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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 161-170     DOI: 10.6046/zrzyyg.2022434
The extraction and analysis of Luding earthquake-induced landslide based on high-resolution optical satellite images
ZHANG Yu1(), MING Dongping1(), ZHAO Wenyi1,2, XU Lu1, ZHAO Zhi1, LIU Ran1
1. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
2. China Geological Environment Monitoring Institute, Beijing 100081, China
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On September 5, 2022, a Ms 6.8 earthquake occurred in Luding County, Ganzi Prefecture, Sichuan Province, inducing numerous landslides. This study collected the pre- and post-earthquake images from the GF-2 and GF-6 satellites, as well as the DEM data of Luding. Then, using the object-oriented method, the stepwise optimization multi-scale segmentation method, and the nearest neighbor classification method, this study extracted the landslide information according to the spectrum, thematic index, geometric texture, and topographic features of the objects in the experimental area. The overall identification accuracy of pre- and post-earthquake landslides was 92.3% and 95.4%, respectively. The comprehensive analysis of the distribution of pre- and post-earthquake landslide landslides shows that 23.91 km2 of new landslides were induced by the earthquake. This study summarized the distribution characteristics of post-earthquake landslides through the spatial statistical analysis of seven topographic factors. The results are as follows: ① The post-earthquake landslides were mainly affected by the Xianshuihe fault zone, and they show a banded distribution along rivers and a lamellar, dense distribution along the hillsides and valleys near the fault zone; ② Compared with the historical landslides, the new landslides have a relatively stable elevation range and a large slope range. Moreover, there is a significantly negative correlation between the area of the post-earthquake landslides and the surface roughness.

Keywords optical remote sensing      object-oriented      landslide information extraction      Ms 6.8 earthquake in Luding County     
ZTFLH:  TP753  
Issue Date: 20 March 2023
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Dongping MING
Wenyi ZHAO
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Yu ZHANG,Dongping MING,Wenyi ZHAO, et al. The extraction and analysis of Luding earthquake-induced landslide based on high-resolution optical satellite images[J]. Remote Sensing for Natural Resources, 2023, 35(1): 161-170.
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Fig.1  Location of the study area
Fig.2  Flowchart of research methods
Fig.3  Variance and ROC variation curve at different scales
Fig.4  Post-processing results of spectral difference segmentation with different parameters
Fig.5  Landslide extraction results of first research area
Fig.6  Landslide extraction results of second research area
Fig.7  Dual-temporal landslide extraction result
Fig.8  Post-earthquake landslide distribution statistics with terrain factors
Fig.9  Landslide distribution distance from seismic fault zones
[1] 李强, 张景发, 罗毅, 等. 2017年“8·8”九寨沟地震滑坡自动识别与空间分布特征[J]. 遥感学报, 2019, 23(4):785-789.
[1] Li Q, Zhang J F, Luo Y, et al. Recognition of earthquake-induced landslide and spatial distribution patterns triggered by the Jiuzhaigou earthquake in August 8,2017[J]. Journal of Remote Sensing, 2019, 23(4):785-789.
[2] 苏凤环, 刘洪江, 韩用顺. 汶川地震山地灾害遥感快速提取及其分布特点分析[J]. 遥感学报, 2008(6):956-963.
[2] Su F H, Liu H J, Han Y S. The extraction of mountain hazard induced by Wenchuan earthquake and analysis of its distributing characteristic[J]. Journal of Remote Sensing, 2008(6):956-963.
[3] 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.
doi: 10.1080/01431160512331314047 url:
[4] 顾海燕, 李海涛, 闫利. 地理本体驱动的遥感影像面向对象分析方法[J]. 武汉大学学报(信息科学版), 2018, 43(1):31-36.
[4] Gu H Y, Li H T, Yan L. A geographic object-based image analysis methodology based on geo-ontology[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1):31-36.
[5] 魏家旺, 惠文华, 程梦真, 等. 地理本体驱动的面向对象滑坡识别[J]. 遥感信息, 2020, 35(2):94-99.
[5] Wei J W, Hui W H, Cheng M Z, et al. Geographic ontology-driven object oriented landslide recognition[J]. Remote Sensing Information, 2020, 35(2):94-99.
[6] 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(4):631-638.
[6] Liu C, Liu X G, Chen Q H, et al. Impact of DEM spatial resolution on landslide extraction using object-oriented methods[J]. Remote Sensing Technology and Application, 2014(4):631-638.
[7] 张群, 赵超英. 基于面向对象的高分遥感数据甘肃黑方台黄土滑坡半自动识别[J]. 灾害学, 2017, 32(3):210-215.
[7] Zhang Q, Zhao C Y. Semiautomatic object-oriented loose landslide recognition based on high resolution remote sensing images in Heifangtai,Gansu[J]. Journal of Catastrophology, 2017, 32(3):210-215.
[8] 丁永辉, 张勤, 杨成生, 等. 基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例[J]. 测绘通报, 2022,(4):51-55.
[8] Ding Y H, Zhang Q, Yang C S, et al. Landslide identification in Jinsha River basin based on high-resolution remote sensing:Taking Wangdalong Village of Batang County as an example[J]. Bulletin of Surveying and Mapping, 2022,(4):51-55.
[9] 彭令, 徐素宁, 梅军军, 等. 地震滑坡高分辨率遥感影像识别[J]. 遥感学报, 2017, 21(4):509-518.
[9] Peng L, Xu S N, Mei J J, et al. Earthquake-induced landslide recognition using high-resolution remote sensing images[J]. Journal of Remote Sensing, 2017, 21(4):509-518.
[10] 唐尧. 利用国产遥感卫星进行金沙江高位滑坡灾害灾情应急监测[J]. 遥感学报, 2019, 23(2):252-261.
[10] Tang Y. Emergency monitoring of high-level landslide disasters in Jinsha River using domestic remote sensing satellites[J]. Journal of Remote Sensing, 2019, 23(2):252-261.
[11] Han Y, Wang P, Zheng Y, et al. Extraction of landslide information based on object-oriented approach and cause analysis in Shuicheng,China[J]. Remote Sensing, 2022, 14(3):502.
doi: 10.3390/rs14030502 url:
[12] Tavakkoli P S, Shahabi H, Jarihani B, et al. Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas[J]. Remote Sensing, 2019, 11(21):2575.
doi: 10.3390/rs11212575 url:
[13] 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 url:
[14] Martha T R K N, Jetten V. Characterising spectral,spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods[J]. Geomorphology, 2010, 116(1-2):24-36.
doi: 10.1016/j.geomorph.2009.10.004 url:
[15] 林齐根. 基于光谱、空间和形态特征的面向对象滑坡识别[J]. 遥感技术与应用, 2017, 32(5):931-937.
[15] Lin Q G. Object-oriented detection of landslides based on the spectral,spatial and morphometric properties of landslides[J]. Remote Sensing Technology and Application, 2017, 32(5):931-937.
[16] Ji S, Yu D, Shen C, et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks[J]. Landslides, 2020, 17(6):1337-1352.
doi: 10.1007/s10346-020-01353-2
[17] Sameen M I, Pradhan B. Landslide detection using residual networks and the fusion of spectral and topographic information[J]. IEEE Access, 2019, 7:114363-114373.
doi: 10.1109/Access.6287639 url:
[18] Cai H, Chen T, Niu R, et al. Landslide detection using densely connected convolutional networks and environmental conditions[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:5235-5247.
doi: 10.1109/JSTARS.2021.3079196 url:
[19] Bragagnolo L, Rezende L R, Dasilva R V, et al. Convolutional neural networks applied to semantic segmentation of landslide scars[J]. Catena, 2021, 201:105189.
doi: 10.1016/j.catena.2021.105189 url:
[20] Prakash N, Manconi A, Loew S. Mapping landslides on EO data:performance of deep learning models vs.traditional machine learning models[J]. Remote Sensing, 2020, 12(3):346.
doi: 10.3390/rs12030346 url:
[21] Liu T, Chen T, Niu R, et al. Landslide detection mapping employing CNN,ResNet,and DenseNet in the Three Gorges Reservoir,China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:11417-11428.
doi: 10.1109/JSTARS.2021.3117975 url:
[22] 王欣, 方成勇, 唐小川, 等. 泸定 Ms 6.8 级地震诱发滑坡应急评价研究[J]. 武汉大学学报(信息科学版), 2023, 48(1):25-35.
[22] Wang X, Fang C Y, Tang X C, et al. Research on emergency evaluation of landslides induced by Luding Ms 6.8 earthquake[J]. Geomatics and Information Science of Wuhan University, 2023, 48(1):25-35.
[23] 陈扬洋. 基于对地观测数据的滑坡灾害解译与分析[D]. 北京: 中国地质大学(北京), 2022.
[23] Chen Y Y. Interpretation and analysis of landslide hazard based on earth observation data[D]. Beijing: China University of Geosciences (Beijing), 2022.
[24] Liu P, Wei Y, Wang Q, et al. Research on post-earthquake landslide extraction algorithm based on improved U-Net model[J]. Remote Sensing, 2020, 12(5):894.
doi: 10.3390/rs12050894 url:
[25] Liu P, Wei Y, Wang Q, et al. A research on landslides automatic extraction model based on the improved mask R-CNN[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):168.
doi: 10.3390/ijgi10030168 url:
[26] Shi W, Zhang M, Ke H, et al. Landslide recognition by deep convolutional neural network and change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6):4654-4672.
doi: 10.1109/TGRS.2020.3015826 url:
[27] 王运生, 程万强, 刘江伟. 川藏铁路廊道泸定段地质灾害孕育过程及成灾机制[J]. 地球科学, 2022, 47(3):950-958.
[27] Wang Y S, Cheng W Q, Liu J W. Forming process and mechanisms of geo-hazards in Luding section of the Sichuan-Tibet railway[J]. Earth Science, 2022, 47(3):950-958.
[28] 黄志坚. 面向对象影像分析中的多尺度方法研究[D]. 长沙: 国防科学技术大学, 2014.
[28] Huang Z J. Research on multiscale methods in object-based image analysis[D]. Changsha: National University of Defence Technology, 2014.
[29] 关元秀, 王学恭, 郭涛, 等. eCognition基于对象影像分析教程[M]. 北京: 科学出版社, 2019.
[29] Guan Y X, Wang X G, Guo T, et al. eCognition object-based image analysis tutorial[M]. Beijing: Science Press, 2019.
[30] 熊华伟, 俞春生, 李小玉, 等. 基于高分辨率遥感影像的不透水面信息快速提取[J]. 国土与自然资源研究, 2015, 1:52-54.
[30] Xiong H W, Yu C S, Li X Y, et al. Rapid extraction of impervious surface information based on high-resolution remote sensing images[J]. Territory and Natural Resources Study, 2015, 1:52-54.
[31] Ming D, Li J, Wang J, et al. Scale parameter selection by spatial statistics for GEOBIA:Using mean-shift based multi-scale segmentation as an example[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106:28-41.
doi: 10.1016/j.isprsjprs.2015.04.010 url:
[32] 范雷, 张琪. 金沙江苏洼龙—奔子栏河段滑坡灾害发育分布规律[J]. 长江科学院院报, 2016, 33(3):38-41.
[32] Fan L, Zhang Q. Occurrence and distribution characteristics of landslides at Suwalong-Benzilan along Jinsha River[J]. Journal of Yangtze River Scientific Research Institute, 2016, 33(3):38-41.
[33] Dragut L, Csillik O, Eisank C, et al. Automated parameterisation for multi-scale image segmentation on multiple layers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 88:119-127.
doi: 10.1016/j.isprsjprs.2013.11.018 url:
[34] Dragut L, Tiede D, Levick S R. ESP:A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J]. International Journal of Geographical Information Science, 2010, 24(6):859-871.
doi: 10.1080/13658810903174803 url:
[35] 黄汀, 白仙富, 庄齐枫, 等. 高分一号汶川极震区滑坡提取研究[J]. 测绘通报, 2018,(2):67-71,82.
[35] Huang T, Bai X F, Zhuang Q F, et al. Research on landslides extraction based on the Wenchuan earthquake in GF-1 remote sensing image[J]. Bulletin of Surveying and Mapping, 2018,(2):67-71,82.
[36] 陈晓利, 刘春国, 传一健, 等. 鲁甸地震的滑坡物质运移规律与地形特征[J]. 地震地质, 2021, 43(1):92-104.
[36] Chen X L, Liu C G, Chuan Y J, et al. Study on the distribution of co-seismic landslides and terrain features in the Ms 6.5 Ludian earthquake affected area[J]. Seismology and Geology, 2021, 43(1):92-104.
[37] Chigira M, Yagi H. Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan[J]. Engineering Geology, 2006, 82(4):202-221.
doi: 10.1016/j.enggeo.2005.10.006 url:
[38] 铁永波, 张宪政, 卢佳燕, 等. 四川省泸定县Ms 6.8级地震地质灾害发育规律与减灾对策[J]. 水文地质工程地质, 2022, 49(6):1-12.
[38] Tie Y B, Zhang X Z, Lu J Y, et al. Characteristics of geological hazards and it’s mitigations of the Ms 6.8 earthquake in Luding County,Sichuan Province[J]. Hydrogeology & Engineering Geology, 2022, 49(6):1-12.
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