Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods

Abstract

Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between −80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends.

Keywords

• Qinghai-Tibet Plateau
• deformation prediction
• LSTM
• MT-InSAR
• permafrost degradation