Remote assessment of shelterbelt conditions after military actions
DOI:
https://doi.org/10.36023/ujrs.2025.12.2.281Keywords:
shelterbelts, post-war restoration, damage assessment, remote sensing, Sentinel-2, EVI, NDMI, S2REPAbstract
Shelterbelts are an essential element for sustainable land use, providing ecological functions such as soil erosion prevention, maintaining water balance, mitigating the effects of dry winds and dust storms, and supporting biodiversity. However, the war in Ukraine caused significant damage, or even destruction, to the shelterbelts. Since ground-based surveys are dangerous due to military actions and cannot effectively assess large territories, remote sensing is the only viable option. Thus, the aim of this study is the remote assessment of shelterbelt conditions after military actions. The given approach should provide information about shelterbelt conditions, including their damage level, which can be used for post-war restoration. In this paper, the shelterbelt conditions assessment is performed based on the three spectral indices evaluated upon the Sentinel-2 satellite's data, namely biomass (EVI), chlorophyll content (S2REP) and moisture content (NDMI). Additionally, a fused map of shelterbelt conditions is created using these indices. The condition of shelterbelts located between the village Klynove (Bakhmut rayon) and Vuhledar's thermal power plant was remotely assessed in our experiment. From May to August 2022, this territory was in the zone of active military actions. Concerning the fused map of shelterbelt conditions, 96.61% of the study area has taken damage, as stated by at least one indicator. Most of these damages correspond to low or medium damage levels, and NDMI gave the highest percentage of high damage level (15.99%). Using high-resolution imagery from Google Earth, visual validation of the obtained maps confirmed the correspondence between the identified damage and its actual state. This indicates the reliability of the obtained maps. Since most damage levels are low or medium, these shelterbelts can be considered for post-war restoration. Further research may include satellite radar data, which will enable the retrieval of continuous time-series data despite the weather conditions, and classification methods for shelterbelt detection and mapping for the developed approach scaling.
Author Contributions: Conceptualization – S.A. Stankevich and A.O. Kozlova; methodology – S.A. Stankevich and A.A. Andreiev; formal analysis – A.O. Kozlova and A.A. Andreiev; investigation – A.A. Andreiev, S.I. Golubov, and A.R. Lysenko; data curation – A.A. Andreiev, S.I. Golubov, and A.R. Lysenko; writing – original draft preparation – A.A. Andreiev, S.I. Golubov and A.R. Lysenko; writing – review and editing – S.I. Golubov and A.R. Lysenko; visualization – S.I. Golubov and A.R. Lysenko. All authors have read and agreed to the published version of the manuscript.
Funding: This research funded by Grant of the National Academy of Sciences of Ukraine to research laboratories/groups of young scientists of the National Academy of Sciences of Ukraine for conducting research in priority areas of science and technology (2025-2026) for the project "Development of a geoinformation toolbox for remote assessment of shelterbelts damaged by military actions".
Data Availability Statement: Data available on reasonable request from the authors.
Acknowledgments: The authors are grateful to the National Academy of Sciences of Ukraine for supporting this research. We are also grateful to the reviewers and editors for their valuable comments, recommendations, and attention to the work.
Conflicts of Interest: The authors declare no conflict of interest
References
Bhattacharya, O., Sinha, S., Mishra, V. N., Kumari, M., Hasher, F. F. B., Barman, J., & Zhran, M. (2024). Harnessing geospatial tools to map the forest fire: risk zonation in Pauri Garhwal, Uttarakhand. Results in Engineering, 103694. https://doi.org/10.1016/ j.rineng.2024.103694.
Fassnacht, F. E., White, J. C., Wulder, M. A., & Næsset, E. (2023). Remote sensing in forestry: current challenges, considerations and directions. Forestry an International Journal of Forest Research, 97(1), 11–37. https://doi.org/10.1093/forestry/cpad024.
Gao, S., Yan, K., Liu, J., Pu, J., Zou, D., Qi, J., Mu, X., & Yan, G. (2024). Assessment of remote-sensed vegetation indices for estimating forest chlorophyll concentration. Ecological Indicators, 162, 112001. https://doi.org/10.1016/j.ecolind.2024.112001.
Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis, J., … Fernandez, V. (2017). Copernicus Sentinel-2A calibration and products Validation status. Remote Sensing, 9(6), 584. https://doi.org/10.3390/rs9060584.
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.
Kędziora, A. (2015). The network of shelterbelts as an agroforestry system controlling the water resources and biodiversity in the agricultural landscape. Papers on Global Change IGBP, 22(1), 63–82. https://doi.org/10.1515/igbp-2015-0016.
Kong, T., Liu, B., Henderson, M., Zhou, W., Su, Y., Wang, S., Wang, L., & Wang, G. (2022). Effects of shelterbelt transformation on soil aggregates characterization and erodibility in China Black soil farmland. Agriculture, 12(11), 1917. https://doi.org/10.3390/agriculture12111917.
Kozlova, A., Stankevich, S., Svideniuk, M., & Andreiev, A. (2021). Quantitative Assessment of Forest Disturbance with C-Band SAR Data for Decision Making Support in Forest Management. In Lecture notes on data engineering and communications technologies, 77, 548–562. https://doi.org/10.1007/978-3-030-82014-5_37.
Lavrov, V., Miroshnyk, N., Grabovska, T., & Shupova, T. (2021). Forest shelter belts in organic agricultural landscape: structure of biodiversity and their ecological role. Folia Forestalia Polonica, 63(1), 48–64. https://doi.org/10.2478/ffp-2021-0005.
Liu, Y., Li, H., Yuan, F., Shen, L., Wu, M., Li, W., Wang, A., Wu, J., & Guan, D. (2022). Estimating the impact of shelterbelt structure on corn yield at a large scale using Google Earth and Sentinel 2 data. Environmental Research Letters, 17(4), 044060. https://doi.org/10.1088/1748-9326/ac58ab.
Matsala, M., Odruzhenko, A., Sydorenko, S., & Sydorenko, S. (2024). War threatens 18% of protective plantations in eastern agroforestry region of Ukraine. Forest Ecologyand Management, 578, 122361. https://doi.org/10.1016/ j.foreco.2024.122361.
Myroniuk, V., Weinreich, A., Von Dosky, V., Melnychenko, V., Shamrai, A., Matsala, M., Gregory, M. J., Bell, D. M., & Davis, R. (2024). Nationwide remote sensing framework for forest resource assessment in war-affected Ukraine. Forest Ecology and Management, 569, 122156. https://doi.org/10.1016/j.foreco.2024.122156.
Popov, M. A., Kussul, N. N., Stankevich, S. A., Kozlova, A. A., Shelestov, A. Y., Kravchenko, O. M., Korbakov, M. B., & Skakun, S. V. (2008). Web service for biodiversity estimation using remote sensing data. International Journal of Digital Earth, 1(4), 367–376. https://doi.org/ 10.1080/17538940802483745.
Stankevich, S. A., Kozlova, A. A. (2024). Remote condition mapping and post-hostilities damage assessment of forest shelterbelts. In Proceedings of the International Scientific, Theoretical and Applied Conference “Restoration of Ecosystems Damaged by Military Actions: Ukrainian and European Challenges” (REDMO-2024), 74–77. Kyiv: National Aviation University.
Wang, J., Patruno, L., Zhao, G., Tamura, Y. (2023). Windbreak effectiveness of shelterbelts with different characteristic parameters and arrangements by means of CFD simulation. Agricultural and Forest Meteorology, 344, 109813. https://doi.org/10.1016/j.agrformet.2023.109813.
Zhao, C., Pan, Y., Zhang, P. (2024). Development of a new indicator for identifying vegetation destruction events using remote sensing data. Ecological Indicators, 166, 112553. https://doi.org/10.1016/j.ecolind.2024.112553.
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