结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别

doi:10.13474/j.cnki.11-2246.2022.0318

测绘通报 ›› 2022, Vol. 0 ›› Issue (11): 13-19.doi: 10.13474/j.cnki.11-2246.2022.0318

结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别

朱智富¹, 甘淑^1,2, 张荐铭¹, 袁希平^1,3, 王睿博¹, 张晓伦¹

1. 昆明理工大学国土资源工程学院, 云南昆明 650093;
2. 云南省高校高原山地空间信息测绘技术应用工程研究中心, 云南昆明 650093;
3. 滇西应用技术大学云南省高校山地实景点云数据处理及应用重点实验室, 云南大理 671006

收稿日期:2021-12-01 修回日期:2022-07-22 发布日期:2022-12-08
通讯作者: 甘淑,E-mail:gs@kust.edu.cn
作者简介:朱智富(1995-),男,硕士生,主要研究方向为InSAR原理及其应用研究。E-mail:1042122083@qq.com
基金资助:
国家自然科学基金(41861054)

Identification of geological potential landslides in Cang Mountain by combining SBAS-InSAR technique and information entropy

ZHU Zhifu¹, GAN Shu^1,2, ZHANG Jianming¹, YUAN Xiping^1,3, WANG Ruibo¹, ZHANG Xiaolun¹

1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;
2. Application Engineering Research Center of Plateau and Mountainous Spatial Information Surveying and Mapping Technology in Yunnan Universities, Kunming 650093, China;
3. Key Laboratory of Cloud Data Processing and Application of Mountain Scenic Spot in Yunnan Universities, West Yunnan University of Applied Technology, Dali 671006, China

Received:2021-12-01 Revised:2022-07-22 Published:2022-12-08

摘要/Abstract

摘要： 针对西南地区滑坡隐患高位隐蔽,传统技术难以全面识别的问题,本文以大理苍山为研究对象,首先利用SBAS-InSAR技术对苍山2019年1月-2021年4月间的滑坡隐患进行识别;然后结合随机概率信息熵模型,对不同坡度等级与边坡稳定性之间的关联性进行定量分析;最后根据典型隐患区的遥感影像以及采样点的形变时序图,探讨了边坡形变时空演化特征及沉降诱因。试验结果表明:①2019年1月-2021年4月,研究区的形变速率为-155.6～92.4mm/a,13个超过-30mm/a的不稳定滑坡隐患被识别;②坡度等级为Ⅳ、Ⅴ级时,信息熵大于0.8,边坡稳定性较弱,不均匀形变严重,与已有研究保持高度一致,证实了该模型的可靠性;③典型隐患区形变趋势呈明显的季节性变化,降雨和冰雪消融是导致边坡失稳的主要因素。

关键词: 苍山, SBAS-InSAR技术, 结合, 信息熵, 滑坡隐患识别

Abstract: In response to the problem of high hidden landslide hazards in southwest China, it is difficult to identify the problem comprehensively by traditional technology. This paper took Dali Cang Mountain as the research object and used SBAS-InSAR technique to identify landslide potential in Cang Mountain between January 2019 and ation between different slope grades and slope stability. Finally, based on the remote sensing images of typical potential landslide areas and the deformation time series maps of sampling points, the spatial and temporal evolution trends of slope stability and deformation inducing factors are discussed. The experimental results show that ① The deformation rate in the study area is -155.6 to 92.4mm/a during January 2019 to April 2021, and 13 unstable potential landslides exceeding -30mm/a are identified. ② The information entropy is greater than 0.8 when the grade of slope is Ⅳ,Ⅴ, the slope stability is weak and the uneven deformation is serious, which maintains high consistency with the existing literature conclusions, confirming the reliability of the model. ③ The deformation trend of typical potential landslide area shows obvious seasonal changes, and rainfall and snow and ice melt are the main factors leading to slope instability.

Key words: Cang Mountain, SBAS-InSAR technology, combination, information entropy, identification of potential landslides

中图分类号:

朱智富, 甘淑, 张荐铭, 袁希平, 王睿博, 张晓伦. 结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别[J]. 测绘通报, 2022, 0(11): 13-19.

ZHU Zhifu, GAN Shu, ZHANG Jianming, YUAN Xiping, WANG Ruibo, ZHANG Xiaolun. Identification of geological potential landslides in Cang Mountain by combining SBAS-InSAR technique and information entropy[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 13-19.

参考文献

[1] 何芳,徐友宁,乔冈,等.中国矿山地质灾害分布特征[J].地质通报,2012,31(2~3):476-485.
[2] 葛大庆, 戴可人, 郭兆成, 等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报(信息科学版), 2019, 44(7):949-956.
[3] 李媛, 孟晖, 董颖, 等. 中国地质灾害类型及其特征——基于全国县市地质灾害调查成果分析[J]. 中国地质灾害与防治学报, 2004, 15(2):29-34.
[4] 张亚迪, 李煜东, 董杰, 等. 时序InSAR技术探测芒康地区滑坡灾害隐患[J]. 遥感学报, 2019, 23(5):987-996.
[5] WU Qiong, JIA Chunting, CHEN Shengbo, et al. SBAS-InSAR based deformation detection of urban land, created from mega-scale mountain excavating and valley filling in the loess plateau:the case study of Yan'an City[J]. Remote Sensing, 2019, 11(14):1673.
[6] MA Peifeng, WANG Weixi, ZHANG Bowen, et al. Remotely sensing large-and small-scale ground subsidence:a case study of the Guangdong-Hong Kong-Macao Greater Bay Area of China[J]. Remote Sensing of Environment, 2019, 232:111282.
[7] 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.
[8] 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.
[9] 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.
[10] BONANO M, MANUNTA M, PEPE A, et al. From previous C-band to new X-band SAR systems:assessment of the DInSAR mapping improvement for deformation time-series retrieval in urban areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4):1973-1984.
[11] 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.
[12] 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.
[13] 李宝龙, 季建清, 罗清华, 等. 滇西点苍山-哀牢山隆升构造样式和隆升时限[J]. 地震地质, 2012, 34(4):696-712.
[14] ZHU Zhifu,GAN Shu,YUAN Xiping, et al. Landslide susceptibility mapping with integrated SBAS-InSAR technique:a case study of Dongchuan District, Yunnan (China)[J]. Sensors, 2022, 22(15):5587.
[15] 张艳梅, 王萍, 罗想, 等. 利用Sentinel-1数据和SBAS-InSAR技术监测西安地表沉降[J]. 测绘通报, 2017(4):93-97.
[16] 王宗明, 张柏, 黄素军,等. 基于GIS和信息熵的松嫩平原土地利用结构演化分析:兼论系统无序度、复杂性与多样性[J]. 农业系统科学与综合研究, 2005, 21(3):196-200.
[17] 王佳佳. 三峡库区万州区滑坡灾害风险评估研究[D]. 武汉:中国地质大学, 2015.
[18] 张继国, 辛格(Vijay P. Singh). 信息熵理论与应用[M]. 北京:中国水利水电出版社, 2012.
[19] 肖洪敏,张文江,田云锋,等.基于SBAS-InSAR方法的岷江上游峡谷区地表沉降的坡向分异规律研究[J/OL].遥感技术与应用:1-11[2021-11-11]. http://kns.cnki.net/kcms/detail/62.1099.TP.20210520.1055.004.html.
[20] 戴可人, 铁永波, 许强, 等. 高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例[J]. 雷达学报, 2020, 9(3):554-568.
[21] 郭果, 陈筠, 李明惠, 等. 土质滑坡发育概率与坡度间关系研究[J]. 工程地质学报, 2013, 21(4):607-612.
[22] 樊晓一, 乔建平. 滑坡危险度评价的地形判别法[J]. 山地学报, 2004, 22(6):730-734.
[23] 熊俊楠,朱吉龙,苏鹏程,等.基于GIS与信息量模型的溪洛渡库区滑坡危险性评价[J].长江流域资源与环境,2019,28(3):700-711.
[24] 齐亚林. 陇东黄土高原地貌特征及其对鄂尔多斯盆地前侏罗纪古地貌刻画的启示[C]//2015年全国沉积学大会沉积学与非常规资源论文摘要集. 武汉:[s.n.], 2015:56-57.
[25] 朱宽兴. 岷江上游叠溪至飞虹段潜在滑坡InSAR早期判识与地表形变研究[D]. 长春:吉林大学, 2021.
[26] HINZMAN L D, KANE D L, GIECK R E, et al. Hydrologic and thermal properties of the active layer in the Alaskan Arctic[J]. Cold Regions Science and Technology, 1991, 19(2):95-110.
[27] KANE D L, HINZMAN L D, ZARLING J P. Thermal response of the active layer to climatic warming in a permafrost environment[J]. Cold Regions Science and Technology, 1991, 19(2):111-122.
[28] 赵林, 程国栋, 李述训,等. 青藏高原五道梁附近多年冻土活动层冻结和融化过程[J]. 科学通报, 2000, 45(11):1205-1211.
[29] 张茂省, 李同录. 黄土滑坡诱发因素及其形成机理研究[J]. 工程地质学报, 2011, 19(4):530-540.

结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别

Identification of geological potential landslides in Cang Mountain by combining SBAS-InSAR technique and information entropy

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