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Use of Remote Sensing to Assess Wetland and Water Quality

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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Use of Remote Sensing to Assess Wetland and Water Quality

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  1. Use of Remote Sensing to Assess Wetland and Water Quality By: Rodney Farris SOIL 4213

  2. Significance/Uses of Wetlands • Filter for clean water supply • Support a diversity of vegetation • Wildlife habitat • Main components • Hydrology • Soil • Vegetation

  3. 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

  4. 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

  5. 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

  6. Landsat TM & MSS SPOT RADARSAT SAR (Synthetic Aperture Radar) Spectron SE-590 Spectroradiometer CASI (Compact Airborne Spectrographic Imager) Aerial Photography Ground Level (low level) Photography Remote Sensors Used

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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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