Automated Detection and Assessment of War-Induced Damage to Agricultural Fields Using Satellite Imagery

Abstract

This paper introduces a methodology based on machine learning and remote sensing for detecting military-induced damages to agricultural lands in Ukraine using free Sentinel-2 satellite data. The most informative spectral bands (B2, B3) and vegetation indices (NDVI, GCI) were experimentally selected for recognizing damaged fields through the Random Forest classification algorithm. Additionally, an anomaly detection method based on the estimation of deviations of pixel values from the mean within each field was applied to determine local damage in the identified affected fields. The proposed methodology demonstrated high classification accuracy with an f1-score of 0.87%, producer’s accuracy of 0.89%, user’s accuracy of 0.85, and sensitivity for detecting local damage. The developed anomaly detection method allows to recognize damage visible on the 10-meter pixel of the Sentinel-2 satellite, but does not identify small craters. Cloudiness of satellite images can significantly impair the accuracy of damage detection, and the method of local damage detection can respond to non-military anomalies and requires careful selection of threshold coefficients for each field. The study conducted a comprehensive assessment of damages inflicted on Ukrainian agricultural fields during the period 2022-2023, revealing that a total of 1,544,952 hectares, equivalent to 5.72% of the total agricultural area, experienced damage. This included 509,107 ha of wheat, 114,302 ha of sunflower, 68,830 ha of maize, 4,029 ha of rapeseed, and 16,561 ha of other crops. The most affected regions were Donetsk, Zaporizhia, and Kherson oblasts. The comprehensive findings of this research provide valuable insights for monitoring the state of agriculture and formulating strategic plans for the recovery of agricultural resources amidst the ongoing military conflict.