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Variations of glaciers are important parameters for monitoring glacial change. Although optical remote sensing method can extract variations of glaciers effectively and accurately in cloudless regions, these variations are difficult to extract in cloudy conditions and bad weather. In this paper, a new method is presented, based on the decorrelation of repeat SAR interferometry, to extract the variations of glaciers. This method uses the decorrelation of the inland glacier’s surface to extract the variation of glacier by comparing the coherence of the glacier and land cover in threshold values. For validation of this method, we compared classification results with that derived from TM images. An accuracy of better than 89% can be achieved if we consider the classification result from TM image as the ground truth. Results show that this method provides an effective way to identify icy areas from the coherent image.
Variations of glaciers are important parameters for monitoring glacial change. Although this paper, a new method is presented , based on the decorrelation of repeat SAR interferometry, to extract the variations of glaciers. This method uses the decorrelation of the inland glacier’s surface to extract the variation of glacier by comparing the coherence of the glacier and land cover in threshold values. For validation of this method, we compared classification results with that derived from TM images. An accuracy of better than 89% can be achieved if we consider the classification result from TM image as the ground truth. Results show that this method provides an effective way to identify icy areas from the coherent image.