PROGRESS IN GEOGRAPHY ›› 2006, Vol. 25 ›› Issue (2): 131-138.doi: 10.11820/dlkxjz.2006.02.015

• Original Articles • Previous Articles    

Study On Land Degradation Mapping by Using Hyperion Data in HengShan Region of China

WU Jian11,2, 21, HE Ting12   

  1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying &|Planning Institute, Beijing 100035, China|2. East China Institute of Technology, Fuzhou 344000
  • Received:2005-12-01 Revised:2006-01-01 Online:2006-03-25 Published:2006-03-25


Land degradation, defined as the loss or the reduction of the potential utility or productivity of the land, is a major environmental problem in the world today. The land degradation process is generally divided into three classes: (1) physical degradation; (2) biological degradation, and (3)chemical degradation. The assessment of land degradation requires the identification of indicators such as soil vulnerability to erosion. Generally, the assessment of the state of land degradation can be carried out by using the Global Assessment of Soil Degradation (GLASOD) method. Hoosbeek et al. recommended this qualitative method to classify soil degradation by using remote sensing data. Degradation features can be detected directly or indirectly by using image data. Based on the Hyperion images, this paper brings forward a new mapping algorithm, called Land Degradation Index, aimed at land degradation in Hengshan region of China. It 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, by 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, i.e. mixture-classification, the spectral angle mapper and mixture-tuned 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.

Key words: endmember, land degradation index, mixture-classification, mixture-tuned matched filtering, the spectral angle mapper

CLC Number: 

  • X144