Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images

Abstract

Retrogressive thaw slumps (RTSs) are among the most dynamic landforms resulting from the thawing of ice-rich permafrost. However, RTS distribution and evolution are poorly quantified because most of them occur in remote and inaccessible areas. In this study, we propose a method that integrates deep learning, change detection, and medial axis transform, aiming to automatically quantify the RTS development on multi-temporal images in the Beiluhe region on the Tibetan Plateau from 2017 to 2019. The images are taken by the Planet CubeSat constellation with high spatial and temporal resolution. The experiments show that automatic delineation based on deep learning can produce similar results to manual delineation, providing the potential of using these results to quantify the changes of RTS boundaries in different years. Our method reveals that among manually-delineated 342 RTSs in the Beiluhe region, 83% and 76% of them expanded from 2017 to 2018 and 2018 to 2019, respectively. For the expansion from 2017 to 2018, the average and maximum expanding areas are 0.20 ha and 1.47 ha, while the average and maximum retreat distances are 21.3 m and 91 m, respectively. For 2018 to 2019 the average and maximum expansion areas and retreat distances are 0.22 ha, 2.53 ha, 25.0 m, and 212 m, respectively. The results show that the method can quantify RTS development automatically on multi-temporal images but may miss some small and subtle RTSs. Moreover, this study provides the very first quantitative report on RTS development on the Tibetan Plateau, which helps to advance the understanding of permafrost degradation.

Keywords

• Change Detection
• Deep Learning
• Medial Axis Transform
• Permafrost
• Retrogressive Thaw Slumps