InSAR滑坡监测研究进展

doi:10.11947/j.AGCS.2022.20220294

测绘学报 ›› 2022, Vol. 51 ›› Issue (10): 2001-2019.doi: 10.11947/j.AGCS.2022.20220294

InSAR滑坡监测研究进展

朱建军^1,2, 胡俊^1,2, 李志伟^1,2, 孙倩³, 郑万基¹

1. 中南大学地球科学与信息物理学院,湖南长沙 410083;
2. 有色金属成矿预测与地质环境监测教育部重点实验室(中南大学),湖南长沙 410083;
3. 湖南师范大学地理科学学院,湖南长沙 410081

收稿日期:2022-05-05 修回日期:2022-08-27 发布日期:2022-11-05
通讯作者: 胡俊 E-mail:csuhujun@csu.edu.cn
作者简介:朱建军(1962—),男,教授,博士生导师,研究方向为测量平差与InSAR数据处理。E-mail:zjj@mail.csu.edu.cn
基金资助:
国家重点研发计划(2018YFC1505101);国家自然科学基金(42030112);湖南省自然科学基金(2020JJ2043);中南大学创新驱动项目(2019CX007)

Recent progress in landslide monitoring with InSAR

ZHU Jianjun^1,2, HU Jun^1,2, LI Zhiwei^1,2, SUN Qian³, ZHENG Wanji¹

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China;
3. College of Geographic Sciences, Hunan Normal University, Changsha 410081, China

Received:2022-05-05 Revised:2022-08-27 Published:2022-11-05
Supported by:
The National Key Research and Development Program of China (No. 2018YFC1505101); The National Natural Science Foundation of China (No. 42030112); Hunan Natural Science Foundation (No. 2020JJ2043); Project of Innovation-driven Plan of Central South University (No. 2019CX007)

摘要/Abstract

摘要： InSAR技术的发展及数据的丰富,使其成为了滑坡监测中的重要手段之一。本文首先从InSAR滑坡监测的数据选择开始,介绍不同数据、场景对于InSAR滑坡监测的影响;其次,对当前影响InSAR滑坡监测精度的主要因素进行分析,综述了目前主流的解决方法;然后,对当前InSAR滑坡三维形变监测的方法做了系统性的分类,总结了各自的优缺点及使用范围;最后,对目前限制InSAR滑坡监测的主要问题、可能的解决途径及未来的发展方向等问题进行了探讨。

关键词: 合成孔径雷达干涉测量, 滑坡, 地质灾害, 形变监测, 综述

Abstract: The development of InSAR technique and the tremendous amount of SAR data have made InSAR the fundamental tool in landslide monitoring.This paper begins with the data selection, the effects of different datasets and scenes in landslide monitoring by InSAR are introduced. We analyze the main limitations affecting the accuracy of landslide monitoring by InSAR, and demonstrate the corresponding solutions. Subsequently, the methods in retrieving landslide-induced 3D displacement using InSAR are then classified, and their advantages, disadvantages, and application scopes are also analyzed. Finally, the limitations in landslide monitoring with InSAR, the corresponding solutions, and future development trends are discussed.

Key words: InSAR, landslide, geo-hazards, deformation monitoring, review

中图分类号:

P227

朱建军, 胡俊, 李志伟, 孙倩, 郑万基. InSAR滑坡监测研究进展[J]. 测绘学报, 2022, 51(10): 2001-2019.

ZHU Jianjun, HU Jun, LI Zhiwei, SUN Qian, ZHENG Wanji. Recent progress in landslide monitoring with InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2001-2019.

参考文献

[1] 张勤, 黄观文, 杨成生. 地质灾害监测预警中的精密空间对地观测技术[J]. 测绘学报, 2017, 46(10): 1300-1307. DOI:10.11947/j.AGCS.2017.20170453. ZHANG Qin, HUANG Guanwen, YANG Chengsheng. Precision space observation technique for geological hazard monitoring and early warning[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1300-1307. DOI:10.11947/j.AGCS.2017.20170453.
[2] 朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10): 1717-1733. DOI: 10.11947/j.AGCS.2017.20170350. ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1717-1733. DOI: 10.11947/j.AGCS.2017.20170350.
[3] FERRETTI A, PRATI C, ROCCA F. Permanent scatterers in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.
[4] LIANG Hongyu, LI Xin, ZHANG Lei, et al. Investigation of slow-moving artificial slope failure with multi-temporal InSAR by combining persistent and distributed scatterers: a case study in northern Taiwan[J]. Remote Sensing, 2020, 12(15): 2403.
[5] DONG Jie, ZHANG Lu, LIAO Mingsheng, et al. Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring[J]. Remote Sensing of Environment, 2019, 233: 111370.
[6] HU Xie, BÜRGMANN R, SCHULZ W H, et al. Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing[J]. Nature Communications, 2020, 11(1): 2792.
[7] HU Xie, BÜRGMANN R, LU Zhong, et al. Mobility, thickness, and hydraulic diffusivity of the slow-moving monroe landslide in California revealed by L-band satellite radar interferometry[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(7): 7504-7518.
[8] LI Menghua, ZHANG Lu, DING Chao, et al. Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event[J]. Remote Sensing of Environment, 2020, 240: 111695.
[9] LIU Yuxin, XU Caijun, LIU Yang. Monitoring and forecasting analysis of a landslide in Xinmo, Mao county, using Sentinel-1 data[J]. Terrestrial, Atmospheric and Oceanic Sciences, 2019, 30(1): 85-96.
[10] SHI Xuguo, HU Xie, SITAR N, et al. Hydrological control shift from river level to rainfall in the reactivated Guobu slope besides the Laxiwa hydropower station in China[J]. Remote Sensing of Environment, 2021, 265: 112664.
[11] HANSSEN R F. Radar interferometry: data interpretation and error analysis[M]. Dordrecht: Springer, 2001.
[12] REN Tianhe, GONG Wenping, BOWA V M, et al. An improved R-index model for terrain visibility analysis for landslide monitoring with InSAR[J]. Remote Sensing, 2021, 13(10): 1938.
[13] KROPATSCH W G, STROBL D. The generation of SAR layover and shadow maps from digital elevation models[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(1): 98-107.
[14] NOTTI D, DAVALILLO J C, HERRERA G, et al. Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: upper tena valley case study[J]. Natural Hazards and Earth System Sciences, 2010, 10(9): 1865-1875.
[15] CHEN Xiaohong, SUN Qian, HU Jun. Generation of complete SAR geometric distortion maps based on DEM and neighbor gradient algorithm[J]. Applied Sciences, 2018, 8(11): 2206.
[16] WILKINSON A J. Synthetic aperture radar interferometry: a statistical model for layover areas[C]//Proceedings of 1999 IEEE International Geoscience and Remote Sensing Symposium. Hamburg, Gernamy: IEEE, 1999: 2392-2394.
[17] 任云. InSAR叠掩与阴影检测技术[D]. 长沙:国防科学技术大学, 2013. REN Yun. Research on layover and shadow detecting in InSAR[D]. Changsha: National University of Defense Technology, 2013.
[18] PLANK S, SINGER J, THURO K, et al. The suitability of the differential radar interferometry method for deformation monitoring of landslides: a new GIS based evaluation tool[C]//Proceedings of the 11th IAEG Congress Geologically Active.Auckland, New Zealand: [s.n.], 2010.
[19] GINI F, LOMBARDINI F, MONTANARI M. Layover solution in multibaseline SAR interferometry[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(4): 1344-1356.
[20] GUO Rui, LI Sumin, CHEN Ya'nan, et al. Identification and monitoring landslides in longitudinal range-gorge region with InSAR fusion integrated visibility analysis[J]. Landslides, 2021, 18(2): 551-568.
[21] COLESANTI C, WASOWSKI J. Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry[J]. Engineering Geology, 2006, 88(3-4): 173-199.
[22] 孙倩. 多基线、多时相和多平台InSAR滑坡监测研究[D]. 长沙:中南大学, 2014. SUN Qian. Investigation of landslides with multi-baseline, multi-temporal and multi-sensor InSAR[D].Changsha: Central South University,2014.
[23] BASELICE F, BUDILLON A, FERRAIOLI G, et al. Layover solution in SAR imaging: a statistical approach[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 577-581.
[24] 张福博, 梁兴东, 吴一戎. 一种基于地形驻点分割的多通道SAR三维重建方法[J]. 电子与信息学报, 2015, 37(10): 2287-2293. ZHANG Fubo, LIANG Xingdong, WU Yirong. 3D reconstruction for multi-channel SAR interferometry using terrain stagnation point based division[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2287-2293.
[25] BVRGI P M, LOHMAN R B. Impact of forest disturbance on InSAR surface displacement time series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 128-138.
[26] SARABANDI K, WILSEN C B. Temporal decorrelation of vegetation by environmental and seasonal effects[C]//Proceedings of 2000 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE, 2000: 1399-1401.
[27] STROZZI T, FARINA P, CORSINI A, et al. Survey and monitoring of landslide displacements by means of L-band satellite SAR interferometry[J]. Landslides, 2005, 2(3): 193-201.
[28] WEI Meng, SANDWELL D T. Decorrelation of L-band and C-band interferometry over vegetated areas in California[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(7): 2942-2952.
[29] MORISHITA Y, HANSSEN R F. Temporal decorrelation in L-, C-, and X-band satellite radar interferometry for pasture on drained peat soils[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1096-1104.
[30] SAMIEI E S. Exploitation of distributed scatterers in synthetic aperture radar interferometry[D]. Delft: Delft University of Technology, 2017.
[31] 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 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): 2039-2049.
[32] SANTORO M, WEGMULLER U, ASKNE J I H. Signatures of ERS-ENVISAT interferometric SAR coherence and phase of short vegetation: an analysis in the case of maize fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 1702-1713.
[33] ZHANG Meimei, LI Zhen, TIAN Bangsen, et al. A method for monitoring hydrological conditions beneath herbaceous wetlands using multi-temporal ALOS PALSAR coherence data[J]. Remote Sensing Letters, 2015, 6(8): 618-627.
[34] MOHAMMADIMANESH F, SALEHI B, MAHDIANPARI M, et al. Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142: 78-93.
[35] ARAB-SEDZE M, HEGGY E, BRETAR F, et al. Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements[J]. Remote Sensing of Environment, 2014, 152: 202-216.
[36] BAI Zechao, FANG Shibo, GAO Jian, et al. Could vegetation index be derive from synthetic aperture radar? The linear relationship between interferometric coherence and NDVI[J]. Scientific Reports, 2020, 10(1): 6749.
[37] CHEN Yongang, SUN Qian, HU Jun. Quantitatively estimating of InSAR decorrelation based on Landsat-derived NDVI[J]. Remote Sensing, 2021, 13(13): 2440.
[38] KURUOGLU E E, ZERUBIA J. Modeling SAR images with a generalization of the rayleigh distribution[J]. IEEE Transactions on Image Processing, 2004, 13(4): 527-533.
[39] FERRETTI A, FUMAGALLI A, NOVALI F, et al. A new algorithm for processing interferometric data-stacks: squeeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3460-3470.
[40] PARIZZI A, BRCIC R. Adaptive InSAR stack multilooking exploiting amplitude statistics: a comparison between different techniques and practical results[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(3): 441-445.
[41] MILLER I. Probability, random variables, and stochastic processes[J]. Technometrics, 1966, 8(2): 378-380.
[42] JIANG Mi, DING Xiaoli, HANSSEN R F, et al. Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1213-1224.
[43] DE ZAN F, ZONNO M, LÓPEZ-DEKKER P. Phase inconsistencies and multiple scattering in SAR interfero-metry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6608-6616.
[44] ANSARI H, DE ZAN F, BAMLER R. Efficient phase estimation for interferogram stacks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(7): 4109-4125.
[45] GUARNIERI A M, TEBALDINI S. On the exploitation of target statistics for SAR interferometry applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3436-3443.
[46] GUARNIERI A M, TEBALDINI S. Hybrid CramÉr-rao bounds for crustal displacement field estimators in SAR interferometry[J]. IEEE Signal Processing Letters, 2007, 14(12): 1012-1015.
[47] TAMBURINI A, CONTE S, LARINI G, et al. Application of squeeSAR^TM to the characterization of deep seated gravitational slope deformations: the Berceto case study (Parma, Italy) [M].Berlin,Heidelberg:Springer,2013:437-443.
[48] PERISSIN D, WANG T. Repeat-pass SAR interferometry with partially coherent targets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 271-280.
[49] FORNARO G, VERDE S, REALE D, et al. CAESAR: an approach based on covariance matrix decomposition to improve multibaseline-multitemporal interferometric SAR processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2050-2065.
[50] CAO Ning, LEE H, JUNG H C. A phase-decomposition-based PSInSAR processing method[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(2): 1074-1090.
[51] SAMIEI-ESFAHANY S, MARTINS J E, VAN LEIJEN F, et al. Phase estimation for distributed scatterers in InSAR stacks using integer least squares estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 5671-5687.
[52] ANSARI H, DE ZAN F, BAMLER R. Sequential estimator: toward efficient InSAR time series analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5637-5652.
[53] CAO Ning, LEE H, JUNG H C. Mathematical framework for phase-triangulation algorithms in distributed-scatterer interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9): 1838-1842.
[54] EVEN M, SCHULZ K. InSAR deformation analysis with distributed scatterers: a review complemented by new advances[J]. Remote Sensing, 2018, 10(5): 744.
[55] DU Yanan, ZHANG Lei, FENG Guangcai, et al. On the accuracy of topographic residuals retrieved by MTInSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 1053-1065.
[56] ZHANG Lei, JIA Hongguo, LU Zhong, et al. Minimizing height effects in MTInSAR for deformation detection over built areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 9167-9176.
[57] 常亮. 基于GPS和美国环境预报中心观测信息的InSAR大气延迟改正方法研究[J]. 测绘学报, 2011, 40(5): 669. CHANG Liang. InSAR atmospheric delay correction based on GPS observations and NCEP data[J].Acta Geodaetica et Cartographica Sinica, 2011, 40(5): 669.
[58] DELACOURT C, BRIOLE P, ACHACHE J A. Tropospheric corrections of SAR interferograms with strong topography: application to Etna[J]. Geophysical Research Letters,1998, 25(15): 2849-2852.
[59] ONN F. Modeling water vapor using GPS with application to mitigating InSAR atmospheric distortions[D]. Stanford: Stanford University, 2006.
[60] LI Zhenhong, MULLER J P, CROSS P. Tropospheric correction techniques in repeat-pass SAR interferometry[C]//Proceedings of 2003 FRINGE Workshop. Frascati, Italy: ESA/ESRIN, 2003.
[61] LI Zhenhong, MULLER J P, CROSSP, et al. Interferometric synthetic aperture radar (InSAR) atmospheric correction: GPS, moderate resolution imaging spectroradiometer (MODIS), and InSAR integration[J]. Journal of Geophysical Research: Solid Earth,2005, 110(B3): B03410.
[62] DING Xiaoli, LI Zhiwei, ZHU Jianjun, et al. Atmospheric effects on InSAR measurements and their mitigation[J]. Sensors,2008, 8(9): 5426-5448.
[63] LI Zhenhong, FIELDING E J, CROSS P, et al. Interferometric synthetic aperture radar atmospheric correction: medium resolution imaging spectrometer and advanced synthetic aperture radar integration[J]. Geophysical Research Letters,2006, 33(6): L06816.
[64] JOLIVET R, GRANDIN R, LASSERRE C, et al. Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data[J]. Geophysical Research Letters, 2011, 38(17): L17311.
[65] KIRUI P K, REINOSCH E, ISYA N, et al. Mitigation of atmospheric artefacts in multi temporal InSAR: a review[J]. PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science,2021, 89(3): 251-272.
[66] GONG Wenyu, MEYER F J, LIU Shizhuo, et al. Temporal filtering of InSAR data using statistical parameters from NWP models[J]. IEEE Transactions on Geoscience and Remote Sensing,2015, 53(7): 4033-4044.
[67] BEKAERT D P S, HOOPER A, WRIGHT T J. A spatially variable power law tropospheric correction technique for InSAR data[J]. Journal of Geophysical Research: Solid Earth,2015, 120(2): 1345-1356.
[68] BEKAERT D P S, WALTERS R J, WRIGHT T W, et al. Statistical comparison of InSAR tropospheric correction techniques[J]. Remote Sensing of Environment,2015, 170: 40-47.
[69] SHEN Lin, HOOPER A, ELLIOTT J. A spatially varying scaling method for InSAR tropospheric corrections using a high-resolution weather model[J]. Journal of Geophysical Research: Solid Earth,2019, 124(4): 4051-4068.
[70] HU Zhongbo, MALLORQUÍ J J, FAN Hongdong, et al. Atmospheric artifacts correction with a covariance-weighted linear model over mountainous regions[J]. IEEE Transactions on Geoscience and Remote Sensing,2018, 56(12): 6995-7008.
[71] LIANG Hongyu, ZHANG Lei, DING Xiaoli, et al. Toward mitigating stratified tropospheric delays in multitemporal InSAR: a quadtree aided joint model[J]. IEEE Transactions on Geoscience and Remote Sensing,2019, 57(1): 291-303.
[72] MAUBANT L, PATHIER E, DAOUT S, et al. Independent component analysis and parametric approach for source separation in InSAR time series at regional scale: application to the 2017—2018 slow slip event in Guerrero (Mexico)[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(3): e2019JB018187.
[73] HYVARINEN A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions on Neural Networks, 1999, 10(3): 626-634.
[74] EBMEIER S K. Application of independent component analysis to multitemporal InSAR data with volcanic case studies[J]. Journal of Geophysical Research: Solid Earth, 2016, 121(12): 8970-8986.
[75] COHEN-WAEBER J, BÜRGMANN R, CHAUSSARD E, et al. Spatiotemporal patterns of precipitation-modulated landslide deformation from independent component analysis of InSAR time series[J]. Geophysical Research Letters, 2018, 45(4): 1878-1887.
[76] LIANG Hongyu, ZHANG Lei, LU Zhong, et al. Nonparametric estimation of DEM error in multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10004-10014.
[77] HU J J, LI Zhiwei, DING Xiaoli, et al. Resolving three-dimensional surface displacements from InSAR measurements: a review[J]. Earth-Science Reviews, 2014, 133: 1-17.
[78] 王志伟. 基于多源InSAR数据的三维地表形变解算方法研究[J]. 测绘学报, 2019, 48(9): 1206. DOI:10.11947/j.AGCS.2019.20180490. WANG Zhiwei. Research on resolving of three-dimensional displacement from multi-source InSAR data[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(9): 1206.DOI:10.11947/j.AGCS.2019.20180490.
[79] MICHEL R, AVOUAC J P, TABOURY J. Measuring near field coseismic displacements from SAR images: application to the Landers earthquake[J]. Geophysical Research Letters, 1999, 26(19): 3017-3020.
[80] RAUCOULES D, DE MICHELE M, MALET J P, et al. Time-variable 3D ground displacements from high-resolution synthetic aperture radar (SAR). application to La Valette landslide (South French Alps)[J]. Remote Sensing of Environment, 2013, 139: 198-204.
[81] SHI Xuguo, ZHANG Lu, BALZ T, et al. Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 128-140.
[82] CAI Jiehua, WANG Changcheng, MAO Xiaokang, et al. An adaptive offset tracking method with SAR images for landslide displacement monitoring[J]. Remote Sensing, 2017, 9(8): 830.
[83] 陈强, 罗容, 杨莹辉, 等. 利用SAR影像配准偏移量提取地表形变的方法与误差分析[J]. 测绘学报, 2015, 44(3): 301-308. DOI: 10.11947/j.AGCS.2015.20130782. CHEN Qiang, LUO Rong, YANG Yinghui, et al. Method and accuracy of extracting surface deformation field from SAR image coregistration[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(3): 301-308.DOI: 10.11947/j.AGCS.2015.20130782.
[84] JIA Hongying, WANG Yingjie, GE Daqing, et al. Improved offset tracking for predisaster deformation monitoring of the 2018 Jinsha River landslide (Tibet, China)[J]. Remote Sensing of Environment, 2020, 247: 111899.
[85] HE Liming, WU Lixin, LIU Shanjun, et al. Mapping two-dimensional deformation field time-series of large slope by coupling DInSAR-SBAS with MAI-SBAS[J]. Remote Sensing, 2015, 7(9): 12440-12458.
[86] LIU Xiaojie, ZHAO Chaoying, ZHANG Qin, et al. Heifangtai loess landslide type and failure mode analysis with ascending and descending Spot-mode TerraSAR-X datasets[J]. Landslides, 2019, 17(1): 205-215.
[87] JOUGHIN I R, KWOK R, FAHNESTOCK M A. Interferometric estimation of three-dimensional ice-flow using ascending and descending passes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(1): 25-37.
[88] SUN Qian, HU Jun, ZHANG Lei, et al. Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets: the Zhouqu case study, China[J]. Remote Sensing, 2016, 8(11): 908.
[89] HU Xie, LU Zhong, PIERSON T C, et al. Combining InSAR and GPS to determine transient movement and thickness of a seasonally active low-gradient translational landslide[J]. Geophysical Research Letters, 2018, 45(3): 1453-1462.
[90] SAMSONOV S, DILLE A, DEWITTE O, et al. Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: a new method illustrated on a slow-moving landslide[J]. Engineering Geology, 2020, 266: 105471.
[91] 杜小平, 郭华东, 范湘涛, 等. 基于ICESat/GLAS数据的中国典型区域SRTM与ASTER GDEM高程精度评价[J]. 地球科学-中国地质大学学报, 2013, 38(4): 887-897. DU Xiaoping, GUO Huadong, FAN Xiangtao, et al. Vertical accuracy assessment of SRTM and ASTER GDEM over typical regions of China using ICESat/GLAS[J]. Earth Science-Journal of China University of Geosciences, 2013, 38(4): 887-897.
[92] NAVARRO-SANCHEZ V D, LOPEZ-SANCHEZ J M, VICENTE-GUIJALBA F. A contribution of polarimetry to satellite differential SAR interferometry: increasing the number of pixel candidates[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(2): 276-280.
[93] LEE J S, POTTIER E. Polarimetric radar imaging: from basics to applications[M]. Boca Raton: CRC Press, 2017.
[94] WU Baolong, TONG Ling, CHEN Yan, et al. Improved SNR optimum method in POLDINSAR coherence optimization[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(7): 982-986.
[95] WANG Guanya, XU Bing, LI Zhiwei, et al. A phase optimization method for DS-InSAR based on SKP decomposition from quad-polarized data[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 4008805.
[96] 朱建军, 杨泽发, 李志伟. InSAR矿区地表三维形变监测与预计研究进展[J]. 测绘学报, 2019, 48(2): 135-144. DOI: 10.11947/j.AGCS.2019.20180188. ZHU Jianjun, YANG Zefa, LI Zhiwei. Recent progress in retrieving and predicting mining-induced 3D displace-ments using InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(2): 135-144. DOI: 10.11947/j.AGCS.2019.20180188.
[97] LIU Xiaoge, HU Jun, SUN Qian, et al. Deriving 3-D time-series ground deformations induced by underground fluid flows with InSAR: case study of Sebei gas fields, China[J]. Remote Sensing, 2017, 9(11): 1129.
[98] HU Xie, BÜRGMANN R, FIELDING E J, et al. Internal kinematics of the Slumgullion landslide (USA) from high-resolution UAVSAR InSAR data[J]. Remote Sensing of Environment,2020, 251: 112057.
[99] HANDWERGER A L, BOOTH A M, HUANG M H, et al. Inferring the subsurface geometry and strength of slow-moving landslides using 3-D velocity measurements from the NASA/JPL UAVSAR[J]. Journal of Geophysical Research: Earth Surface,2021, 126(3): e2020JF005898.
[100] MONSERRAT O, MOYA J, LUZI G, et al. Non-interferometric GB-SAR measurement: application to the Vallcebre landslide (eastern Pyrenees, Spain)[J]. Natural Hazards and Earth System Sciences,2013, 13(7): 1873-1887.
[101] NICO G, BORRELLI L, DI PASQUALE A, et al. Monitoring of an ancient landslide phenomenon by GBSAR technique in the Maierato town (Calabria, Italy)[M]//LOLLINO G, GIORDAN D, CROSTA G B, et al. Engineering Geology for Society and Territory. Cham:Springer,2015: 129-133.
[102] 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.
[103] 巨袁臻, 许强, 金时超, 等. 使用深度学习方法实现黄土滑坡自动识别[J]. 武汉大学学报(信息科学版), 2020, 45(11): 1747-1755. JU Yuanzhen, XU Qiang, JIN Shichao, et al. Automatic object detection of loess landslide based on deep learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755.
[104] 李强, 张景发. 高分三号卫星全极化SAR影像九寨沟地震滑坡普查[J]. 遥感学报, 2019, 23(5): 883-891. LI Qiang, ZHANG Jingfa. Investigation on earthquake-induced landslide in Jiuzhaigou using fully polarimetric GF-3 SAR images[J]. Journal of Remote Sensing, 2019, 23(5): 883-891.
[105] KIM M, CHO K, PARK SE, et al. Development of landslide detection algorithm using fully polarimetric ALOS-2 SAR data[J]. Economic and Environmental Geology, 2019, 52(4): 313-322.
[106] ANANTRASIRICHAI N, BIGGS J, ALBINO F, et al. Application of machine learning to classification of volcanic deformation in routinely generated InSAR data[J]. Journal of Geophysical Research: Solid Earth, 2018, 123(8): 6592-6606.
[107] MA P F, ZHANG F, LIN H. Prediction of InSAR time-series deformation using deep convolutional neural networks[J]. Remote Sensing Letters, 2020, 11(2): 137-145.
[108] QU X D, YANG J, CHANG M. A deep learning model for concrete dam deformation prediction based on RS-LSTM[J]. Journal of Sensors, 2019, 2019: 4581672.
[109] 刘青豪, 张永红, 邓敏, 等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报, 2021, 50(3): 396-404. DOI: 10.11947/j.AGCS.2021.20200038. LIU Qinghao, ZHANG Yonghong, DENG Min, et al. Time series prediction method of large-scale surface subsidence based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 396-404. DOI: 10.11947/j.AGCS.2021.20200038.
[110] XING Yin, YUE Jianping, CHEN Chuang. Interval estimation of landslide displacement prediction based on time series decomposition and long short-term memory network[J]. IEEE Access, 2020, 8: 3187-3196.
[111] ANSAR.A A, SUDHA S. Prediction of earthquake induced landslide using deep learning models[C]//Proceedings of the 5th International Conference on Computing, Communication and Security. Patna, India: IEEE, 2020: 1-6.
[112] MENG Qingxiang, WANG Huanling, HE Mingjie, et al. Displacement prediction of water-induced landslides using a recurrent deep learning model[J]. European Journal of Environmental and Civil Engineering, 2020. DOI: 10.1080/19648189.2020.1763847.
[113] WANG Jing, NIE Guigen, GAO Shengjun, et al. Landslide deformation prediction based on a GNSS time series analysis and recurrent neural network model[J]. Remote Sensing, 2021, 13(6): 1055.
[114] 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. DOI: 10.11947/j.AGCS.2022.20220132. 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. DOI: 10.11947/j.AGCS.2022.20220132.

InSAR滑坡监测研究进展

Recent progress in landslide monitoring with InSAR

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