甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究
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摘要
采用合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)技术对甘肃黑方台地区潜在的黄土滑坡开展了多时相编目、长时序监测以及失稳模式识别研究。首先,采用不同空间分辨率、不同波长的历史存档合成孔径雷达(synthetic aperture radar, SAR)数据对黑方台地区2006-12至2017-11间的潜在滑坡开展了识别研究,在2006-12至2011-03和2016-01至2016-11两个时间段均识别出数10处不稳定坡体,实地调查和光学遥感影像验证了InSAR技术识别结果的可靠性与准确性。然后,对典型不稳定滑坡体采用高空间与高时间分辨率的TerraSAR-X数据开展了长时序监测,结果表明,在InSAR监测期间,累积形变最大的滑坡体在随后的时间里均发生了滑动,并成功地捕获到滑坡体形变加速的时间点。最后,利用升降轨SAR数据开展了黄土滑坡二维形变监测研究,基于滑坡的二维形变特征并结合地形图以及光学遥感影像进一步研究了滑坡的失稳模式,现场调查结果验证了所获得滑坡失稳模式的准确性。
The interferometric synthetic aperture radar(InSAR) technique is used over Heifangtai loess terrace, Gansu province of China to map the distribution of potential loess landslides, the evolution of landslide deformation and the failure mode. Firstly, the archived synthetic aperture radar(SAR) datasets with different spatial resolutions and wavelengths from December 2006 to November 2017 are used to identify the potential landslides. Tens potential landslide areas are identified from December 2006 to March 2011 and from January 2016 to November 2016. Field investigation and optical remote sensing images validate the reliability and accuracy of the identified landslides. Then, the TerraSAR-X data with high spatial and temporal resolution are used to monitor the time series deformation of the typical unstable slopes. Results demonstrate that the landslides with the large accumulative deformation all occur in the following time, and the acceleration dates of failed landslides are successfully captured by InSAR time series results. Finally, two-dimensional deformation monitoring of loess landslide is conducted by combining with ascending and descending SAR datasets. The landslide failure mode are analyzed in depth according to the obtained two-dimensional deformation results, topographic map and remote sensing images. The accuracy of the obtained result is verified by field investigation.
The interferometric synthetic aperture radar(InSAR) technique is used over Heifangtai loess terrace, Gansu province of China to map the distribution of potential loess landslides, the evolution of landslide deformation and the failure mode. Firstly, the archived synthetic aperture radar(SAR) datasets with different spatial resolutions and wavelengths from December 2006 to November 2017 are used to identify the potential landslides. Tens potential landslide areas are identified from December 2006 to March 2011 and from January 2016 to November 2016. Field investigation and optical remote sensing images validate the reliability and accuracy of the identified landslides. Then, the TerraSAR-X data with high spatial and temporal resolution are used to monitor the time series deformation of the typical unstable slopes. Results demonstrate that the landslides with the large accumulative deformation all occur in the following time, and the acceleration dates of failed landslides are successfully captured by InSAR time series results. Finally, two-dimensional deformation monitoring of loess landslide is conducted by combining with ascending and descending SAR datasets. The landslide failure mode are analyzed in depth according to the obtained two-dimensional deformation results, topographic map and remote sensing images. The accuracy of the obtained result is verified by field investigation.
引文
[1] Lin Q,Wang Y.Spatial and Temporal Analysis of a Fatal Landslide Inventory in China from 1950 to 2016 [J].Landslides,2018,15(12):2 357-2 372
[2] Shi X,Liao M,Li M,et al.Wide-Area Landslide Deformation Mapping with Multi-path ALOS PALSAR Data Stacks:A Case Study of Three Gorges Area,China [J].Remote Sensing,2016,8(2):136
[3] Sun L,Muller J P,Chen J.Time Series Analysis of Very Slow Landslides in the Three Gorges Region Through Small Baseline SAR Offset Tracking [J].Remote Sensing,2017,9(12):1 314
[4] Ao Meng,Zhang Qin,Zhao Chaoying,et al.An Improved CR-InSAR Technology Used for Deformation Monitoring in Jiaju Landslide,Sichuan [J].Geomatics and Information Science of Wuhan University,2017,42(3):377-383(敖萌,张勤,赵超英,等.改正的CR-InSAR技术用于四川甲居滑坡形变监测[J].武汉大学学报·信息科学版,2017,42(3):377-383)
[5] Zhang Lu,Liao Mingsheng,Dong Jie,et al.Early Detection of Landslide Hazards in Mountainous Areas of West China Using Time Series SAR Interferometry—A Case Study of Danba,Sichuan [J].Geomatics and Information Science of Wuhan University,2018,43(12):2 039-2 049(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):2 039-2 049)
[6] Zhao C,Kang Y,Zhang Q,et al.Landslide Identification and Monitoring Along the Jinsha River Catchment(Wudongde Reservoir Area),China,Using the InSAR Method [J].Remote Sensing,2018,10(7):993
[7] Zhao C,Zhang Q,He Y,et al.Small-Scale Loess Landslide Monitoring with Small Baseline Subsets Interferometric Synthetic Aperture Radar Technique—Case Study of Xinyuan Landslide,Shaanxi,China [J].J Appl Remote Sens,2016,10(2):1-14
[8] Li M,Zhang L,Shi X,et al.Monitoring Active Motion of the Guobu Landslide Near the Laxiwa Hydropower Station in China by Time-Series Point-Like Targets Offset Tracking [J].Remote Sensing of Environment,2019,221:80-93
[9] Zeng R,Meng X,Zhang F,et al.Characterizing Hydrological Processes on Loess Slopes Using Electrical Resistivity Tomography—A Case Study of the Heifangtai Terrace,Northwest China [J].Journal of Hydrology,2016,541:742-753
[10] Cui S,Pei X,Wu H,et al.Centrifuge Model Test of an Irrigation-Induced Loess Landslide in the Heifangtai Loess Platform,Northwest China [J].Journal of Mountain Science,2018,15(1):130-143
[11] Peng D,Xu Q,Liu F,et al.Distribution and Failure Modes of the Landslides in Heitai Terrace,China [J].Engineering Geology,2017,236:97-110
[12] Xu L,Dai F,Tu X,et al.Landslides in a Loess Platform,North-West China [J].Landslides,2014,11(6):993-1 005
[13] Xu L,Dai F,Gong Q,et al.Irrigation-Induced Loess Flow Failure in Heifangtai Platform,North-West [J].Environmental Earth Sciences,2012,66(6):1 707-1 713
[14] Xu L,Qiao X,Wu C,et al.Causes of Landslide Recurrence in a Loess Platform with Respect to Hydrological Processes [J].Natural Hazards,2012,64(2):1 657-1 670
[15] Lyons S,Sandwell D.Fault Creep Along the Southern San Andreas from Interferometric Synthetic Aperture Radar,Permanent Scatterers,and Stacking [J].J Geophys Res Solid Earth,2003,108(B1):2 047
[16] Berardino P,Fornaro G,Lanari R,et al.A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms [J].IEEE Trans Geosci Remote Sens,2002,40(11):2 375-2 383
[17] Ferretti A,Prati C,Rocca F.Permanent Scatterers in SAR Interferometry [J].IEEE Trans Geosci Remote Sens,2001,39(1):8-20
[18] Samsonov S,d’Oreye N,Smets B.Ground Deformation Associated with Post-mining Activity at the French-German Border Revealed by Novel InSAR Time Series Method [J].International Journal of Applied Earth Observation and Geoinformation,2013,23(1):142-154
[19] Samsonov S,Feng W,Peltier A,et al.Multidimensional Small Baseline Subset(MSBAS) for Volcano Monitoring in Two Dimensions:Opportunities and Challenges.Case Study Piton de la Fournaise Volcano [J].Journal of Volcanology and Geothermal Research,2017,344:121-138
[20] Xu Q,Li H,He Y,et al.Comparison of Data-Driven Models of Loess Landslide Runout Distance Estimation [J].Bull Eng Geol Environ,2017,8:1-14
[21] Qi X,Xu Q,Liu F.Analysis of Retrogressive Loess Flowslides in Heifangtai,China [J].Engineering Geology,2018,236:119-128
[2] Shi X,Liao M,Li M,et al.Wide-Area Landslide Deformation Mapping with Multi-path ALOS PALSAR Data Stacks:A Case Study of Three Gorges Area,China [J].Remote Sensing,2016,8(2):136
[3] Sun L,Muller J P,Chen J.Time Series Analysis of Very Slow Landslides in the Three Gorges Region Through Small Baseline SAR Offset Tracking [J].Remote Sensing,2017,9(12):1 314
[4] Ao Meng,Zhang Qin,Zhao Chaoying,et al.An Improved CR-InSAR Technology Used for Deformation Monitoring in Jiaju Landslide,Sichuan [J].Geomatics and Information Science of Wuhan University,2017,42(3):377-383(敖萌,张勤,赵超英,等.改正的CR-InSAR技术用于四川甲居滑坡形变监测[J].武汉大学学报·信息科学版,2017,42(3):377-383)
[5] Zhang Lu,Liao Mingsheng,Dong Jie,et al.Early Detection of Landslide Hazards in Mountainous Areas of West China Using Time Series SAR Interferometry—A Case Study of Danba,Sichuan [J].Geomatics and Information Science of Wuhan University,2018,43(12):2 039-2 049(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):2 039-2 049)
[6] Zhao C,Kang Y,Zhang Q,et al.Landslide Identification and Monitoring Along the Jinsha River Catchment(Wudongde Reservoir Area),China,Using the InSAR Method [J].Remote Sensing,2018,10(7):993
[7] Zhao C,Zhang Q,He Y,et al.Small-Scale Loess Landslide Monitoring with Small Baseline Subsets Interferometric Synthetic Aperture Radar Technique—Case Study of Xinyuan Landslide,Shaanxi,China [J].J Appl Remote Sens,2016,10(2):1-14
[8] Li M,Zhang L,Shi X,et al.Monitoring Active Motion of the Guobu Landslide Near the Laxiwa Hydropower Station in China by Time-Series Point-Like Targets Offset Tracking [J].Remote Sensing of Environment,2019,221:80-93
[9] Zeng R,Meng X,Zhang F,et al.Characterizing Hydrological Processes on Loess Slopes Using Electrical Resistivity Tomography—A Case Study of the Heifangtai Terrace,Northwest China [J].Journal of Hydrology,2016,541:742-753
[10] Cui S,Pei X,Wu H,et al.Centrifuge Model Test of an Irrigation-Induced Loess Landslide in the Heifangtai Loess Platform,Northwest China [J].Journal of Mountain Science,2018,15(1):130-143
[11] Peng D,Xu Q,Liu F,et al.Distribution and Failure Modes of the Landslides in Heitai Terrace,China [J].Engineering Geology,2017,236:97-110
[12] Xu L,Dai F,Tu X,et al.Landslides in a Loess Platform,North-West China [J].Landslides,2014,11(6):993-1 005
[13] Xu L,Dai F,Gong Q,et al.Irrigation-Induced Loess Flow Failure in Heifangtai Platform,North-West [J].Environmental Earth Sciences,2012,66(6):1 707-1 713
[14] Xu L,Qiao X,Wu C,et al.Causes of Landslide Recurrence in a Loess Platform with Respect to Hydrological Processes [J].Natural Hazards,2012,64(2):1 657-1 670
[15] Lyons S,Sandwell D.Fault Creep Along the Southern San Andreas from Interferometric Synthetic Aperture Radar,Permanent Scatterers,and Stacking [J].J Geophys Res Solid Earth,2003,108(B1):2 047
[16] Berardino P,Fornaro G,Lanari R,et al.A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms [J].IEEE Trans Geosci Remote Sens,2002,40(11):2 375-2 383
[17] Ferretti A,Prati C,Rocca F.Permanent Scatterers in SAR Interferometry [J].IEEE Trans Geosci Remote Sens,2001,39(1):8-20
[18] Samsonov S,d’Oreye N,Smets B.Ground Deformation Associated with Post-mining Activity at the French-German Border Revealed by Novel InSAR Time Series Method [J].International Journal of Applied Earth Observation and Geoinformation,2013,23(1):142-154
[19] Samsonov S,Feng W,Peltier A,et al.Multidimensional Small Baseline Subset(MSBAS) for Volcano Monitoring in Two Dimensions:Opportunities and Challenges.Case Study Piton de la Fournaise Volcano [J].Journal of Volcanology and Geothermal Research,2017,344:121-138
[20] Xu Q,Li H,He Y,et al.Comparison of Data-Driven Models of Loess Landslide Runout Distance Estimation [J].Bull Eng Geol Environ,2017,8:1-14
[21] Qi X,Xu Q,Liu F.Analysis of Retrogressive Loess Flowslides in Heifangtai,China [J].Engineering Geology,2018,236:119-128
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