多源遥感技术在地质灾害早期识别应用中的问题探讨——以西南山区为例

doi:10.19388/j.zgdzdc.2022.04.11

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Abstract In the early identification of geological disasters such as landslides and collapses, multi-source remote sensing technologies based on InSAR, optical remote sensing, and airborne LiDAR have played an important role. In view of some problems in the application, based on the practice of many projects, the authors in this paper summarized the applicable conditions of multi-source remote sensing technology in the field of early identification of geohazards in mountainous areas of southwestern China, and put forward corresponding application suggestions. The main understandings are as follows: ① The InSAR identification effect of potential geohazards is restricted by vegetation coverage, terrain conditions and data source types. And it should be paid attention to the chosen of key data processing parameters in the application of wide-area InSAR. Besides, it is necessary to establish the field judgment methods for the InSAR identification as soon as possible, and unify the evaluation criteria of the “deformation degree” of the surface deformation area and the InSAR identification. ② The early identification of geohazards by optical remote sensing is suitable for the three types, including slope area with obvious deformation characteristics, historical slope geohazard, debris flow and potential debris flow ditch. Appropriate data sources should be chosen according to the requirements of different work tasks. And the analysis of the background conditions of the geological environment should be paid attention, while the shortcoming of “micro-deformation” detection should not be challenged. ③ Airborne LiDAR has obvious advantages in early identification of geohazards in high-density vegetation coverage areas, comparing with InSAR and optical remote sensing. The laser point cloud density should be no less than 30-50 points/m² in actual applications. ④ Nap-of-the-object photogrammetry technology is suitable for early identification of high-position collapse (dangerous rock mass), which belongs to the category of optical remote sensing, and its identification effect is also affected by the degree of vegetation coverage. The reasonable detection distance should be set in application and specific value of distance can be adjusted according to the regularity of the dangerous rock wall, which is recommended to be no less than 30 meters. ⑤ Suggestions were proposed, including the comprehensive application of multi-source remote sensing technology and methods with complementary advantages, multi-level arrangement of work deployment, focusing on interdisciplinary application and comprehensive training of interpreters and establishment of geohazards information acquisition capacity progress as soon as possible.

Key words： geological hazards InSAR LiDAR nap-of-the-object photography remote sensing early identification

Received: 03 March 2022

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	LIANG Jingtao
	ZHAO Cong
	MA Zhigang

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LIANG Jingtao,ZHAO Cong,MA Zhigang. Application discussion on early identification of geohazards based on multi-source remote sensing technology: A case study on mountainous areas of southwestern China[J]. , 2022, 9(4): 92-101.

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http://journal25.magtechjournal.com/Jwk_dzdc/EN/10.19388/j.zgdzdc.2022.04.11 OR http://journal25.magtechjournal.com/Jwk_dzdc/EN/Y2022/V9/I4/92

[1] 许强. 对地质灾害隐患早期识别相关问题的认识与思考[J].武汉大学学报:信息科学版,2020,45(11):1651-1659.
Xu Q.Understanding and consideration of related issues in early identification of potential geohazards[J].Geomat Inf Sci Wuhan Univ,2020,45(11):1651-1659.
[2] 梁京涛,成余粮,王军,等.2013年7月10日四川省都江堰三溪村五里坡特大滑坡灾害遥感调查及成因机制浅析[J].工程地质学报,2014,22(6):1194-1203.
Liang J T,Cheng Y L,Wang J,et al.Remote sensing investigation and formation mechanism on Wulipo landslide of July 10,2013 in Sanxi Village,Dujiangyan,Sichuan Province[J].J Eng Geol,2014,22(6):1194-1203.
[3] 许强,李为乐,董秀军,等.四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J].岩石力学与工程学报,2017,36(11):2612-2628.
Xu Q,Li W L,Dong X J,et al.The Xinmocun landslide on June 24,2017 in Maoxian,Sichuan:characteristics and failure mechanism[J].Chin J Rock Mech Eng,2017,36(11):2612-2628.
[4] 许强,郑光,李为乐,等.2018年10月和11月金沙江白格两次滑坡-堰塞堵江事件分析研究[J].工程地质学报,2018,26(6):1534-1551.
Xu Q,Zheng G,Li W L,et al.Study on successive landslide damming events of Jinsha River in Baige Village on Octorber 11 and November 3,2018[J].J Eng Geol,2018,26(6):1534-1551.
[5] 郑光,许强,刘秀伟,等.2019年7月23日贵州水城县鸡场镇滑坡-碎屑流特征与成因机理研究[J].工程地质学报,2020,28(3):541-556.
Zheng G,Xu Q,Liu X W,et al.The Jichang landslide on July 23,2019 in Shuicheng,Guizhou:characteristics and failure mechanism[J].J Eng Geol,2020,28(3):541-556.
[6] Saito M.Forecasting the time of occurrence of a slope failure[C]//Proceedings of the 6th International Conference on Soil Mechanics and Foundation Engineering.Montreal,Canada:[s.n.],1965:537-541.
[7] 国家能源局.DL/T 2257-2021 大中型水电站地质灾害预警及应急管理技术规范[S].北京:中国电力出版社,2021.
National Energy Administration.DL/T 2257-2021 Technical Specification of Geological Hazard Early Warning and Emergency Management for Large and Medium Scale Hydropower Stations[S].Beijing:China Electric Power Press,2021.
[8] UNAVCO.Synthetic Aperture Radar (SAR) satellites[EB/OL].https://www.unavco.org/instrumentation/geophysical/imaging/sar-satellites/sar-satellites.html.2022-02-23.
[9] Lauknes T R,Zebker H A,Larsen Y.InSAR deformation time series using an L1-norm small-baseline approach[J].IEEE Trans Geosci Remote Sens,2011,49(1):536-546.
[10] Osmanoğlu B,Sunar F,Wdowinski S,et al.Time series analysis of InSAR data:Methods and trends[J].ISPRS J Photogramm Remote Sens,2016,115:90-102.
[11] 王之栋,文学虎,唐伟,等.联合多种InSAR技术的龙门山—大渡河区域地灾隐患早期探测[J].武汉大学学报:信息科学版,2020,45(3):451-459.
Wang Z D,Wen X H,Tang W,et al.Early detection of geological hazards in Longmenshan-Dadu River Area using various InSAR techniques[J].Geomat Inf Sci Wuhan Univ,2020,45(3):451-459.
[12] 蒋弥,丁晓利,李志伟,等.时序InSAR同质样本选取算法研究[J].地球物理学报,2018,61(12):4767-4776.
Jiang M,Ding X L,Li Z W,et al.Homogeneous pixel selection algorithm for multitemporal InSAR[J].Chin J Geophys,2018,61(12):4767-4776.
[13] Yu C,Li Z H,Penna N T,et al.Generic atmospheric correction model for interferometric synthetic aperture radar observations[J].J Geophys Res Solid Earth,2018,123(10):9202-9222.
[14] 张路,廖明生,董杰,等.基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别——以四川丹巴为例[J].武汉大学学报:信息科学版,2018,43(12):2039-2049.
Zhang L,Liao M S,Dong J,et al.Early detection of landslide hazards in mountainous areas of west china using time series InSAR terferometry:A case study of Danba,Sichuan[J].Geomat Inf Sci Wuhan Univ,2018,43(12):2039-2049.
[15] 陆会燕,李为乐,许强,等.光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J].武汉大学学报:信息科学版,2019,44(9):1342-1354.
Lu H Y,Li W L,Xu Q,et al.Early detection of landslides in the upstream and downstream areas of the Baige Landslide,the Jinsha River based on optical remote sensing and InSAR techno-logies[J].Geomat Inf Sci Wuhan Univ,2019,44(9):1342-1354.
[16] 解明礼,赵建军,巨能攀,等.多源数据滑坡时空演化规律研究——以黄泥坝子滑坡为例[J].武汉大学学报:信息科学版,2020,45(6):923-932.
Xie M L,Zhao J J,Ju N P,et al.Research on temporal and spatial evolution of landslide based on multisource data:A case study of Huangnibazi Landslide[J].Geomat Inf Sci Wuhan Univ,2020,45(6):923-932.
[17] 梁京涛,铁永波,赵聪,等.基于贴近摄影测量技术的高位崩塌早期识别技术方法研究[J].中国地质调查,2020,7(5):107-113.
Liang J T,Tie Y B,Zhao C,et al.Technology and method research on the early detection of high-level collapse based on the nap-of-the-object photography[J].Geological Survey of China,2020,7(5):107-113.