CSCD来源期刊

北大中文核心期刊

中国科技核心期刊

地学领域高质量科技期刊分级T2区（2023）

世界期刊影响力指数报告Q1区（2023）

Volume 41 Issue 2
Mar.  2022
Turn off MathJax
Article Contents
Article Navigation >  Bulletin of Geological Science and Technology  > 2022  >  41(2): 187-196
Dong Jiahui, Niu Ruiqin, Qi Mengru, Ding Zan, Xu Hang, He Rui. Identification of geological hazards based on the combination of InSAR technology and disaster background indicators[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 187-196. doi: 10.19509/j.cnki.dzkq.2022.0024
Citation: Dong Jiahui, Niu Ruiqin, Qi Mengru, Ding Zan, Xu Hang, He Rui. Identification of geological hazards based on the combination of InSAR technology and disaster background indicators[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 187-196. doi: 10.19509/j.cnki.dzkq.2022.0024
shu

Identification of geological hazards based on the combination of InSAR technology and disaster background indicators

doi: 10.19509/j.cnki.dzkq.2022.0024
  • Received Date: 13 May 2021
    Abstract
  • Synthetic aperture radar interferometry (InSAR) is an important method to obtain surface deformation information. Due to the limitations of InSAR data acquisition and the accuracy errors produced in the data processing, the identification of hidden dangers also needs to be combined with the analysis of geological hazards themselves, so a method based on InSAR technology combined with the disaster-pregnancy background in the study area is proposed. This study took the Badong section of the Three Gorges Reservoir area as the study area, and ALOS-2 PALSAR radar images were used to obtain the spatial distribution and change rate of deformation in the study area by using time-series InSAR technology. Combining the disaster-prone background of the study area, four indicators of the susceptibility level, slope, engineering rock group and distance from the disaster catalogue point are used as indicators for the identification of hidden dangers of geological disasters. As a result, 19 suspected hidden disaster areas were identified comprehensively, and then the suspected hidden geological disaster areas were verified in the field one by one. The success rate of verification and identification was 78.9%, proving that the method combining the disaster pregnancy background and InSAR results is feasible and can play an important role in regional disaster identification.

     

    • FullText(HTML)
  • loading
    • References(38)
  • [1]
    许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报: 信息科学版, 2019, 44(7): 957-966.

    Xu Q, Dong X J, Li W L. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 957-966(in Chinese with English abstract).
    [2]
    Rosin P L, Hervás J, Barredo J I. Remote sensing image thresholding for landslide motion detection[C]//Anon. 1st Int. Workshop on Pattern Recognition Techniques in Remote Sensing. [S. l. ]: [s. n. ], 2000: 10-17.
    [3]
    Barlow J, Martin Y, Franklin S E. Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British Columbia[J]. Canadian Journal of Remote Sensing, 2003, 29(4): 510-517. doi: 10.5589/m03-018
    [4]
    Chang Y L, Liang L S, Han C C, et al. Multisource data fusion for landslide classification using generalized positive boolean functions[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6): 1697-1708.
    [5]
    Khairunniza-Bejo S, Petrou M, Ganas A. Local similarity measure for landslide detection and identification in comparison with the image differencing method[J]. International Journal of Remote Sensing, 2010, 31(23): 6033-6045. doi: 10.1080/01431160903376365
    [6]
    Hölbling D, Friedl B, Eisank C. An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan[J/OL]. Earth Science Informatics, 2015, 8(2): 327-335.
    [7]
    Marjanovi M, Kovaevi M, Bajat B, et al. Landslide susceptibility assessment using SVM machine learning algorithm[J]. Engineering Geology, 2011, 123(3): 225-234. doi: 10.1016/j.enggeo.2011.09.006
    [8]
    Catani F, Lagomarsino D, Segoni S, et al. Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues[J]. Natural Hazards and Earth System Sciences, 2013, 13(11): 2815-2831. doi: 10.5194/nhess-13-2815-2013
    [9]
    Liu Y, Wu L. Geological disaster recognition on optical remote sensing images using deep learning[J]. Procedia Computer Science, 2016, 91: 566-575. doi: 10.1016/j.procs.2016.07.144
    [10]
    Yu H, Ma Y, Wang L, et al. A landslide intelligent detection method based on CNN and RSG-R[C]//Anon. 2017 IEEE International Conference on Mechatronics and Automation. [S. l. ]: [s. n. ]: IEEE, 2017: 40-44.
    [11]
    王超, 张红, 刘智. 星载合成孔径雷达干涉测量[M]. 北京: 科学出版社, 2002.

    Wang C, Zhang H, Liu Z. Spaceborne synthetic aperture radar interferometry[M]. Beijing: China Science Publishing & Media Ltd., 2002(in Chinese).
    [12]
    Dong J, Zhang L, Tang M, et al. Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China[J]. Remote Sensing of Environment, 2018, 205: 180-198. doi: 10.1016/j.rse.2017.11.022
    [13]
    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. doi: 10.3390/rs8020136
    [14]
    张路, 龚健雅, 廖明生, 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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201812031.htm

    Zhang L, Gong J Y, Liao M S, 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(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201812031.htm
    [15]
    Rosi A, Tfoani 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. doi: 10.1007/s10346-017-0861-4
    [16]
    蔡杰华, 张路, 董杰, 等. 九寨沟震后滑坡隐患雷达遥感早期识别与形变监测[J]. 武汉大学学报: 信息科学版, 2020, 45(11): 17107-1716. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202011007.htm

    Cao J H, Zhang L, Dong J, et al. Detection and monitoring of post-earthquake landslides in Jiuzhaigou using radar remote sensing[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1707-1716(in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202011007.htm
    [17]
    Liu X, Zhao C, Zhang Q, et al. Integration of sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China[J]. Engineering Geology, 2021, 284: 106033. doi: 10.1016/j.enggeo.2021.106033
    [18]
    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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907001.htm

    Ge D Q, Dai K R, Guo Z C, et al. Early identification of serious geological hazards with integrated remote sensing technologies: Thoughts and recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 949-956(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907001.htm
    [19]
    贺凯, 李滨, 赵超英, 等. 基于易滑地质结构与多源数据差异的岩溶山区大型崩滑灾害识别研究[J]. 中国岩溶, 2020, 39(4): 467-477. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYR202004002.htm

    He K, Li B, Zhao C Y, et al. Identification of large-scale landslide hazards based on differences of geological structure prone to sliding and multiple-source data in karst mountainous areas[J]. Carsologica Sinica, 2020, 39(4): 467-477(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYR202004002.htm
    [20]
    胡燕, 李德营, 孟颂颂, 等. 基于证据权法的巴东县城滑坡灾害易发性评价[J]. 地质科技通报, 2020, 39(3): 187-194. doi: 10.19509/j.cnki.dzkq.2020.0320

    Hu Y, Li D Y, Meng S S, et al. andslide susceptibility evaluation in Badong County based on weights of evidence method[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 187-194(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0320
    [21]
    Liu X, Zhao C, Zhang Q, et al. Multi-temporal loess landslide inventory mapping with C-, X- and L-Band SAR datasets: A case study of Heifangtai loess landslides, China[J]. Remote Sensing, 2018, 10(11): 1756. doi: 10.3390/rs10111756
    [22]
    Zhang T, Xie S, Fan J, et al. Detection of active landslides in southwest China using Sentinel-1 and ALOS-2 data[J]. Procedia Computer Science, 2021, 181: 1138-1145. doi: 10.1016/j.procs.2021.01.311
    [23]
    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. doi: 10.1109/TGRS.2002.803792
    [24]
    Dong J, Zhang L, Liao M, et al. Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring[J]. Remote Sensing of Environment, 2019, 233: 111370. doi: 10.1016/j.rse.2019.111370
    [25]
    Tong X, Schmidt D. Active movement of the Cascade landslide complex in Washington from a coherence-based InSAR time series method[J]. Remote Sensing of Environment, 2016, 186: 405-415. doi: 10.1016/j.rse.2016.09.008
    [26]
    刘广全. 基于SBAS-InSAR的丹巴县滑坡探测与监测[D]. 西安: 长安大学, 2015.

    Liu Guangquan. Landslide detection and monitoring of Danba County based on SBAS-InSAR[D]. Xi'an: Chang'an University, 2015(in Chinese with English abstract).
    [27]
    石固林, 徐浪, 张璇钰, 等. 西山村滑坡时序形变的SBAS-InSAR监测[J]. 测绘科学, 2021, 46(2): 93-98, 105. https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD202102014.htm

    Shi G L, Xu L, Zhang X Y, et al. Monitoring time series deformation of Xishancun landslide with SBAS-InSAR[J]. Science of Survering and Mapping, 2021, 46(2): 93-98, 105(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD202102014.htm
    [28]
    孟禹弛. 基于雷达影像的矿区地面沉降监测系统的研究与实现[D]. 江苏徐州: 中国矿业大学, 2017.

    Meng Y C. Study and implementation of mining area land subsidence monitoring system basede on SAR data[D]. Xuzhou Jiangsu: China University of Mining and Technology, 2017(in Chinese with English abstract).
    [29]
    Lauknes T T, Piyush Shanker A, 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. doi: 10.1016/j.rse.2010.04.015
    [30]
    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. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202003012.htm

    Dao K R, Tie Y B, Xu Q, et al. Early identification of potential landslide geohazards in alpine-canyon terrain based on SAR interferometry: A case study of the middle section of yalong river[J]. Journal of Radars, 2020, 9(3): 554-568(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202003012.htm
    [31]
    牛瑞卿, 彭令, 叶润青, 等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报: 地球科学版, 2012, 42(2): 430-439. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201202019.htm

    Niu R Q, Peng L, Ye R Q, et al. Landslide susceptibility assessment based on rough sets and support vector machine[J]. Journal of Jilin University: Earth Science Edition, 2012, 42(2): 430-439(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201202019.htm
    [32]
    连志鹏, 徐勇, 付圣, 等. 采用多模型融合方法评价滑坡灾害易发性: 以湖北省五峰县为例[J]. 地质科技通报, 2020, 39(3): 178-186. doi: 10.19509/j.cnki.dzkq.2020.0319

    Lian Z P, Xu Y, Fu S, et al. Landslide susceptibility assessment based on multi-model fusion method: A case study in Wufeng County, Hubei Province[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 178-186(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0319
    [33]
    Arabameri A, Pradhan B, Rezaei K, et al. Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs[J/OL]. Remote Sensing, 2019, 11(9). doi: 10.3390/rs11090999.
    [34]
    Abedini M, Tulabi S. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: A comparative study of Nojian watershed in Lorestan province, Iran[J/OL]. Environmental Earth Sciences, 2018, 77(11). doi: 10.1007/s12665-018-7524-1.
    [35]
    Chen T, Zhu L, Niu R, et al. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models[J]. Journal of Mountain Science, 2020, 17(3): 670-685. doi: 10.1007/s11629-019-5839-3
    [36]
    Song Y, Niu R, Xu S, et al. Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the three gorges reservoir area (China)[J/OL]. ISPRS International Journal of Geo-Information, 2019, 8(1). doi: 10.3390/ijgi8010004.
    [37]
    Berhane G, Tadesse K. Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: A case study from Gindeberet district, Oromia Regional State, Central Ethiopia[J]. Journal of African Earth Sciences, 2021, 180: 104240. doi: 10.1016/j.jafrearsci.2021.104240
    [38]
    朱冬雪, 许强, 李松林. 三峡库区大型-特大型层状岩质滑坡成因模式及地质特征分析[J]. 地质科技通报, 2020, 39(2): 158-167. doi: 10.19509/j.cnki.dzkq.2020.0217

    Zhu D X, Xu Q, Li S L. Genetic types and geological features of large scale and extra-large scale layered landslides in the Three Gorges Reservoir area[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 158-167(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0217
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(1102) PDF Downloads(172) Cited by()
    Proportional views
    Related

    Copyright © 2010Editorial Department of Bulletin of Geological Science and Technology    

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return