предыдущий|следующий

Research and application of PIE-Engine Studio for spatiotemporal remote sensing cloud computing platform

  • role： First author第一作者
  • Affiliation：

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

  • Email：chengwei@piesat.cn
  • Introduction：程伟，1987年生，女，博士，研究方向为环境变化监测。E-mail： chengwei@piesat.cn
CHENG Wei,  
  • Affiliation：

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

QIAN Xiaoming,  
  • Affiliation：

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

LI Shiwei,  
  • Affiliation：

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

MA Haibo,  
  • role： Corresponding author通信作者
  • Affiliation：

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

  • Email：liudongsheng@piesat.cn
  • Introduction：刘东升，1984年生，男，高级工程师，研究方向为遥感图像处理与应用。E-mail： liudongsheng@piesat.cn
LIU Dongsheng*,  
  • Affiliation：

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

LIU Fuqian,  
  • Affiliation：

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

LIANG Junlong,  
  • Affiliation：

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

HU Ju

реферат

With the arrival of remote sensing big data era, numerous remote sensing cloud computing platforms have emerged inland and overseas to rapidly process and analyze massive remote sensing data. The emergence of remote sensing cloud computing platform makes it possible to quickly analyze and apply remote sensing data on a global scale or for longterm sequences. However, currently, there is lacking of remote sensing cloud computing platform with complete functions in domestic, while foreign remote sensing cloud computing platform has insufficient support for domestic satellite data. Based on this situation, we have independently developed a spatiotemporal remote sensing cloud computing platform, PIE (Pixel Information Expert) -Engine Studio. By adopting container cloud technology, this platform integrating data, computing power and technology, can implements on-demand acquisition of remote sensing data and rapid processing of massive data just driven by the script. (1) This study first introduced the system architecture of PIE-Engine Studio, and then described the data storage and access mode. (2) PIE-Engine Studio provides operations for multiple objects such as number, matrix, image, vector, list, dictionary, etc., also machine learning algorithms and some special satellite algorithms. (3) Furthermore, this study illustrated the calculation flow of the platform in detail. Firstly, the user writes a script in the front-end to describe the calculation process of remote sensing data. Click the “Run” button, these codes automatically build the preliminary chained structure call syntax tree. Then the syntax tree is optimized in the back-end through filter the invalid calculation content. The computing tasks are then distributed to the computing services on multiple nodes through the scheduling center. Finally, the resulting visual map layer or data file is returned to the front-end interface triggered by specific front-end requests or operators (print, addLayer, export).(4) At last, an application case is presented, we adopted Landsat 8 data and taking the calculation of Normalized Difference Vegetation Index (NDVI) in the growing season as an example, the calculation results and running time of this platform are compared with Google Earth Engine (GEE). The results show that, due to the limitation of computing resources, the running and export time of this platform are slightly longer than that of GEE, but the spatial distribution of calculation results is consistent, among which about 68% values are distributed between (0.48, 0.77), and 95.33% of the difference between the two results is concentrated between (-0.13, 0.13). It shows that the results are reliable. Therefore, the remote sensing cloud computing platform constructed by this paper, can provide data resources and computing power for research in the field of earth science, and will help promote the development of remote sensing cloud computing platform in China and the application of domestic satellite data in cloud computing platform.

ключеви́че слова́

remote sensing;big data;remote sensing cloud computing platform;distributed storage;parallel computing

References

  1. 1.
    Bastin J F, Berrahmouni N, Grainger A, Maniatis D, Mollicone D, Moore R, Patriarca C, Picard N, Sparrow B, Abraham E M, Aloui K, Atesoglu A, Attore F, Bassüllü Ç, Bey A, Garzuglia M, García-Montero L G, Groot N, Guerin G, Laestadius L, Lowe A J, Mamane B, Marchi G, Patterson P, Rezende M, Ricci S, Salcedo I, Diaz A S P, Stolle F, Surappaeva V and Castro R. 2017. The extent of forest in dryland biomes. Science, 356(6338): 635-638
  2. 2.
    Bastin J F, Finegold Y, Garcia C, Mollicone D, Rezende M, Routh D, Zohner C M and Crowther T W. 2019. The global tree restoration potential. Science, 365(6448): 76-79
  3. 3.
    Bogard M J, Kuhn C D, Johnston S E, Striegl R G, Holtgrieve G W, Dornblaser M M, Spencer R G M, Wickland K P and Butman D E. 2019. Negligible cycling of terrestrial carbon in many lakes of the arid circumpolar landscape. Nature Geoscience, 12(3): 180-185
  4. 4.
    Bureau of Industry and Security, Commerce. 2020. Addition of software specially designed to automate the analysis of geospatial imagery to the export control classification number 0Y521 series[EB/OL]. (2020-01-06)[2021-04-29]. automate-the-analysis-of-geospatial-imagery-to-the-export
  5. 5.
    Burke M and Lobell D B. 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences of the United States of America, 114(9): 2189-2194
  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.
    Chudley T R, Christoffersen P, Doyle S H, Bougamont M, Schoonman C M, Hubbard B and James M R. 2019. Supraglacial lake drainage at a fast-flowing Greenlandic outlet glacier. Proceedings of the National Academy of Sciences of the United States of America, 116(51): 25468-25477
  8. 8.
    Dong J W, Li S W, Zeng Y L, Yan K and Fu D J. 2020. Remote Sensing Cloud Computing and Scientific Analysis-Application and Practice. Beijing: Science Press: 10-18
  9. 9.
    Finer M, Novoa S, Weisse M J, Petersen R, Mascaro J, Souto T, Stearns F and Martinez R G. 2018. Combating deforestation: from satellite to intervention. Science, 360(6395): 1303-1305
  10. 10.
    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
  11. 11.
    Giezendanner J, Pasetto D, Perez-Saez J, Cerrato C, Viterbi R, Terzago S, Palazzi E and Rinaldo A. 2020. Earth and field observations underpin metapopulation dynamics in complex landscapes: near-term study on carabids. Proceedings of the National Academy of Sciences of the United States of America, 117(23): 12877-12884
  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.
    Hansen A J, Burns P, Ervin J, Goetz S J, Hansen M, Venter O, Watson J E M, Jantz P A, Virnig A L S, Barnett K, Pillay R, Atkinson S, Supples C, Rodríguez-Buritica S and Armenteras D. 2020. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nature Ecology and Evolution, 4(10): 1377-1384
  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.
    Hendershot J N, Smith J R, Anderson C B, Letten A D, Frishkoff L O, Zook J R, Fukami T and Daily G C. 2020. Intensive farming drives long-term shifts in avian community composition. Nature, 579(7799): 393-396
  16. 16.
    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
  17. 17.
    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
  18. 18.
    Li D R. 2016. Towards geo-spatial information science in big data era. Acta Geodaetica et Cartographica Sinica, 45(4): 379-384
  19. 19.
    Li D R, Zhang P L and Xia G S. 2014. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
  20. 20.
    Liu J, Wang W and Zhong H. 2020a. EarthDataMiner: a cloud-based big earth data intelligence analysis platform. IOP Conference Series: Earth and Environmental Science, 509: 012032
  21. 21.
    Liu Y L, Kumar M, Katul G G, Feng X and Konings A G. 2020b. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nature Climate Change, 10(7): 691-695
  22. 22.
    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. 2020c. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 3(7): 564-570
  23. 23.
    Moore R T and Hansen M C. 2011. Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis//Proceedings of the American Geophysical Union, Fall Meeting 2011. [s.l.]: AGU
  24. 24.
    Myers-Smith I H, Kerby J T, Phoenix G K, Bjerke J W, Epstein H E, Assmann J J, John C, Andreu-Hayles L, Angers-Blondin S, Beck P S A, Berner L T, Bhatt U S, Bjorkman A D, Blok D, Bryn A, Christiansen C T, Cornelissen J H C, Cunliffe A M, Elmendorf S C, Forbes B C, Goetz S J, Hollister R D, de Jong R, Loranty M M, Macias-Fauria M, Maseyk K, Normand S, Olofsson J, Parker T C, Parmentier F J W, Post E, Schaepman-Strub G, Stordal F, Sullivan P F, Thomas H J D, Tømmervik H, Treharne R, Tweedie C E, Walker D A, Wilmking M and Wipf S. 2020. Complexity revealed in the greening of the Arctic. Nature Climate Change, 10(2): 106-117
  25. 25.
    Nemani R R, Votava P, Michaelis A, Melton F S, Hashimoto H, Milesi C, Wang W and Ganguly S. 2010. NASA earth exchange: a collaborative earth science platform//Proceedings of the American Geophysical Union, Fall Meeting 2010. [s.l.]: AGU
  26. 26.
    Qin Y W, Xiao X M, Dong J W, Zhang Y, Wu X C, Shimabukuro Y, Arai E, Biradar C, Wang J, Zou Z H, Liu F, Shi Z, Doughty R and Moore III B. 2019. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000-2017. Nature Sustainability, 2(8): 764-772
  27. 27.
    Ryan J C, Smith L C, van As D, Cooley S W, Cooper M G, Pitcher L H and Hubbard A. 2019. Greenland Ice Sheet surface melt amplified by snowline migration and bare ice exposure. Science Advances, 5(3): (eaav3738 )
  28. 28.
    Tuckett P A, Ely J C, Sole A J, Livingstone S J, Davison B J, van Wessem J M and Howard J. 2019. Rapid accelerations of Antarctic Peninsula outlet glaciers driven by surface melt. Nature Communications, 10(1): 4311
  29. 29.
    Venter Z S, Aunan K, Chowdhury S and Lelieveld J. 2020. COVID-19 lockdowns cause global air pollution declines. Proceedings of the National Academy of Sciences of the United States of America, 117(32): 18984-18990
  30. 30.
    Walter T R, Haghighi M H, Schneider F M, Coppola D, Motagh M, Saul J, Babeyko A, Dahm T, Troll V R, Tilmann F, Heimann S, Valade S, Triyono R, Khomarudin R, Kartadinata N, Laiolo M, Massimetti F and Gaebler P. 2019. Complex hazard cascade culminating in the Anak Krakatau sector collapse. Nature Communications, 10(1): 4339
  31. 31.
    Watmough G R, Marcinko C L J, Sullivan C, Tschirhart K, Mutuo P K, Palm C A and Svenning J C. 2019. Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences of the United States of America, 116(4): 1213-1218
  32. 32.
    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
  33. 33.
    Wu X, Braun D, Schwartz J, Kioumourtzoglou M A and Dominici F. 2020. Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Science Advances, 6(29): (eaba5692 )
  34. 34.
    Yeh C, Perez A, Driscoll A, Azzari G, Tang Z Y, Lobell D, Ermon S and Burke M. 2020. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications, 11(1): 2583

Читать полностью

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