Article(id=1242130223613936031, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242130177564672227, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2013.12.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1366905600000, receivedDateStr=2013-04-26, revisedDate=-2209017600000, revisedDateStr=1900-01-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1367078400000, onlineDateStr=2013-04-28, pubDate=1367078400000, pubDateStr=2013-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1367078400000, onlineIssueDateStr=2013-04-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774076522969, creator=sys-migrate, updateTime=1774076522969, updator=sys-migrate, issue=Issue{id=1242130177564672227, tenantId=1146029695717560320, journalId=1146031591421210625, year='2013', volume='31', issue='12', pageStart='2', pageEnd='95', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774076511992, creator=sys-migrate, updateTime=1774076511992, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=37, endPage=41, ext={EN=ArticleExt(id=1242130228156371163, articleId=1242130223613936031, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Airborne Union Observation and Disaster Enhanced Identification of M_s 7.0 Lushan Earthquake, columnId=1242130185257025808, journalTitle=Science & Technology Review, columnName=芦山地震专题, runingTitle=null, highlight=null, articleAbstract=Based on the Manned Aerial Vehicles (MAV) airborne remote sensing images of Sichuan Lushan M_s 7.0 earthquake acquired by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), an enhanced disaster identification system was developed with Multiple Airborne Sensors Monitoring System(MASMS) supported with low-altitude unmanned aerial vehicles (UAV) sensors. The component of the MASMS and the technical process of the disaster monitoring were introduced. The MAV remote sensing and fixed-wing UAV were applied to joint monitor the disasters in Lushan County, which was badly suffered from Lushan M_s 7.0 earthquake. Taking the remote sensing images acquired by MAV and UAV into consideration synthetically, the details of typical building damage, the distribution of landslides and the destruction of power lines were analyzed comprehensively. The results showed that the MASMS can investigate the disaster situations of major electricity facilities, and meanwhile enhance the ability of disaster identification., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=loq77myAufq+5xFS6B2PeA==, pdfFileSize=3484854, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China;2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. Peoples Insurance Company of China, Beijing 100022, China;4. School of Earth Science and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China;5. School of Environment Science and Suney, China University of Mining and Technology, Xuzhou 221008, Jiangsu Province, China, fund=null, authors=XU Zhihua¹, LIU Chunbo², WANG Ping³, WANG Qiuling¹, LI Fashuai⁴, WU Lixin^1,5, authorsList=XU Zhihua;LIU Chunbo;WANG Ping;WANG Qiuling;LI Fashuai;WU Lixin;), CN=ArticleExt(id=1242130225685926092, articleId=1242130223613936031, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=四川芦山M_s 7.0级地震空基联合观测与灾情增强识别, columnId=1242130184183283973, journalTitle=科技导报, columnName=芦山地震专题, runingTitle=null, highlight=null, articleAbstract=以四川芦山M_s 7.0级地震后中国科学院遥感与数字地球研究所的有人机航拍为基础,辅以低空无人机平台进行联合观测,建立了空基多平台联合灾情观测模式下的灾情增强识别系统。介绍了空基多平台航测系统的组成及联合灾情观测的技术流程,使用有人机遥感平台与固定翼无人机遥感平台对重灾区芦山县进行航空联合观测。对震后有人机与无人机遥感影像进行综合对比,分析了地震中房屋典型受损的细节、滑坡体空间变化及重要电力线的破坏情况。结果表明,采用空基多平台的灾情监测模式,可显著增强对灾情的识别能力。, correspAuthors=null, authorNote=null, correspAuthorsNote=吴立新,教授,研究方向为数字矿山与数字减灾、灾变遥感与灾害观测、空间信息理论与方法,电子信箱:awulixin@263.net, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=kXiAjDp2WFfeY7imo+l7+Q==, pdfFileSize=3484854, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 民政部/教育部减灾与应急管理研究院;北京师范大学,北京 100875;2. 中国科学院遥感与数字地球研究所,北京 100094;3. 中国人民财产保险股份有限公司,北京 100022;4. 中国矿业大学(北京)地球科学与测绘工程学院,北京 100083;5. 中国矿业大学环境与测绘学院,江苏徐州 221008, fund=null, authors=许志华¹, 刘纯波², 王平³, 王秋玲¹, 李发帅⁴, 吴立新^1,5, authorsList=许志华;刘纯波;王平;王秋玲;李发帅;吴立新;)}, authors=null, keywords=[Keyword(id=1242130225388130504, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242130223613936031, language=CN, orderNo=1, keyword=四川芦山M_s 7.0级地震), Keyword(id=1242130225476210889, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242130223613936031, language=CN, orderNo=1, keyword=航空遥感), 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