Remote sensing assessment of salinization impacts in the tarim basin: The delta oasis of the ugan and kuqa rivers


Extracting information about saline soils from remote sensing data can be useful, particularly given the environmental significance and changing nature of these soils in arid environments. One interesting case study is the delta oasis of the Ugan and Kuqa rivers in China's Xinjiang region, which was studied using a landsat enhanced thematic mapper plus (ETM+) image collected in August 2001. In recent years, decision tree classifiers have been used successfully 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 improve the classification precision of a decision tree. 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. The third principal component (PC3) is an effective variable in the decision tree classification for salinized soil information extraction. 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 Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Based upon this analysis, a decision tree classifier was applied to classify land cover types with different levels of soil saline. The overall accuracy of the classification was 94.80%, which suggests that the decision tree model was a simple and effective method with relatively high precision. © 2010 Springer Science+Business Media B.V.

Publication Title

Water and Sustainability in Arid Regions: Bridging the Gap Between Physical and Social Sciences