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Current Advances on Cloud-Based Distributed Computing for Forest Monitoring

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Information and Communication Technologies and Sustainable Development (ICT&SD 2022)


One of the most important tasks related to environmental protection is forests monitoring. Meanwhile, specialists deal with the problem of big data and the need to utilize powerful computing resources that are not always available. Cloud solutions (CREODIAS, Google Earth Engine, etc.) provide instant satellite data access and the ability to quickly and conveniently process geospatial data in the cloud and use it to search for information products. Forest monitoring is supported by the European Commission (EU project SWIFTT), the World Wildlife Fund and others. This work analyzes Sentinel-2 satellite spectral channels, which distribution of pixel values was constructed for diseased and healthy forests, and the possibility of separating these two classes was analyzed based on the Bhattacharya distance. The informativeness of time series application of the normalized difference vegetation index (NDVI) was analyzed. The assumption that the average value of NDVI decreases and the standard deviation increases when the forest changes is confirmed. Getting results for large areas will lead to a big data problem. Therefore, the structure of the pilot information system is proposed as the basis for a further cloud solution with the development of a machine (deep) learning model for forest monitoring in any territory (including Ukraine). This system allows monitoring forests dynamics based on time series of satellite data at the country level and worldwide. This will be an important step for Ukraine as a potential member of the EU in the field of providing information services and monitoring the most sensitive natural resources.

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  1. Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., et al.: ESA WorldCover 10 m 2020 v100. (2021).

  2. Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., et al.: ESA WorldCover 10 m 2021 v200. (2021).

  3. Hansen, M., Potapov, P., Moore, R., et al.: High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342, 850–853 (2013). Data available online from:, last accessed 2023/05/27

  4. Buchhorn, M., Lesiv, M., Tsendbazar, N., et al.: Copernicus Global Land Cover Layers-Collection 2. Remote Sensing 108, 1044 (2020).

    Article  Google Scholar 

  5. Giannetti, F., Barbati, A., Mancini, L., et al.: European forest types: toward an automated classification. Ann. For. Sci. 75(1), 1–14 (2018)

    Article  Google Scholar 

  6. Huo, L., Persson, H.J., Lindberg, E.: Early detection of forest stress from european spruce bark beetle attack, and a new vegetation index: normalized distance red & SWIR (NDRS). Remote Sens. Environ. 255, 112240 (2021)

    Article  Google Scholar 

  7. Google Earth Engine. Retrieved from, last accessed 2023/05/27

  8. Kussul, N., Lemoine, G., Gallego, J., Skakun, S., Lavreniuk, M.: Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 165–168 (2015)

    Google Scholar 

  9. Kussul, N., Shelestov, A., Basarab, R., Skakun, S., Kussul, O., Lavrenyuk, M.: Geospatial Intelligence and Data Fusion Techniques for Sustainable Development Problems. ICTERI 1356, 196–203 (2015)

    Google Scholar 

  10. European Space Agency. MultiSpectral Instrument (MSI). Retrieved from, last accessed 2023/05/27

  11. SENTINEL-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. European Space Agency. ESA SP-1322/2, March 2012

    Google Scholar 

  12. S2 MPC Level-1 Algorithm Theoretical Bases Document. European Space Agency, 2023. Retrieved from, last accessed 2023/05/27

  13. European Space Agency. 6. Passive sensors. Retrieved from

  14. Huang, S., Tang, L., Hupy, J.P., Wang, Y., Shao, G.: A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32(1), 1–6 (2021)

    Article  Google Scholar 

  15. Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., Joergensen, R.G.: Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosys. Eng. 129, 341–351 (2015)

    Article  Google Scholar 

  16. Xu, H.: Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27(14), 3025–3033 (2006)

    Article  Google Scholar 

  17. Ill, J.E.P., McLeod, K.W.: Indications of relative drought stress in longleaf pine from thematic mapper data. Photogramm. Eng. Remote Sens. 65(4), 495–501 (1999)

    Google Scholar 

  18. Barnes, E. M., Clarke, T. R., Richard, S., et al.: Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 1619, 6 (2000, July)

    Google Scholar 

  19. Louhaichi, M., Borman, M., Johnson, D.: Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int. 16(1), 65–70 (2001)

    Article  Google Scholar 

  20. Gao, B.C.: NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58(3), 257–266 (1996)

    Article  Google Scholar 

  21. Galvao, L.S., Formaggio, A.R., Tisot, D.A.: Discrimination of sugarcane varieties in southeastern brazil with EO-1 hyperion data. Remote Sens. Environ. 94(4), 523–534 (2005)

    Article  Google Scholar 

  22. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by probability distributions. Indian J. Stat. 7(4), 1933–1960 (1946)

    Google Scholar 

  23. Kailath, T.: The divergence and bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15(1), 52–60 (1967).

    Article  Google Scholar 

  24. Keinosuke, F.: Introduction to Statistical Pattern Recognition (2nd ed.). Academic Press. ISBN 978–0–12–269851–4 (1990)

    Google Scholar 

  25. Wilson, N.R., Norman, L.M.: Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI). Int. J. Remote Sens. 39(10), 3243–3274 (2018)

    Article  Google Scholar 

  26. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., Skakun, S.: Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci. 5, 17 (2017)

    Article  Google Scholar 

  27. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., Skakun, S.: Large-scale crop classification using Google Earth Engine platform. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3696–3699 (2017)

    Google Scholar 

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The authors acknowledge the funding received by the project SWIFTT - Satellites for Wilderness Inspection and Forest Threat Tracking – funded by the European Union under Grant Agreement 101082732, and National research foundation of Ukraine project No. 2020.02/0284 «Geospatial models and information technologies of satellite monitoring of smart city problems».

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Correspondence to Andrii Shelestov .

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Shelestov, A., Salii, Y., Hordiiko, N., Yailymova, H. (2023). Current Advances on Cloud-Based Distributed Computing for Forest Monitoring. In: Dovgyi, S., Trofymchuk, O., Ustimenko, V., Globa, L. (eds) Information and Communication Technologies and Sustainable Development. ICT&SD 2022. Lecture Notes in Networks and Systems, vol 809. Springer, Cham.

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