Mapping and Characterizing Rock Glaciers in the Arid Western Kunlun Mountains Supported by InSAR and Deep Learning

Abstract

AbstractRock glaciers (RGs) manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. There is a complete lack of knowledge about RGs in the Western Kunlun Mountains, one of the driest mountain ranges in Asia, where extensive permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active RGs based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then, we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well‐trained model was applied for a region‐wide extensive delineation of RGs from Sentinel‐2 images to map the landforms that were previously missed due to the limitations of the InSAR‐based identification. Finally, we mapped 413 RGs across the Western Kunlun Mountains: 290 of them were active RGs mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The RGs are categorized by their spatial connection to the upslope geomorphic units. All the RGs are located at altitudes between 3,390 and 5,540 m with an average size of 0.26 km2 and a mean slope angle of 17°. Characteristics of the inventoried RGs provided insights into permafrost distribution in the Western Kunlun Mountains. The median and maximum surface downslope velocities of the active ones are 17 ± 1 and 127 ± 6 cm yr−1, respectively.