                    change detection map (Lunetta et al., 199 1; Jensen and
                                                                                    Narumalani, 1992). Even for a single-time database,
                Accuracy Assessment for Individual Date                             existing procedures are ineffective for past land cover
                Classification of Water and Submerged Land                          since the recommended "source of higher accuracy"
                Data                                                                cannot include actual field verification. Change detec-
                                                                                    tion databases compound the difficulty because they
                Accuracy assessment for submersed habitat is similar to             always include a past time period and a large number of
                that for emergent and upland habitat but it should be               "from" and "to" categories (potentially the square of
                noted that data for submersed habitat is intrinsically              the number of categories for each time). This large
                vector, not raster. Positional accuracy of polygon bor-             change matrix can make accuracy assessment more
                ders and attribute accuracy of a point location can both            expensive than the original classification and change
                be assessed. Habitat polygons or areas of potential habi-           detection effort. Furthermore, if the distribution of
                tat should be stratified by class and region (water body)           error is thoroughly depicted by class and position, the
                and randomly selected. Additional sample locations                  accuracy database may be as large as the thematic data-
                from potential habitat sites (i.e. sites of suitable depth          base itself. Even worse, both the distribution of error
                but apparently devoid of habitat) should be randomly                and the distribution of actual change tend to concen-
                selected. Verification locations should be identified by            trate on the same circumstances (for example, polygon
                latitude and longitude coordinates and visited in the               edges and transitional classes, such as marsh and
                field with GPS navigation. The nature of the habitat, if            palustrine forest). C-CAP is sponsoring workshops to
                present, should be documented by inspection or sam-                 develop improved methods for assessing the accuracy
                pling if necessary and the position of the sample or                of change databases and maps.
                observation recorded to a CEP of <5 in. The entire
                perimeter of a small polygon or a section, e.g. 0.5 kin of
                the perimeter of a large polygon, should be positioned              Comparison and Statistical Analysis
                by differentially corrected GPS at a point spacing of 3 to
                20 in depending upon the degree of curvature in the                 The C-CAP land-cover database and the field-mapped
                perimeter. Differential GPS provides CEP of <5 in for               verification database should be compared and mea-
                single position fixes. C-CAP projects found that single             sured to determine differences in attributes for the
                point differential GPS position fixes did not exceed 10             base time period and for change that can be recog-
                in (Ferguson, R. L.,J. A. Scope, and L. L. Wood, unpub-             nized in the field. The measures obtained from this
                lished data). Habitats at the minimum mapping limit,                comparison are numerical differences relating to the
                i.e. with diameters on the order of 20 in, therefore,               sample sites only. It will then be necessary to employ
                should be located and delineated with multiple posi-                statistical algorithms to determine what the differences
                tion estimates. GPS manufacturers recommend collec-                 reveal about the accuracy of the entire regional data-
                tion of multiple position fixes for a time period of                base. These algorithms should be designed to estimate
                about 4 min to achieve CEP on the order of I or 2 in                the attribute accur-acy and positional accuracy of the change
                with differentially collected GPS. Multiple position fixes          database. The necessary algorithms are not currently avail-
                obtained at strategic points around the perimeter of                able in the remote-sensing, cartographic, and geographic
                the smallest mapped polygons would be required to                   literature (Congalton et al., 199 I;Jensen and Narumalani,
                ensure mapping the polygon rather than generating a                 1992). GCAP ftinded two protocol development projects
                scattered pattern of points.                                        in an attempt to remedy this deficiency.






           50       NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program






                                                                    Chapter 6
                                                          Product Availability

               Digital Product                                                  acknowledge NOAA as the source of the product when-
                                                                                ever data are redistributed and must provide an ac-
               Description and Availability                                     counting to NOAA stating who received copies of the
                                                                                database. If the redistributed data are modified, an
               Regional databases generated     by C-CAP participants           accompanying disclaimer must acknowledge NOAA as
               will be provided to the C-CAP project director in accor-         the source of the original data, must state the nature of
               dance with procedures specified in research funding              the modifications, and must relieve NOAA of responsi-
               proposals (RFP), statements of work (SOW), funding               bility for the modified data.
               documents, memoranda of understanding (MOU), or
               other applicable documents under which each regional             Liability Disclaimer-The user of C-CAP data will hold
               project is authorized and conducted. The purpose of              the U.S. Government and its agencies, officers, and
               this transfer is to place each regional database into a          employees harmless against any and all liability, includ-
               central archive from which all data will be made avail-          ing costs and expenses, which may result from or arise
               able to the public. It will be the responsibility of the         out of any use of the data.
               regional participants to document and certify that the
               data have been prepared in accordance with C-CAP
               protocols. C-CAP may conduct additional data quality             Digital Product Format and Contents
               and accuracy assessment tests before final submission
               to the archive. The data should adhere to the Spatial            The goal is to exchange the digital products in the
               Data Transfer Standard (SDTS) proposed by the Fed-               Federal Spatial Data Transfer Standard (SDTS) format
               eral Geographic Data Committee and adopted as a                  for raster data. Until the SDTS raster standard is avail-
               Federal Information Processing Standard (FIPS) (NIST,            able, the initial data products may not adhere to the
               1992). Commercial implementations are not currently              final standard.
               available but will be marketed by software vendors in
               the near future. At a minimum, the standard should be            Product Identifiers and Characteristics-Each data
               considered a near-term goal with one or more de facto            transmittal from NODC to the user will be accompa-
               standards-such as DLG, ARC, DXF (geometry only),                 nied by documentation provided by the data producers
               and ERDAS-accepted in the interim. Lineage, quality,             stating the following:
               and format information should be transmitted with the
               data disseminated to users.                                      0Geographical coverage in UTM coordinates
                  The digital product for each region will be a change          aUTM zone number
               matrix of land cover by class for coastal submersed              0Computer and operating systems used to create the
               habitats, emergent coastal wetlands, and adjacent up-             file
               lands. T'he only regional database currently completed           0Precision of the computer system (e.g. 16-bit, 32-bit)
               and available to the public is the Chesapeake Bay Land-          eSoftware used to create the file (e.g. ERDAS Imagine
               Cover Change Database for 1984 and 1988-89.                       8.2, ARC-Info 7.0)
                  Digital products are available from                           0Type of file (ASCII, binary, ERDAS.IMG, ARC-Info
                                                                                 coverage)
                  National Oceanographic Data Center (NODC)                     0Description and format of header file
                  1825 Connecticut Avenue, NW                                   *Data record format
                  Washington, DC 20235                                          0Number of classes
                  (202) 606-4549                                                0Class names
                                                                                0Number of pixels by class and by file, including the
               When more C-CAP regions have been completed, an                   null class (i.e. no data in pixel)
               on-line electronic catalog will be created for users to
               browse.                                                          The header file for each database will repeat the quan-
                                                                                titative portion of this information.
               Digital Product Redistribution Restrictions-The prod-
               uct file will contain statements defining the responsibil-       Product Data Quality-The documentation will describe
               ity of the user in regard to C-CAP data. The user must           the lineage, date and source of data (i.e. instrument,

                                                                                                                                        51







           52        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


           and platform), resolution, positional accuracy and pre-         Digital Product Ancillary Documentation
           cision, attribute accuracy and precision, logical consis-
           tency, completeness, and temporal accuracy and preci-           General Protocol-A copy of the "C-CAP Guidance for
           sion of the data being transferred.                             Regional Implementation" for the version used to pro-
                                                                           duce the product will be available from the NODC in
           Guidance Version-The product file will contain a field          digital form for the cost of reproduction.
           indicating the "C-CAP Guidance for Regional Imple-
           mentation" version used to produce the image product.           Specific Digital Products Documentation-Regional
                                                                           analysts may provide ancillary documentation for dis-
           Transfer Verification Parametem--Each C-CAP prod-               semination by NODC if both the documentation and
           uct will contain unique verification parameters for the         the corresponding database are provided to NODC in a
           image raster data and a confirmation algorithm that             standard digital format.
           can be applied to the image values. The algorithm tests
           whether the database received by the user is equivalent
           to the original. If an image has been damaged or modi-
           fied, application of the algorithm will produce results         Hardcopy Products
           different from the master values in the original data file
           maintained at the NODC. The occurrence of each class            Upland and Wetland Habitats
           value (including the no data class) can be tabulated
           and compared with the original summary statistics.              Hardcopy maps of uplands and wetlands for selected
                                                                           areas will be produced for informational purposes, pri-
           Derived Data and Quality-The data product may in-               marily to illustrate database content. At present there
           clude derived data, such as tabular summaries of land           are no plans to publish hardcopy maps for general sale
           cover and accuracy assessments for specified areas (e.g.        and distribution to the public. Requests for informa-
           counties, watersheds, wildlife management areas). Data          tional maps will be considered on a case-by-case basis.
           are defined as "derived data" if they cannot be used to            Individuals and organizations should make their re-
           reconstitute the C-CAP data at the pixel level.                 quests in writing to

           Digital Data Values--The data values in raster format are          Dr. Ford A Cross, Director
           numerical values representing the land-cover categories            Beaufort Laboratory
           described in this document (see "The CCAP Coastal Land-            NOAA/National Marine Fisheries Service
           Cover Classification System"). A lookup table or other             10 1 Pivers Island Road
           accompanying statement will define the relationship be-            Beaufort, NC 28516-9722
           tween the stored values and the land-cover categories.
                                                                           Organizations wishing to serve as value-added vendors
                                                                           of hardcopy products derived from C-CAP data should
           Digital Product Medium                                          write to this address.

           Digital data products are available on 9-track magnetic
           tapes and CD-ROM. As the completed coverage ex-                 Submersed Habitats
           pands, these data may be available on other magnetic
           and optical media.                                              Hardcopy maps are routinely produced as part of the
                                                                           submersed habitat change analysis because the tech-
                                                                           niques are currently based on aerial photographic in-
           Digital Product Cost                                            terpretation in analog form. A limited number of publi-
                                                                           cation-quality maps are reproduced at the completion
           The organization conducting each regional project will          of each regional task. Individuals and organizations
           receive one copy of the final database as distributed by        may request copies on a "first come, first served" basis
           NODC at no cost. All other users will be charged the            by writing to Dr. Ford A Cross at the address listed
           standard NODC reproduction fee.                                 above.






                                                                            Chapter 7
                                                          Users and Information Needs

                 Table 6 presents a matrix developed by participants in                 from their own regional perspective and submit their
                 the regional concerns breakout group at the C-CAP                      modifications to C-CAP. This will enable C-CAP to gen-
                 Rhode Island Workshop (see Appendix 4). The matrix                     erate matrices for each region or a single national
                 matches potential uses with C-CAP products and indi-                   matrix that will help ensure that C-CAP products meet
                 cates the relative value of the product according to use.              the broadest range of user needs possible.
                 Interested parties are encouraged to modify this table



                                                                                 Table 6
                                  The potential utility of C-CAP coastal land-cover information; abbreviations: H=high; M=moderate; L=Iow.

                    Potential uses                    Map data          Digital data      Tables        Physical boundaries         Error estimation

                    Technical review                      M                 M                L                   L                          L

                    Decisions                             M                 M                L                   L                          L
                    Modeling                              L                 H                H                   H                          H
                    Interstate coordination               H                 H                H                   H                          H

                    Enforcement                           L                 L                L                   L                          L
                    Hazard response                       H                 H                L                   L                          L
                    Policy                                L                 L                H                   H                          H
                    Management and planning               H                 H                H                   H                          H
                    Education                             H                 H                M                   M                          H

                    Citizens                              H                 L                L                   L                          L

                    Commerce                              H                 H                L                   L                          H

                    Research                              H                 H                M                   M                          H










































                                                                                                                                                       53







           54        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program







                                                                      Oiapter 8
                                                         Regional Participation

               Purpose                                                          Universities of Connecticut and Rhode Island

               NOAA C-CAP will endeavor to cooperate with all ongo-             Faculty members of the Universities of Connecticut
               ing wetlands mapping and change-detection programs               and Rhode Island worked cooperatively to examine
               at the Federal, State, and regional levels. Priorities for       several issues concerning coastal land-cover classifica-
               NOAA funding allocation will be                                  tion and change detection in the Northeast (Hurd et
                                                                                al., 1992). In the first part of the project, detailed GIS
               1) biogeographic diversity,                                      data on coastal wetlands in Rhode Island derived from
               2) joint funding efforts, and                                    aerial photography were used to establish coastal wet-
               3) existing field-based studies.                                 lands signatures for input to a digital classification of
                                                                                Landsat TM imagery. This work is crucial in assessing
               Other considerations will include:                               the extent to which an existing coastal wetlands dataset
                                                                                (e.g. NWI digital data) can be used to establish a classi-
               1) areas of rapid development,                                   fication for a larger TM dataset. Other areas of impor-
               2) areas disturbed by major storms or other natural              tance to C-CAP include assessments of 1) classification
                  events, and                                                   approaches best suited to characterize wetlands in south-
               3) areas disturbed by hazardous technologies (e.g. oil           ern New England; 2) techniques for monitoring coastal
                  spills).                                                      wetlands change in the Northeast using several change
                                                                                detection techniques to look at TM imagery from the
                                                                                same location for 1988 and 1982; and 3) multistate,
               Regional Project Summaries                                       multi-institutional collaboration in southern New England.

               St. Croix River Estuary (Border of Maine
               and New Brunswick, Canada)                                       University of Delaware

               This is a cooperative effort involving the U.S. Fish and         University of Delaware faculty members at the Center
               Wildlife Service (USFWS), the Gulf of Maine Program,             for Remote Sensing played a lead role in developing
               and Environment Canada. A change detection analysis              the interagency land-cover classification system used by
               was performed using TM imagery from 1985 and 1992.               C-CAP (Klemas et al., 1993). The system was developed
               The image processing and change detection analysis               during joint meetings with representatives from key
               was performed at Oak Ridge National Library (ORNL).              government agencies including NOAA, USGS, USEPA,
               Five field verification exercises were carried out in con-       USFWS, and COE.
               junction with USFWS personnel. The C-CAP change                     Currently, University of Delaware faculty members
               detection product has been completed and submitted               are developing remote-sensing and field techniques for
               to NODC.                                                         measuring indicators of wetland condition and func-
                                                                                tional health over large wetland areas. An overview of
                                                                                wetland health assessment techniques has been pre-
               Coastal Massachusetts                                            pared, with special emphasis on wetland condition and
                                                                                functional health indicators that can be monitored with
               This is a cooperative submerged land effort involving            remote sensors (Patience and Klemas, 1993). The over-
               the Massachusetts Department of Environmental Pro-               view report contains a comprehensive literature search
               tection (DEP) Wetlands Conservancy Program. A pilot              and chapters describing the techniques and their sta-
               project focusing on training Massachusetts personnel             tus. A joint study has been initiated with investigators
               in current SRV mapping techniques and adapting the               from Louisiana State University and USEPA to work on
               C-CAP protocol for use in Massachusetts was conducted            impaired and healthy pilot test sites in Louisiana
               in the spring and summer of 1993. Photo interpreta-              marshes. Field data, including measures of biomass,
               tion and mapping are being performed by DEP person-              soils, hydrology, chemistry, biology, and light reflec-
               nel with technical assistance from the NMFS Beaufort             tance, are being correlated with Landsat TM imagery to
               Laboratory. The SRV polygons will be added to wetland            assess biomass and stress indicators over large areas
               data on coastal orthophoto maps.                                 with the help of modified models and techniques devel-

                                                                                                                                          55







            56        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

            oped during previous studies. The data derived from                areas were prepared to illustrate static land cover for
            these investigations are crucial to C-CAP for early detec-         1984 and 1988/1989 and land-cover change between
            tion of functional change in habitat.                              these dates. The final database was delivered to NODC
                                                                               and is available on CD-ROM for purchase by the public.
                                                                               This analysis was conducted on graphics workstations
            Oak Ridge National Laboratory                                      employing ERDAS image processing software, ERDAS
                                                                               raster GIS software, and Oak Ridge Geographics GIS
            The C-CAP prototype and first regional project was                 software. Processing and verification techniques were
            conducted in the Chesapeake Bay region by ORNL                     similar to those employed in the initial MSS/TM analy-
            (Dobson and Bright, 1991, 1992, and 1993). In the first            sis. Investigators revisited the area in the spring and
            phase, a land-cover classification was completed for a             summer of 1991 and participated in the Maryland field
            four-scene area using MSS data for 24-25 October 1978,             verification workshop (See Appendix 4). Finally, the
            and change detection was completed for portions of a               database was modified to accommodate the new C-CAP
            scene in the vicinity of Metomkin Inlet, VA, using MSS             land-cover classification scheme'and to incorporate sug-
            data for 12 September 1974, MSS data for 24 October                gestions and corrections resulting from the Maryland
            1978, and TM data for 18 November 1982. The Virginia               workshop. The protocols developed for the Chesapeake
            Institute of Marine Sciences (VIMS) was contracted to              Bay project have been incorporated into the C-CAP
            assist in field verification and training-sample identifi-         protocols. Thus the final C-CAP Chesapeake Bay Land-
            cation. The results of this prototype served as a proof-           Cover Change Database complies with this document.
            of-principle for large-area change analysis, and the meth-
            ods and techniques served as the basis for the draft
            protocol presented at the first protocol development               Virginia Institute of Marine Sciences
            workshop.
              This initial prototype and proof-of-principle was con-           The Virginia Institute of Marine Sciences has been
            ducted at the Oak Ridge Geographics Laboratory. All                conducting photographic mapping of submersed veg-
            cartographic and geographic information processing                 etation in the entire Chesapeake Bay beginning in 1978
            was conducted by ORNL personnel using Oak Ridge                    and annually since 1984 (Orth et al., 1990 and 1991).
            Geographics software. Tentative land-cover classes were            Although not funded by C-CAP, this important work is
            determined on the basis-of supervised training samples             considered a regional C-CAP project because of the
            in areas of known land cover. The tentative classes were           voluntary collaboration among principal investigators.
            checked with information available from other sources              Methodology for the Chesapeake Bay project was a
            such as 1:24,000 USGS topographic maps and wetlands                starting point for the C-CAP protocol. Data from Chesa-
            inventories (NWI and county marsh inventories). In-                peake Bay have been provided to C-CAP to attempt to
            vestigators visited the area on 4-6 November 1985 for              overlay it with the land-cover data for Chesapeake Bay
            field verification of the tentative classes and for identifi-      generated by ORNL. Historically, Chesapeake Bay has
            cation of additional training samples in the Wacha-                suffered a dramatic decline in SRV and associated fish-
            preague, Metomkin Inlet, and Saxis areas of Virginia.              eries. From 1984 to 1990, however, SRV habitat in-
            Land-cover classes were determined through iterative               creased from 15,400 to 24,313 ha.
            refinement of supervised training samples. Investiga-
            tors visited the area in August 1986 for field verification
            of final land-cover classes in the York River estuary of           North Carolina State University
            Virginia and the Tangier Island and Blackwater Na-
            tional Wildlife Refuge areas of Maryland. Finally, the             A land-cover classification project was conducted by the
            entire dataset was compared digitally on a cell-by-cell            Computer Graphics Center at North Carolina State
            basis to land-cover data from the USGS Land Use Data               University (NCSU) prior to the University's involve-
            Analysis (LUDA) database in order to resolve certain               ment in C-CAP. Coincidentally, the four-scene area ana-
            classes.                                                           lyzed by NCSU was contiguous with the four-scene area
               In the second phase, the change detection was ex-               analyzed by ORNL in the Chesapeake Bay project. Scene
            tended to cover the full four-scene area by using TM               dates are contemporaneous with the 1988 Chesapeake
            data for 27 August 1984, 21 September 1984, 3 Novem-               Bay scenes. Faculty members of NCSU cooperated with
            ber 1988, and 10 October 1989. The final product                   ORNL research staff to investigate the potential for
            consisted of a classified land-cover change matrix data-           merging portions of these two independently conducted
            base for the entire Chesapeake Bay area. Regional maps             land-cover classifications based on TM digital data. The
            at 1:500,000 scale and numerous local area maps cover-             goal was to merge the project areas and form a seamless
            ing individual USGS 1:100,000 and 1:24,000 quadrangle              regional land-cover classification from the Chesapeake






                                                                                             Chapter 8: Regional Participation         57

               Bay to Dare County, N.C. One of the major problems              tent with current photographs, on stable media. The
               investigated was the development of a classification            topographic maps are virtually complete for North Caro-
               scheme adaptable to both areas. This research was a             lina (with the exception of Currituck Sound) but are
               crucial test of the C-CAP concept of regional compat-           out of date (mid-forties photography with occasional
               ibility among neighboring databases developed by dif-           photo-revision for cultural features dated in the seven-
               ferent organizations.                                           ties or eighties). NOAA shoreline manuscripts are based
                                                                               on 1988 or more recent photography but are not com-
                                                                               plete for North Carolina. Necessary photographs for
               Beaufort Laboratory, National Marine Fisheries                  construction of manuscripts to complete shorelines in
               Service                                                         North Carolina were obtained by NOAA Photogram-
                                                                               metry Branch in 1988-92.
               The project in North Carolina is researching protocol             Habitat polygons were coordinate digitized by State
               for conducting and verifying change detection in SRV,           of North Carolina personnel and incorporated into a
               including seagrasses and low-salinity-tolerant grasses and      statewide ARC-Info database referenced to the State
               forbs. Simultaneously the project is completing the first       Plane Coordinate System.
               comprehensive inventory of such habitat in North                  Three two-color charts of seagrass habitat at a scale of
               Carolina. The project wasjointly funded by the Albemarle        1:36,000 and measuring about 3 x 4 ft were published
               Pamlico Program of EPA's National Estuary Program.              and are available at no cost.
                 Aerial photography to delineate SRV was first com-
               missioned in 1985 (Bogue, Back, and southern Core
               Sounds). The rest of the aerial photography for all             University of South Carolina
               areas of potential SRV between Bogue Inlet and the
               Virginia border were taken between 1988 and 1992. All           University of South Carolina faculty members performed
               photography was subcontracted to the NOAA Photo-                a detailed investigation of the geographic area cen-
               grammetry Unit and acquired at scales of 1:12,000,              tered on two 7.5' U.S. Geological Survey quadrangles
               1:20,000, 1:24,000, or 1:50,000. The smallest scale pho-        (quads) along South Carolina's coastal plain. These
               tography provided a bridge between parallel flightlines         quads, representative of many other quads in coastal
               (at 1:24,000) in eastern Pamlico Sound, where minor             South Carolina, provide an opportunity to examine two
               dimensions of some habitat areas exceeded 3 nmi.                very different wetland communities. One quad is di-
                 All aerial photography from 1985 through 1991 was             rectly on the coast and contains extensive Spartina
               interpreted and most was compiled on base maps. The             alterniWora marsh, developed and undeveloped beach
               interpretation was supported by extensive systematic            front, and a mature maritime forest. The other quad is
               and directed sampling throughout the study area. At             40 river miles inland and contains significant inland
               the time of photography, stations were selected by              freshwater wetlands with extensive bottoraland hard-
               statified random sampling, visited, and sampled for             woods. The project identified optimum parameters for
               species of submersed plants and ancillary data (sedi-           conducting accurate coastal change detection includ-
               ment particle size and organic content, water depth,            ing, but not limited to, 1) an optimum wetlands classifi-
               salinity, temperature, and Secchi depth and the pres-           cation scheme; 2) an optimum type of remotely sensed
               ence of exposed peat deposits, shells, algae, or debris         data; 3) optimum digital image processing pattern rec-
               which might confuse signature identification). All loca-        ognition algorithms for C-CAP land-cover classification;
               tions of known and potential habitat, water <6 ft MLLW          4) the applicability and utility of including ancillary
               on nautical charts, were sampled by positioning a rect-         data (e.g. NWI digital data) in the classification pro-
               angular matrix of points over the nautical chart. Station       cess; 5) optimum change detection algorithm logic;
               positions approximately two scaled nautical miles apart         and 6) detailed error evaluation. Results were reported
               were extracted from the chart and visited with the aid          injensen et al. (1993a).
               of LORAN C, now, preferably, GPS. After receipt and
               preliminary interpretation of the photographs, field
               surveys were conducted to verify the range of habitat           State of Florida, Department of Environmental
               signatures and confirm false signatures.                        Protection
                  Photographs initially interpreted monoscopically are
               now interpreted stereoscopically. Polygons are traced           State of Florida personnel are mapping submersed habi-
               on stable film at the photograph scale, rectified, and          tat in the Florida Keys, Florida Bay, Biscayne Bay, and
               transferred to base maps with a zoom transfer scope.            Tampa Bay. Photography was conducted in the winter
               Base maps are NOAA shoreline manuscripts, if avail-             of 1991 and 1992 by the NOAA Photogrammetry Branch.
               able, or USGS 7.5' topographic series maps, if consis-          The effort in the Keys is cooperative with NOAA's Ma-







           58        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis program


           rine Sanctuary Program. The Keys were photographed                CREST and its cooperators in conjunction with ORNL.
           at 1:48,000 because of cost considerations and will dem-          Several field verification exercises have been performed,
           onstrate resolution of signatures of submersed habitat            and a final change detection project is expected in fall
           at a scale smaller than that acceptable with the current          1994. This information should be useful to a variety of
           C-CAP protocol. The motion-compensating camera used               managers that are presently dealing with severely stressed
           in this case should enhance resolution. Photography in            salmon stocks throughout the study area.
           Florida Bay is being interpreted and ground verified in
           fiscal year 1993. C-CAP is partially funding the cost of
           photography and interpretation.                                   The Hubbard Glacier and Russell Fjord, Alaska

                                                                             This is a cooperative effort involving NMFS's Auke Bay
           Texas Parks and Wildlife Department                               Laboratory. A 1986 image is the only image available at
                                                                             this time that meets C-CA-P cloud cover specifications.
           This agency is currently processing TM imagery, as per            The implications of the future movements of the
           the steps outlined in this document, for the entire               Hubbard Glacier make this project unique. During 1986
           Texas coast with technical assistance from ORNL. Two              the Hubbard Glacier blocked off the mouth of the
           scenes in the Galveston Bay area for 2 December 1988              Russell Fjord and created the world's largest glacier-
           have already been classified, and a change detection              formed lake. Within months, rising water levels caused
           analysis was performed, comparing a November 1992                 the glacier to burst, restoring tidal flow to the Fjord.
           scene with the southernmost of the 1988 scenes. Classi-           Glacier experts predict that there is a 90% chance that
           fication has been aided by an abundance of ground                 the Hubbard Glacier will block off the mouth of the
           reference data as well as digital NWI data that are               Russell Fjord again within the next 10 years. Because
           available for most of the Texas coast.                            the portion of the glacier that will block off the Fjord is
                                                                             bigger than the one in 1986, it is predicted that the
                                                                             glacier will not burst. This would cause the rising waters
           Columbia River, Tillamook Bay, and Willapa Bay                    to exit the Fiord at the end opposite the glacier, flowing
           (Oregon and Washington)                                           into Old Situk Creek. This may significantly affect a
                                                                             very important salmon fishery, crucial to the inhabit-
           This is a cooperative effort involving cooperating agen-          ants of nearby Yakutat, Alaska. C-CAP is presently look-
           cies within the Columbia River Estuary Study Taskforce            ing for another image to perform a change analysis and
           (CREST), NMFS's Point Adams Field Station, Ham-                   provide more baseline information for future change-
           mond, Oreg., and Washington State personnel. Imag-                detection activities, should the glacier again close off
           ery for September 1989 and 1992 has been obtained                 Russell Fjord. The 1986 image has been processed by
           and a change-detection analysis is being performed by             ORNL and the data has been submitted to NODC.






                                                            Acknowledgments

               Many individuals contributed to this effort at regional        poration; Richard Kleckner, Kathy Lins, Keven Roth,
               C-CAP workshops, at meetings, and through private              and Peg Rawson, U.S. Geological Survey; Mark Laustrup,
               communications. We wish to thank all of them, espe-            National Biological Survey; Doug Norton, U.S. Envi-
               cially the following who have helped us continuously           ronmental Protection Agency; Robert Peplies, East Ten-
               over several years to improve this classification system:      nessee State University; and Warren Pulich, Texas De-
               Michael DeMers, New Mexico State University; Francis           partment of Parks and Wildlife. Brent Moll of Oak
               Golet, University of Rhode Island; Steven Hoffer,              Ridge National Laboratory assisted in the technical re-
               Lockheed Engineering and Sciences Co.; Michael                 vision of a preliminary draft. The Chesapeake bay pro-
               Hodgson, Oak Ridge National Laboratory; Jimmy                  totype project was funded in part by NOAA's Chesa-
               Johnston, National Biological Survey; Bill Wilen, U.S.         peake Bay Program. The development of this docu-
               Fish and Wildlife Service; Donley Kisner, Bionetics Cor-       ment was funded by NOAA's Coastal Ocean Program.










































                                                                                                                                      59







          60        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program








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                                                          Appendix 1
                        U.S. Geological Survey Land-Cover Classification Scheinefor Remote Sensor Data


                                                        Appendix Table I
                              Summary of Level I and Level II elements of the U.S. Geological Survey "Land Use
                              and Land Cover Classification System for Use with Remote Sensor Data" (Anderson et
                              al., 1976; USGS, 1992).


                              Level  Land-use and land-cover class


                                I    Urban or Built-Up Land
                                        11 Residential
                                        12 Commercial and Services
                                        13 Industrial
                                        14 Transportation, Communications, and Utilities
                                        15 Industrial and Commercial Complexes
                                        16 Mixed Urban or Built-up
                                        17 Other Urban or Built-up Land

                                2    Agricultural Land
                                        21 Cropland and Pasture
                                        22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas
                                        23 Confined Feeding Operations
                                        24 Other Agricultural Land
                                3    Rangeland
                                        31 Herbaceous Rangeland
                                        32 Shrub-Brushland Rangeland
                                        33 Mixed Rangeland

                                4    Forest Land
                                        41 Deciduous Forest Land
                                        42 Evergreen Forest Land
                                        43 Mixed Forest Land

                                5    Water
                                        51 Streams and Canals
                                        52 Lakes
                                        53 Reservoirs
                                        54 Bays and Estuaries

                                6    Wedand
                                        61 Forested Wettand
                                        61 Nonforested Wetland

                                7    Barren Land
                                        71 Dry Salt Flats
                                        72 Beaches
                                        73 Sandy Areas Other than Beaches
                                        74 Bare Exposed Rock
                                        75 Strip Mines, Quarries, and Gravel Pits
                                        76 Transitional Areas
                                        77 Mixed Barren Land

                                8    Tundra
                                        81 Shrub and Brush Tundra
                                        82 Herbaceous Tundra
                                        83 Bare Ground Tundra
                                        84 Wet Tundra
                                        85 Mixed Tundra

                                9    Perennial Snow or lee
                                        91 Perennial Snowfields
                                        92 Glaciers







                                                                                                                 65






           66       NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program -








                                                                                                                  Appendix 2
                                                                    U.S. Fish and Wildlife Smdce Ketland Classification Schente


                                                                                                              Appendix Table 2
                              Summary of the classification hierarchy of wetlands and deepwater habitats, showing systems, subsystems, and classes of the U.S.
                              Fish and Wildlife Service "Classification of Wetlands and Deepwater Habitats of the United States" (Cowardin et al., 1979).


                                                                                      System                Subsystem                                    Class

                                                                                                                                                         Rock Bottom
                                                                                                            Subtidal                          F-_        Unconsolidated Bottom
                                                                                                                                                         Aquatic Bed
                                                                                                                                                         Reef
                                                                                      Marine
                                                                                                                                                         Aquatic Bed
                                                                                                         - Interlical                         F-_        Reef
                                                                                                                                                         Rocky Shore
                                                                                                                                                         Unconsolidated shore


                                                                                                                                                         Rock Bottom
                                                                                                         - Subtidal                           F-_        Unconsolidated Shore
                                                                                                                                                         Aquatic Bed
                                                                                                                                                         Reef

                                                                                      Estuarine                                                          Aquatic Bed
                                                                                                                                                         Reef
                                                                                                                                                         Streambed
                                                                                                         - Intertidal                                    Rocky Shore
                                                                                                                                                         Unconsolidated Shore
                                                                                                                                                         Emergent Welland
                                                                                                                                                         Scrub-Shrub Welland
                                                                                                                                                         Forested Wetland


                                                                                                                                                         Rock Bottom
                                                                                                                                                         Unconsolidated Bottom
                                                                                                                                                         Aquatic Bed
                                                                                                            Tidal                                        Streambed
                                                                        CO                                                                               Rocky Shore
                                                                        <                                                                                Unconsolidated Shore
                                                                                                                                                         Emergent Welland

                                                                                                                                                         Rock Bottom
                                                                        Lu                                                                               Unconsolidated Bottom
                                                                        Lu
                                                                                                            Lower Perennial                              Aquatic Bed
                                                                                                                                                         Rocky Shore
                                                                        z
                                                                        <             Riverine                                                           Unconsolidated Shore
                                                                        0                                                                                Emergent Wetland
                                                                        0
                                                                        z
                                                                        :5                                                                               Rock Bottom
                                                                        Lu                                                                               Unc nsolidated Bottom
                                                                                                                                                               0
                                                                                                            Upper Perennial                              Aquatic Bed
                                                                                                                                                         Rocky Shore
                                                                                                                                                         Unconsolidated Shore


                                                                                                            Intermittent                                 Streambed


                                                                                                                                                         Rock Bottom
                                                                                                            Limnetic                                     Unconsolidated Bottom
                                                                                                                                                         Aquatic Bed
                                                                                      Lacustrine
                                                                                                                                                         Rock Bottom
                                                                                                                                                         Unconsolidated Bottom
                                                                                                            Littoral                                     Aquatic Bed
                                                                                                                                                         Rocky Shore
                                                                                                                                                         Unconsolidated Shore
                                                                                                                                                         Emergent Wetland

                                                                                                                                                         Rock Bottom
                                                                                                                                                         Unconsolidated Bottom
                                                                                                                                                         Aquatic Bed
                                                                                      palustrine                                                         Unconsolidated Shore
                                                                                                                                                         Moss-Lichen Welland
                                                                                                                                                         Emergent Welland
                                                                                                                                                         Scrub-Shrub Wetland
                                                                                                                                                         Forested Welland





                                                                                                                                                                                                                             67







           68       NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program -








                                                                                Appendix 3
                                          C-CAP Coastal Land-Cover aassification System Definitions


                  The C-CAP Coastal Land-Cover Classification System, de-                  areas in which a significant land area is covered by concrete
                  scribed in Chapter 2 (Table 2), was developed to meet C-CAP              and asphalt or other constructed materials. Vegetation, if
                  requirements (KIemas et al., 1993). It is intended to be com-            present, occupies <20% of the landscape. Examples of such
                  patible with other classification systems to facilitate the ex-          areas include apartment buildings, skyscrapers, shopping cen-
                  change of data among related programs, especially USGS,                  ters, factories, industrial complexes, large barns, airport run-
                  NWI, and EPA's EMAP. Those classes underlined in Table 2                 ways, and interstate highways.
                  arc of greatest importance to the C-CAP program and most
                  can be detected by satellite sensors such as TM and SPOT.                1.12-Low Intensity (Mixed Pixels)-Low-In tensity Developed
                                                                                           Land includes areas with a mixture of constructed materials
                                                                                           (e.g. roofing, metal, concrete, asphalt) and vegetation or
                   Categories of the C-CAP Classification System                           other cover. Constructed materials account for 50-79% of
                                                                                           total area. These areas commonly include single-family hous-
                  The system starts with three superclasses: 1.0-Uplands, 2.0-             ing areas, especially in suburban neighborhoods, but may in-
                  Wetlands, and 3.0-Water and Submerged Land. These super-                 clude scattered surfaces associated with all types of land use. As
                  classes are subdivided into classes and subclasses at the sec-           the percentage of constructed material cover decreases, this
                  ond and third levels, respectively. Most of the classes and              category grades into Cultivated, Grassland, Woody, and other
                  subclasses in the C-CAP system are taken from Anderson et                land-cover classes. A large building surrounded by several acres
                  al. (1976), Cowardin et al. (1979), and USGS (1992). How-                of grass, for example, might appear as one or more pixels of
                  ever, a few definitions have been modified to resolve conflicts          High-Intensity Developed Land, one or more pixels of Low-
                  between the Anderson et al. and Cowardin et al. categories,              Intensity Developed Land, and many pixels of Grassland.
                  and some finer categories have been added (e.g. High-Inten-
                  sity Developed Land and Low-Intensity Developed Land) -                  1.2-Cultivated Land

                  1.0-Upland                                                               Agricultural Land in the Anderson et al. (1976) classification
                                                                                           system was defined as
                  The superclass   1.0-Upland is divided into seven classes: 1.1-
                  Developed Land, 1.2-Cultivated Land, 1.3-Grassland, 1.4-                          land used primarily for production of food and
                  Woody Land, 1.5-Bare Land, 1.6-Tundra, and 1.7-Snow/Ice.                   fiber. On high-altitude imagery, the chief indications of
                                                                                             agricultural activity will be distinctive geometric field
                                                                                             and road patterns on the landscape and the traces pro-
                  1.1 -Developed Land                                                        duced by livestock or mechanized equipment."

                  This class is composed of areas of intensive anthropogenic               C-CAP renamed this class "Cultivated Land" to emphasize
                  use. Much of the land is covered by structures and impervi-              land cover rather than land use. This category contains areas
                  ous surfaces. Anderson et al. (1976) called these areas "Ur-             that have been planted, tilled, or harvested. Pastures and
                  ban or Built-up Land" although the definition clearly in-                hayfields that are in a state of tilling or planting are also
                  cluded suburban and rural areas:                                         included. Otherwise, pasture or hayfield with well-established
                                                                                           grasses are placed in the Grassland category (3.0). The Culti-
                     "Included in this category are cities; towns; villages; strip         vated Land class is divided into three subclasses: 1.21-Orchards/
                     developments along highways; transportation, power, and               Groves/Nurserics, 1.22-Vincs/Bushes, and 1.23-Cropland.
                     communications facilities; and areas such as those occu-
                     pied by mills, shopping centers, industrial and commer-               1.21-Orchards/Groves/Nurseri,es-This category includes
                     cial complexes, and institutions that may, in some in-                woody-stemmed crops that are dominated by single-stemmed,
                     stances, be isolated from urban areas."                               woody vegetation that is unbranched 0.6 to 0.9 m (2 to 3 ft)
                                                                                           above the ground, having a height >3 m (10 ft). Some ex-
                  To clarify this apparent contradiction, C-CAP specifies all              amples of the crops included are apple and cherry orchards,
                  constructed surfaces regardless of land use. Developed Lands             and palm date groves. Anderson et al. (1976) states
                  are divided into two Level 11 groups: 1.11-High Intensity and
                  1.12-Low Intensity.                                                        "Orchards and groves produce the various fruits and nut
                                                                                             crops. Tree nurseries that provide seedlings for planta-
                  1.1 I-High Intensity (Solid Cover)-High-Intensity Developed                tion forestry also are included."
                  Land includes heavily built-up urban centers and large con-
                  structed surfaces in suburban and rural areas with a variety of          Isolated fruit trees and other orchards substantially smaller
                  different land uses. The High-Intensity category contains                than the areal unit of observation are not included. Pine

                                                                                                                                                           69






            70         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            plantations are not included in this class; they are assigned to        1.31-Unmanaged-The Unmanaged, Herbaceous, Grasslands
            the Forest, Evergreen, Woody category (1.421).                          category refers to herbaceous cover that is allowed to grow
                                                                                    naturally and is not fertilized, cut, or tilled and planted
            1. 22-Vines/Bushes-Vines/ Bushes refers to areas of multiple            regularly. This category includes, but is not limited to, the
            stemmed, woody-stemmed crops that are shrubs <3 rn (10 ft)              Anderson et al. (1976) "Herbaceous Rangeland" category:
            in height. Examples of crops included in this category are
            blueberries, grapes, and other vines and bushes producing                 "the tall grass (or true prairie), short grass, bunch grass
            various fruit or nut crops (Anderson et al., 1976). This group            or palouse grass, and desert grass regions ... Bunch grass
            has a different spectral signature than other Cultivated Land             and desert grass are found in many locations, represent-
            groups because of the size and spatial configuration of the               ing transitional situations to desert shrub. Typical occur-
            vines, shrubs, and bushes.                                                rences of grasslands include such species as the various
                                                                                      bluestems (Andropogon), grama grasses (Bouteloua), wheat-
            1.23-Cropland-This class of Cultivated Land refers to any                 grasses (Agropyron), needle-grasses (Stipa), and fescues
            crop type that is planted on a regular basis. Crops may be                (Festuca). This category also includes the palmetto prai-
            planted annually in the same field year after year or on a                rie areas of south-central Florida, which consist mainly
            rotating schedule. Anderson et al. (1976) states                          of dense stands of medium length and tall grasses such
                                                                                      as wiregrass (Aristida stricta) and saw palmettos (Seronoa
               "The several components of Cropland now used for agri-                 ripens), interspersed occasional palms (Sabal palmetto),
               cultural statistics include: cropland harvested, including             and shrubs."
               bush fruits; cultivated summer-fallow and idle cropland,
               land on which crop failure occurs; and cropland in soil-             Unmanaged grasslands are found throughout the United
               improvement grasses and legumes."                                    States, often as a transitional phase in the regrowth of aban-
                                                                                    doned Cropland, clearcut Woody Land, or land affected by
            C-CAP has modified this category to emphasize the instanta-             natural disturbance,
            neous state of the land at the time of observation. Hence, for
            example, Cultivated Land in a five-year rotation scheme will            1.32-Managed-These grasslands are maintained by human
            be categorized as Cropland for the four years the land is               activity and include lawns, golf courses, pastures, hayfields,
            tilled and as Grassland for the one year the land is fallow and         and other areas of grassland in which seeding, fertilization,
            covered by grasses. A fallow period of several years may result         or irrigation enhance biomass productivity. This category
            in a transition from Cropland to Grassland to Scrub/Shrub               may contain vegetation that grows as fallow if vigorous growth
            and back to Cropland.                                                   persists due to the residual effects of management practices
               Nurseries and horticultural areas (which include floriculture,       in the nonfallow state.
            seed, and sod areas) used perennially for those purposes are
            included in this category if woody-stemmed plants are not grown.
            Greenhouses normally fall in the Developed Land category.               1.4-Woody Land

                                                                                    The Woody Land class includes any species with an aerial
            1.3-Grassland                                                           stem that persists for more than one season. The class is
                                                                                    divided into three subclasses: 1.41-Deciduous, 1.42-Evergreen,
            The Grassland category includes lands covered by natural                and 1.43-Mixed.
            and managed herbaceous cover. Historically, grassland has
            been defined as land where the potential natural vegetation             1.4 I-Deciduous-The Deciduous Woody subclass includes all
            is predominantly grasses, grasslike plants, and forbs, and              forest and shrub areas having a predominance of trees and
            where natural herbivory was an important influence in its               shrubs that lose their leaves or needles at the end of the frost-
            pre-civilization state. Anderson et al. (1976) state                    free season or at the beginning of a dry season. Areas in this
                                                                                    category are composed of greater than two-thirds deciduous
               "Some grasslands have been or may be seeded to intro-                trees and shrubs. The Deciduous Woody category can be
               duce or domesticate plant species. The Grassland (Her-               divided into two groups: 1.41 I-Forest and 1.412-Scrub/Shrub.
               baceous) category contains both managed and                            1.41 I-Forest-Deciduous Forest includes areas dominated
               unmanaged or natural herbaceous cover. The Grassland                 by single stemmed, woody vegetation unbranched 2-3 ft above
               (Herbaceous) category can be found in every state in the             the ground having a height @!6 in (20 ft). Forest Deciduous
               United States along with Canada and Mexico."                         Woody areas have a tree-crown areal density (crown closure
                                                                                    percentage) of @!10 percent, are stocked with trees capable of
            The C-CAP category includes lands with herbaceous cover at the          producing timber or other wood products, and exert an
            time of observation regardless of origin or potential. Pastures,        influence on the climate or water regime. In most parts of the
            hayfields, and natural rangelands are included. Also included           United States, these would be the hardwoods such as oak,
            are lawns and other managed grassy areas such as parks, ce-             Quercus, maple, Acer, or hickory, Caiya, and the "soft" hard-
            eteries, golf courses, road fights-of-way, and other herbaceous-        woods, such as aspen, Populus tremuloides. Tropical hardwoods
            covered, landscaped areas. The Grassland class is divided into          are included in the Evergreen Forest (Woody Land) category
            two subclasses: 1.31-Unmanaged and 1.32-Managed.                        (1.421). Deciduous forest types characteristic of Wetland,







                                                                                   Appendix 3: C-CAP Land-Cover Classification Definitions                        71


                    such as tupelo, Nyssa, or cottonwoods, Populus deltoides, are              1.43-Mixed-The Mixed Wood), class includes all forest and
                    not included in this category.                                             shrub areas where both evergreen and deciduous trees and
                      1.412-Scrub/Shrub-Deciduous Scrub/Shrub includes all                     shrubs grow and neither predominates. When evergreen and
                    areas having a predominance of shrub that lose their leaves                deciduous species each respectively occupy @:33 % of an area,
                    or needles at the end of the frost-free season or at the begin-            the land is classified as Mixed Woody. The Mixed Woody
                    ning of the dry season (Anderson et al., 1976). This category              category is subdivided into two additional categories: 1.431-
                    contains vegetation that is <6 in (20 ft) in height. The species           Forest and 1.432-Scrub/Shrub.
                    include true shrubs, young trees, and trees or shrubs that are               1.431 Forest-This class includes all forested areas where
                    small or stunted because of environmental conditions. True                 both evergreen and deciduous trees are growing and neither
                    shrubs are those woody@stemmed species that exhibit several                predominate.
                    erect, spreading, or prostrate stems and a general bushy                     1.432 Scrub/Shrub-This class includes all shrub areas
                    appearance. Shrub Lands may represent a successional stage                 where both evergreen and deciduous shrubs are growing and
                    leading to forests or they may be relatively stable communi-               neither predominate.
                    ties. Forest regrowth composed of young trees <6 in tall is also
                    included in this category.
                                                                                               1.5-Bare Land
                    1.42-Evergreen-The Evergreen Woody subclass contains for-
                    eIsts and shrubs that do not lose their leaves or needles at the           The Bare Land class, modified from "Barren Land" in Ander-
                    end of a frost-free season or at the beginning of a dry season.            son et al. (1976) is composed of bare rock, sand, silt, gravel,
                    The Evergreen Woody category is subdivided into two addi-                  or other earthen material with little or no vegetation regard-
                    tional categories: 1.421-Forest and 1.422-Scrub/Shrub.                     less of its inherent ability to support life. Vegetation, if present,
                      1.421-Forest-Evergreen Forest includes areas in which                    is more widely spaced and scrubby than that in the vegetated
                    @!67% of the trees remain green throughout the year. Both                  categories. Unusual conditions, such as a heavy rainfall, occa-
                    coniferous and broad-leaved evergreens are included in this                sionally may result in a short-lived, luxuriant plant cover.
                    category. Coniferous evergreens predominate except in tropi-               Wet, nonvegetated exposed lands are included in the wet-
                    cal regions where broad-leaved evergreens are indigenous.                  land categories.
                    Coniferous evergreens, often called softwoods, include such                  Categories of Bare Land include Dry Salt Flats; Beaches;
                    eastern species as the longleaf pine, Pinus palustris, slash               Sandy Areas other than Beaches; Bare Exposed Rock; Strip
                    pine, P. ellioti, shortleaf pine, P. echinata, loblolly pine, P.           Mines, Quarries; Gravel Pits; Transitional Areas; and Mixed
                    taeda, and other southern yellow pines; various spruces, Picea,            Barren Land:
                    and balsam fir, Abies balsamea; white pine, P. strobus, red pine,
                    P. resinosa, and jack pine, P. banksiana; and hemlock, Tsuga               Dry Salt Flats are level bottoms of interior desert basins that
                    canadensis-, and such western species as Douglas fir, Pseudotsuga          capture infrequent rainfall and do not qualify as Wetland.
                    menziesii, redwood, Sequoia sempervirens, ponderosa pine,                  Salt concentrations result in highly reflective surfaces.
                    P. monticola, Sitka spruce, P. sitchensis, Engelmann spruce,
                    P. engelmanni, western redcedar, ThyJa plicata, and western                Beaches are the smooth sloping accumulations of sand and
                    hemlock, Tsuga heterophylla. Evergreen species commonly associ-            gravel along shorelines. The inland face is usually stable, but
                    ated with Wedand, such as tamarack, Larix laficina, or black               the shoreward face is subject to erosion by wind and water
                    spruce, P. mariana, are not included in this category.                     and subject to deposition in protected areas.
                      1.422-Scrub/Shrub-Evergreen Scrub/Shrub includes ar-
                    eas in which @!67% of the shrubs remain green throughout                   Sandy Areas other than Beaches are composed primarily of
                    the year. Anderson et al. (1976) states                                    dunes-accumulations of sand transported by the wind. Sand
                                                                                               accumulations most commonlyare found in deserts although
                      "Both coniferous and broad-leaved         evergreens are in-             they also occur on coastal plains, river flood plains, and
                      cluded in this category. The typical Shrub Lands are                     deltas and in periglacial environments. When such sand ac-
                      found in those and and semiarid regions characterized                    cumulations are encountered in tundra areas, they are not
                      by such xerophytic vegetative types with woody stems as                  included here but are placed in the Bare Ground Tundra
                      big sagebrush, A?Iemisia tridentata, shadscale, Atiiplex                 category.
                      confertifolia, greasewood, Sarcobatus vermiculatus, and
                      creosotebush, Larrea divaricata. When bottom lands and                   Bare Exposed Rock includes areas of bedrock, desert pave-
                      moist flats are characterized by dense stands of typical                 ment, scarps, talus, slides, volcanic material, glacial debris,
                      wetland species ... they are considered Wetland. Where                   and other accumulations of rock without vegetative cover,
                      highly alkaline soils are present, halophytes such as desert             with the exception of such rock exposures in tundra regions.
                      saltbush, Atyiplex may occur. The type, density, and asso-
                      ciation of these various species are useful as indicators of             Strip Mines, Quarries, and Gravel Pits are areas of extractive
                      the local hydrologic and pedologic environments. Also                    mining activities with significant surface expression. Vegeta-
                      included in this category is chaparral, a dense mixture                  tive cover and overburden are removed to expose such de-
                      of broadleaf evergreen sclerophyll shrubs, and the oc-                   posits as coal, iron ore, limestone, and copper. Quarrying of
                      currences of mountain mahogany, Cercocarpus ledifolius                   building and decorative stone and recovery of sand and gravel
                      and scrub oaks, Quercus."                                                deposits also result in large open-surface pits. Active, inac-







            72         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            tive, and unreclaimed strip mines, quarries, borrow pits, and            rennial Snow/Ice cover is of paramount interest. A combina-
            gravel pits are included in this category until other cover has          tion of environmental factors may cause snow and ice to survive
            been established, after which the land is classified in accor-           the summer melting season. Areas of Perennial Snow/Ice cover
            dance with the resulting cover. Unused pits or quarries that             are defined as those where snow, firn (coarse, compacted granu-
            have been flooded, however, are placed in the appropriate                lar snow), or ice accumulation exceeds ablation. Ablation is the
            Water category.                                                          combined loss of snow or ice mass by evaporation and melt-
                                                                                     water run-off (Anderson et al., 1976). The class Snow/Ice con-
            Transitional Areas are dynamically changing from one land                tains two subclasses: 1.71-Perennial Snow/Ice and 1.72-Glacier.
            cover to another, often because of land use activities. This
            transitional phase occurs when, for example, forest lands are            1.71-Perennial Snow/Ice-This class contains areas covered
            cleared for agriculture and wetlands are drained for develop-            year-round with snow and ice but which have not accumu-
            ment. Often land becomes temporarily bare as construction                lated sufficient ice to be considered Glaciers. Snowfields can
            initiates the transition from Woody Land or Grassland to a               be extensive and thus representative of a regional climate, or
            future cover associated with residential, commercial, or other           can be isolated and localized, where they are known by vari-
            intensive land use. Lands, such as spoil banks and sanitary              ous terms, such as snowbanks. The regional snowline is con-
            landfills, that are temporarily altered by grading and filling           trolled by general climatic conditions and closely parallels
            are considered transitional.                                             the regional 32* F (0' Q isotherm for the average tempera-
                                                                                     ture of the warmest summer month. The use of the term
                                                                                     "line" is somewhat misleading because the "snowline" repre-
            1.6-Tundra                                                               sents an irregular transitional boundary, which is determined
                                                                                     at any single location by the combination of snow accumula-
            Tundra is the term applied to the treeless cover beyond the              tion, snow melt, and ablation, variables that can change rap-
            latitudinal limit of the boreal forest in poleward regions and           idly within short distances because of changes in local topo-
            above the elevation range of the boreal forest in high moun-             graphy and slope orientation. Snowfields normally can be
            tains. In the United States, tundra is found primarily in                distinguished from the following Glacier subclass by their
            Alaska, several areas of the western high mountain ranges,               relative lack of flow features (Anderson et al., 1976).
            and isolated enclaves in the high mountains of New England
            and northern New York.                                                   1.72-Glaciers-Glacial ice originates from the compaction of
              The vegetative cover of the tundra is low and dwarfed,                 snow into firn and finally into ice under the weight of several
            often forming a continuous mat. Plant characteristics are an             successive annual accumulations. Refrozen melt water usu-
            adaptation to an extreme physical environment in which                   ally contributes to the increasing density of the glacial ice
            temperatures may average above freezing only one or two                  mass. With sufficient thickness, weight, and bulk, flow begins;
            months of each year, strong desiccating winds may occur,                 all glaciers exhibit evidence of present or past motion in the
            great variation exists in solar energy, and permafrost is ubiq-          form of moraines, crevasses, and other glacial geomorphic
            uitous beneath the surface.                                              features.
              The number of species in the flora is relatively small com-              Where the snowline of adjacent ice-free areas extends across
            pared with typical middle- and low-latitude flora, and the               the glacier, it is known as the firn limit, which represents the
            number decreases as the environment becomes more severe                  dividing line between the glacier's two major zones, the zone
            with increasing latitude and elevation. The tundra vegetation            of accumulation and the zone of ablation. While glaciers
            is most luxuriant near the boreal forest. Conversely, plant              normally are recognized easily, certain glacial boundaries
            density and species diversity are lowest near the boundaries             may be subject to misinterpretation, even by the experienced
            of permanent ice and snow areas, where only isolated patches             interpreter. Flow features up-glacier from the firn limit typi-
            of vegetation occur on generally bare surfaces.                          cally are obscured by fresh snow, forcing the image inter-
              Tundra may be further subdivided into Shrub and Brush                  preter to depend on secondary information, such as valley
            Tundra, Herbaceous Tundra, Bare Ground Tundra, Wet Tun-                  shapes, or to seek a more discriminating sensor. Similarly, gla-
            dra, and Mixed Tundra (Anderson et al., 1976).                           cial drift materials (rock and soil) may stripe the surface of a
                                                                                     glacier, and moraine material may cover the ter-minus (or snout)
                                                                                     because of ablation, making boundary determination in that
            1.7-Snow/Ice                                                             vicinity difficult. This later problem occasionally is compounded
                                                                                     by the presence of considerable vegetation rooted in the insulat-
            The temporal dimension is crucial in determining snow and                ing blanket of ablation moraine (Anderson et al., 1976).
            ice cover. Any snowfall, for example, deep enough to conceal
            another land cover, no matter how briefly, comprises the
            visible surface at that time and technically constitutes the             2.0-Wetland
            land cover for the period of its duration. As a practical mat-
            ter, of course, analysts usually need to characterize the land           Cowardin et al. (1979) define wetlands as lands where satura-
            cover persisting for a greater portion of the year. At higher            tion with water is the dominant factor determining soil devel-
            latitudes and elevations, snow and ice persist for greater               opment and the types of plant and animal communities
            portions of the year, and seasonal coverage becomes a more               living in the soil and on its surface. The single feature that all
            important concern. At extreme latitudes and elevations, Pe-              wetlands share is soil or substrate that is at least periodically







                                                                              Appendix 3: C-CAP Land-Cover Classification Definitions                    73

                  saturated with or covered by water. The upland limit for                wetland or deepwater habitat under the Cowardin et al. (1979)
                  vegetated wetlands with soil is 1) the boundary between land            classification system. The Level. 11 C-CAP Water and Sub-
                  with predominantly hydrophytic cover and land with pre-                 merged Land classes are modified from Cowardin et al. (1979)
                  dominantly mesophytic or xerophytic cover; 2) for nonvege-              (Appendix 2), and include Water, Reef, and Aquatic Beds.
                  tated wetlands with soil, the boundary between soil that is             Marine and Estuarine Reefs and Marine and Estuarine Aquatic
                  predominantly hydric and soil that is predominantly                     Beds are combined into two classes, 3.2-Marine/ Estuarine
                  nonhydric; or 3) in the case of wetlands without vegetation or          Reef and 3.3-Marine/EstuarineAquatic Bed. Aquatic bed in
                  soil, the boundary between land that is flooded or saturated            rivers, lakes, and streams are assigned to 3.4-Riverine Aquatic
                  sometime during the growing season each year and land that              Bed, 3.5-Lacustrine Aquatic Bed, and 3.6-Palustrine Aquatic
                  is not. Most wetlands are vegetated and are found on soil.              Bed classes. This last class also, includes Cowardin et al.'s
                    In the C-CAP Coastal Land-Cover Classification System                 (1979) Rock Bottom and Unconsolidated Bottoms.
                  (Table 2), "Wetland" includes all areas considered wetland                Most C-CAP products will designate water as a single class
                  by Cowardin et al. (1979) except for Wetland Bottoms, Aquatic           (3.1) regardless of system type. fir is recognized, however, that
                  Beds, and Nonpersistent Emergent Wetlands. Subdivision of               the ma or systems (Marine/Estuarine, Riverine, Lacustrine,
                  the Wetlands class closely resembles the Cowardin et al. sys-           Palustrine) are ecologically quite different from one another.
                  tem (Appendix 2). At Level II, C-CAP uses certain Cowardin              Hence, the four systems at Level III are shown as subclasses:
                  et al. classes (e.g. Rocky Shore, Unconsolidated Shore, Emer-           3.1 I-Marine/Estuarine, 3.12-Riverine, 3.13-Lacustrine, and
                  gent Weiland) or grouped Cowardin et al. classes (e.g. Woody            3.14-Palustrine. Even though C-CAP does not commit itself to
                  Wetland = Scrub-Shrub + Forested Wetland) in combination                provide the subclass data, this option is encouraged for re-
                  with Cowardin et al. systems (i.e. Marine, Estuarine, Riverine,         gional participants. Incorporating water system information
                  Lacustrine, Palustrine). Thus, C-CAP Level 11 wetland classes           makes the C-CAP scheme more compatible with the Cowardin
                  became 2.1-Marine/Estuarine Rocky Shore, 2.2-Marine/Es-                 et al. system. The subclass 3.1l.-Marine/Estuarine includes
                  tuarine Unconsolidated Shore, 2.3-Marine/ Estuarine Emer-               bottoms and undetected reefs and aquatic beds. The sub-
                  gent Wetland, 2.4-Estuarine Woody Wetland, 2.5-Riverine                 classes 3.12-Riverinc, 3.13-Lacustrine, and 3.14-Palustrine in-
                  Unconsolidated Shore, 2.6-Lacustrine Unconsolidated Shore,              clude bottoms and undetected aquatic beds or non-persis-
                  2.7-Palustrine Unconsolidated Shore, 2.8-Palustrine Emergent            tent emergent wetlands.
                  Wetland (persistent), and 2.9-Palustrine Woody Wetland.                   3.3-Marine/Estuarine Aquatic Beds includes the subclass
                    Salinity displays a horizontal gradient in marshes typical of         Rooted Vascular, which is broken into High Salinity (>5 ppt)
                  coastal plain estuaries. This is evident not only through the           and Low Salinity (<5 ppt). The break was made at 5 ppt
                  direct measurement of salinity but also in the horizontal               salinity because it separates true! seagrasses that require high
                  distribution of marsh plants in marshes with positive correla-          salinity from low salinity species that are tolerant of or re-
                  tions between vertical rise and landward location (Daiber,              quire fresh water. Both low and high salinity types of SRV are
                  1986). Therefore Marine Estuarine Emergent Wetland was                  important to the C-CAP project. High Salinity includes
                  partitioned into Haline (Salt) and Mixohaline (Brackish)                Cowardin et al.'s mesohaline, polyhaline, euhaline, and
                  Marshes. For both subclasses, the definitions used in Cowardin          hyperhaline salinity categories. Low Salinity includes Cowardin
                  et al. (1979) were used, i.e. the salinities for Mixohaline             et al.'s oligohaline and fresh categories.
                  range from 0.5 to 30 ppt, and Haline include salinities >30
                  ppt. Within a marsh, plant zonation is usually quite evident.
                  Along the Atlantic coast of North America the pioneer plant                  Systems and Classes of Cowardin et al.
                  is saltmarsh cordgrass, Spa?tina alterniflora, which often ap-
                  pears in pure stands. Higher up the slope saltmeadow hay,               Most of the C-CAP wetland and water definitions are taken
                  Spa7fina patens, becomes dominant, while the upland edges               directly from Cowardin et al. (1979). This classification is
                  are bordered by marsh elder, Ivafrutescens, and groundsell              hierarchical, progressing from systems and subsystems, at the
                  tree, Bacchaiis halimifolia. Thus, salt marshes could be subdi-         most general level, to classes, subclasses, and dominance
                  vided further into High Marsh and Low Marsh.                            types. Appendix Table 2 illustrates the hierarchical structure
                    C-CAP does not attempt to identify Nonpersistent Emer-                to the class level. Modifiers for water regime, water chemistry,
                  gent Wetlands, because they are seasonal. These wetlands are            and soils are applied to classes, subclasses, and dominance
                  classified as "Riverine Water" and "Lacustrine Water." Ma-              types. Special modifiers describe wetlands and deepwater
                  rine and Estuarine Rocky Shores were combined into a single             habitats that have been either created or highly modified by
                  class, Marine/Estuarine Rocky Shore. The same logic was                 human or beaver activity.
                  applied to create Marine and Estuarine Unconsolidated
                  Shores, Aquatic Beds, and Water.                                        Systems
                  3.0-Water and Submerged Land                                            The term system refers to a complex of wetlands and
                                                                                          deepwater habitats that share the influence of similar hydro-
                  All areas of open water with <30% cover of trees, shrubs,               logic, geomorphologic, chemical, or biological factors. Sys-
                  persistent emergent plants, emergent mosses, lichens, or other          tems are subdivided into subsystems.
                  land cover are grouped under the heading, Water and Sub-                  The characteristics of the five major systems-Marine, Es-
                  merged Land, regardless of whether the area is considered               tuarine, Riverine, Lacustrine, and Palustrine-have been dis-







            74         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            cussed at length in the scientific literature and the concepts            wetlands dominated by trees, shrubs, persistent emergents,
            are well recognized. However, there is disagreement as to                 emergent mosses, or lichens and 2) habitats with water con-
            which attributes should be used to bound the systems in                   taining ocean-derived salts >0.5%. A channel is "an open
            space. For example, both the limit of tidal influence and the             conduit either naturally or artificially created which periodi-
            limit of ocean-derived salinity have been proposed,as defini-             cally or continuously contains moving water, or which forms
            tions of the upstream limit of Estuarine Systems (Caspers,                a connecting link between two bodies of standing water."
            1967). As Bormann and Likens (1969) affirm, boundaries of                    Limits. The Riverine System is bounded on the landward
            ecosystems are defined to meet practical needs.                           side by upland, by the channel bank (including natural and
                                                                                      man-made levees), or by wetland dominated by trees, shrubs,
                                                                                      persistent emergents, emergent mosses, or lichens. In braided
            Marine System                                                             streams, the system is bounded by the banks forming the
                                                                                      outer limits of the depression within which the braiding
            Definition. The Marine system consists of the open ocean                  occurs.
            overlying the continental shelf and its associated high-energy               The Riverine System terminates downstream where the
            coastline. Marine habitats are exposed to the waves and cur-              concentration of ocean-derived salts in the water exceeds
            rents of the open ocean, and the water regimes are deter-                 0.5% during the period of annual average low flow, or where
            mined primarily by the ebb and flow of ocean tides. Salinities            the channel enters a lake. It terminates upstream where
            exceed 30%, with little or no dilution except near the mouths             tributary streams originate or where the channel originates
            of estuaries. Shallow coastal indentations or bays without                from a lake. Springs discharging into a channel are consid-
            appreciable freshwater inflow, and coasts with exposed rocky              ered part of the Riverine System.
            islands that provide the mainland with little or no shelter
            from wind and waves, are also considered part of the Marine
            System because they generally support typical marine biota.               Lacustrine System
              Limits. The Marine System extends from the outer edge of
            the continental shelf shoreward to one of three lines: 1) the             Definition. The Lacustrine System includes wetlands and
            landward limit of tidal inundation (extreme high water of                 deepwater habitats with all of the following characteristics: 1)
            spring tides), including the splash zone from breaking waves;             situated in a topographic depression or a dammed river
            2) the seaward limit of wetland emergents, trees, or shrubs;              channel; 2) lacking trees, shrubs, persistent emergents, emer-
            or 3) the seaward limit of the Estuarine System, where this               gent mosses or lichens with >30% areal coverage; and 3) total
            limit is determined by factors other than vegetation. Deepwater           area >8 ha (20 acres). Similar wetland and deepwater habitats
            habitats lying beyond the seaward limit of the Marine System are          totaling less than 8 ha are also included in the Lacustrine
            outside the scope of this classification system.                          system if an active wave-formed or bedrock shoreline feature
                                                                                      makes up all or part of the boundary, or if the water depth in
                                                                                      the deepest part of the basin is >2 in (6.6 feet) at low water.
            Estuarine Svstem                                                          Lacustrine waters may be tidal or nontidal, but ocean-derived
                           J
                                                                                      salinity is always <0.5%.
            Definition. The Estuarine system consists of deepwater tidal                 Limits. The Lacustrine System is bounded by upland or by
            habitats and adjacent tidal wetlands that are usually semi-               wetland dominated by trees, shrubs, persistent emergents,
            enclosed by land but have open, partly obstructed, or spo-                emergent mosses, or lichens. Lacustrine systems formed by
            radic access to the open ocean, and in which ocean water is at            damming a river channel are bounded by a contour approxi-
            least occasionally diluted by freshwater runoff from the land.            mating the normal spillway elevation or normal pool eleva-
            The salinity may be periodically increased above that of the              tion, except where Palustrine wetlands extend lakeward of
            open ocean by evaporation. Along some low-energy coast-                   that boundary. Where a river enters a take, the extension of the
            lines there is appreciable dilution of sea water. Offshore                Lacustrine shoreline forms the Riverine-Lacustrine boundary.
            areas with typical estuarine plants and animals, such as red
            mangroves, Rhizophora mangle, and eastern oysters, Crassostrea
            virginica, are also included in the Estuarine system.                     Palustrine System
               Limits. The Estuarine system extends 1) upstream and landward
            to where ocean-derived salts measure <0.5% during the period of           Definition. The Palustrine System includes all nontidal wet-
            -average annual low flow; 2) to an imaginary line closing the             lands dominated by trees, shrubs, persistent emergents, emer-
            mouth of a river, bay, or sound; and 3) to the seaward limit of           gent mosses or lichens, and all such wetlands that occur in
            wetland emergents, shrubs, or trees where they extend beyond              tidal areas where salinity due to ocean-derived salts is below
            the river mouth defined by (2). The Estuarine System also                 0.5%. It also includes wetlands lacking such vegetation, but
            includes offshore areas of continuously diluted sea water.                with all of the following four characteristics: 1) the area is
                                                                                      <8 ha (20 acres); 2) active wave-formed or bedrock shoreline
                                                                                      features are lacking; 3) water depth in the deepest part of
            Riverine System                                                           basin is <2 in at low water; and 4) salinity due to ocean-
                                                                                      derived salts is <0.5%.
            Definition. The Riverine system includes all wetlands and                    Limits. The Palustrine System is bounded by upland or by
            deepwater habitats contained within a channel, except 1)                  any of the other four Systems.







                                                                             Appendix 3: C-CAP Land-Cover Classification Definitions                   75


                   Description. The Palustrine System was developed to group            In the Marine and Estuarine Systems, Bottoms are Subtidal,
                the vegetated wetlands traditionally called by such names as            whereas Streambeds and Shores are Intertidal. Bottoms,
                marsh, swamp, bog, fen, and prairie pothole, which are found            Shores, and Streambeds are further divided at the class level
                throughout the United States. It also includes small, shallow,          on the basis of the important characteristic of rock versus
                permanent, or intermittent water bodies often called ponds              unconsolidated substrate. Subclasses are based on finer dis-
                (except in New England and New York where the term pond                 tinctions in substrate material unless, as with Streambeds and
                often refers to substantial lakes). Palustrine wetlands may be          Shores, the substrate is covered by, or shaded by, an areal
                situated shoreward of lakes, river channels, or estuaries on            coverage of pioneering vascular plants (often nonhydro-
                river floodplains, in isolated catchments, or on slopes. They           phytes) @!30%. The subclass is then simply "vegetated." Fur-
                may also occur as islands in lakes or rivers. The erosive forces        ther detail as to the type of vegetation must be obtained at
                of wind and water are of minor importance except during                 the level of dominance type. Reefs are a unique class in which
                severe floods.                                                          the substrate itself is composed primarily of living and dead
                                                                                        animals. Subclasses of Reefs are designated on the basis of
                                                                                        the type of organism that formed the reef.
                Classes and Subclasses                                                    As shown in Appendix Table 2, the classes defined in
                                                                                        Cowardin et al. (1979) include
                The class is the highest taxonomic unit below the Subsystem
                level. It describes the general appearance of the habitat in              Rock Bottom (not used in the C-CAP system)
                terms of either the dominant life form of the vegetation or               Unconsolidated Bottom (not used in the C-CAP system)
                the physiography and composition of the substratc-features                Aquatic Bed
                that can be recognized without the aid of detailed environ-               Reef
                mental measurements. Vegetation is used at two different                  Streambed (not used in the C-CAP system)
                levels in the classification. Five life-forms-trees, shrubs,              Rocky Shore
                emergents, emergent mosses, and lichens-are used to de-                   Unconsolidated Shore
                fine classes because they are relatively easy to distinguish, do          Emergent Wetland
                not change distribution rapidly, and have traditionally been              Scrub-Shrub Wetland
                used as criteria for classification of wetlands. Other forms of           Forested Wetland
                vegetation, such as submersed or floating-leaved rooted vas-
                cular plants, free-floating vascular plants, submergent mosses,
                and algae, though frequently more difficult to detect, are              Aquatic Bed
                used to define the class Aquatic Bed. Pioneer species that
                briefly invade wetlands when conditions are favorable are               Definition. The Aquatic Bed class includes wetlands and
                treated at the subclass level because they are transient and            deepwater habitats dominated by plants that grow principally
                often are not true wetland species (Cowardin et al., 1979).             on or below the surface of the water for most of the growing
                   Using life-forms at the class level has two ma or advantages:        season in mostyears. Water regimes include subtidal, irregularly
                1) extensive biological knowledge is not required to distin-            exposed, regularly flooded, permanently flooded, intermittently
                guish between various life-forms and 2) various life-forms are          exposed, semi-permanently flooded, and seasonally flooded.
                easily recognizable on a great variety of remote sensing prod-            Description. Aquatic beds represent a diverse group of
                ucts (Anderson et al., 1976) If vegetation (except pioneer              plant communities that require surface water for optimum
                species) covers @!30% of the substrate, classes are distinguished       growth and reproduction. They are best developed in rela-
                on the basis of the life form of the plants that constitute the         tively permanent water or under conditions of repeated flood-
                uppermost layer of vegetation and that occupy an areal cover-           ing. The plants are either attached to the substrate or float
                age @!50% of vegetative cover. Finer differences in life-forms          freely in the water above the bottom or on the surface. The
                are recognized at the subclass level. For example, in the               subclasses are Algal, Aquatic Moss (not used by C-CAP and
                C-CAP system Estuarine Woody Wetland is divided into the                not defined here), and Rooted Vascular.
                subclasses Scrub-Shrub and Forest categories, each of which
                may be further characterized as Deciduous, Evergreen, and               Algal-Algal beds are widespread and diverse in the Marine
                Mixed on the basis of the predominant life-form. This differs           and Estuarine Systems, where they occupy substrates charac-
                somewhat from the Cowardin et al. system which distinguishes            terized by a wide range of sediment depths and textures.
                trees from shrubs at the class level.                                   They occur in both the Subtidal and Intertidal subsystems
                   If vegetation covers <30% of the substrate, the physiogra-           and may grow to depths of 30 in (98 ft). Coastal algal beds are
                phy and composition of the substrate are the principal char-            most luxuriant along the rocky shores of the Northeast and
                acteristics used to distinguish classes. The nature of the sub-         West. Kelp (Macrocystis) beds arc especially well developed on
                strate reflects regional and local variations in geology and the        the rocky substrates of the Pacific coast. Dominance types
                influence of wind, waves, and currents on erosion and depo-             such as the rockweeds Fucus and Ascophyllum and the kelp
                sition of substrate materials. The classes Bottoms, Shores,             Lamina7ia are common along both coasts. In tropical regions,
                and Streambeds are separated on the basis of duration of                green algae, including forms containing calcareous particles,
                inundation. In the Riverine, Lacustrine, and Palustrine Sys-            are more characteristic; Halimeda and Peniellus are common
                tems, Bottoms are submerged all or most of the time, whereas            examples. The red alga Laurencia and the green algae CauLe@pa,
                Streambeds and Shores are exposed all or most of the time.              Enteromorpha, and Ulva are also common Estuarine and Ma-







              76         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


              rine dominance types; Enteromorpha and [Rva are tolerant of                singly or in combination have an areal cover :@75% and an
              fresh water and flourish near the upper end of some estuar-                areal coverage by vegetation of <30%. Water regimes are
              ies. The stonewort Chara is also found in estuaries.                       restricted to irregularly exposed, regularly flooded, irregu-
                Inland, the stoneworts Chara, Nitella, and Tolypella are ex-             larly flooded, seasonally flooded, temporarily flooded, and
              amples of algae that look much like vascular plants and may                intermittently flooded.
              grow in similar situations. However, meadows of Chara may                     Description. In Marine and Estuarine Systems, Rocky Shores
              be found in Lacustrine water as deep as 40 m (131 ft) where                are generally high-energy habitats that lie exposed as a result
              hydrostatic pressure limits the survival of vascular submergents           of continuous erosion by wind-driven waves or strong cur-
              (phanaerogams). Other algae bearing less resemblance to                    rents. The substrate is stable enough to permit the attach-
              vascular plants are also common. Mats of filamentous algae                 ment and growth of sessile or sedentary invertebrates as well
              may cover the bottom in dense blankets, may rise to the                    as attached algae or lichens. Rocky shores usually display a
              surface under certain conditions, or may become stranded                   vertical zonation that is a function of tidal range, wave action,
              on unconsolidated or rocky shores.                                         and degree of exposure to the sun. In the Lacustrine and
                                                                                         Riverine Systems, Rocky shores support sparse plant and
              Rooted Vascular-Rooted Vascular beds include a large ar-                   animal communities. The subclasses are Bedrock and Rubble.
              ray of vascular species in the Marine and Estuarine Systems.               More detailed definitions are provided in Cowardin et al.
              They have been referred to as temperate grass flats (Phillips,             (1979).
              1974), tropical marine meadows, as well as eelgrass beds,
              turtlegrass beds, and seagrass beds. The greatest number of
              species occur in shallow, clear tropical, or subtropical waters            Unconsolidated Shore (Tidal Flats)
              of moderate current strength in the Caribbean and along the
              Florida and Gulf Coasts. Principal dominance types in these                Definition. The Unconsolidated Shore class includes all wet-
              areas include turtle grass, Thalassia testudinum, shoalgrass,              land habitats having three characteristics: 1) unconsolidated
              Halodule unightfi, manatee grass, Cymodoceafiliformis, widgeon             substrates with < 75% areal cover of stones, boulders, or
              grass, Ruppia ma7itima, sea grasses, Halophila spp., and wild              bedrock; 2) <30% areal cover of vegetation other than pio-
              celery, Vallisneria anzeyicana.                                            neering plants; and 3) any of the following water regimes:
                                                                                         irregularly exposed, regularly flooded, irregularly flooded,
              Reef                                                                       seasonally flooded, temporarily flooded, intermittently
                                                                                         flooded, saturated, or artificially flooded. Intermittent or
              Definition. The Reef class includes ridgelike or moundlike                 intertidal channels of the Riverine System and intertidal chan-
              structures formed by the colonization and growth of seden-                 nels of the Estuarine System are classified as Streambed.
              tary invertebrates. Water regimes are restricted to subtidal,                 Description. Unconsolidated Shores are characterized by
              irregularly exposed, regularly flooded, and irregularly flooded.           substrates lacking vegetation except for pioneering plants
                Description. Reefs are characterized by being elevated above             that become established during brief periods when growing
              the surrounding substrate and interfering with normal wave                 conditions are favorable. Erosion and deposition by waves
              flow; they are primarily subtidal, but parts of some reefs may             and currents produce a number of landforms such as beaches,
              be intertidal as well. Although corals, oysters, and tube worms            bars, and flats, all of which are included in this class. Uncon-
              are the most visible organisms and are mainly responsible for              solidated Shores may be found adjacent to Unconsolidated
              reef formation, other mollusks, foraminifera, coralline algae,             Bottoms in all systems. As in the class Unconsolidated Bot-
              and other forms of life also contribute substantially to reef              toms, the particle size of the substrate and the water regime
              growth. Frequently, reefs contain far more dead skeletal ma-               are the important factors determining the types of plant and
              terial and shell fragments than living matter. The subclasses              animal communities present. Different substrates usually sup-
              are Coral, Mollusk, and Worm. Only the first subclass is                   port characteristic invertebrate fauna. The subclasses are
              emphasized by C-CAP; the other two definitions are omitted.                Cobble-gravel, Sand, and Mud. More detailed definitions are
                                                                                         provided in Cowardin et al. (1979).
              Coral-Coral reefs are widely distributed in shallow waters of
              warm seas, in Hawaii, Puerto Rico, the Virgin Islands, and
              southern Florida. Odum (1971) characterized them as stable,                Emergent Wedand
              well-adapted, highly diverse, and highly productive ecosys-
              tems with a great degree of internal symbiosis. Coral reefs lie            Definition. The Emergent Wetland class is characterized by
              almost entirely within the Subtidal subsystem of the Marine                erect, rooted, herbaceous hydrophytes (excluding mosses
              System, although the upper part of certain Reefs may be                    and lichens) which are present for most of the growing
              exposed. Examples of dominance types are the corals Porites,               season in most years. These wetlands are usually dominated
              Acropora, and Montipora. The distribution of these types re-               by perennial plants. All water regimes are included except
              flects primarily elevation, wave exposure, and reef age.                   subtidal and irregularly exposed.
                                                                                            Description. In areas with relatively stable climatic condi-
              Rocky Shore                                                                tions, emergent wetlands maintain the same appearance year
                                                                                         after year. In other areas, such as the prairies of the central
              Definition. The Rocky Shore class includes wetland environ-                United States, violent climatic fluctuations cause them to
              ments characterized by bedrock, stones, or boulders which                  revert to open water in some years. Emergent wetlands are






                                                                                Appendix 3: C-CAP Land-Cover Classification Definitions                      77


                 found throughout the United States and occur in all systems                species include true shrubs, young trees, and trees or shrubs
                 except Marine. Emergent wetlands are known by many names,                  that are small or stunted because of environmental condi-
                 including marsh, meadow, fen, prairie pothole, and slough.                 tions. All water regimes except subtidal are included.
                 Areas dominated by pioneer plants that become established                    Description. Scrub-shrub wetlands may represent a succes-
                 during periods of low water are not emergent wetlands and                  sional stage leading to forested wetlands, or they may be
                 should be classified as Vegetated Unconsolidated Shores or                 relatively stable communities. They occur only in the Estua-
                 Vegetated Streambeds. The subclasses in the Cowardin et al.                rine and Palustrine Systems but are one of the most wide-
                 system are Persistent and Nonpersistent.                                   spread classes in the United States. Scrub-shrub wetlands are
                                                                                            known by many names, such as shrub swamp, shrub, bog, and
                                                                                            pocosin. The C-CAP category includes forests composed of
                                                                                            young trees <6 m tall regardless of potential height at maturity.
                 Woody Wedand                                                                 Broad-leaved. Deciduous--In Estuarine System wetlands
                                                                                            the predominant deciduous and broad-leaved trees or shrubs
                 The Woody Wetland class includes any species with an aerial                are plants such as sea-myrtle, Bacchafis halimifolia, and marsh
                 stem that persists for more than one season. The Woody                     elder, Iva frutescens. In the Palustrine System typical domi-
                 Wetland class is divided into three subclasses: Deciduous,                 nance types are alders, Alnus spp., willows, Salix spp., button-
                 Evergreen, and Mixed.                                                      bush, Cephalanthus cocidentalis, red osier dogwood, Cornus
                                                                                            stolonifera, honeycup, Zenobia pulverulenta, spirea, Spiraea
                 Deciduous-The Deciduous WoodyWeaand subclass includes                      douglasii, bog birch, Betula pumila, and young trees of species
                 all wetland forest and shrub areas having a predominance of                such as red maple, Acer rubrum, or black spruce, Picea mariana.
                 trees and shrubs that lose their leaves or needles at the end of             Needle-leaved Deciduous-This group, consisting of wet-
                 the frost-free season or at the beginning of a dry season. This            lands where trees or shrubs are predominantly deciduous
                 category contains greater than two-thirds deciduous trees                  and needle-leaved, is represented by young or stunted trees
                 and shrubs. The Deciduous Woody Wetland category can be                    such as tamarack or bald cypress, Taxodium dislichum.
                 divided into three categories: Forest, Scrub-Shrub, and Dead.                Dead-Definition. Dominated by dead deciduous woody
                    Forest-Definition. Forested wetland is characterized by                 vegetation, these wetlands are usually produced by a pro-
                 woody vegetation 2:6 m in height. All water regimes are in-                longed rise in the water table resulting from impoundment
                 cluded except subtidal.                                                    of water by landslides, human activity, or beaver activity. Such
                    Description. Forested wetlands are most common in the                   wetlands may also result from various other factors such as
                 eastern United States and in those sections of the West where              fire, salt spray, insect infestation, air pollution, and
                 moisture is relatively abundant, particularly along rivers and             herbicides.
                 in mountains. They occur only in the Palustrine and Estua-
                 rine Systems and normally contain an overstory of trees, an                Evergreen-The Evergreen Woody Wetland subclass contains
                 understory of young trees or shrubs, and a herbaceous layer.               wedand forests and shrubs that do not lose their leaves or
                 Forested wetlands in the Estuarine System, including the                   needles at the end of a frost-free season or at the beginning of a
                 mangrove forests of Florida, Puerto Rico, and the Virgin                   dry season. The Evergreen Woody Wetland category is subdi-
                 Islands, are known by such names as swamps, hammocks,                      vided into two additional categories: Forest and Scrub-Shrub.
                 heads, and bottoms. These names often occur in combina-                      Forest-Definition. Forested wetland is characterized by
                 tion with species names or plant associations, such as cedar               woody vegetation @!6 m in height. All water regimes are in-
                 swamp or bottom land hardwoods.                                            cluded except subtidal.
                    Broad-leaved Deciduous-Dominant trees typical of broad-                   Broad-leaved Evergreen-In the Southeast, broad-leaved
                 leaved deciduous wetlands, which are represented through-                  evergreen wetlands reach their greatest development. Red
                 out the United States, are most common in the South and                    bay, Persea borbonia, loblolly bay, Gordonia lasianthus, and sweet
                 East. Common dominants are species such as red maple,                      bay, Magnolia virginiana, are prevalent, especially on organic
                 American elm, Ulmus americana, the ashes Fraxinus                          soils. This group also includes red mangrove, black man-
                 pennsylvanica and F. nigTa, black gum, Nyssa sylvatica, tupelo             grove, Avicennia germinans, and white mangrove, Langunculayia
                 gum, N. aquatica, swamp white oak, Quercus bicolor, overcup                racemosa, which are adapted to varying levels of salinity.
                 oak, Q lyrata, and basket oak, Q michauxii. These wetlands                   Needle-leaved Evergreen-Black spruce, growing on or-
                 generally occur on mineral soils or highly decomposed or-                  ganic soils, represents a major dominant of the Needle-leaved
                 ganic soils.                                                               Evergreen subclass in the north. Though black spruce is
                    Needle-leaved Deciduous--The southern representative of                 common on nutrient-poor soils, northern white cedar, Thuja
                 the Needle-leaved Deciduous Wetland subclass is bald cy-                   occidentalis, dominates northern wetlands on more nutrient-
                 press, Taxodium distichum, which is noted for its ability to               rich sites. Along the Atlantic Coast, Atlantic white cedar,
                 tolerate long periods of surface inundation. Tamarack is                   Chamaecypaiis thyoides, is one of the most common dominants
                 characteristic of the boreal forest region, where it occurs as a           on organic soils. Pond pine, Pinus serotina, is a common
                 dominant on organic soils. Relatively few other species are                necdle-leaved evergreen found in the Southeast in associa-
                 included in this subclass.                                                 tion with dense stands of broad-leaved evergreen and decidu-
                                                                                            ousshrubs.
                 Scrub-Shrub-Definition. Scrub-Shrub wetland includes ar-                     Scrub-Shrub-Definition. Scrub-shrub wetland includes
                 eas dominated by woody vegetation <6 m (20 ft) tall. The                   areas dominated by woody vegetation <6 m (20 ft) tall. The







              78          NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


              species include true shrubs, young trees, and trees or shrubs               an area, the land is classified as Mixed Woody. The Mixed
              that are small or stunted because of environmental condi-                   Woody category is subdivided into two additional categories:
              tions. All water regimes except subtidal are included.                      Forest and Scrub/Shrub.
                Broad-leaved Evergreen-In the Estuarine System, vast wet-                   Forest-This category includes all forested areas where
              land areas are dominated by mangroves (Rhizophora mangle,                   both evergreen and deciduous trees are growing and neither
              Languncularia racemosa, Conocarpus erectus, and Avicennia                   predominate.
              genninans) that are I m to <6 m tall. In the Palustrine System,               Scrub/Shrub-This category includes all shrub areas where
              the broad-leaved evergreen species are typically found on                   both evergreen and deciduous shrubs are growing and nei-
              organic soils. Northern representatives are labrador tea, Ledum             ther predominate.
              groenlandicum, bog rosemary, Andromeda glaucophylla, bog lau-                 Dead-Wetland areas dominated by dead mixed woody
              rel, Kalmia polifolia, and the semi-evergreen leatherleaf,                  vegetation are, like dead deciduous woody wetlands, most
              Chamaedaphne calyculata. In the south, fetterbush, Lyonia lu-               common in, or around the edges of, impoundments and
              cida, coastal sweetbells, Leucothoe axillaris, inkberry, Ilex glabra,       beaver ponds. The same factors that produce dead deciduous
              and the semi-evergreen black ti-ti, Cyyilla racemiflora, are char-          woody wetlands produce dead mixed woody wetlands.
              acteristic broad-leaved evergreen species.
                Needle-leaved Evergreen-The dominant species in needle-
              leaved evergreen wetlands are young or stunted trees such as                Water
              black spruce or pond pine.
                                                                                          Cowardin et al. (1979) define deepwater habitats as perma-
              Dead-Definition. These wetland areas are dominated by                       nently flooded lands lying below the deepwater boundary of
              dead evergreen woody vegetation. Like dead deciduous woody                  wetlands. Deepwater habitats include environments where
              wetlands, they are most common in, or around the edges of,                  surface water is permanent and often deep, so that water, rather
              impoundments and beaver ponds. The same factors that                        than air, is the principal medium within which the dominant
              produce dead deciduous woody wetlands produce dead ever-                    organisms live, whether or not they are attached to the substrate.
              green woody wetlands.                                                       As in wetlands, the dominant plants are hydrophytes. However,
                Mixed-The Mixed Woody Wetland subclass includes all                       the substrates are considered nonsoil because the water is too
              forest and shrub wetland areas where both evergreen and                     deep to support emergent vegetation. The class Water includes
              deciduous trees and shrubs grow and neither predominates.                   Marine/Estuarine, Lacustrine, Palustrine, and Riverine
              When evergreen and deciduous species each occupy2!33% of                    Deepwater subclasses as defined by Cowardin et al. (1979).








                                                                              Appendix 4
                                                                        GCAP Wo?*shops


                  This guidance document results from the participation of               mend protocols for accuracy assessment of C-CAP TM data.
                  more than 200 scientists, technical specialists, managers, and         The accuracy assessment procedures outlined in this report
                  regional experts in nine protocol-development workshops. Four          are a direct result of those workshops. The classification
                  regional workshops were held in the Northeast, Southeast, West         workshop was a multi-agency group organized to develop a
                  Coast, and Great Lakes regions to address a broad range of             classification system that would suit C-CA-P needs. Through
                  issues including those specific to each major coastal region.          the workshop and a long iterative process thereafter, the
                  Also, a major national workshop was held that focused specifi-         classification presented in this report was developed. The
                  cally on mapping submerged aquatic vegetation. Participation           Maryland Field Reconnaissance workshop was unique in that
                  was encour-aged across all Federal and State agencies involved in      the organizers were not from C-CAP but from the Maryland
                  coastal research, management, and policy and many other agen-          Department of Natural Resources and Salisbury State Univer-
                  cies concernedwith remote sensing and land-cover analysis. The         sity. The principal objective of the workshop was to provide
                  seagrass workshop followed the same for-mat, focusing specifi-         recommendations concerning C-CAP products. The recon-
                  cally on submersed habitats. Each workshop was presented with          naissance consisted of field visits to sites in the vicinity of
                  a draft C-CAP protocol, based initially on the Chesapeake Bay          Salisbury, Maryland, identified in a preliminary version of the
                  prototype, and participants were encouraged to refine the draft        C-CAP Chesapeake Bay Land-Cover Change Database.
                  and resolve remaining issues. Issues not resolved in the five          In addition, the preliminary C-CAP data were compared
                  major workshops were addressed through dedicated topical               with other types of ancillary data supplied by workshop
                  workshops. Finally, the issues that could not be resolved through      participants.
                  workshops were explored through research funding proposals.,             Findings and recommendations from these workshops and
                    Two accuracy assessment workshops involved leading spe-              from other meetings of specialists (not listed here) were
                  cialists in spatial error estimation who were asked to recom-          crucial in the development of this document.



                  Southeast Regional Workshop                                            Classification Scheme Workshop
                  Location: University of South Carolina                                 Location: Silver Spring, MD
                              Columbia, SC                                               Date:        12 February 1991
                  Dates:      29-31 May 1990                                             Co-Chairs: James Johnston
                  Host:       University of South Carolina                                            Vic Klemas
                  Co-Chairs: Jerome Dobson
                              Kenneth Haddad
                                                                                         West Coast Regional Workshop
                  Seagrass Mapping Workshop                                              Location: Embassy Suites Hotel
                  Location:   Embassy Suites, Tampa Airport Hotel                                     Seattle, WA
                              Tampa, FL                                                  Dates:       29 April-1 May 1991
                  Dates:      23-25 July 1990                                            Co-Chairs: Jerome Dobson
                  Co-Chairs:  Randolph Ferguson                                                       Kenneth Haddad
                              Robert Orth


                  Accuracy Assessment Workshop                                           Maryland Field Reconnaissance Workshop
                  Location:   National Marine Fisheries Service                          Location:    Salisbury State University
                              Beaufort Laboratory                                                     Salisbury, MD
                              Beaufort, NC                                               Dates:       16-18july 1991
                  Date:       25 September 1990                                          Host:        Salisbury State University
                  Host:       Beaufort Laboratory                                        Workshop     William Burgess, Maryland Department of
                  Chair:      Jerome Dobson                                                 Design:   Natural Resources;
                                                                                                      Edward W. Christoffers, NOAA/NMFS
                  Northeast Regional Workshop                                                         Jerome Dobson, Oak Ridge National Laboratory
                  Location:   Whispering Pines Conference Center                                      Randolph Ferguson, NOAA/NMFS
                              W. Altonjones Campus                                                    Adam Fisch, Virginia Council on the
                              University of Rhode Island                                              Environment
                              Kingston, RI                                                            K. Peter Lade, Salisbury State University
                  Dates:      8-10january 1991                                                        James Thomas, NOAA/NMFS
                  Host:       University of Rhode Island                                              Bill Wilen, USFWS, National Wetlands Inventory.
                  Co-Chairs: Jerome Dobson                                               Sponsors:,   Salisbury State University, Maryland Department
                              Kenneth Haddad                                                            of Natural Resources, NOAA, and USFWS

                                                                                                                                                          79







            80        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            Accuracy Assessment Workshop                                        Great Lakes Regional Workshop
            Location:  Oak Ridge National Laboratory                            Location: Best Western Ann Arbor Regent
                       Oak Ridge, TN                                                       Ann Arbor, MI
            Date:      1-2 August 1991                                          Dates:     19-27 August 1991
            Host:      Oak Ridge National Laboratory                            Host:      NOAA Great Lakes Environmental Research Lab
            Chair:     Jerome Dobson                                            Co-Chairs: Jerome Dobson
                                                                                           Kenneth Haddad








                                                                           Appendix 5
                                                       C-C4P Notocol Development Research

                C-CAP funded research to refine various aspects of the proto-         University of South Carolina
                col based on workshop recommendations and on findings                    Determine the impact of tides on coastal change detection
                from the upland and wetland prototype (Chesapeake Bay),
                the water and submerged land prototype (North Carolina                North Carolina State University
                Coast), and the Salisbury field experience. These research               Develop methodologies for accuracy assessment for change
                projects are intended to increase the geographical coverage              detection databases
                of the C-CAP change detection database. The following is a
                list of the institutions that performed the research and the          University of Virginia
                topics addressed in fiscal year 1991:                                    Examine influence of tides on TM data with the aid of
                                                                                         digital elevation models
                University of South Carolina
                ï¿½ Test change detection methodologies                                 University of Maine
                ï¿½ Identify optimum pattern recognition algorithms                        Develop improved methodologies for detecting forested
                                                                                         wetlands
                North Carolina State University
                   Develop a seamless database from two independently de-             University of New Hampshire
                   veloped land-cover databases derived from TM data                     Develop methodologies for accuracy assessment of change
                Universities of Rhode Island and Connecticut                             detection databases
                ï¿½ Test change detection methodology
                ï¿½ Test the use of available digital wetlands data as an aid for       Two other studies were also funded by C-CAP in fiscal year
                   classifying TM imagery                                             1992:

                Beaufort, NMFS                                                        Universities of Rhode Island and Connecticut
                o Develop change detection methodologies for SAV                      9 Funded for six months to finish work started in 1991

                An announcement of availability of funds for protocol devel-          Beaufort, NMFS
                opment research was distributed in March 1992. The follow-               Develop change detection methodologies for SAV using
                ing five studies were funded:                                            CPS technology.







           82       NOAA Technical Report NMFS 123: Dobson et aL: Coastal Change Analysis Program








                                                                        Appendix 6
                                                                Workshop Pat-ficipants

                Mr. Bob Ackerman                                                   Thomas E Bigford
                MD, DNR, FPWS                                                      NOAA/National Marine Fisheries Service
                Chesapeake Bay Programs (Forestry)                                 Habitat Conservation Branch
                Tawes State Office Building                                        One Blackburn Drive
                Annapolis, MD 21401                                                Gloucester, MA 01930-2298

                Steve Ackleson                                                     Elaine Blok
                Bigelow Laboratory for Ocean Science                               Geonex Martel, Inc.
                McKown Point                                                       8950 9th Street North
                West Boothbay Harbor, ME 04575                                     St. Petersburg, FL 33703

                Dean Albro                                                         Nate Boyer
                Division of Freshwater Wetlands                                    EOSAT
                Rhode Island Dept. of Environmental Management                     4300 Forbes Blvd.
                291 Promenade St.                                                  Lanham, MD 20706
                Providence, RI 02908
                                                                                   Earl Bradley
                Warren Alward                                                      Coastal Resources Division
                Dept. of Natural Resources                                         Tawes State Office Bldg. B-3
                Land and Water Management Division                                 Annapolis, MD 21401
                P.O. Box 30028
                Lansing, MI 48909                                                  James Brewer
                                                                                   USDA,SCS
                Roy A. Armstrong                                                   339 Revell Highway
                NASA, Ames Research Center                                         Annapolis, MD 21401
                37250 Sequoia Terrace #1032
                Freemont, CA 94536                                                 Douglas A. Bulthuis
                                                                                   Padilla Bay National Estuarine
                Peter V. August                                                      Research Reserve
                Environmental Data Center                                          1043 Bayview-Edison Road
                Dept. of Natural Resources Science                                 Mount Vernon, WA 98273
                University of Rhode Island
                Kingston, RI 02881                                                 Bill Burgess
                                                                                   Maryland Water Resources Admin.
                John Banta                                                         Tawes State Office Building, D-2
                Director of Planning                                               Annapolis, MD 21401
                Adirondack Park Agency
                Ray Brook, NY 12977                                                Alice Chalmers
                                                                                   University of Georgia
                Franklin S. Baxter                                                 Marine Institute
                U.S. Geological Survey                                             Sapelo Island, GA 31327
                Mail Stop 514
                National Center                                                    Michael Chambers
                Reston, VA 22092                                                   USGS, National Mapping Division
                                                                                   590 National Center
                A] Beeton                                                          Reston, VA 22092
                Director
                Great Lakes Enviromental                                           Heather Cheshire
                  Research Lab/NOAA                                                Computer Graphics Center
                2205 Commonwealth                                                  Box 7106, North Carolina State University
                Ann Arbor, MI 48105                                                Raleigh, NC 27695





                                                                                                                                            83







           84        NOAA Technical Report NMYS 123: Dobson et al.: Coastal Change Analysis Program

           Alexander J. Chester                                               Joseph E. Costa
           NOAA/National Marine Fisheries Service                             Massachusetts Coastal Zone Management
           Beaufort Laboratory                                                Buzzards Bay Project
           Beaufort, NC 28516-9722                                            2 Spring Street
                                                                              Marion, MA 02738
           Nicholas Chrisman
           Dept. of Geography                                                 Paul Crawford
           University of Washington                                           Olympic National Park
           Smith Hall/DP-10                                                   National Park Service
           Seattle, WA 98195                                                  600 East Park Avenue
                                                                              Port Angeles, WA 98362
           Eric Christensen
           Science Technology Laboratory                                      Ford A Cross, Director
           Lockheed 1210                                                      NOAA/National Marine Fisheries Service
           Stennis Space Center, MS 39529                                     Southeast Fisheries Science Center
                                                                              Beaufort Laboratory
           Edward W. Christoffers;                                            101 Pivers Island Road
           NOAA/CBP Science Coordinator                                       Beaufort, NC 28516-9722
           Tawes State Office Building, C-4
           Annapolis, MD 21401                                                Pat Cummens
                                                                              Division of Science and Research
           Barbara Cintron                                                    GIS Laboratory
           Puerto Rico Dept. of Natural Resources                             401 East State Street
           P.O. Box 5887                                                      Trenton, NJ 08625
           Puerta de Pierra, PR 00906
                                                                              Thomas E. Dahl
           Daniel Civco                                                       National Wetlands Inventory
           Dept. of Natural Resources                                         U.S. Fish and Wildlife Service
           Management and Engineering, Box U-87                               9720 Executive Center Drive
           1376 Storrs Road                                                   Suite 101, Monroe Building
           University of Connecticut                                          St. Petersburg, FL 33702
           Storrs, CT 06269-4087
                                                                              Barbara D'Angelo
           Elaine Collins                                                     3WMOO U.S. EPA Region III
           NOAA/NESDIS                                                        841 Chestnut Building
           1825 Connecticut Ave., N.W., Room 406                              Philadelphia, PA 19107
           Washington, DC 20235
                                                                              Rick Dawson
           Commander                                                          U.S. Department of the Interior
           U.S. Army Corps of Engineers                                       National Park Service
           Waterways Experiment Station                                       Southeast Regional Office
           Attn: CEWES-ER-W/Buddy Clarain                                     75 Spring Street SW
           Vicksburg, MS 39180-6199                                           Atlanta, GA 30303

           Russell G. Congalton                                               Michael DeMers
           Dept. of Natural Resources                                         New Mexico State University
           215 James Hall                                                     Dept. of Geography
           University of New Hampshire                                        Box MAP
           Durham, NH 03824                                                   Las Cruces, NM 88003

           Robert Costanza                                                    Larry Deysher
           Chesapeake Biological Laboratory                                   Coastal Resources Assoc.
           Solomons Island, MD 20688-0038                                     2270 Camino Vina Roble
                                                                              Suite L
           Dave Cowen                                                         Carlsbad, CA 92009
           Statistical and Behavioral
             Sciences Laboratory
           University of South Carolina
           Columbia, SC 29208






                                                                                                 Appendix 6: Workshop Participants             85


                 Jerome E. Dobson                                                    Don Field
                 Oak Ridge National Laboratory                                       NOAA/National Marine Fisheries Service
                 Geographic Information Systems                                      Southeast Fisheries Science Center
                   and Computer Modelling                                            Beaufort Laboratory
                 P.O. Box 2008, 4500 N, MS 6237                                      101 Pivers Island Road
                 Oak Ridge, TN 37831                                                 Beaufort, NC 28516-9722

                 Bill Dunn                                                           J. Michael Flagg
                 USDA SCS                                                            Virginia Dept. of Conservation and Recreation
                 339 Revell Highway                                                  Division of Soil and Water
                 Annapolis, MD 21401                                                 203 Governor St., Suite 206
                                                                                     Richmond, VA 23219
                 Sandy Wyllie Echeverria
                 Institute of Marine Science                                         Ms. Bellory Fong
                 University of Alaska                                                California Dept of Water Resources
                 Fairbanks, AK 99775                                                 3251 "S" Street (RM B-5)
                                                                                     Sacramento, CA 95816
                 Robert Emmett
                 NOAA/National Marine Fisheries Service                              Andrew Frank
                 Pt. Adams Biological Field Station                                  Dept. of Civil Engineering
                 P. 0. Box 155                                                       119 Boardman Hall
                 Hammond, OR 97121                                                   University of Maine
                                                                                     Orono, ME 04469
                 William Enslin
                 Center for Remote Sensing                                           Adam Frisch
                 Michigan State University                                           Virginia Council on the Environment
                 302 Berkey Hall                                                     202 N. 9th Street, Suite 900
                 East Lansing, MI 48824-1111                                         Richmond, VA 23219


                 Ron Erickson                                                        Ellen Fritts
                 U.S. Fish and Wildlife Service                                      Alaska Dept. of Fish and Game
                 4101 E. 80th Street                                                 Habitat Division
                 Bloomington, MN 55425-1600                                          P.O. Box 3-2000
                                                                                     1255 West 8th Street
                 Maggie Ernst                                                        Juneau, AK 99802-2000
                 NOAA/Coastal Ocean Program Office
                 1315 East-West Highway                                              Gregory Fromm
                 Silver Spring, MD 20910                                             NOAA/National Ocean Service
                                                                                     Photogrammetry Branch
                 Tamra Faris                                                         1315 East-West Highway
                 NOAA/National Marine Fisheries Service                              Silver Spring, MD 20910
                 P.O. Box 21668
                 Juneau, AK 99802                                                    Carolyn Gates
                                                                                     California Coastal Commission
                 Robin Fegeas                                                        45 Frcemont St.
                 U.S. Geological Survey                                              Suite 2000
                 Mail Stop 521                                                       San Francisco, CA 94105
                 National Center
                 Reston, VA 22092                                                    Len Gaydos
                                                                                     Ames Research Center
                 Randolph L. Ferguson                                                P.O. Box 1000
                 NOAA/National Marine Fisheries Service                              Moffett Field, CA 94035-1000
                 Southeast Fisheries Science Center
                 Beaufort Laboratory                                                 Bess Gillelan
                 101 Pivers Island Road                                              Chief
                 Beaufort, NC 28516-9722                                             NOAA/Chesapeake Bay Office
                                                                                     410 Severn Me, Suite 107A
                                                                                     Annapolis, MD 21403







            86        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


            Frank Golet                                                         Michael E. Hodgson
            Dept. of Natural Resources Science                                  Oak Ridge National Laboratory
            University of Rhode Island                                          Geographic Information Systems and Computer Modelling
            Kingston, RI 02881                                                  P.O. Box 2008, 4500 N. MS6274
                                                                                Oak Ridge, TN 37831
            Al Green
            Texas Parks and Wildlife Dept.                                      Sherman Hollander
            4200 Smith School Road                                              Michigan Resource Information Program
            Austin, TX 78745                                                    P.O. Box 30028
                                                                                Lansing, MI 48909
            Christie Guy
            Assateaque Island National Seashore                                 Bob Holman
            Rt 611-7206                                                         WRRI
            National Seashore Lane                                              Box 7912
            Berlin, MD 21811                                                    North Carolina State University
                                                                                Raleigh, NC 27695-7912
            Kenneth D. Haddad
            Marine Research Institute                                           Frank Horvath
            FL Dept. of Environmental Protection                                Chief Scientist
            100 8th. Avenue SE                                                  Great Lakes Information System
            St. Petersburg, FL 33701-5059                                       Michigan Dept. of Natural Resources
                                                                                Land and Water Management Division
            Anne Marie Hale Miglarese                                           Lansing, MI 48909
            South Carolina Water Resources Commission
            1201 Main Street, Suite 1100                                        Karl Huber
            Columbia, SC 29201                                                  Virginia Dept. of Conservation and Recreation
                                                                                Division of Soil & Water
            Larry Handley                                                       203 Governor Street, Suite 206
            U.S. Fish and Wildlife Service                                      Richmond, VA 23219
            National Wetlands Research Center
            700 Cajun Dome Blvd.                                                Kent Hughes
            Lafayette, LA 70506                                                 Dept. of Commerce, NOAA/NESDIS
                                                                                Federal Office Building 41069
            Bud Harr-is                                                         Washington, DC 20233
            University of Wisconsin at Green Bay
            2420 Nicolet Drive                                                  Paul G. Huray
            Green Bay, WI 54311                                                 106 Osborne Administration Building
                                                                                University of South Carolina
            Greg Hellyer                                                        Columbia, SC 29208
            U.S. Environmental Protection Agency
            Region 1, WWP 424                                                   Merton Ingham
            JFK Federal Building                                                NOAA/National Marine Fisheries Service
            Boston, MA 02203                                                    Northeast Fisheries Science Center
                                                                                Narragansett Laboratory
            Gary Hendrix                                                        28 Tarzwell Drive
            U.S. Department of the Interior                                     Narragansett, RI 02882-1199
            National Park Service
            Room 1092, Sciences and Natural Resources                           Harry Iredale
            75 Spring Street SW                                                 NOAA/NESDIS
            Atlanta, GA 30303                                                   Universal Building
                                                                                1825 Connecticut Avenue, NW
            Rex C. Herron                                                       Washington, DC 20235
            NOAA/National Marine Fisheries Service
            Building 1103                                                       EugeneJaworski
            Stennis Space Center, MS 39529                                      SER-GEM Center
                                                                                3075 Washtenaw Avenue
            Carl Hershner                                                       Ypsilanti, MI 48197
            Virginia Institute of Marine Science
            College of William and Mary
            Gloucester Point, VA 23062


                                   1






                                                                                                  Appendix 6: Workshop Participants              87


                John Jensen                                                          Vic Klemas
                Dept. of Geography                                                   Director
                University of South Carolina                                         Center for Remote Sensing
                Columbia, SC 29208                                                   College of Marine Studies
                                                                                     University of Delaware
                Lisajipping                                                          Newark, DL 19716
                U.S. Army Corps of Engineers
                Great Lakes Hydraulics and Hydrology Branch                          Frederick Kopfler
                P.O. Box 1027                                                        U.S. Environmental Protection Agency
                Detroit, MI 48231                                                    Gulf of Mexico Program
                                                                                     Bldg. 1103
                JimmyJohnston                                                        Stennis Space Center, MS 39529
                National Biological Survey
                National Wetlands Research Center                                    K Koski
                700 Cajun Dome Blvd.                                                 NOAA/National Marine Fisheries Service
                Lafayette, LA 70506                                                  Auke Bay Lab
                                                                                     11305 Glacier Highway
                James R. Karr                                                        Auke Bay, AK 99821
                Institute for Environmental Studies
                Engineering Annex, FM-12                                             Rose Kress
                University of Washington                                             U.S. Army Corps of Engineers
                Seattle, WA 98195                                                    Waterways Experiment Station
                                                                                     3909 Halls Ferry Road
                Dick Kelly                                                           Vicksburg, MS 39180
                Maine State Planning Office
                184 State Street                                                     Tom Kunneke
                Augusta, ME 04333                                                    Geonex Martel, Inc.
                                                                                     8950 9th Street North
                Dick Kempka                                                          St. Petersburg, FL 33703
                Ducks Unlimited, Inc.
                9823 Old Winery Place, #16                                           Kathy Kunz
                Sacramento, CA 95827                                                 U.S. Army Corps of Engineers
                                                                                     Seattle District
                Siamak Khorram                                                       P. 0. C-3755
                Computer Graphics Center                                             Seattle, WA 98124-2255
                5112jordan Hall, Box 7106
                North Carolina State University                                      Charles LaBash
                Raleigh, NC 27695-7106                                               Environmental Data Center
                                                                                     Dept. of Natural Resources Science
                Donley Kisner                                                        University of Rhode Island
                Bionetics Corp.                                                      Kingston, RI 02881
                P.O. Box 1575
                VHFS                                                                 K.-Peter Lade
                Warrenton, VA 22186                                                  Salisbury State University
                                                                                     Image Processing Center
                Bjorn Kjerfve                                                        I 10 Power Street
                Baruch Institute                                                     Salisbury, MD 21801
                University of South Carolina
                Columbia, SC 29208                                                   Rene Langi
                                                                                     Dept. of Biology
                Richard Kleckner                                                     San Diego State University
                U.S. Geological Survey                                               San Diego, CA 92182
                Mail Stop 590
                National Center                                                      Lewis A. Lapine
                Reston, VA 22092                                                     NOAA, National Ocean Service
                                                                                     Photogrammetry Branch, N/CG23
                                                                                     1315 East-West Highway
                                                                                     Silver Spring, MD 20910







             88        NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program


             M. Larson                                                             Carl Markon
             No Address                                                            U.S. Geological Survey
                                                                                   4230 University Drive
             George Leshkevich                                                     Anchorage, AK 99508-4664
             Great Lakes Environmental Res. Lab.
             2205 Commonwealth Blvd                                                Garry Mayer
             Ann Arbor, MI 48105-1593                                              NMFS Restoration Center
                                                                                   1315 East-West Highway
             Roy R. Lewis, III                                                     Silver Spring, MD 20910
             Lewis Environmental Services, Inc.
             P.O. Box 20005                                                        L. Nelson Mayjr.
             Tampa, FL 33622-0005                                                  Coastal Fisheries Institute
                                                                                   Louisiana State University
             Nancy Liebowitz                                                       Baton Rouge, LA 70803
             U.S. Environmental Protection Agency
             1600 S.W. Western Blvd.                                               Bruce McCarnmon
             Corvallis, OR 97333                                                   USDA/USFW
                                                                                   Ecology Range and Watershed
             Katherine Lins                                                        P.O. Box 3623
             U.S. Geological Survey                                                Portland, OR 97208
             Mail Stop 512
             National Center                                                       Keith McLaughlin
             Reston, VA 22092                                                      1720 Peachtree Road, N.W.
                                                                                   Atlanta, CA 30367
             Robyn Loudermilk
             Hawaii Office of State Planning                                       Peter McRoy
             State Capitol - Room 406                                              Institute of Marine Science
             Honolulu, HI 96813                                                    University of Alaska
                                                                                   Fairbanks, AK
             Ross Lunetta                                                            or
             U.S. Environmental Protection Agency                                  Dept of Botany
             Environmental Monitoring System                                       University of Hawaii
             Laboratory                                                            3190 Mailae Way
             P.O. Box 93478                                                        Honolulu, HI 96822
             Las Vegas, NV 89193-3478
                                                                                   Todd Mecklenberg
             David Lusch                                                           Geonex Martel, Inc.
             Center For Remote Sensing                                             8950 9th Street North
             302 Berkey Hall, Michigan State University                            St. Petersburg, FL 33703
             East Lansing, MI 48824-1111
                                                                                   Norman Melvin
             Anne Lynn                                                             Plant Ecologist
             State Biologist                                                       USDA\SCS
             USDA SCS                                                              339 Revell Highway
             339 Revell Highway                                                    Annapolis, MD 21401
             Annapolis, MD 21401
                                                                                   Robert E. Menzer
             Andreas Magerjr.                                                      Director
             NOAA/National Marine Fisheries Service                                U.S. Environmental Protection Agency
             Southeast Region                                                      Environmental Research Laboratory
             9450 Koger Blvd.                                                      Sabine Island
             St. Petersburg, FL 33702                                              Gulf Breeze, FL 32561-5299

             Leslie Manfull                                                        Carolyn Merry
             National Park Service                                                 Dept of Civil Engineering
             ADNR 470                                                              470 Hitchcock Hall
             P.O. Box 37127                                                        Ohio State University
             Washington, DC 20013-7127                                             2070 Neil Avenue
                                                                                   Columbus, OH 43210-1275







                                                                                                 Appendix 6: Workshop Participants             89


                William Michner                                                     Robert Orth
                Baruch Institute                                                    Virginia Institute of Marine Science
                University of South Carolina                                        The College of William and Mary
                Columbia, SC 29208                                                  Gloucester Point, VA 23062


                Chris Mobley                                                        Jacquelyn Ott
                NOAA/National Marine Fisheries Service                              ERIM-Room C148
                777 Sonoma Avenue - Room 325                                        Box 8618
                Santa Rosa, CA 95404                                                Ann Arbor, MI 48107

                John Mooney                                                         Keith Patterson
                NOAA/National Ocean Service                                         Geonex Martel, Inc.
                Photogrammetry Branch, N/CG2                                        8950 9th Street, North
                1315 East-West Highway                                              St. Petersburg, FL 33703
                Silver Spring, MD 20910
                                                                                    Brian Pawlak
                Tom Mumford                                                         NOAA/National Marine Fisheries Service
                Division of Aquatic Lands                                           Southeast Fisheries Science Center
                Washington Dept. of Natural                                         Beaufort Laboratory
                  Resources, EX12                                                   101 Pivers Island Rd.
                Olympia, WA 98504                                                   Beaufort, NC 28516-9722

                Ron Myszka                                                          Ed Pendleton
                U.S. Environmental Protection Agency                                U.S. Fish and Wildlife Service
                Great Lakes National Program Office (SGB)                           180 Admiral Cochran Drive, Suite 535
                230 South Dearborne Street                                          Annapolis, MD 21401
                Chicago, IL 60604
                                                                                    Robert Peplies
                Scott Nixon                                                         Dept. of Geography
                Rhode Island Sea Grant Program                                      East Tennessee State University
                Graduate School of Oceanography                                     P.O. Box 22870A
                Narragansett, RI 02882-1197                                         Johnson City, TN 37614

                Douglas Norton                                                      Dennis Peters
                U.S. Environmental Protection Agency                                U.S. Fish and Wildlife Service
                WH553, Room E-7,431)                                                NWI Regional Wetland Coordinator
                Washington, DC 20460                                                Eastside Federal Complex
                                                                                    911 NE Ilth Ave.
                Richard P. Novitzki                                                 Portland, OR 97232-4181
                U.S. Environmental Protection Agency
                1600 S.W. Western Blvd.                                             Ron Phillips
                Corvallis, OR 97333                                                 Beak Consultants, Inc.
                                                                                    12931 NE 126th Place
                Charles E. Olsonjr.                                                 Kirkland, WA 98034-7716
                School of Natural Resources
                University of Michigan                                              Steve Phillips
                Samuel Trask Dana Building                                          Division of Forestry
                430 East University Street                                          Alaska Dept. of Natural Resources
                Ann Arbor, MI 48109-1115                                            P.O. Box 107005
                                                                                    Anchorage, AK 99510-7005
                Chris Onuf
                U.S. Fish and Wildlife Service                                      Larry Pomatto
                National Wetlands Research Center                                   Wetlands and Aquatic Protection
                Campus Box 339, CCSU                                                DNREC
                6300 Ocean Drive                                                    P.O. Box 1401
                Corpus Christi, TX 78412                                            Dover, DE 19903







             90         NOAA Technical Report NMFS 123:. Dobson et al.: Coastal Change Analysis Program


             John Posenau                                                           Tony Reyer
             Virginia Institute of Marine Science                                   NOAA/National Ocean Service
             College of William and Mary                                            Strategic Environmental Assessments Div.
             Gloucester Point, VA 23062                                             1305 East West Highway
                                                                                    SSMC-4, 9th Floor
             Sandy Prisloe                                                          Silver Spring, MD 20910
             Natural Resources Center
             Dept. of Environmental Protection                                      Steve Robb
             Room 545                                                               Salisbury State University
             State Office Building                                                  Image Processing Center
             Hartford, CT 06106                                                     110 Power Street
                                                                                    Salisbury, MD 21801
             Jane Provancha
             NASA                                                                   Robert Rolley
             Biomedical and Environmental Laboratories                              Division of Fish and Wildlife
             Mail Code BIO-2                                                        300 West Ist Street
             Kennedy Space Center, FL 32899                                         Bloomington, IN 47403

             Warren Pulich Jr.                                                      Charles Roman
             Texas Parks and Wildlife Dept.                                         U.S. Department of the Interior
             Resource Protection Division                                           National Park Service
             4200 Smith School Road                                                 Cooperative Research Unit
             Austin, TX 78745                                                       Graduate School of Oceanography
                                                                                    University of Rhode Island
             Charles Racine                                                         Narragansett, RI 02882-1197
             U.S. Army Corps of Engineers
             Cold Regions Research and                                              Lynn Sampson
             Engineering Laboratory                                                 USDA/Soil Conservation Service
             72 Lyme Road                                                           Manley Miles Building
             Hanover, NH 03755                                                      1405 South Harrison
                                                                                    East Lansing, MI 48823
             David Rackley
             NOAA/National Marine Fisheries Service                                 Don Scavia, Director
             Southeast Region                                                       NOAA/Coastal Ocean Program Office
             9450 Koger Blvd.                                                       1315 East-West Highway
             St. Petersburg, FL 33702                                               Silver Spring, MD 20910

             Ann Rasberry                                                           Charles Simenstad
             Programmer/Analyst                                                     Wetland Ecosystem Team
             Forest Park and Wildlife Service                                       Fisheries Research Institute
             Tawes State Office Bldg. B-2                                           University of Washington
             Annapolis, MD 21401                                                    Seattle, WA 98195

             Jill Reichert                                                          Tim Schlagenhaft
             Maryland Water Resources Admin.                                        Minnesota Dept. of Natural Resources
             Tawes State Office Bldg. D-2                                           Section of Fisheries
             Annapolis, MD 21401                                                    Box 12
                                                                                    500 Lafayette Road
             Kenneth R. Reisinger                                                   St. Paul, MN 55155
             Div. of Rivers and Wetlands Conservation
             Bureau of Water Resources Management                                   Peter F. Sheridan
             P.O. Box 8761                                                          NOAA/National Marine Fisheries Service
             Harrisburg, PA 17105-8761                                              4700 Avenue U
                                                                                    Galveston, TX 77551-5997
             Becky Ritter
             Ducks Unlimited, Inc.
             One Waterfowl Way
             Long Grove, IL 60047







                                                                                                    Appendix 6: Workshop Participants              91


                 Fred Short                                                            Kevin Summers
                 Jackson Estuarine Laboratory                                          U.S. Environmental Protection Agency
                 University of New Hampshire                                           EMA-P-NC Gulf Province Manager
                 RFD 2                                                                 Gulf Breeze Environmental Research Manager
                 Adams Point                                                           Gulf Breeze, FL 32561-5299
                 Durham, NH 03824
                                                                                       John Sutherland
                 Karen Siderelis                                                       NOAA/National Sea Grant College Program
                 North Carolina Center for Geographic                                  R/ORI
                   Information and Analysis                                            1335 East-West Highway
                 North Carolinia Dept. of Environment, Health                          Silver Spring, MD 20910
                   and Natural Resources
                 Raleigh, NC 27611-7687                                                Billy Teels, National Biologist
                                                                                       USDA Soil Conservation Service
                 Gene Silberhorn                                                       South Ag Bldg.
                 Virginia Institute of Marine Science                                  P.O. Box 2890
                 The College of William and Mary                                       Room 6144
                 Gloucester Point, VA 23062                                            Washington, DC 20013

                 Jerry Spratt                                                          Michael Thabault
                 California Dept. of Fish and Game                                     NOAA/National Marine Fisheries Service
                 2201 Garden Road                                                      777 Sonoma Avenue, Rm. 325
                 Monterey, CA 93940                                                    Santa Rosa, CA 95404

                 Louisa Squires                                                        James P. Thomas
                 U.S. Environmental Protection Agency                                  NOAA/National Marine Fisheries Service
                 1600 SW Western Blvd.                                                 Restoration Center
                 Corvallis, OR 97333                                                   Office of Protected Resources, F/PR5
                                                                                       1315 East-West Highway
                 Kristen Stout                                                         Silver Spring, MD 20910
                 Bionetics Corp.
                 P.O. Box 1575 (VHFS)                                                  Greg Tilley
                 Warrenton, VA 22186                                                   Maryland Water Resources Admin.
                                                                                       Tawes State Office Bldg. D-2
                 Judy Stout                                                            Annapolis, MD 21401
                 University of South Alabama
                 Dauphine Island Sea Laboratory                                        Ralph Tiner
                 Marine Environmental Sciences Consortium                              U.S. Fish and Wildlife Service
                 P.O. Box 369                                                          NWI Regional Wetland Coodinator
                 Dauphine Island, AL 36528                                             300 Westgate Center Drive
                                                                                       Route 2 & 116 North
                 Larry Strong                                                          Hadley, MA 01035
                 U.S. Fish and Wildlife Service
                 Northern Prairie Wildlife Research Center                             Tom Tiner
                 Route 1, Box 96C                                                      Salisbury State University
                 Jamestown, ND 58401                                                   Image Processing Center
                                                                                       110 Power Street
                 Richard P. Stumph                                                     Salisbury, MD 21801
                 USGS Center for Coastal Geology
                 600 4th Street South                                                  Bill Tippets
                 St. Petersburg, FL 33701                                              California Dept. of Parks & Recreation
                                                                                       8885 Rio San Diego Drive
                 Michael Sullivan                                                      Suite 270
                 Mississippi State University                                          San Diego, CA 92108
                 Dept. of Biological Sciences
                 P.O. Iirawer GY                                                       Robert Virnstein
                 Mississippi State, MS 39762-5759                                      St. Johns River Water Management District
                                                                                       P.O. Box 1429
                                                                                       Palatka, FL 32178






           92         NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

           Joel Wagner                                                         Donald Williams
           National Park Service - Air                                         c/o Army Corps of Engineers
           12795 W. Alameda                                                    Planning Division
           Lakewood, CO 80225                                                  P.O. Box 1027
                                                                               Detroit, MI 48231
           Carl Weaver
           MDE, Chesapeake Bay and                                             Ken Winterberger
           Special Programs                                                    United States Forest Service
           2500 Broening Highway                                               Forestry Science Laboratory
           Baltimore, MD 21224                                                 201 E. 9th Avenue
                                                                               Suite 303
           David Weaver                                                        Anchorage, AK 99501
           Massachusetts Office of Environ. Affairs
           GIS Division                                                        Lisa Wood
           20 Somerset St., 3rd Floor                                          NOAA/National Marine Fisheries Service
           Boston, MA 02108                                                    Southeast Fisheries Science Center
                                                                               Beaufort Laboratory
           Jeff Weber                                                          101 Pivers Island Road
           Oregon Coastal Program                                              Beaufort, NC 28516-9722
           Dept. of Land Conservation & Development
           320 SW Stark, Rm 530                                                Jay Zieman
           Portland, OR 97204                                                  University of Virginia
                                                                               Dept. of Environmental Sciences
           Fred Weinmann                                                       Clark Hall
           U.S. Environmental Protection Agency                                Charlottesville, VA 22903
           (WD-138)
           1200 6th Avenue                                                     Kevin Zytkovicz
           Seattle, WA 98101                                                   Minnesota Dept. of Natural Resources
                                                                               Ecological Services, Box 25
           jennette Whipple                                                    500 Lafayette Road
           NOAA/National Marine Fisheries Service                              St. Paul, MN 55155
           Tiburon Laboratory
           3150 Paradise Dr.
           Tiburon, CA 94920


           Bill Wilen
           National Wetlands Inventory
           U.S. Fish and Wildlife Service
           400 ARL - SQ
           l8th and C Streets, NW
           Washington, DC 20240








                                             NOAA TECHNICAL REPORTS NMFS


                     The major responsibilities of the National Marine Fisheries Service (NMFS) are to monitor and assess the abundance
                  and geographic distribution of fishery resources, to understand and predict fluctuations in the quantity and distribution of
                  these resources, and to establish levels for their optimum use. NMFS is also charged with the development and implemen-
                  tation of policies for managing national fishing grounds, with the development and enforcement of domestic fisheries
                  regulations, with the surveillance of foreign fishing off U.S. coastal waters, and with the development and enforcement of
                  international fishery agreements and policies. NMFS also assists the fishing industry through marketing services and
                  economic analysis programs and through mortgage insurance and vessel construction subsidies. It collects, analyzes, and
                  publishes statistics on various phases of the industry.



                                   Recently Published NOAA Technical Reports NMFS

                     111. Control of disease in aquaculture: proceed-                              California species of rockfishes (Scorpaenidae:
                  ings of the nineteenth U.S.-Japan meeting on aquacul-                            Sebastes) from rearing studies, by Guillermo Moreno.
                  ture; Ise, Mie Prefecture, Japan, 29-30 October 1990,                            November 1993, 18 p.
                  edited by Ralph S. Svrjcek. October 1992, 143 p.
                                                                                                      117. Distribution, abundance, and biological char-
                     112. Variability of temperature and salinity in the                           acteristics of groundfish off the coast of Washington,
                  Middle Atlantic Bight and Gulf of Maine, by Robert L.                            Oregon, and California, 1977-1986, by Thomas A. Dark
                  Benway, Jack W. Jossi, Kevin P. Thomas, and Julien R.                            and Mark E. Wilkins. May 1994, 73 p.
                  Goulet. April 1993, 108 p.
                                                                                                      118. Pictorial guide to the groupers (Teleostei: Ser-
                     113. Maturation of nineteen species of finfish off the                        ranidae) of the western North Atlantic, by Mark Grace,
                  northeast coast of the United States, 1985-1990, by                              Kevin R. Rademacher, and Mike Russell. May 1994, 46 p.
                  Loretta O'Brienjay Burnett, and Ralph K. Mayo. June 1993, 66 p.
                                                                                                      119. Stocks of dolphins (Stenella spp. and Del-
                     114. Structure and historical changes in the                                  phinus delphis) in the eastern tropical Pacific: a
                  groundfish complex of the eastern Bering Sea, by                                 phylogeographic classification, by Andrew E. Dizon, Wil-
                  Richard G. Bakkala. July 1993, 91 p.                                             liam F. Perrin, and Priscilla A. Akin. June 1994, 20 p.

                     115. Conservation biology of elasmobranchs, edited                               120.      Abundance and distribution of ich-
                  by Steven Branstetter. September 1993, 99 p.                                     thyoplankton along an inshore-offshore transect in
                                                                                                   Ouslow Bay, North Carolina, by Allyn B. Powell and
                     116. Description of early larvae of four northern                             Roger E. Robbins. June 1994, 28 p.





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