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PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING
Citation:
Williams, D J., T. M. O'Brien, N. B. Rybicki, AND R. B. Gomez. PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING. ENVIRONMENTAL MONITORING AND ASSESSMENT 81(1):383-392, (2003).
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:
The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral reflectance measurements using a field spectrometer, were obtained for the pilot sites in spring and early fall of 2000. Field-based shoreline surveys for the study area determined SAV presence, species, and distribution. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The goal of the spectral database is to automate the image processing of hyperspectral 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 two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. Map accuracy was high and had very low false positive detections. ne algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.