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









               W.      NOAA Technical Memorandum NMFS-SEFSC-319





               EW Of
                       WETLAND FUNCTIONAL HEALTH ASSESSMENT
                       USING REMOTE SENSING AND OTHER TECHNIQUES:
                       LITERATURE SEARCH




                       N. Patience and V. Klemas



























                                     Id




                       March 1993




                       U.S. Department of Commerce
                       National Oceanic and Atmospheric Administration
                       National Marine Fisheries Services
    SH1 1              Southeast Fisheries Science Center
                       Beaufort Laboratory
           /4































    .A2S65             Beaufort, NC 28516-9722
    1993








                'WAT OF C0

                          OM
                                                                  -SEFSC-319
                            NOAA Technical Memorandum NMFS



                 "ArEs Of







                            WETLAND FUNCTIONAL HEALTH ASSESSMENT
                            USING REMOTE SENSING AND OTHER TECHNIQUES:
                            LITERATURE SEARCH AND OVERVIEW







                            N. Patience and V. Klemas


                            College of Marine Studies
                            University of Delaware
                            Newark, DE 19716









                            U.S. DEPARTMENT OF COMMERCE
                            Ronald H. Brown, Secretary
                            NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION
                            Diana H. Josephson, Acting Administrator
                            NATIONAL MARINE FISHERIES SERVICE
                            Nancy M. Foster, Acting Assistant Administrator for Fisheries



                            March 1993



                            This Technical Memorandum series is used for documentation and timely
                            communication of preliminary results, interim reports, or similar special-purpose
                            information. Although the memoranda are not subject to complete formal review,
                            editorial control, or detailed editing, they are expected to reflect sound
                            profession@@ work.
     <    M                           LIBRARY
          q-q                       NOAA/CCEH
                                1990 HOBSON AVE.
                               CHAS. SC 2.9408-2623









             NOTICE



             The National Marine Fisheries Service (NMFS) does not approve, recommend or endorse any
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                                           Correct citation of this report is:

             Patience, N. and V. V. Klemas. 1993. Wetland functional health assessment using remote
                    sensing and other techniques: literature search. NOAA Technical Memorandum NMFS-
                    SEFSC-319, 114 p.







                                      Copies of this report can be obtained from:

                                          National Marine Fisheries Service
                                          Southeast Fisheries Science Center
                                                 Beaufort Laboratory
                                                101 Pivers Island Road
                                              Beaufort, NC 28516-9722



                                                          or



                                        National Technical Information Service
                                                5258 Port Royal Road
                                                Springfield, VA 22161









                      WETLAND FUNCTIONAL HEALTH ASSESSMENT USING REMOTE SENSING
                                          AND OTHER TECHNIQUES: LITERATURE SEARCH

                                                                     Table of Contents

                      Abstract  .........................................................................................................      v

                      1.        INTRODUCTION            .................................................................................  1-1

                      2.        REMOTE SENSING OF WETLAND BIOMASS AND OTHER WETLAND
                                CONDITION INDICATORS                 ..................................................................... 2-1
                                2.1.     Spectral Properties of Plants       ............................................................. 2-2
                                2.2.     Wetland Biomass and Productivity            ..................................................... 2-3
                                2.3.     Early Vegetative Stress Detection         ....................................................... 2-6
                                2.4.     Waterfowl Habitat Quality         ............................................................... 2-6
                                2.5.     Hydrology      .................................................................................  2-7
                                2.6.     Conclusions and Recommendations               ................................................... 2-9
                                2.7.     References     .................................................................................  2-9

                      3.        CONCEPTUAL APPROACHES IN WETLAND ASSESSMENT                                       ......................... 3-1
                                3.1.     Introduction    ...............................................................................   3-1
                                3.2.     Assessment of Ecosystem Health            ....................................................... 3-1
                                3.3.     Importance of a Landscape Approach in Wetland Assessment                     ..................... 3-3
                                3.4.     Evaluation of Wetland Functions and Values               ......................................... 3-4
                                3.5.     Conclusions and Research Needs            ....................................................... 3-4
                                3.6.     References     .................................................................................  3-5

                      4.        WETLAND EXTENT AND TYPE                      ............................................................. 4-1
                                4.1.     Introduction    ...............................................................................   4-1
                                4.2.     Approach     ...................................................................................  4-1
                                4.3.     Important Parameters in Remote Sensing of Wetlands                 ............................... 4-3
                                4.4.     Interpretation of Wetland Changes           ..................................................... 4-4
                                4.5.     Conclusions     ...............................................................................   4-5
                                4.6.     References     .................................................................................  4-5

                      5.        LANDSCAPE AND WETLAND PATTERNS                             ............................................... 5-1
                                5.1.     Introduction    ...............................................................................   5-1
                                5.2.     Importance of Scale in Pattern Analysis           ............................................... 5-1
                                5.3.     Quantifying Landscape Structure           ....................................................... 5-2
                                5.4.     Relating Landscape Patterns and Ecological Processes               ............................... 5-4
                                5.5.     Conclusions and Research Needs            ....................................................... 5-5
                                5.6.     References     .................................................................................  5-6







                 6.       WETLAND BIOMASS AND PRODUCTIVITY                              ............................................. 6-1
                          6.1.      Introduction    ...............................................................................   6-1
                          6.2.      Traditional Biomass/Productivity Assessment Techniques                   ......................... 6-1
                          6.3.      Remote Sensing Technique            ............................................................. 6-2
                          6.4.      Conclusions and Research Needs           .......................................................  6-4
                          6.5.      References     .................................................................................  6-5

                 7.       WETLAND VEGETATION                    ..................................................................... 7-1
                          7.1.      Introduction    ...............................................................................   7-1
                          7.2.      Vegetation Assessment Techniques            ..................................................... 7-1
                          7.3.      Uncertainties Associated With the Assessment of Vegetation                 ....................... 7-5
                          7.4.      Some Observations About Recovery Rates of Wetlands                   ............................. 7-7
                          7.5.      Conclusions and Research Needs           .......................................................  7-7
                          7.6.      References     .................................................................................  7-7

                 8.       WETLAND HABITAT QUALITY                       ............................................................. 8-1
                          8.1.      Introduction    ...............................................................................   8-1
                          8.2.      Habitat Quality Assessment Techniques             ............................................... 8-1
                          8.3.      Conclusion and Research Needs            .....................................................    8-11
                          8.4.      References     ...............................................................................    8-11

                 9.       WETLAND HYDROLOGY                     ..................................................................... 9-1
                          9.1.      Introduction    ...............................................................................   9-1
                          9.2.      Critical Hydrologic Features of a Wetland           ............................................. 9-1
                          9.3.      Assessment of Wetland Hydrology             ..................................................... 9-4
                          9.4.      Use of Remote Sensing in Hydrology              ................................................. 9-6
                          9.5.      Disturbances of Wetland Hydrology             ................................................... 9-8
                          9.6.      Relationships Between Hydrologic Functions and Wetlands                    ....................... 9-8
                          9.7.      Conclusions and Research Needs           .....................................................    9-10
                          9.8.      References     ...............................................................................    9-10

                 10.      CONCLUSIONS AND RECOMMENDATIONS                               ...........................................   10-1

                 11.      BIBLIOGRAPHY             ...............................................................................    11-1

























                                                                             iv








                                 WETLAND FUNCTIONAL HEALTH ASSESSMENT
                             USING REMOTE SENSING AND OTHER TECHNIQUES:
                                                  LITERATURE SEARCH

                                                N. Patience and V. Klernas
                                                 College of Marine Studies
                                                   University of Delaware
                                                     Newark, DE 19716


                                                            Abstract

                      The objective of this report is to provide a literature search -and a short review of wetland
              functional health determination techniques which are relevant to the NOAA CoastWatch Change
              Analysis Program and other related programs, e.g., the Environmental Monitoring and Assessment
              Program (EMAP-Wetlands) of the Environmental Protection Agency. The report also suggests
              areas where further research is needed. In chapter 2, we review those remote sensing techniques
              which appear effective for mapping abundance (biomass). We also outline the contributions of
              remote sensing to early vegetative stress detection, habitat quality, and hydrology. In Chapter 3,
              we provide an overview of conceptual approaches for the assessment of wetland health, function
              and value. Then each of the proposed indicators of wetland condition is described in a chapter.
              Their importance is underlined, the techniques used for indicator sampling and measurement are
              briefly explained, and the remaining issues that must be resolved are outlined. Complete details of
              every technique are not discussed in this overview. The reader is encouraged to consult the
              references in each section for additional information.

                      Our choice of the health indicators is essentially based on conclusions drawn from various
              interagency reports and planning meetings attended by us and the EMAP-Wetlands major
              assessment endpoints: productivity and biodiversity defined by the variety of species inhabiting
              the wetland, and sustainability defined as the wetland persistence over time. The review includes
              all the remote sensing techniques that can be substituted for the conventional methods, or that are
              used in conjunction with them. Some indicators of wetland condition, such as wetland extent and
              type, habitat structure, and the floral component of wetland productivity, can be studied primarily
              by means of remote sensing; while others (e.g., vegetation, hydrology, habitat quality) still require
              the use of more conventional techniques. Satellite and airborne sensors have been used for several
              decades in wetland mapping, but new remote sensing techniques have recently been developed,
              that allow researchers to determine wetland biomass production. These new techniques should
              enhance our ability to determine wetland condition and functional health over large areas and at
              various repeat intervals.
















                                                                 v












                                                     1 - INTRODUCTION



                Only within the past few decades have we begun to recognize the importance of wetlands as
                productive and valuable ecosystems with numerous functions that benefit society. Wetlands are
                transitional lands between terrestrial and aquatic systems where the water table is usually at or
                near the surface, or the land is covered by shallow water (U.S. Fish and Wildlife Service
                definition). Wetlands are often characterized by high rates of primary production (Gross et al.,
                1990). They are nursery areas for many commercially and recreationally important species of
                fish, shellfish, and wildlife. They often act as a protective buffer against storms by water storage
                and flood abatement, and against erosion damage, by sediment stabilization. They contribute to
                water quality improvement by immobilizing various pollutants and nutrients and also play a role
                in geochernical cycling.

                Often viewed as wastelands, wetlands have been continually converted for other uses since the
                1800s. As wetlands values are better perceived, new legislation is being introduced in many
                parts of the country to preserve these vital resources. Natural resource managers and scientists
                are now confronted with a number of questions (Leibowitz et al., 1991): What proportion of
                wetlands are in good condition; how many are in poor condition? Are conditions improving or
                degrading over time? In what proportion of the wetland resource are conditions continuing to
                decline and at what rate? What are the most likely causes of a poor or degrading condition?
                Which stress factors seem to be most important, adversely affecting the greatest numbers of
                wetlands? Answers to these questions require the use of standardized and accurate assessment
                techniques.

                Timely quantification of wetland area, location and rate and cause of loss, is needed, now.
                Management decisions can then be proactive and based on fact rather than supposition of the
                effects of coastal development on coastal wetlands and wetland dependent fisheries. Current
                projections for U.S. population growth in the coastal zone suggest accelerating losses of wetlands
                and adjacent habitats, as waste loads and competition for limited space and resources increase.
                Agencies responsible for coastal management must be kept current with regard to extent and
                status of wetlands and adjacent uplands. Changes in wetlands are occurring too fast and too
                pervasively to be monitored once a decade. 'fberefore, NOAA within its Coastal Ocean Program,
                has initiated a cooperative interagency and state/federal effort to map coastal wetlands and
                adjacent upland cover and change in the coastal region of the U.S. every two to five years and
                monitor annually, areas of significant change. The program is called the NOAA Coastwatch
                Change Analysis Program (C-CAP).

                NOAA's Change Analysis Program is being designed to determine land/habitat cover and change
                of seagrass, emergent wetlands and adjacent uplands in the coastal regions of the U.S. on a one
                to five year repeating basis. The 1-5 year monitoring cycle will provide feedback to habitat








                                                            1-2

             managers on the success or failure of habitat management policies and programs. Frequent
             feedback to managers will help assure the continued integrity or recovery of coastal ecosystems
             and the attendant productivity and health of fish and other living marine resources at minimal
             cost. In addition, the geographical data base developed under the program, will allow both the
             manager and the researcher to evaluate and ultimately to predict cumulative direct and indirect
             effects of coastal development on wetland habitats and living marine resources.

             Remote sensing (from satellites and aircraft) and other techniques will be used to quantify and
             map coastal wetlands and adjacent uplands. The first cycle will document status and change
             (retroactively). The data base, increasing with each subsequent monitoring cycle will be an
             invaluable resource for research; evaluation of local, state and federal wetland management
             strategies; and construction of predictive models.

             Satellite and airborne remote sensors have been used for several decades to map the acreage of
             wetlands lost to natural processes and anthropogenic activities. Determining only the area of
             marsh lost, however, may not give an accurate description of the total degree of environmental
             degradation. A marsh may have lost only 20% of its area, yet if the hydrology has been
             seriously disturbed, the productivity of the remaining 80% may be only a small fraction of its
             previous level. In response to this and other needs, new remote sensing techniques are being
             developed by NOAA and other researchers to determine not only the change in marsh area but
             also its biomass production and other indicators of condition and functional health. Therefore
             in addition to determining losses in wetlands acreage over time, C-CAP is also trying to satisfy
             the following requirements:

                     1.     To be able to see early functional change before areal change in habitat.
                            We expect this to be a more sensitive approach for "early warning."

                    2.      To be able to assess large areas rapidly and efficiently in order to allow
                            for an early response to stress or impending environmental degradation.


             The purpose of this report is to present the results of a literature search and an overview of
             approaches for the assessment of wetland health, functions and values. In Chapter 2, we review
             those remote sensing techniques which appear effective for mapping abundance (biornass). We
             also outline the contributions of remote sensing to early vegetative stress detection, habitat
             quality, and hydrology. In Chapter 3, we provide an over-view of conceptual approaches for the
             assessment of wetland health, function and value. Then each of the proposed indicators of
             wetland condition is described in a chapter. Their importance is underlined, the techniques used
             for indicator sampling and measurement are briefly explained, and the remaining issues that must
             be resolved are outlined. Complete details of every technique are not discussed in this overview.
             The reader is encouraged to consult the references in each section for additional information.
             The indicators order is random and is not a function of their importance. They all are important
             parameters to be considered eventually for wetland health assessment.







  3





                                    2 - REMOTE SENSING OF WETLAND BIOMASS
                                  AND OTHER WETLAND CONDITION INDICATORS



                NOAA's Change Analysis Program (C-CAP) is being designed to determine land/habitat cover
                and change of sea grass, emergent wetlands and adjacent uplands in the coastal regions of the
                U.S. on a one to five year repeating basis. C-CAP's long term objectives can be summarized as
                follows:


                       1.      To be able to see early functional change before areal change in habitat.
                               We expect this to be a more sensitive approach for "early warning."

                       2.      To be able to assess large areas rapidly and efficiently in order to allow
                               for an early response to stress or impending environmental degradation.

                The Environmental Monitoring and Assessment Program (EMAP) was initiated in 1988 by EPA
                to provide improved information on the current status and long-term trends in the condition of
                the nation's ecological resources. The overall goal of EMAP is to provide a quantitative
                assessment of the current status and long-term trends in ecological condition on regional and
                national scales. In the short-term, EMAP will provide standardized protocols for measuring and
                describing ecological condition, provide estimates of condition in several regions, and develop
                formats for reporting program results. Trend detection will clearly require longer periods of data
                collection and evaluation, and therefore is an intermediate goal. Diagnostic analyses, to identify
                or eliminate plausible causes for degraded or improved condition, is considered the long term
                goal of for EMAP.

                NOAA's C-CAP program is designed to determine land/habitat cover and change of seagrass,
                emergent wetlands, and adjacent uplands in the coastal region of the U.S. using satellite data to
                obtain continuous coverage of large areas. Thus if a small number of EMAP grid point samples
                could be used to "calibrate" C-CAP's satellite products, a continuous measurement of biomass
                and a few other key wetland condition or health indicators becomes possible. In other words,
                we can use the C-CAP satellite data to interpolate between EMAP grid points or extrapolate to
                large areas several of the key condition indicators.

                To accomplish these objectives cost-effectively over large coastal areas, the C-CAP is cooperating
                with EPA's Environmental Monitoring and Assessment Program (EMAP), since both programs
                complement each other. ENIAP-Wetlands will use standardized sampling methods and a
                probability-based sampling design to monitor wetlands over broad geographic areas and for
                multiple decades. The outputs from this program will be for the estimates of wetland condition
                for the regional wetland population (i.e., all wetlands of interest, with a given region), not site-
                specific information. The proposed design strategy is based on a permanent national sampling








                                                                  2-2

              framework consisting of systematic triangular point grid placed randomly over the conterminous
              United States; a similar array is available for Alaska and Hawaii.                  This grid identifies
              approximately 12,600 locations at which all ecological resources will be catalogued and
              classified. Using existing maps, aerial photography, and satellite imagery, the numbers, classes,
              and sizes of wetlands will be determined for the area included within a 40 km2hexagon centered
              on each grid point. These 40 km@ hexagons (40-hexes) describe an area sample representing one-
              sixteenth of the area of the United States, and provide the basis for the Tier 1 estimates of
              wetland extent and distribution.


              The question of which indicators should be sensed remotely can be resolved as follows. The
              three major assessment endpoints suggested by EMAP Wetlands are:

                       1.     Productivity, including both floral and faunal components.

                       2.     Biodiversity, defined by the variety of floral and faunal species inhabiting
                              the wetland, in terms of both community composition and structure, as
                              well as the functional niches that are represented.

                       3.     Sustainability, defined as the robustness of the wetland; its resistance to
                              changes in structure and. function and persistence over long periods of
                              time, as measured by both a wetland's size and hydrology.

              Also, at recent interagency meetings, including implementation meetings for the Louisiana
              Coastal Wetland Planning, Protection and Restoration Act (Mitchell, 1991), it was concluded that
              vegetative abundance (biomass) and species composition (biodiversity) were the two most
              practical indicators for monitoring wetlands condition over large areas. It is fortuitous that both
              of these properties can be detected and mapped from satellites and aircraft with considerable
              success.


              Wetland mapping by remote         sensing (areal extent and floral species composition) is amply
              discussed in chapter 4, and in the Implementation Manual being developed by C-CAP. This
              chapter will, therefore, turn its attention to remote sensing of wetland biomass, and the
              contributions of remote sensing to early vegetative stress detection, waterfowl habitat quality, and
              hydrology. These contributions are also described in the other sections of the report.

              In order to understand the principles of remote sensing technology used in wetland monitoring,
              first let us examine some spectral properties of plants.


              2.1.     Spectral Properties of Plants

              Early reports describing the interaction of leaf tissue with light indicated that changes in the
              spectral quality of reflected electromagnetic radiation were directly related to the quantity of leaf
              tissue and pigment concentrations (Allen and Richardson, 1986; Colwell, 1974; Gausman, 1974).








                                                                        2-3

                  As green aboveground biomass increased, the most significant of the spectral changes were a
                  decrease in red radiation resulting from strong absorption by the chlorophylls, and an increase
                  in near infrared radiation resulting from intra- and interleaf scattering (see the figure below).
                  Although these findings were made in non-wetland environments, wetland plants show the same
                  patterns (Bartlett, 1976; Bartlett and Klemas, 1981; Drake,             1976, Jensen, 1980).


                                                                                          Dominant Factor
                                       Leaf       Cell                                    Controlling Leat
                                     P9 o s-:-Structure-: -Water Content-
                                                                                          Ref lectance

                                  80.
                                  70 -Chlorophyll                                         Primary
                                  60.Absorption                    Water bsorption        Absorption
                                                                                          Bands
                                  50-
                                  40.
                               130-
                               'QE) 20 -
                               2
                                  10-
                                   0"                        4 t .8 2.0 2.2 2A 2f)
                                     .4   .6       11"0 1'2 1'   .6 1,
                                                     wavelength (micrometers)

                                   -vis:ble-E- Reflective infrared                        Spectral
                                    %                                                     Region
                                          I'D E-Near Intrared-E- Middle Infrared
                                          Cc

                                     Reflectance characteristic or healthy, green leaves (derived from HOFFER and JOHANNSEN 1969). It ap-
                               plies in the shape of the curve also for trees with rull, green foliage and for dense canopies of healthy forest
                               stands.




                  The low reflectance in the blue and red regions is due to strong absorbance of these wavelengths
                  by the chlorophyll. There is a slight peak in the green region, because plants do not absorb
                  green, but reflect it. The high reflectance in the near infrared region is controlled by plant tissue
                  structure and results from the scattering effects of the mesophyll (Boyer et al., 1988). Beyond
                  1200 nm, the decrease in infrared absorption is due to the absorption by water (Knipling, 1968).


                  2.2. Wetland Biomass and Productivity


                  2.2.1 Definition


                  The most commonly used and accepted parameter for evaluating an ecosystem condition is
                  biomass and/or net primary productivity of the emergent macrophytes. Both terms refer to the
                  dry weight of plants (expressed as grams dry weight per square meter -gdw/m@- for the biomass
                  and usually as gdw/m' per year for the productivity).








                                                              2-4

             2.2.2 Reflectance and aboveground biomass

             Early work by Bartlett (1976, 1979) determined green biomass of wetland grasses to be strongly
             correlated with the near infrared/red reflectance ratio. Other investigators found good correlations
             between green biomass and the spectral reflectance of different marsh shrub communities
             (Hardisky et al., 1986). In most cases, the combination of red and near infrared radiance
             provided the best correlation with canopy biomass.

             Several years ago, a simple linear regression model equating spectral reflectance to biomass was
             formulated for Delaware Spartina alterniflora, one of the most common salt marsh plants in
             eastern North America (Hardisky, 1982, 1984). Spectral reflectance measurements were made
             in selected portions of the marsh using a hand-held radiometer that gathered data in three
             wavebands, spectrally configured to simulate bands 3, 4, and 5 of the Landsat Thematic Mapper:
             a red band (630-690 nm, TM3) sensitive to chlorophyll concentration, a near infrared (NIR) band
             (760-900 nm, TM4), sensitive to plant tissue structure, and a middle infrared band (1550-1750
             nm, TM5) sensitive to water absorption.

             The raw radiance data were transformed and expressed as a normalized difference of two bands
             as follows:


                    VI = [NIR - red] / [NIR + red]

                    H = [NIR - middle IR] / [NIR + middle IR]

             where VI is the Vegetation Index and II the Infrared Index. Index values were preferred to raw
             radiance data because the normalization procedure tended to compensate for both short- and
             long-term changes in solar iffadiance and atmospheric conditions (Tucker et al., 1981). Both
             indices, VI and 11, correlated strongly with the changes in biomass (Hardisky, Smart and Klernas,
             1983; Hardisky, Gross and Klemas, 1986). Measurements of biomass were combined over a
             growing season to yield an estimate of NAPP (net aerial primary productivity). The NAPP
             estimates were generally within ten percent of harvest estimates (Hardisky et al., 1984).

             Biomass evaluation has also been attempted with satellite imagery (Gross et al., 1987). The
             problem of atmospheric effects on the satellite-measured radiance data was solved by converting
             the satellite data to the equivalent ground-measurement reflectance. This was done using
             equations relating the reflectance of certain large, homogeneous sites measured from the ground
             at the time of the satellite overpass, to their satellite-measured radiance. The satellite-derived
             estimates were found to be within 13 percent of ground-based biomass estimates. The nature of
             the relationship linking VI and the aerial biomass was consistent from year to year and between
             marshes, although there was a difference between northern and southern marshes (Gross et al.,
             1990).

             By comparing satellite data from several years, it was found that in some parts of marshes, the
             growth of Spartina alterniflora was more sensitive to the amount of precipitation than in other








                                                              2-5

               parts of the marsh (Gross et al., 1990). The most sensitive areas seemed to be the closest to the
               saltiest water. The plants nearest freshwater areas showed the least response to quantity of
               precipitation. From this work, it will be possible to predict in advance which parts of the marsh
               will be most affected by drought, by unusually wet weather, or by man-made hydrologic
               disturbances (Gross et al., 1987; 1990).

               2.2.3 Belowground biomass estimation

               Light does not penetrate soil, making it impossible to measure root biomass directly by optical
               remote sensing. However, Gross et al. (1990; 1991) report a strong positive relationship (r2 =
               0.86) between the natural logarithm of live aboveground biomass and the natural logarithm of
               live belowground biomass of S. alterniflora ("short" plants only). Therefore, belowground
               biomass can be indirectly measured using a non-destructive method.

               Another promising technique for belowground biomass estimation is the use of
               ground-penetrating radar (GPR), but it is still under evaluation. Traditionally, GPR has been used
               to locate things such as archaeological sites, toxic waste drums, and divisions between contrasting
               soil types like sand and clay. A radar antenna is dragged along the surface of the marsh,
               emitting electromagnetic waves. These waves penetrate the soil, and are reflected back by
               objects in the soil. The return signal is recorded and printed in graph form (Gross, 1989).
               Researchers hope that the characteristics of the return signal will reveal something about root
               material.


               2.2.4 Factors influencing spectral estimates

               One of the factors that influence the spectral radiance of the marsh is the solar angle which can
               easily be corrected (Hardisky, Gross and Klemas 1986). Two other factors are the quantity and
               orientation of dead biomass, and the amount of soil reflectance (Hardisky et al., 1986). The
               presence of dead material tends to decrease the vegetation index. Except in marshes with very
               sparse canopy (<30% cover), soil reflectance is not usually a problem. Richardson and Wiegand
               (1977) have proposed a perpendicular vegetation index (PVI), which factors out the influence of
               soil reflectance. The infrared index is less attenuated by dead biomass and soil reflectance than
               the vegetation index.

               2.2.5 Conclusions and research needs


               Remote sensing is considered an accurate and effective non-destructive biomass assessment
               technique in salt marshes despite its limitations: sampling can only be done on sunny days, for
               four hours, and only during a tidal stage when the marsh surface is not flooded (Hardisky et al.,
               1984). Hand-held radiometers have been extensively used to assess biomass and NAPP of small
               wetland tracts, but satellite imagery is more useful for sampling larger areas. The aerial biomass
               estimation technique is based on the use of simple regression models equating the green biomass
               with spectral radiance indices. Root biomass can then be estimated using equations linking
               aboveground and belowground biomass.








                                                              2-6

              Limited remote sensing work has been conducted in other types of wetlands such as brackish
              marshes and coastal mangrove systems (Hardisky et al., 1986).              Salt marshes are often
              characterized by large monospecific stands of vegetation. In contrast, the physiognomy of
              brackish marshes is usually more varied because a particular plant community often comprises
              many species. Different plant morphologies thus coalesce to produce canopy architectures that
              reflect incident radiation differently from a monospecific canopy (Hardisky et al., 1986).
              Hardisky and Klemas (1985) analyzed the effects of three canopy types on the vegetation index.
              Since the quality of reflected radiation (expressed as a vegetation index) differs for each canopy
              architecture, accurate biomass predictions must rely on separate models describing each type.
              Studies by Hardisky (1984) suggested that biomass could indeed be -predicted for communities
              of one canopy type using a single model. The work conducted by Hardisky et al. (1986) in the
              black mangrove, Avicennia germinans, in Costa Rica, describes a positive relationship (r=0.79)
              between the TM vegetation index and live leaf biomass. The more ubiquitous taller mangrove
              forms will require extensive ground comparisons before an operational biomass estimation
              procedure can be developed (Hardisky et al., 1986).


              2.3.   Early Vegetative Stress Detection

              When plants are subjected to stressful conditions, certain physiological changes occur, that can
              be detected by remote sensing, because of the consequent changes in plants spectral reflectance.
              These physiological changes relate to chlorophyll density, cellular size and arrangement, and
              moisture content. As a plant is exposed to various stressful conditions (disease, insects, moisture
              and mineral stress, etc.), two changes in reflectance are observed: 1) visible reflectance increases,
              because there is less chlorophyll and/or the chlorophyll is less efficient in absorbing red and blue
              light; 2) near infrared reflectance decreases, be@ause of a deterioration of the mesophyll cells
              (Campbell, 1987). Reflectance changes can be detected before visible sympt         'oms appear, and
              thus, are good indicators of plant stress (Knipling, 1968). Moisture stress is usually evidenced
              by an increased radiant erriission from the plant and thus lighter tones in images (Weaver et al.,
              1968). Nitrogen deficiency will result in increased reflectivity of a single leaf, but in decreased
              reflectivity of the whole canopy, because of the decrease of leaf surface area per unit ground area
              (Hardisky, 1984).


              2.4.    Waterfowl Habitat Quality

              Waterfowl habitat quality is a function of both water conditions and terrain characteristics of the
              surrounding wetland and upland cover types (Colwell et al., 1978). Habitat quality, according
              to Colwell et al., relates to the potential of the habitat to attract breeding waterfowl and furnish
              the requirements for survival and successful rearing of broods. They developed a model for the
              assessment of waterfowl habitat quality based on the various relationships between ponds and the
              surrounding terrain types.









                                                               2-7

               The model developed by Colwell et al. (1978) evaluates waterfowl habitat quality on the basis
               of water conditions and terrain characteristics. The specific water conditions are pond number,
               pond area, and pond size-class distribution. Once these factors were calculated, they integrated
               them into one single pond factor. 'Me terrain characteristics they evaluated were the presence
               and spatial arrangement of certain terrain types (hay, grasses, pasture). They incorporated
               presence and spatial arrangement into a single factor represented by the amount of edge between
               desirable terrain types. The resulting model for waterfowl habitat quality combined pond and
               terrain factors, and generated ratings on a section-by-section analysis. This habitat model wa's
               preliminary; no detailed analysis of the accuracy of the model ratings has been made. Colwell
               et al. (1978) used Landsat data in their model. Pond and terrain characteristics were determined
               from multidate Landsat imagery and aerial photography. Remote sensing data allow monitoring
               changes in the habitat quality over time. With the advent of satellites with better spatial
               resolution (e.g., SPOT), it is possible to improve the accuracy of the pond and terrain factors.


               2.5.   Hydrology

               Remote sensing can provide some information on the hydrology regime of the wetland, such as
               changes in surface level, in open areas, and in soil moisture. A number of studies have used
               remote sensing as a method for flood analysis and soil moisture assessments (Sollers et al., 1978;
               Harker and Rouse, 1977; Ragan, 1977; McGinnis and Rango, 1975; Rango and Anderson, 1974;
               Moore and North, 1974; Rango and Salomonson, 1974; Williamson, 1974; American Water
               Resources Associations, 1974; Piech and Walker, 1971), (Schmugge, 1983; Cihlar, 1978;
               Schmugge et al., 1977; Myers et al., 1977; Idso et al., 1975; Blanchard et al., 1974; Waite et al.,
               1973; Werner et al., 197 1). Microwave radiometric sensors are very effective at measuring water
               content, both in the atmosphere and on the earth's surface. These systems can be used to map
               areal distribution and variations in rainfall, water absorption rates of surface soils and map flood
               water distribution and flow patterns on an all-weather synoptic basis (Kennedy 1968). The
               microwave radiometer functions as a temperature-measuring device. The capability of the
               radiometer to measure atmospheric hydrology derives from the electromagnetic properties of
               atmospheric water vapor, oxygen, clouds, rain, and the earth's surface which differ greatly in
               electromagnetic properties. The dielectric properties of surface materials are strongly dependent
               on moisture content. Changes in the dielectric constant result in major changes in the emissivity
               and radiometric brightness temperature (Kennedy 1968).

               2.5.1 Flood monitoring

               Aircraft and satellite data have been used to perform floodplain mapping by two complementary
               approaches: static and dynamic (Sollers et al., 1978). The static approach is based on the
               recognition of geornorphological features formed by historical floods such as terraces, alluvial
               fans, natural levees, bars, oxbows, marshes, deltas, etc. Floodprone areas tend to have
               multispectral signatures that are distinctly different from those of surrounding nonfloodprone
               areas. The dynamic approach uses images of floods as they occur or soon afterward. Visible
               evidence of inundation in the near infrared region of the spectrum remains for up to two or more








                                                              2-8

              weeks after the flood. The near infrared reflectivity is reduced in the flooded areas because of
              the presence of increased surface-layer soil moisture, moisture stressed vegetation, and isolated
              pockets of standing water. The inundated areas are characterized by the water absorption band
              (700-1100 nm). Visible and near infrared channels are recommended for analysis. The features
              observed here are the atmospheric conditions (clouds, air mass characteristics, precipitation),
              flood water levels, and soil and vegetation characteristics after the high waters have receded.
              Soil moisture and sediment traces in water can indicate the path and extent of flood damage to
              a plain (Currey, 1977). Vegetation also exhibit patterns related to flood conditions: flood stressed
              plants reflect more blue and less infrared radiation (Sollers et al., 1978).

              Satellites, such as ERTS, NOAA, Landsat, and SPOT could help reduce short- and long-term
              flood losses and provide regional water resources planning information. Data from these
              satellites would therefore complement aircraft and conventional surveying methods to ascertain
              the areal extent of flooding (McGinnis and Rango, 1975). Despite its usefulness in flood
              monitoring, remote sensing has limitations: 1) some systems don't have the resolution needed to
              delineate the boundary of flooded areas; 2) the scale of floodplain mapping is not large enough
              for most legal requirements; and 3) clear weather conditions are necessary with passive sensors.
              When possible, a combination of sensors should be used. Remote sensing data can serve as a
              base for assessment of potential flood damage, in identifying areas where further surveys are
              merited.


              2.5.2 Soil moisture assessment


              Soil moisture and its spatial and temporal behavior is of critical importance to disciplines such
              as agriculture, hydrology, and climatology. Specifically, soil moisture assessments are needed
              to study flood water distribution and flow patterns, distribution and variations in precipitation
              (especially rainfall), runoff following precipitation, and evapotranspiration (Kennedy 1968; Cihlar,
              1978).

              Most techniques developed for soil moisture measurement provide point estimates, therefore are
              not suited for large areas (Cihlar, 1978). The traditional method of soil moisture measurement
              is to weigh a sample of soil, oven-dry it, and reweigh it. The difference between the wet and
              dry weights represents the soil moisture, and the percent moisture is then extrapolated to the
              entire field. This method is time-consuming and representative of only small areas. The status
              of remote sensing techniques for soil moisture estimation was reviewed in a workshop organized
              in 1978 in Maryland (Cihlar, 1978). The techniques discussed were: 1) the reflected solar
              technique; 2) the thermal infrared technique; 3) the active microwave (radar) technique; 4) the
              passive microwave (radiometer) technique; and 5) the gamma radiation technique. The review
              indicated the complementary nature of the various techniques. Thus, it is likely that a
              combination of sensors will be needed to provide accurate soil moisture estimates from satellites.
              Thermal infrared and both microwave approaches have shown potential for estimating
              near-surface water contents, but the sensitivity to water at greater depths and under canopy
              seemed limited to the thermal infrared technique (Cihlar, 1978).






  4                                                             2-9

               2.6.   Conclusions and Recommendations


               Remote sensing is a very helpful tool in wetland management. Remote sensors on aircraft and
               satellites can be used to select sampling sites, establish field transects, identify and delineate the
               major vegetation types, map wetland changes and to detect early vegetative stress. It also allows
               to document present conditions for use in future trend analyses. Methodologies and algorithms
               for the determination of biomass and productivity of coastal wetlands habitat by remote sensing
               have been recently developed and will significantly enhance our ability to determine wetland
               condition over time on a regional scale. Remote sensing also provides information on physical
               alterations to the wetland (flooding, human activities, etc.), soil moisture, and the wetland
               hydrological regime. By comparing two or more time periods, change in biomass, productivity,
               wetland extent, type, and patterns, wetland vegetation community composition, or any other
               factor correlated with spectral reflectance could be used to index functional health. The activity
               requires ground-based research to relate remotely sensed spectral radiances to these indicators.
               Various remote sensing methods are available, and the choice of the method will depend on the
               project objectives and monetary restraints. Low-resolution data may be sufficient for the study
               of certain parameters, and higher resolution data will be required for detailed studies of selected
               sample sites. Our management effectiveness in the future will depend on our ability to collect
               and analyze data on a regional, and eventually, global scale. The advances in instrumentation
               and in computer analysis techniques will greatly improve the types of data available.

               Vegetative abundance (biomass) and species composition (biodiversity) have been identified as
               two practical indicators for monitoring wetland condition over large areas by remote sensing.
               Therefore, there is an urgent need for research to test the applicability of remote sensing for
               operational determination of biomass, productivity and species diversity over a large range of
               wetland types.     Simultaneously, other applications of remote sensing for wetland health
               evaluation should also be investigated.



               2.7     References


               Allen, W.A., and A.J. Richardson. 1968. Interaction of light with a plant canopy. J. Opt. Soc.
               Am. 58: 1023-1028.


               American Water Resources Association. 1974. Satellite analyses of the 1973 Mississippi River
               floods. Water Resources Bull. 10(5): 1023-1096.

               Bartlett, D.S. 1976. Variability of wetland reflectance and its effect on automatic categorization
               of satellite imagery. M.S. thesis, University of Delaware, Newark. 108 pp.

               Bartlett, D.S. 1979. Spectral reflectance of tidal wetland plant canopies and implications for
               remote sensing. Ph.D. dissertation, University of Delaware, Newark. 239 pp.








                                                            2-10

             Bartlett, D.S., and V. Klemas. 1981. In situ spectral reflectance studies of tidal wetland grasses.
             Photogramm. Eng. Remote Sens. 47: 1695-1703.

             Blanchard, M.B., R. Greeley, and R. Goettelman. 1974. Use of visible, near-infrared, and thermal
             infrared remote sensing to study soil moisture. Ninth International Symposium on Remote
             Sensing of Environment. Environmental Research Inst. of Michigan. pp. 693-700.

             Boyer, M., J. Miller, M. Belanger, and E. Hare. 1988. Senescence and spectral reflectance in
             leaves of Northern Pin Oak (Quercu palusais Muenchh). Remote Sens. Envir. 25(t): 71-87.

             Campbell, J.B. 1987. Introduction to remote sensing. The Guilford Press, New York. pp. 366-403.

             Cihlar, J. 1978. Soil moisture information: needs and remote sensing capabilities. Remote Sensing
             news briefs. Energy, Mines and Resources Canada. Canada Center for Remote Sensing, 588
             Booth Street, Ottawa, Canada KIYOY7

             Colwell, J.E. 1974. Vegetation canopy reflectance. Remote Sens. Environ. 3: 175-183.

             Colwell, J.E., D.S. Gilmer, E.A. Work, Jr., D.L. Rebel, and N.E.G. Roller. 1978. Use of Landsat
             data to assess waterfowl habitat quality. National Aeronautics and Space Administration.

             Currey, D.T. 1977. Identifying flood water movement. Remote Sensing of the Environment 6:
             51-61.


             Drake, B.G. 1976. Seasonal changes in reflectance and standing crop biomass in three salt
             marshes communities. Plant Physiol. 58: 696-699.

             Gausman, H.W. 1974. Leaf reflectance of near-infrared. Photogramm. Eng. Remote Sens. 40:
             183-191.


             Gross, M.F. 1989. CMS researchers use satellites to measure the health of wetlands. At Sea,
             College of Marine Studies Newsletter, vol.9, no.3, University of Delaware, pp. 1-2.

             Gross, M.F., M.A. Hardisky, V. Klemas, and P.L. Wolf. 1987. Quantification of biomass of the
             marsh grass Spartina alterniflora Loisel using Landsat Tbernatic Mapper imagery. Photogramm.
             Eng. Remote Sens. 53: 1577-1583.

             Gross, M.F., M.A. Hardisky, and V. Klemas. 1988. Effects of solar angle on reflectance from
             wetland vegetation. Remote Sens. Environ. 26: 195-212.

             Gross, M.F., V. Klemas, and M.A. Hardisky. 1990. Long-term remote monitoring of salt marsh
             biomass. Proceedings, SPIE's 1990 technical symposium on Optical Eng. and Photonics in
             Aerospace Sensing, Earth Observing Systems, April 16-20, 1990, Orlando, Fl. 12 p.








                                                             2-11

               Gross, M.F., M.A. Hardisky, and V. Klemas. 1990b. Inter-annual spatial variability in the
               response of Spartina alterniflora biomass to amount of precipitation. J. Coastal Research 6(4):
               949-960.

               Gross, MY, M.A. Hardisky, P.L. Wolf, and V. Klemas. 1991. Relationship between aboveground
               and belowground biomass of Spartina alterniflora (Smooth Cordgrass). Estuarine Research
               Federation. Estuaries 14(2): 180-191.

               Hardisky, M.A., MY Gross, and V. Klemas. 1986. Remote sensing of coastal wetlands.
               Bioscience 36: 453-460.

               Hardisky, M.A. 1982. The relationship between spectral radiance and aboveground biomass of
               Spartina alterniflora Loisel. M.S. thesis, University of Delaware, Newark. 112 pp.

               Hardisky, M.A. 1984. Remote sensing of aboveground biomass and annual net aerial primary
               productivity in tidal wetlands. Ph.D. dissertation, University of Delaware, Newark. 252 pp.

               Hardisky, M.A., F.C. Daiber, C.T. Roman, and V. Klemas. 1984. Remote sensing of biomass
               and annual net aerial primary productivity of a salt marsh. Remote Sens. Environ. 16: 91-106.

               Hardisky, M.A.,and V. Klemas. 1985. Remote sensing of coastal wetlands biomass using
               Thematic Mapper wavebands. pp. 251-269 In: Landsat-4 Early Results Symposium, vol.IV.
               NASA Goddard Space Flight Center, Greenbelt, MD.

               Hardisky, M.A., R.M. Smart, and V. Klemas. 1983. Seasonal spectral characteristics and
               aboveground biomass of the tidal marsh plant, Spartina alterniflora. Photogramm. Eng. Remote
               Sens. 49: 85-92.


               Harker, G.R. and J.W. Rouse, Jr. 1977. Floodplain delineation using multispectral. data analysis.
               Photogrammetric Eng. and Remote Sensing 43(l): 81-87.

               Idso, S.B., R.D. Jackson, and R.J. Reginato. 1975. Detection of soil moisture by remote
               surveillance. American Scientist 63: 549-556.


               Jensen, A. 1980. Seasonal changes in near-infrared reflectance ratio and standing crop biomass
               in a salt marsh community dominated by Halimione portulacoides (L.) Aellen. New Phytol. 86:
               57-67.


               Kennedy, J.M. 1968. Microwave sensors for water management and hydrology from space. Ryan
               Aeronautical Company, San Diego, California. A/AA Paper No. 68-1076, A/AA 5 th Annual
               Meeting and Technical Display, Philadelphia, PA, Oct 21-24, 5 pp.

               Knipling, E.B. 1969. Leaf reflectance and image formation on color infrared film. In: Remote
               Sensing in Ecology, University of Georgia Press, pp. 17-29.








                                                         2-12

             McGinnis, D.F., and A. Rango. 1975. Earth Resources Satellite systems for flood monitoring.
             Geophys. Res. Letters 2(4): 132-135.

             Moore, G.K., and G.W. North. 1974. Flood inundation in the southeastern United States from
             aircraft and satellite imagery. Water Resources Bull. 10(5): 1082-1280.

             Myers, V.I., J.L. Heilman, and D.G. Moore. 1977. Remote soil moisture measurements: need,
             present methods and obstacles. Microwave Symposium, Houston, Texas, sponsored by NASA and
             Texas A & M Univ. 20 pp.

             Piech, K.R. and J.E. Walker. 1971. T'hematic mapping of flooded acreage. Photogrammetric Eng.,
             1972, pp. 1081-1090.

             Ragan, R.M. 1977. Utilization of remote sensing observations in hydrologic models. Proceedings
             of the Eleventh International Symposium on Remote Sensing of Environment 1: 87-99.

             Rango, A. and A.T. Anderson. 1974. Flood hazard studies in the Mississippi River basin using
             remote sensing. Water Resources Bull. 10(5): 1060-1081.

             Rango, A. and V.V. Salomonson. 1974. Regional flood mapping from space. Water Resources
             Research 10(3): 473-484.

             Richardson, A.J., and C.L. Wiegand. 1977. Distinguishing vegetation from soil background
             information. Photogramm.Eng. Remote Sens. 43: 1541-1552.

             Schmugge, T.J. 1983. Remote sensing of soil moisture: recent advances. EEEE Transactions on
             Geoscience and Remote Sensing, Vol. GE-21, no.3, pp. 336-344.

             Schmugge, T.J., J.M. Meneely, A. Rango, and R.Neff. 1977. Satellite microwave observations
             of soil moisture variations. Water Resources Bull. 13(2): 265-281.

             Sollers, S.C., A. Rango, and D.L. Henniger. 1978. Selecting reconnaissance strategies for
             floodplain surveys. American Water Resources Association. Water Resources Bull. 14(2):
             359-373.


             Tucker, C.J., B.N. Holben, J.H. Elgin, and J.E. McMurtrey. 1981. Remote sensing of total
             dry-matter accumulation in winter wheat. Remote Sens. Environ. 11: 171-189.

             Tucker, C.J., W.H. Jones, W.A. Kley, and G.J. Sundstrom. 1981b. A three-band hand-held
             radiometer for field use. Science 211: 281-283.


             Waite, W.P., K.R. Cook, and B.B. Bryan. 1973. Broad spectrum microwave systems for remotely
             measuring soil moisture content.     Water Resources Research Center Publication #18 in
             cooperation with University of Arkansas Eng. Expt. Station Report #23. 166 pp.








                                                           2-13

              Weaver, D.K., W.E. Butler, C.E. Olson, Jr. 1968. Observations on interpretation of vegetation
              from infrared imagery. In: Remote Sensing in Ecology, University of Georgia Press, pp. 132-137.

              Werner, H.D., F.A. Schmer, M.L. Horton, and F.A. Waltz. 197 1. Application of remote sensing
              techniques to monitoring soil moisture. Remote Sensing Inst. Report #RS171-4. South Dakota
              State Univ. 33 p.

              Williamson, AN 1974. Mississippi River flood maps from Erts-I digital data. Water Resources
              Bull. 10(5): 1050-1059.










                           3 9 CONCEPTUAL APPROACHES IN WETLAND ASSESSMENT



               3.1.    Introduction

               Development of a monitoring strategy for wetlands depends on the objectives of a study (Brooks
               1989). Is the purpose of the study to detect an improvement or decliiie in a wetland's condition?
               Is the reason for monitoring related to a specific concern or function, such as water quality,
               species diversity, etc.? Is the purpose of the study to evaluate the success of a mitigation
               project? The investigator's objectives will determine the amount of time and funds required for
               a monitoring program. This program can range from single site visits to assess the condition of
               a wetland after an anticipated event (e.g., flood occurrence or completion of a mitigation project),
               to long-term studies of the cumulative impacts on wetlands and their surroundings (Brooks,
               1989).



               3.2.    Assessment of Ecosystem Health

               3.2.1. Definition of functional health

               The complexity of an ecosystem makes it difficult to define its state of health.                       The
               environmental biologist is confronted by an infinite number of parameters which might be
               measured. But in practice, a healthy ecosystem may be defined only by reference to a few
               parameters, and the absence of disease is based on comparison to one or more poorly qua            ,ntified
               "ideal" ecosystems (Schaeffer et al., 1988). According to Karr et al. (1986), "a biological system
               ... can be considered healthy when its inherent potential is realized, its condition is stable, its
               capacity for self-repair when perturbed is preserved, and minimal external support for
               management is needed".

               Like physicians, we define health in ecosystems as the absence of disease. The relationships
               between measures of structure (numbers and kinds of organisms, biomass, etc.) and function
               (activity, production, decomposition, etc.) help identify some diseases (Schaeffer et al., 1988).
               Ecosystem impairment has short-term and long-term, major and minor effects. Illness may be
               defined in relation to short-term shifts in ecosystem elements considered critical for the
               maintenance of ecosystem function, and may be viewed as acceptable if the degree of degradation
               is limited by ecosystem resiliency or managerial intervention. Disease, then, would be related
               to long-term and permanent shifts in critical ecosystem elements; these shifts include falling
               numbers of native species, overall regressive succession, rapid alteration in the quantity of either
               living or dead biomass, changes in energy production and flow, changes in mineral macronutrient
               stocks and in the capacity of the ecosystem to damp undesirable oscillations of contaminant
               concentrations (Schaeffer et al., 1988). Because an ecological system is comprised of many








                                                              3-2

              interacting species, a functional definition of a healthy ecosystem must be given as a set of
              ecological requirements (Schaeffer et al., 1988).

              3.2.2 Wetland health evaluation


              No indices of wetland health currently exist that are widely accepted and have been tested and
              applied on regional scales.      Ecosystem health evaluation involves: 1) the identification of
              systematic indicators of ecosystem structural and functional integrity, 2) the measurement of
              ecological sustainability and 3) the detection of potential symptoms of ecosystem disease or stress
              (Rapport, 1989). Four types of indicators may be distinguished (Leibowitz et al., 1991)-
              1) response indicators, which provide a metric of biological condition (e.g., vegetation community
              composition); 2) exposure indicators, which assess the occurrence and magnitude of contact with
              a physical, chemical, or biological stressor (e.g., nutrient concentrations); 3) habitat indicators,
              which characterize the natural physical, chemical, and biological conditions necessary to support
              an organism, a population, or a community (e.g., wetland hydrology); and 4) stressor indicators,
              which quantify natural processes, environmental hazards, or management actions that result in
              changes in exposure or habitat (e.g., changes in land use).           Indicator selection must be
              parsimonious, including only those that most effectively define wetland condition.               This
              procedure requires extensive testing and evaluation. To classify ecosystems as healthy or
              unhealthy, it is important to use objective criteria for a range of parameters that are appropriate
              to each ecosystem (Schaeffer et al., 1988). Thus, region-based quantitative definitions of
              ecological health are required (Karr, 1991).

              For each wetland class, in each region, wetland condition may be evaluated by comparing the
              measured indicator values with: 1) expected normal ranges for each response variable, derived
              from measurements at reference sites, historical records, the available literature arid/or expert
              judgment; 2) information on stress-damage thresholds for each exposure indicator, obtained from
              the literature and available data (Leibowitz et al., 1991). Reference sites may be monitored for
              each wetland class and region, representing the least disturbed and most disturbed wetland in the
              landscape. Wetlands classified as healthy are assumed to perform as expected for a wetland of
              that type, within that region and for the specific wetland value of interest. Classification of a
              wetland as healthy or unhealthy should not rely on a single indicator, but on the fun set of
              monitored response, exposure, habitat and stressor indicators (Leibowitz et al., 199 1). Additional
              criteria are needed to distinguish between acceptable and unacceptable degrees of a diseased state
              because recovery from disease is possible (Schaeffer et al., 1988). Health parameters must be
              assessed differentially with the age or developmental stage of the ecosystem.             Also, the
              assessment should reflect our knowledge of normal succession or expected sequential changes,
              which occur naturally in ecosystems. This requires change in target measurements and
              modification of criteria as ecosystems change (Schaeffer et al., 1988).

              Three assessment endpoints are considered in EMAP-Wetlands program (Leibowitz et al., 1991)
              to reflect the major social and biological values associated w*ith natural wetlands: 1) productivity,
              including both floral and faunal components; 2) biodiversity, defined by the variety of floral and
              faunal species existing in the wetland, in terms of community composition and structure, as well








                                                               3-3

               as the functional niches that are represented; and 3) sustainability, defined as the robustness of
               the wetland, i.e., its resistance to changes in structure and function and persistence over long
               periods of time, as measured by both wetland's size and hydrology.

               The assessment of ecosystem health requires the analysis of selected parameters based on criteria
               developed from experimentation coupled with some reference to healthy ecosystems (Schaeffer
               et al., 1988). It requires standard procedures, precision and accuracy in analyses. Unfortunately,
               these standardized techniques do not yet exist.

               3.2.3 Use of biological indexes

               Early efforts to develop biological indexes concentrated on detecting a narrow range of variation
               in biological integrity (Taub, 1987; Ford, 1989; Fausch et al., 1990), resulted in indexes sensitive
               to only a few types of degradation, or provided only a binary (degraded/not degraded) evaluation
               (Karr, 1991). Many existing biological indexes may only apply to a narrow geographic area,
               and do not screen complex cumulative impacts. Such approaches are appropriate when specific
               narrow impacts are known to be present, but protection of natural resources from a broad range
               of human impacts requires a more comprehensive approach. The ideal index would be sensitive
               to all stresses placed on biological systems by human society while also having limited sensitivity
               to natural variation in physical and biological environments (Karr, 1991). An array of indicators
               would be combined into one or more simple indexes and could be used to detect degradation and
               identify its cause, and to determine if improvement results from management actions (Karr,
               1991).

               Many indicators of the health of biological systems have been tested in recent years (National
               Academy of Sciences, 1986; Schindler, 1987; Taub, 1987; Ford, 1989; Gray, 1989; Pontasch et
               al., 1989; Karr, 1990). Each has sensitivity at different levels of degradation and to different
               kinds of anthropogenic: stress. The complexity of biological systems and the diversity of factors
               responsible for degradation, makes it unlikely that any metric will have sufficient sensitivity to
               be useful under all circumstances. Karr (1991) suggests to integrate aspects of those promising
               indicators to create a more robust approach to biological monitoring.


               3.3.    Importance of a Landscape Approach in Wetland Assessment

               Traditional assessment procedures are species oriented, assume linear, causal relationships, are
               focused on individual or segmented processes and sites, and tend to base decisions on a static
               "snapshot" of the site in its present condition (Gosselink and Ixe, 1986). In contrast, cumulative
               impact assessment requires a landscape orientation in which processes are highly interactive and
               often nonlinear. Although much has been said about the importance of cumulative impact
               assessment, there have been a few successful attempts to deal with it within a regulatory
               framework. Failure to work on appropriate delimited systems continues to be a frequent problem.
               Besides, cumulative impact assessment is constrained by lack of adequate understanding of the
               relationships between physical properties of wetlands and their functions.








                                                            3-4

             A larger scale approach that integrates both spatial and temporal dynamics in wetland assessment
             is needed for several reasons (Bedford and Preston, 1988). First, wetland functions operate at
             watershed, landscape, regional, or larger scales; they are not primarily the product of particular
             wetlands but of their relationships to other wetlands, and other land use types; therefore, they
             need to be studied within these larger contexts. Second, the occurrence and maintenance of
             wetlands reflect large-scale and long-term characteristics of watersheds, landscapes, and regions,
             as well as more local processes (Winter, 1988; Siegel, 1988; O'Brien, 1988). Finally, the scale
             of environmental problems is expanding from the local to the regional and global level. Shifting
             the scale of impact assessment to larger scales requires that measurable properties or indicators
             of wetland and landscape condition be defined (Bedford and Preston, 1988). Refer to Gosselink
             and Lee (1986) and Bedford and Preston (1988) for an overview of cumulative impact assessment
             principles.



             3.4.   Evaluation of Wetland Functions and Values


             A wide variety of wetland evaluation methods have been developed by federal or state agencies,
             private consulting firms, and the academic community to ascertain all or selected wetland
             functions and values (Adamus, 1983; Adamus and Stockwell, 1983; Smith, 1984; Hollands and
             McGee, 1985; Winchester, 1985; Wencek, 1985; Animam et al., 1985; McColligan, Jr., 1985;
             Adamus, 1985). Most of these methods are. designed to be fast and easily applied, even by non-
             specialists. Iley allow a quick functional evaluation of wetlands and often use a system of
             weight and scores to rank the wetlands for a-variety of functional values (biological, hydrologic,
             socio-cultural). The Federal Highway Administration (FHWA) method for wetland functional
             assessment (Adamus and Stockwell, 1983) has been very popular, but still needed improvement
             and standardization. 25 wetland evaluation methodologies are summarized in Lonard and
             Clairain, Jr. (1985).



             3.5.   Conclusions and Research Needs


             A number of wetland evaluation methods have been proposed, but they all still need improvement
             and standardization. The assessment of wetland health requires the analysis of indicators that are
             appropriate to each ecosystem; the evaluation should take into account the full set of parameters
             that best indicate the ecosystem conditipn. As the scale of environmental problems is expanding
             from the local to the global level, a more comprehensive approach in wetland assessment is
             fundamental. Further research is needed to: 1) understand the relationships between physical
             properties of wetlands and their functions; 2) find better health indexes that are sensitive to
             cumulative impacts; 3) develop standardized and accurate wetland evaluation procedures.








                                                            3-5

               3.6.  References


               Adamus, P.R. 1983. A method for wetland functional assessment: Vol. H. FHWA assessment
               method. Washington, DC: Federal Highway Administration, Report No. FHWA-EP-82-24. 134
               pp-

               Adamus, P.R. 1985. Uses and proposed revisions for the Adamus assessment methodology. pp.
               73-77, In: Proceedings of National Wetland Assessment Symposium. June 17-20, 1985. Portland,
               Maine. Association of State Wedand Managers, Inc., Berne, NY..

               Adamus, P.R., and L.T. Stockwell. 1983. A method for wetland functional assessment: Vol. I.
               Critical review and evaluation concepts. Washington, DC: Federal Highway Administration.
               Report No. FHWA-IP-82-23. 181 pp.

               Aminam, A.P., R.W. Franzen, and J.L. Johnson. 1985. Connecticut's wedand evaluation method
               designed for town officials and other nonspecialists. pp. 98-102, In: Proceedings of National
               Wedand Assessment Symposium. June 17-20, 1985. Portland, Maine. Association of State
               Wetland Managers, Inc., Berne, NY.

               Bedford, B.L., and E.M. Preston. 1988. Developing the scientific basis for assessing cumulative
               effects of wetland loss and degradation on landscape functions: status, perspectives, and
               prospects. Environmental Management 12(5): 751-771.

               Brooks, R.P. 1989. Monitoring wetlands. In: Wetlands Ecology and Conservation: Emphasis in
               Pennsylvania. Eds S.K. Majumdar, R.P. Brooks, F.J. Brenner, and R.W. Tiner, Jr. The
               Pennsylvania Academy of Science, Chapter 24, pp. 283-297.

               Fausch, K.D., J. Lyons, J.R. Karr, and P.L. Angermeier. 1990. Fish communities as indicators
               of environmental degradation. In: Biological indicators of stress in fish. American Fisheries
               Society Symposium 8, Bethesda, Maryland, USA, in press.

               Ford, J. 1989. The effects of chemical stress on aquatic species composition and community
               structure. pp. 99-144 In: S.A. Levin, M.A. Harwell, J.R. Kelly, and K.D. Kimball, eds.
               Ecotoxicology: problems and approaches. Springer-Verlag, New York, NY, USA.

               Gosselink, J.G., and L.C. Lee. 1986. Cumulative impact assessment principles. pp. 196-203, In:
               Proceedings of National Wetland Symposium: Mitigation of Impacts and Losses. October 8-10,
               1986. New Orleans, Louisiana. Association of State Wetland Managers, Inc., Berne, NY.

               Gray, J.S. 1989. Effects of environmental stress on species of rich assemblages. Biol. Jour.
               Linnean Soc. 37: 19-32.








                                                              3-6

              Hollands, G.G., and D.W. McGee. 1985. A method for assessing the functions of wetlands. pp.
              108-117, In: Proceedings of National Wetland Assessment Symposium. June 17-20, 1985.
              Portland, Maine. Association of State Wetland Managers, Inc., Berne, NY.

              Karr, J.R. 1990. Bioassessment and non-point source pollution: an overview. pp. 4-1 - 4-18 In:
              Second National Symposium on Water Quality Assessment. Meeting summary. October 16-19,
              1989. Ford Collins, Colorado, USA. U.S. Environmental Protection Agency, Washington, DC.

              Karr, J.R. 1991. Biological integrity: a long-neglected aspect of water resource management.
              Ecological Applications l(l): 66-84. Ecological Society of America.

              Karr, J.R., K.D. Fausch, P.L. Angenneier, P.R. Yant, and I.J. Schlosser. 1986. Assessing
              biological integrity in running waters: a method and its rationale. Illinois Natural History Survey,
              Champaign, Illinois, Special Publication 5.

              Leibowitz, N.C., L. Squires, J.P. Baker, and others. 1991. Research plan for monitoring wetland
              ecosystems. Environmental Monitoring and Assessment Program. U.S. Environmental Protection
              Agency, Office of Research and Development, Washington, DC. EPA/600/3-91/010. 191 pp.

              Lonard, R.I. and E.J. Clairain, Jr. 1985. Identification of methodologies for the asse   ssment of
              wetland functions and values. pp. 66-72, In: Proceedings of National Wetland Assessment
              Symposium. June 17-20, 1985. Portland, Maine. Association of State Wetland Managers, Inc.,
              Berne, NY

              McColligan, E.T., Jr. 1985. The New Jersey computer program for the wetland functional
              assessment method: an environmental management perspective. pp. 87-92, In: Proceedings of
              National Wetland Assessment Symposium. June 17-20, 1985. Portland, Maine. Association of
              State Wetland Managers, Inc., Berne, NY

              National Academy of Sciences. 1986. Ecological knowledge and environmental problem solving:
              concepts and case studies. National Academy Press, Washington, DC, USA. 388 pp.

              O'Brien, A.L. 1988. Evaluating the cumulative effects of alteration on New England wetlands.
              Environmental Management 12: 627-636.

              Pontasch, K.W., B.R. Niederlehner, and J. Cairns, Jr. 1989. Comparisons of single-species,
              microcosm, and field responses to a complex effluent. Environmental Toxicology and Chemistry
              8: 521-532.


              Preston, E.M., and B.L. Bedford. 1988. Evaluating cumulative effects on wetland functions: a
              conceptual overview and generic framework. Environmental Management 12(5): 565-583.

              Rapport, D.J. 1989. Ecosystem health. Persp. Biol. and Med. 33: 120-132.









                                                           3-7

              Schaeffer, D.J., E.E. Herricks, and H.W. Kerster. 1988. Ecosystem health: I. Measuring
              ecosystem health. Environmental Management 12(4): 445-455.

              Schindler, D.W. 1987. Detecting ecosystem responses to anthropogenic stress. Canadian Journal
              of Fisheries and Aquatic Sciences 44: 6-25.

              Siegel, D.I. 1988. Evaluating cumulative effects of disturbance on the hydrologic function of
              bogs, fens, and mires. Environmental Management 12: 621-626.

              Smith, D.L. 1984. Method for wetland functional assessment. Transportation Research Record
              969, Transportation Research Board-National Research Council, Washington, DC.

              Taub, F.B. 1987. Indicators of change in natural and human-impacted ecosystems: status. pp.
              115-144, In: S. Draggan, J.J. Cohrssen, and R.E. Morrison, eds. Preserving ecological systems:
              the agenda for long-term research and development. Praeger, New York, NY, USA.

              Wencek, M.D. 1985. Application of the Golet system in assessing wildlife and habitat functions
              in Rhode Island. pp. 239-243, In: Proceedings of National Wetland Assessment Symposium. June
              17-20, 1985. Portland, Maine. Association of State Wetland Managers, Inc., Berne, NY.

              Winchester, B.H. 1985. WET: a wetland evaluation technique for southeastern coastal plain
              wetlands. pp. 119-121, In: Proceedings of National Wedand Assessment Symposium. June 17-20,
              1985. Portland, Maine. Association of State Wetland Managers, Inc., Berne, NY.

              Winter, T.C. 1988. A conceptual framework for assessing cumulative impacts on the hydrology
              of non-tidal wetlands. Environmental Management 12: 605-620.










                                          4 - WETLAND EXTENT AND TYPE



              4.1.   Introduction

              With the rapid disappearance of North American wetlands, there is a great need for long-term
              data documenting changes in wetland ecosystems. A knowledge of wetland dynamics should be
              an integral part of wetland management, but detailed studies of this topic are scarce in the
              scientific literature (Golet and Parkhurst, 1981). Many functional attributes of wetlands are
              related directly to their size (areal extent) and type. These two parameters are important
              indicators of wetland sustainability, defined as its resistance to changes in structure and function
              over long periods of time (Leibowitz et al., 1991). Documenting losses and gains in wetland area
              and monitoring changes in wetland types are critical for understanding regional trends and
              identifying the areas that need immediate attention.


              4.2.    Approach

              Remote sensing is ideal for mapping wetlands as large areas can be surveyed relatively quickly
              without having to conduct extensive and expensive ground work in a difficult environment. The
              first step in surveying wetlands is to define them. Classification systems have generally been
              based on water conditions -- permanently flooded, temporarily flooded, saturated soils, etc. -- and
              vegetation cover -- herbaceous, shrub or tree -- (Cowardin et al., 1979). Once the definition and
              classification system have been determined, the most suitable remote sensing techniques and
              equipment can be chosen to meet the objectives of wetlands investigations. Although aerial
              photography remains the primary data source in many wetlands mapping and inventory programs,
              new remote sensors are likel@ to contribute to this effort in the future.

              4.2.1 Aerial photography

              Inventories of wetlands using primarily color infrared aerial photography began in the late 1960s
              as legislation protecting coastal wetlands was being formulated (Daiber, 1986). The aerial
              photography served primarily as a means of delineating plant communities and as a template
              from which the areal extent of each community could be determined. All investigators reported
              better tonal contrast for species discrimination with color infrared (CIR) photography. Best
              results could be obtained using a combination of color and CIR as color was better for detecting
              submerged vegetation, often an important ecological component of freshwater marshes.

              The usual procedure in wetlands inventories is to compare aerial photography from different
              years and to construct maps that reflect the changes that have occurred (Hardisky and Klemas,
              1983). Wetlands are identified on the photographs and classified primarily on the basis of water








                                                              4-2

              regime, soil, and the life form of the dominant vegetation -- such as deep marsh, shallow marsh,
              meadow, shrub swamp etc'. -- (Golet and Parkhurst, 1981). There is an overlap of 60% between
              consecutive photographs within a flightline to insure complete coverage and allow photography
              to be viewed in three dimensions using stereoscopic equipment (Hardisky and Klemas, 1983).
              Such stereo viewing is useful in identification of some plant species. Photointerpretation is
              generally based on tone, hue, texture, pattern and canopy height (Seher and Tueller, 1973;
              Howland, 1980). These parameters produce the most information on the color infrared imagery.
              Infrared wavelengths are superior for wetlands discrimination due to the relative high infrared
              reflectance of vegetation and the very low infrared reflectance of -water and water-logged soils.
              After photointerpretation is completed, class and subclass boundaries for all wetlands are
              transferred to mylar topographic base maps, the size of each wedand and its component classes
              and subclasses is measured with a polar planimeter, and the amount of change calculated (Golet
              and Parkhurst, 1981). Small or subtle changes can be detected with the aid of the zoom transfer
              scope (Hardisky and Klemas, 1983). Variability in the characterization of wetlands by aerial
              photography can occur during the interpretation of the imagery by different observers. A
              standardized method of classification and ground control are required to produce accurate maps.

              Interpretation of aerial imagery can be used in conjunction with a computer-based geographic
              information system (GIS). Sasser et al. (1986) developed a computerized data base to determine
              the amount, rate, and location of Louisiana's Barataria Basin marshes change over time. Data
              sets were assembled into a controlled data base, classified by the size and the spatial distribution
              (percentage) of water bodies within the marsh. Marshes were classified according to percentage
              of water. Output of the GIS was numerical and graphic (computer-generated gray maps and
              color images). The sequence and spatial patterns of water body development (indicative of marsh
              deterioration) were monitored to determine the phases and causal mechanisms of marsh loss.

              4.2.2. Satellite imagery

              Multispectral scanners have two main advantages over aerial photography when a high degree
              of resolution is not required; data can be obtained in a digital form and can be automatically
              classified by computer, thus speeding the interpretation process considerably and allowing direct
              quantitative treatment of the radiance data; large areas can be rapidly covered where photographic
              mapping is either cost prohibitive or too slow, The disadvantages are that the equipment is very
              expensive and resolution and accuracy of boundaries are much lower than with photography.
              Automatic classification techniques are based on either reflectance values or pattern recognition.
              The best discrimination of marsh vegetation occurs in the infrared bands.

              A statewide computerized Landsat land cover and classification system has been developed in
              Florida in 1985 (Groce and Craig-Ayotte) to monitor changes and generate numeric acreage data,
              as well as color graphic displays of various wedand systems and other land use features.
              Computer tapes are obtained from the NASA for the areas needed and the time period desired.
              The computer provides 22 different classifications of land cover, including seven different classes
              of wetlands. It also gives acres of each classification, on a county by county basis. By obtaining
              the data for different time periods, it is possible to locate each wetland class and to detennine






 6
                                                              4-3

               if there is a net gain or loss of each classification. This Landsat system has two major
               advantages: firstly, it provides its users with accurate, up-to-date information on the status and
               trends of land use within the state; secondly, land use planners have much more complete data
               to use in their planning processes.

               As a result of the coarse resolution, multispectral (MSS) data have been generally supplemented
               with high-resolution aerial photography. Usually, the aerial photography provides a familiar data
               source for plant community identification, and the MSS data serve as a template for extrapolation
               to large areas. The combination of MSS data and aerial photography has been used to develop
               vegetation maps of forested wetlands, such as the Great Dismal Swamp (Garret and Carter, 1977;
               Carter et al., 1977). The upgraded multispectral scanner, the Thematic Mapper (TM), has a better
               ground resolution (30 m) and has three more spectral bands than the original MSS. This has
               improved species discrimination capabilities in wetland systems. The spectral and spatial
               improvements of the TM are likely to enhance our ability to discriminate species as well as to
               estimate percentage cover in wetlands. Digitized data from Landsat or SPOT constitute important
               data bases that may be used to provide temporal verification of selected wetland sites and
               updated information on land use.


               4.3.   Important Parameters in Remote Sensing of Wetlands

               4.3.1 Scale


               An important parameter in wetland investigations by remote sensing is image scale. Scale is
               generally related to the accuracy in determining the wetland boundary and the minimum size
               mapping unit obtained. Regulatory maps require as precise a location of wetland boundaries as
               possible, so large scale images are usually desirable. A high degree of accuracy is possible for
               tidal wetlands since they are generally large tracts of homogeneous vegetation communities that
               are closely related to tidal boundaries. Freshwater wetlands have proved to be more difficult to
               map. The majority of freshwater wetlands are relatively small, often less than an acre, with
               highly diverse, heterogeneous vegetation communities, making them difficult to discern and
               separate on all but low altitude images. A series of investigations conducted on the Georgia
               coast identified color infrared photography at scales ranging from 1:2,500 to 1:40,000 as the best
               photographic product for discrimination of salt marsh plant communities (Gallagher et al., 1972a,
               1972b; Reimold et al., 1972). Brackish tidal marshes required larger-scale photography --
               1:5,000 -- (Gallagher and Reimold, 1973). Larger-scale imagery in brackish or salt marshes
               provided more vegetation detail, yielding better discrimination.          However, the cost of
               photography rises with increases in scale. Small-scale (1:12,000 to 1:24,000) color infrared
               photography usually acquired in tandem with natural color photography became the standard for
               state wetland inventories (Daiber, 1986).








                                                            4-4

             4.3.2 Time of the year and water level

             The time of the year the imagery is taken is extremely important in investigating wetlands. The
             size of many wetland areas changes significantly with seasonal and annual variations in
             precipitation. More than one season is generally required unless the wetlands involved contain
             only emergent vegetation. In this case, imagery taken from late August through mid-October at
             the end of the growing season is sufficient. If the only objective is to delineate wetlands,
             regardless of vegetation cover, late winter-early spring imagery alone is sufficient. Wetlands are
             best delineated in this period as ground water is most likely to be at or above the surface. If
             wooded wetlands or a mixture of wedand types are to be surveyed, better accuracy can be
             achieved through multiseasonal analysis. Freshwater wetlands do not maintain the relatively
             constant boundaries of tidal wetlands from year to year or even from season to season;
             hydrologic characteristics and the resulting vegetation change through the year altering the
             appearance and reflective patterns of these wetlands on the images. The extent of forested
             wetlands can be difficult to assess through a leaf-covered canopy; thus early spring photography
             is usually best (Leibowitz et al., 1991). Water level must be taken into consideration in the
             analyses; if water level is too high, or too low, then the comparative interpretations are
             compromised. Submerged vegetation appears emergent, mudflats are extensive (or absent), etc.
             (R.E. Turner, personal communication).


             4.4.    Interpretation of Wetland Changes

             Wetlands are dynamic ecosystems deriving much of their unique qualities from the fluctuating
             and oftentimes catastrophic environmental disturbances that mold them. These natural changes,
             however, are dwarfed by the potential manipulative power of humans, (Hardisky and Klemas,
             1983). Once comparative data are available, the factor(s) responsible for observed changes in
             wedand area and type will be identified whenever possible from the aerial photos and/or the
             satellite imagery or the site will be visited on the ground so that a positive determination as to
             the nature of the alteration could be made. Aerial photography is probably more reliable than
             satellite imagery for identifying causes of observed changes. Distinctions between major impacts,
             such as drought, manmade disturbances, and natural succession can be made (Golet and
             Parkhurst, 1991); but according to Keddy (1983), defining human-induced changes is difficult
             since there is a continuum of effects. Keddy gives examples to illustrate how numerous and
             subtle indirect human impacts could be. At one extreme, there are direct and obvious human-
             induced effects, such as drainage and infiffing. The observed changes are rapid. and major
             changes in species composition occur. There are the more subtle human-induced effects such as
             rising atmospheric C02 levels, acid rain, or low-level radioactive fallout. The resulting changes
             in wetlands might be slow. In between these extremes lie all other human impacts on wetlands.
             Keddy suggests that it is virtually impossible to recognize many human-induced effects, even
             with careful field observation (such as atmospheric C02 or acid rain). Studies that attempt to
             recognize human-induced effects on ecosystems must consider indirect human-induced
             effects, but the difficulty is to define which human-induced effects to include and to recognize
             them in the field.








                                                               4-5



               4.5.   Conclusions

               Remote sensing contributions to wetland management have essentially been in the areas of
               mapping and vegetal classification. A variety of remote sensing methods are available to locate
               and map wetlands. The choice of the method is dependent on project objectives plus time and
               monetary restraints. Infrared wavelengths appear to offer the greatest amount of information for
               discriminating and identifying wetlands whether interpretation is done manually or by computer.
               Large and medium scale color infrared photography is preferred where high resolution and
               accuracy are required. However, automatic techniques using digital data are constantly being
               improved and offer the advantages of speed and objectivity over human interpreters. Satellite
               imagery provides frequent coverage and is adequate for identifying landscape patterns of all kinds
               of ecosystems, but aerial photography can provide the resolution necessary to evaluate individual
               wetlands and the impacts that affect them. The advances in instrumentation and in computer
               analysis techniques will greatly enhance the types of data available for use by wetland managers.
               The imaging spectrometer is a major advance in remote sensing, since it allows to collect images
               in many narrow, contiguous spectral bands spanning the visible, near-infrared, and middle-
               infrared regions (Hardisky et al., 1986). As a result, a complete reflectance spectrum can be
               derived for each pixel in the image. Imaging spectrometry can reveal reflectance features unique
               to a particular vegetation type or environmental condition. Such features could either be hidden
               within a broad spectral band or, by residing outside of the wavelengths sensed, be missed entirely
               by MSS, TM or SPOT sensors (Hardisky et al.,1986). Our management effectiveness in the
               future will be directly related to our ability to collect and analyze data on a regional, ecosystem,
               and, eventually global scale.



               4.6.    References


               Carter, V., M.K. Garret, L. Shima, and P. Gammon. 1977. The Great Dismal Swamp:
               management of a hydrologic resource with the aid of remote sensing. Water Resour. Bull. 13:
               1-12.


               Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of wetlands and
               deep water habitats of the United States. U.S. Fish and Wildlife Service, Washington D.C.,
               FWS/OBS-79/31. 103 pp.

               Daiber, F.C. 1986. Conservation of tidal marshes. Van Nostrand Reinhold, 341p.

               Gallagher, J.L., R.J. Reimold, and D.E. Thompson. 1972a. Remote sensing and salt marsh
               productivity. 38th Ann. Meeting Proc., American Society of Photogrammetry, Washington, D.C.,
               pp. 338-348.








                                                            4-6

             Gallagher, J.L., R.J. Reimold, and D.E. Thompson. 1972b. A comparison of four remote sensing
             media for assessing salt marsh productivity. In: 8th International Symposium on Remote Sensing
             of Environment Proc., Ann Arbor, Mich., pp. 1287-1295.

             Gallagher, J.L., and R.J. Reimold. 1973. Tidal marsh plant distribution and productivity patterns
             from the sea to freshwater-A challenge in resolution and discrimination. In: 4th Biennial
             Workshop on Aerial Color Photography in the Plant Sciences, University of Maine, Orono, pp.
             165-183.


             Garret, M.K., and V. Carter. 1977. Contribution of remote sensing to habitat evaluation and
             management in a highly altered ecosystem. In: 42nd North American Wildlife and Natural
             Resources Conf. Trans., Wildlife Management Inst., Washington, D.C., pp. 56-65.

             Golet, F.C., and J.A. Parkhurst. 1981. Freshwater wetland dynamics in South Kingstown, Rhode
             Island, 1939-1972. Environ. Management, Vol.5, No.3, pp. 245-251.

             Groce, R., and A. Craig-Ayotte. 1985. Land use assessment in Florida using LandSat satellite
             data and a local government perspective of its use. pp. 154-155, In: National Wetland Assessment
             Symposium Proceedings, June 17-20, 1985, Portland, Maine. Association of State Wetland
             Managers Inc., Berne, NY

             Hardisky, M.A., M.F. Gross, and V. Klemas. 1986. Remote sensing of coastal wetlands.
             Bioscience 36: 453-460.


             Hardisky, M.A., and V. Klemas. 1983. Tidal wetlands natural and human-made changes from
             1973 to 1979 in Delaware: mapping techniques and results. Environ. management, Vol.7, No.4,
             pp. 339-344.

             Howland, W.C. 1980. Multispectral aerial photography for wetland vegetation mapping.
             Photogram. Eng. and Remote Sensing 46(l): 87-99.

             Keddy, P.A. 1983. Freshwater wetlands human-induced changes: indirect effects must also be
             considered. Environ. Management, Vol.7, No.4, pp. 299-302.

             Leibowitz, N.C., L. Squires, J.P. Baker, and others. 1991. Research plan for monitoring wetland
             ecosystems. Environmental Monitoring and Assessment Program. U.S. Environmental Protection
             Agency, Office of Research and Development, Washington, DC. EPA/600/3-91/010. 191 pp.

             Penny, M.E., and H.H. Gordon. 1975. Remote sensing of wetlands in Virginia. In: 10th
             International Symposium on Remote Sensing of Environment Proceedings, Ann Arbor, Mich.,
             pp. 495-503.












                                                            -4- /


              Reimold, R.J., J.L. Gallagher, and D.E. Thompson. 1972. Coastal mapping with remote sensors.
              In: Coast Mapping Symposium Proceedings, American Society of Photograrnmetry, Washington,
              D.C., pp. 99-112.

              Sasser, C.E., M.D. Dozier, J.G. Gosselink, and J.M. Hill. 1986. Spatial and temporal changes in
              Louisiana's Barataria Basin marshes, 1945-1980. Environ. Management, 10(5): 671-680.

              Seher, J.S., and P.T. Tueller. 1973. Color aerial photos for marshland. Photogramm. Eng. 39(5):
              489-499.


              Turner, R.E., Professor and Chair, Department of Oceanography and Coastal Sciences, Louisiana
              State University, Baton Rouge, LA 70803.













                                      5 - LANDSCAPE AND WETLAND PATTERNS



               5.1.    Introduction

               Landscape pattern or structure is now recognized as an important factor that affects ecological
               functions. The architectural qualities of structure and the arrangement or separateness of
               structural elements in the landscape particularly influence faunal diversity and abundance. The
               impact of separateness is related to dispersal ability of the species. The topic of habitat structure
               is poorly represented in historical reviews of ecology. Bell et al. (1991) discuss this concept and
               synthesize a number of ideas concerning habitat structure from different ecological perspectives.
               The importance of this parameter is well documented for birds (Roth, 1976; Keller et al., 1983;
               Robbins et al., 1989; Gosselink et al., 1990). The term "patchiness" has been commonly used
               to refer to habitat heterogeneity. It is the spatial variability within a habitat of any resource or
               feature critical to a taxon's existence in that habitat (Roth, 1976). Numerous indicators of spatial
               patterns have been suggested (see O'Neill et al., 1988 and Turner, 1989 for reviews), although
               relatively few have received rigorous empirical scrutiny. Some indicators describe landscape
               heterogeneity as a function of patch characteristics; others emphasize the arrangement of patches.
               In all instances, the choice of scale is critical to the measurement and interpretation of pattern
               indicators (Leibowitz et al., 1991).


               5.2.   Importance of Scale in Pattern Analysis

               Because landscapes are spatially heterogeneous areas, their structure, function and change are
               themselves scale-dependent (Turner, 1989).           The measurement of spatial pattern and
               heterogeneity is dependent upon the scale at which the measurements are made. Gardner et al.
               (1987) demonstrated that the number, sizes, and shapes of patches in a landscape were dependent
               on the linear dimension of the map. Observations of landscape function, such as the flow of
               organisms, also depend on scale. The scale at which humans perceive boundaries and patches
               in the landscape may have little relevance for numerous flows and fluxes (Turner, 1989). If we
               are interested in a particular organism, we are unlikely to discern the important elements of patch
               structure   or dynamics, unless we adopt an organism-centered view of the environment
               (Whittaker, 1975). We must possess sufficient information about the organism's behavior to
               understand its responses to structure, that is how it perceives and uses structure. But despite such
               data on behavioral traits, it is not always obvious exactly at what spatial scale a particular
               interaction should be judged (Bell et al., 1991). Finally, changes in landscape structure or
               function are scale-dependent. A dynamic landscape may exhibit a stable mosaic at one spatial
               scale but not at another (Turner, 1989).








                                                                5-2

              The scale at which studies are conducted may profoundly influence the conclusions; processes
              and parameters important at one scale may not be as important at another scale. Thus,
              conclusions regarding landscape patterns and processes must be drawn with an acute awareness
              of scale (Turner, 1989).

              5.3.   Quantifying Landscape Structure

              Landscape structure must be identified and quantified in meaningful ways in order to understand
              the interactions between landscape patterns and ecological processes. The spatial patterns
              observed in a landscape result from complex interactions between physical, biological, and social
              forces (Turner, 1989). Quantitative methods are required to compare different landscapes,
              identify significant changes through time, and relate landscape patterns to ecological function.
              Considerable progress in analyzing and interpreting changes in landscape structure has been made
              (for detailed methods and applications, see Turner and Garner, 1990; statistical approaches are
              reviewed in Turner et al., 1990).

              5.3.1 Important aspects of landscape structure

              Most landscapes are a mixture of natural and human-managed patches that vary in size, shape,
              and arrangement (Forman and Godron, 1981; Krummel et al., 1987; Turner and Ruscher, 1988).
              The size and distribution of patches in the landscape may be of particular importance for species
              that require habitat patches of a minimum size or specific arrangement (Turner, 1989), and thus
              are critical to the maintenance of biodiversity. Franklin and Forman (1987) analyzed the
              potential effects of changes in patch structure on the persistence of interior and edge species.
              Patch size and arrangement may also reflect environmental factors, such as topography or soil
              type (Sharpe et al., 1987). The amount of edge between different landscape elements is another
              parameter of critical importance for the movement of certain organisms or materials across
              boundaries (Hansen et al., 1988; McCoy et al., 1986; Turner and Bratton, 1987; Wiens et al.,
              1985); and the importance of edge habitat for various species is well know. Thus, it may be
              essential to monitor changes in edges when one quantifies spatial patterns and integrates pattern
              with function (Turner, 1989). It is important to distinguish between edges formed along expected
              environmental gradients, such as bands of vegetation along a moisture gradient, versus edges
              considered to be a negative result of human activities -- utility corridors, agricultural fields, etc. -
              - (Leibowitz et al., 1991).

              5.3.2 Spatial pattern indices

              Three complementary pattern indices -- dominance, contagion, and fractal dimension -- allow the
              discrimination of major landscape types, such as urban coastal landscapes, mountain forests, and
              agricultural areas (O'Neill et al., 1988). They appear to provide information at different scales,
              with the fractal dimension and dominance indices reflecting gross features of landscape pattern,
              and the contagion index reflecting the fine-scale attributes of the landscape. This type of scale
              sensitivity might be useful in selecting measures of pattern that can be easily monitored through
              time (e.g., by remote sensing) and related to different processes (Turner, 1989). It is important







  7
                                                                5-3

                to note that the value of any measurement is a function of how the landscape units were
                classified (e.g., land use categories vs. successional stages) and of the spatial scale of the analysis
                (e.g., grain and extent). "Grain" refers to the level of spatial or temporal resolution within a data
                set, and "extent" refers to the area of study (Turner, 1989).

                Dominance index. The dominance index measures the extent to which one or a few land uses
                dominate the landscape (O'Neill et al., 1988):

                                                                         m
                                                Dominance = ln(n) +         PXP)


                where Pi is the proportion of the landscape in land use i, and n is the total number of land use
                categories in a particular scene. The term, In(n), represents the maximum diversity with all land
                use types present in equal proportions. Large values of the index indicate that the landscape is
                dominated by one or a few land uses. Small values suggest that many land use types are present
                in approximately equal proportions.

                Contagion index. The contagion index is a measure of the probability of patches to be adjacent
                to each other (O'Neill et al., 1988):

                                                                      m    m
                                            Contagion      2nln(n) + E E PiJ ln(PV).
                                                                     i=1 J=1

                where Pij is the probability of a grid point of land use i being adjacent to a grid point of land
                use j. The term, 2n In(n), represents a maximum in which all adjacency probabilities are equal.
                High values of contagion are obtained when large, contiguous patches are found on the
                landscape; whereas, low values indicate that the landscape is fragmented into many small patches
                (O'Neill et al., 1988).

                Fractal dimension index. Fractal geometry (Mandelbrot 1983; Bell et al., 1991) has been
                introduced as a method to study shapes that are partially correlated over many scales. It has been
                used to compare simulated and actual landscapes (Gardner et al., 1987; Turner, 1987), and to
                compare the geometry of different landscapes (Krummel et al., 1987; Milne, 1988; O'Neill et al.,
                1988; Turner and Ruscher, 1988). The fractal. dimension is an index of the complexity of shapes
                on the landscape. It is estimated by regressing polygon area against perimeter for each patch on
                a digitized map. The fractal dimension is related to the slope of the regression, S, by the
                relationship (Lovejoy, 1982):


                                                      Fractal dimension = 2 S


                If the landscape is composed of simple geometric shapes like squares and rectangles, the fractal
                dimension will be small, approaching 1.0 (Krummel et al., 1987). If the landscape contains many
                patches with complex and convoluted shapes, the fractal dimension will be large. Krummel et
                al. (1987) suggest that human-influenced landscapes exhibit simpler patterns than natural ones,








                                                                 5-4

              as measured by the fractal. dimension. An increase or decrease in the fractal dimension through
              time indicates the degree to which human activities disturb and simplify the landscape patterns,
              regardless of the specific land uses (O'Neill et al., 1988).

              5.3.3 Keller method


              Keller (1985) developed a method for the quantification of edge and the spatial arrangement of
              habitat. His aim was to identify the characteristics of patches associated with particular groups
              of birds. A grid of hexagonal cells is superimposed over the imagery of the studied site (air
              photos or other remotely sensed imagery), and each cell is classified as to habitat component
              type. Then, the cells are numbered on a cartesian (X,Y) coordinate system so that each cell can
              be located in two-dimensional space in relation to every other cell (spatial arrangement). In order
              to apply this technique, it is important to:

                      1)      identify the species of interest;
                      2)      use a habitat classification appropriate to the taxonomic group of interest;
                      3)      choose a biologically meaningful grid-cell size (i.e., equivalent to the smallest
                              resolvable habitat component of interest). Then, we can abstract the remotely
                              sensed habitat information to the grid and store the data base on a computer for
                              later analyses. A series of algorithms that provide a variety of measures of
                              horizontal habitat structure is available. The collective name of this system is
                              SPADIST (Spatial Distribution) (Keller, 1979, 1980, 1985).


              5.4.    Relating Landscape Patterns and Ecological Processes

              Identifying the relationship between landscape pattern and ecological processes is a difficult goal
              for landscape research. The broad spatial-temporal scales involved make experimentation more
              challenging, and we may have to extrapolate the results obtained from small-scale experiments
              to broad-scales (Turner, 1989).

              5.4.1 Landscape heterogeneity and disturbance

              The spread of disturbance across a landscape is an important ecological process that is influenced
              by spatial heterogeneity (Risser et al., 1984; Romme, 1982; Turner 1987). Disturbance can be
              defined as "any relatively discrete event in time that disrupts ecosystem, community, or
              population structure and changes resources, substrate availability, or the physical environment"
              (Pickett and White, 1985). Disturbances operate in a heterogeneous manner in the landscape;
              gradients of frequency, severity, and type are often controlled by physical and vegetational
              features. The differential exposure to disturbance, in concert with previous history and edaphic
              conditions, leads to the vegetation mosaic observed in the landscape. The spatial spread of the
              disturbance may be enhanced or retarded by landscape heterogeneity, depending on its mode of
              propagation. If the disturbance'is likely to propagate within a community, high landscape
              heterogeneity should retard its spread. If the disturbance is likely to move between communities,








                                                                 5-5

                increased landscape heterogeneity should enhance it (Turner, 1989). The relationship between
                landscape pattern and disturbance regimes must be studied further, particularly in light of
                potential global climatic change (Turner, 1989).           Disturbances operate at many scales
                simultaneously, but their interactive effects are not well known, partly because we often tend to
                study single disturbances in small areas rather than multiple disturbances in whole landscapes.

                5.4.2 Movement and persistence of organisms

                Landscape connectivity may be quite important for species persistence. Modifications of habitat
                connectivity can have strong influences on species abundance and movement patterns. The size,
                shape, and diversity of patches also influence species abundance. Woodlot size was found to
                be the best single predictor of bird species richness in the Netherlands (Van Dorp and Opdam,
                1987). Theoretical approaches are being developed to identify scale-dependent patterns of
                resource utilization by organisms on a landscape (Turner, 1989). The interaction between
                dispersal processes and landscape pattern influences the temporal dynamics of populations.
                Results suggested that if an organism disperses along corridors, then the spatial relationships
                between habitat patches are important. If, however, the organism disperses large distances in
                random directions from patches, then the spatial arrangement of habitat patches will have less
                effect on population dynamics (Turner, 1989).

                5.4.3 Redistribution of matter and nutrients


                Few studies have addressed the influence that spatial pattern may have upon the flow of matter
                and nutrients, although there is increasing recognition that such an influence is important (Gosz,
                1986).



                5.5.    Conclusions and Research Needs


                Spatial pattern has been shown to influence many ecologically important processes. Therefore,
                the effects of pattern on process must be considered in future ecological studies, particularly at
                broad scales, and in resource management decisions. Habitat fragmentation may progress with
                little effect on a population until the critical pathways of connectivity are disrupted; then, a slight
                change near a critical threshold can have dramatic consequences for the persistence of the
                population. Methods for characterizing landscape structure and predicting changes are now
                available. Current research suggests that different landscape indexes may reflect processes
                operating at different scales. The broad-scale indexes of landscape structure may provide an
                appropriate metric for monitoring regional ecological changes. Such an application is of
                particular importance because changes in broad-scale patterns can be measured with remote
                sensing technology (Turner, 1989).

                Important questions remain about landscape patterns and their changes (Turner, 1989). What
                constitutes a significant change in landscape structure? Which measures best relate to ecological
                processes? How do the measurements of pattern relate to the scale of the underlying processes?








                                                           5-6

             Which measures of structure give the best indications of landscape change; that is can any serve
             as "early warning" signals? Answers to these and other questions are necessary for the
             development of broad-scale experiments and for the design of strategies to monitor landscape
             responses to global change.



             5.6.   References


             Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witiner. 1976. A land use and land cover
             classification system for use with remote sensor data. USDI, Geol. Surv. Prof Pap. 964.
             Washington, DC.

             Bell, S.S., E.D. McCoy, H.R. Mushinsky, eds. 1991. Habitat structure: the physical arrangement
             of objects in space. Chapman and Hall. 438 pp.

             Forman, R.T.T., M. Godron. 1981. Patches and structural components for a landscape ecology.
             Bioscience 31: 733-740.


             Franklin, J.F., R.T.T. Forman. 1987. Creating landscape patterns by forest cutting: ecological
             consequences and principles. Landscape Ecol. 1: 5-18.

             Gardner, R.H., B.T. Milne, M.G. Turner, R.V. O'Neill. 1987. Neutral models for the analysis of
             broad-scale landscape pattern. Landscape Ecol. 1: 19-28.

             Gosselink, J.G. and L.C. Lee. 1989. Cumulative impact assessment in bottomland hardwood
             forests. Wetlands 9: 1-174.


             Gosselink, J.G, G.P. Shaffer, L.C. Lee, D.M. Burdick, D.L. Childers, N.C. Leibowitz, S.C.
             Hamilton, R. Bournans, D. Cushman, S. Fields, M. Koch, and J.M. Visser. 1990. Landscape
             conservation in a forested wetland watershed: can we manage cumulative impacts? Bioscience
             40: 588-600.


             Gosz, J.R. 1986. Biogeochen-dstry research needs: observations from the ecosystem studies
             program of The National Science Foundation. Biogeochemistry 2: 101-112.

             Hansen, A.J., F. Di Castri, R.J. Naiman. 1988. Ecotones: what and why? In: A New Look at
             Ecotones, ed. F. Di Castri, A.J. Hansen, M. M. Holland. Biol. Int. Spec. Issue 17: 9-46.

             Keller, J.K., D. Heimbuch, and M. Richmond. 1979. Optimization of grid cell shape for analysis
             wildlife habitat. pp. 1419-1428 In: Proc. 13th Internat. Symp. on Remote Sensing of
             Environment, Vol.III. 23-27 April 1979, Environ. Res. Inst., Ann Arbor, Mich.









                                                            5-7

              Keller, J.K., D. Heimbuch, and M. Richmond. 1980. Optimization of grid cell shape for
              quantification of spatial arrangement. pp. 153-162, In: F. Shahrokhi and T. Paluden (eds). Remote
              sensing of earth resources. Vol.VIII. Earth Resources Conference, Tullahoma, Tennessee. 372 pp.

              Keller, J.K. and C. Smith. 1983. Birds in a patchwork landscape. The Living Bird Quarterly 2:
              20-23.


              Krummel, J.R., R.H. Gardner, G. Sugihara, R.V. O'Neill, P.R. Coleman. 1987. Landscape
              patterns in a disturbed environment Oikos 48: 321-324.

              Leibowitz, N.C., L. Squires, J.P. Baker, and others. 1991. Research plan for monitoring wetland
              ecosystems. Environmental Monitoring and Assessment Program. U.S. Environmental Proteciion
              Agency, Office of Research and Development, Washington, DC. EPA/600/3-91/010. 191 pp.

              Lovejoy, S. 1982. Area-perimeter relation for rain and cloud areas. Science 216: 185-187.


              Mandelbrot, B.B. 1983. The fractal geometry of nature. W. H. Freeman and Co. New York, NY.
              468 pp.

              McCoy, E.D., S.S. Bell, K. Walters. 1986. Identifying biotic boundaries along environmental
              gradients. Ecology 67: 749-759.

              Milne, B.T. 1988. Measuring the fractal dimension of landscapes. Appl. Math. Comput. 27:
              67-79.


              O'Neill, R.V., J.R. Krummel, R.H. Gardner, G. Sugihara, B. Jackson et al. 1988. Indices of
              landscape pattern. Landscape Ecol. 1: 153-162.

              Pickett, S.T.A., P.S. White, eds. 1985. The Ecology of Natural Disturbance and Patch Dynamics.
              Academic Press, Orlando, FL, 472 pp.

              Risser, P.G., J.R. Karr, R.T.T. Forman. 1984. Landscape ecology: directions and approaches.
              Special Publ. No.2. III. Nat. Hist. Surv., Champaign, III.

              Robbins, C.S., D.L. Dawson, and B.A. Dowell. 1989. Habitat area requirements of breeding forest
              birds of the middle Atlantic states. Wildl. Monogr. 103: 1-34.

              Romme, W.H. 1982. Fire and landscape diversity in subalpine forests of Yellowstone National
              Park. Ecol. Monogr. 52: 199-221.

              Roth, R.R. 1976. Spatial heterogeneity and bird species diversity. Ecology 57: 773-782.








                                                            5-8

              Shannon, C.E. and W. Weaver. 1962. The mathematical theory of communication. University of
              Illinois Press, Urbana, IL. 125 pp.

              Sharpe, D.M., G.R. Guntenspergen, C.P. Dunn, L.A. Leitner, F. Steams. 1987. Vegetation
              dynamics in a southern Wisconsin agricultural landscape. See reference Turner, 1987a, pp.
              139-158.


              Turner, M.G. 1987a. Landscape Heterogeneity and Disturbance. Soringer-Verlag, New York, NY.

              Turner, M.G. 1987b. Spatial simulation of landscape changes in Georgia: a comparison of 3
              transition models. Landscape Ecol. 1: 29-36.

              Turner, M.G. 1989. Landscape ecology: the effect of pattern on process. Annu. Rev. Ecol. Syst.
              20: 171-197.


              Turner, M.G. and S.P. Bratton. 1987. Fire, grazing and the landscape heterogeneity of a Georgia
              barrier island. See reference Turner, 1987a., pp. 85-101.

              Turner, M.G., R.H. Gardner, eds. 1990. Quantitative methods in landscape ecology. The analysis
              and interpretation of landscape heterogeneity. Springer-Verlag. 536 pp.

              Turner, M.G., C.L. Ruscher. 1988. Changes in the spatial patterns of land use in Georgia.
              Landscape Ecol. 1: 242-251.

              Turner, M.G., R.V. O'Neill, W. Conley. 1990. Pattern and scale: statistics for landscape ecology.
              See reference Turner and Gardner, 1990.

              Van Dorp, D., P.F.M. Opdam.1987. Effects of patch size, isolation and regional abundance on
              forest bird communities. Landscape Ecol. 1: 59-73.

              Whittaker, R.H. 1975. Communities and ecosystems. Macm illan, NY. 385 pp.

              Wiens, J.A., C.S. Crawford, J.R Gosz. 1985. Boundary dynamics: a conceptual framework for
              studying landscape ecosystems. Oikos 45: 421-427.











                                   6 - WETLAND BIOMASS AND PRODUCTIVITY



               6.1.   Introduction


               The most commonly used and accepted parameter for evaluating an ecosystem condition is
               biomass and/or net primary productivity of the emergent macrophytes. Both terms refer to the
               dry weight of plants (expressed as grams dry weight per square meter -- gdw/m2 -- for the
               biomass and usually as gdw/m2 per year for the productivity). Biomass represents the amount
               of fixed energy available to consumer organisms. Net aerial primary productivity (NAPP) is
               defined by Odum (1983) as the rate of storage of organic matter in aboveground plant tissues
               exceeding the respiratory use by the plants during the period of measurement. Changes in live
               and dead biomass over the growing season can be combined in a variety of ways to yield an
               estimate of NAPP.


               Rapid changes in biomass and primary production are the signs of illness or profound disease in
               the ecosystem (Schaeffer et al., 1988). Change can include both accumulation and loss of
               biomass. The accumulation of biomass reflects the inability of the ecosystem to reach a new
               stable state. Ecosystems can be viewed as systems which maintain their structural integrity by
               degrading energy while avoiding entropy. If the amount of energy available changes, the system
               changes. The onset of illness occurs when subtle shifts in production occur; profound disease
               is indicated when energy is lost from the ecosystem in an uncontrollable manner (Schaeffer et
               al., 1988).

               The high productivity of wetlands makes them an important part of local nutrient cycles, such
               that biogeochemical cycling in wetlands is intricately linked to vegetation biomass production.
               (Gross et al., 1990). In recent years, the role of soil microorganisms in wetland nutrient cycles
               has been an extensively researched topic. Soil microbes feed on senescing roots and their
               exudates, producing reduced gases, such as hydrogen sulfide and methane, that are important in
               biogeochemical cycles. The amount of plant biomass can serve as an indicator of potential
               production of these microbially-generated gases. Methane, is involved in the greenhouse effect,
               the unnatural warming of the earth's atmosphere, caused by the buildup of certain gases.


               6.2.   Traditional Biomass/Productivity Assessment Techniques

               6.2.1 Destructive harvesting technique

               The traditional method of measuring aboveground biomass is to clip the plants at the soil surface,
               sort the materials into live and dead components and dry them in an oven, to obtain a constant
               dry weight. This method is both destructive and time consurning. Determining belowground








                                                              6-2

              biomass (root) by traditional techniques is even more tedious and damaging. A stainless steel
              or plastic tube is used to extract a cylinder of the soil; the soil is washed away and the live and
              dead roots sorted before the materials are dried in an oven and weighed. Each time a core is
              removed, a hole is left in the marsh.

              Because of the severe damage to vegetation and soil, these techniques cannot be repeated in one
              area longer than a year, making year-to-year biomass comparisons for the same site impossible.
              Such comparisons are needed in order to detect changes and determine the sensitivity of wetlands
              to phenomena such as pollution, drought or manmade disturbances (e.g. highway construction).

              6.2.2 Tagging technique

              Another technique of monitoring plant production is to tag individual stalks of marsh plants to
              determine changes in live and dead tissues. Each site is visited periodically and each live and
              dead culm is measured for height, and the number of live and dead leaves counted. The
              differences in the measurements between successive intervals are analyzed to detern-iine leaf
              production, senescence and abcission which approximates the rate of tissue transition from the
              live to the dead component. Recruitment is assessed by counting and measuring all new sprouts
              too short to be tagged. Culms which died during a sampling period are used as an estimate of
              mortality. This method estimates live culm production in the form of culm mortality as it passes
              to the dead component. A reasonable estimation of net aerial primary productivity (NAPP) can
              be determined by deriving a simple model equating live culm height and dry weight biomass, and
              estimating leaf abcission, culm mortality and a mean weight for dead leaves. For more details
              on the destructive and tagging procedures, refer to Hardisky 1980.

              The frequent inaccessibility of wetlands makes biomass assessment by traditional harvesting or
              tagging techniques difficult. In addition, these techniques are tedious and very labor-intensive,
              making them impractical for studying large areas.


              6.3.    Remote Sensing Technique

              Remote sensing is a rapid and non-destructive technique that allows repetitive studies over time.
              One of the instruments commonly used, a hand-held radiometer, is very easy to work with and
              saves a lot of time compared to a typical harvest study. With this more efficient method, more
              stations can be sampled in less time. Although it is useful for assessing biomass over small
              areas, measurements for entire marshes are more practical if satellite imagery is used.

              6.3.1 Spectral properties of plants

              In order to understand the principles of remote sensing techniques used to monitor wetland
              health, let us examine some spectral properties of plants. Early reports describing the interaction
              of leaf tissue with light indicated that changes in the spectral quality of reflected electromagnetic
              radiation were directly related to the quantity of leaf tissue and pigment concentrations (Allen







 8

                                                               6-3

               and Richardson 1968, Colwell 1974, Gausman 1974). As green aboveground biomass increased,
               the most significant of the spectral changes were a decrease in red radiation resulting from strong
               absorption by the chlorophylls, and an increase in near infrared radiation resulting from intra- and
               interleaf scattering. Although these findings were made in non-wetland environments, wetland
               plants show the same patterns (Bartlett 1976, 1979, Bartlett and Klemas 1981, Drake 1976,
               Jensen 1980).

               6.3.2 Reflectance and aboveground biomass

               Early work by Bartlett (1976, 1979) determined green biomass of wetland grasses to be strongly
               correlated with the near infrared/ red reflectance ratio.        Other investigators found good
               correlations between green biomass and the spectral reflectance of different marsh shrub
               communities (Hardisky et al., 1986). In most cases, the combination of red and near infrared
               radiance provided the best correlation with canopy biomass.

               Several years ago, a simple linear regression model equating spectral reflectance to biomass was
               formulated for Delaware Spartina alterniflora, one of the most common salt marsh plants in
               eastern North America (Hardisky 1982, 1984). Spectral reflectance measurements were made
               in selected portions of the marsh using a hand-held radiometer that gathered data in three wave-
               bands, spectrally configured to simulate bands 3, 4, and 5 of the Landsat Thematic Mapper: a red
               band (630-690 nm, TM3) sensitive to chlorophyll concentration, a near infrared (NIR) band
               (760-900 nm, TM4), sensitive to plant tissue structure, and a middle infrared band (1550-1750
               nm, TM5) sensitive to water absorption.

               The raw radiance data were transformed and expressed as a normalized difference of two bands
               as follows:


                                                VI = [NIR - red]     [NIR + red]

                                          11 = [NIR - middle IR]    [NIR + middle IR]

               where VI is the Vegetation Index and 11 the Infrared Index. Index values were preferred to raw
               radiance data because the normalization procedure tended to compensate for both short- and long-
               term changes in solar irradiance and atmospheric conditions (Tucker et al., 1981). Both indices,
               VI and 11, correlated strongly with the changes in biomass (Hardisky et al., 1983; Hardisky et al.,
               1986). Measurements of biomass were combined over a growing season to yield an estimate of
               NAPP. The NAPP estimates were generally within ten percent of harvest estimates (Hardisky
               et al., 1984).

               Biomass evaluation has also been attempted with satellite imagery (Gross et al., 1987). The
               problem of atmospheric effects on the satellite-measured radiance data was solved by converting
               the satellite data to the equivalent ground-measurement reflectance. This was done using
               equations relating the reflectance of certain large, homogeneous sites measured from the ground
               at the time of the satellite overpass, to their satellite-measured radiance. The satellite-derived








                                                             6-4

              estimates were found to be within 13 percent of ground-based biomass estimates. 'Me nature of
              the relationship linking VI and the aerial biomass was consistent from year to year and between
              marshes, although there was a difference between northern and southern marshes (Gross et al.,
              1990).

              6.3.3 Belowground biomass estimation

              Light does not penetrate soil, making it impossible to measure root biomass directly by optical
              remote sensing. However, Gross et al. (1990) report a strong positive relationship (r' = 0.86)
              between the natural logarithm of live aboveground biomass and the natural logarithm of live
              belowground biomass of S. alterniflora ("short" plants only). Therefore, belowground biomass
              can be indirectly measured using a non-destructive method.

              Another promising technique for belowground biomass estimation is the use of ground-
              penetrating radar (GPR), but it is still under evaluation. Traditionally, GPR has been used to
              locate things such as archaeological sites, toxic waste drums, and divisions between contrasting
              soil types like sand and clay. A radar antenna is dragged along the surface of the marsh,
              emitting electromagnetic waves. These waves penetrate the soil, and are reflected back by objects
              in the soil. The return signal is recorded and printed in graph form (Gross, 1989). Researchers
              hope the. characteristics of the return signal will reveal something about root material.

              6.3.4 Factors influencing spectral estimates

              One of the factors that influence the spectral radiance of the marsh is the solar angle which can
              easily be corrected (Hardisky et al., 1986). Two other factors are the quantity and orientation
              of dead biomass and the amount of soil reflectance. The presence of dead material tends to
              decrease the vegetation index VI. Except in marshes with very sparse canopy (<30% cover), soil
              reflectance is not usually a problem. Richardson and Wiegand (1977) have proposed a
              perpendicular vegetation index (PVI), which factors out the influence of soil reflectance. The
              infrared index is less attenuated by dead biomass and soil reflectance than the vegetation index.



              6.4.   Conclusions and Research Needs


              Remote sensing is considered an accurate and effective non-destructive biomass assessment
              technique in salt marshes despite its limitations: sampling can only be done on sunny days, for
              four hours, and only during a tidal stage when the marsh surface is not flooded (Hardisky et al.,
              1984). Hand-held radiometers have been extensively used to assess biomass and NAPP of small
              wetland tracts, but satellite imagery is more useful for sampling larger areas. The aerial biomass
              estimation technique is based on the use of simple regression models equating the green biomass
              with spectral radiance indices. Root biomass can then be estimated using equations linking
              aboveground and belowground biomass.








                                                               6-5

               Limited remote sensing work has been conducted in other types of wetlands such as brackish
               marshes and coastal mangrove systems (Hardisky et al., 1986).             Salt marshes are often
               characterized by large monospecific stands of vegetation. In contrast, the physionomy of
               brackish marshes is usually more varied because a particular plant community often comprises
               many species. Different plant morphologies thus coalesce to produce canopy architectures that
               reflect incident radiation differently than monospecific canopy (Hardisky et al., 1986). Hardisky
               and Klemas (1985) analyzed the effects of the three canopy types on the vegetation index. Since
               the quality of reflected radiation (expressed as a vegetation index) differs for each canopy
               architecture, accurate biomass predictions must rely on separate models describing each type.
               Studies by Hardisky (1984) suggested that biomass could indeed be predicted for communities
               of one canopy type using a single model. The work conducted by Hardisky et al. (1986) in the
               black mangrove, Avicennia germinans, in Costa Rica, describes a positive relationship (r = 0.79)
               between the TM vegetation index and live leaf biomass. The more ubiquitous taller mangrove
               forms will require extensive ground comparisons before an operational biomass estimation
               procedure can be developed (Hardisky et al., 1986).



               6.5.   References


               Allen, W.A., and A.J. Richardson. 1968. Interaction of light with a plant canopy. J. Opt. Soc.
               Am. 58: 1023-1028.


               Bartlett, D.S. 1976. Variability of wetland reflectance and its effect on automatic categorization
               of satellite imagery. M.S. Thesis, University of Delaware, Newark. 108 pp.

               Bartlett, D.S. 1979. Spectral reflectance of tidal wetland plant canopies and implications for
               remote sensing. Ph.D. Dissertation, University of Delaware, Newark. 239 pp.

               Bartlett, D.S., and V. Klemas. 1981. In situ spectral reflectance studies of tidal wetland grasses.
               Photogramm. Eng. Remote Sens. 47: 1695-1703.

               Colwell, J.E. 1974. Vegetation canopy reflectance. Remote Sens. Environ. 3: 175-183.

               Drake, B.G. 1976. Seasonal changes in reflectance and standing crop biomass in three salt marsh
               communities. Plant Physiol. 58: 696-699.

               Gausman, H.W. 1974. Leaf reflectance of near-infrared. Photogramm. Eng. Remote Sens. 40:
               183-191.


               Gross, M.F. 1989. CMS researchers use satellites to measure the health of wetlands. At Sea,
               College of Marine Studies Newsletter, vol.9, no.3, University of Delaware. pp 1-2.








                                                          6-6

            Gross, M.F., M.A. Hardisky, V. Klemas, and P.L. Wolf. 1987. Quantification of biomass of the
            marsh grass Spartina alterniflora Loisel using Landsat Thematic Mapper imagery. Photogramm.
             Eng. Remote Sens. 53: 1577-1583.

            Gross, M.F., V. Klemas, and M.A. Hardisky. 1990. Long-term remote monitoring of salt marsh
            biomass. In: Proceedings, SPIE's 1990 technical symposium on Optical Eng. and Photonics in
            Aerospace Sensing, Earth Observing Systems, April 16-20, 1990, Orlando, Fl. 12 pp.

            Hardisky, M.A., 1980. A comparison of Spartina alterniflora primary production estimated by
            destructive and non-destructive techniques. Coastal Resources Division, Georgia Department of
            Natural Resources, 1200 Glynn ave., Brunswick, GA, pp 223-234.

            Hardisky, M.A., 1982. The relationship between spectral radiance and aboveground biomass of
            Spartina alterniflora Loisel. M.S. Thesis, University of Delaware, Newark. 112 pp.

            Hardisky, M.A., 1984. Remote sensing of aboveground biomass and annual net aerial primary
            productivity in tidal wetlands. Ph.D. Dissertation, University of Delaware, Newark. 252 pp.

            Hardisky, M.A., F.C. Daiber, C.T. Roman, and V. Klemas. 1984. Remote sensing of biomass
            and annual net aerial primary productivity of a salt marsh. Remote Sens. Environ. 16: 91-106.

            Hardisky, M.A., M.F. Gross, and V. Klemas. 1986. Remote sensing of coastal wetlands.
            Bioscience 36: 453-460.


            Hardisky, M.A., and V. Klemas. 1985. Remote sensing of coastal wetlands biomass using
            Thematic Mapper wavebands. Pages 251-269 In: Landsat-4 Early Results Symposium, vol.IV.
            NASA Goddard Space Flight Center, Greenbelt, MD.

            Hardisky, M.A., R.M. Smart, and V. Klemas. 1983. Seasonal spectral characteristics and
            aboveground biomass of the tidal marsh plant, Spartina alterniflora. Photogramm. Eng. Remote
            Sens. 49: 85-92.


            Jensen, A. 1980. Seasonal changes in near-infrared reflectance ratio and standing crop biomass
            in a salt marsh community dominated by Halimione portulacoides (L.) Aellen. New Phytol. 86:
            57-67.


            Odum, E.P., 1983. Basic ecology, Saunders, Philadelphia, pp. 98-120.

            Richardson, A.J., and C.L. Wiegand. 1977. Distinguishing vegetation from soil background
            information. Photogramm. Eng. Remote Sens. 43: 1541-1552.

            Schaeffer, D.J., E.E. Herricks, and H.W. Kerster. 1988. Ecosystem health: 1. Measuring
            ecosystem health. Environmental Management 12(4): 445-455. Springer-Verlag New York Inc.









                                                         6-7

             Tucker, C.J., B.N. Holben, J.H. Elgin, and J.E. McMurtrey- 1981. Remote sensing of total dry-
             matter accumulation in winter wheat. Remote Sens. Environ. 11: 171-189.











                                               7 * WETLAND VEGETATION



               7.1.   Introduction


               Wetlands are primarily described and classified according to their vegetation (Cowardin et al.,
               1979). The vegetation is intimately associated with the hydrologic and edaphic characteristics
               of the ecosystem (D'Avanzo, 1986), so it plays a crucial role in the various wetland processes
               (Nickerson et al., 1989). Both the community level and the organism level should be considered
               in the vegetation assessment.

               Vegetation community composition and abundance are important parameters in wetland health
               assessment because they reflect habitat suitability for wildlife and fisheries, ecological
               productivity, water chemistry, and landscape aesthetics (Brooks and Hughes, 1986). Plant
               communities and their characteristics have been extensively studied and sampling methods are
               well developed (e.g., Frederickson and Reid, 1988). Because of their immobility, plants are
               reliable indicators of certain types of stress, such as changes in hydrology and nutrient/pollutant
               loadings (Leibowitz and Brown, 1990). The composition and density of herbaceous communities
               and the forest understory will respond readily to short-term impacts; whereas, trees and shrubs
               are better indicators of long-term disturbance. Changes in the community composition or density
               should coincide with the coarse spatial pattern indicators (Leibowitz et al., 1991).

               At the organism level, eco-physiological indicators of plant stress have shown promise - in
               environmental impact assessment: proline concentration in the leaf, root alcohol dehydrogenase
               activity, activities of nitrogen metabolizing enzymes, and adenylate compounds (Mendelssohn and
               McKee, in press). These indicators respond, respectively, to salt stress, soil oxygen and
               nitrogen deficiencies, and to cumulative impacts. Physiological stress can be detected by remote
               sensing techniques, even before visible symptoms appear in the field.


               7.2.   Vegetation Assessment Techniques

               7.2.1 Community level

               Plant communities are commonly described by their floristics (species list), vertical structure (life
               form, layers), and horizontal arrangement (coverage, density) (Leibowitz et al., 1991).

               Sampling methods. The sampling methods outlined below are essentially those suggested by
               Leibowitz et al. (1991) for their wetland monitoring program.








                                                             7-2

              Permanent transects, are established in each major plant community of the wedand, allowing
              change detection over time. Aerial photography can be used to identify and delineate these major
              vegetation types. Transects are usually oriented parallel to certain ecological gradients within
              each community. The number and length of transects, will depend on the shape, orientation,
              hydrologic gradients, and interspersion of plant communities. Sampling points are marked on the
              ground with iron rods or other suitable markers and located on the map. Different kinds of
              sampling methods for study site selection are discussed in Shirnwell (1971): subjective samples,
              partially random samples, regularly spaced samples in a checkerboard arrangement, contiguous
              study sites in a belt sample arrangement etc. The size and number of sites to select can be based
              on the added species approach (Whittaker, 1975).

              For herbaceous vegetation, 1 m@ square plots or rectangular (2: 1) quadrants are the standard,
              although microplots also may be used (Federal Interagency Committee for Wetland Delineation,
              1989). Herbaceous communities must be sampled during the growing season. A multiseasonal
              analysis is recommended to account for non-persistent species (Brooks and Hughes, 1986). For
              shrubs, saplings, and vines, sampling should be done using a circular plot with a 9-meter
              (30-foot) radius centered on the transect as recommended in the Federal Wetland Delineation
              manual (Federal Interagency Committee for Wedand Delineation, 1989). This allows some
              standardization in the method throughout the United States. Coverage for each plant or
              multisternmed clump is estimated by measuring the diameter of the maximum extent of foliage
              and assuming a circular outline. Trees may be sampled by determining their basal area with a
              prism or angle gauge, within the 9-meter circular plot and beyond its perimeter (FICWD, 1989).
              The basal area factor appropriate for each region or forest type is used. Sampling for shrubs
              and trees may be done in the dormant season, but the growing season is preferred. Alternative
              sampling methods used include nested 2:1 rectangles for herbs (I m), shrubs (10 m), and trees
              (100 m2) (Brower and Zar, 1984; Brooks and Hughes, 1986); 20 x 50 cm microplots for herbs,
              a 2:1 rectangle of 50 rn@ for riparian shrubs, and 375 or 500 rn@ plot for trees in the western
              United States (Platts et al., 1987).

              The total area occupied by each vegetation type can be estimated by planimetry (Roman et al.,
              1984). Their dominant and associated plant species are identified, and percent cover of each
              species estimated. Plant identification is usually done in the field. For more consistency
              nationwide, it is recommended to follow the National Wetland Inventory plant nomenclature
              (Reed, 1988). Photographs can be taken to document the general appearance and extent of the
              vegetation.

              Other characteristics that could be useful in vegetation analysis include (Leibowitz et al., 1991):
              the occurrence of wetland indicator species, the ratio of wetland obligate species to facultative
              species, the occurrence of species considered intolerant or tolerant of wetland stressors of
              concern, the ratio of exotic species to native species, the occurrence of competitive species such
              as Phragmites australis, indicators of a disturbation in the marsh (Roman et al., 1984), vegetation
              height, and vegetation age.








                                                               7-3

               Measures of community composition. Five measures of community composition are commonly
               used to compare vegetation communities (Nickerson et al., 1989): number of species, and total
               stem count per plot, calculated directly from the raw data; plant diversity, species richness, and
               species evenness, that derive from the two first measures. Plant diversity is based on the
               heterogeneity index of Shannon and Weaver (1949) and is calculated using the following
               equation:


                                                    H


               where H is the general diversity, n, is the stem count per species per plot, and N the total stem
               count per plot. General diversity is influenced by both the total number of the species and their
               distribution (Krebs, 1978). To distinguish between the contributions of these two factors, species
               richness and species evenness are also calculated.

               Species richness, r, is computed as follows (Margalef, 1957):

                                                        r = (S - 1)lln(N)


               where S is the total number of species in a plot.

               Species evenness, e, (developed by Shannon and Weaver, 1949) is calculated as follows:

                                                           e == HI ln(S)


               Nickerson and Thibodeau (1984) and Thibodeau and Nickerson (1986) discuss in more detail the
               use of these standard ecological descriptive measures.

               7.2.2 Individual level


               Salinity, soil anaerobiosis, and nitrogen deficiency are environmental factors that affect plants
               vigor and productivity. Some indicators of these types of stress have been tested (Mendelssohn
               and McKee, in press): leaf proline concentration, root alcohol dehydrogenase activity, nitrogen
               metabolizing enzymes activities, and adenylate energy charge ratio and related adenine nucleotide
               concentrations. The first indicators are " stressor- specific", whereas the adenylate parameters are
               considered "integrative" that respond to the cumulative impact of all stressors to which vegetation
               is exposed. Stress-related physiological changes that occur in the plant result in changes in their
               reflectance properties; thus, remote sensing allows early detection of plant disturbance.

               Proline concentration: an indicator of salt stress. Salinity is a major factor that can affect
               species composition and productivity of coastal wetland vegetation (Mitsch and Gosselink, 1986).
               Many species of plants accumulate specific low molecular weight solutes in response to elevated
               salinities (Jeffries et al., 1979). Some species (e.g., salt marsh plants) will accumulate amino
               acids such as proline, while others will accumulate carbohydrates such as sugars and polyols








                                                              7-4

              (Flowers et al., 1977; Jeffries, 1981).        These compounds are considered to have an
              osmoregulatory function: they act as compatible solutes to reduce plant osmotic stress due to the
              increased salinity (Mendelssohn and McKee, in press). The concentration of proline in salt marsh
              plants increases significantly as the salt concentration increases above a threshold level, which
              is species specific (Cavalieri and Huang, 1979; Mendelssohn and McKee, 1987b). The greater
              the salt tolerance of the plant, the higher the threshold level at which there is a proline increase
              (Jain et al., 1987). Leaf proline concentration seems to be a good salt stress indicator for the
              plants that accumulate this compound. The proline analysis technique is described by Bates et
              al. (1973).

              Alcohol dehydromenase: an indicator of soil anaerobiosis. Wetland plants are often subjected
              to increased submergence due to sea level rise or to man-induced changes in hydrology.
              Excessive soil and plant submergence may result in root oxygen deficiencies, even in relatively
              flood-tolerant species, affecting their growth (Kozlowski, 1984). When soil oxygen is lacking,
              the enzyme alcohol dehydrogenase (ADH) becomes very active, catalyzing the reduction of
              acetaldehyde to ethanol. ADH activity has been used extensively in the literature as an index of
              waterlogging response in vegetation (Mendelssohn and McKee, 1987a; Smith et al., 1986;
              Mendelssohn and Burdick, 1987). However, this indicator is limited to those wetland conditions
              where sulfide and possibly other unidentified soil toxins do not accumulate to concentrations that
              inhibit this enzyme (Mendelssohn and McKee, in press).

              Nitrogen metabolizing enzymes: indicators of nitrozen deficiency. Nitrogen is a determining
              factor in the growth of salt marsh vegetation (Mendelssohn et al., 1982; Howes et al., 1986) and
              is often the primary growth-limiting factor for emergent wetland macrophytes in general
              (Mendelssohn and McKee, in press). Two enzymes, glutarnate dehydrogenase (GDH) and
              glutamate synthetase (GS), which catalyze the initial incorporation of ammonium into amino
              acids, have been used to measure nitrogen metabolism and quantify nitrogen deficiencies
              (Mendelssohn and McKee, in press). GS is considered the primary nitrogen assimilatory enzyme
              in plants (Rhodes et al., 1976; Miflin and Lea, 1976). Mendelssohn (1979) used GDH activity
              to show differences in nitrogen utilization among the height forms of Spartina alterniflora in a
              North Carolina marsh.


              Adenylate enerpry charge ratio: an inteprative stress indicator. The adenylate energy charge
              ratio (AEC, Bromsel and Pradet, 1968) is considered to provide an integrated measure of the
              physiological "health" of a particular community, population, or organism (Stewart and Guinn,
              1969; Jones, 1979; Wiebe and Bankcroft, 1975; Ivanovici, 1979; Mendelssohn and McKee, 1981;
              McKee and Mendelssohn, 1984; Sklar and McKee, 1984; Mendelssohn and McKee, 1985). An
              organism maintains a particular cellular ratio of ATP (adenosine triphosphate), which depends
              on the organism's physiological vigor and state of growth (Mendelssohn and McKee, in press).
              AEC ratio (ATP + 0.5 ADP)/(ATP + ADP + AMP) is a measure of the "energy rich" adenylate
              compounds present in the organism. Adenylate analyses are described by Mendelssohn and
              McKee(1981). AEC index is superior to growth measurements, because itis more sensitive to
              subtle environmental alterations and thus can indicate plant stress before symptoms are visually
              apparent. The adenylate parameters are presently being compared with other integrative stress








                                                                 7-5

                indicators (e.g., photosynthesis, leaf reflectance and fluorescence) to determine which are most
                efficient in quantifying plant stress (Mendelssohn and McKee, in press). Seasonal fluctuations
                of AEC and adenylate concentrations must be considered when evaluating the effect of a natural
                or man-induced stressor in the field (Mendelssohn and McKee, in press).

                Use of remote sensine for plant stress detection. When plants are subjected to stressful
                conditions, certain physiological changes occur, that can be detected by remote sensing, because
                of the consequent changes in plants' spectral reflectance. These physiological changes relate to
                chlorophyll density, cellular size and arrangement, and moisture content. The figure below shows
                the typical reflectance curve of a leaf. The low reflectance in the blue and red regions is due to
                strong absorbance of these wavelengths by chlorophyll. There is a slight peak in the green region,
                because plants do not absorb green, but reflect it. The high reflectance in the near infrared
                region is controlled by plant tissue structure and results from the scattering effects of the
                mesophyll (Boyer et al., 1988). Beyond 1200 nm, the decrease in infrared absorption is due to
                the absorption by water (Knipling, 1969).

                As a plant is exposed to various stressful conditions (disease, insects, moisture and mineral stress,
                etc.), two changes in reflectance are observed: 1) visible reflectance increases because there is
                less chlorophyll and/or the chlorophyll is less efficient in absorbing red and blue light; and 2)
                near infrared reflectance decreases because of a deterioration of the mesophyll cells (Campbell,
                1987). Reflectance changes can be detected before visible symptoms appear, and thus, are good
                indicators of plants stress (Knipling, 1969). Moisture stress is usually evidenced by an increased
                radiant emission from the plant and thus lighter tones in images (Weaver et al., 1968). Nitrogen
                deficiency will result in increased reflectivity of a single leaf, but in decreased reflectivity of the
                whole canopy, because of the decrease of leaf surface area per unit ground area (Hardisky, 1984).


                7.3.    Uncertainties Associated With the Assessment of Vegetation

                Ecosystem are dynamic and in a constant state of change. Consequently, data representing a
                single year reflect conditions only for that year and can be misleading if compared to any other
                year (Treshow and Allan, 1985). In addition to this temporal variation, considerable spatial
                variation exists in species composition and cover. Treshow and Allan (1985) discuss the
                uncertainties associated with vegetation assessment techniques.

                7.3.1 Sources of uncertainties


                Considerable spatial variation exists in the relative abundance of the dominant species, as well
                as vegetal cover, depending on the aspect, elevation, and latitude (Tueller et al., 1979). On a
                more local basis, the patchiness, that is the biotic heterogeneity of the plant cover, is governed
                by both biotic and abiotic disturbance, climatic and soil effects, the reproductive patterns of the
                vegetation, and effects such as grazing preferences, seed dispersal, and nutrient deposition
                (Pierneisel, 1951; Wells, 1960; West, 1969; Wiens, 1976).







9L








                                                              7-6

              The temporal variation in a plant community is even more critical than the spatial variation.
              The year the study is conducted may probably not be representative over time, because of
              climatic fluctuations. Data necessary to incorporate this variability are rarely obtained and little
              is known about the probability that baseline conditions established one year will remain the same
              in subsequent years, even in the absence of perturbation (Treshow and Allan, 1985).

              In addition to these sources of uncertainties, "noise" can be caused by chance distribution and
              establishment of individuals, by statistical limitations of finite samples, and from limitations in
              estimating the species data (Gauch, 1980 and 1982).

              7.3.2 Minimizing the uncertainties

              To compensate for the spatial heterogeneity, ample sampling is required. Sampling methods
              should portray a realistic and accurate picture of the community composition. Teshow and Allan
              (1985) suggest a random sampling plan coupled with a stratification based on proportional
              representation of the major components of the community.

              Yearly variation can be minimized by adequate sampling time frames of at least two years
              (Treshow and Allan, 1985). Another approach is considered in which the similarity of the plant
              cover among the study sites is examined over time. A similarity index (SI) compares the
              variation of each stand every year (Treshow and Allan, 1985). The minimum and maximum
              frequency of occurrence and percent cover of each species in each stand is compared using an
              equation modified from Ruzicka (1958):


                                  SI         minimum value           maximum value x 100


              The similarity is analyzed by a weighted pair group method (Sneath and Sokal, 1973). 'Me plots
              are arranged according to their highest similarities. The ordered plots are then graphed into
              dendograms.

              Similarity matrices serve to delineate plant communities that are floristically distinctive and to
              determine those which are most similar. Selection of stands with some initial similarity would
              allow the use of fewer replicated stands and reduce the uncertainty due to the differences among
              them. This could be done by placing the highest value on the sites that are closest to the center
              of the gradient. Although the use of similarity indexes reduces the yearly variation, it does not
              eliminate it (Treshow and Allan, 1985).


              7.4.   Some Observations About Recovery Rates of Wetlands

              The recovery rate of an ecosystem is a consequence of its productivity, regrowth ability of climax
              species, and harshness of the environment (reviewed in Thorhaug, 1980). Results of a ten-year
              analysis (Nickerson et al., 1989) demonstrate that once disturbed, different wetlands display
              distinctive recovery rates. While some wetland ecotypes recuperate within one year (e.g., cattail








                                                              7-7

              marsh), other areas (e.g., bogs) are much more sensitive to perturbation and consequently require
              longer recovery time periods. For adaptable protection and management of natural resources, it
              is critically important to address and, whenever possible, to capitalize on differential resilience
              and stability attributes of ecological systems (Holling 1973). It should be emphasized that the
              vegetation recovery may or may not be indicative of functional recovery. Unfortunately, the
              scientific understanding of the relationships between vegetation and wedand functions is limited
              and fragmented (Nickerson et al., 1989).



              7.5.    Conclusions and Research Needs


              Vegetation is a fundamental indicator of wetland health. It is the primary short-term parameter
              that can be measured in disturbed ecosystems. Specific and integrative indicators of plant stress
              have been successfully tested in a variety of environmental conditions; some of them may allow
              the early detection of disturbance. Remote sensing is also an efficient tool to detect early
              vegetative stress. These indicators should be used in conjunction with community level response
              indicators to provide a comprehensive evaluation of ecosystem health. A number of measures
              are available to describe vegetation communities, but further investigation is required to analyze
              the sensitivity of these measures for wetland health assessment. Additional research is also
              needed to evaluate the role of vegetation in wetland functions and the cause and effect
              relationships between them. Yet, there is no single analytical method that can be used to predict
              how wetland plants will respond to changing environmental conditions. Vegetation systems are
              dynamic and vary significantly in space and time. If comparisons, predictions, or estimates of
              potential impact are to be made, interpretations of data must consider yearly variations. Sampling
              methods should be sought and tested to minimize the natural background "noise" inherent in any
              ecological system. Variation can be reduced by sampling over a period of at least two years and
              by the use of similarity indexes.



              7.6. References


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              Bates, L.S., R.P. Waldren, and I.D. Teare. 1973. Rapid determination of free proline for water-
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                                                          7-8

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              Mendelssohn, I.A. 1979. Nitrogen metabolism in the height forms of Spartina alterniflora in
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  10
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               Illinois Press, Urbana, Illinois. 117 pp.

               Shimwell, D.W. 197 1. The description and classification of vegetation. University of Washington
               Press, Seattle. 322 pp.

               Sklar, F.H., and K.L. McKee. 1984. Adenylate energy charge (AEC) response to stress and
               extraction technique in the Louisiana swamp crayfish, Procambarus clarkii. Bull. Environ.
               Contam. Toxicol. 33: 584-591.


               Smith, A.M., C.M. Hylton, L. Koch, and H.W. Woolhouse. 1986. Alcohol dehydrogenase
               activity in the roots of marsh plants in naturally occurring waterlogged soils. Planta 168: 130-138.

               Sneath, P.H.A., and P.R. Sokal. 1973. Numerical taxonomy: the principals and practice. W.N.
               Freeman, San Francisco. 573 pp.

               Stewart, J.M. and G. Guinn. 1969. Chilling injury and changes in adenosine triphosphate of
               cotton seedlings. Plant Physiol. 44: 605-608.

               Thibodeau, F.R., and N.H. Nickerson. 1986. Impact of power utility rights-of-way on wooded
               wedand. Environmental Management 10: 809-814.

               Thorhaug, A. 1980. Recovery patterns of restored plant communities in the United States: High
               to low altitude, desert to marine. pp. 113-124. In: J.Cairns (ed.), The recovery process in
               damaged ecosystems. Ann Arbor Science, Ann Arbor, Michigan, 167pp.

               Treshow, M., and J. Allan. 1985. Uncertainties associated with the assessment of vegetation.
               Environmental Management 9: 471-478.








                                                          7-12

             Tueller, P.T., C.D. Beeson, R.J. Tausch, N.E. West, and K.H. Rea. 1979. Pinyon-juniper
             woodlands of the Great Basin: distribution, flora, vegetal cover. USDA Forest Service research
             paper INT-229. US Forest Service, Ogden, UT. 23 pp.

             Weaver, D.K., W.E. Butler, C.E. Olson, Jr. 1968. Observations on interpretation of vegetation
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             Wells, P.V. 1960. Physionomic integration of vegetation of the Pine Valley Mountains in
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             Wentworth, T.G., G.P. Johnson, and R.L. Kologiski. 1988. Water Resource Bulletin. 24(2):
             389-396.


             West, N.E. 1969. Tree patterns in central Oregon ponderosa pine forests. Am. Midl. Naturalist
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                                           8 9 WETLAND HABITAT QUALITY



                8.1.   Introduction


                Monitoring vertebrate communities can be an effective way to evaluate changing environments
                (Karr, 1987; Brooks et al., 1990; Root, 1990). Vertebrates are readily observable, and their
                presence represents an integration of the environmental features over relatively large areas
                (Brooks and Hughes, 1988). Thus, they are good candidates for response indicators of
                cumulative impacts (Leibowitz et al., 1991). Many investigators used birds as indicators of
                wildlife habitat quality (Roth, 1976; Adamus, 1983; Harris et al., 1983; Keller, 1985; Cable et
                al., 1989). Karr's (1981) "Index of Biotic Integrity" (IBI), based on fish assemblages, has been
                a widely applied method in North America to biologically assess aquatic habitats. Invertebrates
                have been also used as indicators of water quality and biological integrity of the ecosystem (Ohio
                EPA, 1988; Plafkin et al., 1989; Berkman et al., 1986; Lenat, 1988). Biological integrity is
                defined by Karr and Dudley (198 1) as the ability to support and maintain "a balanced, integrated,
                adaptive. community of organisms having species composition, diversity, and functional
                organization comparable to that of natural habitat of the region".

                Biological criteria are valuable for assessing the alterations caused by human activities, because
                they directly measure the condition of the resource at risk, and detect problems that other
                methods may miss or underestimate (EPA, 1990). They do not replace chemical and
                toxicological methods, but they do increase the probability that an assessment program will detect
                degradation due to anthropogenic influences (Karr, 1991).


                8.2.   Habitat Quality Assessment Techniques

                8.2.1 Habitat Evaluation Procedures (HEP)

                One widely used assessment method is the U.S. Fish and Wildlife Service Habitat Evaluation
                Procedures (USFWS 1976, 1980). This method is based on models that allow the assessment
                of habitat quality and quantity for selected wildlife species. The habitat is the target of
                assessment; the species themselves are not studied and need not to be present. The HEP
                methodology has been primarily developed for application to terrestrial and inland aquatic
                habitats. However, the concepts of habitat evaluation may be applicable to other systems
                (USFWS, 1980).

                The procedures provide a quantification of wildlife habitat that is based on two primary variables:
                the Habitat Suitability Index (HSI) and the total area of available habitat. Two types of wildlife
                habitat comparisons are combined: the relative value of different areas at the same point in time









                                                              8-2

              and the relative value of the same area at future points in time. The HEP is based on the
              assumption that habitat for selected wildlife species can be described by an HSI. This index
              value is multiplied by the area of available habitat to obtain Habitat Units which are used in the
              above comparisons.

              The first step generally involves delineating the study area, determining cover types (by remote
              sensing), and selecting evaluation species. Ile next step is to describe baseline conditions in
              terms of Habitat Units (HU's). The third step is the projection of future habitat conditions. So,
              a HEP analysis is structured around the calculation of HU's for each evaluation species in the
              study area.

              Total area of available habitat. It is calculated by summing the areas of all cover types likely
              to be used by the evaluation species. If the study area is not subdivided into different cover
              types, then the total available habitat area is identical to the entire study area.

              Habitat Suitability Index for available habitat. HSI values are obtained from models. We can
              use existing habitat models and convert them to HSI format, or develop an HSI model. The
              models must be in index form and linear:


                             HSI = study area habitat conditions / optimum habitat conditions

              The HSI ranges between 0.0 and 1.0. The ideal HSI model is an index with a proven, quantified,
              positive relationship to carrying capacity (i.e., units of biomass/unit area or units of biomass
              production/unit area).

              The HSI for available habitat is a function of the suitability of all cover types used by the
              species. Each cover type is-assigned a suitability index. When all habitat needs are met by one
              cover type, the HSI is equivalent to the cover type suitability index. But if the available habitat
              consists of two or more cover types, then methods are required to aggregate cover type indices
              into an HSI for available habitat. Interspersion between cover types is important when all habitat
              needs are not provided by each cover type. In this case, the model should aggregate cover type
              HSI's into one HSI value. If interspersion is not important, then a different aggregation method
              is required, that is a simple weighted mean of the suitability indices for the cover types (weighted
              by the area of each cover type).

              Habitat assessments usinp, habitat units. Habitat assessments involve measurement and
              description of habitat conditions for baseline (present) assessments and impact (future)
              assessments. Baseline assessments describe existing ecological conditions. They identify wildlife
              resource capabilities at one point in time. For each evaluation species, the number of HU's at
              one point in time is computed, i.e., the area of available habitat is multiplied by the mean HSI.
              The HU's are evaluated and compared directly, if the objective of the assessment is to compare
              existing conditions in two or more areas. Additional calculations are required to quantify habitat
              conditions at several points in time (impact assessments).









                                                              8-3

              For further details concerning the HEP methodology, refer to U.S. Fish and Wildlife Service
              (1976, 1980). The procedures may have been improved since then, because of certain
              inconsistencies (Keller, 1985).

              8.2.2 Methods using bird communities

              Birds are easily identified and occur in nearly every habitat type. Their responses to various
              types of stressors are fairly well known. They are sensitive to cumulative negative effects on the
              environment, that may be detected by the absence or reduction of certain specific species. The
              availability of historical data bases throughout the U.S., such as the Breeding Bird Survey
              (BBS), Breeding Bird Censuses (BBC), Christmas Bird Counts (CBQ, and state Breeding Bird
              Atlases (BBA), provides a benchmark for future monitoring (Leibowitz et al., 1991).

              The standard protocol for an avian census is a five-minute visual and auditory observation at
              selected vegetation plots during the early morning hours and ideally during the breeding season
              (Leibowitz et al., 1991). If the avian community is especially important, taped songs can be
              played and responses noted (Brooks and Hughes, 1988). The occurrence and relative abundance
              of bird species are recorded, and diversity indices (Krebs, 1989) and guild analyses (e.g., Short,
              1984; DeGraff et al., 1985; Brooks and Croonquist, 1990) can then be extracted. Additional
              sampling during spring or fall migrations and/or during winter residency may be conducted for
              subpopulations of particular concern (Leibowitz et al., 1991).

              The community composition and abundance of birds are subject to considerable spatial and
              temporal variability. However, long-term monitoring can help identify trends. It is not necessary
              to detect all the individuals present in a given area. By using only data on species presence or
              absence, we can ascertain the functional composition of the community. It is important to
              determine which avian taxa and guild combinations provide the most information about changes
              in the wetland resource (Leibowitz et al., 1991).

              Habitat Assessment Technique (HAT). Cable et al. (1989) present a wetland habitat assessment
              technique using birds as indicators of habitat quality, but in theory, any taxa could be used. The
              technique is a refinement of the Graber and Graber method (1976) and its modified versions
              (Holmes et al., 1986; Brack et al., 1987). The technique is quick, simple, inexpensive, and can
              be used to screen large numbers of wetlands. Measures of species diversity and rarity are used
              to assess the quality of the wetland. The presence of more species and uncommon species makes
              an area more valuable. Generally, a habitat of greater quality retains a greater number of species
              (Cable et al., 1989).

                      Faunal index. A faunal index is used as a basis for assessing the quality of the wedand.
              It is calculated by dividing a measure of species diversity and uniqueness (total species points)
              by a factor that accounts for wetland size (area factor).

                      Species index. To calculate the species index, wetland-dependent species are assigned
              base values (species points) on the state's (or other appropriate geographic area) breeding









                                                               8-4

               population. Wetland-dependent species are those that require wetlands for a major life function,
               such as reproduction or feeding. For example, a wetland-dependent species with a state breeding
               population of 5000 or more individuals may have an assigned base value of 10 points, whereas
               a species with 2500-4999 individuals may have an assigned value of 20 points. Extremely rare
               species (e.g., less than 50 individuals or any species on a state or federal endangered species list)
               are not assigned points. Instead, they are considered "red flag" species. Accidentals and exotic
               species are excluded from the calculations.

               A field survey is required to assess the avifauna composition of each area if reliable information
               for the site is not available. For each area, the species are enumerated and the species points
               totaled. The point totals are then averaged, thereby providing a spdcies index for the site. This
               index, by virtue of the species composition, reflects habitat quality. To adjust for habitat size
               when calculating the faunal index, the total species points are divided by an area factor.

                      Area factor. The importance of wetland size in the assessment is dictated by both the
               theory of island biogeography and a concern for the efficient use and allocation of resources
               (Cable et al., 1989). The theory of island biogeography addresses the relationship between island
               size and species richness (e.g., MacArthur and Wilson, 1967; Simberloff, 1974). Specifically,
               isolated pieces of habitat, such as islands, will not retain a high species complement over time.
               The extensive body of literature on this subject indicates that species richness is associated most
               with tract size. HAT incorporates the notion of "optimum size" and penalizes wetlands that are
               too small or excessively large. Optimum is based upon the area required to maintain species
               diversity, especially of rarer species. To 'Calculate the area factor, wetland habitat type and area
               size must first be determined from aerial photographs, maps, or field measurements.

                      Discussion. Besides focusing on wetland size, HAT is sensitive to other biogeographical
               factors. For example, the proximity of smaller tracts to one another and their configuration can
               affect the number of species they can support (Wilson and Willis, 1975). Small tracts connected
               by corridors hold species better than separate tracts. Tracts in a small cluster hold species better
               than tracts in a linear association (Cable et al., 1989). HAT requires minimal field time, since
               the only environmental variable of interest is wedand-dependent bird species. A field visit is not
               required at all, if site-specific bird records can be obtained. When field surveys are necessary,
               they are best conducted when the likelihood of migrants is low and breeding birds are vocal.

               For wetlands, HAT has a variety of uses that supplement existing habitat assessment techniques.
               The technique is fast and provides a comparison of sites as part of the output. The raw data or
               output of 'HAT can frequently be used as input to some of the more extensive evaluation
               techniques. HAT avifauna data may be helpful in selecting appropriate birds for a HEP analysis.
               The wetland values derived from HAT are directly related to species diversity, an important
               component of habitat value. The rarity of species and the importance of habitat patch size are
               taken into account in the assessment.


               Edge diversity and cover type diversity indices. Harris et al. (1983) tested two habitat indices
               -- edge diversity (ED) and cover type diversity (CTD) -- to predict bird species diversity (BSD)








                                                             8-5

              in freshwater coastal marshes. Edge and unit size are important aspects of habitat structural
              diversity. They are important in the habitat selection of marsh-nesting birds (Kiel, 1955; Willson,
              1966; Burt, 1970). BSD is directly associated with the structure of the vegetation (see Hilden,
              1965, for review) and is often used as an indicator of ecological quality (Harris et al., 1983).

              Harris et al. (1983) conducted breeding bird censuses and calculated BSD from the census data
              using the Shannon-Wiener information theory formula (Shannon and Weaver, 1949):

                                                             5
                                                       H         p, Inp,


              where, s is the number of categories (species) and pi is the proportion observed in the ith
              category.

              They mapped major cover types found along the transects, using aspect dominance at the time
              bird nest searching occurred. Estimates of cover type proportions were derived from the cover
              maps and area was measured with a polar planimeter. CTD was then calculated with the
              Shannon-Wiener formula.


              Cover maps were also,used to estimate edge diversity. ED was calculated as follows:
                                                              TE Va-(
                                                      ED =            7,)


              where, TE is the total linear edge between cover-cover and cover-water, and A is the area of the
              study plot.

              The relationship between CTD, ED, and BSD were determined through multiple regression
              analysis. They found a linear relationship between BSD and CTD and a curvilinear relationship
              between BSD and ED. This means that BSD is related not only to the diversity of vegetation
              types, but also to the arrangement of those cover types within the marsh, as measured by edge
              diversity.

              Harris et al. (1983) suggest that, for freshwater marshes, CTD and ED are useful measures of
              community change, and further, that they may be used as a "sign stimuli" (Hilden, 1965) by
              breeding birds in their selection among available marshes (Milligan, 1981). Habitat quality for
              marsh-nesting birds is thus equated to habitat diversity as measured by CTD and ED. BSD is
              an indicator of ecological quality because habitat heterogeneity is a function of the number of
              spatial niches in the habitat and it reflects the number of species populations present in a
              particular habitat (MacArthur and MacArthur, 1961; Anderson, 1979; Asherin et al., 1979;
              Milligan, 1981).

              Harris et al. (1983) believe that the quantitative relationships they described can provide a rapid
              evaluation technique for use in assessing ecological quality in marshes. The potential of aerial








                                                                8-6

               small-scale imagery coupled with computer-assisted digital analysis presents considerable promise
               in ecological evaluation.

               Roth's index of heterogeneity Pianka (1966), Murdoch et al. (1972), Blondel et al. (1973) and
               Wiens (1973, 1974) measured the horizontal component of habitat diversity with various
               techniques, and related it to the diversity of the animal community being studied. MacArthur et
               al. (1962) concluded that patchiness -- horizontal variability in the types of profiles in a habitat -
               - was the principle factor affecting bird species diversity, and that its effect was much more
               important than that of additional vegetation layers to support ground species, canopy species, etc.
               (Roth, 1976).     Since vertical measures, such as foliage height diversity (MacArthur and
               MacArthur, 1961) and percent vegetation cover (Karr and Roth, 1971), do not measure horizontal
               patchiness, a measure that combined both horizontal and vertical variability of the vegetation
               seemed desirable. Roth (1976) developed an index of heterogeneity that is a measure of
               patchiness on a scale important to birds.

                      Index of heterogeneity (D). In searching for a measure of heterogeneity of ecological
               relevance to birds, Roth (1976) considered habitat features which seemed to contribute
               significantly to habitat patchiness. A view of Texas brush-grasslands suggested the shrubs and
               their spacing as a logical choice. A regular distribution of shrubs or trees of uniform size and
               shape is the ultimate in homogeneity for a habitat (Roth, 1976). We could expect only a few
               bird species to be able to partition this habitat spatially because of the difficulty of discriminating
               specific patch types. A change in dispersion of the plants in either direction should create
               patches with shrubs and trees of different densities, and consequently, patches preferred and
               recognizable by several different bird species (Roth, 1976). Hence, more species can live in the
               area. Therefore, variation in spacing of vegetation dominants, coupled with variations in their
               height and shape and in associated plant cover, is considered a major cause of spatial
               heterogeneity.

               The heterogeneity index is derived from the point-centered quarter technique that involves
               measuring the distances from a central point to the nearest plant in each quadrant of a circle.
               Since these distances give information about dispersion and density, they should measure
               heterogeneity. Distances collected from a habitat with regularly distributed vegetation should
               have less variation than those collected from habitat with random or clumped distributions. Thus,
               the coefficient of variation (CV) of the distances from a homogeneous distribution should be
               lowest. The index of heterogeneity (D), or coefficient of variation of distance (CV) is calculated
               as follows:

                                                           D     I    SD
                                                                      x


               where, SD is the standard deviation for CV and x is the mean of the point-to-plant distances.
               Roth used D to emphasize the distance concept. Where D is used without subscript, it refers to
               the general index without specifying the plant life form being sampled. An appropriate sampling
               is added when discussing D calculated for a particular life form, e.g., D,, for point-to-shrub








                                                               8-7

               distances. Difficulty of identification of a common plant form which is important in all habitats
               may limit the usefulness of the index.

                      Correlation between D and BSD. The heterogeneity index was significantly correlated
               with BSD for several shrub and forest areas (Roth, 1976). D predicted BSD for a series of
               similar brushlands where other indices had failed (Roth, 1976). Roth gives a biological
               explanation of the positive correlation between vegetation patchiness and BSD. Individuals of
               a species select sites of certain vegetational configuration for breeding purposes that are more or
               less unique to that species. A species is likely to be present in a habitat where its patch type(s)
               is present, assuming that other resources in that habitat are adequate, and competitive pressure
               from other species is low. For additional species to be accommodated in the same habitat, there
               must be either an increase in the number of kinds of patches available to permit spatial
               segregation, or more spatial overlap by species in their utilization of the patches available. To
               find out which hypothesis is correct, we can examine changes in species overlap with increased
               species packing. Roth's explanation of heterogeneity and species packing agrees with results and
               suggestions of other work (MacArthur et al., 1962; Karr and Roth, 1971; Willson, 1974).

                       Conclusion. Earlier work has failed to find significant BSD/heterogeneity correlations
               especially when the common index of foliage height diversity (FHD) was used. FHD predicts
               BSD on a coarse scale because vertical layers of vegetation have only a coarse effect on BSD.
               Subtle differences in BSD among similar habitats are due to subtle differences in habitat structure
               to which FHD is insensitive. Thus, as Willson (1974) has noted, there is a need for an efficient,
               simple, and biologically reasonable heterogeneity index which will take both horizontal variation
               and layering into account. While D holds promise, it is only an indirect measure and it requires
               sampling of a common plant life form to be universally applicable (Roth, 1976).

               Use of remote sensing to assess waterfowl habitat guality Waterfowl habitat quality is a
               function of both water conditions and terrain characteristics of the surrounding wetland and
               upland cover types (Colwell et al., 1978). Habitat quality, according to Colwell et al., relates to
               the potential of the habitat to attract breeding waterfowl and furnish the requirements for survival
               and successful rearing of broods. They developed a model for the assessment of waterfowl
               habitat quality based on the various relationships between ponds and the surrounding terrain
               types. They also tried to estimate annual duck production by monitoring the number of breeding
               and brood ponds present in the habitat. Numerous investigators have indicated a relationship
               between amount and timing of water bodies and current year's duck production (Colwell et al,
               1978). Early-season ponds are of some importance in attracting the migrating ducks; in the
               absence of adequate ponds, some of the potential breeding population may overfly the area.
               Later-season ponds are also important, for breeding pairs and brood rearing.

                       The model. The model developed by Colwell et al. (1978) evaluates waterfowl habitat
               quality on the basis of water conditions and terrain characteristics. The specific water conditions
               are pond number, pond area, and pond size-class distribution. The literature suggests that 10 or
               more ponds per section are optimal for duck production, depending on the species of duck and
               the region. Both large and small ponds are important for good waterfowl habitat, although there








                                                              8-8

              is some disagreement about their relative importance (Colwell et al., 1978). They suggest that
              17.5 ha of water area is an optimal habitat. Once these factors were calculated, they integrated
              them into one single pond factor. The terrain characteristics they evaluated were the presence
              and spatial arrangement of certain terrain types (hay, grasses, pasture). They incorporated
              presence and spatial arrangement into a single factor represented by the amount of edge between
              desirable terrain types. The resulting model for waterfowl habitat quality combined pond and
              terrain factors, and generated ratings on a section-by-section basis.

              This habitat model was preliminary; no detailed analysis of the accuracy of the model ratings has
              been made. The available knowledge of the relative importance of habitat characteristics and
              their relationships with each other was limited at that time.

                     Use of remote sensing. Colwell et al. (1978) used Landsat data in their model for
              assessing waterfowl habitat quality. Pond and terrain characteristics were determined from
              multidate Landsat imagery and aerial photography. Remote sensing data allow monitoring
              changes in the habitat quality over time. With the advent of satellites with a better spatial
              resolution (e.g., SPOT), it is possible to improve the accuracy of the pond and terrain factors.

              8.2.3 Assessing fish communities

              Fish are not particularly suited for monitoring many wetland types because of the high variability
              of water depths; however, they should be included in the biological sampling of wetlands
              associated with deepwater habitats and rivers. Sampling methodologies vary with the habitat type
              and species expected (Brooks and Hughes, 1988). Seines suffice in shallow waters with little
              woody debris. Backpack electrofishers are often more effective where woody debris make
              seining impractical. In waters too deep for wading, or in those with dense macrophyte beds,
              experimental gill nets (multiple mesh sizes) and minnow traps or grabs should be used. All
              habitat types should be sampled. Brooks and Hughes (1988) recommend sampling in early spring
              (adults) and late summer Ouveniles). See Pardue and Huish (1981) for applications in wetlands.

              Many individual studies demonstrate correlations between degradation and some biological
              indicator (e.g., species richness, abundance of adindicator species, production/respiration ratio).
              Karr (1981) developed an index -- the Index of Biotic Integrity -- that integrated several of these
              indicators into a single index.      He used a set of attributes that measure fish community
              organization and structure. This index has now been widely applied in North America.

              Karr's Index of Biotic Integrity OEM). The IBI provides a broadly based and ecologically
              sound tool to evaluate biological conditions in streams (KarT, 1981). Each metric is compared
              to a regional reference site with little or no influence from human society (Fausch et al., 1984).
              For each metric, an index score of 5 is assigned if the study site deviates only slightly from the
              reference site, 3 if it deviates moderately, and I if it deviates strongly from the undisturbed
              conditions. Twelve attributes of a fish community are rated. The sum of those ratings (5, 3,
              or 1) provides an IBI value, that reflects the local biological integrity. IBI uses three groups of
              metrics: species richness and composition, trophic composition, and fish abundance and condition.








                                                                8-9

                       Species richness and composition metrics. Because richness varies as a function of
                region, stream size, elevation, and stream gradient, all sites must be compared to the expected
                richness from a similar undisturbed site (Karr, 1991). This group of metrics includes the total
                number of fish species, the number of benthic species, water-column species, long-lived species,
                intolerant species, and the percentage of tolerant species.

                       Trophic composition metrics. This group of three metrics -- respective proportions of
                omnivores, insectivorous cyprinids and top carnivores -- evaluates the trophic: composition of the
                fish community to assess the energy base and trophic dynamics of the resident biota. Instead of
                measuring the productivity at several trophic levels directly, Karr (1991) suggests to measure
                divergence from expectation as a way to assess energy flow through the community.

                       Fish abundance and condition metrics. They include the total number of individuals in
                the sample, the frequency of hybridization, and the proportion of individuals with disease, tumors,
                fin damage, and major skeletal anomalies that can be discovered by external examination.

                IBI scores can be used to 1) evaluate current conditions at a site; 2) determine trends over time
                in a given area; 3) compare sites; and 4) to some extent, identify the cause of local degradation
                (Karr et al., 1986). This index is quantitative, and there is no loss of information from
                constituent metrics when the total index is determined (Karr, 1991). IBI does not serve all needs
                of biological monitoring (Karr et al., 1986), and certainly cannot replace physical and chemical
                monitoring or toxicity testing (Karr, 1991).

                Successful use of IBI in a variety of contexts and in a diversity of geographic areas prove the
                utility of its concept (Karr et al., 1986; Miller et al., 1988; Steedman, 1988; Fausch et al., 1990).
                IBI can be modified to incorporate other aspects of the fish community, such as growth rates,
                population structure, etc. Adaptation of IBI to geographic regions outside the midwestern United
                States requires modification, deletion, or replacement of selected IBI metrics. Miller et al. (1988)
                provide the most up-to-date review of changes needed to reflect regional differences in biological
                communities and fish distribution. Efforts should be made to develop IBI-type indexes for use
                in other environments, such as wetlands, lakes, and terrestrial ecosystems. Brooks and Hughes
                (1988) have advanced the evaluation of wetlands.


                8.2.4 Assessment of other taxa


                Although initially developed for use with fish communities, the ecological foundation of IBI can
                be used to develop analogous indexes that apply to other taxa, or even to combine taxa into a
                more comprehensive assessment of biotic integrity (Karr, 1991).

                'Invertebrates. The framework of the fish IBI has been adopted by invertebrate biologists to
                develop assessment methods that use benthic: invertebrates. The most extensively tested,
                integrative effort is the Invertebrate Community Index (ICI) developed by Ohio EPA (1988). ICI
                is a 10-metric index that emphasizes structural attributes of invertebrate communities. Another
                method, The Rapid Bioassessment Protocol III (Plafkin et al., 1989) is similar to the ICI but has








                                                             8-10

              only eight metrics. The RPB III has been less extensively tested, and many validation studies
              remain to be done (Karr, 1991). These and other approaches that use invertebrates for the
              assessment of biotic integrity (Berkman et al., 1986; Lenat, 1988) are not as widely validated as
              is IBI, but they show considerable promise as additional water resource tools.

              Brooks and Hughes (1988), in their guidelines for assessing the biotic communities of freshwater
              wetlands, describe some sampling methodologies for invertebrates. Terrestrial flying insects and
              emerging aquatic insects can be sampled either by clear, plastic funnel traps or sweep nets.
              Insects are captured in a removable jar at the top of the cone or pyramid-shaped funnel
              (McCauley, 1976) and placed in 70% ethanol for later identification. Aquatic macroinvertebrates
              can be sampled with activity traps (Murkin et al., 1983) placed underwater where water depth
              is sufficient. Traps are checked daily. Benthic invertebrates are sampled from hydric sediments
              or soils using a lightweight coring device.

              Mammals. Mammals tend to be more sedentary than birds, but they are often more difficult to
              detect. Mammals sampling requires the use of several trapping techniques. Small ground-
              dwelling mammals are sampled using pairs of baited live and snap traps placed along the
              transects, (Brooks and Hughes, 1988). The presence of larger mammals is ascertained by looking
              for signs, such as tracks or droppings. If presence of large marnmals is of particular concern for
              a given wetland, then additional methods, such as scent stations (Linscombe et al., 1983) can be
              used. However, the attraction of mammals with large ranges to a scent station in a small wetland
              may bias the results (Brooks and Hughes, 1988). Brooks et al. (1990) and Croonquist (1990)
              found only weak correlations between the occurrence of mammals and the variable levels of
              disturbance affecting wetland and riparian areas. Many marnmalian species are adaptable to
              habitat alterations, and only a few species are sensitive to the negative impacts that threaten
              aquatic systems.

              Herpetofauna (reptiles and    amphibians) Herpetofauna are common in wetlands. Reptiles,
              such as turtles, with their long life span and amphibians sensitive to water pollution are good
              indicators of cumulative impacts, thus good potential candidates in a monitoring program
              (Phillips, 1990). Arrays of pitfall traps and drift fences have been recommended as the best
              means of sampling herpetofauna (Vogt and Hines, 1982; Campbell ind Christman, 1982). Less
              labor intensive techniques include timed searches of concealed herpetiles under logs, slash, and
              rocks, and recordings of vocalizations during the breeding season (Brooks and Hughes, 1988).
              For most regions of the United States, herpetofaunal communities may be too small and elusive
              to give comparable results across a broad spectrum of wetlands (Leibowitz et al., 1991).



              8.3.   Conclusion and Research Needs


              Diversity is one of the most frequently used criterion to assess conservation potential and
              ecological value (Margules and Usher, 1981). Though diversity has been studied in many
              community or habitat types (Asherin et al., 1979; Fuller, 1980), it has not been validated for
              marsh communities. Current wetland evaluation procedures employ concepts of vegetation









              structure and complexity but have not quantitatively demonstrated the relationship between
              vegetation structure and habitat quality (Harris et al., 1983). Birds are broadly used to assess
              biological integrity of the habitat, because relative to other taxa, they are conspicuous, easy to
              identify, and they are present in almost all habitats, thereby minimizing field time. Karr's Index
              of Biotic Integrity is a widely applied method to assess water resource quality. The ecological
              foundation of IBI can serve as a basis to other indexes that apply to other taxa. As suggested
              by Karr et al. (1986), biological assessments require an integrative approach relying on several
              taxa and variables.



              8.4.   References


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              Anderson, S.H. 1979. Habitat assessment for breeding bird populations. Trans. N. Am. Wildl. &
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              Asherin, D.A., H.L., Short, and J.E. Roelle. 1979. Regional evaluation of wildlife habitat quality
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              Berkman, H.E., C.F. Rabeni, and T.P. Boyle. 1986. Biomonitors of stream quality in agricultural
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              Blondel, J., C. Ferry, and B. Frochot. 1973. Avifaune et vegetation: essai d'analyse de la
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              Brack, V., Jr., V.R. Holmes, T.T. Cable, and G.K. Hess. 1987. A wetland habitat assessment
              method using birds. In: Proceedings Coastal Zone 87- the 5th symposium on coastal and ocean
              management. Seattle, Washington. pp. 1155-1170.

              Brooks, R.P., D.E. Arnold, E.D. Bellis, C.S. Keemer, and M.J. Croonquist. 1990. A methodology
              for biological monitoring of cumulative impacts on wetland, stream, and riparian components of
              watersheds. In: J.A. Kusler and G. Brooks, eds. Proceedings International Symposium: Wetlands
              and River Corridor Management. Assoc. Wedand Managers, Inc., Berne, NY.

              Brooks, R.P., and M.J. Croonquist. 1990. Wetland, habitat, and trophic response guilds for
              wildlife species in Pennsylvania. J.PA Acad. Sci.

              Brooks, R.P., and R.M. Hughes. 1988. Guidelines for assessing the biotic communities of
              freshwater wetlands. pp. 276-282. In: J.A.       Kusler, M.L. Quarnmen, and G. Brooks, eds.
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              Managers, Technical Report No.3. Berne, NY. 460 pp.








                                                             8-12

              Burt, D.E. 1970. Habitat selection and species interaction of some marsh passerines. MS Thesis,
              Iowa State University, Ames, Iowa.
              122 pp.

              Cable, T.T., V. Brack, Jr., V.R. Holmes. 1989. Simplified method for wetland habitat assessment.
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              Campbell, H.W. and S.P. Christman. 1982. Field techniques for herpetofaunal community
              analysis. In: N.J. Scott, Jr. (ed.), Herpetological Communities. Research Report No. 13, U.S. Fish
              and Wildlife Service. Washington, DC.

              Colwell, J.E., D.S. Gilmer, E.A. Work, Jr., D.L. Rebel, and N.E.G. Roller. 1978. Use of Landsat
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              Croonquist, M.J. 1990. Avian and mammalian community comparisons between protected and
              altered watersheds: A landscape approach. Pennsylvania State University, University Park, PA.
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              DeGraaf, R.M., N.G. Tilghman, and S.H. Anderson. 1985. Foraging guilds of North American
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              EPA. 1990. Biological criteria: national program guidance for surface waters. Office of Water
              Regulations and Standards. U.S. Environmental Protection Agency. Washington, DC.
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              Fausch, K.D., J.R. Karr, and P.R. Yant. 1984. Regional application of an index of biotic integrity
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              Fausch, K.D., J. Lyons, J.R. Karr, and P.L. Angermeier. 1990. Fish communities as indicators
              of environmental degradation. In: Biological indicators of stress in fish. American Fisheries
              Society Symposium 8. Bethesda, MD.

              Fuller, R.J. 1980. A method for assessing the ornithological interest of sites for conservation.
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              Graber, J.W., and R.R. Graber. 1976. Environmental evaluations using birds and their habitats.
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              Harris, H.J., M.S. Milligan, and G.A. Fewless. 1983. Diversity: quantification and ecological
              evaluation in freshwater marshes. Biological Conservation 27: 99-110.

              Hilden, 0. 1965. Habitat selection in birds. Ann. Zool. Fenn. 2: 53-57.








                                                              8-13

              Holmes, C.V.R., T.T. Cable, and V. Brack, Jr. 1986. Avifauna as indicators of habitat quality in
              some wetlands of Northern Indiana. Proceedings of Indiana Academy of Science 95: 523-528.

              Karr, J.R. 1981. Assessment of biotic integrity using fish community. Fisheries 6(6): 21-27.

              Karr, J.R. 1987. Biological monitoring and environmental assessment: a conceptual framework.
              Environ. Manag. 11: 249-256.

              Karr, J.R. 1991. Biological integrity: a long-neglected aspect of water resource management.
              Ecological Applications l(l): 66-84.

              Karr, J.R., and D.R. Dudley. 1981. Ecological perspective on water quality goals. Environ.
              Manag. 5: 55-68.

              Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant, and I.J. Schlosser. 1986. Assessing
              biological integrity in running waters: a method and its rationale. Illinois Natural History Survey,
              Champaigne, Illinois. Special Publication 5.

              Karr, J.R., and R.R. Roth. 1971. Vegetation structure and avian diversity in several New World
              areas. Ain. Nat. 105: 423-435.


              Keller, J.K. 1985. A method for the quantification of edge and the spatial arrangement of habitat.
              pp. 34-37. In: Proceedings National Wetland Assessment Symposium. Portland, Maine, 1985.
              Assoc. Wetland Managers, Inc., Berne, NY.

              Kiel, W.H. 1955. Nesting studies of the coot in Southwestern Manitoba. J. Wildl. Mgmt. 19:
              199-198.


              Krebs, C.J. 1989. Ecological methodology. Harper and Row, NY. 654 pp.

              Leibowitz, N.C., L.Squires, J.P. Baker, and others. 1991. Research plan for monitoring wetland
              ecosystems. Environmental Monitoring and Assessment Program. U.S. Environmental Protection
              Agency, Office of Research and Development, Washington, DC. EPA/600/3-91/010. 191 pp.

              Lenat, D.R. 1988. Water quality assessment of streams using a qualitative collection method for
              benthic macroinvertebrates. Journal of the North American Benthological. Society 7: 222-233.

              Linscombe, G., N. Kinler, and V. Wright. 1983. An analysis of scent station response in
              Louisiana. Proceedings Annual Conference Southeast Association Fish Wildlife Agencies 37:
             .190-200.


              MacArthur, R.H., and J.W. MacArthur. 1961. On bird species diversity. Ecology 42: 594-598.








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              MacArthur, R.H., J.W. MacArthur, and J. Preer. 1962. On bird species diversity. 111. Prediction
              of bird census from habitat measurements. Am. Nat. 96: 167-174.


              MacArthur, R.H., and E.O. Wilson. 1967. The theory of island biogeography. Princeton
              University Monographs in Population Biology 1: 1-203.

              Margules, C., and M.B. Usher. 1981. Criteria used in assessing wildlife conservation potential:
              a review. Biol. Conserv. 21: 79-109.


              McCauley, V.J.E. 1976. Efficiency of a trap for catching and retaining insects emerging from
              standing water. Oikos 27: 339-345.

              Miller, D.L., P.M. Leonard, R.M. Hughes, J.R. KarT, P.B. Moyle, L.H. Schrader, B.A. Thompson,
              R.A. Daniels, K.D. Fausch, G.A. Fitshugh, J.R. Gammon, D.B. Haliwell, P.L. Angermeier, and
              D.J. Orth. 1988. Regional applications of an index of biotic integrity for use in water resource
              management. Fisheries 13: 12-20.

              Milligan, M.S. 198 1. Resource partitioning: spatial and behavioral patterns in a freshwater coastal
              marsh avian community. MS Thesis, University of Wisconsin, Green Bay. 98 pp.

              Murdoch, W.W., F.C. Evans, and C.H. Peterson. 1972. Diversity and pattern in plants and insects.
              Ecology 53: 819-829.

              Murkin, H.R., P.G. Abbott, and J.A. Kadlec. 1983. A comparison of activity traps and sweep nets
              for sampling nektonic invertebrates in wetlands. Freshwater Invertebrate Biology 2: 99-106.

              Ohio EPA. 1988. Biological criteria for the protection of aquatic life. Ohio Environmental
              Protection Agency. Division of Water Quality Monitoring and Assessment, Surface Water
              Section, Columbus, Ohio, USA. (3 Volumes).

              Pardue, G.B., and M.T. Huish. 1981. An evaluation of methods for collecting fishes in swamp
              streams. In: L.A. Krumholz. The Warmwater Streams Symposium. American Fisheries Society.
              Bethesda, Maryland.

              Phillips, K. 1990. Where have all the frogs and toads gone? BioScience 40(6): 422-424.

              Pianka, E.R. 1966. Convexity, desert lizards and spatial heterogeneity. Ecology 47: 1055-1059.

              Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross, and R.M.               Hughes. 1989. Rapid
              Bioassessment Protocols for use in streams and rivers: benthic macroinvertebrates and fish. U.S.
              Environmental Protection Agency, Washington, DC. EPA/444/4-89-001. (1 volume, various
              pagings)

              Root, M. 1990. Biological monitors of pollution. BioScience 40(2): 83-86.







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               Roth, R.R. 1976. Spatial heterogeneity and bird species diversity. Ecology 57(4): 773-782.

               Shannon, C.E., and W. Weaver. 1949. The mathematical theory of communication. Urbana,
               University of Illinois Press. 117 pp.

               Short, H.L. 1984. Habitat suitability index models: the Arizona guild and layers of habitat
               models. USDI, Fish and Wildlife Service. FWS/OBS-82/10.70. 37 pp.

               Simberloff, D.S. 1974. Equilibrium theory of island biogeography and ecology. Annual Review
               of Ecology and Systematics 5: 161-182.

               Steedman, R.J. 1988. Modification and assessment of an index of biotic integrity to quantify
               stream quality in Southern Ontario. Canadian Journal of Fisheries and Aquatic Sciences 45:
               492-501.


               U.S. Fish and Wildlife Service. 1976. Habitat Evaluation Procedures. Division of Ecological
               Services, Department of Interior, Washington, DC. 30 pp.

               U.S. Fish and Wildlife Service. 1980. Habitat Evaluation Procedures. Division of Ecological
               Services, Department of Interior, Washington, DC. 84 pp.

               Vogt, R.C. and R.L. Hine. 1982. Evaluation of techniques for assessment of amphibian and
               reptile populations in Wisconsin. pp. 201-217. In: N.J. Scott, Jr. ed. Herpetological Conununities.
               USDI, Fish and Wildlife Service. Wildlife Research Report No. 13. Washington, DC.

               Wiens, J.A. 1973. Pattern and process in grassland bird communities. Ecol. Monogr. 43: 237-270.

               Wiens, J.A. 1974. Habitat heterogeneity and avian community structure in North American
               grasslands. Am. Midl. Nat. 43: 237-270.

               Willson, M.F. 1966. The breeding ecology of the yellowheaded blackbird. Ecol. Monogr. 36:
               51-77.


               Willson, M.F. 1974. Avian community organization and habitat structure. Ecology 55: 1017-1029.

               Wilson, E.O., and E.O. Willis. 1975. Applied biogeography. pp. 522-534. In: M.L. Cody and J.M.
               Diamond, eds. Ecology and evolution of communities. Harvard University Press, Cambridge,
               Mass. 545 pp.












                                              9 9 WETLAND HYDROLOGY


               9.1.   Introduction


               Hydrology is probably the single most important determinant for the establishment and
               maintenance of specific types of wetlands and wetland processes (Mitsch and Gosselink, 1986).
               The presence or absence of ground and surface waters and the frequency of inundation
               detem-iines the existence of a wetland. Disturbances of wetland" hydrology are commonly
               associated with human activities on the landscape. When the hydrologic pattern remains similar
               from year to year, a wetland's structural and functional integrity may persist for many years. But
               if hydrologic conditions change even slightly, the biota may respond with massive changes in
               species richness and ecosystem productivity (Mitsch and Gosselink, 1986). Wedand functions,
               such as flood storage, and sediment and contaminant retention, as well as nutrient fluxes may
               also be altered. In the longer term, hydrology determines, through erosion and deposition, the
               shape, size, depth, and even the location of a wetland (Kusler, 1987). Some types of wetlands
               (e.g., bogs and mires) exhibit little variation in water levels from year to year, whereas other
               types (e.g., floodplains) may depend on frequent and drastic changes in the amount of water
               entering and leaving the system (Leibowitz and Brown, 1990).

               The literature dealing with wetland hydrology is meager and topically specific (NEtsch and
               Gosselink, 1986). Emphasis is often placed upon narrow water budget calculations and depth
               of water without taking into account other factors. The role of long-term fluctuations in
               precipitation and water level and interactions of wetlands with hydrology are rarely considered
               (Kusler, 1987).



               9.2.   Critical Hydrologic Features of a Wetland

               Critical hydrologic features of a wetland may be broadly grouped into two categories: those
               relating directly to the water in the wetland, and those which interact with the water to produce
               or affect certain characteristics or functions (Kusler, 1987).


               9.2.1 Water


               Sources of wetland water and vath of water discharge. Sources of wetland water (direct
               precipitation, groundwater, surface water) determine specific wetland characteristics.         For
               example, water entering wetlands from the ocean generally has some salinity and a high energy
               profile due to tidal action, determining vegetation and fauna. The way the water is discharged
               from the wetland is also important: discharge primarily through evaporation or transpiration
               indicates that the wetland is probably an effective sink for sediment and very sensitive to









                                                              9-2

              nutrients, sediment, or dissolved/suspended substances; whereas, if discharge is through a surface
              outlet and active flow, the wetland is less likely to be a sink (Kusler, 1987).

              Number of water exchanges The number of water exchanges into and out of the wetlands is
              very important to sediment exchanges, and is a good indicator of sediment accumulation and
              fisheries movement (Turner, personal communication).

              Water depth. The depth of water in various portions of a wetland during "normal" periods,
              during long-term fluctuations in precipitation, and during rare but extreme events such as floods
              is critical to many wetland characteristics and functions. Generally, depth determines vegetated
              vs. open water areas of a wetland and its vegetation type. Many plant species, particularly
              forested wetland species, are very sensitive to depth. Depth is also critical to use of particular
              portions of wetlands by muskrats, fish, wading birds, turtles, and other species. Average depth
              at a particular point over a period of months or years may be critical for certain plant and animal
              use. Water depth reflects the topographic contours of the wetland and the amounts and levels
              of incoming and outflowing waters including direct precipitation and surface and subsurface
              flows. Depth varies with precipitation and tide levels and also varies over time as deposition and
              erosion occur within a wetland (Kusler, 1987).

              Water velocity The velocity of the water entering and passing through a wetland determines
              some wetland functions such as: 1) flood conveyance (i.e., its ability to convey a given amount
              of water from upstream to downstream within a certain period of time); 2) flood storage; 3)
              sediment transport and trapping; 4) potential for short and long term flushing of sediments out
              of the wetland; and 5) pollution control. In general, the higher the velocity, the greater the flood
              conveyance capability. The higher the velocity of the water entering the wetland and the lower
              the velocity of the water exiting, the greater the sediment trapping and pollution control functions
              (Kusler, 1987).

              Wave action. Wave action is important where the wetland contains substantial open water or
              is adjacent to open water with at least moderate depth (over four feet). Wave height and the
              erosive force of waves depend on water depth, the "fetch" (width) of open water, the presence
              or absence of particular types of vegetation, and the substrate material. Wave action determines,
              in part, the type and condition of wetland vegetation and soils. Most wetland plants cannot
              germinate and grow in moderate to high energy zones. However, some plants may survive in
              a less moderate energy area if protected until maturity (Kusler, 1987).

              Fluctuations in water sources, velocities, sediment loadings etc..            Fluctuations in the
              characteristics of the water flowing into and out of a wetland may be as important as "normal"
              or mean conditions. The timing and duration of maxima and minima for water depths and
              velocities determine many wetland characteristics and functions. Relatively short-term inundation
              by flood flows, which is common for coastal and riverine wetlands, may not affect vegetation
              much, unless it changes salinity or causes erosion; whereas, long-term inundation in isolated
              wetlands will often kill trees and other plants (Kusler, 1987; Weller, 1987).









                                                               9-3

               Dissolved/sus vended materials content in water, turbidity, and temperature. Although
               dissolved and suspended substances in waters (nutrients, sediment, detritus) flowing into, through,
               and out of a wetland may not be considered a "hydrologic characteristic" per se, such materials
               play major roles in determining wetland habitat, food chain support, pollution control, and other
               functions (Kusler, 1987). Suspended solids also affect flow rates and the erosive force of water.
               Waters with a very high sediment load flow more slowly through wetlands than clear waters, and
               have less erosive force. Suspended and dissolved substances determine the long-term shape, size,
               depth, and even location of the wetland and its long-term functions. For example, high sediment
               loadings may fill a wetland, reducing or destroying its flood conveyance and storage potential,
               and virtually all other functions. Similarly, high loadings of nutrients and organics may lead to
               rapid filling of the wetland by organic matter, altering its hydrology. Water temperature and
               turbidity influence vegetation and fauna, affecting habitat values and indirectly affecting flood
               conveyance, pollution control, and other functions.

               9.2.2 Wetland features that interact with water flow


               A wetland develops certain features in response to its hydrology, which, in turn, affect wetland
               hydrology.

               The size, shape, contours, and depth of most naturally occurring wetlands are formed by the
               forces of ice or flowing water. The shape of the wetland determines, to some extent, its
               hydrology and its functions, especially its flood conveyance capability. However, the same
               erosional and depositional forces that create the wetland continue to modify its shape over time.
               These changes may be accelerated by impacts to the watershed such as drainage, urbanization,
               tree-cutting, which increase peak flood flows and sediment loadings (Kusler, 1987).

               Wetland hydrology determines its vegetation; but also, the vegetation modifies the hydrologic
               characteristics of the wetland. Vegetation produces detritus and organic soils which may
               gradually decrease wetland depths. The type and density of vegetation affect water velocities and
               flood storage and conveyance potential. It determines, in part, sediment and detritus trapping
               potential, affects erosion rates, and in some cases (forested wetlands) groundwater levels through
               evapotranspiration (Kusler, 1987).

               The suspended and dissolved substances in the inflowing waters influence wetland soil
               characteristics, which, in turn, determine, to some extent, vegetation, substance retention potential
               of the wetland, and erosion rates during periods of high flows (Kusler, 1997).

               Animal life also interacts with wetland hydrology. Microorganisms and small organisms feed
               and break down detritus, thereby influencing wetland soils and wetland depth; beavers alter
               drainage; muskrats and alligators deepen some wetland areas or reduce vegetation in others;
               waterfowl may denude some areas of a wetland; carp destroy aquatic vegetation and increase
               water turbidity (Kusler, 1987).








                                                            9-4

             9.3.    Assessment of Wetland Hydrology

             Wetland hydrology assessment includes the study of water flow (precipitation, ground water,
             surface water) into, through, and out of the wetland, the characteristics of this flow, and its
             interaction with the wetland. It would be ideal to have site-specific and quantitative hydrologic
             data for every wetland, but this is quite unrealistic, because of the high costs and manpower and
             the long time frame required for evaluation. Besides, the calculations of water budgets are
             complex, and there are limits to short-term quantified measurements. For example, it may take,
             at minimum, several years of monitoring with a series of wells to determine groundwater
             relationships (Kusler, 1987). Wedand hydrology can be approached with varying levels of
             generality and quantification (Kusler, 1987):

                            General, unquantified presumptions based on wetland origin and type,
                            location in the watershed, or other factors.        Generalized scientific
                            information can usefully be used for some purposes, although
                            generalizations must be made with care.

                            More specific unquantified evaluation of the functions of a particular
                            wetland based on its location, its shape and size, the topography of the
                            surrounding land, and other site-specific and readily observable factors.

                            Site-specific quantitative evaluations based on flood and stream flow data,
                            topographic maps, aerial photography, superficial field surveys, and
                            hydrologic monitoring (time series).

             9.3.1 Water depth

             Depth can be directly measured in a field survey through the use of a stadia rod or similar
             device. However, it is often difficult to decide what is "bottom" when the substrate includes
             many feet of unconsolidated organics. Depth during long-term precipitation cycles and flood
             events is harder to predict. Flood maps, stream flow records, and various modeling approaches
             based on precipitation may be used (Kusler, 1987).

             9.3.2 Water velocity

             "Guesstimates" of water velocity in a wetland can be made based on the overall characteristics
             of the wetland and the adjacent water body. In general, low velocities can be expected in
             isolated wetlands and along very low gradient streams. Whereas, relatively high velocities may
             be expected for coastal wetlands impacted by hurricane waves and for riverine wetlands along
             high gradient rivers and streams.       "Guesstimates" are also sometimes possible based on
             examination of soils including the organic content and size of materials. Deep organic soils
             imply low velocities, whereas mineral soils, particularly those containing small rocks, imply
             higher velocities (Kusler, 1987).









                                                               9-5

               More accurate values of water velocity can be obtained using continuous recorders attached to
               weirs or flumes (Leibowitz et al., 1991). Weirs are devices used to determine the quantity of
               water flowing over it, based. on measurements of water depth over the crest or sill and known
               dimensions of the device. A flume is a channel placed in a stream of water to measure the
               volume or rate of flow. The discharge of channelized surface water can be measured very
               accurately (Rantz et al., 1982). Groundwater fluxes determination is more complex, because of
               the difficulty of defining flow-system boundaries, dynamics of recharge and discharge, hydraulic
               gradients, and permeability distribution (Winter, 198 1). The groundwater flow-system modeling
               studies by Toth (1963), Freeze (1969), and Winter (1976, 1978) are mostly of steady-state,
               generalized systems and are most appropriate for understanding regional flow systems (Winter,
               1988).

               9.3.3 Wave energies

               "Guesstimates" of wave energies may be made based on air photos or topographic maps
               indicating open water areas and the depths of such areas. The lack of vegetation or wetland soil
               along the shores of lakes, larger rivers, or the ocean may also infer moderate to high wave
               energies. Various models and other predictive approaches can be used to calculate potential wave
               energies at particular points (Kusler, 1987).

               9.3.4 Changes in surface level

               The land surface can gain water from the atmosphere by precipitation and lose water by
               evaporation and transpiration. Precipitation that falls on the land surface will flow over it, remain
               ponded in depressions, and/or infiltrate into the subsurface. The relative distribution of water
               involved in each of these processes depends on the slope and permeability of the land surface
               (Winter, 1988).

               Field observations and aerial photography can be used in conjunction to define the maximum and
               minimum reaches of surface waters in and around wetlands. Continuous water-level recorders
               are desirable, but are more expensive to install and maintain than staff gauges that are read at
               periodic intervals (Leibowitz et al., 1991). Otheir similar devices can be used, such as iron rods
               or PVC wells. The wells must be placed at strategic stations along transects established for plant
               and animal sampling. Slotted PVC pipes allow measurements of water level above the surface
               during wet periods, and below the surface during dry periods. All types of gauges must be
               anchored below the frost line or tied into a surveying grid to maintain accurate depths over a
               period of years (Leibowitz et al., 1991).

               Research on evaporation and transpiration from wetlands has been minimal. Evapotranspiration
               is determined by 1) differences in the water budget; 2) evaporation pan data; 3) evaporitneters
               and lysimeters; or 4) any of several empirical formulas (Winter, 1988). If pans are used, large
               errors in the calculated evapotranspiration are common because the openwater evaporation from
               a pan is not a surrogate for transpiration by plants. Studies of evapotranspiration using lysimeters
               and evaporimeters containing wetland vegetation are not common (Carter, 1986). When








                                                              9-6

              empirical formulas are used to estimate evapotranspiration, the sensors that provide the data are
              often located at the nearest weather station, not at the site of interest, leading to large errors.

              Long-term fluctuations in water levels and hydroperiod (duration) are often difficult to predict.
              Direct gauging of water levels is, of course, desirable. But long-term records are rarely
              available (Kusler, 1987). Where hydrologic gauging stations and other relevant records are not
              available, there may be natural indicators of hydrologic history. Sediments often contain
              information on their source, rate of sedimentation, and distribution within the wetland (Cooper
              et al., 1987). Dendrochronology and aberrations in the form of tree growth (Sigafoos, 1964;
              Everitt, 1968) may help interpret the magnitude of flood events where no other records are
              available. However, the existing plant community species composition may not be a reliable
              indicator of hydrologic conditions (Brinson, 1988).


              9.4.   Use of Remote Sensing in Hydrology

              Remote sensing can provide some information on the hydrologic regime of the wetland, such as
              changes in open areas, in surface level, and in soil moisture. A number of studies have used
              remote sensing as a method for flood analysis and soil moisture assessments (Sollers et al., 1978;
              Harker and Rouse, 19771; Ragan, 1977; McGinnis and Rango, 1975; Rango and Anderson, 1974;
              Moore and North, 1974; Rango and.Salomonson, 1974; Williamson 1974; American Water
              Resources Associations, 1974; Piech and Walker, 1971), (Schmugge, 1983; Cihlar, 1978;
              Schmugge et al., 1977; Myers et al., 1977; Idso et al., 1975; Blanchard et al., 1974; Waite et al.,
              1973; Werner et al., 197 1). Microwave radiometric sensors are very effective at measuring water
              content, both in the atmosphere and on the earth's surface. These systems can be used to map
              areal distribution and variations in rainfall, water absorption rates of surface soils and map flood
              water distribution and flow patterns on an all-weather synoptic basis (Kennedy). The microwave
              radiometer functions as a temperature-measuring device. The capability of the radiometer to
              measure atmospheric hydrology derives from the electromagnetic properties of atmospheric water
              vapor, oxygen, clouds, rain, and the earth's surface which differ greatly in electromagnetic
              properties. The dielectric properties of surface materials are strongly dependent on moisture
              content Changes in the dielectric constant result in major changes in the emissivity and
              radiometric brightness temperature (Kennedy).

              9.4.1 Flood monitoring

              Aircraft and satellite data have been used to perform floodplain mapping by two complementary
              approaches: static and dynamic (Sollers et al., 1978). The static approach is based on the
              recognition of geornorphological features formed by historical floods such as terraces, alluvial
              fans, natural levees, bars, oxbows, marshes, deltas, etc.        Floodprone areas tend to have
              multispectral. signatures that are distinctly different from those of surrounding nonfloodprone
              areas. The dynamic approach uses images of floods as they occur or soon afterward. Visible
              evidence of inundation in the near infrared region of the spectrum remains for up to two or more
              weeks after the flood. The near infrared reflectivity is reduced in the flooded areas because of






 13

                                                                9-7

                the presence of increased surface-layer soil moisture, moisture stressed vegetation, and isolated
                pockets of standing water. The inundated areas are characterized by the water absorption band
                (700-1100 nm). Visible and near infrared channels are recommended for analysis. The features
                observed here are the atmospheric conditions (clouds, air mass characteristics, precipitation..),
                flood water levels, and soil and vegetation characteristics after the high waters have receded.
                Soil moisture and sediment traces in water can indicate the path and extent of flood damage to
                a plain (Currey, 1977). Vegetation also exhibit patterns related to flood conditions: flood stressed
                plants reflect more blue and less infrared radiation (Sollers et al., 1978). Satellites, such as ERTS,
                NOAA, Landsat, SPOT could help reduce short and long term flood losses and provide regional
                water resources planning information. Data from these satellites would therefore complement
                aircraft and conventional surveying methods to ascertain the areal extent of flooding (McGinnis
                and Rango, 1975). Despite its usefulness in flood monitoring, remote sensing has limitations:
                1) some systems don't have the resolution needed to delineate the boundary of flooded areas; 2)
                the scale of floodplain mapping is not large enough for most legal requirements; and 3) clear
                weather conditions are necessary with passive sensors. When possible, a combination of sensors
                should be used. Remote sensing data can serve as a base for assessment of potential flood
                damage, in identifying areas where further surveys are merited.

                9.4.2 Soil moisture assessment


                Soil moisture and its spatial and temporal behavior is of critical importance to disciplines such
                as agriculture, hydrology, and climatology. Specifically, soil moisture assessments are needed
                to study flood water distribution and flow patterns, distribution and variations in precipitation
                (especially rainfall), runoff following precipitation, and evapotranspiration (Kennedy; Cihlar,
                1978).

                Most techniques developed for soil moisture measurement provide point estimates, therefore are
                not suited for large areas (Cihlar, 1978). The traditional method of soil moisture measurement
                is to weigh a sample of soil, oven-dry it, and reweigh it. The difference between the wet and
                dry weights represents the soil moisture, and the percent moisture is then extrapolated to the
                entire field. This method is time-consuming and representative of only small areas. The status
                of remote sensing techniques for soil moisture estimation was reviewed in a workshop organized
                in 1978 in Maryland (Cihlar, 1978). The techniques discussed were: 1) the reflected solar
                technique; 2) the thermal infrared technique; 3) the active microwave (radar) technique; 4) the
                passive microwave (radiometer) technique; and 5) the gamma radiation technique. The review
                indicated the complementary nature of the various techniques.             Thus, it is likely that a
                combination of sensors will be needed to provide accurate soil moisture estimates from satellites.
                Thermal infrared and both microwave approaches have shown potential for estimating near-
                surface water contents but the sensitivity to water at greater depths and under canopy seemed
                limited to the thermal infrared technique (Cihlar, 1978).








                                                            9-8

             9.5.    Disturbances of Wetland Hydrology

             Because the hydrologic system is a continuum, any modification of one component will have an
             effect on contiguous components. Disturbances commonly affecting the hydrology of wetlands
             include weather modification, alteration of plant communities, storage of surface water in
             reservoirs, road construction, drainage of surface water and soil water, and alteration of
             groundwater recharge and discharge areas (Winter, 1988).

             Weather modification refers principally to inducing precipitation through cloud seeding (Winter,
             1988). Alteration of plant communities refers to the removal of biomass through harvests. If
             plants are removed from a wetland, the loss of water by evapotranspiration may change, thereby
             changing the quantity of water available for surface water flow or groundwater recharge.
             Complete removal of the transpiration process, which usually accounts for the greatest loss of
             water from wetlands, could result in considerably more water available for surface runoff, and/or
             groundwater recharge (Winter, 1988). The effects of drainage on nutrient concentrations flowing
             from wetlands (Kuenzler et al., 1977) and aboveground biomass production (Bayley et al., 1985;
             Carteret al., 1973) can be detected soon after alteration of hydrology. In contrast, the species
             composition of forested wetlands may persist for decades. Therefore, wetland inventories based
             only on aerial photographs may not detect changes resulting from altered hydrology (Brinson,
             1988). Impacts that reverse depositional tendencies may cause wetlands to be large exporters
             rather than importers. Increases in rates as well as direction can cause stocks of materials,
             accumulated over centuries in wedand sediments, to be lost within decades, resulting in nutrient
             loading to downstream aquatic systems (Brinson, 1988). Wetlands should not only be assessed
             for their capacity to accumulate sediments, which may be slow, but for their vulnerability to
             export when hydrologically altered, which is potentially rapid.

             The consequences of these alterations are fairly predictable on a local scale and are described by
             Brinson et al. (1981). In contrast to these single impacts on individual wetlands, cumulative
             impacts present a scale of complexity in time and space that is much more difficult to describe
             and predict (Brinson, 1988). In dealing with the spatial scale, we are confronted with complex
             watersheds units, often containing wetlands of several types.

             The sensitivity of wetlands to various impacts is related to hydrologic characteristics. For
             example, salt marshes and marshes along rivers with continuous water supply and periodic
             flushing by flood flows are quite "self healing", provided the wetland topographic contours are
             maintained (Kusler, 1987). Pothole wetlands and other closed systems with perched water may
             be very sensitive to impacts and have a very slow recovery rate.


             9.6.   Relationships Between Hydrologic Functions and Wetlands

             Wetlands are determined by their physiographic setting and the water balance that favor the
             accumulation or retention of soil water and/or surface water for a period of time (Winter, 1988).
             To determine the hydrologic support function of a wetland, the wetland must be placed in a








                                                              9-9

               regional hydrogeologic context (Winter, 1976). Hydrogeologic classification of wetlands is
               important to understand a wetland's water balance and the effect of hydrology on other wetland
               functions (Hollands, 1985). The classification that appears to be most used by wedand regulators
               is that of Novitzki (1978). This classification combines topography, surface water, and
               groundwater parameters.

               Flood storage and sediment retention processes are reasonably well defined (Novitzki, 1986).
               Flood desynchronization and nutrient retention are defined for some circumstances but not all.
               But the relations between wetlands and seasonal strearnflow patterns, and between wetlands and
               groundwater are poorly defined (Novitzki, 1986). O'Brien (1988.) explores the relationships
               between wetlands and flood reduction, wetlands and strearnflow, wetlands and groundwater. A
               year or more of groundwater elevation observations (using piezometers) are required before
               recharge/discharge functions can be understood (Hollands, 1985).

               Many wetlands have a capacity for maintaining water quality by buffering surface and ground
               waters from potentially damaging compounds (Brinson, 1988). The biological and.non-biological
               mechanisms by which wetlands retain and transform high concentrations of nutrients and toxic
               compounds are covered in a review by Hemond and Benoit (1988). Sampling of water
               constituents is essential; however, it is too expensive to analyze them all and sometimes difficult
               to select those that deserve more interest. Besides, their concentrations fluctuate considerably,
               especially after climatic events and anthropogenic disturbances.         Water quality data, by
               themselves, cannot be used to illustrate trends that imply deterioration due to wetland
               degradation. Data are not available for all surface waters of interest, nor are they generally
               available over a long enough period to detect changes in water quality. Brinson (1988) presents
               a geornorphological classification of wetlands that has relevance for water quality.       He also
               shows how the position of these wetland types in the landscape may influence strategies for
               protecting water quality in different areas of a watershed.

               Wetlands are intrinsically depositional landforms (Brinson, 1988). They tend to import many
               elements. But when a wetland is filled, drained, or deprived of its sedimentary function, it
               exports rather than imports elements. Data are required on both sedimentation and erosion to
               place the sedimentation process within a meaningful context at a cumulative impact scale
               (Brinson, 1988). A common technique to measure sediment flux is to place artificial surfaces
               into the substrate, and quantify the accumulation of sediment during the sampling period.
               Measurement of erosion rates is more difficult. The sediment inventory may best reflect the
               integrated history of a wetland (Hemond and Benoit, 1988). This is particularly useful where the
               manager must assess previous impacts on a particular site.            Many pollutants are more
               concentrated in sediments than in water, thus simplifying the problem of making accurate
               measurements.








                                                             9-10

              9.7.    Conclusions and Research Needs


              Hydrology is the primary and critical force that creates and modifies wetlands, thus it is a good
              indicator of wedand condition. Unfortunately, wetland hydrology and its changes over time
              (years, decades and longer) are not adequately understood. Many of the studies are empirical and
              focus on particular aspects of hydrology, and a few articles deal with practical assessment needs.
              Site-specific and quantitative data are essential for the assessment, but they are missing because
              of the high costs and the long time frame required for evaluation. Detailed hydrologic budgets
              for different wedand types must be calculated for a period long enough to include a range from
              relatively wet to relatively dry years. Studies on both reference sites and disturbed wetlands are
              needed to calculate flux rates under a variety of environmental conditions before threshold values
              associated with disturbance can be determined. Sediment analysis is a potential tool for the
              wedand manager, and is a fruitful area for future research (Hemond and Benoit, 1988). Remote
              sensing has been found to be very helpful in soil moisture and flood damage
              assessments. A combination of sensors seems to provide more accurate estimates.

              It is important to understand the regional differences that exist in wetlands and in their potential
              hydrologic function. Considerable care should be exercised in extrapolating results from one
              region to another. Additional research is needed before hydrologic function can be reliably
              correlated with physica 'I properties of wetlands and landscapes. Special effort should be
              directed toward understanding surface and near-surface flow processes within the wedand, the
              relation of the wedand to the local groundwater system, and the relation of the hydrologic regime
              to the wetland plant, community. More research is also required to evaluate the relationships
              between the hydrologic regime and the cycling and transport of nutrients and pollutants- The
              ambitious and time-consuming studies required to answer all these questions would be facilitated
              by open cooperation between researchers, wetland managers, and funding agencies.



              9.8.   References


              American Water Resources Association. 1974. Satellite analyses of the 1973 Mississippi River
              floods. Water Resources Bull. 10(5): 1023-1096.

              Bayley, S.E., J. Zoltek, Jr., A.J. Hermann, T.J. Dolan and L. Tortora. 1985. Experimental
              manipulation of nutrients and water in a freshwater marsh: Effects on biomass, decomposition,
              and nutrient accumulation. Limnology and Oceanography 30: 500-512.

              Blanchard, M.B., R. Greeley, and R. Goettelman. 1974. Use of visible, near-infrared, and thermal
              infrared remote sensing to study soil moisture. Ninth International Symposium on Remote
              Sensing of Environment. Environmental Research Inst. of Michigan. pp. 693-700.

              Brinson, M.M. 1988. Strategies for assessing the cumulative effects of wetland alteration on
              water quality. Environmental Management 12(5): 655-662.









                                                             9-11

               Brinson, M.M., B.L. Swift, R.C. Plantico, and J.S. Barclay. 1981. Riparian ecosystems: their
               ecology and status. FWS/OBS-81/17. U.S. Fish and Wildlife Service, Washington, DC. 155pp.

               Carter, M.R., L.A. Bums, T.R. Cavinder, K.R. Dugger, P.L. Fore, D.B. Hicks, H.L. Revells, and
               T.W. Schmidt. 1973. Ecosystems analysis of the Big Cypress Swamp and estuaries. U.S.
               Environmental Protection Agency report 904/9-74-002. Atlanta, Georgia. 379 pp.

               Carter, V. 1986. An overview of the hydrologic concerns related to wetlands in the United States.
               Canadian Journal of Botany 64: 364-374.

               Cihlar, J. 1978. Soil moisture information: needs and remote sensing capabilities. In: Remote
               Sensing news briefs. Energy, Mines and Resources Canada. Canadian Center for Remote
               Sensing, 588 Booth Street, Ottawa, Canada, KlYOY7.

               Cooper, J.R., J.W. Gilliam, R.B. Daniels, and W.P. Robarge. 1987. Riparian areas as filters for
               agricultural sediment. Soil Science Society of America Journal 51: 416-420.

               Currey, D.T. 1977. Identifying flood water movement. Remote Sensing of the Environment 6:
               51-61.


               Everitt, B.L. 1968. Use of the cottonwood in an investigation of the recent history of a flood
               plain. American Journal of Science 288: 417-439.

               Freeze, R.A. 1969. Theoretical analysis of regional groundwater flow. Canadian Department of
               Energy, Mines, and Resources, Inland Waters Branch Scientific Series 3, 147 pp.

               Harker, G.R. and J.W. Rouse., Jr. 1977. Floodplain delineation using multispectral data analysis.
               Photogrammetric Eng. and Remote Sensing 43(l): 81-87.

               Hemond, H.F. and J. Benoit. 1988. Cumulative impacts on water quality functions of wetlands.
               Environmental Management 12(5): 639-653.

               Hollands, G.G. 1985. Assessing the relationship of groundwater and wetlands. In: Proceedings
               of National Wetlands Assessment Symposium, Portland, Maine. Association of State Wetland
               Managers, Inc., Berne, NY.

               Idso, S.B., R.D. Jackson, and R.J. Reginato. 1975. Detection of soil moisture by remote
               surveillance. American Scientist 63: 549-556.


               Kennedy, J.M. 1968. Microwave sensors for water management and hydrology from space. Ryan
               Aeronautical Company, San Diego, California. A/AA Paper No. 68-1076, AJAA 5 th Annual
               Meeting and Technical Display, Philadelphia, PA, Oct 21-24, 5 pp.








                                                            9-12

             Kuenzler, E.J., P.J. Mulholland, L.A. Ruley, and R.P. Sniffen. 1977. Water quality in North
             Carolina coastal plain streams and effects of channelization. In: Water Resources Research Inst.
             Report 127, Univ. of North Carolina, Raleigh, North Carolina. 160 pp.

             Kusler, J. 1987. Hydrology: an introduction for wetland managers pp. 4-24. In: Proceedings of
             National Wetland Symposium: Wetland Hydrology. Chicago, Illinois. Association of State
             Wetland Managers, Inc., Berne, NY.

             Leibowitz, N.C.T. and M.T. Brown. 1990. Indicator strategy for wetlands, pp. 5-1 - 5-15. In: C.T.
             Hunsaker and D.E. Carpenter, eds. Ecological indicators for the environmental monitoring and
             assessment program. EPA 600/3-90/060. U.S. Environmental Protection Agency, Office of
             Research and Development, Research Triangle Park, NC.

             Leibowitz, N.C., L. Squires, J.P. Baker, and others. 1991. Research plan for monitoring wetland
             ecosystems. Environmental Monitoring and Assessment Program. U.S. Environmental Protection
             Agency, Office of Research and Development, Washington, DC. EPA/600/3-91/010. 191 pp.

             McGinnis, D.F., and A. Rango. 1975. Earth Resources Satellite systems for flood monitoring.
             Geophys. Res. Letters 2(4): 132-135.

             Mitsch, W.J. and J.G. Gosselink. 1986. Wetlands. Van Nostrand Reinhold Company, NY 539
             pp-

             Moore, G.K. and G.W. North. 1974. Flood inundation in the south-eastern United States from
             aircraft and satellite imagery. Water Resources Bull. 10(5): 1082-1280.

             Myers, V.I., J.L. Heilman, and D.G. Moore. 1977. Remote soil moisture measurements: need,
             present methods and obstacles. In: Microwave Symposium, Houston Texas, sponsored by NASA
             and Texas A & M Univ. 20 pp.

             Novitzki, R.P. 1978. Hydrogeological characteristic of Wisconsin's wetlands and their influences
             on floods, stream flow and sediment. In: Good et al. eds, Wetland functions and values, the state
             of our understanding. Proceedings of the National Symposium of Wetlands, American Water
             Resources Association, Minneapolis, Minnesota 55414.

             Novitzki, R.P. 1986. Summary of hydrologic functions workshop: state of scientific knowledge
             and unmet research needs. In Proceedings of National Wetland Symposium: Mitigation of
             Impacts and Losses. New Orleans, Louisiana. Association of - State Wetland Managers, Inc.,
             Berne, NY.

             O'Brien A.L. 1988. Evaluating the cumulative effects of alteration on New England Wetlands.
             Environmental Management 12(5): 627-636.









                                                            9-13

              Piech, K.R. and J.E. Walker. 1971. Thematic mapping of flooded acreage. Photogrammetric Eng.,
              1972, pp. 1081-1090.

              Ragan, R.M. 1977. Utilization of remote sensing observations in hydrologic models. Proceedings
              of the Eleventh International Symposium on Remote Sensing of Environment 1: 87-99.

              Rango, A. and A.T. Anderson. 1974. Flood hazard studies in the Mississippi River basin using
              remote sensing. Water Resources Bull. 10(5): 1060-1081.

              Rango, A. and V.V. Salomonson. 1974. Regional flood mapping from space. Water Resources
              Research 10(3): 473-484.

              Rantz, S.E. and others. 1982. Measurement and computation (2 volumes). U.S. Geological Survey
              Water Supply Paper 2175, 631 pp.

              Schmugge, T.J. 1983. Remote sensing of soil moisture: recent advances. IEEE Transactions on
              Geoscience and Remote Sensing. Vol. GE-21(3): 336-344.

              Schmugge, T.J., J.M. Meneely, A. Rango, and R. Neff. 1977. Satellite microwave observations
              of soil moisture variations. Water Resources Bull. 13(2): 265-281.

              Sigafoos, R.S. 1964. Botanical evidence of floods and floodplain deposition. U.S. Geological
              Survey Professional Paper 485-A. U.S. Government Printing Office, Washington, DC.

              Sollers, S.C., A. Rango, and D.L. Henniger. 1978. Selecting reconnaissance strategies for
              floodplain surveys. American Water Resources Association. Water Resources Bulletin 14(2):
              359-373.


              Toth, J. 1963. A theoretical analysis of groundwater flow in small drainage basins. pp 75-96. In:
              Proceedings of Hydrology Symposium 3, Groundwater, Queen's Printer, Ottawa, Canada.

              Turner, R.E., Professor and Chair, Department of Oceanography and Coastal Sciences, Louisiana
              State University, Baton Rouge, LA 70803.

              Waite, W.P., K.R. Cook, and B.B. Bryan. 1973. Broad spectrum n-dcrowave systems for remotely
              measuring soil moisture content.      Water Resources Research Center Publication #18 in
              cooperation with Univ. of Arkansas Eng. Expt. Station Report #23. 166 pp.

              Weller, M.W. 1987. The influence of hydrologic maxima and minima, on wildlife habitat and
              production values of wetlands. pp. 55-60. In: Proceedings of National Wetland Symposium:
              Wetland Hydrology. Chicago, Illinois. Association of State Wetland Managers, Inc., Berne, NY.








                                                          9-14

             Werner, H.D., F.A. Schmer, M.L. Horton, and F.A. Waltz. 1971. Application of remote sensing
             techniques to monitoring soil moisture. Remote Sensing Inst. Report #RS171-4. South Dakota
             State Univ. 33 pp.

             Williamson, A.N. 1974. Mississippi River flood maps from ERTS- l,digital data. Water Resources
             Bull. 10(5): 1050-1059.

             Winter, T.C. 1976. Numerical simulation analysis of the interaction of lakes and groundwater.
             U.S. Geological Survey Professional Paper 1001, 45 pp.

             Winter, 1978. Numerical simulation of steady state three-dimensional groundwater flow near
             lakes. Water Resources Research 14: 245-254.


             Winter, T.C. 1981. Uncertainties in estimating the water balance of lakes. Water Resources
             Bulletin 17: 82-115.


             Winter, T.C. 1988. A conceptual framework for assessing cumulative impacts on the hydrology
             of nontidal wetlands. Environmental Management 12(5): 605-620,






  14





                                    10 - CONCLUSIONS AND RECOMMENDATIONS



               Currently, there are no standard methods for monitoring wetlands, but a consensus is beginning
               to form among the scientific and regulatory communities. The combination of biological
               monitoring and evaluation of physical and chemical data may improve our understanding of
               processes that operate at higher levels of biological organization, such as communities and
               ecosystems (Brooks, 1989). Vertebrates, invertebrates, and vascular plant communities, when
               analyzed in conjunction with selected abiotic parameters, serve as ecological indicators of change.
               Biotic communities are likely to integrate the incremental changes that occur in ecosystems over
               time, and therefore, reflect long-term, cumulative effects (Brooks, 1990).

               As the scale of environmental problems is expanding quickly and spatially, it becomes urgent to
               develop standardized and fast procedures to manage our natural resources and stop or prevent
               degradation. Remote sensing technology (satellite imagery and aerial photography) is a very
               promising tool in environmental monitoring. It allows large areas of wetlands to be surveyed
               rapidly and easily. Some indicators of wetland condition, such as wetland extent and type,
               habitat structure, and the floral component of wetland productivity, can be determined primarily
               by means of remote sensing; while others (e.g., vegetation, hydrology, habitat quality) still require
               the use of more conventional techniques. In most cases, a combination of remote sensing and
               other techniques is used to collect data and assess the environmental condition of wetlands.

               Aerial photography can be used to select sampling sites, establish field transects, identify and
               delineate the major vegetation types and to detect early vegetative stress. It also allows us to
               document present conditions for use in future trend analyses. Methodologies and algorithms for
               the determination of biomass and productivity of coastal wetlands habitat by remote sensing have
               been recently developed and will significantly enhance our ability to determine wetland condition
               over time on a regional scale. Remote sensing also provides information on physical alterations
               to the wetlands (flooding, human activities, etc.), soil moisture, and the wetland hydrological
               regime. By comparing two or more time periods, change in biomass, productivity, wetland
               extent, type, and patterns, wetland vegetation community composition, or some other factor
               correlated with spectral reflectance (e.g., greenness) could be used to index functional health. The
               activity requires ground based research to relate remotely sensed spectral radiances to these
               indicators. Various remote sensing methods are available and the choice of the method will
               depend on the project objectives and monetary constraints. Low-resolution data may be sufficient
               for the study of certain parameters, and higher resolution data will be required for detailed studies
               of selected sample sites. Our effectiveness for managing wetland resources in the future will
               depend on our ability to collect and analyze data on a regional and eventually global scale. The
               advances in instrumentation and in computer analysis techniques will greatly improve the types
               of data available.








                                                             10-2

             Wetland health assessment procedures still require improvement through further research and
             testing; this would be facilitated by more cooperation between researchers, wetland managers and
             funding agencies. The importance of a more comprehensive approach in the protection of
             ecological resources is now being recognized. A large set of parameters must be taken into
             account to accurately assess the overall condition of wetlands. Sampling methods should
             minimize the natural variation inherent in any ecological system, and should be standardized
             throughout the country to provide a national assessment of wetlands status, changes, and long-
             term trends.


             Further research is needed to better understand the physical and biological processes that
             contribute to the ecological integrity of wetlands, i.e., -understand the relationships between
             physical properties of wedand and their functions. Some of the questions that still need to be
             answered are: which measures of structure best indicate landscape changes? What are the
             ecological processes that are related to landscape pattern? How does pattern affect disturbance
             regime, movement and persistence of organisms, redistribution of matter and nutrients? What
             is the role of vegetation in wedand functions? How much does vegetation structure influence
             habitat quality? How important are habitat characteristics for the maintenance of biodiversity?
             Is it possible to predict how wedand plants will respond to changing environmental conditions?
             What are the relationships, between the wetland hydrologic regime and the cycling and transport
             of nutrients and pollutants? Hydrology is the driving force of wetlands and needs to be better
             understood.


             Particular emphasis should be placed on conducting the following specific research:

                    0       Test the applicability of remote sensing techniques for biomass and
                            productivity determination in a large range of wetland types.

                    0       Find other applications of remote sensing technology in wetland health
                            evaluation.


                    0       Analyze the sensitivity of the proposed indicators for wedand health
                            assessment; a number of methods described here need more testing and
                            validation in wetland environments.


                    0       Find early warning indicators that would detect initial impairment in
                            wetlands.


                    0       Test other indicators of wetland condition, such as sediment characteristics,
                            chemical contamination, bioaccumulation in tissues (Leibowitz et al.,
                            1991).

                    0       Develop. health indexes sensitive to cumulative impacts (Karr, 1991).












                                                   11 9 BIBLIOGRAPHY



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                                                             11-2

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