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SUBMERSED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL IMAGERY
Citation:
Williams, D J., T. M. O'Brien, N. B. Rybicki, AND R. B. Gomez. SUBMERSED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL IMAGERY. Presented at Environmental Monitoring and Assessment Program (EMAP) 2001, Coastal Management Through Partnerships, Pensacola, FL, April 24-27, 2001.
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:
Submersed aquatic vegetation (SAV) beds are an important resources for aquatic life and
wildfowl in the Potomac River and Chesapeake Bay region. SAV habitat is threatened in part by nitrogen loadings from human activities. Monitoring and assessing this resource using field based sampling and mapping using aerial photography is time consuming and costly. The use of airborne hyperspectral remote sensing imagery for automated mapping was investigated for near to real-time resource assessment and monitoring. Field surveys for several pilot sites determined SAV species, density, and distribution as well as water quality and optical parameters. Airborne hyperspectral imagery, together with m-situ spectral reflectance measurements using a field spectrometer, were obtained for the pilot sites in spring and early fall. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. My goal of the spectral database is to automate the image processing of hyper-spectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map several species of SAV, suspended sediment concentrations, chlorophyll, and wetland vegetation. The resulting imagery derived vegetation maps were assessed for overall accuracy using aerial photography and field based sampling, Ultimately, the species data could be used to study SAV population dynamics and relationships between environmental variables and invasive and native species of SAV. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.