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DING Hui, ZHANG Maosheng, ZHU Weihong, ZHANG Tao. 2019. High Resolution Remote Sensing for the Identification of Loess Landslides: Example from Yan'an City. Northwestern Geology, 52(3): 231-239. doi: 10.19751/j.cnki.61-1149/p.2019.03.022
Citation: DING Hui, ZHANG Maosheng, ZHU Weihong, ZHANG Tao. 2019. High Resolution Remote Sensing for the Identification of Loess Landslides: Example from Yan'an City. Northwestern Geology, 52(3): 231-239. doi: 10.19751/j.cnki.61-1149/p.2019.03.022
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High Resolution Remote Sensing for the Identification of Loess Landslides: Example from Yan'an City

  • 1. Xi'an Center of China Geological Survey/Northwest China Center for Geoscience Innovation, Xi'an 710054, Shaanxi, China;

  • 2. Key Laboratory for Geo-hazards in Loess Area, MNR, Xi'an 710054, Shaanxi, China

Received Date:  18 September 2018
Revised Date:  28 March 2019
    Abstract
  • The Loess Plateau in West China covers a wide area and has many landslides. Due to problems with field investigations and visual interpretation, such as the time and effort involved, as well as relatively long cycles of data acquisition and update, the high spatial resolution remote sensing data and digital elevation model (DEM) have been used for regional identification of loess landslides in Baota District, Yan'an City, Shaanxi Province, China. The regional loess landslides have been identified by using the object-oriented classification method, this method is to use the image segmentation combined with spectral-spatial and geomorphological features and based on band selection and scale analysis to analyze the landslides. Analysis of the study site showed that the recognition accuracy for the landslide back scarp and landslide mass is 78.9% and 73.6%, respectively; the landslide back scarp is easier to recognize than the landslide mass. This method is important for landslide cataloging, earth science analysis, and image interpretation.
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    • References
  • 刘东生.黄土与环境[M].北京:科学出版社,1985.

    Google Scholar

    LIU Dongsheng. Loess and Environment[M]. Beijing:Science Press,1985.

    Google Scholar

    文宝萍,李媛,王兴林,等. 黄土地区典型滑坡预测预报及减灾对策研究[M].北京:地质出版社,1997.

    Google Scholar

    WEN Baoping, LI Yuan, WANG Xinglin, et al. Study on Typical Landslide Prediction and Mitigation counter mesure in losses region[M]. Beijing:Geology Press, 1997.

    Google Scholar

    郝立贞,白世彪,徐红波,等.基于CBERS-02 卫星数据的地震滑坡识别-以青川县为例[J]. 防灾科技学院学报,2010,12(4):46-52.

    Google Scholar

    HAO Lizhen, BAI Shibiao, XU Hongbo, et al. Landslide identification after earthquake based on CBERS-02 remote sensing data-the case of Qinchuan[J].Journal of Institute of Disaster-Prevention, 2010, 12(4):46-52.

    Google Scholar

    沈永林,李晓静,吴立新.基于航空影像和LiDAR 数据的海地地震滑坡识别研究[J]. 地理与地理信息科学,2011,27(1):16-20.

    Google Scholar

    SHEN Yonglin, LI Xiaojing, WU Lixin. Detection of Haiti earthquake induce landslides from aerial images and LiDAR data[J]. Geography and Geo-Information Science, 2011, 27(4):16-20.

    Google Scholar

    杨文涛,汪明,史培军.利用ndvi时间序列识别汶川地震滑坡的分布[J]. 遥感信息,2012,27(6):45-56.

    Google Scholar

    YANG Wentao, WANG Ming,SHI Peijun. Identification of landslides in Wenchuan earthquake affected region using NDVI time series[J]. Remote Sensing Information, 2012,27(6):45-56.

    Google Scholar

    杨文涛,汪明,史培军,等.基于地形因子分割、分类的面向对象滑坡快速识别方法[J].自然灾害学报,2015,24(4):1-6.

    Google Scholar

    YANG Wentao, WANG Ming,SHI Peijun. Object-oriented rapid identification of landslides based on terrain factor segmentation and classification[J].Journal of Natural Disasters,2015,24(4):1-6.

    Google Scholar

    周志华,林维芳,许高程,等.基于面向对象的滑坡快速识别技术研究[J].安徽农业科学, 2012,40(5):3017-3071.

    Google Scholar

    ZHOU Zhihua, LIN Weifang, XU Gaocheng, et al. Research of fast landslide recognition based on object-oriented technology[J]. Journal of Anhui Agriculture, 2012,40(5):3017-3071.

    Google Scholar

    杨树文,谢飞,韩惠,等. 基于SPOT5 遥感影像的浅层滑坡体自动提取方法[J]. 测绘科学,2012,37(1):71-88.

    Google Scholar

    YANG S W,XIE F,HAN H, et al.Automatic extraction of shallow landslides based on SPOT-5 remote sensing images[J]. Science of Surveying and Mapping, 2012,37(1):71-88.

    Google Scholar

    张毅,谭龙,陈冠,等.基于面向对象分类法的高分辨率遥感滑坡信息提取[J].兰州大学学报(自然科学版),2014,50(5):745-750.

    Google Scholar

    ZHANG Yi, TAN Long, CHEN Guan, et al. Landslide information extracted from high resolution remote sensing based on the object-oriented classification method[J]. Journal of Lanzhou University(Natural Sciences), 2014,50(5):745-750.

    Google Scholar

    李松,李亦秋,安裕伦.基于变化检测的滑坡灾害自动识别[J].遥感信息,2010,1(6):27-31.

    Google Scholar

    LI Song, LI Yiqiu, AN Yulun. Automatic recognition of landslides based on change detection[J].Remote Sensing Information, 2010, 1(6):27-31.

    Google Scholar

    李松,邓宝昆,徐红勤,等.地震型滑坡灾害遥感快速识别方法研究[J].遥感信息,2015,30(4):25-28.

    Google Scholar

    LI Song, DENG Baokun, XU Hongqin, et al.Fast interpretation methods of landslides triggered by earthquake using remote sensing imagery[J]. Remote Sensing Information, 2015,30(4):25-28.

    Google Scholar

    彭令,徐素宁,梅军军.地震滑坡高分辨率遥感影像识别[J].遥感学报, 2017, 21(4):509-518.

    Google Scholar

    PENG Lin, XU Suning, MEI Junjun. Earthquake-induced landslide recognition using high-resolution remote sensing images[J].Journal of Remote Sensing, 2017, 21(4):509-518.

    Google Scholar

    李勋,杨环,殷宗敏,等.基于DEM和遥感影像的区域黄土滑坡体识别方法研究[J].地理与地理信息科学,2017,33(4):86-92.

    Google Scholar

    LI Xun, YANG Huan, YIN Zongming, et al.Regional loess landslide recognition method research based on DEM and remote sensing image[J].Geography and Geo-Information Science, 2017,33(4):86-92.

    Google Scholar

    丁辉,张茂省,李林. 基于多特征面向对象区域滑坡现象识别[J]. 遥感技术与应用, 2013, 28(6):1107-1113.

    Google Scholar

    DING Hui, ZHANG Maosheng, LI Lin. Regional Landslide Identification base on Multi-feature Object-oriented Image Classification[J]. Remote Sensing Technology and Application, 2013, 28(6):1107-1113.

    Google Scholar

    丁辉, 张茂省, 李林. 西北黄土高原区滑坡遥感解译研究-以陕西延安、宁夏彭阳等地为例[J]. 第四纪研究, 2011, 31(6):1077-1085.

    Google Scholar

    DING Hui, ZHANG Maosheng, LI Lin. Interpretation NTERPRETING Landslides in the Northwest Loess Plateau Using Remote Sensing Images[J].Quaternary Sciences,2011, 31(6):1077-1085.

    Google Scholar

    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.

    Google Scholar

    ZHANG Maosheng, LI Tonglu. Triggering factors and mechanism of losses landslides[J].Journal of Engineer Geology, 2011,19(4):530-540.

    Google Scholar

    祝俊华,陈志新,祝艳波.延安市滑坡分布规律及发育特征[J].地质科技情报,2017,36(2):236-243.

    Google Scholar

    ZHU Junhua,CHEN Zhixin, ZHU Yanbo. Distribution Regularity and Development Characteristic of Landslides in Yan'an[J]. Geological Sciences and Technology Information, 2017, 36(2):236-243.

    Google Scholar

    薛强,张茂省.延安淹土安滑坡监测预警及变形特征[J].西北地质,2018,51(2):220-226.

    Google Scholar

    XUE Qiang, ZHANG Maosheng. Monitoring, Early Warning and Deformation Characteristics of Yantu'an Landslides in Yan'an[J].Northwestern Geology,2018,51(2):220-226.

    Google Scholar

    段钊,彭建兵,陈伟,等.泾河下游黄土台塬区滑崩灾害空间分异研究[J].西北地质,2018,51(3):214-222.

    Google Scholar

    DUAN Zhao,PENG Jianbing,CHEN Wei,et al. Distribution Difference of Landslide and Collapse in the Loess Tableland Area at the Downstream of Jing River[J]. Northwestern Geology,2018,51(3):214-222.

    Google Scholar

    张树轩, 杨为民, 程小杰, 等. 甘肃天水红旗山黄土滑坡群成因及稳定性分析[J].中国地质, 2017,44(5):924-937.

    Google Scholar

    ZHANG Shuxuan, YANG Weimin, CHENG Xiaojie, et al. Genetic mechanism and stability analysis of loess landslides group in Tianshui Hongqishan, Gansu Province[J]. Geology in China, 2017,44(5):924-937.

    Google Scholar

    丁华,丁辉.遥感技术在滑坡灾害解译中的应用-以陕西省延安市子长县为例[J].自然灾害学报,2013, 22(2):229-233.

    Google Scholar

    DING Hua, DING Hui. Application of remote sensing technology to interpretation of landslides disaster:a case study of Zichang country, Shaanxi province[J]. Journal of Nature Disasters, 2013,22(2):229-233.

    Google Scholar

    刘辰,刘修国,陈启浩,等.面向对象滑坡信息提取中DEM空间分辨率影响分析[J].遥感技术与应用,2014,29(4):631-638.

    Google Scholar

    LIU Chen,LIU Xiuguo,CHEN Qihao,et al. Impact of DEM spatial resolution on landslide extraction using object-oriented methods[J]. Remote Sensing Technology and Application, 2014,29(4):631-638.

    Google Scholar

    王耀南,李树涛,毛建旭.计算机图像处理与识别技术[M].北京:高等教育出版社,2001.

    Google Scholar

    WAO Yaonan, LI Shutao, MAO Janxu. Computer image process and recognition technology[M]. Beijing:High Education Press,2001.

    Google Scholar

    BARLOW J, MARTIN Y, FRANLIN 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.

    Google Scholar

    BARLOW J, FRANKLIN S E,MARTIN Y. High spatial resolution satellite imagery,DEM derivatives, and image segmentation for the detection of mass wasting processes[J]. Photogrammetric Engineering and Remote Sensing, 2006,72(6):687-692.

    Google Scholar

    MARTIN Y E, FRANKLIN S E. Classification of soil and bedrock-dominated landsides in British Columbia using segmentation of satellite imagery and DEM data[J]. International Journal of Remote Sensing, 2005,26(7):1505-1509.

    Google Scholar

    NICHOL J, WONG M S. Satellite remote sensing for detailed landslide inventories using change detection and image fusion[J]. International Journal of Remote Sensing, 2005,26(9):1913-1926.

    Google Scholar

    BORGHUIS A M, CHANG K, LEE H Y. Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery[J]. International Journal of Remote Sensing, 2007,28(8):1843-1856.

    Google Scholar

    MARTHA T R, KERLE N,JETTEN V, et al. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods[J]. Geomorphology, 2010,116(1):24-36.

    Google Scholar

    MARTHA T R, KERLE N,VANWESTEN C J, et al. Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67(2):105-119.

    Google Scholar

    MARTHA T R,VANWESTEN C J,KERLE N, et al. landslide hazard and risk assessment using semi-automatically created landslide inventories[J].Geomorphology, 2013,184:139-150.

    Google Scholar

    MONDINI A C, MARCHESINI I, ROSSI M, et al. Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data[J]. Geomorphology, 2013, 201:135-147.

    Google Scholar

    MONDINI A C, GUZETTI F, REICHENBACH P, et al. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images[J].Remote Sensing of Environment, 2011,115(7):1743-1757.

    Google Scholar

    LU P, STUMPF A, KERLE N, et al. Object-Oriented change detection for landslides rapid mapping[J]. IEEE Geo-science and Remote Sensing letters, 2011,8(4):701-705.

    Google Scholar

    STUMPF A, KERLE N. Object-oriented mapping of landslides using Random Forests[J]. Remote Sensing of Environment, 2011,115(10):2564-2577.

    Google Scholar

    AKSOY B, ERCANOGLU M. Landslide identification and classification by object-based image analysis and fuzzy logic:An example from the Azdavay region[J]. Computers& Geosciences, 2012,38(1):87-98.

    Google Scholar

    LI Y G, CHEN G Q, WANG B, et al. A new approach of combining aerial photography with satellite imagery for landslide detection[J]. Nature Hazard, 2013,66(2):649-669.

    Google Scholar

    RUDY A C A, LAMOUREUX S F, TREITZ P, et al. Identifying permafrost slope disturbance using multi-temporal optical satellite images and change detection techniques[J]. Cold Regions Science and Technology, 2013,88:37-49.

    Google Scholar

    CHENG G, GUO L, ZHAO TY, et al. Automatic landslide detection rom remote-sensing imagery using a scene classification method based on BOVW and PLSA[J]. International Journal of Remote Sensing, 2013,34(1):45-59.

    Google Scholar

    MOOSAVI V,TALEBI A,SHIRMOHAMMADI,B. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method[J]. Geomorphology, 2014,204:646-656.

    Google Scholar

    KURTZ C,STUMPF A,MALET J P, et al. Hierarchical extraction of landslides from multiresolution remotely sensed optical images[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87:122-136.

    Google Scholar

    GOLOVKO D,ROESSNER S,BEHLINGR, et al. Development of multi-temporal landslide inventory information system for southern Kyrgyzstan using GIS and satellite remote sensing[J]. Photogrammetrie Ferner Kundung Geoinformation, 2015,2:157-172.

    Google Scholar

    BEHLING R, Roessner S, Golovko D, et al. Derivation of long-term spatiotemporal landslide activity a multi-sensor time series approach[J].Remote Sensing of Environment, 2016,186:88-104.

    Google Scholar

    LI Z B,SHI W Z,MYINT S W, et al. Semi-automated landslide inventory mapping from bi temporal aerial photographs using change detection and level set method[J]. Remote Sensing of Environment, 2016, 175:215-230.

    Google Scholar

    YU B, CHEN F. A new technique for landslide mapping from a large-scale remote sensed image:A case study of central Nepal[J]. Computers & Geoscience, 2017,100:115-124.

    Google Scholar

    CHEN F, YU B, XU C, et al. Landslide detection using probability regression,a case study of Wenchuan,northwest of Chengdu[J].Applied Geography,2017,89:32-40.

    Google Scholar

    HARIS K, EFSTRATIADISf N S, MAGLAVERAS N,et al. Hybirdimage segmentation using watersheds and fast region merging[J]. IEEE Transactions on Image Processing,1998,7(12):1684-1699.

    Google Scholar

    ROBINSON D J, REDDING N J, CRISP D J. Implementation of a fastalgorithm for segmenting SAR imagery[R]. Scientific and Technical Report,Australia:Defense Science and Technology Organization,2002.

    Google Scholar

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