An enhanced image stacks method for mapping long-term retrogressive thaw slumps in the Tibetan Plateau

Abstract

Retrogressive thaw slumps (RTSs) are severe manifestations of permafrost degradation with profound implications for regional environments and ecosystems. Previous studies heavily rely on high-resolution imagery and deep learning methods for RTS mapping. However, the acquisition of high-resolution imagery and the extensive computation of the deep learning-based method present challenges for long-term and large-scale monitoring. The image stacks method can overcome the defects of deep learning but is not sensitive in low-productivity ecosystems. This study proposes a feature-enhanced image stacks method. Instead of utilizing the Normalized Difference Vegetation Index (NDVI) directly in time-series change detection, the method employs the ratio of NDVI to its background value to enhance weak RTS signals caused by little change in NDVI or climate variation. A case study applied in the source region of the Yangtze River (SRYR), Tibetan Plateau, indicates that the method can amplify the RTS signal by more than 50 %, yielding accuracy slightly lower than the deep learning methods based on high-resolution imagery, but with a speed advantage of nearly an order of magnitude. The overall precision is 0.74, the F1 score is 0.73, and the maximum Intersection over Union (IOU) is 0.8. The delineation of RTSs takes about half an hour for the entire study area (158,000 km²), even with relatively low hardware specifications. Besides, the experiment conducted in the Horton Delta in the Arctic also demonstrates a good generalization of the method, with signal enhancement exceeding 80 %. This study confirms the feasibility of using medium-resolution data for long-term and large-scale RTS monitoring and will contribute to understanding the impact of climate change on permafrost dynamics in cold regions.