摘要
滑坡识别是滑坡灾害研究的工作前提,准确而快速的滑坡识别与滑坡制图对滑坡易发性、滑坡机理、滑坡监测和预警等研究具有十分重要的意义.在最近20年里,滑坡识别在学术和实践领域都获得了极大的发展.然而,针对滑坡识别领域的文献计量学分析却很少,我们通过收集分析Web of Science(WOS)TM核心合集中2003—2022年所发表的1830篇滑坡识别领域的文章,根据年度文章量、研究领域、研究机构、有影响力的期刊、核心作者、高被引文章、关键词的时间趋势和研究主题地图等,对该领域进行了全面的统计分析.结果表明:滑坡识别每年发表的文章数量呈逐年上升趋势,年均增长率达到14.14%;其中《Remote Sensing》期刊的文章发表数量近几年呈现出爆炸式增长,特别是2020年后排在了首位,显示了全球滑坡遥感研究进入到高发展期;通过高被引文章、关键词和研究主题地图分析发现滑坡识别当前有潜力的热门研究方向主要有深度学习、机器学习、InSAR等.这些结果可以有效地帮助相关研究人员更好地了解滑坡识别领域研究的过去和现状,更好地追踪当前的研究热点.
Landslide identification is a vital component of landslide disaster research,with precise and rapid identification and mapping of landslides being of major importance for the study of landslide susceptibility,mechanisms,monitoring,and early warning.Despite the substantial increase in landslide detection research over the past two decades,little bibliometric analysis has been undertaken on this topic.However,this paper collects and analyzes 1830 publications on landslide identification from the Web of Science(WOS)TM core collection between 2003 and 2022.It provides a comprehensive statistical analysis of the field based on annual publication volume,research field,institution,influential journals,core authors,highly cited literature,time trend of keywords,and research topic map.With an average annual growth rate is 14.14%,the number of publications related to landslide identification is rising steadily.Notably,the journal Remote Sensing has experienced explosive growth in recent years,particularly after the year 2020,and is now ranked first,indicating that global landslide remote sensing research has entered a period of rapid expansion.Deep learning,machine learning,and InSAR are current and prospective research avenues in landslide identification,according to an analysis of highly referenced literature,keywords,and study themes.These results can successfully aid researchers in comprehending past and present research on the subject of landslide identification and in identifying hotspots for current research.Overall,this study sheds light on the most recent advances in landslide identification research and offers important direction for future research in this field.
作者
王涛
苏怀
董铭
WANG Tao;SU Huai;DONG Ming(Faculty of Geography,Yunnan Normal University,Kunming 650500,Yunnan,China;College of Geography and Land Engineering,Yuxi Normal University,Yuxi 653100,Yunnan,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第S01期331-339,共9页
Journal of Yunnan University(Natural Sciences Edition)
基金
云南省地方本科高校基础研究项目(202001BA070001-109)
云南省科技厅科技人才与平台计划(202305AC160086)
国家自然科学基金(42262024).
