Land degradation mapping based on hyperion data in desertification region of northwest China

Desertification is an alarming sign of land degradation in Henshan county of northwest china. Due to the considerable costs of detailed ground surveys of this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of land degradation. Degradation features can be detected directly or indirectly by using image data. In this paper, based on the Hyperion images of Hengshan desertification region of northwest china, a new algorithm aimed at land degradation mapping, called Land Degradation Index (LDI), was put forward. This new algorithm is based on the classified process. We applied the linear spectral unmixing algorithm with the training samples derived from the formerly classified process so as to find out new endmembers in the RMS error imagine. After that, using neutral net mapping with new training samples, the classified result was gained. In addition, after applying mask processing, the soils were grouped to 3 types (Kappa =0.90): highly degraded soils, moderately degraded soils and slightly degraded soils. By analyzing 3 mapping methods: mixture-classification, the spectral angle mapper and mixturetuned matched filtering, the results suggest that the mixture-classification has the higher accuracy (Kappa=0.7075) than the spectral angle mapper (Kappa=0.5418) and the mixture-tuned matched filter (Kappa=0.6039). As a result, the mixture-classification is selected to carry out Land Degradation Index analysis.