Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

Rock glaciers 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. Knowledge of rock glaciers is completely lacking in the West Kunlun, one of the driest mountain ranges in Asia, where widespread permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers 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 rock glaciers 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 rock glaciers across the West Kunlun: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,389 m and 5,541 m with an average size of 0.26 km 2 and a mean slope angle of 17°. The mean and maximum surface downslope velocities of the active ones are 24 cm yr -1 and 127 cm yr -1 , respectively. Characteristics of the rock glaciers of different categories hold implications on the interactions between glacial and periglacial processes in the West Kunlun.

Rock glaciers 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 rock glaciers 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 rock glaciers 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 rock glaciers 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 rock glaciers across the Western Kunlun Mountains: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,390 m and 5,540 m with an average size of 0.26 km2 and a mean slope angle of 17°. The median and maximum surface downslope velocities of the active ones are 17±1 cm yr-1 and 127±6 cm yr-1, respectively. Characteristics of the inventoried rock glaciers provided insights into permafrost distribution in the Western Kunlun Mountains.

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