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Spectral mixture analysis in land degradation

Spectral mixture analysis was applied to multi temporal Landsat thematic mapper (TM) and advanced visible infrared imaging spectrometer (AVIRIS) data in order to analyse the suitability of linear spectral unmixing for identifying soil degradation and the erosional state of soils on landscape scale (soil condition mapping). These experiment objectives were successfully achieved, providing both quantified assessments of soil conditions and precise mapping of soil conditions with AVIRIS and Landsat TM data. It was possible to overlay the resulting maps on topographic maps at scale 1 to 50 000, so that they can be considered a valuable alternative to conventional mapping approaches. One of the most important aspects of the work is that the method is reproducible (standardized data processing, including the use of spectral libraries), and that it can be applied to other study sites with equivalent or similar lithological conditions. For the purpose of mapping green vegetation abundance, a strategy was adapted which aimed at minimizing the number of endmember spectra while optimizing the selection of these endmembers for each pixel. Band residuals and, to a lesser extent, the root mean squared unmixing error seemed to provide valid criteria for controlling this selection process. The approach was based on the assumption that the mixed reflectance signature of a pixel is primarily conditioned by 3 components which represent so called foreground and background materials, and shade (in order to account for albedo differences). The method was applied to the high spectral resolution AVIRIS data from the study site. In this case, several 3 endmember configurations were tested for each pixel, and the algorithm finally selects the endmember set which produces the smallest band residuals between measured and modelled spectrum.

Reported by

Joint Research Centre, Ispra
21020 Ispra
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