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RIPARIAN CHARACTERIZATION USING SUB-PIXEL ANALYSIS OF LANDSAT TM IMAGERY FOR USE IN ECOLOGICAL RISK ASSESSMENT
Citation:
Williams, D J., D. A. White, AND A. Englemann. RIPARIAN CHARACTERIZATION USING SUB-PIXEL ANALYSIS OF LANDSAT TM IMAGERY FOR USE IN ECOLOGICAL RISK ASSESSMENT. Presented at American Society for Photogrammetry & Remote Sensing Annual Conference, Portland, OR, May 19, 1999.
Impact/Purpose:
The objectives of this task are to:
Assess new remote sensing technology for applicability to landscape characterization; Integrate multiple sensor systems data for improved landscape characterization;
Coordinate future technological needs with other agencies' sensor development programs;
Apply existing remote sensing systems to varied landscape characterization needs; and
Conduct remote sensing applications research for habitat suitability, water resources, and terrestrial condition indicators.
Description:
Landuse/land cover and riparian corridor characterization for 7 major watersheds in western Ohio was accomplished using sub-pixel analysis and traditional classification techniques. Areas
representing forest, woodland, shrub, and herbaceous vegetation were delineated using a global positioning system (GPS). Vegetation type and percent dominance was quantified for use in the sub- pixel classification procedure. Two LandsatThematic Mapper (TM) imagery dates, late spring/early summer and midsummer, were used in the classification methodology to determined riparian vegetation composition, abundance, and spatial distribution. Riparian fragmentation indices were derived from the classification products for use in statistical model development in an ecological risk assessment, Landuse/land cover in arm adjacent to the riparian corridors (i.e. upland regions) were determined using a combination of unsupervised classification and spectral indices. Agricultural intensity as defined by NRCS runoff curve numbers, nutrient loads, and annual soil Joss using USLE was determined from the classified images. The ecological risk assessment (ECORISK) model uses the derived riparian patch metrics and agricultural intensity values with ancillary data, such as GIS calculated watershed infrastructure to predict in-stream aquatic biological integrity, The ECORISK model's unique use of sub-pixel analysis with traditional image classification methods is being explored for use in other ecoregions.