Detection and assessment of potential landslides in the Xiaojiang River Basin using SBAS-InSAR

The Xiaojiang River Basin is susceptible to debris flows and landslides due to its unique geological environment and historically frequent human activities. For large-scale landslide identification, traditional ground-based surveying techniques are insufficient due to limitations in data acquisition and operation capabilities. Interferometric Synthetic Aperture Radar (InSAR) is an advanced technology for wide-area ground deformation monitoring and has proven to be highly effective in detecting subtle surface deformations. To better understand the distribution and activity characteristics of landslides in the Xiaojiang River Basin, the ascending and descending SAR images from the Sentinel-1 A satellite between 2014 and 2018 were processed with SBAS-InSAR. Through multi-directional deformation analysis, 59 potential landslides were identified, with a total area of approximately 55.91 km<sup>2</sup>. By collating previous studies and field surveying results, we found that 25 of these landslides had been previously recorded, while 34 were newly detected. Analysis of the impact of rainfall on landslide deformation indicated that prolonged precipitation was a primary driving factor for landslide destabilization. Additionally, we assessed the accuracy of the InSAR measurements using geometric distortions and the monitoring sensitivity model. This research could advance our understanding of landslide dynamics in the Xiaojiang River Basin, providing crucial insights for future mitigation and research strategies.

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Publication Details

Journal
Scientific Reports
Published
2025-05-08
DOI
https://doi.org/10.1038/s41598-025-00490-9
Citations
4
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
19.97

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article

Detection and assessment of potential landslides in the Xiaojiang River Basin using SBAS-InSAR

Yongfa Li, Xiang D. Tang, Mengyao Shi, Cheng Huang et al.
4 citations
Scientific Reports
Synthetic Aperture Radar (SAR) Applications and Techniques
19.97
article

Detection and assessment of potential landslides in the Xiaojiang River Basin using SBAS-InSAR

Yongfa Li, Xiang D. Tang, Mengyao Shi, Cheng Huang, Chao Shi, Jianming Zhang, Xiaoqing Zuo
article en
4 citations

Abstract

The Xiaojiang River Basin is susceptible to debris flows and landslides due to its unique geological environment and historically frequent human activities. For large-scale landslide identification, traditional ground-based surveying techniques are insufficient due to limitations in data acquisition and operation capabilities. Interferometric Synthetic Aperture Radar (InSAR) is an advanced technology for wide-area ground deformation monitoring and has proven to be highly effective in detecting subtle surface deformations. To better understand the distribution and activity characteristics of landslides in the Xiaojiang River Basin, the ascending and descending SAR images from the Sentinel-1 A satellite between 2014 and 2018 were processed with SBAS-InSAR. Through multi-directional deformation analysis, 59 potential landslides were identified, with a total area of approximately 55.91 km<sup>2</sup>. By collating previous studies and field surveying results, we found that 25 of these landslides had been previously recorded, while 34 were newly detected. Analysis of the impact of rainfall on landslide deformation indicated that prolonged precipitation was a primary driving factor for landslide destabilization. Additionally, we assessed the accuracy of the InSAR measurements using geometric distortions and the monitoring sensitivity model. This research could advance our understanding of landslide dynamics in the Xiaojiang River Basin, providing crucial insights for future mitigation and research strategies.

Scientific ReportsVol. 15(1)
Kunming University of Science and Technology (CN), Center For Remote Sensing (United States) (US), Ministry of Natural Resources (RW), Yunnan Environmental Protection Bureau (CN), National Marine Environmental Forecasting Center (CN), Institute of Geological Sciences (UA), China Institute of Geological Environmental Monitoring (CN)
European Space Agency, National Natural Science Foundation of China, Chinese Academy of Sciences
Openalex Percentile: Top 1%
Synthetic Aperture Radar (SAR) Applications and Techniques
19.97
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