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

• SPOT• RADARSAT• SAR (Synthetic

Aperture Radar)• Spectron SE-590

Spectroradiometer

• CASI (Compact Airborne Spectrographic Imager)

• Aerial Photography

• Ground Level (low 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 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

ReferencesBaghdadi, 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