Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain

Abstract Desertification is a severe stage of land degradation, manifested by “desert-like” conditions in dryland areas. Climatic conditions together with geomorphologic processes help to mould desert-like soil surface features in arid zones. The identification of these soil features serves as a useful input for understanding the desertification process and land degradation as a whole. In the present study, imaging spectrometer data were used to detect and map desert-like surface features. Absorption feature parameters in the spectral region between 0.4 and 2.5 μm wavelengths were analysed and correlated with soil properties, such as soil colour, soil salinity, gypsum content, etc. Soil groupings were made based on their similarities and their spectral reflectance curves were studied. Distinct differences in the reflectance curves throughout the spectrum were exhibited between groups. Although the samples belonging to the same group shared common properties, the curves still showed differences within the same group. Characteristic reflectance curves of soil surface features were derived from spectral measurements both in the field and in the laboratory, and mean reflectance values derived from image pixels representing known features. Linear unmixing and spectral angle matching techniques were applied to assess their suitability in mapping surface features for land degradation studies. The study showed that linear unmixing provided more realistic results for mapping “desert-like” surface features than the spectral angle matching technique.