摘要
为摸清深圳市龙岗区斜坡地质灾害隐患底数,查明该地区灾害易发区域分布情况,以多源遥感卫星数据为基础,利用地质灾害解译平台,采用专家判读方法解译斜坡地质灾害隐患,并采用野外验证与百度街景数据相结合的方式对部分解译隐患进行验证。最后利用信息量法,以坡高、坡度、降雨量、地表岩性和土地覆被等为评价因子,获得龙岗区斜坡地质灾害隐患易发区分布情况;同时利用已发地质灾害点与易发性评价结果进行叠加分析。结果显示评价结果与已发灾害点分布完全吻合,说明该方法在斜坡地质灾害易发性评价的有效性,同时也侧面证明了遥感解译斜坡地质灾害隐患的准确性。
This study aims to investigate the fundamental facts concerning slope geological hazards in Longgang District,Shenzhen City,as well as the distributions of disaster-prone zones in the district.Based on the multi-source remote sensing satellite data,this study interpreted the slope geological hazards using the expert interpretation method on a geological hazard interpretation platform.Furthermore,some interpreted geological hazards were verified through field verification combined with Baidu Street View data.Finally,the distributions of zones susceptible to slope geological hazards in Longgang District were determined using the information value method,with the slope height,slope gradient,rainfall,surface lithology,and land cover as assessment factors.Additionally,existing geological hazard sites were superimposed with the susceptibility assessment results for analysis,yielding completely consistent results.This confirms the effectiveness of the method used in this study for assessing the susceptibility of slope geological hazards,as well as the accuracy of remote sensing interpretation of slope geological hazards.
作者
王宁
姜德才
郑向向
钟昶
WANG Ning;JIANG Decai;ZHENG Xiangxiang;ZHONG Chang(China Aero Geophysical Survey and Remote Sensing Center for Natural Resources,Beijing 100083,China;University of Chinese Academy of Sciences,Beijing 100049,China;Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处
《自然资源遥感》
CSCD
北大核心
2023年第4期122-129,共8页
Remote Sensing for Natural Resources
基金
中国地质调查局项目“基础地质遥感调查”(编号:DD20230011)
自然资源部航空地球物理与遥感地质重点实验室课题“基于深度学习的黄河源区岩屑坡提取及成因机理研究”(编号:2023YFL25)共同资助。
关键词
地质灾害隐患识别
时序InSAR
易发性评价
信息量模型
identification of geological hazards
time-series InSAR
susceptibility assessment
information value model