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


									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
  • Some Areas Around
    Wetlands are
     – Some used/converted for
       agricultural use
       (crops, forage, timber)
     – Irrigation source
     – Reduction or prevention of
     – Flood control
     – Non-point/point source runoff
Wetland and Water Quality Monitoring
 • Water Storage Capability
   – Size of wetlands
   – Extent of water-spread and its seasonal
   – 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
 • SAR (Synthetic
                       • Aerial Photography
   Aperture Radar)
                       • Ground Level (low
 • Spectron SE-590
                         level) Photography
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
 • 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
 • Low reflectance of water in infrared
 • 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
 • 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
SAR–Synthetic Aperture Radar               (C-Band)

 • Detects changes in surface soil moisture
 • 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
   – 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
Ground Level (low level) Photography

• Photographs, video, time lapse
  – Used at fixed or surveyed points of
  – Photos taken at specific times
  – Document scale with range poles
  – Photos can be pieced together to form
  – 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
 • Wetlands on private lands can be
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
 Baghdadi, N., 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.

 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.

 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.)

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