Creating a Pan-Arctic Retrogressive Thaw Slump Dataset with Harmonized Sentinel-2 Data and Deep Learning Methods

In a rapidly changing permafrost environment driven by climate change and anthropogenic disturbances, tracking geomorphological dynamics is a crucial task, not only to provide hazard monitoring, but also to evaluate climatological feedback processes. Yet, the impact on rapid permafrost disturbances on the Earth system is still uncertain, making the availability of reliable, long term data a very important building block to understand the interconnections and feedbacks between several environmental subsystems. 

Specifically, Retrogressive Thaw Slumps (RTS) are a major mass-wasting phenomenon and a rapid disturbance in ground-ice rich permafrost landscapes. They can mobilize large quantities of formerly frozen ground and consequently sediment, carbon, and nutrients. Once initiated they can grow and develop broader erosion disturbances. Over years and decades they can undergo polycyclic behaviour of initialization, growth, stabilization, and re-activation. The spatial distribution and temporal dynamics of RTS are generally poorly quantified so far on a pan-arctic scale, except for some regions covered by more intensive research. 

Multiple methods and data are used to map permafrost disturbances like RTS, including in-situ mapping. However, due to the remoteness and reduced accessibility, earth observation data is the primary source of RTS inventories. While RTS mapping is also done manually utilizing expert knowledge from high-resolution remote sensing imagery, machine learning techniques are increasingly used to segment permafrost features from satellite images. However, due to the requirement to process large amounts of data and also the reduced availability of suitable image data, especially in the high-latitudes, these datasets still often lack the temporal and spatial coverage to derive insights related to the recent global environmental changes. Current advancements in artificial intelligence based inference methods make feature segmentation now much more feasible and efficient, so activities for mapping RTS based on high resolution PlanetScope images and deep-learning methods, such as the DARTS dataset, already cover large RTS affected regions. Nevertheless, a full pan-arctic coverage over multiple time-steps is still lacking, thus far. 

To expand the existing body of RTS inventories, we use a convolutional neural network to detect these permafrost features from Sentinel 2 imagery to create a multi-year dataset of detected thaw slumps in the circumpolar arctic. The comparison with existing manually labelled and automatically derived high resolution thaw slump inventories provides a quantifiable verification to estimate uncertainties. This is crucial for evaluating Sentinel-2 as a high resolution dataset with favourable properties in terms of data availability and processing requirements compared to commercial and access restricted VHR imagery. Our work can underpin downstream tasks to extend RTS classification, understanding trigger mechanisms and improve vulnerability mapping. Also, time series of RTS disturbance data may be used for the temporal and spatial correlation with climate reanalysis and atmospheric datasets for large scale climate change impact modelling and feedback evaluation over the permafrost domain. Additionally, the open architecture of the processing pipeline can be used to implement near real-time monitoring services based on the Sentinel-2 data release stream for public access. We present ongoing work on the RTS segmentation dataset and current key downstream results.