Study On Monitoring Mechanism And Method Of Soil Salinization In Songliao Plain

China is one of the most serious salinization countries. In the late of last century, for the drought caused by global climate change and over-exploitation of water, soil and biological resources, problem of environment degradation, soil salinization and grass degradation grows more and more serious in Songliao Plain, such as expansion of soil salinization, seriously damaged of ecological environment, which has serious impact on the local sustainable development. As a rapid and accurate method to get spatial information, RS technology has an important meaning on monitoring, management and utilization of soil salinization, maintaining regional ecological sustainable development.Salinization information can be extracted from MSS image in 1975, TM image in 1990 and ETM image in 2001 by interactive method, and the salinization change of nearly 30 years in Songliao Plain can be obtained based on the analysis of GIS. Corrected ASTER images and TM images of the study area using 6S atmospheric correction model to study the spectral characteristics of ASTER image and TM image, and with the soil sampling data soil salinity was quantitatively inversed in Songliao Plain. To improve the retrieval accuracy, Research conducted on the reflectivity including the number of countdown, the form of a variety of differential mathematical transformation. The results show that these transformations can improve the retrieval accuracy. In order to isolate soil, three kinds of classification methods: the maximum likelihood value of the classification, artificial neural networks and decision tree classification are discussed. Results show that the decision tree classifier can achieve higher classification accuracy. THSI paper tries to use the HSI hyper-spectral data of environmental mitigation Satellite (HJ-1A) to quantitative inversed soil salinity. In the course of the study, used FLAASH model for atmospheric correction on hyperspectral data, and carried out correlation analysis of single-band on the corrected image and its transformation to determine the salinity sensitive band in the HSI. To eliminate the high correlation impact between the spectral bands and improve the retrieval accuracy, used partial least squares regression (PLSR) method to extract the image reflectance and its biggest transformation in the form to explain the main component of salt content, and predicted local soil salt content and salt ion content.In order to study the number variation of salinization and transfer rules, using the result of interpretation and classification, several main class will be generated in Markov transition matrix, analyzed the temporal and spatial variation characteristics of soil salinity and the law. Finally, in the guidance of landscape ecology, using the landscape index, carried on the quantitative analysis on changes in the landscape pattern, revealed the characteristics and process of landscape pattern.