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遥感云计算平台相关文献计量可视化分析
引用本文:闫凯,陈慧敏,付东杰,曾也鲁,董金玮,李世卫,吴秋生,李翰良,杜姝渊. 遥感云计算平台相关文献计量可视化分析[J]. 遥感学报, 2022, 26(2): 310-323
作者姓名:闫凯  陈慧敏  付东杰  曾也鲁  董金玮  李世卫  吴秋生  李翰良  杜姝渊
作者单位:1.中国地质大学(北京) 土地科学技术学院, 北京 100083;2.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室, 北京 100101;3.美国斯坦福卡内基研究所 全球生态学系, 加州 94305;4.中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室, 北京 100101;5.中国科学院大学 资源与环境学院, 北京 100101;6.航天宏图信息技术股份有限公司, 北京 100195;7.美国田纳西大学 地理系, 田纳西州 37996
基金项目:国家自然科学基金(编号:41901298);中央高校基本科研业务费(编号:2652018031)
摘    要:在遥感大数据时代背景下,遥感云计算平台的出现改变了遥感数据处理和分析的传统模式,极大地提高了运算效率,使得全球尺度的快速分析成为可能.国内外已有众多学者利用遥感云计算平台开展研究,然而相对缺乏对遥感云计算平台发展和应用的客观性综述.本文基于Web of Science (WoS)和中国知网CNKI(China Nati...

关 键 词:文献计量  可视化  遥感  大数据  遥感云计算平台
收稿时间:2021-05-18

Bibliometric visualization analysis related to remote sensing cloud computing platforms
YAN Kai,CHEN Huimin,FU Dongjie,ZENG Yelu,DONG Jinwei,LI Shiwei,WU Qiusheng,LI Hanliang,DU Shuyuan. Bibliometric visualization analysis related to remote sensing cloud computing platforms[J]. Journal of Remote Sensing, 2022, 26(2): 310-323
Authors:YAN Kai  CHEN Huimin  FU Dongjie  ZENG Yelu  DONG Jinwei  LI Shiwei  WU Qiusheng  LI Hanliang  DU Shuyuan
Affiliation:1.School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China;2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;3.Department of Global Ecology, Carnegie Institution for Science, Stanford CA 94305, USA;4.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, Beijing 100101, China;5.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China;6.Beijing Piesat Information Technology Co., Ltd., Beijing 100195, China;7.Department of Geography, University of Tennessee, Knoxville, TN 37996, United States
Abstract:In the context of big data Remote Sensing (RS), the development of RS cloud computing platforms has changed the mode of RS traditional data processing and analysis. It also has greatly improved the computing efficiency, which enables it to quickly analyze long-term time-series on the global scale. Although many scholars have conducted related works with RS cloud computing platforms, an objective review on the development and application of RS cloud computing platforms is still lacking. In this study, we retrieved the research literature related to RS cloud computing platforms between January 2011 and April 2021 based on the Web of Science and China National Knowledge Infrastructure. The retrieved data were analyzed in terms of publication volume, collaboration analysis, keyword co-occurrence analysis, and co-citation analysis using bibliometric methods. Results show that (1) the number of studies based on RS cloud computing platforms is increasing. China and the United States are the most active countries in this field, and the Chinese Academy of Sciences (CAS) is the most active institution. (2) The intersection of related disciplines is extensive, and it involves RS, environmental science and ecology, computer science, engineering, electrical and electronics, and other disciplines. Among them, RS is the most researched field using cloud computing platforms, and environmental science and ecology and computer science are more closely connected with other disciplinary fields. (3) At present, Google Earth Engine is a widely used RS cloud computing platform. In addition, Amazon Web Services Cloud, Earth Data Miner (a pioneering earth data mining and analysis system of CAS), PIE-Engine, and other platforms are also in a rapid development stage. (4) Large-scale land cover mapping, land use, vegetation dynamics, and climate change have been the main application areas. Environmental health assessment and research on the impact of human activities on the environment will also be important application areas of the platforms in the future. These results quantitatively demonstrated the development history, research hotspots, and applications of RS cloud computing platforms, which provide a reference for relevant researchers to grasp the development dynamics of the field and explore valuable new research directions.
Keywords:bibliometric  visualisation  remote sensing  big data  remote sensing cloud computing platform
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