2017年“8.8”九寨沟地震滑坡自动识别与空间分布特征
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摘要
2017年8月8日发生的7.0级九寨沟地震诱发九寨沟熊猫海附近产生大量的滑坡体,造成道路阻塞,严重影响地震应急救援进度。为快速准确地识别滑坡分布范围,本文在深入分析滑坡遥感影像特征的基础上,引入面向对象分析方法,实现了基于无人机影像的震后滑坡体的自动识别。通过多尺度分割算法获取滑坡多层次影像对象,利用SEaTH算法自动构建每一层次特征规则集,实现基于不同层次分析的滑坡体自动识别。分析滑坡体在地形、活动断层等因子中的空间分布特征,为地震滑坡预测与危险性评价奠定基础。与人工目视解译结果相比较,基于面向对象的滑坡自动识别方法提取精度可达94.8%,Kappa系数为0.827,在电脑配置相同的情况下,自动识别方法的效率是人工目视解译效率的一倍。空间分布特征分析表明,地震滑坡的空间分布与斜坡坡度、地形起伏度呈正相关关系,与地表粗糙度存在负相关关系,研究区滑坡体分布存在明显的断层效应。
The Jiuzhaigou earthquake with the magnitude of 7.0 occurred in August 8, 2017 and resulted in a large number of landslides near the panda sea area in Jiuzhaigou. These landslides caused road congestion and seriously affected the progress of earthquake emergency rescue. The landslide caused by earthquake has wide distribution and large quantity. Given the urgency of the disaster and high resolution of unmanned aerial vehicle(UAV) images, the traditional artificial visual interpretation model cannot meet the needs of earthquake emergency response. Therefore, an automatic information identification method must be developed to identify the distribution range of landslide rapidly and accurately.On the basis of comprehensive analysis of the features of remote sensing images of landslide, an automatic information identification model for object-oriented analysis was constructed. First, the remote sensing images were segmented at different scales to obtain different levels of image objects depending on different types and scales of land objects. Then, SEath algorithm was used to construct feature rule set automatically by the comprehensive utilization of the information of spectrum, texture, and shape of object at every level, and the distribution of earthquake-induced landslides was identified. Thereafter, the accuracy and efficiency of recognition were evaluated on the basis of artificial visual interpretation. Finally, the spatial distribution features of landslide body in topographic factor and fracture distribution layer were analyzed by statistical analysis.Using the acquired aerial image data of UAV, the earthquake landslide near the panda sea area of Jiuzhaigou earthquake was identified.The overall accuracy was 94.8%, and the Kappa coefficient was 0.827. The present method was twice as efficient as the artificial visual interpretation method under the same configuration of computer.The spatial distribution of landslide was positively related to slope and topographic relief but was negatively correlated with roughness.No evident relationship was found between the spatial distribution of landslide mass and the topographic factors such as slope and gradient of slope. Evident fault effects were observed in the distribution of landslide.In this study, the object-oriented analysis method was developed to realize automatic identification of earthquake-induced landslide using UAV images. On the basis of the comprehensive utilization of spectrum, texture, and shape of image objects at each segmentation level,an automatic construction method of feature rule set based on SEaTH algorithm was established, Finally, an automatic, efficient extraction of earthquake-induced landslides was realized. Compared with the artificial visual interpretation method, the automatic method of object-oriented analysis could effectively improve the efficiency and timeliness of disaster information identification after earthquake, which could break the pattern of multiple interpretations and save time for earthquake emergency response.The earthquake-induced landslide distribution features in elevation, slope, aspect, fault distance, and other factors were also analyzed.The correlation between landslide and topographic factors was found. Overall, the earthquake-induced landslide in the study area is mainly controlled by the Tazang fault. The spatial distribution rule can provide information support for landslide risk assessment, disaster investigation, prediction, and prevention.
The Jiuzhaigou earthquake with the magnitude of 7.0 occurred in August 8, 2017 and resulted in a large number of landslides near the panda sea area in Jiuzhaigou. These landslides caused road congestion and seriously affected the progress of earthquake emergency rescue. The landslide caused by earthquake has wide distribution and large quantity. Given the urgency of the disaster and high resolution of unmanned aerial vehicle(UAV) images, the traditional artificial visual interpretation model cannot meet the needs of earthquake emergency response. Therefore, an automatic information identification method must be developed to identify the distribution range of landslide rapidly and accurately.On the basis of comprehensive analysis of the features of remote sensing images of landslide, an automatic information identification model for object-oriented analysis was constructed. First, the remote sensing images were segmented at different scales to obtain different levels of image objects depending on different types and scales of land objects. Then, SEath algorithm was used to construct feature rule set automatically by the comprehensive utilization of the information of spectrum, texture, and shape of object at every level, and the distribution of earthquake-induced landslides was identified. Thereafter, the accuracy and efficiency of recognition were evaluated on the basis of artificial visual interpretation. Finally, the spatial distribution features of landslide body in topographic factor and fracture distribution layer were analyzed by statistical analysis.Using the acquired aerial image data of UAV, the earthquake landslide near the panda sea area of Jiuzhaigou earthquake was identified.The overall accuracy was 94.8%, and the Kappa coefficient was 0.827. The present method was twice as efficient as the artificial visual interpretation method under the same configuration of computer.The spatial distribution of landslide was positively related to slope and topographic relief but was negatively correlated with roughness.No evident relationship was found between the spatial distribution of landslide mass and the topographic factors such as slope and gradient of slope. Evident fault effects were observed in the distribution of landslide.In this study, the object-oriented analysis method was developed to realize automatic identification of earthquake-induced landslide using UAV images. On the basis of the comprehensive utilization of spectrum, texture, and shape of image objects at each segmentation level,an automatic construction method of feature rule set based on SEaTH algorithm was established, Finally, an automatic, efficient extraction of earthquake-induced landslides was realized. Compared with the artificial visual interpretation method, the automatic method of object-oriented analysis could effectively improve the efficiency and timeliness of disaster information identification after earthquake, which could break the pattern of multiple interpretations and save time for earthquake emergency response.The earthquake-induced landslide distribution features in elevation, slope, aspect, fault distance, and other factors were also analyzed.The correlation between landslide and topographic factors was found. Overall, the earthquake-induced landslide in the study area is mainly controlled by the Tazang fault. The spatial distribution rule can provide information support for landslide risk assessment, disaster investigation, prediction, and prevention.
引文
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Martin Y E and Franklin S E.2005.Classification of soil-and bedrockdominated landslides in British Columbia using segmentation of satellite imagery and DEM data.International Journal of Remote Sensing,26(7):1505-1509[DOI:10.1080/01431160412331330202]
Mauro C and Eufemia T.2001.Accuracy assessment of Per-field classification integrating very fine spatial resolution satellite imagery with topographic data.Journal of Geospatial Engineering,3(2):127-134
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Niu Q F,Cheng W M,Lan H X,Liu Y,Xie Y W and Cao Y Y.2010.The extraction of landslide hazard induced by Yushu earthquake and analysis of its distributing characteristic//Proceedings of Symposium on Emergency Handling and Disaster Prevention and Control Technology for Sudden Geological Disasters in China.Lanzhou:China Association for Disaster Prevention:39-45(牛全福,程维明,兰恒星,刘勇,颉耀文,曹玉尧.2010.玉树地震滑坡灾害的遥感提取与分布特征分析//全国突发性地质灾害应急处置与灾害防治技术高级研讨会论文集.兰州:中国灾害防御协会:39-45)
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Park N W and Chi K H.2008.Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model.International Journal of Remote Sensing,29(1):247-264[DOI:10.1080/01431160701227661]
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current strain state in the southeastern Qinghai-Tibet plateau.
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华,王启欣,陈立春,任治坤,许冲,魏占玉,鲁人齐,谭锡斌,董
绍鹏,石峰.2017a.九寨沟地震发震断层属性及青藏高原东南
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Blaschke T.2010.Object based image analysis for remote sensing.IS-PRS Journal of Photogrammetry and Remote Sensing,65(1):2-16[DOI:10.1016/j.isprsjprs.2009.06.004]
Chen T B,Hu Z W,Wei L and Hu S Q.2017.Data processing and landslide information extraction based on UAV remote sensing.Journal of Geo-information Science,19(5):692-701(陈天博,胡卓玮,魏铼,胡顺强.2017.无人机遥感数据处理与滑坡信息提取.地球信息科学学报,19(5):692-701)[DOI:10.3724/SP.J.1047.2017.00692]
Cheng K S,Wei C and Chang S C.2004.Locating landslides using multi-temporal satellite images.Advances in Space Research,33(3):96-301[DOI:10.1016/S0273-1177(03)00471-X]
Dai L X,Xu Q,Fan X M,Chang M,Yang Q,Yang F and Ren J.2017.A preliminary study on spatial distribution patterns of landslides triggered by Jiuzhaigou earthquake in Sichuan on august 8th,2017and their susceptibility assessment.Journal of Engineering Geology,25(4):1151-1164(戴岚欣,许强,范宣梅,常鸣,杨琴,杨帆,任敬.2017.2017年8月8日四川九寨沟地震诱发地质灾害空间分布规律及易发性评价初步研究.工程地质学报,25(4):1151-1164)[DOI:10.13544/j.cnki.jeg.2017.04.03]
Fu W J and Hong J Y.2006.Discussion on application of support vector machine technique in extraction of information on landslide hazard from remote sensing images.Research of Soil and Water Conservation,13(4):120-121,124(付文杰,洪金益.2006.基于支持向量机的滑坡灾害信息遥感图像提取研究.水土保持学报,13(4):120-121,124)[DOI:10.3969/j.issn.1005-3409.2006.04.038]
Giada S,De Groeve T,Ehrlich D and Soille P.2003.Information extraction from very high resolution satellite imagery over Lukole refugee camp,Tanzania.International Journal of Remote Sensing,24(22):4251-4266[DOI:10.1080/0143116021000035021]
Hervás J,Barredo J I,Rosin P L,Pasuto A,Mantovani F and Silvano S.2003.Monitoring landslides from optical remotely sensed imagery:the case history of Tessina landslide,Italy.Geomorphology,54(1/2):63-75[DOI:10.1016/S0169-555X(03)00056-4]
Hu D Y,Li J,Zhao W J and Peng G X.2008.Object-oriented landslide detection from remote sensing imageries with high resolution.Journal of Natural Disasters,17(6):42-46(胡德勇,李京,赵文吉,彭光雄.2008.基于对象的高分辨率遥感图像滑坡检测方法.自然灾害学报,17(6):42-46)[DOI:10.13577/j.jnd.2008.0609]
Hua L Z,Cui S H,Li X H,Yin K and Qiu Q Y.2008.Remote sensing identification of earthquake trigged landsides and their impacts on ecosystem services:a case study of Wenchuan County.Acta Ecologica Sinica,28(12):5909-5916(花利忠,崔胜辉,李新虎,尹锴,邱全毅.2008.汶川大地震滑坡体遥感识别及生态服务价值损失评估.生态学报,28(12):5909-5916)[DOI:10.3321/j.issn:1000-0933.2008.12.017]
Huang H P,Wu B F and Fan J L.2003.Analysis to the relationship of classification accuracy,segmentation scale,image resolution//Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium.Toulouse,France:IEEE:3671-3673[DOI:10.1109/IGARSS.2003.1295233]
Huang R Q.2009.Mechanism and geomechanical modes of landslide hazards triggered by Wenchuan 8.0 earthquake.Chinese Journal of Rock Mechanics and Engineering,28(6):1239-1249(黄润秋.2009.汶川8.0级地震触发崩滑灾害机制及其地质力学模式.岩石力学与工程学报,28(6):1239-1249)[DOI:10.3321/j.issn:1000-6915.2009.06.021]
Li S,Li Y Q and An Y L.2010.Automatic recognition of landslides based on change detection.Remote sensing information(1):27-31(李松,李亦秋,安裕伦.2010.基于变化检测的滑坡灾害自动识别.遥感应用(1):27-31)[DOI:10.3969/j.issn.1000-3177.2010.01.006]
Li Y S,Huang C,Yi S J and Wu C H.2017.Study on seismic fault and source rupture tectonic dynamic mechanism of Jiuzhaigou MS7.0earthquake.Journal of Engineering Geology,25(4):1141-1150(李渝生,黄超,易树健,伍纯昊.2017.九寨沟7.0级地震的地震断裂及震源破裂的构造动力学机理研究.工程地质学报,25(4):1141-1150)[DOI:10.13544/j.cnki.jeg.2017.04.029]
Lobo A,Chic O and Casterad A.1996.Classification of Mediterranean crops with multisensor data:per-pixel versus per-object statistics and image segmentation.International Journal of Remote Sensing,17(12):2385-2400[DOI:10.1080/01431169608948779]
Lu P,Stumpf A,Kerle N and Casagli N.2011.Object-oriented change detection for landslide rapid mapping.IEEE Geoscience and Remote Sensing Letters,8(4):701-705[DOI:10.1109/LGRS.2010.2101045]
Martin Y E and Franklin S E.2005.Classification of soil-and bedrockdominated landslides in British Columbia using segmentation of satellite imagery and DEM data.International Journal of Remote Sensing,26(7):1505-1509[DOI:10.1080/01431160412331330202]
Mauro C and Eufemia T.2001.Accuracy assessment of Per-field classification integrating very fine spatial resolution satellite imagery with topographic data.Journal of Geospatial Engineering,3(2):127-134
Nichol J and Wong M S.2005.Satellite remote sensing for detailed landslide inventories using change detection and image fusion.International Journal of Remote Sensing,26(9):1913-1926[DOI:10.1080/01431160512331314047]
Niu Q F,Cheng W M,Lan H X,Liu Y,Xie Y W and Cao Y Y.2010.The extraction of landslide hazard induced by Yushu earthquake and analysis of its distributing characteristic//Proceedings of Symposium on Emergency Handling and Disaster Prevention and Control Technology for Sudden Geological Disasters in China.Lanzhou:China Association for Disaster Prevention:39-45(牛全福,程维明,兰恒星,刘勇,颉耀文,曹玉尧.2010.玉树地震滑坡灾害的遥感提取与分布特征分析//全国突发性地质灾害应急处置与灾害防治技术高级研讨会论文集.兰州:中国灾害防御协会:39-45)
Nussbaum S,Niemeyer I and Canty M J.2006.SEaTH-a new tool for automated feature extraction in the context of object-based image analysis//Proceedings of the 1st International Conference on Object-Based Image Analysis.Salzburg,Austria:Salzburg University
Park N W and Chi K H.2008.Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model.International Journal of Remote Sensing,29(1):247-264[DOI:10.1080/01431160701227661]
Ren Y H,Liu Y L,Wei C J and Hu L Q.2009.Study on information extraction method to road damage in Wenchuan earthquake based on high-resolution remote sensing images.Remote Sensing Technology and Application,24(1):52-56(任玉环,刘亚岚,魏成阶,胡蕾秋.2009.汶川地震道路震害高分辨率遥感信息提取方法探讨.遥感技术与应用,24(1):52-56)[DOI:10.11873/j.issn.1004-0323.2009.1.52]
Tang G A and Yang X.2012.ArcGIS Geographic Information System Spatial Analysis Experiment Course.2nd ed.Beijing:Science Press(汤国安,杨昕.2012.ArcGIS地理信息系统空间分析实验教程.2版.北京:科学出版社)
Trigila A,Iadanza C and Spizzichino D.2010.Quality assessment of the Italian Landslide Inventory using GIS processing.Landslides,7(4):455-470[DOI:10.1007/s10346-010-0213-0]
Xu Q and Li W L.2010.Study on the direction effects of landslides triggered by Wenchuan earthquake.Journal of Sichuan University(Engineering Science Edition),42(S1):7-14(许强,李为乐.2010.汶川地震诱发滑坡方向效应研究.四川大学学报(工程科学版),42(S1):7-14)[DOI:10.15961/j.jsuese.2010.s1.007]
Xu X W,Chen G H,Wang Q X,Chen L C,Ren Z K,Xu C,Wei Z Y,
Lu R Q,Tan X B,Dong S P and Shi F.2017a.Discussion on seis-
mogenic structure of Jiuzhaigou earthquake and its implication for
current strain state in the southeastern Qinghai-Tibet plateau.
Chinese Journal of Geophysics,60(10):4018-4026(徐锡伟,陈桂
华,王启欣,陈立春,任治坤,许冲,魏占玉,鲁人齐,谭锡斌,董
绍鹏,石峰.2017a.九寨沟地震发震断层属性及青藏高原东南
缘现今应变状态讨论.地物物理学报,60(10):4018-4026)[DOI:10.6038/cjg20171028]
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