测绘学报 ›› 2022, Vol. 51 ›› Issue (10): 2056-2068.doi: 10.11947/j.AGCS.2022.20220306

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孕灾机理与综合遥感结合的三峡库首顺层岩质滑坡隐患识别

黄海峰1,2,3,4, 薛蓉花1,2,3,4, 赵蓓蓓1,2,3,4, 易武1,4, 邓永煌5, 董志鸿5, 柳青5, 易庆林1,4, 张国栋1   

  1. 1. 三峡大学湖北长江三峡滑坡国家野外科学观测研究站,湖北 宜昌 443002;
    2. 三峡大学三峡库区地质灾害教育部重点实验室,湖北 宜昌 443002;
    3. 三峡大学湖北省水电工程智能视觉监测重点实验室,湖北 宜昌 443002;
    4. 三峡大学土木与建筑学院,湖北 宜昌 443002;
    5. 宜昌市地质环境监测站,湖北 宜昌 443002
  • 收稿日期:2022-05-05 修回日期:2022-07-26 发布日期:2022-11-05
  • 通讯作者: 薛蓉花 E-mail:xuerh2020@qq.com
  • 作者简介:黄海峰(1978—),男,博士,教授,研究方向为滑坡灾害监测、3S技术应用。E-mail:hhf@ ctgu.edu.cn
  • 基金资助:
    国家自然科学基金(U21A2031;42007237;42107489);水电工程智能视觉监测湖北省重点实验室开放基金(2020SDSJ02);三峡库区地质灾害教育部重点实验室开放基金(2020KDZ09)

The bedding rock landslide identification in the head area of the Three Gorges Reservoir combined with disaster pregnant mechanism and comprehensive remote sensing method

HUANG Haifeng1,2,3,4, XUE Ronghua1,2,3,4, ZHAO Beibei1,2,3,4, YI Wu1,4, DENG Yonghuang5, DONG Zhihong5, LIU Qing5, YI Qinglin1,4, ZHANG Guodong1   

  1. 1. National Field Observation and Research Station of Landslides in Three Gorges Reservoir Area of Yangtze River, China Three Gorges University, Yichang 443002, China;
    2. Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang 443002, China;
    3. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China;
    4. College of Civil Engineering & Architecture, China Three Gorges University, Yichang 443002, China;
    5. Yichang Geological Environment Monitoring Station Yichang, Yichang 443002, China
  • Received:2022-05-05 Revised:2022-07-26 Published:2022-11-05
  • Supported by:
    The National Natural Science Foundation of China (Nos. U21A2031;42007237;42107489);The Opening Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,China Three Gorges University (No. 2020SDSJ02);The Opening Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area,Ministry of Education,China Three Gorges University(No. 2020KDZ09)

摘要: 隐患识别是实现地质灾害从注重灾后救助向注重灾前预防转变的重要技术工作。本文以三峡库首秭归沙镇溪镇周边岸坡段顺层岩质滑坡隐患识别为基础,提出基于孕灾机理与综合遥感相结合的地质灾害隐患识别方法。首先,借助资料整理分析、遥感调查和现场调查等查明孕灾环境,并建立孕灾指标体系;其次,针对典型灾害体开展地质结构与致灾机理分析,以揭示典型孕灾模式,并建立综合遥感判识标志;再次,采用易发性分区评价,结合高分光学卫星遥感与InSAR等天基遥感变化检测技术,圈定隐患识别的易发重点靶区;然后,针对高易发靶区,利用无人机摄影测量、LiDAR等空基遥感技术识别疑似隐患体;最后,通过地面核查与专家判识,确认并圈定地质灾害隐患。利用该套技术方法,在工作区内共识别出8处地质灾害隐患,其中5处为具备孕灾模式但尚未出现明显变形的顺层岩质滑坡隐患体。结果表明,该套技术方法以查明孕灾环境及建立孕灾模式为核心与前提、以综合遥感探测为重要技术支撑,可以弥补目前主要依赖遥感变化探测开展隐患识别易造成精度较低甚至失效的缺陷,尤其适合于山高坡陡、植被覆盖茂密地区的隐蔽性、突发性地质灾害的隐患识别。

关键词: 三峡库首, 顺层岩质滑坡, 隐患识别, 孕灾机理, 综合遥感

Abstract: The identification of hidden dangers is an important technical work to realize the transformation of potential geological hazards from post-disaster relief to pre-disaster prevention.This paper proposes a method for identifying the bedding rock landslide based on disaster pregnant mechanism and comprehensive remote sensing detection technology. Firstly, data analysis, remote sensing survey and field survey is used for identifying disaster-pregnant environment and establishing a disaster-pregnant index system; at the same time, typical hazard mode and the identification mark of integrated remote sensing are established. Then, key target areas and suspected hidden dangers of geological disasters are delineated. And geological hazard identification is realized relied on ground detailed assessments and professional identification. By using this set of technical methods, a total of 8 potential catastrophic geohazards have been identified in the work area, of which 5 are potential rockslides with hazard-pregnancy modes but not yet apparently deformed. The results show that this method can make up for the disadvantages of low accuracy or even failure mainly relying on remote sensing change detection. It is especially suitable for hidden and sudden geological hazard identification in areas with steep hills and dense vegetation.

Key words: head area of the TGR, consequent bedding rockslide, potential hazard detection, disaster pregnant mechanism, comprehensive remote sensing

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