滑坡是指岩土体在重力作用下，由于外界因素导致失稳而向下滑动的现象。与地震、洪水等其他重大且罕见的自然灾害相比较，滑坡是一种常见且普遍发生的地质灾害。滑坡的形成原因主要包括降雨、水系、地震和人类活动等多个方面。我国广袤而又富饶的土地上分布着高山、高原和盆地等各种类型的区域，由于复杂的地形导致我国是全球受地质灾害危害最严重的国家之一，尤其在西南山区，经常发生大规模滑坡等地质灾害。据统计，全世界每年因各种自然因素或人为诱因引起的地质灾害造成的经济损失高达数百亿美元，其中一半是由滑坡引起的。因此对滑坡进行科学有效地监测与防治显得尤为重要。近年来，合成孔径雷达测量（Interferometric Synthetic Aperture Radar，InSAR）成为滑坡研究中可靠的技术手段，但是如何将其与滑坡易发性研究进行有机结合并提高滑坡易发性结果的精度还有待研究。综合上述讨论，本文以重庆市武隆区为研究对象，开展时序InSAR技术在重庆武隆区滑坡易发性评价中的应用研究，主要研究内容如下：（1）研究区地表形变分析。采用时序InSAR技术中的小基线集干涉测量（Small Baseline Subset，SBAS）对重庆市武隆区2015年7月至2018年12月的7景ALOS-2卫星SAR影像数据进行处理，得到年平均形变速率。根据年平均形变速率提取地表形变点，对提取出的地表形变点进行最大坡向形变速率投影、滑坡形变点筛选等操作验证其可靠性，结果表明，筛选后的地表变形速率为-75mm/a~-10mm/a。并将所得到的形变点采用克里金插值构成连续面状图层，作为参与滑坡易发性评价的形变速率因子。采用时序InSAR技术中的永久性散射体技术（Permanent Scatterers，PS）对重庆市武隆区2019年至2022年的110景Sentinel-1A卫星影像SAR数据进行处理分析和筛选，圈定不稳定区域共59处。（2）研究区滑坡影响因子分析。根据重庆市武隆区30m分辨率的SRTM DEM数据提取高程、坡度、坡向、曲率、河流、地形湿度指数和地形起伏度七个影响因子，使用重庆市武隆区基础地质资料及该区域的地质图提取地层岩性影响因子，将这些因子结合形变速率因子通过信息量模型进行计算，得到各个因子分级状态下的信息量值。（3）研究区滑坡易发性评价。建立基于层次分析法的信息量模型，利用信息量权值在每个评价单元上进行结果叠加，划分出极高易发区、高易发区、中易发区和低易发区4种类型。结果显示，高易发区和极高易发区面积占研究区面积的12.76%，两区滑坡数占总滑坡数的86.74%，说明划分滑坡易发性区间的有效性较好。根据PS-InSAR技术圈定出的不稳定区域与滑坡易发性分区结果进行对比验证，说明滑坡易发性评价的分区结果的准确性较高。将纳入形变速率因子与未纳入形变速率因子的滑坡易发性分区结果进行对比分析，并绘制ROC曲线，结果表明，纳入形变速率因子的滑坡易发性评价预测性较优。

Landslide refers to the phenomenon that the rock and soil mass slides downward due to the instability caused by external factors under the action of gravity. Compared with other major and rare natural disasters such as earthquakes and floods, landslides are a common and ubiquitous geological disaster. The causes of landslides mainly include rainfall, water systems, earthquakes and human activities. Various types of areas such as high mountains, plateaus and basins are distributed on the vast and fertile land of our country. Due to the complex terrain, our country is one of the countries most affected by geological disasters in the world, especially in the southwest mountainous areas, where large-scale disasters often occur Landslides and other geological disasters. According to statistics, the economic losses caused by geological disasters caused by various natural factors or man-made causes in the world are as high as tens of billions of dollars every year, half of which are caused by landslides. Therefore, it is particularly important to monitor and control landslides scientifically and effectively. In recent years, Interferometric Synthetic Aperture Radar (InSAR) measurement has become a reliable technical means in landslide research, but how to organically combine it with landslide susceptibility research and improve the accuracy of landslide susceptibility results remains to be studied. Based on the above discussion, this paper takes Wulong District of Chongqing as the research object to carry out the application research of time series InSAR technology in landslide susceptibility evaluation in Wulong District of Chongqing. The main research contents are as follows:(1) Analysis of surface deformation in the study area. Using Small Baseline Subset (SBAS) in time-series InSAR technology to process the 7-view ALOS-2 satellite SAR image data from July 2015 to December 2018 in Wulong District, Chongqing, and obtain the annual average deformation rate. Extract the surface deformation points according to the annual average deformation rate, and verify the reliability of the extracted surface deformation points by performing operations such as projection of the maximum slope deformation rate and screening of landslide deformation points. The results show that the selected surface deformation rate is -75mm/ a~-10mm/a. And the obtained deformation points are constructed by kriging interpolation to form a continuous surface layer, which is used as the deformation rate factor involved in the evaluation of landslide susceptibility. The permanent scatterers (PS) technology in the time-series InSAR technology is used to process, analyze and screen the 4 scenes of ALOS-2 satellite image SAR data from May 2019 to May 2022 in Wulong District, Chongqing City. There are 59 stable areas in total.(2) Analysis of landslide impact factors in the study area. According to the 30m resolution SRTM DEM data in Wulong District, Chongqing City, seven influencing factors of elevation, slope, aspect, curvature, river, topographic humidity index and topographic relief were extracted, using the basic geological data of Wulong District in Chongqing City and the area’s The formation lithology influencing factors are extracted from the geological map, and these factors are combined with the deformation rate factor to calculate through the information quantity model to obtain the information quantity value of each factor in the graded state.(3) Evaluation of landslide susceptibility in the study area. The information volume model based on the analytic hierarchy process was established, and the weight of the information volume was used to superimpose the results on each evaluation unit, and four types were divided into extremely high-prone areas, high-prone areas, medium-prone areas and low-prone areas. The results show that the area of high-risk area and extremely-high-risk area accounted for 12.76% of the study area, and the number of landslides in the two areas accounted for 86.74% of the total number of landslides, indicating that the effectiveness of dividing the landslide susceptibility interval is better. According to the comparison and verification between the unstable area delineated by PS-InSAR technology and the landslide susceptibility zoning results, it shows that the accuracy of the zoning results of landslide susceptibility evaluation is high. The landslide susceptibility zoning results with and without the deformation rate factor were compared and analyzed, and the ROC curve was drawn. The results showed that the landslide susceptibility evaluation with the deformation rate factor was more predictive.

[1] 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.

[2] 赵志明, 潘岳, 陈理. 四川茂县新磨村滑坡高速启动机理研究[J]. 工程地质学报, 2023, 31(01): 145-153.

[3] 张涛, 杨志华, 张永双, 等. 四川茂县新磨村高位滑坡铲刮作用分析[J]. 水文地质工程地质, 2019, 46(03): 138-145.

[4] 殷跃平, 王文沛, 张楠, 等. 强震区高位滑坡远程灾害特征研究——以四川茂县新磨滑坡为例[J]. 中国地质, 2017, 44(05): 827-841.

[5] 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.

[6] 李俊峰. 西藏自治区江达县“10. 11”白格滑坡、堰塞湖[J]. 中国地质, 2018, 45(06): 1329.

[7] 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[J]. 工程地质学报, 2018, 26(06): 1534-1551.

[8] Li Y, Jiao Q, Hu X, et al. Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 84: 101949.

[9] Achache J, Fruneau B, Delacourt C. Applicability of SAR interferometry for operational monitoring of landslides[C]. Proceedings of the SecondERS Applications Workshop, 1995: 165-168.

[10] 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): 2202-2212.

[11] Hilley G E, Burgmann R, Ferretti A, et al. Dynamics of slow-moving landslides from permanent scatterer analysis[J]. Science, 2004, 304(5679): 1952-1955.

[12] Lauknes T R, Shanker A P, Dehls J F, et al. Detailed rockslide mapping in northern Norway with small baseline and persistent scatterer interferometric SAR time series methods[J]. Remote Sensing of Environment, 2010, 114(9): 2097-2109.

[13] Casagli N, Frodella W, Morelli S, et al. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning[J]. Geoenvironmental Disasters, 2017, 4: 1-23.

[14] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. Ieee Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383.

[15] 范钟. DINSAR技术在滑坡信息提取中的应用研究[D]. 成都: 电子科技大学, 2013.

[16] 王立伟. 基于DInSAR数据分析的高山峡谷区域滑坡位移识别[D]. 北京: 北京科技大学, 2015.

[17] 高思远. 基于Sentinel-1数据的PS-InSAR技术在金沙江白格滑坡监测中应用研究[D]. 北京: 中国水利水电科学研究院, 2019.

[18] 朱玉. 基于PS-InSAR的贵州省大方县、纳雍县滑坡易发性评价[D]. 中国地质大学(北京), 2020.

[19] 吴东霖, 葛伟鹏, 魏聪敏, 等. 黄河流域刘家峡—兰州段滑坡灾害的 InSAR 识别及成因分析[J]. 地震工程学报, 2021, 43(3): 607-614.

[20] 谢谟文, 王增福, 胡嫚, 等. 高山峡谷区DInSAR滑坡监测数据特征分析[J]. 测绘通报, 2012(04): 18-21+40.

[21] 敖萌, 张勤, 赵超英, 等. 改进的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(03): 377-383.

[22] 白艳萍. 基于SBAS-InSAR技术的白龙江中游地表变形特征分析与潜在滑坡早期识别[D]. 兰州: 兰州大学, 2020.

[23] 刘筱怡. 基于SBAS-InSAR鲜水河断裂带及蠕滑型滑坡特征研究[D]. 北京: 中国地质大学(北京), 2017.

[24] 赵宝强. 基于InSAR技术的白龙江流域地表变形特征与潜在滑坡早期识别研究[D]. 兰州: 兰州大学, 2019.

[25] 赵超英, 刘晓杰, 张勤, 等. 甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究[J]. 武汉大学学报(信息科学版), 2019, 44(07): 996-1007.

[26] 王振林, 廖明生, 张路, 等. 基于时序Sentinel-1数据的锦屏水电站左岸边坡形变探测与特征分析[J]. 国土资源遥感, 2019, 31(02): 204-209.

[27] 代聪, 李为乐, 陆会燕, 等. 甘肃省舟曲县城周边活动滑坡InSAR探测[J]. 武汉大学学报(信息科学版), 2020: 1-11.

[28] 张佳佳, 高波, 刘建康, 等. 基于SBAS-InSAR技术的川藏铁路澜沧江段滑坡隐患早期识别[J]. 现代地质, 2021, 35(01): 64-73.

[29] 聂兵其. 基于InSAR的滑坡形变探测隐患识别研究[D]. 成都: 成都理工大学, 2018.

[30] Tan Q, Bai M, Zhou P, et al. Geological hazard risk assessment of line landslide based on remotely sensed data and GIS[J]. Measurement, 2021, 169: 108370.

[31] Patriche C V, Pirnau R, Grozavu A, et al. A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrov River Basin, Romania[J]. Pedosphere, 2016, 26(3): 335-350.

[32] Hodasová K, Bednarik M. Effect of using various weighting methods in a process of landslide susceptibility assessment[J]. Natural Hazards, 2021, 105: 481-499.

[33] Cascini L, Calvello M, Cuomo S, et al. LARAM School 2020 goes online: the 15th doctoral school on “LAndslide Risk Assessment and Mitigation”[J]. 2020.

[34] Biçer Ç T, Ercanoglu M. A semi-quantitative landslide risk assessment of central Kahramanmaraş City in the Eastern Mediterranean region of Turkey[J]. Arabian Journal of Geosciences, 2020, 13: 1-26.

[35] Ercanoglu M, Gokceoglu C. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach[J]. Environmental geology, 2002, 41(6): 720-730.

[36] Guzzetti F, Reichenbach P, Ardizzone F, et al. Estimating the quality of landslide susceptibility models[J]. Geomorphology, 2006, 81(1-2): 166-184.

[37] Kawabata D, Bandibas J. Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)[J]. Geomorphology, 2009, 113(1-2): 97-109.

[38] Pourghasemi H R, Moradi H R, Aghda S M F. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances[J]. Natural Hazards, 2013, 69(1): 749-779.

[39] Ahmed B, Dewan A. Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh[J]. Remote Sensing, 2017, 9(4): 304.

[40] 刘明学, 陈祥, 杨珊妮. 基于逻辑回归模型和确定性系数的崩滑流危险性区划[J]. 工程地质学报, 2014, 22(06): 1250-1256.

[41] 谭龙, 陈冠, 王思源, 等. 逻辑回归与支持向量机模型在滑坡敏感性评价中的应用[J]. 工程地质学报, 2014, 22(01): 56-63.

[42] 黄健敏, 赵国红, 廖芸婧, 等. 基于Logistic回归的降雨诱发区域地质灾害易发性区划及预报模型建立——以安徽歙县为例[J]. 中国地质灾害与防治学报, 2016, 27(03): 98-105.

[43] 樊芷吟, 苟晓峰, 秦明月, 等. 基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价[J]. 工程地质学报, 2018, 26(02): 340-347.

[44] 郭小莹, 肖天贵. 川东北地区滑坡易发性区划及气象预警模型研究[J]. 成都信息工程大学学报, 2019, 34(06): 625-631.

[45] 杜谦, 范文, 李凯, 等. 二元Logistic回归和信息量模型在地质灾害分区中的应用[J]. 灾害学, 2017, 32(02): 220-226.

[46] 张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报, 2016, 35(02): 284-296.

[47] 任敬, 范宣梅, 赵程, 等. 贵州省都匀市滑坡易发性评价研究[J]. 水文地质工程地质, 2018, 45(05): 165-172.

[48] 孟田, 许晓露, 刘汉湖. 基于斜坡单元优化的高海拔地区滑坡危险性评价—以金沙江白格滑坡为例[J]. 河南理工大学学报 (自然科学版), 2021, 40(1): 65-72.

[49] 凌晓, 刘甲美, 王涛, 等. 基于致灾因子对称法分级的信息量模型在地震滑坡危险性评价中的应用[J]. 国土资源遥感, 2021, 33(2): 172-181.

[50] 刘国祥. InSAR基本原理[J]. 四川测绘, 2004(04): 187-190.

[51] 李德仁, 周月琴, 马洪超. 卫星雷达干涉测量原理与应用[J]. 测绘科学, 2000(01): 9-12+1.

[52] Hilley G E, Burgmann R, Ferretti A, et al. Dynamics of slow-moving landslides from permanent scatterer analysis[J]. Science, 2004, 304(5679): 1952-1955.

[53] Froese C, Poncos V, Skirrow R, et al. Characterizing complex deep seated landslide deformation using corner reflector InSAR(CR-INSAR): Little Smoky Landslide, Alberta[C]. //Proc. 4th Can. Conf. Geohazards. 2008: 1-4.

[54] Crosetto M, Gili J A, Monserrat O, et al. Interferometric SAR monitoring ofthe Vallcebre landslide (Spain) using corner reflectors[J]. Natural hazards and earth system sciences, 2013, 13(4): 923-933.

[55] Jebur M N, Pradhan B, Tehrany M S. Detection of vertical slope movementin highly vegetated tropical area of Gunung pass landslide, Malaysia, usingL-band InSAR technique[J]. Geosciences Journal, 2014, 18(1): 61-68.

[56] Rosi A, Tofani V, Tanteri L, et al. The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution[J]. Landslides, 2018, 15(1): 5-19.

[57] Zhu W, Zhang Q, Ding X L, et al. Landslide monitoring by combining of CR-InSAR and GPS techniques[J]. Advances in Space Research, 2014, 53(3): 430-439.

[58] Hooper A, H Zebker, Segall P, et al. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers[J]. Geophysical Research Letters, 2004, 31(23).

[59] Zhang L, Ding X, Lu Z. Modeling PSInSAR Time Series Without Phase Unwrapping[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(1): 547-556.

[60] Touski M Y, Veiskarami M, Dehghani M. Interferometric Point Target Analysis (Ipta) for Landslide Monitoring[J]. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, 42: 1079-1083.

[61] Sarychikhina O, Palacios D G, Argote L A D, et al. Application of satellite SAR interferometry for the detection and monitoring of landslides along the Tijuana-Ensenada Scenic Highway, Baja California, Mexico[J]. Journal of South American Earth Sciences, 2021, 107: 103030.

[62] 周玉龙. 融入时序InSAR形变的毛尔盖库区滑坡易发性评价研究[D]. 成都:. 成都理工大学, 2020.

[63] 石元帅. 乡村崩塌地质灾害风险评价与管控研究[D]. 成都: 成都理工大学, 2019.

[64] 王雁林, 郝俊卿, 赵法锁等. 地质灾害风险评价与管理研究[M]. 科学出版社, 2014.

[65] 任景珂, 王凤元, 王生力等. 基于GIS的公路地质灾害防治管理体系的设计与开发[J]. 科学时代, 2012(21).

[66] 郭富赟. 兰州市地质灾害特征与风险管控对策[J]. 城市与减灾, 2019(03): 59-63.

[67] 薛强, 唐亚明, 白轩. 吕梁山区大宁县城地质灾害破坏模式及风险管控[J]. 山地学报, 2021, 39(01): 151-162.

[68] 唐亚明, 张茂省, 李政国, 冯卫. 国内外地质灾害风险管理对比及评述[J]. 西北地质, 2015, 48(02): 238-246.

[69] 尚长生. 山西省柳林县地质灾害发育特征[J]. 华北国土资源, 2005(04): 30-32.

[70] 张香香, 贾文毓. 柳林县主要地质灾害的分布及其防治[J]. 山西师范大学学报(自然科学版), 2014, 28(01): 103-106.

[71] 段宇英, 汤军, 刘远刚, 高贤君, 段宇雄. 基于随机森林的山西省柳林县黄土滑坡空间敏感性评价[J]. 地理科学, 2022, 42(02): 343-351.

[72] 李艳杰, 唐亚明, 邓亚虹, 宋焱勋, 慕焕东, 山聪, 崔思颖. 降雨型浅层黄土滑坡危险性评价与区划—以山西柳林县为例[J]. 中国地质灾害与防治学报, 2022, 33(02): 105-114.

[73] 于成龙. 和龙市典型地质灾害风险性区划与地质环境承载力综合评价研究[D]. 吉林大学, 2021.

[74] 地方标准《地质灾害调查规范》（DB14/T 2122-2020）.

[75] 曹文霞. 基于组合赋权法的交口县地质灾害易发性评价研究[D]. 太原: 太原理工大学, 2021.

[76] 崔少华. 山西省平顺县地形地貌与地质灾害的关系分析[J]. 西部探矿工程, 2018, 30(05): 17-20.

[77] 霍斐斐. 陕西省永寿县地质灾害发育特征及易发性评价研究[D]. 西安: 西北大学, 2012.

[78] 缪信, 汤明高, 王自高, 等. 地质灾害危险性评价模型的比较分析与应用[J]. 水利水电技术, 2016, 47(4): 4.

[79] 孟庆华. 秦岭山区地质灾害风险评估方法研究[D]. 北京: 中国地质科学院, 2011.

[80] 杨强. 基于时序InSAR技术的皮力青河流域滑坡易发性研究[D]. 成都: 成都理工大学, 2019.

[81] 杜国梁, 张永双, 吕文明, 张广泽, 周成灿, 郭长宝. 基于加权信息量模型的藏东南地区滑坡易发性评价[J]. 灾害学, 2016, 31(02): 226-234.

[82] 桂蕾. 三峡库区万州区滑坡发育规律及风险研究[D]. 武汉: 中国地质大学, 2014.

[83] 田春阳, 张威, 张戈, 等. 基于信息量法的西丰县地质灾害易发性评价[J]. 北京: 首都师范大学学报(自然科学版), 2020, 41(02): 32-40.

[84] 张婷, 汤国安, 王春等. 黄土丘陵沟壑区地形定量因子的关联性分析[J]. 地理科学, 2005(04): 85-90.

[85] 张福浩, 朱月月, 赵习枝, 等. 地理因子支持下的滑坡隐患点空间分布特征及识别研究[J]. 武汉大学学报(信息科学版), 2020, 45(08): 1233-1244.

[86] 吴常润, 赵冬梅, 刘澄静, 等. 基于GIS和信息量模型的陇川县滑坡易发性评价[J]. 西北地质, 2020, 53(02): 308-320.

[87] 刘阳. 延长县滑坡地质灾害风险评估和管理研究[D]. 西安: 长安大学, 2009.

[88] 杨光, 徐佩华, 曹琛, 等. 基于确定性系数组合模型的区域滑坡敏感性评价[J]. 工程地质学报, 2019, 27(05): 1153-1163.

[89] 刘坚, 李树林, 陈涛. 基于优化随机森林模型的滑坡易发性评价[J]. 武汉大学学报(信息科学版), 2018, 43(07): 1085-1091.

[90] 李宁, 陈永东, 古学超, 等. 基于GIS和信息量法的川藏铁路雅江县段滑坡易发性评价[J]. 资源信息与工程, 2020, 35(02): 113-117.

[91] Pradhan B, Youssef A, Varathrajoo R. Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model[J]. Geo-spatial Information Science, 2010, 13(2): 93-102.

[92] Scheidegger A E. On the prediction of the reach and velocity of catastrophic landslides[J]. Rock mechanics, 1973, 5(4): 231-23