多源遥感地质灾害早期识别技术进展与发展趋势

doi:10.11947/j.AGCS.2022.20220132

摘要/Abstract

摘要： 随着全球气候变化、矿产资源开采和大型人类工程活动的不断加剧,冰崩、塌陷、滑坡、地面沉降和地裂缝等多类型地质灾害呈现高频性和链生性的趋势,灾害后果更加严重。大范围高效率地质灾害的早期识别是防灾减灾的重要前提,也是工程安全的技术保障。本文首先介绍了多类型地质灾害的特点和常规识别方法;然后,重点介绍了光学遥感、微波遥感、机载LiDAR及多源遥感数据融合技术在不同类型地质灾害识别中的技术特点和典型应用,并对当前地质灾害早期识别存在问题和下一步发展趋势进行了总结与展望。

关键词: 地质灾害, 早期识别, 光学遥感, 合成孔径雷达, LiDAR, 深度学习

Abstract: With the intensification of global climate change mineral resource exploitation and human engineering activities, the geological disasters including ice collapse, collapse, landslide, land subsidence and ground fissure are triggered with the trend of high frequency and in chain mode, which result in serious consequence. The early identification of geological disasters in large area and with high efficiency is the prerequisite for the hazard mitigation and prevention and the technical support of the engineering safety. In this paper, the characteristics of diverse geological disasters and traditional methods of their identification are introduced firstly. Then we focus on optical remote sensing, synthetic aperture radar, LiDAR and the fusion of multi-source remote sensing, where the technical flow and typical applications are given. Lastly, the current difficulties and the future trends are summarized and forwarded.

Key words: geological disasters, early identification, optical remote sensing, synthetic aperture radar, LiDAR, deep learning

中图分类号:

P228

张勤, 赵超英, 陈雪蓉. 多源遥感地质灾害早期识别技术进展与发展趋势[J]. 测绘学报, 2022, 51(6): 885-896.

ZHANG Qin, ZHAO Chaoying, CHEN Xuerong. Technical progress and development trend of geological hazards early identification with multi-source remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 885-896.

参考文献

[1] 李铁锋. 灾害地质学[M]. 北京:北京大学出版社, 2002. LI Tiefeng. Disaster Geology[M]. Beijing. Peking University Press, 2002.
[2] ROWAN A V, EGHOLM D L, QUINCEY D J, et al. Modelling the feedbacks between mass balance, ice flow and debris transport to predict the response to climate change of debris-covered glaciers in the Himalaya[J]. Earth and Planetary Science Letters, 2015, 430:427-438.
[3] BRABB E E. The world landslide problem[J]. Episodes, 1991, 14(1):52-61.
[4] KÄÄB A, LEINSS S, GILBERT A, et al. Massive collapse of two glaciers in western Tibet in 2016 after surge-like instability[J]. Nature Geoscience, 2018, 11(2):114-120.
[5] QUINCEY D J, BRAUN M, GLASSER N F, et al. Karakoram glacier surge dynamics[J]. Geophysical Research Letters, 2011, 38(18):L18504.
[6] HU Kaiheng, ZHANG Xiaopeng, YOU Yong, et al. Landslides and dammed lakes triggered by the 2017 Ms6.9 Milin earthquake in the Tsangpo gorge[J]. Landslides, 2019, 16(5):993-1001.
[7] 张永军, 张祖勋, 龚健雅. 天空地多源遥感数据的广义摄影测量学[J]. 测绘学报, 2021, 50(1):1-11. DOI:10.11947/j.AGCS.2021.20200245. ZHANG Yongjun, ZHANG Zuxun, GONG Jianya. Generalized photogrammetry of spaceborne, airborne and terrestrial multi-soruce remote sensing dataset[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1):1-11. DOI:10.11947/j.AGCS.2021.20200245.
[8] 许强, 陆会燕, 李为乐, 等. 滑坡隐患类型与对应识别方法[J]. 武汉大学学报(信息科学版), 2022, 47(3):377-387. XU Qiang, LU Huiyan, LI Weile et al. Types of potential landslide and corresponding identification technologies[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3):377-387.
[9] 王治华. 滑坡遥感[M]. 北京:科学出版社, 2012. WANG Zhihua. Remote sensing for landslide[M]. Beijing:Science Press, 2012.
[10] CRUDEN D M, VARNES D J. Landslide types and processes[J]. Special Report-National Research Council, Transportation Research Board, 1996, 247:36-75.
[11] FIORUCCI F, GIORDAN D, SANTANGELO M, et al. Criteria for the optimal selection of remote sensing optical images to map event landslides[J]. Natural Hazards and Earth System Sciences, 2018, 18(1):405-417.
[12] 王治华. 数字滑坡技术及其在天台乡滑坡调查中的应用[J]. 岩土工程学报, 2006, 28(4):516-520. WANG Zhihua. Digital landslide technology and its application in investigation of Tiantai Town landslide[J]. Chinese Journal of Geotechnical Engineering, 2006, 28(4):516-520.
[13] 许冲, 戴福初, 陈剑, et al. 汶川Ms8.0地震重灾区次生地质灾害遥感精细解译[J]. 遥感学报, 2009, 13(4):754-762. XU Chong, DAI Fuchu, CHEN Jian, et al.. Remote sensing interpretation of secondary geological Hazards in Wenchuan Ms8.0 earthquake[J]. Journal of Remote Sensing, 2009, 13(4):754-762.
[14] HE Jianan, SUN Mingwei, CHEN Qi, et al. An improved approach for generating globally consistent seamline networks for aerial image mosaicking[J]. International Journal of Remote Sensing, 2019, 40(3):859-882.
[15] 何佳男. 贴近摄影测量及其关键技术研究[D]. 武汉:武汉大学, 2019. HE Jianan. Nap-of-the-object photogrammetry and its key techniques[D]. Wuhan:Wuhan University, 2019.
[16] LI Zhongbin, SHI Wenzhong, LU Ping, et al. Landslide mapping from aerial photographs using change detection-based Markov random field[J]. Remote Sensing of Environment, 2016, 187:76-90.
[17] KYRIOU A, NIKOLAKOPOULOS K. Landslide mapping using optical and radar data:a case study from Aminteo, Western Macedonia Greece[J]. European Journal of Remote Sensing, 2020, 53(S2):17-27.
[18] QU Feihang, QIU Haijun, SUN Hesheng, et al. Post-failure landslide change detection and analysis using optical satellite Sentinel-2 images[J]. Landslides, 2021, 18(1):447-455.
[19] HUANG Qingqing, WANG Chengyi, MENG Yu, et al. Landslide monitoring using change detection in multitemporal optical imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(2):312-316.
[20] LEPRINCE S, BARBOT S, AYOUB F, et al. Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6):1529-1558.
[21] ROSU A M, PIERROT-DESEILLIGNY M, DELORME A, et al. Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 100:48-59.
[22] DILLE A, KERVYN F, HANDWERGER A L, et al. When image correlation is needed:Unravelling the complex dynamics of a slow-moving landslide in the tropics with dense radar and optical time series[J]. Remote Sensing of Environment, 2021, 258:112402. DOI:10.1016/j.rse.2021.112402.
[23] SCHERLER D, LEPRINCE S, STRECKER M R. Glacier-surface velocities in alpine terrain from optical satellite imagery-Accuracy improvement and quality assessment[J]. Remote Sensing of Environment, 2008, 112(10):3806-3819.
[24] HILLEY G E, BVRGMANN R, FERRETTI A, et al. Dynamics of slow-moving landslides from permanent scatterer analysis[J]. Science, 2004, 304(5679):1952-1955.
[25] STEPHENSON O L, KÖHNE T, ZHAN E, et al. Deep learning-based damage mapping with InSAR coherence time series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-17.
[26] SHIBAYAMA T, YAMAGUCHI Y, YAMADA H. Polarimetric scattering properties of landslides in forested areas and the dependence on the local incidence angle[J]. Remote Sensing, 2015, 7(11):15424-15442.
[27] ZHAO Chaoying, LU Zhong, ZHANG Qin, et al. Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA[J]. Remote Sensing of Environment, 2012, 124:348-359.
[28] LU Ping, CASAGLI N, CATANI F, et al. Persistent scatterers interferometry hotspot and cluster analysis (PSI-HCA) for detection of extremely slow-moving landslides[J]. International Journal of Remote Sensing, 2012, 33(2):466-489.
[29] LIU Xiaojie, ZHAO Chaoying, ZHANG Qin, et al. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China[J]. Engineering Geology, 2021, 284:106033. DOI:10.1016/j.enggeo.2021.106033.
[30] WANG Chao, TANG Yixian, ZHANG Hong, et al. First mapping of China surface movement using supercomputing interferometric SAR technique[J]. Science Bulletin, 2021, 66(16):1608-1610.
[31] LANARI R, BONANO M, CASU F, et al. Automatic generation of sentinel-1 continental scale DInSAR deformation time series through an extended P-SBAS processing pipeline in a cloud computing environment[J]. Remote Sensing, 2020, 12(18):2961.
[32] COSTANTINI M, FERRETTI A, MINATI F, et al. Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data[J]. Remote Sensing of Environment, 2017, 202:250-275.
[33] GUZZETTI F, MONDINI A C, CARDINALI M, et al. Landslide inventory maps:new tools for an old problem[J]. Earth-Science Reviews, 2012, 112(1-2):42-66.
[34] SINGLETON A, LI Z, HOEY T, et al. Evaluating sub-pixel offset techniques as an alternative to D-InSAR for monitoring episodic landslide movements in vegetated terrain[J]. Remote Sensing of Environment, 2014, 147:133-144.
[35] LIU X, ZHAO C, ZHANG Q, et al. Deformation of the baige landslide, Tibet, China, revealed through the integration of cross-platform ALOS/PALSAR-1 and ALOS/PALSAR-2 SAR observations[J]. Geophysical Research Letters, 2020, 47(3):e2019GL086142.
[36] HANDWERGER A L, JONES S Y, HUANG M-H, et al. Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine[J]. Natural Hazards Earth System Sciences Discussions, 2020, 315, 1-24.
[37] ESPOSITO G, MARCHESINI I, MONDINI A C, et al. A spaceborne SAR-based procedure to support the detection of landslides[J]. Natural Hazards and Earth System Sciences, 2020, 20(9):2379-2395.
[38] NIU Chaoyang, ZHANG Haobo, LIU Wei, et al. Using a fully polarimetric SAR to detect landslide in complex surroundings:case study of 2015 Shenzhen landslide[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 174:56-67.
[39] LIN S Y, LIN Chengwei, VAN GASSELT S. Processing framework for landslide detection based on synthetic aperture radar (SAR) intensity-image analysis[J]. Remote Sensing, 2021, 13(4):644.
[40] SANTANGELO M, CARDINALI M, BUCCI F, et al. Exploring event landslide mapping using Sentinel-1 SAR backscatter products[J]. Geomorphology, 2022, 397:108021.
[41] WAN Jie, WANG Changcheng, SHEN Peng, et al. Forest height and underlying topography inversion using polarimetric SAR tomography based on SKP decomposition and maximum likelihood estimation[J]. Forests, 2021, 12(4):444.
[42] 周建民, 李震, 邢强. 基于雷达干涉失相干特性提取冰川边界方法研究[J]. 冰川冻土, 2010, 32(1):133-138. ZHOU Jianmin, LI Zhen, XING Qiang. Deriving glacier border information based on analysis of decorrelation in SAR interferometry[J]. Journal of Glaciology and Geocryology, 2010, 32(1):133-138.
[43] FOURNIER T J, PRITCHARD M E, RIDDICK S N. Duration, magnitude, and frequency of subaerial volcano deformation events:new results from Latin America using InSAR and a global synthesis[J]. Geochemistry, Geophysics, Geosystems, 2010, 11(1):Q01003.
[44] BELL R E, STUDINGER M, SHUMAN C A, et al. Large subglacial lakes in East Antarctica at the onset of fast-flowing ice streams[J]. Nature, 2007, 445(7130):904-907.
[45] MOHR J J, REEH N, MADSEN S N.Three-dimensional glacial flow and surface elevation measured with radar interferometry[J]. Nature, 1998, 391:6664.
[46] LI Jia, LI Zhiwei, WU Lixin, et al. Deriving a time series of 3D glacier motion to investigate interactions of a large mountain glacial system with its glacial lake:use of Synthetic Aperture Radar Pixel Offset-Small Baseline Subset technique[J]. Journal of Hydrology, 2018, 559:596-608.
[47] ERTEN E. Glacier velocity estimation by means of a polarimetric similarity measure[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6):3319-3327.
[48] TONG Xiaohua, YE Zhen, XU Yusheng, et al. A novel subpixel phase correlation method using singular value decomposition and unified random sample consensus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8):4143-4156.
[49] HU Xie, WANG Teng, LIAO Mingsheng. Measuring coseismic displacements with point-like targets offset tracking[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1):283-287.
[50] ZHAO Chaoying, LU Zhong, ZHANG Qin, et al. Mining collapse monitoring with SAR imagery data:a case study of Datong mine, China[J]. Journal of Applied Remote Sensing, 2014, 8(1):083574.
[51] 彭建兵, 范文, 李喜安, 等. 汾渭盆地地裂缝成因研究中的若干关键问题[J]. 工程地质学报, 2007, 15(4):433-440. PENG Jianbing, FAN Wen, LI Xian, et al. Some key questions in the formation of ground fissures in the Fen-Wei Basin[J]. Journal of Engineering Geology, 2007, 15(4):433-440.
[52] 赵超英, 张勤, 丁晓利, 等. 利用InSAR技术定位西安活动地裂缝[J]. 武汉大学学报(信息科学版), 2009, 34(7):809-813. ZHAO Chaoying, ZHANG Qin, DING Xiaoli, et al. Positioning of Xi'an active ground fissures with SAR interferometry[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7):809-813.
[53] YANG Chengsheng, LU Zhong, ZHANG Qin, et al. Deformation at Longyao ground fissure and its surroundings, North China plain, revealed by ALOS PALSAR PS-InSAR[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 67:1-9.
[54] ABDULWAHID W M, PRADHAN B. Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)[J]. Landslides, 2017, 14(3):1057-1076.
[55] PALENZUELA J A, MARSELLA M, NARDINOCCHI C, et al. Landslide detection and inventory by integrating LiDAR data in a GIS environment[J]. Landslides, 2015, 12(6):1035-1050.
[56] EKER R, AYDIN A, HVBL J. Unmanned aerial vehicle (UAV)-based monitoring of a landslide:Gallenzerkogel landslide (Ybbs-Lower Austria) case study[J]. Environmental Monitoring and Assessment, 2017, 190(1):1-14.
[57] MORA O, LENZANO M, TOTH C, et al. Landslide change detection based on multi-temporal airborne LiDAR-derived DEMs[J]. Geosciences, 2018, 8(1):23.
[58] 杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10):1509-1516. DOI:1011947/j.AGCS.2017.20170351. YANG Bisheng, LIANG Fuxun, HUANG Ronggang. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1509-1516. DOI:1011947/j.AGCS.2017.20170351.
[59] ZHANG Fan, HU Zhenqi, FU Yaokun, et al. A new identification method for surface cracks from UAV images based on machine learning in coal mining areas[J]. Remote Sensing, 2020, 12(10):1571.
[60] 熊艳, 高仁强, 徐战亚. 机载LiDAR点云数据降维与分类的随机森林方法[J]. 测绘学报, 2018, 47(4):508-518. DOI:10.11947/J.AGCS.2017.20170417. XIONG Yan, GAO Renqiang, XU Zhanya. Random forest method for dimension reduction and point cloud classification based on airborne LiDAR[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(4):508-518. DOI:10.11947/J.AGCS.2017.20170417.
[61] MAXWELL A E, WARNER T A, STRAGER M P, et al. Combining RapidEye satellite imagery and lidar for mapping of mining and mine reclamation[J]. Photogrammetric Engineering & Remote Sensing, 2014, 80(2):179-189. DOI:10.11947/J.AGCS.2017.20170417.
[62] STUMPF A, KERLE N. Object-oriented mapping of landslides using random forests[J]. Remote Sensing of Environment, 2011, 115(10):2564-2577.
[63] STUMPF A, LACHICHE N, MALET J P, et al. Active learning in the spatial domain for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5):2492-2507.
[64] CHEN Tao, TRINDER J, NIU Ruiqing. Object-oriented landslide mapping using ZY-3 satellite imagery, random forest and mathematical morphology, for the three-gorges reservoir, China[J]. Remote Sensing, 2017, 9(4):333.
[65] SIMON PLANK. DANIEL HÖLBLING C E, BARBARA FRIEDL, SANDRO MARTINIS, ANDRO TWELE. Comparing object-based landslide detection methods based on polarimetric SAR and optical satellite imagery-a case study in Taiwan[C]//Proceedings of the 7th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry. Frascati, Italy:[s.n.], 2015.
[66] CASAGLI N, CIGNA F, BIANCHINI S, et al. Landslide mapping and monitoring by using radar and optical remote sensing:examples from the EC-FP₇ project SAFER[J]. Remote Sensing Applications:Society and Environment, 2016, 4:92-108.
[67] ZHENG Xiangxiang, HE Guojin, WANG Shanshan, et al. Comparison of machine learning methods for potential active landslide hazards identification with multi-source data[J]. ISPRS International Journal of Geo-Information, 2021, 10(4):253.
[68] GHORBANZADEH O, BLASCHKE T, GHOLAMNIA K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection[J]. Remote Sensing, 2019, 11(2):196.
[69] JI Shunping, YU Dawen, SHEN Chaoyong, et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks[J]. Landslides, 2020, 17(6):1337-1352.
[70] SHI Wenzhong, ZHANG Min, KE Hongfei, et al. Landslide recognition by deep convolutional neural network and change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6):4654-4672.
[71] ANANTRASIRICHAI N, BIGGS J, KELEVITZ K, et al. Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(4):2940-2950.