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Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
by
Dai, Keren
, Xiang, Jianming
, Zhang, Hongyuan
, Li, Zhoujiang
, Shi, Xianlin
, Zhuo, Guanchen
in
Accuracy
/ China
/ Clustering
/ Deformation
/ dynamic landslide susceptibility
/ Environmental risk
/ geodetector-based factor analysis
/ Geological hazards
/ Geology
/ Hazard assessment
/ Hydrology
/ Landslides
/ Landslides & mudslides
/ Machine learning
/ Mountain regions
/ Mountainous areas
/ Precipitation
/ Real time
/ Risk
/ Risk assessment
/ Rivers
/ SBAS-InSAR
/ Seismic engineering
/ slope unit-based modeling
/ Software
/ Soil erosion
/ Soil types
/ Susceptibility
/ Synthetic aperture radar
/ time-series InSAR
/ Topography
/ Velocity
/ Yalong River
2025
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Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
by
Dai, Keren
, Xiang, Jianming
, Zhang, Hongyuan
, Li, Zhoujiang
, Shi, Xianlin
, Zhuo, Guanchen
in
Accuracy
/ China
/ Clustering
/ Deformation
/ dynamic landslide susceptibility
/ Environmental risk
/ geodetector-based factor analysis
/ Geological hazards
/ Geology
/ Hazard assessment
/ Hydrology
/ Landslides
/ Landslides & mudslides
/ Machine learning
/ Mountain regions
/ Mountainous areas
/ Precipitation
/ Real time
/ Risk
/ Risk assessment
/ Rivers
/ SBAS-InSAR
/ Seismic engineering
/ slope unit-based modeling
/ Software
/ Soil erosion
/ Soil types
/ Susceptibility
/ Synthetic aperture radar
/ time-series InSAR
/ Topography
/ Velocity
/ Yalong River
2025
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Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
by
Dai, Keren
, Xiang, Jianming
, Zhang, Hongyuan
, Li, Zhoujiang
, Shi, Xianlin
, Zhuo, Guanchen
in
Accuracy
/ China
/ Clustering
/ Deformation
/ dynamic landslide susceptibility
/ Environmental risk
/ geodetector-based factor analysis
/ Geological hazards
/ Geology
/ Hazard assessment
/ Hydrology
/ Landslides
/ Landslides & mudslides
/ Machine learning
/ Mountain regions
/ Mountainous areas
/ Precipitation
/ Real time
/ Risk
/ Risk assessment
/ Rivers
/ SBAS-InSAR
/ Seismic engineering
/ slope unit-based modeling
/ Software
/ Soil erosion
/ Soil types
/ Susceptibility
/ Synthetic aperture radar
/ time-series InSAR
/ Topography
/ Velocity
/ Yalong River
2025
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Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
Journal Article
Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
2025
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Overview
Dynamic susceptibility assessment is essential for mitigating evolving landslide risks in alpine gorge regions. To address the static limitations and unit mismatch issues in conventional landslide susceptibility assessments in alpine gorge regions, this study proposes a dynamic framework integrating time-series InSAR-derived deformation. Applied to the Xinlong–Kangding section of the Yalong River, annual surface deformation velocities were retrieved using SBAS-InSAR with Sentinel-1 data, identifying 24 active landslide zones (>25 mm/a). The Geodetector model quantified the spatial influence of 18 conditioning factors, highlighting deformation velocity as the second most significant (q = 0.21), following soil type. Incorporating historical landslide data and InSAR deformation zones, slope unit delineation was optimized to construct a refined sample dataset. A Random Forest model was then used to assess the contribution of deformation factors. Results show that integrating InSAR data substantially improved model performance: “Very High” risk landslides increased from 67.21% to 87.01%, the AUC score improved from 0.9530 to 0.9798, and the Kappa coefficient increased from 0.7316 to 0.8870. These results demonstrate the value of InSAR-based dynamic monitoring in enhancing landslide susceptibility mapping, particularly for spatial clustering, classification precision, and model robustness. This approach offers a more efficient dynamic evaluation pathway for dynamic assessment and early warning of landslide hazards in mountainous regions.
Publisher
MDPI AG
Subject
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