Use of Remote Sensing to Assess Wetland and Water Quality by 9h9O9MR

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									Use of Remote Sensing to Assess
   Wetland and Water Quality


         By: Rodney Farris

             SOIL 4213
Significance/Uses of Wetlands
 • Filter for clean water supply
 • Support a diversity of vegetation
 • Wildlife habitat

 • Main components
   – Hydrology
   – Soil
   – Vegetation
Significance/Uses of Wetlands
  • Improve Water Quality
     – Mobilize heavy metals
     – Regulate the flow of water and
       nutrients
  • Some Areas Around
    Wetlands are
    Pasture/Agricultural
    Croplands
     – Some used/converted for
       agricultural use
       (crops, forage, timber)
     – Irrigation source
     – Reduction or prevention of
       erosion
     – Flood control
     – Non-point/point source runoff
       filtration
Wetland and Water Quality Monitoring
 • Water Storage Capability
   – Size of wetlands
   – Extent of water-spread and its seasonal
     variation
   – Water flow
   – Water fluctuations
 • Vegetation
   – Patterns, abundance, richness, composition
   – Weed infestations
Wetland and Water Quality Monitoring
 • Water Quality
   – Turbidity levels
   – Eutrophication
   – Siltation/sediment concentration
      • Chlorophyll concentration/Algal biological parameters
   – Herbicides
      • Change detected in short lived taxa
   – Bioaccumulation of metals
      • Change detected in long lived taxa
 • Wetland Wildlife
Remote Sensors Used

 • Landsat TM & MSS    • CASI (Compact
 • SPOT                  Airborne
                         Spectrographic
 • RADARSAT
                         Imager)
 • SAR (Synthetic
                       • Aerial Photography
   Aperture Radar)
                       • Ground Level (low
 • Spectron SE-590
                         level) Photography
   Spectroradiometer
Landsat TM or MSS
 • High spatial resolution,
   data at 16 day
   intervals, 25 years of
   archived data
 • 95% accuracy in
   mapping wetlands
   compared to manual
   mapping
 • Bands 4, 5, 7 best for
   detecting water
Landsat TM or MSS (cont.)
 • (TM) Thematic Mapper
   – 30m spatial resolution (all Bands*)
   *Exception: for Band 6 resolution is 120m
 • Incident infrared wavelengths shows
   water body better than visible Bands.
   – Strong absorption of light by water, giving
     a low spectral response
 • Detect open water
Landsat TM or MSS (cont.)
 • Able to classify vegetation
   – Dense green
   – Sparse green
   – Very sparse green
 • Problems
   – Clouds or cloud shadows
   – Dense vegetation makes it difficult to
     define soil/water boundaries
   – Can only classify vegetation based on
     density
SPOT
 • Low reflectance of water in infrared
   Bands
 • Searches a smaller area than Landsat
   images (20 m spatial resolution)
 • Records reflected radiation in green,
   red and near-infrared spectrum
 • Detect changes in aquatic vegetation
 • Used to measure algal growth and
   respiration rates
RADARSAT
 • Daily access over an area
 • Able to penetrate clouds, vegetative
   canopies, sensitive to moisture changes
   in targets
 • Specular signal scattering over water
   surface and diffuse over soil surface
 • Able to pick up corner reflection effects
   between water surface and vegetative
   stems/trunks
SAR–Synthetic Aperture Radar               (C-Band)

 • Detects changes in surface soil moisture
   conditions
 • Detects wetland and non-wetland vegetation
 • Better detection in fall or senescence period
 • Open water appears dark
 • With image filtrations:
   – Marshes (bright red, green, and blue due to
     reflective effects
   – Non-forested bogs appear reddish
Spectron SE-590 Spectroradiometer
 • Detects suspended sediment
   concentrations
   – Better detection at 740 – 900nm or
     infrared wavelengths
   – Based on function of bottom brightness
     and reflection of suspended sediments
CASI–Compact Airborne Spectrographic Imager
 • Wetland mapping
 • Vegetative health
    – Density, position, composition
    – Determine wetland vegetation based on
      lushness, vigor, intensity
      • Compared to upland/dry sites
 • Detect sediments, wildlife, algal
   concentrations
Ground Level (low level) Photography

• Photographs, video, time lapse
  photography
  – Used at fixed or surveyed points of
    reference
  – Photos taken at specific times
  – Document scale with range poles
  – Photos can be pieced together to form
    panorama
  – Detect changes in vegetation, distribution/
    loss of wildlife
Importance of Remote Sensing for
Wetland/Water Quality Assessment
 • Ground access is often difficult
 • Able to sense a large area at a given
   point in time
 • Assess the impacts of point/non-point
   pollution
 • Wetlands on private lands can be
   monitored
Importance of Remote Sensing for
Wetland/Water Quality Assessment
 • Wetlands are included in Water Quality
   Standards (WQS)
   – Basis for wetland status/trend monitoring
     of state wetland resources
   – Wetland assessment, over the years, will
     help define spatial extent (quantity),
     physical structure (plant types, diversity,
     distribution), users, and wetland health
References
 Baghdadi, N., et.al. 2001. Evaluation of C-band SAR data for wetlands mapping. Int. J.
 of Remote Sensing. 22:71-88.

 Chopra, R., V.K. Verma, and P.K. Sharma. 2001. Mapping, monitoring and conservation
    of Harike wetland ecosystem, Punjab, India, through remote sensing. Int. J. of Remote
    Sensing. 22:89-98.

 Durand, Dominique, J. Bijaoui, and F. Cauneau. 2000. Optical remote sensing of
     shallow-water environmental parameters: a feasibility study. Remote Sensing of
     Environment. 73:152-161.

 Frazier, P.S., and K.J. Page. 2000. Water body detection and delineation with Landsat
 TM data. Photogrammetric. Engineering & Remote Sensing. 66:1461-1467.

 Jorgensen, P.V. and K. Edelvang. 2000. CASI data utilized for mapping suspended
     matter concentrations in sediment plumes and verification of 2-D hydredynamic modeling.
     Int. J. of Remote Sensing. 21:2247-2258.

 Keiner, Louis E. and X. Yan. 1998. A neural network model for estimating sea surface
     chlorophyll and sediments from Thematic Mapper imagery. Remote Sensing of
     Environment. 66:153-165.
References (cont.)
 Munyati, C. 2000. Wetland change detection on the Kafue Flats, Zambia, by
    classification of a multitemporal remote sensing image database. Int. J. of Remote Sensing.
    21:1787-1806.

 Rio, Julie N.R., and D.F. Lozano-Garcia. 2000. Spatial filtering of radar data
      (RADARSAT) for wetlands (brackish marshes) classification. Remote Sensing of Environment.
      73:143-151.

 Shepherd, I., et. al. 2000. Monitoring surface water storage in the north Kent marshes
 using Landsat TM images. Int. J. of Remote Sensing. 21:1843-1865.

 Tolk, B.L., et. al. 2000. The impact of bottom brightness on spectral reflectance of
 suspended sediments. Int. J. of Remote Sensing. 21:2259-2268.

 Toyra, Jessika, A. Pietroniro, and L.W. Martz. 2001. Multisensor hydrological
     assessment of a freshwater wetland. Remote Sensing of Environment. 75:162-173.

 Yang, M.D., R.M. Sykes, and C.J. Merry. 2000. Estimation of algal biological parameters
    using water quality modeling and SPOT satellite data. Ecological Modelling. 125:1-13.
References (cont.)
 http://baby.indstate.edu/gerstt/rscc/isurs2.html

 http://www.ducks.org/conservation/greatplains.asp

 http://www.epa.gov/owow/wetlands/wqual.html

 http://sfbay.wr.usgs.gov/access/quality.html

 http://terraweb.wr.usgs.gov/TRS/projects/SFBay/

 http://water.usgs.gov/nwsum/WSP2425.html

								
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