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<p>The Sichuan-Xizang transportation corridor (SXTC) traverses a geologically active region with complex topography and high landslide susceptibility. However, the sparse ground-based monitoring network constrains comprehensive landslide inventory mapping across this extensive and challenging terrain. To address this limitation, this study integrates Interferometric Synthetic Aperture Radar (InSAR) with spatial clustering to automatically detect active landslides along the SXTC using Sentinel-1 ascending and descending data. The methodology first applies Stacking InSAR to generate robust deformation velocity fields through temporal averaging of multiple interferograms. High-velocity pixels are then extracted and clustered using a spatial density-based algorithm to delineate coherent deformation zones indicative of potential landslide activity. The analysis identifies 741 potential landslides from ascending orbit data and 705 from descending orbit acquisitions, with 128 landslides consistently detected in both viewing geometries. In the Jinsha River section, comparison with existing inventories indicates that approximately 70% of previously reported landslides are successfully detected by the proposed method, Meanwhile, systematic visual interpretation confirms all mapped landslides, and detailed field investigations are conducted for 105 representative landslides, supporting the overall reliability of the results. The spatial distribution of detected landslides exhibits significant heterogeneity, mainly concentrated in the Lhasa, Jinsha River, and Dadu River regions. Multi-geometry SAR integration demonstrates enhanced spatial coverage and improved detection capabilities in topographically complex terrain. This study establishes that combining InSAR with spatial clustering provides an effective strategy for regional-scale landslide detection. The resulting landslide inventory contributes essential datasets for hazard assessment, infrastructure vulnerability analysis, and risk mitigation strategies along the SXTC.</p>

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Regional-scale landslide detection along the Sichuan-Xizang transportation corridor using stacking InSAR and spatial clustering approaches

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Yaning Yi,
  • Guangyu Xu,
  • Xiwei Xu,
  • Wenjun Kang

摘要

The Sichuan-Xizang transportation corridor (SXTC) traverses a geologically active region with complex topography and high landslide susceptibility. However, the sparse ground-based monitoring network constrains comprehensive landslide inventory mapping across this extensive and challenging terrain. To address this limitation, this study integrates Interferometric Synthetic Aperture Radar (InSAR) with spatial clustering to automatically detect active landslides along the SXTC using Sentinel-1 ascending and descending data. The methodology first applies Stacking InSAR to generate robust deformation velocity fields through temporal averaging of multiple interferograms. High-velocity pixels are then extracted and clustered using a spatial density-based algorithm to delineate coherent deformation zones indicative of potential landslide activity. The analysis identifies 741 potential landslides from ascending orbit data and 705 from descending orbit acquisitions, with 128 landslides consistently detected in both viewing geometries. In the Jinsha River section, comparison with existing inventories indicates that approximately 70% of previously reported landslides are successfully detected by the proposed method, Meanwhile, systematic visual interpretation confirms all mapped landslides, and detailed field investigations are conducted for 105 representative landslides, supporting the overall reliability of the results. The spatial distribution of detected landslides exhibits significant heterogeneity, mainly concentrated in the Lhasa, Jinsha River, and Dadu River regions. Multi-geometry SAR integration demonstrates enhanced spatial coverage and improved detection capabilities in topographically complex terrain. This study establishes that combining InSAR with spatial clustering provides an effective strategy for regional-scale landslide detection. The resulting landslide inventory contributes essential datasets for hazard assessment, infrastructure vulnerability analysis, and risk mitigation strategies along the SXTC.