Geoinformation tool for remote assessment of shelterbelts damaged by military actions
DOI:
https://doi.org/10.36023/ujrs.2025.12.3.290Keywords:
shelterbelts, post-war restoration, damage assessment, remote sensing, Sentinel-2, EVI, NDMI, S2REP, Google Earth EngineAbstract
Field forest shelterbelts are considered as the key element of sustainable land use, performing functions such as preventing soil erosion, reducing the impact of dry winds and dust storms, improving the water balance, and serving as centres of biodiversity in agricultural landscapes.
However, forest shelterbelts suffer extensive damage or even destruction during military actions, which creates serious long-term threats to the environment and agricultural areas. Given that ground-based surveys for in-situ data retrieval are dangerous and have certain limitations in speed and scalability, the use of remote sensing (RS) methods is the most appropriate. Assessing the condition of the forest shelterbelts requires the use of a large amount of data and significant computational power, so it is suitable to involve cloud platforms for geospatial analysis, in particular Google Earth Engine (GEE). Therefore, this work aims to develop a geoinformation tool for assessing the condition of the forest shelterbelts after military actions based on the GEE platform. This tool is based on a methodology, the algorithm of which consists of comparing the condition of the forest shelterbelts before and after the damage based on the biophysical indicators, information about which is obtained from the remote sensing data. The methodology is flexible, as it allows changing the involved spectral indices depending on the selected biophysical indicators. The developed geoinformation tool involves Sentinel-2 satellite images to evaluate three biophysical indicators through their respective spectral indices: biomass (spectral index – EVI), chlorophyll content (S2REP) and moisture content (NDMI). Since the geoinformation tool is based on the GEE platform, it uses and provides users with GEE tools for entering, loading, and visualising data, and processing is fast due to the use of the computing powers of the Google Cloud servers. The developed geoinformation tool was tested in 2 study areas within the Donetsk region, which were in the zone of active military actions in 2022–2023 and suffered significant damage. Visual analysis confirmed the effectiveness of the developed tool, showing the correspondence between the damage zones marked on the maps and the actual condition of the forest shelterbelts. Therefore, this geoinformation tool can be used for monitoring the condition of forest shelterbelts located in combat zones and for planning their restoration. Further research can be aimed at applying additional data sources and biophysical indicators, as well as assessing the impact of each of the indicators to provide recommendations on expert estimates of the spectral index weighting coefficients when constructing a fused map of the shelterbelts condition.
Author Contributions: Conceptualization – S.A. Stankevich and A.O. Kozlova; methodology – S.A. Stankevich and A.A. Andreiev; formal analysis – A.O. Kozlova, A.A. Andreiev and L.M. Artiushyn ; 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
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