Improved classification of soil salinity by decision tree on remotely sensed images

Soil salinity, caused by natural or human-induced processes, is not only a major cause of soil degradation but also a major environmental hazard all over the world. This results in increasing impact on crop yields and agricultural production in both dry and irrigated areas due to poor land and water management. Multi-temporal optical and microwave remote sensing can significantly contribute to detecting spatial-temporal changes of salt-related surface features. The study area is located in the west of Jilin Province, Northeast China, which is one of most important saline-alkalized areas in semi-arid and and area in North China. Decision tree classifiers are used to improve the classification of soil salinity on Landsat Thematic Mapper (TM) images in later autumn of 1996. The Kauth-Thomas (K-T) transformation was performed after TM image preprocessing including image registration, mosaic and resizing for the study area. Then the first component of KT transformation, TM 6 imagery (thermal infrared imagery), and NDVI (Normalized Difference Vegetation Index) from TM 4 and TM 3 images, were density-sliced respectively to establish suitable feature classes of soil salinity as the decision nodes. Thus, the classification of soil salinity was improved using decision trees based on these feature classes. Compared with the conventional maximum likelihood classification, this method is more effective to distinguish soil salinity from mixed residential and sand areas in the west of Jilin Province. China.