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.
Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.
