基于SBAS-InSAR监测技术的城池村古滑坡形变特征研究

王仕兴; 徐正宣; 赵思为; 唐宇豪; 郭晨; 赵文龙

doi:10.13928/j.cnki.wrahe.2025.S1.101

水利水电技术（中英文） ›› 2025, Vol. 56 ›› Issue (S1) : 681 -688. DOI: 10.13928/j.cnki.wrahe.2025.S1.101

工程地质

基于SBAS-InSAR监测技术的城池村古滑坡形变特征研究

王仕兴 ¹^,² ,
徐正宣 ¹^,² ,
赵思为 ¹^,²^,³ ,
唐宇豪 ¹^,² ,
郭晨 ¹ ,
赵文龙 ¹

作者信息 +

Study on the deformation characteristics of ancient landslides in Chengchi Village based on SBAS-InSAR monitoring technology

Shixing WANG ¹^,² ,
Zhengxuan XU ¹^,² ,
Siwei ZHAO ¹^,²^,³ ,
Yuhao TANG ¹^,² ,
Chen GUO ¹ ,
Wenlong ZHAO ¹

Author information +

文章历史 +

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摘要

滑坡地质灾害对人类生命财产安全构成严重威胁，因此，对滑坡的形变特征进行深入研究具有重要的科学与实践意义。短基线集合成孔径雷达干涉测量时序分析技术(SBAS-InSAR)是监测大范围滑坡缓慢地表形变的有效手段之一。该技术能够高效识别滑坡的时空形变特征。利用SBAS-InSAR技术，分析覆盖城池村古滑坡区域的76景Sentinel-1A卫星升轨数据和12景ALOS-1/PALSAR卫星升轨数据，对该区域的滑坡时空形变特征进行解析。结果表明，城池村古滑坡体的显著形变区域集中于中上部南侧，最大年均形变速率为-90 mm/a。2020年至2022年间，最大形变点的累积形变量达到-237 mm,形变斜率介于0至-0.5之间。研究结果表明，城池村古滑坡处于长期持续蠕动状态。结合L波段与C波段的监测，显著提高了滑坡形变监测的精度与时效性，为滑坡的早期识别和灾害预警提供了重要数据支持，并为减灾防灾和提升灾害预警时效性奠定了科学基础。

Abstract

Landslide-related geological disasters pose a significant threat to human life and property, making the in-depth study of the deformation characteristics of landslides both scientifically valuable and practically important. The Small Baseline Subset Interferometric Synthetic Aperture Radar(SBAS-InSAR) technique is one of the most effective method for monitoring large-scale, slow-moving landslide surface deformations. The technology efficiently identifies the spatiotemporal deformation characteristics of landslides. In this study, we applied the SBAS-InSAR method to analyze the spatiotemporal deformation of the ancient landslide area in Chengchi Village, using 76 ascending orbit images from the Sentinel-1A satellite and 12 ascending orbit images from the ALOS-1/PALSAR satellite. The results indicate that the most significant deformation area of the ancient landslide in Chengchi Village is concentrated in the southern part of the upper-middle section, with a maximum annual average deformation rate of-90 mm/year. Between 2020 and 2022, the cumulative deformation at the most affected point reached-237 mm, with a deformation gradient ranging from 0 to-0.5. The research results indicate that the ancient landslide in Chengchi Village is in a state of long-term, continuous creep. By integrating L-band and C-band monitoring, the precision and timeliness of landslide deformation detection have been significantly enhanced. This approach provides crucial data support for the early identification of landslides and disaster warnings, laying a solid scientific foundation for disaster prevention, mitigation, and improving the effectiveness of early warning systems.