Use of remote sensing to assess wetland and water quality
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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

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


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


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
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
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


References
References Assessment

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
References (cont.) Assessment

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.) Assessment

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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