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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.

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significance uses of wetlands
Significance/Uses of Wetlands
  • Filter for clean water supply
  • Support a diversity of vegetation
  • Wildlife habitat
  • Main components
    • Hydrology
    • Soil
    • Vegetation
significance uses of wetlands1
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
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 monitoring1
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



SAR (Synthetic Aperture Radar)

Spectron SE-590 Spectroradiometer

CASI (Compact Airborne Spectrographic Imager)

Aerial Photography

Ground Level (low level) Photography

Remote Sensors Used
landsat tm or mss
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
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 cont1
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
  • 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
  • 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
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
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
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
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
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 assessment1
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
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 cont1
References (cont.)