Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Abstract

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, occurring when ice-rich permafrost is exposed and thaws. However, large-scale quantification of RTS-related mass wasting in Arctic permafrost landscapes is currently lacking, despite its importance to understand impacts on local environments and the global permafrost carbon cycle. Generating differential digital elevation models (dDEMs) from TanDEM-X single-pass Interferometric SAR (InSAR) observations enables us to quantify volume changes induced by rapid permafrost thaw. To extend this capability across the entire Arctic permafrost region, automation in data processing and RTS detection is essential. This study introduces a method that employs deep learning on InSAR-derived dDEMs to map RTSs and quantify volume changes from RTS activity. We chose eleven study sites with a total area of 71 400 km² to reflect the diverse character of Arctic environments for model training, testing, and inference. Our trained UNet++ model delivers a scalable solution for mapping RTSs and quantifying mass wasting towards a pan-Arctic scale, achieving segmentation accuracies of 0.58 (Intersection over Union) and classification accuracies of 0.75 (F1) on previously unseen test sites, with volume change estimates from model predictions being within $\pm$ 20% of the actual values. We found a total of almost 5000 RTSs active between 2010 and 2021 with volume change rates between 40.75 m³yr⁻¹km${}^2$ for sites in the Siberian to 1164.11 m³yr⁻¹km${}^2$ in the Canadian Arctic.