Spectral unmixing versus spectral angle mapper for land degradation assessment: a case study in Southern Spain

Unlike conventional sensor systems such as Landsat-TM, Spot-MX or IRS-LISS, which acquire data in only a few spectral bands, the development of scanner systems that acquire data in many narrow-wavelength bands allows the use of almost continuous reflectance data in studies of the Earth’s surface. This not only produces laboratory-like reflectance spectra with absorption bands specific to object properties, but also helps increase accuracy of mapping surface features. Classification by means of spectral matching thus becomes more feasible. With so much information, the well-known problem of mixed pixels can be solved by a mixture model, which is commonly assumed to occur in a linear fashion. In this study, we compare linear unmixing and spectral angle matching techniques to assess the classification performance for identifying and mapping ‘desert like’ surface features in southern Spain. These features include desert pavements, calcareous, gypsiferous and saline surface soils. Although spectral unmixing helps to assign a pixel to a dominant class, the data is affected by illumination variations caused by topography, so that selection of end member can be biased. By comparison, the spectral angle matching technique compares only the angle between known and unknown spectra, which uses only the direction and not the length of the spectral vector. It is therefore insensitive to the gain factor caused by surface illumination conditions and thus more suitable in areas with high illumination differences. On the other hand, linear unmixing calculates, for each pixel, the abundance of pixel components. Present study shows that linear unmixing seems to provide more realistic results for mapping “desert like” surface features as compared to spectral angle mapper.