Identifying active retrogressive thaw slumps from ArcticDEM

Abstract

Permafrost degradation in the Arctic is accelerating and affects northern communities, ecosystems, and global soil carbon storage. However, the extent, distribution, and rates of permafrost degradation in the pan-Arctic remain unknown, contributing to the challenges of mapping and monitoring it in a harsh and remote environment. We applied feature extraction, deep learning, and crowdsourcing to an open-access, high-resolution (2 m), and multi-temporal digital elevation models (i.e. ArcticDEM), to identify retrogressive thaw slumps (RTSs), a dynamic form of permafrost degradation widespread across the Arctic. Specifically, we (1) developed an automated pipeline to process approximately 200 TB of ArcticDEM data; (2) designed feature extractors (pixel-wise elevation differences, polygons of elevation reductions, lines of narrow-steep slopes, and RTS headwall lines) to identify slumps; (3) trained a super-efficient object detection algorithm based on deep learning (YOLOv4) and used it to locate RTSs from composite imagery derived from the ArcticDEM; (4) combined the extracted features and the bounding boxes output by YOLOv4 to obtain mapping results at a manageable level; and (5) developed a crowdsourcing system and invited volunteers to validate and refine the results. The final map included 2494 RTSs (actively expanding during ArcticDEM observation) across the Arctic. The results also show that (1) it is necessary to combine the extracted features and deep learning to remove many false positives in the scenario with limited training data, but large regions to map; (2) some hotspots of RTSs need further and detailed investigation, including an RTS cluster in Greenland; (3) the crowdsourcing system is useful for the validation of a large dataset, but responses to this work were limited, possibly because RTSs are a quite specific topic that not many people are familiar with. The results likely miss many RTSs due to the limitation in the method for identifying RTSs and uncertainties as well as short observation periods of ArcticDEM at many locations. This study provides data and serves as a starting point to develop a global inventory and better understand permafrost thaw in the pan-Arctic using very high resolution remote sensing.