Glacial Lake Detection from GaoFen-2 Multispectral Imagery Using an Integrated Nonlocal Active Contour Approach: A Case Study of the Altai Mountains, Northern Xinjiang Province

Due to recent global climate change, glacial lake outburst floods (GLOFs) have become a serious problem in many high mountain areas. Accurately and rapidly mapping glacial lakes is the basis of other glacial lake studies that are associated with water resources management, flood hazard assessment, and climate change. Most glacial lake detection studies have mainly used medium to coarse resolution images, whose application is limited to large lakes. Because small glacial lakes are abundant and because changes in these lakes are small and occur around the lake shores, fine-resolution satellite imagery is required for adequate assessments. In addition, the existing detection methods are mainly based on simply applying a threshold on various normalized difference water indices (NDWIs); this cannot give appropriate results for glacial lakes that have a wide range of turbidity, mineral, and chlorophyll content. In the present study, we propose a region-dependent framework to overcome the spectral heterogeneity of glacial lake areas using a nonlocal active contour model that is integrated with the NDWI. As the first trial, the glacial lakes were detected using high-resolution GaoFen-2 multispectral imagery in the test site of Altai Mountains (northern Xinjiang Province). The validation of the results was carried out using the manually digitized lake boundaries. The average probabilities of false positives $P_{FP}$ and false negatives $P_{FN}$ were found to be 0.0106 and 0.0039, respectively. After taking into consideration the spectral features of the water and making slight NDWI threshold adjustments, this method can also be used for lake detection in any glaciated environment elsewhere in the world.