Response of Ecological Environment to Climate
Change in the Source Area of the Yangtze River
Based on the Observation During 2005-2015

Song, Lianchun; Tian, Qing; Li, Zongjie

doi:10.15244/pjoes/136183

Cited by 1 publication

(1 citation statement)

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“…The Tuotuo River Basin is a fragile ecosystem susceptible to external disturbances and promptly responds to temperature increases. In recent years, due to rising global temperatures, glacier ablation, and soil erosion, permafrost degradation in the Tuotuo River Basin has been increasing every year [ 11 , 12 ]. The Tuotuo River Basin and the whole QTP are recognized as one of the world’s most ideal and distinctive ecological and environmental change research areas.…”

Section: Introductionmentioning

confidence: 99%

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

Ping

Liu

Zhang

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%