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Bibliometric visualization analysis related to remote sensing cloud computing platforms

  • role： First author第一作者
  • Affiliation：

    School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China

  • Email：kaiyan@cugb.edu.cn
  • Introduction：闫凯，1988 年生，男，副教授，研究方向为植被定量遥感模型、遥感产品业务化生产与分析。E-mail：kaiyan@cugb.edu.cn
YAN Kai1,  
  • Affiliation：

    School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China

CHEN Huimin1,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

FU Dongjie25,  
  • Affiliation：

    Department of Global Ecology, Carnegie Institution for Science, Stanford CA 94305, USA

ZENG Yelu3,  
  • Affiliation：

    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

DONG Jinwei45,  
  • Affiliation：

    Beijing Piesat Information Technology Co., Ltd., Beijing 100195, China

LI Shiwei6,  
  • Affiliation：

    Department of Geography, University of Tennessee, Knoxville, TN 37996, United States

WU Qiusheng7,  
  • Affiliation：

    School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China

LI Hanliang1,  
  • Affiliation：

    School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China

DU Shuyuan1

résumé

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.

mots-clés

bibliometric;visualisation;remote sensing;big data;remote sensing cloud computing platform

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    Giachetta R. 2015. A framework for processing large scale geospatial and remote sensing data in MapReduce environment. Computers and Graphics, 49: 37-46
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    Kessler M M. 1963. Bibliographic coupling between scientific papers. American Documentation, 14(1): 10-25
  29. 29.
    Kumar L and Mutanga O. 2018. Google earth engine applications since inception: usage, trends, and potential. Remote Sensing, 10(10): 1509
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    Li L and Zhu Q H. 2008. An empirical study of coauthorship analysis using social network analysis. Information Science, 26(4): 549-555
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    Smith J M and Kerbache L. 2017. Topological network design of closed finite capacity supply chain networks. Journal of Manufacturing Systems, 45: 70-81
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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
  47. 47.
    Wei R B. 2009. An empirical study of keywords network analysis using social network analysis. Journal of Intelligence, 28(9): 46-49
  48. 48.
    Wolfe A W. 1995. Social network analysis: methods and applications. Contemporary Sociology, 91(435): 219-220
  49. 49.
    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
  50. 50.
    Wu Q S. 2020. Geemap: a Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software, 5(51): 2305
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    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
  56. 56.
    Zupic I and Čater T. 2015. Bibliometric methods in management and organization. Organizational Research Methods, 18(3): 429-472
  57. 57.
    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

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