The study is focused on identifying potential areas of wildfire hazard in Ukraine using the Google Earth Engine (GEE) cloud platform, which provides processing of large volumes of satellite and geospatial data. The relevance of the topic is due to the increased risk of wildfires caused by climate change, anthropogenic impact, and the consequences of full-scale war, which pose a threat to natural ecosystems and biodiversity. The study did not distinguish between types of fires, but covered the natural forest and steppe ecosystems characteristic of Ukraine. The methodology involved determining the fire-hazardous period (April–November) and forming an integrated fire hazard index based on six key factors: surface temperature, precipitation, soil moisture, vegetation moisture, wind speed, and slope. The eight open satellite datasets from the GEE catalogue cover the period from 2003 to 2024. The datasets were processed by reclassifying the index values and generating thematic maps. The results showed that the highest level of wildfire hazard is observed in the south and east of Ukraine, while the mountainous regions of the Carpathians and northern regions have a lower level of risk. The data obtained are consistent with MODIS archival satellite observations of fire areas. The study can aid in planning preventive fire safety measures in areas with higher risk.
Chen, Y., Morton, D. C., & Randerson, J. T. (2024). Remote sensing for wildfire monitoring: Insights into burned area, emissions, and fire dynamics. One Earth, 7(6), 1022-1028. https://doi.org/10.1016/j.oneear.2024.05.014
Cinar, T., & Aydin, A. (2023). Exploring the potential of the Google Earth Engine (GEE) platform for analysing forest disturbance patterns with big data. Earth Sciences Research Journal, 27(4), 437-448. https://doi.org/10.15446/esrj.v27n4.110128
Costa-Saura, J. M., Bacciu, V., Ribotta, C., Spano, D., Massaiu, A., & Sirca, C. (2022). Predicting and mapping potential fire severity for risk analysis at regional level using Google Earth engine. Remote Sensing, 14(19), 4812. https://doi.org/10.3390/rs14194812
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Jodhani, K. H., Patel, H., Soni, U., Patel, R., Valodara, B., Gupta, N., ... & Omar, P. J. (2024). Assessment of forest fire severity and land surface temperature using Google Earth Engine: A case study of Gujarat State, India. Fire Ecology, 20(1), 23. http://dx.doi.org/10.1186/s42408-024-00254-2
Karpinskyi, Y., Lyashchenko, A., Lazorenko-Hevel, N., Cherin, A., Kin, D., & Havryliuk, Y. (2021). Main state topographic map: Structure and principles of the creation A database. Paper presented at the 20th International Conference Geoinformatics: Theoretical and Applied Aspects, https://doi.org/10.3997/2214-4609.20215521043
Kin, D., Lazorenko, N., Karpinskyi, Y., Lyashchenko, A., Pliushch, T., & Pomortseva, О. (2025). Using Remote Sensing to Detect Destroyed Urban Landscapes for Their Future Restoration. Journal of Digital Landscape Architecture, 339-348. http://dx.doi.org/10.14627/537754032
Matsala, M., Odruzhenko, A., Hinchuk, T. et al. (2024). War drives forest fire risks and highlights the need for more ecologically-sound forest management in post-war Ukraine. SciRep, 14, 4131. https://doi.org/10.1038/s41598-024-54811-5
Mohammed, Khan, A., Kuri, A., Ahammed, S., Al Muqtadir Abir, K., & Arfin-Khan, M. A. (2025). A google earth engine approach for anthropogenic forest fire assessment with remote sensing data in Rema-Kalenga wildlife sanctuary, Bangladesh. Geology, Ecology, and Landscapes, 9(1), 45-66. https://doi.org/10.1080/24749508.2023.2165297
Piao, Y., Lee, D., Park, S., Kim, H. G., & Jin, Y. (2022). Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea. Geomatics, Natural Hazards and Risk, 13(1), 432-450. https://doi.org/10.1080/19475705.2022.2030808
dos Santos, S. M. B., Duverger, S. G., Bento-Gonçalves, A., Franca-Rocha, W., Vieira, A., & Teixeira, G. (2023). Remote sensing applications for mapping large wildfires based on machine learning and time series in northwestern Portugal. Fire, 6(2), 43. https://doi.org/10.3390/fire6020043
Tavakkoli Piralilou, S., Einali, G., Ghorbanzadeh, O., Nachappa, T. G., Gholamnia, K., Blaschke, T., & Ghamisi, P. (2022). A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions. Remote sensing, 14(3), 672. https://doi.org/10.3390/rs14030672