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
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摘要
中国地质灾害点多面广,且大多地处高位并被植被覆盖,传统的人工调查排查在一些地区进行地质灾害隐患识别已显得无能为力,这也是近年来绝大多数灾难性地质灾害事件都不在预案点范围内的主要原因。提出通过构建天-空-地一体化的"三查"体系进行重大地质灾害隐患的早期识别,再通过专业监测,在掌握地质灾害动态发展规律和特征的基础上,进行地质灾害的实时预警预报,以此破解"隐患点在哪里""什么时候可能发生"这一地质灾害防治领域的难题和国家急切需求。"三查"体系首先通过光学遥感和合成孔径雷达干涉测量技术(interferometric synthetic aperture radar,InSAR)实现区域扫面性地质灾害隐患的普查,随后利用机载激光雷达测量技术(light laser detection and ranging,LiDAR)和无人机摄影测量实现高地质灾害风险区段和重大地质灾害隐患的详查,最后采用现场调查、地面与坡体内部监(探)测等手段,实现重大地质灾害隐患的复核确认和排除,即核查。监测预警则是通过InSAR和地面观测手段(如全球导航卫星系统、裂缝计等),在掌握滑坡崩塌的变形规律和阶段以及时间-空间变形特征的基础上,建立分级综合预警体系,并利用地质灾害实时监测预警系统,逐步实现地质灾害监测预警的实用化和业务化运行。
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar(InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles(UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging(LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system(GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar(InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles(UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging(LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system(GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
引文
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[16] Fan X,Qiang X,Scaringi G,et al.Failure Mechanism and Kinematics of the Deadly June 24th 2017 Xinmo Landslide,Maoxian,Sichuan,China[J].Landslides,2017,14(6):2 129-2 146
[17] Xu Qiang,Zheng Guang,Li Weile,et al.Study on Successive Landslide Damming Events of Jinsha River in Baige Village on October 11 and November 3,2018[J].Journal of Engineering Geology,2018,26(6):1 534-1 551(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):1 534-1 551)
[18] Intrieri E,Raspini F,Fumagalli A,et al.The Maoxian Landslide as Seen from Space:Detecting Precursors of Failure with Sentinel-1 Data[J].Landslides,2018,15(1):123-133
[19] Dong J,Zhang L,Li M,et al.Measuring Precursory Movements of the Recent Xinmo Landslide in Mao County,China with Sentinel-1 and ALOS-2 PALSAR-2 Datasets[J].Landslides,2018,15(1):135-144
[20] Xu Qiang,Tang Minggao,Huang Runqiu.Monitoring,Early Warning and Emergency Disposal of Large Landslides[M].Beijing:Science Press,2015(许强,汤明高,黄润秋.大型滑坡监测预警与应急处置[M].北京:科学出版社,2015)
[2] Hilley G E.Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis[J].Science,2004,304(5 679):1 952-1 955
[3] Zhao C,Lu Z,Zhang Q,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
[4] Wasowski J,Bovenga F.Investigating Landslides and Unstable Slopes with Satellite Multi-temporal Interferometry:Current Issues and Future Perspectives[J].Engineering Geology,2014,174(8):103-138
[5] Sun Q,Zhang L,Ding X L,et al.Slope Deformation Prior to Zhouqu,China Landslide from InSAR Time Series Analysis[J].Remote Sensing of Environment,2015,156:45-57
[6] Dai K,Li Z,Tomás R,et al.Monitoring Activity at the Daguangbao Mega-landslide (China) Using Sentinel-1 TOPS Time Series Interferometry[J].Remote Sensing of Environment,2016,186:501-513
[7] Liao Mingsheng,Wang Teng.Time Series InSAR Technology and Its Applications [M].Beijing:Science Press,2014(廖明生,王腾.时间序列InSAR技术与应用[M].北京:科学出版社,2014)
[8] Ferretti A,Prati C,Rocca F.Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(5):2 202-2 212
[9] Zhang L,Lu Z,Ding X,et al.Mapping Ground Surface Deformation Using Temporarily Coherent Point SAR Interferometry:Application to Los Angeles Basin[J].Remote Sensing of Environment,2012,117:429-439
[10] Liao Mingsheng,Zhang Lu,Shi Xuguo,et al.Methodology and Practice of Landslide Deformation Monitoring with SAR Remote Sensing[M].Beijing:Science Press,2017(廖明生,张路,史绪国,等.滑坡变形雷达遥感监测方法与实践[M].北京:科学出版社,2017)
[11] Dong J,Liao M,Xu Q,et al.Detection and Displacement Characterization of Landslides Using Multi-temporal Satellite SAR Interferometry:A Case Study of Danba County in the Dadu River Basin[J].Engineering Geology,2018,240:95-109
[12] Dong J,Zhang L,Tang M,et al.Mapping Landslide Surface Displacements with Time Series SAR Interferometry by Combining Persistent and Distributed Scatterers:A Case Study of Jiaju Landslide in Danba,China[J].Remote Sensing of Environment,2018,205:180-198
[13] Liu X,Zhao C,Zhang Q,et al.Multi-temporal Loess Landslide Inventory Mapping with C-,X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides,China[J].Remote Sensing,2018,10(11):1 756
[14] 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
[15] Xu Qiang,Li Weile,Dong Xiujun,et al.The Xinmocun Landslide on June 24,2017 in Maoxian,Sichuan:Characteristics and Failure Mechanism[J].Chinese Journal of Rock Mechanics and Engineering,2017,36(11):17-33(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):17-33)
[16] Fan X,Qiang X,Scaringi G,et al.Failure Mechanism and Kinematics of the Deadly June 24th 2017 Xinmo Landslide,Maoxian,Sichuan,China[J].Landslides,2017,14(6):2 129-2 146
[17] Xu Qiang,Zheng Guang,Li Weile,et al.Study on Successive Landslide Damming Events of Jinsha River in Baige Village on October 11 and November 3,2018[J].Journal of Engineering Geology,2018,26(6):1 534-1 551(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):1 534-1 551)
[18] Intrieri E,Raspini F,Fumagalli A,et al.The Maoxian Landslide as Seen from Space:Detecting Precursors of Failure with Sentinel-1 Data[J].Landslides,2018,15(1):123-133
[19] Dong J,Zhang L,Li M,et al.Measuring Precursory Movements of the Recent Xinmo Landslide in Mao County,China with Sentinel-1 and ALOS-2 PALSAR-2 Datasets[J].Landslides,2018,15(1):135-144
[20] Xu Qiang,Tang Minggao,Huang Runqiu.Monitoring,Early Warning and Emergency Disposal of Large Landslides[M].Beijing:Science Press,2015(许强,汤明高,黄润秋.大型滑坡监测预警与应急处置[M].北京:科学出版社,2015)
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