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自然资源遥感  2025, Vol. 37 Issue (3): 221-232    DOI: 10.6046/zrzyyg.2024020
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
“三查”体系在高植被山区地质灾害隐患识别与监测中的应用——以四川省乐山市为例
周胜森1(), 李为乐1(), 陆会燕1, 任娟2, 付豪2, 李雪清1, 王新程1, 李雨森1, 韦春豪1
1.成都理工大学地质灾害防治与地质环境保护国家重点实验室,成都 610059
2.四川省国土空间生态修复与地质灾害防治研究院,成都 610081
Application of the space-air-ground investigation system in the identification and monitoring of geohazards in highly vegetation-covered mountain areas: A case study of Leshan City, Sichuan Province
ZHOU Shengsen1(), LI Weile1(), LU Huiyan1, REN Juan2, FU Hao2, LI Xueqing1, WANG Xincheng1, LI Yusen1, WEI Chunhao1
1. State Key Laboratory Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2. Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610081, China
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摘要 

自天-空-地一体化的“三查”体系提出以来,地质灾害隐患识别与监测示范性工作取得显著成效。四川省乐山市是示范区中的地质灾害重点防治区,高植被覆盖率和集中降雨导致地质灾害具有高位隐蔽和突发性。该文以乐山市为研究区,采用天-空-地一体化的“三查”技术体系,开展高植被山区地质灾害隐患识别与监测应用研究。主要得出以下结论: ①研究区共识别地质灾害隐患75处,通过现场核查确认地质灾害隐患解译数为51处,识别准确率为68%,新识别隐患点36处; ②地质灾害隐患主要集中分布在2个区域,集中区内识别地质灾害隐患37处,占研究区地质灾害隐患的72.5%; ③Stacking-InSAR可以作为斜坡初始变形阶段的地质灾害识别手段; 斜坡处于显著变形阶段且在InSAR探测能力范围内时,Stacking-InSAR、高分辨率光学卫星影像、LiDAR均能作为地质灾害识别的技术手段; ④在高植被覆盖的山区,可利用LiDAR技术去除地表植被的影响,结合一定的专家经验开展地质灾害隐患识别工作; ⑤目前利用遥感手段很难对“非显性地质灾害隐患”进行有效识别。

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周胜森
李为乐
陆会燕
任娟
付豪
李雪清
王新程
李雨森
韦春豪
关键词 地质灾害隐患高植被山区“三查”体系识别与监测乐山市    
Abstract

The space-air-ground investigation system has achieved remarkable results in the identification and monitoring demonstration work of geohazards. Leshan City in Sichuan Province, China, is a key zone for preventing and controlling geohazards within the demonstration area. The high vegetation cover and concentrated rainfall lead to the high concealment and sudden occurrence of geohazards in Leshan City, necessitating the identification and monitoring of geohazards in this city. Hence, under the guidance of the space-air-ground investigation system, this study explored the identification and monitoring of geohazards in the highly vegetation-covered mountain areas within Leshan City. The results indicate that 75 geohazards were identified in the study areas, with 51 confirmed through field verification, suggesting an identification accuracy rate of 68 %. Among them, 36 geohazards were newly identified. The geohazards were primarily concentrated in two areas, where 37 were identified, representing 72.5 % of the total geohazards in the study areas. Concerning techniques for identifying geohazards at different deformation stages of slopes, stacking-interferometric synthetic aperture radar (InSAR) can be employed to detect geohazards at the initial deformation stage of slopes. For slopes experiencing significant deformation within the detection range of InSAR, techniques like stacking-InSAR, high-resolution optical satellite imagery, and light detection and ranging (LiDAR) can all be used for geohazard identification. For highly vegetation-covered mountain areas, the LiDAR technique, which can be utilized to remove the effects of surface vegetation, combined with expert knowledge, can be used for geohazard identification. Additionally, remote sensing techniques face challenges in effectively identifying concealed geohazards.

Key wordsgeohazard    highly vegetation-covered mountain area    space-air-ground investigation system    identification and monitoring    Leshan City
收稿日期: 2024-01-10      出版日期: 2025-07-01
ZTFLH:  TP79  
  P694  
基金资助:国家重点研发计划“重大崩滑灾害危险源识别指标体系研究”(2021YFC3000401);四川省重点研发项目“山区重大地质灾害智能识别与动态风险评价关键技术研究”(2023YFS0435);四川省自然资源厅 “四川省地质灾害隐患遥感识别监测(2022年)和高分遥感应用服务项目”(N5100012022001470)
通讯作者: 李为乐(1982-),男,博士,教授,主要从事地质灾害早期识别与监测预警研究。Email: liweile08@mail.cdut.edu.cn
作者简介: 周胜森(1997-),男,博士研究生,主要从事地质灾害早期识别与监测研究。Email: zhoushengsen@stu.cdut.edu.cn
引用本文:   
周胜森, 李为乐, 陆会燕, 任娟, 付豪, 李雪清, 王新程, 李雨森, 韦春豪. “三查”体系在高植被山区地质灾害隐患识别与监测中的应用——以四川省乐山市为例[J]. 自然资源遥感, 2025, 37(3): 221-232.
ZHOU Shengsen, LI Weile, LU Huiyan, REN Juan, FU Hao, LI Xueqing, WANG Xincheng, LI Yusen, WEI Chunhao. Application of the space-air-ground investigation system in the identification and monitoring of geohazards in highly vegetation-covered mountain areas: A case study of Leshan City, Sichuan Province. Remote Sensing for Natural Resources, 2025, 37(3): 221-232.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024020      或      https://www.gtzyyg.com/CN/Y2025/V37/I3/221
Fig.1  研究区位置与数据覆盖范围图
Fig.2  技术路线图
县域 识别
总数/处
确认为隐患
数/处
准确率/%
峨边彝族自治县 40 34 85.0
峨眉山市 2 2 100.0
金口河区 8 3 37.5
马边彝族自治县 15 9 60.0
沐川县 2 0 0.0
沙湾区 8 3 37.5
合计 75 51 68.0
Tab.1  地质灾害隐患县域识别准确率统计表
Fig.3  新识别地质灾害隐患点统计图
县域 地质灾害隐患类型 合计
滑坡 崩塌 泥石流 地面沉降
峨边彝族自治县 29 3 2 34
峨眉山市 1 1 2
金口河区 3 3
马边彝族自治县 6 3 9
沙湾区 2 1 3
合计 39 7 2 3 51
Tab.2  地质灾害隐患统计表
Fig.4  地质灾害隐患空间分布与集中区
Fig.5  勒乌村滑坡综合遥感识别结果
Fig.6  A-B地质剖面图
Fig.7  勒乌村滑坡现场调查照片
Fig.8  勒乌村滑坡SBAS-InSAR监测结果
Fig.9  干溪拉达滑坡综合遥感识别与现场调查照片
Fig.10  柑子口滑坡综合遥感识别与现场调查照片
Fig.11  大板村滑坡综合遥感识别与现场调查照片
Fig.12  余坪村滑坡综合遥感识别结果
Fig.13  金口河石板沟滑坡无人机照片
Fig.14  金口河石板沟滑坡综合遥感识别结果
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