Investigating Automatic Detection Methods for Retrogressive Thaw Slumps with TanDEM-X-Derived Digital Elevation Models
Creators: name: Maier, Kathrin
name: Bernhard, Philipp
name: Hajnsek, Irena
Publisher: name: ETH Zurich
Year: 2023

description: Permafrost underlies approximately 15 % of the landmass in the Northern Hemisphere and is becoming more susceptible to rapid thawing as the climate continues to warm (Obu et al. 2019). When ice-rich permafrost thaws it can alter the surface characteristics of a landscape which is commonly referred to as thermokarst. Retrogressive Thaw Slumps (RTS) are emerging as one of the most dynamic types of thermokarst, varying strongly in shape and thawing behavior. The prevalence and distribution of RTSs on a pan-Arctic scale are not well understood and so is its potential contribution in the Arctic carbon-climate feedback  (Kokelj et al. 2013). High-resolution Digital Elevation Models (DEMs) are a valuable tool for monitoring surface characteristics of thermokarst features and  track changes over time, especially being able to directly measure volumetric changes in RTS development essential for deriving carbon mobilisation rates which in turn has a great potential to improve our understanding of the impact of rapid thaw to carbon release and climate feedback mechanisms (Turetsky et al. 2020). The TanDEM-X missions merits investigation for a comprehensive monitoring of rapid permafrost thaw and directly retrieve information about volumetric change rates and thus carbon mobilization by differencing multi-temporal DEMs due to the full Arctic coverage and suitable spatial resolution. This approach has already been successfully applied to single-pass InSAR-based time-series DEM analysis to detect and quantify volumetric change rates and potential carbon mobilization of RTSs in several test sites in the Arctic permafrost region between 2011 and 2017 (Bernhard et al. 2020, Bernhard et al. 2022b).    In this paper we present the learnings from a case study in the Northwestern Territories, Canada investigating the potential of TanDEM-X derived DEMs for automatic detection of RTSs over large geographic scales based on deep learning. We developed a detection method using the instance segmentation model Mask R-CNN (He et al. 2017) and tested its performance on the difference DEM images on accurately detecting and delineating active RTS development. The focus of this study lays on the preparation of input data for training and inference by testing with various input data combinations, tile sizes and data augmentation. Growing vegetation and late laying snow packs impact the height estimates (Nagler and Rott 2000, Zwieback et al. 2018), as well as viewing geometries from different orbit directions of the satellite platforms which in turn could potentially lead to false RTS detections or incorrect volumetric change estimates. We therefore assessed height and volume change accuracies with respect to uncertainties due to seasonal vegetation and snow cover changes, penetration characteristics of different SAR polarizations as well as influences of different viewing geometries of the TanDEM-X observations at known RTS locations. Our test sites include the upland region to the east of the Mackenzie River Delta and the Peel Plateau which both showed recent RTS activity (Kokelj et al. 2013). We selected this region due to the availability of TanDEM-X acquisitions with a variety of different observation settings as well as mapped RTS locations (Bernhard et al. 2022a).   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