滑坡作为我国地质灾害的主控灾型，严重威胁我国人民的生命财产安全。伊犁地区因其地质背景和冻融作用等，致使滑坡灾害频发，相关学者在此主要集中于已发生的冻融型黄土滑坡的动力学特征研究及冻融失稳机理的探索，缺少区域尺度下潜在滑坡的识别编目、变形时空演化监测、监测方法适用性分析以及易发性评价等研究。针对上述问题，本文以伊犁地区伊宁县为例，首先联合多源SAR数据，采用不同InSAR技术并结合光学遥感识别了伊宁县的潜在滑坡。然后以伊犁地区典型地质灾害区域喀拉亚尕奇为例，分别采用四种InSAR技术识别了该区域的潜在滑坡，并进行了可靠性验证，探究了不同InSAR技术在伊犁地区的适用性，探讨了伊犁地区滑坡灾害的诱发因子。最后以InSAR技术识别的伊宁县潜在滑坡为样本开展了伊宁县滑坡灾害易发性评价，相关成果如下：

（1）伊宁县滑坡InSAR早期识别。分别采用Stacking-InSAR和SBAS-InSAR技术对伊宁县时间覆盖范围为2020-1至2021-12期间的122景Sentinel-1A数据进行处理，得到地表形变信息，结合光学遥感解译识别研究区潜在滑坡。分析两种InSAR技术的优缺点并结合ALOS-2数据的特点，选取SBAS-InSAR技术处理覆盖研究区2016-10至2020-5期间的10景ALOS-2数据。综合两种SAR数据滑坡识别结果，伊宁县共识别418处潜在滑坡，其中包括67处历史滑坡点，主要分布在喀拉亚尕奇乡、阿乌利亚乡以及麻扎乡。

（2）伊犁地区滑坡时序InSAR技术识别监测适用性分析。以伊犁地区典型地质灾害区域—喀拉亚尕奇为研究对象，分别采用Stacking-InSAR、SBAS-InSAR、PS-InSAR和DS-InSAR四种技术反演覆盖研究区2020-1至2021-12期间的Sentinel-1A数据的形变信息，结合当地多时相光学遥感影像共识别区域80处潜在滑坡，并进行野外核查和精度检验，结果表明了InSAR技术识别滑坡的可靠性和准确性。引入降水量数据对区域内典型滑坡隐患点-胡吉尔特滑坡群的时空演化特征进行重点分析，结合当地气候推断冻融作用和降雨为区域内滑坡变形的主要诱发因子，且冻融期形变速率大于降雨期。最后结合伊犁地区目前SAR数据实际情况，综合考虑四种不同InSAR技术的优缺点、滑坡识别数目以及实验结果精度，得出利用SBAS-InSAR技术在伊犁地区识别滑坡效果最优，其次依次是Stacking-InSAR和DS-InSAR，PS-InSAR不适用于伊犁地区的滑坡识别。综合四种InSAR技术在伊犁地区的适用情况，构建了伊犁地区滑坡InSAR早期识别方法，即联合Stacking和SBAS两种InSAR技术结合光学遥感解译进行潜在滑坡的广域识别，采用SBAS-InSAR或DS-InSAR技术进行重点单体滑坡隐患的精细形变监测与分析。

（3）基于多源SAR数据的伊宁县滑坡易发性评价。为有效提升区域滑坡易发性评价结果的准确度，本文以联合C波段和L波段SAR数据，基于时序InSAR技术识别的潜在滑坡作为滑坡样本，构建伊宁县滑坡易发性评价体系。综合考虑伊宁县滑坡孕灾环境和成灾因素，最终选取坡度、坡向、高程、工程岩组、距断层距离、距水系距离、植被覆盖指数、土地利用类型、地形湿度指数9个影响因子。然后基于CF模型和CF-LR耦合模型对伊宁县滑坡进行易发性评价，从合理性和精度两方面检验两种模型的评价结果，合理性检验结果表明CF-LR模型较CF模型更为合理，ROC精度检验结果中CF模型AUC为0.852，CF-LR模型AUC为0.884，其表明CF-LR模型精度更高，由此可得CF-LR模型在伊宁县进行易发性评价适用更优。最后基于CF-LR模型预测结果进行评价分析，伊宁县极高易发区主要位于喀拉亚尕奇乡、麻扎乡和阿乌利亚乡等，该区域人类活动频繁，发育多条断裂带，地质条件脆弱，利于滑坡灾害的发生。

Landslides, as the main disaster type of geological disasters in our country, seriously threaten the safety of life and property of our people. Due to the geological background and freeze-thaw effects in the Yili area, landslide disasters frequently occur. Relevant scholars here mainly focus on the dynamic characteristics of the freeze-thaw loess landslides that have occurred and the exploration of the freeze-thaw instability mechanism, lacking a regional scale. The identification and cataloging of potential landslides, the monitoring of deformation spatio-temporal evolution, the applicability analysis of monitoring methods, and the evaluation of susceptibility, etc. In response to the above problems, this paper takes Yining County in Yili Prefecture as an example, firstly combines multi-source SAR data, uses different InSAR technologies and combines optical remote sensing to identify potential landslides in Yining County. Then, taking Kalayagaqi, a typical geological disaster area in Yili area, as an example, four InSAR technologies were used to identify potential landslides in this area, and the reliability verification was carried out to explore the applicability of different InSAR technologies in Yili area. Inducing factors of landslide disaster in Yili area. Finally, taking the potential landslides in Yining County identified by InSAR technology as samples, the susceptibility evaluation of landslide hazards in Yining County was carried out. The relevant results are as follows:

(1) InSAR early identification of landslide in Yining County. Using Stacking-InSAR and SBAS-InSAR technologies to process the Sentinel-1A data of 122 scenes in Yining County from January 2020 to December 2021 to obtain surface deformation information and identify potential landslides in the study area by combining optical remote sensing interpretation . By analyzing the advantages and disadvantages of the two InSAR technologies and combining the characteristics of ALOS-2 data, the SBAS-InSAR technology was selected to process the 10 scenes of ALOS-2 data covering the study area from October 2016 to May 2020. Combining the landslide identification results of the two SAR data, a total of 418 potential landslides were identified in Yining County, including 67 historical landslide points, mainly distributed in Kalayagaqi Township, Aulia Township and Maza Township.

(2) Applicability analysis of landslide time series InSAR technology identification and monitoring in Yili area. Taking Kalayagaqi, a typical geological disaster area in Yili area, as the research object, four technologies of Stacking-InSAR, SBAS-InSAR, PS-InSAR and DS-InSAR were used to invert and cover the research area from January 2020 to December 2021 The deformation information of the Sentinel-1A data, combined with the local multi-temporal optical remote sensing images, identified 80 potential landslides in the area, and conducted field verification and precision testing. The results showed the reliability and accuracy of InSAR technology in identifying landslides. Introducing precipitation data to analyze the spatio-temporal evolution characteristics of the typical hidden landslides in the region - the Hujierte landslide group, and combining the local climate to infer that freeze-thaw and rainfall are the main inducing factors of landslide deformation in the region, and the deformation rate during the freeze-thaw period greater than the rainfall period. Finally, combined with the actual situation of the current SAR data in Yili area, comprehensively considering the advantages and disadvantages of four different InSAR technologies, the number of landslide identification and the accuracy of experimental results, it is concluded that the use of SBAS-InSAR technology in Yili area has the best landslide identification effect, followed by Stacking- InSAR and DS-InSAR, PS-InSAR are not suitable for landslide identification in Yili area. Based on the application of four InSAR technologies in Yili area, an early identification method of InSAR landslides in Yili area was constructed, that is, two InSAR technologies Stacking and SBAS combined with optical remote sensing interpretation were used for wide-area identification of potential landslides, and SBAS-InSAR or DS -InSAR technology for fine deformation monitoring and analysis of key individual landslide hazards.

(3) Evaluation of landslide susceptibility in Yining County based on multi-source SAR data. In order to effectively improve the accuracy of regional landslide susceptibility evaluation results, this paper uses C-band and L-band SAR data and potential landslides identified based on time-series InSAR technology as landslide samples to construct a landslide susceptibility evaluation system in Yining County. Comprehensively considering the landslide hazard-forming environment and hazard-causing factors in Yining County, 9 influencing factors were finally selected: slope, aspect, elevation, engineering rock group, distance from fault, distance from water system, vegetation cover index, land use type, and topographic humidity index. Then, based on the CF model and the CF-LR coupling model, the landslide susceptibility of Yining County was evaluated, and the evaluation results of the two models were tested from two aspects of rationality and accuracy. The rationality test results showed that the CF-LR model was more reasonable than the CF model , the AUC of the CF model is 0.852, and the AUC of the CF-LR model is 0.884 in the ROC accuracy test results, which shows that the accuracy of the CF-LR model is higher, and it can be concluded that the CF-LR model is more suitable for susceptibility evaluation in Yining County. Finally, based on the prediction results of the CF-LR model, the evaluation and analysis shows that the extremely high-risk areas in Yining County are mainly located in Kalayagaqi Township, Mazha Township, and Aulia Township. Human activities are frequent in this area, and many fault zones are developed. The fragile conditions are conducive to the occurrence of landslide disasters.

[1] 杨迁, 王雁林, 马园园. 2001~2019 年中国地质灾害分布规律及引发因素分析[J]. 地质灾害与环境保护, 2020, 31(4): 43-48.

[2] 丁继新, 杨志法, 尚彦军, 等. 降雨型滑坡时空预报新方法[J]. 中国科学.D 辑:地球科学, 2006(6): 579-586.

[3] Hu X, Wang T, Pierson T C, et al. Detecting Seasonal Landslide Movement Within the Cascade Landslide Complex (washington) Using Time-series Sar Imagery[J]. Remote Sensing of Environment, 2016, 187.

[4] 庄茂国, 魏云杰, 邵海, 等. 新疆伊犁皮里青河黄土滑坡类型及其发育特征[J]. 中国地质灾害与防治学报, 2018, 29(1): 54-59.

[5] 朱赛楠, 殷跃平, 王文沛, 等. 新疆伊犁河谷黄土滑坡冻融失稳机理研究[J]. 地球学报, 2019, 40(2): 339-349.

[6] 杨龙伟, 魏云杰, 王文沛, 等. 新疆伊宁县喀拉亚尕奇滑坡动力学特征研究[J].地质力学学报, 2018, 24(05): 699-705.

[7] 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), 2021, 46(7): 994-1002.

[8] 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(7): 996-1007.

[9] Genger L, Bo H, Hui L, et al. Early Identifying and Monitoring Landslides in Guizhou Province with Insar and Optical Remote Sensing[J]. Journal of Sensors, 2021, 2021.

[10] Lele Z, Keren D, Jin D, et al. Identifying Potential Landslides By Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR[J]. Remote Sensing, 2021, 13(18):3662.

[11] 陆会燕, 李为乐, 许强, 等. 光学遥感与 InSAR 结合的金沙江白格滑坡上下游滑坡隐患早期识别[J]. 武汉大学学报(信息科学版), 2019, (9): 1342-1354.

[12] Ren T, Gong W, Gao L, et al. An interpretation approach of ascending-descending SAR data for landslide identification[J]. Remote Sensing, 2022, 14(5): 1299.

[13] Dong J, Niu R, Li B, et al. Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results[J]. Geomatics, Natural Hazards and Risk, 2023, 14(1):52-75.

[14] 陈宝林, 李为乐, 陆会燕, 等. 基于SBAS-InSAR的黄河干流军功古滑坡形变分析[J/OL].武汉大学学报(信息科学版): 1-17[2023-03-15].

[15] 闫怡秋, 郭长宝, 钟宁, 等. 基于InSAR形变监测的四川甲居古滑坡变形特征[J].地球科学, 2022, 47(12): 4681-4697.

[16] 郭澳庆, 胡俊, 郑万基, 等. 时序InSAR滑坡形变监测与预测的N-BEATS深度学习法—以新铺滑坡为例[J]. 测绘学报, 2022, 51(10): 2171-2182.

[17] 吴琼, 葛大庆, 于峻川, 等. 广域滑坡灾害隐患InSAR显著性形变区深度学习识别技术[J]. 测绘学报, 2022, 51(10): 2046-2055.

[18] 黄海峰, 薛蓉花, 赵蓓蓓, 等. 孕灾机理与综合遥感结合的三峡库首顺层岩质滑坡隐患识别[J]. 测绘学报, 2022, 51(10): 2056-2068.

[19] 戴可人, 沈月, 吴明堂, 等.联合InSAR与无人机航测的白鹤滩库区蓄水前地灾隐患广域识别[J]. 测绘学报, 2022, 51(10): 2069-2082.

[20] 周晓亭. 基于多源数据的滑坡识别及其易发性动态评价[D]. 南昌: 东华理工大学, 2022.

[21] Dai K, Deng J, Xu Q, et al. Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements[J]. GIScience & Remote Sensing, 2022, 59(1): 1226-1242.

[22] 毛佳睿. 基于多源遥感的白龙江流域地质灾害监测与易发性动态评价[D]. 北京: 中国地质大学, 2021.

[23] 申朝永. 西南山区滑坡隐患易发性及森林对其影响研究[D]. 北京: 北京林业大学, 2020.

[24] Hao J M, Wu T H, Wu X D,et.al. Investigation of a small landslide in the Qinghai-Tibet Plateau by InSAR and absolute deformation model[J]. Remote Sensing, 2019, 11(18): 2126.

[25] Rouyet L, Liu L, Strand S M, et.al. Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen[J]. Remote Sensing, 2021, 13(5): 2977.

[26] Gong W Y, Darrow M M, Meyer F J, et al. Reconstructing movement history of frozen debris lobes in northern Alaska using satellite radar interferometry[J]. Remote Sensing of Environment, 2019, 221: 722-740.

[27] 焦志平, 江利明, 牛富俊, 等. 藏东冻土区滑坡形变时序 InSAR 监测分析—以 317 国道矮拉山为例[J]. 冰川冻土, 2021, 43(5): 1312-1322.

[28] 李珊珊, 李志伟, 胡俊, 等. SBAS-InSAR 技术监测青藏高原季节性冻土形变. 地球物理学报, 2013, 56(5): 1476-1486.

[29] 刘世博, 赵林, 汪凌霄,等. InSAR 技术在多年冻土区形变监测的应用[J]. 冰川冻土, 2021, 43(4): 964-975.

[30] Qu T, Zhang H, Niu F,et al. Characterization of the frost heave deformations in high latitude and deep seasonally frozen soil of inner Mongolia with sentinel-1 insar observations[C]//XXIV ISPRS Congress. Newcastle University, 2020.

[31] 窦杰, 向子林, 许强, 等. 机器学习在滑坡智能防灾减灾中的应用与发展趋势[J/OL]. 地球科学: 1-18[2023-03-15].

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

[33] Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas: differential radar interferometry [J]. Journal of Geophysical Research Atmospheres, 1989, 94(B7): 9183-9191.

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

[35] 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.

[36] 李广宇, 张瑞, 刘国祥, 等. Sentinel-1A TS-DInSAR 京津冀地区沉降监测与分析[J]. 遥感学报, 2018, 22(4): 633-646.

[37] 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.

[38] 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).

[39] Bianchini S,Herrera G,Mateos R M,et al. 2013. Landslide activity maps generation by means of Persistent Scatterer InterferometryJ]. Remote Sensing, 5(12):6198-6222.

[40] Van Natijne A L, Bogaard T A, van Leijen F J, et al. World-wide InSAR sensitivity index for landslide deformation tracking[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 111: 102829.

[41] Xia Y, Kaufmann H, Guo X F. Landslide monitoring in the Three Gorges area using D-InSAR and corner reflectors[J]. Photogrammetric Engineering and Remote Sensing, 2004, 70(10): 1167-1172.

[42] 刘路遥. 基于分布式目标雷达干涉测量的滑坡形变监测[D]. 成都: 西南交通大学, 2018.

[43] 李媛茜, 张毅, 苏晓军, 等.白龙江流域潜在滑坡InSAR识别与发育特征研究[J]. 遥感学报, 2021, 25(02): 677-690.

[44] 张成龙, 李振洪, 余琛, 等. 利用GACOS辅助下InSAR Stacking对金沙江流域进行滑坡监测[J]. 武汉大学学报(信息科学版), 2021, 46(11): 1649-1657.

[45] Yao J, Yao X, Liu X. Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR:A Case Study in Gongjue County , Tibet, China. Remote Sensing. 2022, 14,4728.

[46] 付豪, 李为乐, 陆会燕, 等. 基于“三查”体系的丹巴县滑坡隐患早期识别与监测[J/OL]. 武汉大学学报(信息科学版): 1-17[2023-03-20].

[47] Brabb E E. Innovative approaches to landslide hazard and risk mapping[C]. International Landslide Symposium Proceedings. 1984: 17-22.

[48] Carrara A, Cardinali M, Guzzetti F, et al. GIS technology in mapping landslide hazard[J]. Geographical information systems in assessing natural hazards, 1995: 135-175.

[49] 乔建平. 不稳定斜坡危险度的判别[J]. 山地研究, 1991(02): 117-122.

[50] 王文俊, 向喜琼, 黄润秋等. 区域崩塌滑坡的易发性评价—以四川省珙县为例[J]. 中国地质灾害与防治学报, 2003(02): 33-36.

[51] 王哲, 易发成. 基于层次分析法的绵阳市地质灾害易发性评价[J]. 自然灾害学报, 2009, 18(01): 14-23.

[52] 朱阿兴, 裴韬, 乔建平, 等.基于专家知识的滑坡危险性模糊评估方法[J]. 地理科学进展, 2006(04): 1-12.

[53] Myronidis D, Papageorgiou C, Teophanous S. Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)[J]. Nat. Hazards, 2016, 81(1): 245-263.

[54] 刘福臻, 王灵, 肖东升, 等. 基于模糊综合评判法的宁南县滑坡易发性评价[J]. 自然灾害学报, 2021, 30(05): 237-246.

[55] 宋小川, 刘汉湖, 张春等. 确定性系数模型的滑坡易发性评价应用与比较[J]. 矿山测量, 2021, 49(05): 65-71.

[56] Tang Y, Feng F, Guo Z, et al. Integrating principal component analysis with statistically-basedmodels for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)[J]. Journal of Cleaner Production, 2020, 277:124159.

[57] 仉文岗, 何昱苇, 王鲁琦, 等. 基于水系分区的滑坡易发性机器学习分析方法—以重庆市奉节县为例[J/OL].地球科学:1-18[2023-02-12].

[58] 王世宝, 庄建琦, 郑佳, 等.基于深度学习的CZ铁路康定—理塘段滑坡易发性评价[J]. 工程地质学报, 2022, 30(03): 908-919.

[59] 李信, 薛桂澄, 夏南等. 基于CF模型､ CF-LR模型和CF-AHP模型的国家热带雨林公园地质灾害易发性研究: 以海南保亭为例[J/OL]. 现代地质: 1-12[2023-02-12].

[60] 于晶涛. 星载SAR干涉技术的理论与方法研究[J]. 测绘学报, 2004(04): 368-369.

[61] 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.

[62] Liu L, Zhao Q, Lin H, et al.Ground deformation monitoring in Pearl River Delta region with Stacking D-InSAR technique[J]. 2008, 7145: 714513-714519.

[63] 何敏, 何秀凤. 利用时间序列干涉图叠加法监测江苏盐城地区地面沉降[J]. 武汉大学学报(信息科学版), 2011, 36(12): 1461-1465.

[64] 董继红. InSAR技术在金沙江流域高位远程滑坡识别与监测中的应用研究[D]. 西安: 长安大学, 2021.

[65] 杨成生. 差分干涉雷达测量技术中水汽延迟改正方法研究[D]. 西安: 长安大学, 2011.

[66] He K, Liu B, X Hu. Preliminary reports of a catastrophic landslide occurred on August 21, 2020, in Hanyuan County, Sichuan Province, China[J]. Landslides, 2020, 18(1): 503-507.

[67] 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.

[68] 董雅竹. 基于PS-InSAR 技术的地表形变过程研究[D]. 合肥: 中国科学技术大学, 2020.

[69] 陶威, 贾洪果, 亢邈迒, 等. 基于时序合并的PS-InSAR方法在雄县及周边区域地表形变监测中的应用[J]. 测绘通报, 2023(01): 101-106.

[70] 刘毅. 伊犁黄土滑坡特征､ 成因及稳定性分析[D]. 石河子: 石河子大学, 2015.

[71] 曹小红, 尚彦军, 古丽波斯坦·吐逊江,等.霍城县滑坡灾害发育特征及诱发因素相关性分析[J]. 新疆地质, 2021, 39(3): 487-492.

[72] 赵吉海, 安永刚, 孙耀锋. 新疆伊宁县滑坡灾害发育特征及影响因素分析[J]. 西部探矿工程, 2022, 34(10): 35-41.

[73] 胡列群, 武鹏飞, 梁凤超, 等. 新疆冬春季积雪及温度对冻土深度的影响分析[J]. 冰川冻土, 2014, 36(1): 48-54.

[74] 朱赛楠, 殷跃平, 王文沛, 等. 新疆伊犁河谷黄土滑坡冻融失稳机理研究[J]. 地球学报, 2019, 40(2): 339-349.

[75] Hooper A, Zebker H, 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).

[76] Hooper A, Bekaert D, Spaans K, et al. Recent advances in SAR interferometry time series analysis for measuring crustal deformation[J]. Tectonophysics, 2012, 514: 1-13.

[77] Jiang M, Ding X, 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, 2014, 53(3): 1213-1224.

[78] Jiang M. Sentinel-1 TOPS co-registration over low-coherence areas and its application to velocity estimation using the all pairs shortest path algorithm[J]. Journal of Geodesy, 2020, 94(10): 95.

[79] Jiang Mi, Ding Xiaoli, Li Zhiwei. Hybrid Approach for Unbiased Coherence Estimation for Multi-Temporal InSAR, IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5): 2459-2473.

[80] Jiang M, Ding X, Li Z, et al. InSAR coherence estimation for small data sets and its impact on temporal decorrelation extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6584-6596.

[81] Jiang M, Miao Z, Gamba P, et al. Application of multitemporal InSAR covariance and information fusion to robust road extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3611-3622.

[82] Jiang M, Hooper A, Tian X, et al. Delineation of built-up land change from SAR stack by analysing the coefficient of variation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169: 93-108.

[83] Wasowski J, Bovenga F. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives[J] , 2014, 174: 103-138.

[84] 饶品增, 曹冉, 蒋卫国. 基于地理加权回归模型的云南省地质灾害易发性评价[J]. 自然灾害学报, 2017, 26(2): 134-143.

[85] 黄润秋, 李为乐. “5.12”汶川大地震触发地质灾害的发育分布规律研究[J]. 岩石力学与工程学报, 2008, 27 (12): 2585-2592.

[86] 庞茂康. 白龙江流域滑坡发育特征及其成因的地质环境条件研究[D]. 成都: 成都理工大学, 2011.

[87] 毕华兴, 谭秀英, 李笑吟. 基于 DEM 的数字地形分析 [J]. 北京林业大学学报, 2005, (02): 49-53.

[88] 熊十力. 基于岩性因子的滑坡易发性评价研究[D]. 武汉: 湖北工业大学, 2020.

[89] 鲁霞, 兰安军, 母浩江, 等. 基于信息量模型的盘州市地质灾害易发性评价[J]. 科学技术与工程, 2020, 20(14): 5544-5551.

[90] Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine[J]. Mathematical biosciences, 1975, 23(3-4): 351-379.

[91] Heckerman D. Probabilistic interpretations for MYCIN's certainty factors[M]//Machine intelligence and pattern recognition. North-Holland, 1986, 4: 167-196.

[92] 刘希林, 庙成, 田春山. 区域滑坡和泥石流灾害两种危险性评价方法的比较分析[J]. 防灾减灾工程学报, 2017, 37(01): 71-78.