Spectral mixture analysis for mapping land degradation in semi-arid areas

Spectral mixture analysis (SMA) is an image-processing technique used for t he analysis of airborne hyperspectral remote-sensing data which consist of a large number of spectral bands, typically over 100. In this paper the pos sibilities are examined of using SMA for the analysis of Landsat Thematic M apper satellite data with only six bands in the visible to shortwave-infrar ed wavelength. We use data from a semi-arid area in the Sanmatenga province of Burkina Faso, an area known to experience land-degradation problems. In SMA, we assume that pixels in an image consist of one or more homogeneous (uniform in character) or pure surfaces, the so called end-members. The end -members were derived from the image data on the basis of specific image ch aracteristics. Field data was collected to describe the characteristics of these end-members in terms of land cover, soil and degradation phenomena. T he end-members derived from the image analysis, although statistically pure in terms of image spectral characteristics, prove to be mixtures at a fiel d scale. This lack of purity is explained by the nature of semi-arid areas which is more heterogeneous than that of most other areas. The SMA yielded cover percentages for the end-members from which an unsupervised classifica tion was made. Comparison of the classification with the results of SMA sho ws that the latter provides information on the amount and spatial distribut ion of land characteristics such as land degradation.