A Glacial Lake Mapping Framework in High Mountain Areas: A Case Study of the Southeastern Tibetan Plateau

Mapping glacial lakes is a prerequisite for understanding their responses to climate changes and assessing potential danger of glacial lake outburst floods (GLOFs). Although remote sensing technology has enabled continuous monitoring and assessment of global glacial lake evolution, accurately and reliably extracting glacial lakes in high mountain areas remains challenging. This study proposed a glacial lake mapping framework based on multisource remote sensing technique and an improved deep learning (DL) model to address diverse challenges associated with glacial lake mapping in high mountain areas. Test results obtained in the Southeast Tibetan Plateau (TP) region demonstrate that the framework achieves high accuracy, with measures of Dice, precision, recall, and intersection over union (IOU) reaching 0.8986, 0.9009, 0.8963, and 0.8287, respectively. It effectively mitigates the impacts of cloud cover, shadowing, glacial debris, lake-water turbidity, and freeze-thaw lake-water conditions on glacial lake delineation. This study provided a concrete solution for glacial lake mapping in high mountain areas with complex topography, and it supported technical advancements in GLOF risk identification.