Analysis of landslide deformation in eastern Qinghai Province, Northwest China, using SBAS-InSAR

Haibo Tian, Pinglang Kou (), Qiang Xu, Yuxiang Tao, Zhao Jin, Ying Xia, Jiangfan Feng, Rui Liu and Yongcheng Gou
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Haibo Tian: Chongqing University of Posts and Telecommunications
Pinglang Kou: Chongqing University of Posts and Telecommunications
Qiang Xu: Chengdu University of Technology
Yuxiang Tao: Chongqing University of Posts and Telecommunications
Zhao Jin: Chinese Academy of Sciences
Ying Xia: Chongqing University of Posts and Telecommunications
Jiangfan Feng: Chongqing University of Posts and Telecommunications
Rui Liu: Chongqing Normal University
Yongcheng Gou: Chongqing University of Posts and Telecommunications

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 6, No 33, 5763-5784

Abstract: Abstract In eastern Qinghai Province, China, landslides are a frequent hazard, yet their large-scale monitoring and assessment are under-researched. This study utilized 31 Sentinel-1A satellite images from January 4, 2020, to August 9, 2022, and applied the Small Baseline Subset Interferometry Synthetic Aperture Radar (SBAS-InSAR) method to quantify surface subsidence and infer landslide deformation rates in the Loess Plateau. We identified 491 hazardous landslides, with 14 posing significant risks to the Yellow River, major highways, and over 10,000 residents. The average line-of-sight (LOS) surface displacement rate was 118 mm/year, peaking at 298 mm. Satellite imagery revealed rapid and continuous landslide front activity. The landslides' uneven distribution aligns with the area's complex geology and environment, predominantly occurring on 20°–40° slopes with the Normalized Difference Vegetation Index values below 0.3, and aligning with nearby faults in the Hualong basin. Detailed analysis of 14 key landslides showed a marked correlation between landslide movement and monthly precipitation, offering new insights into landslide deformation mechanisms and driving factors.

Keywords: Landslides; Loess Plateau; SBAS-InSAR; Sentinel-1A; Optical images (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06442-2

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