Assessment of military impacts on protected areas of Ukraine using Sentinel-1 and machine learning - HoroshkovаL. A.; Menshov , O. I.; Maslov , D. V. (Man and Environment. Issues of Neoecology, 2026)
Purpose. To assess military impacts on protected areas of Ukraine using Sentinel-1 Radar Vegetation Index (RVI) data and machine learning methods in order to identify spatial and temporal patterns of vegetation disturbance and ecosystem transformation under wartime conditions Methods. Spatial and temporal changes are analyzed using remote sensing techniques combined with machine learning methods, including unsupervised classification algorithms to detect patterns of vegetation disturbance and ecosystem transformation. Additionally, comparative analysis and time-series analysis are applied to assess the impact of military activities on forest ecosystems under wartime conditions. Results. This study assesses the impact of military activity on forest ecosystems in eastern Ukraine using Sentinel-1 SAR data, the Radar Vegetation Index (RVI), baseline-relative change analysis, and unsupervised machine learning. The primary objective was to detect, quantify, and characterize war-related forest disturbance in the Serebrianskyi Botanical Reserve which is directly exposed to active military operations and to understand the extent, severity, and temporal dynamics of that damage relative to a pre-conflict baseline. A conflict-free control site, Homilsha Forests National Nature Park, was used to distinguish military-driven change from background ecological variability. The study addresses whether Sentinel-1 RVI, VV, and VH backscatter can capture the spatial patterns and progressive development of military-induced forest disturbance over the period 2020–2025. Sentinel-1 data were processed in Google Earth Engine and restricted to forest pixels using a land-cover mask. Annual summer composites were generated for each year, and a pre-conflict baseline (2020–2021) was used to quantify post-disturbance change. The analysis encompassed annual RVI trend assessment, rule-based damage classification, K-means clustering, and detection of isolated forest anomalies. After 2022, Serebrianskyi ROI showed a marked RVI decline from stable values in 2020–2022, with changed forest pixels in 2025, including severely disturbed pixels increasing. Homilsha ROI remained stable, and no deterioration trend. Machine-learning results were consistent. Conclusions. SAR methods have proven to be effective for remote monitoring with limited field access, although derived categories of damage should be interpreted as remote sensing indicators and not as field validated categories of damage.