Précédent|Prochain

Benefits of Google Earth Engine in remote sensing

  • role： First author第一作者
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：2190902117@cnu.edu.cn
  • Introduction：王小娜，1997年生，女，硕士研究生，研究方向为湿地遥感。E-mail： 2190902117@cnu.edu.cn
WANG Xiaona13,  
  • role： Corresponding author通信作者
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

  • Email：tjyremote@126.com
  • Introduction：田金炎，1988年生，男，讲师，研究方向为湿地遥感、时空大数据融合。E-mail： tjyremote@126.com
TIAN Jinyan12*,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

LI Xiaojuan13,  
  • Affiliation：

    Department of Geography, The State University of New York at Buffalo, Buffalo, NY, 14261, USA

WANG Le4,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

GONG Huili2,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

CHEN Beibei1,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

LI Xiangcai1,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

GUO Jinghan3

résumé

The rapid development of remote sensing technology has enabled accumulation of massive earth observation data in recent years. Traditional desktop-based remote sensing platforms (e.g., ERDAS and ENVI) cannot satisfy the current application requirements of remote sensing big data. Google Earth Engine (GEE), as a leading cloud-based remote sensing platform, has not only changed the traditional processing and analysis means of data but also brought new opportunities for the rapid processing and information mining of massive data. To date, many researchers have successfully conducted a large number of works and published many academic papers and reviews based on GEE. However, no one has systematically analyzed how GEE promotes the development of remote sensing science. Therefore, this study aims to explore the innovative changes caused by GEE in the aspect of resources, methods, and applications, compared with the traditional desktop-based platform. (1) In terms of resources, GEE breaks the separation of traditional data, model algorithms, and computing power, realizes cloud deployment of the three, and shows great potential in the rapid processing and analysis of massive data through combining big data and cloud computing together. (2) With regard of methods, innovative remote sensing analysis methods provided by GEE break through the bottleneck of traditional remote sensing technology and promote the technological innovation of data processing and analysis. Thus, the efficiency of massive data processing and information mining greatly improves. (3) In the perspective of applications, GEE not only brings development opportunities for the rapid global-scale long-time analysis but also promotes the rapid sharing of data, algorithms, and products, which further ushers in the era of opening and sharing remote sensing. Systematically summarizing the advantages of GEE will not only help the potential new users understand GEE as well as deepen the existing users’ understanding but also encourage Google developers to perfect and improve GEE while catalyzing new discoveries in the scientific research of the earth system.

mots-clés

Google Earth Engine;cloud-based remote sensing platform;desktop-based remote sensing platform;remote sensing big data;pixel-based analysis method

References

  1. 1.
    Alonso A, Muñoz-Carpena R, Kennedy R E and Murcia C. 2016. Wetland landscape spatio-temporal degradation dynamics using the new Google earth engine cloud-based platform: opportunities for non-specialists in remote sensing. Transactions of the ASABE, 59(5): 1331-1342
  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.
    Badgley G, Field C B and Berry J A. 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3): e1602244
  4. 4.
    Baumann P, Mazzetti P, Ungar J, Barbera R, Barboni D, Beccati A, Bigagli L, Boldrini E, Bruno R, Calanducci A, Campalani P, Clements O, Dumitru A, Grant M, Herzig P, Kakaletris G, Laxton J, Koltsida P, Lipskoch K, Mahdiraji A R, Mantovani S, Merticariu V, Messina A, Misev D, Natali S, Nativi S, Oosthoek J, Pappalardo M, Passmore J, Rossi A P, Rundo F, Sen M, Sorbera V, Sullivan D, Torrisi M, Trovato L, Veratelli M G and Wagner S. 2016. Big Data Analytics for Earth Sciences: the EarthServer approach. International Journal of Digital Earth, 9(1): 3-29
  5. 5.
    Bey A, Jetimane J, Lisboa S N, Ribeiro N, Sitoe A and Meyfroidt P. 2020. Mapping smallholder and large-scale cropland dynamics with a flexible classification system and pixel-based composites in an emerging frontier of Mozambique. Remote Sensing of Environment, 239: 111611
  6. 6.
    DeLancey E R, Simms J F, Mahdianpari M, Brisco B, Mahoney C and Kariyeva J. 2019. Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada. Remote Sensing, 12(1): 2
  7. 7.
    Dong J W, Li S W, Zeng Y L, Yan K and Fu D J. 2020. Cloud Computing in Remote Sensing and Science Analysis—Application and Practice. Beijing: Science Press
  8. 8.
    Dong J W, Xiao X M, Menarguez M A, Zhang G L, Qin Y W, Thau D, Biradar C and Moore B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185: 142-154
  9. 9.
    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
  10. 10.
    Gong P, Li X C, Wang J, Bai Y Q, Chen B, Hu T Y, Liu X P, Xu B, Yang J, Zhang W and Zhou Y Y. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236: 111510
  11. 11.
    Goodchild M F, Guo H D, Annoni A, Bian L, De Bie K, Campbell F, Craglia M, Ehlers M, Van Genderen J, Jackson D, Lewis A J, Pesaresi M, Remetey-Fülöpp G, Simpson R, Skidmore A, Wang C L and Woodgate P. 2012. Next-generation digital earth. Proceedings of the National Academy of Sciences of the United States of America, 109(28): 11088-11094
  12. 12.
    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
  13. 13.
    Guo H D. 2014. Big data, big science, big discovery—review of Codata workshop on big data for international scientific programmes. Bulletin of Chinese Academy of Sciences, 29(4): 500-506
  14. 14.
    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
  15. 15.
    Hao B F, Han X J, Ma M G, Liu Y T and Li S W. 2018. Research Progress on the Application of Google Earth Engine in Geoscience and environmental Sciences. Remote Sensing Technology and Application, 33(4): 600-611
  16. 16.
    He G J, Wang G Z, Long T F, Peng Y, Jiang W, Yin R Y, Jiao W L and Zhang Z M. 2018. Opening and sharing of big earth observation data: challenges and countermeasures. Bulletin of Chinese Academy of Sciences, 33(8): 783-790
  17. 17.
    Ielpi A and Lapôtre M G A. 2020. A tenfold slowdown in river meander migration driven by plant life. Nature Geoscience, 13(1): 82-86
  18. 18.
    Jia M M, Wang Z M, Mao D H, Ren C Y, Wang C and Wang Y Q. 2021. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google earth engine. Remote Sensing of Environment, 255: 112285
  19. 19.
    Johnson D M. 2019. Using the Landsat archive to map crop cover history across the United States. Remote Sensing of Environment, 232: 111286
  20. 20.
    Joshi A R, Dinerstein E, Wikramanayake E, Anderson M L, Olson D, Jones B S, Seidensticker J, Lumpkin S, Hansen M C, Sizer N C, Davis C L, Palminteri S and Hahn N R. 2016. Tracking changes and preventing loss in critical tiger habitat. Science Advances, 2(4): e1501675
  21. 21.
    Kumar L and Mutanga O. 2018. Google earth engine applications since inception: usage, trends, and potential. Remote Sensing, 10(10): 1509
  22. 22.
    Laskin D N, McDermid G J, Nielsen S E, Marshall S J, Roberts D R and Montaghi A. 2019. Advances in phenology are conserved across scale in present and future climates. Nature Climate Change, 9(5): 419-425
  23. 23.
    Li D R. 2016. Towards geo-spatial information science in big data era. Acta Geodaetica et Cartographica Sinica, 45(4): 379-384
  24. 24.
    Li D R. 2018. Brain cognition and spatial cognition: on integration of geo-spatial big data and artificial intelligence. Geomatics and information Science of Wuhan University, 43(12): 1761-1767
  25. 25.
    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
  26. 26.
    Liu X P, Huang Y H, Xu X C, Li X C, Li X, Ciais P, Lin P R, Gong K, Ziegler A D, Chen A P, Gong P, Chen J, Hu G H, Chen Y M, Wang S J, Wu Q S, Huang K N, Estes L and Zeng Z Z. 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 3(7): 564-570
  27. 27.
    MacDonald A J and Mordecai E A. 2020. Correction for MacDonald and Mordecai, Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing. Proceedings of the National Academy of Sciences of the United States of America, 117(33): 20335-20335
  28. 28.
    Maggiori E, Tarabalka Y, Charpiat G and Alliez P. 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 645-657
  29. 29.
    Mueller N, Lewis A, Roberts D, Ring S, Melrose R, Sixsmith J, Lymburner L, McIntyre A, Tan P, Curnow S and Ip A. 2016. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174: 341-352
  30. 30.
    Murray N J, Phinn S R, DeWitt M, Ferrari R, Johnston R, Lyons M B, Clinton N, Thau D and Fuller R A. 2019. The global distribution and trajectory of tidal flats. Nature, 565(7738): 222-225
  31. 31.
    Mutanga O and Kumar L. 2019. Google earth engine applications. Remote Sensing, 11(5): 591
  32. 32.
    Ordway E M, Naylor R L, Nkongho R N and Lambin E F. 2019. Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nature Communications, 10: 114
  33. 33.
    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
  34. 34.
    Phan T N, Kuch V and Lehnert L W. 2020. Land cover classification using google earth engine and random forest classifier—the role of image composition. Remote Sensing, 12(15): 2411
  35. 35.
    Richards D R and Belcher R N. 2019. Global changes in urban vegetation cover. Remote Sensing, 12(1): 23
  36. 36.
    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
  37. 37.
    Tian J Y, Wang L, Li X J, Gong H L, Shi C, Zhong R F and Liu X M. 2017. Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. International Journal of Applied Earth Observation and Geoinformation, 61: 22-31
  38. 38.
    Tian J Y, Wang L, Yin D M, Li X J, Diao C Y, Gong H L, Shi C, Menenti M, Ge Y, Nie S, Ou Y, Song X N and Liu X M. 2020. Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion. Remote Sensing of Environment, 242: 111745
  39. 39.
    Wang L, Diao C Y, Xian G, Yin D M, Lu Y, Zou S Y and Erickson T A. 2020a. A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248: 112002
  40. 40.
    Wang L, Xu M, Liu Y, Liu H X, Beck R, Reif M, Emery E, Young J and Wu Q S. 2020b. Mapping freshwater chlorophyll-a concentrations at a regional scale integrating multi-sensor satellite observations with Google earth engine. Remote Sensing, 12(20): 3278
  41. 41.
    Wang L Z, Yan J N and Yan M. 2019. Cloud Computing in Remote Sensing. Boca Raton: Chapman and Hall/CRC
  42. 42.
    Weiss D J, Nelson A, Gibson H S, Temperley W, Peedell S, Lieber A, Hancher M, Poyart E, Belchior S, Fullman N, Mappin B, Dalrymple U, Rozier J, Lucas T C D, Howes R E, Tusting L S, Kang S Y, Cameron E, Bisanzio D, Battle K E, Bhatt S and Gething P W. 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688): 333-336
  43. 43.
    Wu B F, Gao F, He G J, Zhang N and Zeng H W. 2016. Big data on global changes: Data sharing platform and recognition. Journal of Remote Sensing, 20(6): 1479-1484
  44. 44.
    Wu Q S. 2020. Geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software, 5(51): 2305
  45. 45.
    Wulder M A and Coops N C. 2014. Satellites: Make Earth observations open access. Nature, 513(7516): 30-31
  46. 46.
    Xiong J, Thenkabail P S, Tilton J C, Gumma M K, Teluguntla P, Oliphant A, Congalton R G, Yadav K and Gorelick N. 2017. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using sentinel-2 and landsat-8 data on Google earth engine. Remote Sensing, 9(10): 1065
  47. 47.
    Yin D M, Cao X, Chen X H, Shao Y J and Chen J. 2013. Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery. International Journal of Remote Sensing, 34(19): 6529-6538
  48. 48.
    Zhao Z M, Gao L R, Chen D, Yue A Z, Chen J B, Liu D S, Yang J and Meng Y. 2019. Development of satellite remote sensing and image processing platform. Journal of Image and Graphics, 24(12): 2098-2110
  49. 49.
    Zhou Y and Dong J W. 2019. Review on monitoring open surface water body using remote sensing. Journal of Geo-information Science, 21(11): 1768-1778

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website