LAUSR

In order to monitor the response of thermokarst lakes on the Qinghai–Tibet Plateau (QTP) to rapid climatic changes and human activities, an automated method for extracting shorelines from Chinese GaoFen-2 (GF-2) imagery is proposed. First, the water index (WI) images and the potential lake areas are calculated from the preprocessed multispectral imagery and digital elevation model data, respectively. Second, the initial segmentation obtained by global thresholding of the WI images and masking in the potential lake areas are used to implement the contour initialization of active contours models efficiently. Finally, the nonlocal active contours (NLAC) approach is applied to refine the initial segmentation of the WI images, and the final shoreline vector files are produced by some simple and automatic postprocessing steps. Experiments on the GF-2 imagery demonstrate that 1) by exploiting the capability of WI to locate the approximate shoreline effectively around the evolving contour, the processing time of the proposed method can be saved significantly; 2) the NLAC approach can efficiently identify the shoreline by integrating the nonlocal interactions between pairs of patches inside and outside the lake; and 3) the proposed method can conveniently adapt to the multitemporal and multifeature image analysis. Using the manual digitized shorelines as the reference data, an average error of less than one pixel with standard deviation of 0.1320 can be obtained. These results prove that the proposed method is feasible for the identification and monitoring of thermokarst lakes on the QTP.