Tracking Rapid Permafrost thaw Through Time: Exploring the Potential of Convolutional Neural Network based Models

This paper presents the novel use of convolutional neural network (CNN)-based machine learning models for remotely detecting and monitoring retrogressive thaw slumps (RTS) in high latitude northern permafrost using open-source Sentinel-2 satellite data. RTS are indicative of rapid permafrost thaw (RPT), the accelerated release of greenhouse gases (GHG) and potentially runaway changes in the cryosphere. Attempts to quantify GHG emissions from RTS are inhibited by a lack of information on RTS incidence and area affected. We show that site-specific CNN models can be used to produce time series data on rapid RTS development that allow for the approximation of associated GHG emissions. For the sites assessed we achieve good model precision, recall and F1 values of > 0.8. The short time series studied so far do not reveal clear trends in RTS development. These limitations arise from the low resolution of Sentinel-2 data (10 m) and limited availability and diversity of validated training data. The capability shown here is the first step towards achieving automated monitoring of rapid environmental change in permafrost using satellite data. This work highlights the need for ready access to open-source high resolution satellite data and permafrost field data if the potential of such approaches is to be fully realized.