小尺度山区地质灾害隐患的无人机精细化识别方法与实践

doi:10.13474/j.cnki.11-2246.2024.0102

测绘通报 ›› 2024, Vol. 0 ›› Issue (1): 6-11.doi: 10.13474/j.cnki.11-2246.2024.0102

• 地质灾害探测与隐患识别 • 上一篇下一篇

小尺度山区地质灾害隐患的无人机精细化识别方法与实践

黄海峰^1,2,3, 张瑞^1,2, 周红^1,2, 易武^1,2, 薛蓉花^1,2, 董志鸿⁴, 柳青⁴, 邓永煌⁴, 张国栋^1,2

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. 宜昌市地质环境监测站, 湖北宜昌 443002

收稿日期:2023-04-12 发布日期:2024-01-30
通讯作者: 张瑞。E-mail:18603439281@163.com
作者简介:黄海峰(1978—),男,博士,教授,主要从事地质灾害识别与监测方面的科研与教学工作。E-mail:hhf@ctgu.edu.cn
基金资助:
国家自然科学基金(U21A2031;42007237;42107489);三峡库区地质灾害教育部重点实验室开放基金(2020KDZ09);水电工程智能视觉监测湖北省重点实验室开放基金(2020SDSJ02)

Refined identification method and practice of unmanned aerial vehicle for geological hazards in small-scale mountainous areas

HUANG Haifeng^1,2,3, ZHANG Rui^1,2, ZHOU Hong^1,2, YI Wu^1,2, XUE Ronghua^1,2, DONG Zhihong⁴, LIU Qing⁴, DENG Yonghuang⁴, ZHANG Guodong^1,2

1. National Field Observation and Research Station of Landslides in Three Gorges Reservoir Area of Yangtze River, 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. Yichang Geological Environment Monitoring Station, Yichang 443002, China

Received:2023-04-12 Published:2024-01-30

摘要/Abstract

摘要： 本文针对小空间尺度范围内以滑坡、崩塌为主的山区地质灾害,提出了一套基于小型无人机摄影测量的精细化隐患识别方法。首先,针对工作区开展至少两期无人机摄影测量作业,经处理后得到实景三维模型、数字正射影像(DOM)、数字表面模型(DSM)等精细化成果;其次,以两期DOM与DSM变化检测为主实现灾害体识别;然后,基于灾害体共性特征建立典型识别标志,并依此采用三维实景目视解译为主方法实现孕灾体识别;最后,通过地面核查确认或排除隐患。将该套方法应用到三峡库首秭归泄滩河左岸顺向斜坡区域,共识别出10处不同类型隐患,证明了方法的可行性。

关键词: 山区地质灾害, 隐患识别, 孕灾特征, 无人机, 变化检测

Abstract: In this paper, aiming at the geological disasters in small-scale mountainous areas, such as landslides and collapses, a set of refined hidden danger identification method based on small UAV photogrammetry is proposed. Firstly, carry out at least two drone photogrammetry operations for the work area, and obtain refined results such as realistic 3D models, digital orthophoto images (DOM), and digital surface models (DSM) after processing.Secondly, the main focus is on detecting changes in DOM and DSM in two phases to achieve disaster body recognition.Once again, summarize the common characteristics of disaster bodies and establish identification indicators for typical disasters.Then, based on the identification markers, the three-dimensional visual interpretation method is mainly used to identify the pregnant body. Finally, identify or eliminate potential hazards through ground inspections. Applying this method to the left bank slope area of the Xietan River in Zigui, the head of the Three Gorges Reservoir, and 10 different types of hidden dangers are identified, which proves the feasibility of the method.

Key words: geological disasters in mountainous areas, identification of hidden dangers, characteristics of pregnancy disaster, unmanned aerial vehicle, change detection

中图分类号:

黄海峰, 张瑞, 周红, 易武, 薛蓉花, 董志鸿, 柳青, 邓永煌, 张国栋. 小尺度山区地质灾害隐患的无人机精细化识别方法与实践[J]. 测绘通报, 2024, 0(1): 6-11.

HUANG Haifeng, ZHANG Rui, ZHOU Hong, YI Wu, XUE Ronghua, DONG Zhihong, LIU Qing, DENG Yonghuang, ZHANG Guodong. Refined identification method and practice of unmanned aerial vehicle for geological hazards in small-scale mountainous areas[J]. Bulletin of Surveying and Mapping, 2024, 0(1): 6-11.

参考文献

[1] 中华人民共和国自然资源部.自然资源部关于做好2021年地质灾害防治措施[EB/OL].(2021-03-15) [2022-04-29]. http://gi.mnr.gov.cn/202103/t20210318-2617541.html.
[2] 许强,董秀军,李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版),2019,44(7): 957-966.
[3] 佘金星,许强,杨武年,等. 九寨沟地震地质灾害隐患早期识别与分析研究[J]. 工程地质学报,2023,31(1): 207-216.
[4] 葛大庆,戴可人,郭兆成,等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报(信息科学版),2019,44(7): 949-956.
[5] 余斌,李松,谢凌霄,等. 改进TS-InSAR方法的白格滑坡灾前-灾后形变演化特征分析[J]. 测绘通报,2022(11): 8-12.
[6] 赵蓓蓓,黄海峰,邓永煌,等. 基于Sentinel-1A的三峡库区范家坪滑坡InSAR监测分析[J]. 人民长江,2022,53(10): 103-107.
[7] 朱智富,甘淑,张荐铭,等. 结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别[J]. 测绘通报,2022(11): 13-19.
[8] 孙涛,徐明宇,董秀军,等. 机载LiDAR技术应用于茂密植被山区地质灾害调查[J]. 测绘通报,2021(4): 90-97.
[9] GUO Chen,XU Qiang,DONG Xiujun,et al. Geohazard recognition and inventory mapping using airborne LiDAR data in complex mountainous areas[J]. Journal of Earth Science,2021,32(5): 1079-1091.
[10] SHE Jinxing,MABI Awei,LIU Zhongming,et al. Analysis using high-precision airborne LiDAR data to survey potential collapse geological hazards[J]. Advances in Civil Engineering,2021,2021: 1-10.
[11] 黄海峰,易武,张国栋. 地质灾害防治中的小型无人机应用方法与实践[M]. 北京: 科学出版社,2020.
[12] 黄海峰,薛蓉花,赵蓓蓓,等. 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[J]. 测绘学报,2022,51(10): 2056-2068.
[13] LIAN Xugang,LI Zoujun,YUAN Hongyan,et al. Rapid identification of landslide,collapse and crack based on low-altitude remote sensing image of UAV[J]. Journal of Mountain Science,2020,17(12): 2915-2928.
[14] MA Jianxiong,LIU Jiwen,MING Jing,et al. Geological hazard interpretation and hazard evaluation of openpit mines based on unmanned aerial vehicle images[J]. Journal of Physics: Conference Series,2021,2006(1): 012072.
[15] 黄海峰,易武,张国栋,等. 引入小型无人机遥感的滑坡应急治理勘查设计方法[J]. 防灾减灾工程学报,2017,37(1): 99-104.
[16] 陈巧,袁飞云,付霞,等. 无人机摄影测量技术在阿娘寨滑坡应急调查中的应用[J]. 测绘通报,2023(1): 77-83.
[17] 郭晨,许强,董秀军,等. 无人机在重大地质灾害应急调查中的应用[J]. 测绘通报,2020(10): 6-11.
[18] 许强,郭晨,董秀军. 地质灾害航空遥感技术应用现状及展望[J]. 测绘学报,2022,51(10): 2020-2033.
[19] KOUTALAKIS P D,TZORAKI O A,PRAZIOUTIS G I,et al. Can drones map earth cracks？ landslide measurements in North Greece using UAV photogrammetry for nature-based solutions[J]. Sustainability,2021,13(9): 4697.
[20] 戴可人,沈月,吴明堂,等. 联合InSAR与无人机航测的白鹤滩库区蓄水前地灾隐患广域识别[J]. 测绘学报,2022,51(10): 2069-2082.
[21] 黄海峰,林海玉,吕奕铭,等. 基于小型无人机遥感的单体地质灾害应急调查方法与实践[J]. 工程地质学报,2017,25(2): 447-454.
[22] 董秀军,邓博,袁飞云,等.航空遥感在地质灾害领域的应用:现状与展望[J/OL]. 武汉大学学报(信息科学版). [2022-12-18]. http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20220151.
[23] BAI Shihan,BAI Shaoyun. Application of unmanned aerial vehicle multi-vision image 3D modeling in geological disasters[J]. Journal of Information and Optimization Sciences,2017,38(7): 1101-1115.
[24] 吕权儒,曾斌,孟小军,等. 基于无人机倾斜摄影技术的崩塌隐患早期识别及影响区划分方法[J]. 地质科技通报,2021,40(6): 313-325.
[25] YIN Yueping,HUANG Bolin,ZHANG Quan,et al. Research on recently occurred reservoir-induced Kamenziwan rockslide in Three Gorges Reservoir,China[J].Landslides,2020,17(8): 1935-1949.

小尺度山区地质灾害隐患的无人机精细化识别方法与实践

Refined identification method and practice of unmanned aerial vehicle for geological hazards in small-scale mountainous areas

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