Primary study on the multi-layer remote sensing information extraction of desertification land types by using decision tree technology

Studies and discusses the auto-classification of desertification land types by using Landsat TM data in a typical desertification region of the oasis of Minqin county, China. According to the spectrum-reflecting characteristic, identifying and extracting methods are correspondingly used in order that the remote sensing data are drawn on maximally. At first, the non-desertification lands can be extracted from TM image by characteristics of Soil-Adjusted Vegetation Index (SAVI) and digital numbers. Then the major research focuses on the multi-layer information discernment and extraction of three types of desertification land-covers and their respondent grades. Each grade is an object and is processed to yield a layer. Overlaying can create a complete map of desertification, geometry and texture properties analysis and NDVI have been respectively utilized for distinguishing and classifying the different land types. The results show the application of decision tree and multi-layer technology could decrease the possibility of interaction and impact with others message and make target simplified in pixel identification. Meanwhile, the investigation and adjustment in the fields is quite important for analyzing the land-cover distribution and comparing the difference between grades.