Early detection of potential landslides along high‐speed railway lines: A pressing issue

• Published in: Earth surface processes and landforms Vol. 48; no. 15; pp. 3302 - 3314
• Main Authors: Zhu, Yaru, Qiu, Haijun, Cui, Peng, Liu, Zijing, Ye, Bingfeng, Yang, Dongdong, Kamp, Ulrich
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.12.2023
• Subjects: Analysis; Deformation; Deformation effects; Detection; Disaster management; Disasters; early detection; Environmental risk; Geological hazards; high‐speed railway; Image acquisition; Image analysis; Image processing; InSAR; Landslides; Landslides & mudslides; Mitigation; monitoring; Railroads; Rainfall; Satellite imagery; Solifluction; Tertiary
• ISSN: 0197-9337; 1096-9837
• DOI: 10.1002/esp.5697

Early detection of landslides is important for prevention and mitigation of landslide disasters. Especially, accurately identifying potential landslides along high‐speed railway is becoming a pressing issue for operation safety of high‐speed railway. Here, 161 Sentinel‐1A satellite images from March 2017 to September 2022 were acquired to detect potential landslides along the Lanzhou‐Urumqi high‐speed railway (LUHR) in Qinghai. Results show that rainfall is the main cause for the transition from secondary creep (steady state) to tertiary creep (accelerated) at the Jiujiawan landslide. The hot spot analysis method was used to cluster the average line‐of‐sight deformation velocity along the LUHR, effectively extracting five deformation zones that may pose a threat. Optical satellite image analysis revealed four potential landslides and one unstable slope. Furthermore, the rescaled range analysis documented that the Hurst exponent is greater than 0.8 for all four potential landslide instability points, implying that the identified potential landslide areas are still in motion and may pose a threat to the operation of the LUHR. On this basis, we propose a framework for early identification and monitoring of landslides along the high‐speed railway lines that supports landslide hazard managing with the goal to reduce landslide risk. We propose a framework for detecting potential landslides along high‐speed railway lines. The hot spot analysis method can quickly extract the deformation area based on InSAR result. Stability of potential landslide can be assessed by combining InSAR and Hurst exponent.