Method for remote sensing monitoring of desertification based on MODIS and NOAA/AVHRR data

Desertification is one of the most serious ecological and environmental problems in the west of China. Understanding the distribution and development trend of desertification provides researchers important scientific basis for desertification control and rehabilitation. In this paper the authors proposed a method suitable for large-scale desertification monitoring using remote sensing techniques. First, five desertification indexes(MSAVI, FVC, Albedo, LST and TVDI) suitable for large-scale desertification monitoring using remote sensing technique were selected. In terms of the desertification climate types, the potential extent of desertification in China was respectively divided into four categories: dry sub-humid areas, semi-arid areas, arid areas, high and cold areas. Second, different desertification index systems were built for each area. Then based on analysis and comparison of current retrieval algorithms, the authors utilized a suitable algorithm on large scale to retrieve five desertification indexes with ten-day NOAA/AVHRR data set in 1995 and 16-day MODIS data set in 2001 in China. By assessing the classification accuracies of three types of classifiers, the authors selected decision tree classifier for desertification monitoring. Supported by desertification index system and the database of desertification indexes, the desertification status in China in 1995 and 2001 was classified by decision tree classifier, and analysis of desertification changes from 1995 to 2001 was also completed. Statistical result showed that the speed of desertification development was faster than that of rehabilitation, there was a trend of development as a whole and improvement locally in desertificated areas in China. The desertification monitoring results confirmed the practicability of the method founded in this paper for desertification monitoring.