浏览全部资源
扫码关注微信
Published： 07 February 2022 ，
扫 描 看 全 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.2022.遥感云计算平台相关文献计量可视化分析.遥感学报，26（2）： 310-323
Yan K，Chen H M，Fu D J，Zeng Y L，Dong J W，Li S W，Wu Q S，Li H L and Du S Y. 2022. Bibliometric visualization analysis related to remote sensing cloud computing platforms. National Remote Sensing Bulletin， 26（2）：310-323
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 National Knowledge Infrastructure）的文献数据，检索了2011-01—2021-04与遥感云计算平台相关的文献，借助文献计量方法对检索到的数据进行了发文量分析、合作分析、关键词共现分析以及文献共被引分析。结果表明：（1）国内外基于遥感云计算平台的应用研究均呈上升趋势，中国和美国是利用遥感云计算平台进行研究最活跃的国家，中国科学院是最活跃的机构；（2）相关学科交叉较为广泛，涉及遥感、环境科学与生态学、计算机科学、电子电力工程等领域，其中遥感学科是利用遥感云计算平台研究最多的领域，环境科学与生态学以及计算机科学与其他学科领域联系较密切；（3）目前谷歌地球引擎GEE（Google Earth Engine）是应用最为广泛的遥感云计算平台，此外亚马逊网络服务云（Amazon Web Services Cloud）、中科院先导地球大数据挖掘分析系统（EarthDataMiner）、PIE-Engine等平台也处于迅速发展阶段；（4）大范围的土地覆被制图、土地利用、植被变化、气候变化是遥感云平台的应用热点领域，而环境健康评估和人类活动对环境的影响研究也将成为遥感云平台未来的重要应用领域。本文研究结果定量展示了遥感云计算平台的发展历程、研究热点和应用情况，为相关研究人员把握领域发展动态并挖掘有价值的新研究方向提供了参考。
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.
文献计量可视化遥感大数据遥感云计算平台
bibliometricvisualisationremote sensingbig dataremote sensing cloud computing platform
Adam E, Mutanga O and Rugege D. 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management, 18(3): 281-296 [DOI: 10.1007/s11273-009-9169-zhttp://dx.doi.org/10.1007/s11273-009-9169-z]
Amani M, Ghorbanian A, Ahmadi S A, Kakooei M, Moghimi A, Mirmazloumi S M, Moghaddam S H A, Mahdavi S, Ghahremanloo M, Parsian S, Wu Q S and Brisco B. 2020. Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5326-5350 [DOI: 10.1109/JSTARS.2020.3021052http://dx.doi.org/10.1109/JSTARS.2020.3021052]
Benz S A, Bayer P and Blum P. 2017. Identifying anthropogenic anomalies in air, surface and groundwater temperatures in Germany. Science of the Total Environment, 584-585: 145-153 [DOI: 10.1016/j.scitotenv.2017.01.139http://dx.doi.org/10.1016/j.scitotenv.2017.01.139]
Boothroyd R J, Williams R D, Hoey T B, Barrett B and Prasojo O A. 2021. Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change. WIREs Water, 8(1): e21496 [DOI: 10.1002/wat2.1496http://dx.doi.org/10.1002/wat2.1496]
Bornmann L and Mutz R. 2015. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11): 2215-2222 [DOI: 10.1002/asi.23329http://dx.doi.org/10.1002/asi.23329]
Ceccherini G, Duveiller G, Grassi G, Lemoine G, Avitabile V, Pilli R and Cescatti A. 2020. Abrupt increase in harvested forest area over Europe after 2015. Nature, 583(7814): 72-77 [DOI: 10.1038/s41586-020-2438-yhttp://dx.doi.org/10.1038/s41586-020-2438-y]
Chaussard E and Kerosky S. 2016. Characterization of black sand mining activities and their environmental impacts in the Philippines using remote sensing. Remote Sensing, 8(2): 100 [DOI: 10.3390/rs8020100http://dx.doi.org/10.3390/rs8020100]
Chen C M. 2006. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3): 359-377 [DOI: 10.1002/asi.20317http://dx.doi.org/10.1002/asi.20317]
Chen C M, Hu Z G, Liu S B and Tseng H. 2012a. Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace. Expert Opinion on Biological Therapy, 12(5): 593-608 [DOI: 10.1517/14712598.2012.674507http://dx.doi.org/10.1517/14712598.2012.674507]
Chen Y, Chen C M, Liu Z Y, Hu Z G and Wang X W. 2015. The methodology function of CiteSpace mapping knowledge domains. Studies in Science of Science, 33(2): 242-253
陈悦, 陈超美, 刘则渊, 胡志刚, 王贤文. 2015. CiteSpace知识图谱的方法论功能. 科学学研究, 33(2): 242-253 [DOI: 10.16192/j.cnki.1003-2053.2015.02.009http://dx.doi.org/10.16192/j.cnki.1003-2053.2015.02.009]
Chen Y C, Yeh H Y, Wu J C, Haschler I, Chen T J and Wetter T. 2011. Taiwan’s national health insurance research database: administrative health care database as study object in bibliometrics. Scientometrics, 86(2): 365-380 [DOI: 10.1007/s11192-010-0289-2http://dx.doi.org/10.1007/s11192-010-0289-2]
Chen Z Q, Chen N C, Yang C and Di L P. 2012b. Cloud computing enabled web processing service for earth observation data processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6): 1637-1649 [DOI: 10.1109/JSTARS.2012.2205372http://dx.doi.org/10.1109/JSTARS.2012.2205372]
Cui X T, Guo X Y, Wang Y D, Wang X L, Zhu W H, Shi J H, Lin C Y and Gao X. 2019. Application of remote sensing to water environmental processes under a changing climate. Journal of Hydrology, 574: 892-902 [DOI: 10.1016/j.jhydrol.2019.04.078http://dx.doi.org/10.1016/j.jhydrol.2019.04.078]
Daim T U, Rueda G, Martin H and Gerdsri P. 2006. Forecasting emerging technologies: use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8): 981-1012 [DOI: 10.1016/j.techfore.2006.04.004http://dx.doi.org/10.1016/j.techfore.2006.04.004]
Deng M X, Di L P, Han W G, Yagci A L, Peng C M and Heo G. 2013. Web-service-based monitoring and analysis of global agricultural drought. Photogrammetric Engineering and Remote Sensing, 79(10): 929-943 [DOI: 10.14358/PERS.79.10.929http://dx.doi.org/10.14358/PERS.79.10.929]
DeVries B, Huang C Q, Armston J, Huang W L, Jones J W and Lang M W. 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240: 111664 [DOI: 10.1016/j.rse.2020.111664http://dx.doi.org/10.1016/j.rse.2020.111664]
Dong J W, Kuang W H and Liu J Y. 2017. Continuous land cover change monitoring in the remote sensing big data era. Science China Earth Sciences, 60(12): 2223-2224
董金玮, 匡文慧, 刘纪远. 2018. 遥感大数据支持下的全球土地覆盖连续动态监测. 中国科学: 地球科学, 48(2): 259-260 [DOI: 10.1007/s11430-017-9143-3http://dx.doi.org/10.1007/s11430-017-9143-3]
Donthu N, Kumar S and Pattnaik D. 2020. Forty-five years of Journal of Business Research: a bibliometric analysis. Journal of Business Research, 109: 1-14 [DOI: 10.1016/j.jbusres.2019.10.039http://dx.doi.org/10.1016/j.jbusres.2019.10.039]
Fang Y, Yin J and Wu B H. 2018. Climate change and tourism: a scientometric analysis using CiteSpace. Journal of Sustainable Tourism, 26(1): 108-126 [DOI: 10.1080/09669582.2017.1329310http://dx.doi.org/10.1080/09669582.2017.1329310]
Fu D J, Xiao H, Su F Z, Zhou C H, Dong J W, Zeng Y L, Yan K, Li S W, Wu J, Wu W Z and Yan F Q. 2021. Remote sensing cloud computing platform development and Earth science application. National Remote Sensing Bulletin, 25(1): 220-230
付东杰, 肖寒, 苏奋振, 周成虎, 董金玮, 曾也鲁, 闫凯, 李世卫, 吴进, 吴文周, 颜凤芹. 2021. 遥感云计算平台发展及地球科学应用. 遥感学报, 25(1): 220-230 [DOI: 10.11834/jrs.20210447http://dx.doi.org/10.11834/jrs.20210447]
Fu K, Sun X, Qiu X L, Diao W H, Yan Z Y, Huang L J and Yu H F. 2021. Multi-satellite integrated processing and analysis method under remote sensing big data. National Remote Sensing Bulletin, 25(3): 691-707
付琨, 孙显, 仇晓兰, 刁文辉, 闫志远, 黄丽佳, 于泓峰. 2021. 遥感大数据条件下多星一体化处理与分析. 遥感学报, 25(3): 691-707 [DOI: 10.11834/jrs.20211058http://dx.doi.org/10.11834/jrs.20211058]
Giachetta R. 2015. A framework for processing large scale geospatial and remote sensing data in MapReduce environment. Computers and Graphics, 49: 37-46 [DOI: 10.1016/j.cag.2015.03.003http://dx.doi.org/10.1016/j.cag.2015.03.003]
Gingras Y. 2010. Revisiting the “Quiet Debut” of the Double Helix: A bibliometric and methodological note on the “Impact” of scientific publications. Journal of the History of Biology, 43(1): 159-181 [DOI: 10.1007/s10739-009-9183-2http://dx.doi.org/10.1007/s10739-009-9183-2]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-Century forest cover change. Science, 342(6160): 850-853 [DOI: 10.1126/science.1244693http://dx.doi.org/10.1126/science.1244693]
Hou J H and Hu Z G. 2013. Review on the application of CiteSpace at home and abroad. Journal of Modern Information, 33(4): 99-103
侯剑华, 胡志刚. 2013. CiteSpace软件应用研究的回顾与展望. 现代情报, 33(4): 99-103 [DOI: 10.3969/j.issn.1008-0821.2013.04.022http://dx.doi.org/10.3969/j.issn.1008-0821.2013.04.022]
Hu K, Qi K L, Guan Q F, Wu C Q, Yu J M, Qing Y, Zheng J, Wu H Y and Li X. 2017. A scientometric visualization analysis for night-time light remote sensing research from 1991 to 2016. Remote Sensing, 9(8): 802 [DOI: 10.3390/rs9080802http://dx.doi.org/10.3390/rs9080802]
Kessler M M. 1963. Bibliographic coupling between scientific papers. American Documentation, 14(1): 10-25 [DOI: 10.1002/asi.5090140103http://dx.doi.org/10.1002/asi.5090140103]
Kumar L and Mutanga O. 2018. Google earth engine applications since inception: usage, trends, and potential. Remote Sensing, 10(10): 1509 [DOI: 10.3390/rs10101509http://dx.doi.org/10.3390/rs10101509]
Lewis A, Oliver S, Lymburner L, Evans B, Wyborn L, Mueller N, Raevksi G, Hooke J, Woodcock R, Sixsmith J, Wu W J, Tan P, Li F Q, Killough B, Minchin S, Roberts D, Ayers D, Bala B, Dwyer J, Dekker A, Dhu T, Hicks A, Ip A, Purss M, Richards C, Sagar S, Trenham C, Wang P and Wang L W. 2017. The Australian Geoscience Data Cube—Foundations and lessons learned. Remote Sensing of Environment, 202: 276-292 [DOI: 10.1016/j.rse.2017.03.015http://dx.doi.org/10.1016/j.rse.2017.03.015]
Li D R, Zhang L P and Xia G S. 2014. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
李德仁, 张良培, 夏桂松 2014. 遥感大数据自动分析与数据挖掘. 测绘学报, 43: 1211-1216 [DOI: 10.13485/j.cnki.11-2089.2014.0187http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0187]
Li L, Liu Y, Zhu H H, Ying S, Luo Q Y, Luo H, Kuai X, Xia H and Shen H. 2017. A bibliometric and visual analysis of global geo-ontology research. Computers and Geosciences, 99: 1-8 [DOI: 10.1016/j.cageo.2016.10.006http://dx.doi.org/10.1016/j.cageo.2016.10.006]
Li L and Zhu Q H. 2008. An empirical study of coauthorship analysis using social network analysis. Information Science, 26(4): 549-555
李亮, 朱庆华. 2008. 社会网络分析方法在合著分析中的实证研究. 情报科学, 26(4): 549-555 [DOI: 10.3969/j.issn.1007-7634.2008.04.017http://dx.doi.org/10.3969/j.issn.1007-7634.2008.04.017]
Liu C L and Gui Q C. 2016. Mapping intellectual structures and dynamics of transport geography research: a scientometric overview from 1982 to 2014. Scientometrics, 109(1): 159-184 [DOI: 10.1007/s11192-016-2045-8http://dx.doi.org/10.1007/s11192-016-2045-8]
Liu J, Wang W and Zhong H. 2020. EarthDataMiner: a cloud-based big earth data intelligence analysis platform. IOP Conference Series: Earth and Environmental Science, 509(1): 012032 [DOI: 10.1088/1755-1315/509/1/012032http://dx.doi.org/10.1088/1755-1315/509/1/012032]
Liu X P, Hu G H, Chen Y M, Li X, Xu X C, Li S Y, Pei F S and Wang S J. 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine platform. Remote Sensing of Environment, 209: 227-239 [DOI: 10.1016/j.rse.2018.02.055http://dx.doi.org/10.1016/j.rse.2018.02.055]
Ma Y, Wu H P, Wang L Z, Huang B, Ranjan R, Zomaya A and Jie W. 2015. Remote sensing big data computing: challenges and opportunities. Future Generation Computer Systems, 51: 47-60 [DOI: 10.1016/j.future.2014.10.029http://dx.doi.org/10.1016/j.future.2014.10.029]
Masocha M, Dube T, Mpofu N T and Chimunhu S. 2018. Accuracy assessment of modis active fire products in southern african savannah woodlands. African Journal of Ecology, 56(3): 563-571 [DOI: 10.1111/aje.12494http://dx.doi.org/10.1111/aje.12494]
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]
Reddington C L, Butt E W, Ridley D A, Artaxo P, Morgan W T, Coe H and Spracklen D V. 2015. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nature Geoscience, 8(10): 768-771 [DOI: 10.1038/ngeo2535http://dx.doi.org/10.1038/ngeo2535]
Schulz K, Hänsch R and Sörgel U. 2018. Machine learning methods for remote sensing applications: an overview//Proceedings Volume 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX. Berlin, Germany: SPIE [DOI: 10.1117/12.2503653http://dx.doi.org/10.1117/12.2503653]
Small H. 1973. Co-citation in the scientific literature: a new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4): 265-269 [DOI: 10.1002/asi.4630240406http://dx.doi.org/10.1002/asi.4630240406]
Smith J M and Kerbache L. 2017. Topological network design of closed finite capacity supply chain networks. Journal of Manufacturing Systems, 45: 70-81 [DOI: 10.1016/j.jmsy.2017.08.001http://dx.doi.org/10.1016/j.jmsy.2017.08.001]
Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S and Brisco B. 2020. Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152-170 [DOI: 10.1016/j.isprsjprs.2020.04.001http://dx.doi.org/10.1016/j.isprsjprs.2020.04.001]
Van Raan A F J. 2014. Advances in bibliometric analysis: research performance assessment and science mapping//Bibliometrics: Use and Abuse in the Review of Research Performance. London: Portland Press Limited: 17-28
Wang L, Diao C Y, Xian G, Yin D M, Lu Y, Zou S Y and Erickson T A. 2020. A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248: 112002 [DOI: 10.1016/j.rse.2020.112002http://dx.doi.org/10.1016/j.rse.2020.112002]
Wei R B. 2009. An empirical study of keywords network analysis using social network analysis. Journal of Intelligence, 28(9): 46-49
魏瑞斌. 2009. 社会网络分析在关键词网络分析中的实证研究. 情报杂志, 28(9): 46-49 [DOI: 10.3969/j.issn.1002-1965.2009.09.010http://dx.doi.org/10.3969/j.issn.1002-1965.2009.09.010]
Wolfe A W. 1995. Social network analysis: methods and applications. Contemporary Sociology, 91(435): 219-220
Wu B F, Tian F Y, Zhang M, Zeng H W and Zeng Y. 2020. Cloud services with big data provide a solution for monitoring and tracking sustainable development goals. Geography and Sustainability, 1(1): 25-32 [DOI: 10.1016/j.geosus.2020.03.006http://dx.doi.org/10.1016/j.geosus.2020.03.006]
Wu Q S. 2020. Geemap: a Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software, 5(51): 2305 [DOI: 10.21105/JOSS.02305http://dx.doi.org/10.21105/JOSS.02305]
Xiong J, Thenkabail P S, Gumma M K, Teluguntla P, Poehnelt J, Congalton R G, Yadav K and Thau D. 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 225-244 [DOI: 10.1016/j.isprsjprs.2017.01.019http://dx.doi.org/10.1016/j.isprsjprs.2017.01.019]
Yan K, Zou D X, Yan G J, Fang H L, Weiss M, Rautiainen M, Knyazikhin Y and Myneni R B. 2021. A bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020. Journal of Remote Sensing, 2021: 7410921 [DOI: 10.34133/2021/7410921http://dx.doi.org/10.34133/2021/7410921]
Yan L, Liao X H, Zhou C H, Fan B K, Gong J Y, Cui P, Zheng Y Q and Tan X. 2019. The impact of UAV remote sensing technology on the industrial development of China: a review. Journal of Geo-Information Science, 21(4): 476-495
晏磊, 廖小罕, 周成虎, 樊邦奎, 龚健雅, 崔鹏, 郑玉权, 谭翔. 2019. 中国无人机遥感技术突破与产业发展综述. 地球信息科学学报, 21(4): 476-495 [DOI: 10.12082/dqxxkx.2019.180589http://dx.doi.org/10.12082/dqxxkx.2019.180589]
Yang C W, Goodchild M, Huang Q Y, Nebert D, Raskin R, Xu Y, Bambacus M and Fay D. 2011. Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?. International Journal of Digital Earth, 4(4): 305-329 [DOI: 10.1080/17538947.2011.587547http://dx.doi.org/10.1080/17538947.2011.587547]
Zhong S X, Qu B, Su X Y, Mao P and You X B. 2014. Progress in Chinese geography research reflected from Acta Geographica Sinica during 1934-2013: a bibliometrics analysis. Acta Geographica Sinica, 69(8): 1077-1092
钟赛香, 曲波, 苏香燕, 毛鹏, 游细斌. 2014. 从《地理学报》看中国地理学研究的特点与趋势——基于文献计量方法. 地理学报, 69(8): 1077-1092 [DOI: 10.11821/dlxb201408005http://dx.doi.org/10.11821/dlxb201408005]
Zupic I and Čater T. 2015. Bibliometric methods in management and organization. Organizational Research Methods, 18(3): 429-472 [DOI: 10.1177/1094428114562629http://dx.doi.org/10.1177/1094428114562629]
Zyoud S H and Zyoud A H. 2021. Coronavirus disease-19 in environmental fields: a bibliometric and visualization mapping analysis. Environment, Development and Sustainability, 23(6): 8895-8923 [DOI: 10.1007/s10668-020-01004-5http://dx.doi.org/10.1007/s10668-020-01004-5]
相关作者
相关机构
京公网安备11010802024621