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<p>The thawing of ice-rich permafrost leads to the formation of thermokarst landforms. Precise mapping of retrogressive thaw slumps (RTSs) is imperative for assessing the degradation and carbon exchange of permafrost at both local and regional scales on the Tibetan Plateau (TP). However, previous methods for RTSs mapping rely on a large number of samples and complex classifiers with low automation level or unnecessary complexity. We propose an automatic mapping network (AmRTSNet) for producing decimeter-level RTSs maps from GaoFen-7 images based on deep learning. Both the quantitative metrics and qualitative evaluations show that AmRTSNet trained in the Beiluhe offers significant advantages over previous methods. Without further fine-tuning, we conducted RTSs automatic mapping based on AmRTSNet in the Wulanwula, Chumarhe, and Gaolinggo. Over 141,312 ha on the TP have been automatically mapped, comprising 926 RTS regions with a total RTS area of 2318.72 ha. The average statistics of the mapped RTSs show low roundness (0.38), moderate rectangularity (0.61), and high convexity (0.79). About 90% of the RTSs are smaller than 6 ha. The average aspect ratio is 2.18. RTSs are unevenly distributed in belt-like aggregations with dominant density peaks. RTSs often concentrate in hillslopes and along lateral streams, with more dense areas more likely to have larger RTSs.</p>

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Automatic mapping and pattern analysis of retrogressive thaw slumps on the central Tibetan Plateau using deep learning

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Yi Yuan,
  • Guiyun Zhou,
  • Jinzhi Ding,
  • Shihua Li,
  • Ziyin Liu,
  • Binbin He

摘要

The thawing of ice-rich permafrost leads to the formation of thermokarst landforms. Precise mapping of retrogressive thaw slumps (RTSs) is imperative for assessing the degradation and carbon exchange of permafrost at both local and regional scales on the Tibetan Plateau (TP). However, previous methods for RTSs mapping rely on a large number of samples and complex classifiers with low automation level or unnecessary complexity. We propose an automatic mapping network (AmRTSNet) for producing decimeter-level RTSs maps from GaoFen-7 images based on deep learning. Both the quantitative metrics and qualitative evaluations show that AmRTSNet trained in the Beiluhe offers significant advantages over previous methods. Without further fine-tuning, we conducted RTSs automatic mapping based on AmRTSNet in the Wulanwula, Chumarhe, and Gaolinggo. Over 141,312 ha on the TP have been automatically mapped, comprising 926 RTS regions with a total RTS area of 2318.72 ha. The average statistics of the mapped RTSs show low roundness (0.38), moderate rectangularity (0.61), and high convexity (0.79). About 90% of the RTSs are smaller than 6 ha. The average aspect ratio is 2.18. RTSs are unevenly distributed in belt-like aggregations with dominant density peaks. RTSs often concentrate in hillslopes and along lateral streams, with more dense areas more likely to have larger RTSs.