Extracting information about saline soils from remote sensing data is useful, particularly given the environmental significance and changing nature of these areas in arid environments. One interesting case study to consider is the delta oasis of the Weigan and Kuqa rivers, China, which was studied using a Landsat Enhanced Thematic Mapper Plus (ETM+) image collected in August 2001. In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Principal component analysis (PCA) is a popular data reduction technique used to help build a decision tree; it reduces complexity and can help the classification precision of a decision tree to be improved. A decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area. According to the research, the third principal component (PC3) is an effective variable in the decision tree classification for salinized soil information extraction. The research demonstrated that the PC3 was the best band to identify areas of severely salinized soil; the blue spectral band from the ETM+ sensor (TM1) was the best band to identify salinized soil with the salt-tolerant vegetation of tamarisk (Tamarix chinensis Lour); and areas comprising mixed water bodies and vegetation can be identified using the spectral indices MNDWI (modified normalized difference water index) and NDVI (normalized difference vegetation index). Based upon this analysis, a decision tree classifier was applied to classify landcover types with different levels of soil saline. The results were checked using a statistical accuracy assessment. The overall accuracy of the classification was 94.80%, which suggested that the decision tree model is a simple and effective method with relatively high precision.
This paper uses 3S technology in macroscopic. Combining the integrated technology of ecological quantity analytical method with GIS technology through ArcGIS and Fragstats, the authors study the images of 1972, 1990, 2001, and 2005 and obtained land use data in Jinghe County. Then, the change of land use/cover and landscape pattern had been analyzed in the Jinghe County of Xinjiang. The conclusions were as follows: (1) The trend of LUCC is that the area of oasis expands slowly in nearly 33 years between 1972 to 2005 in Jinghe County. (2) The water area is mainly influenced by Ebinur Lake, so the area expands a little in this period. (3) The area of salinization-land expands at first and reduces later. The area of sand land decreases and the other land class increases, while the probability of transfer is always high. (4) Landscape change is also obvious throughout the decades. Overall, landscape density increases, the largest path index decreases at first and expends later, the weight area index decreases, and the shape of landscape becomes regulated. The nearest distances, the degrees of reunite, and outspread decreases. It shows that the connection of the main path in 1972 is better than 2005, wherein the patch becomes more complex. From the changes of Shannon’s Diversity Index and Shannon’s Evenness Index, we know that the diversity of landscape and the Interspersion Juxtaposition Index increase. The degree of diversity landscape and fragmentation increase also shows that the land uses become more complex. All in all, it is essential to intensify the spatial relationships among landscape elements and to maintain the continuity of landscape ecological process and pattern in the course of area expansion.
