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A major problem for mapping shallow water zones by the analysis of remotely sensed data is that contrast effects due to water depth obscure and distort the spectral nature of the substrate. This paper outlines a new method which unmixes the exponential influence of depth in each pixel by employing a mathematical constraint. This leaves a multispectral residual which represents relative substrate reflectance. Input to the process are the raw multispectral data and water attenuation coefficients derived by the co-analysis of known bathymetry and remotely sensed data. Outputs are substrate-reflectance images corresponding to the input bands and a greyscale depth image. The method has been applied in the analysis of Landsat TM data at Hamelin Pool in Shark Bay, Western Australia. Algorithm derived substrate reflectance images for Landsat TM bands 1, 2, and 3 combined in colour represent the optimum enhancement for mapping or classifying substrate types. As a result, this colour image successfully delineated features, which were obscured in the raw data, such as the distributions of sea-grasses, microbial mats and sandy areas.
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