Spatial analysis of interannual changes in thermokarst lakes across the Qinghai-Tibet Plateau with Time-Series SAR imagery

• Obtained the changes at the freezing-thawing transition point of thermokarst lake. • Constructed thermokarst lake features from Sentinel-1A SAR images. • Introduced CBAM to improve U-Net and enhanced thermokarst lake extraction accuracy. • Obtain the interannual variation dataset of thermokarst lake on the QTP (2015–2022) • Thermokarst lake area changes were highly correlated with temperature from 2015 to 2022. The climate changes on the Qinghai-Tibet Plateau (QTP) have led to the widespread development of thermokarst lakes in permafrost regions. The seasonal variations in temperature and precipitation in the permafrost regions of the QTP affect the intra-annual state of thermokarst lakes. The intra-annual seasonal variations of thermokarst lakes pose challenges for accurately capturing inter-annual changes. To accurately obtain inter-annual change information of thermokarst lakes, this study proposes a method for capturing inter-annual changes of thermokarst lakes based on the “thaw-freeze” transition points and time-series SAR imagery. This method identifies the “thaw-freeze” transition points for thermokarst lakes across the entire Qinghai-Tibet Plateau, ensuring that each lake is in a similar thermal state, and proposes an improved U-net-based extraction algorithm for thermokarst lakes. This algorithm leverages the transition points and SAR imagery’s polarization, statistical, and temporal features to accurately capture the changes in thermokarst lakes over the years. The study obtained the extent and inter-annual changes of thermokarst lakes from 2015 to 2022, achieving an accuracy of 91.01% and an F1 score of 0.92. The change extraction results identified spatial heterogeneity in the changes of thermokarst lakes on the QTP. Further investigation explored the impacts of surface temperature, precipitation, and evapotranspiration on the changes in thermokarst lakes. The analysis revealed a higher correlation between thermokarst lake area changes and land surface temperature, while the correlation with precipitation and evapotranspiration changes was relatively lower.

Authors

Institutions

Publication Details

Journal
Journal of Hydrology
Published
2025-04-08
DOI
https://doi.org/10.1016/j.jhydrol.2025.133269
Citations
2
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
2.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatial analysis of interannual changes in thermokarst lakes across the Qinghai-Tibet Plateau with Time-Series SAR imagery

Qikai Shen, Qihao Chen, Xiuguo Liu
2 citations
Journal of Hydrology
Cryospheric studies and observations
2.60
article

Spatial analysis of interannual changes in thermokarst lakes across the Qinghai-Tibet Plateau with Time-Series SAR imagery

Qikai Shen, Qihao Chen, Xiuguo Liu
article en
2 citations

Abstract

• Obtained the changes at the freezing-thawing transition point of thermokarst lake. • Constructed thermokarst lake features from Sentinel-1A SAR images. • Introduced CBAM to improve U-Net and enhanced thermokarst lake extraction accuracy. • Obtain the interannual variation dataset of thermokarst lake on the QTP (2015–2022) • Thermokarst lake area changes were highly correlated with temperature from 2015 to 2022. The climate changes on the Qinghai-Tibet Plateau (QTP) have led to the widespread development of thermokarst lakes in permafrost regions. The seasonal variations in temperature and precipitation in the permafrost regions of the QTP affect the intra-annual state of thermokarst lakes. The intra-annual seasonal variations of thermokarst lakes pose challenges for accurately capturing inter-annual changes. To accurately obtain inter-annual change information of thermokarst lakes, this study proposes a method for capturing inter-annual changes of thermokarst lakes based on the “thaw-freeze” transition points and time-series SAR imagery. This method identifies the “thaw-freeze” transition points for thermokarst lakes across the entire Qinghai-Tibet Plateau, ensuring that each lake is in a similar thermal state, and proposes an improved U-net-based extraction algorithm for thermokarst lakes. This algorithm leverages the transition points and SAR imagery’s polarization, statistical, and temporal features to accurately capture the changes in thermokarst lakes over the years. The study obtained the extent and inter-annual changes of thermokarst lakes from 2015 to 2022, achieving an accuracy of 91.01% and an F1 score of 0.92. The change extraction results identified spatial heterogeneity in the changes of thermokarst lakes on the QTP. Further investigation explored the impacts of surface temperature, precipitation, and evapotranspiration on the changes in thermokarst lakes. The analysis revealed a higher correlation between thermokarst lake area changes and land surface temperature, while the correlation with precipitation and evapotranspiration changes was relatively lower.

Journal of HydrologyVol. 659
China University of Geosciences (Beijing) (CN)
Openalex Percentile: Top 13%
Cryospheric studies and observations
2.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.