Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan
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摘要:
中国黄土滑坡灾害频发且分布广泛，传统的地质灾害调查对于地处高位、形变特征不明显和隐蔽型的滑坡隐患难以有效识别，是滑坡灾害监测预警成功率低的主要原因之一。如何有效超前判识别地质灾害隐患是地质灾害防治工作的前提和基础，时序InSAR技术在此领域具有良好的应用潜力，但如何更好地将InSAR技术融入到滑坡灾害相关研究中仍处于探索阶段。笔者以西安市白鹿塬西南区为研究区，在高精度三维倾斜摄影、ALOS-2雷达影像集等数据基础上，以时序InSAR技术反演得到104处地表形变明显区域；结合黄土滑坡易发指数、航拍影像和野外核查，快速识别黄土滑坡及隐患23处，其中包括新识别的滑坡隐患20处和在册的滑坡灾害3处，通过与传统地灾调查数据比对和实地调查核实验证了时序InSAR方法探测结果的优势和有效性，并构建了基于高精度InSAR和DEM数据的黄土滑坡隐患早期识别方法。
Abstract:Loess landslide disasters occur frequently and are widely distributed in China. Traditional geological hazard surveys are difficult to effectively identify hidden landslide hazards that are located at high altitudes, have unclear deformation characteristics, and are hidden. This is also one of the main reasons for the low success rate of landslide hazard monitoring and warning. How to effectively identify geological hazard hazards beyond prejudgment is the premise and foundation of geological hazard prevention and control work. Time series InSAR technology has good application potential in this field, but how to better integrate InSAR technology into landslide disaster related research is still in the exploratory stage. The author takes the southwest area of Bailuyuan in Xi’an City as the research area, and on the basis of high−precision 3D oblique photography, ALOS-2 radar image set, and other data, uses time-series InSAR technology to invert 104 areas with obvious surface deformation. By combining the susceptibility index of loess landslides, aerial images, and field verification, 23 loess landslides and hidden dangers were quickly identified, including 20 newly identified landslide hazards and 3 registered landslide disasters. The advantages and effectiveness of the time−series InSAR method detection results were verified through comparison with traditional geological disaster investigation data and on−site investigation verification. A high−precision InSAR and DEM data based early identification method for loess landslide hazards was constructed.
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图 2 白鹿塬地质剖面示意图（李宝田等，2021）
Figure 2.
表 1 ALOS-2数据参数表
Table 1. ALOS-2 data parameters
影像采集时间 影像数量 雷达波长 轨道方向 空间分辨率 视角 垂直基线分布范围 极化方式 2020-01-18-2021-09-11 19景 23 cm 升轨 10 m 32° 91 m HH+HV 
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表 2 新识别滑坡隐患和历史滑坡灾害活动性分类表
Table 2. Classification of newly identified landslide hazards and activities of historical landslide hazards
类型 划分标准 活动性 统计 历史滑坡灾害（点位信息） 年变形量＜10 mm/yr 稳定 HP1～HP7、HP8～HP24、HP26～HP28（共26个） 年变形量＞10 mm/yr 复活/活动 HP8、HP25、HP29（共3个） 滑坡隐患（识别范围） 年变形量＞10 mm/yr 活动 X1～X2、X4～X8、X10～X13、X15～X23（共20处） 复活/活动 X3、X9、X14（共3处）
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