Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning

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dc.contributor.authorKussul, Nataliia
dc.contributor.authorDrozd, Sofiia
dc.contributor.authorYailymova, Hanna
dc.contributor.authorShelestov, Andrii
dc.contributor.authorLemoine, Guido
dc.contributor.authorDeininger, Klaus
dc.date.accessioned2023-11-15T08:02:51Z
dc.date.available2023-11-15T08:02:51Z
dc.date.issued2023
dc.description.abstractThe ongoing full-scale Russian invasion of Ukraine has led to widespread damage of agricultural lands, jeopardizing global food security. Timely detection of impacted fields enables quantification of production losses, guiding recovery policies and monitoring military actions. This study presents a robust methodology to automatically identify agricultural areas damaged by wartime ground activities using free Sentinel-2 satellite data. The 10 m resolution spectral bands and vegetation indices are leveraged, alongside their statistical metrics over time, as inputs to a Random Forest classifier. The algorithm efficiently pinpoints damaged fields, with accuracy metrics around 0.85. Subsequent anomaly detection delineates damages within the fields by combining spectral bands and indices. Applying the methodology over 22 biweekly periods in 2022, approximately 500 thousand ha of cropland across 10 regions of Ukraine were classified as damaged, with the most significant impacts occurring from March to September. The algorithm provides updated damage information despite cloud cover and vegetation shifts. The approach demonstrates the efficacy of automated satellite monitoring to assess agricultural impacts of military actions, supporting recovery analysis and documentation of war crimes.uk
dc.format.pagerangeP. 1-21uk
dc.identifier.citationAssessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning / Nataliia Kussul, Sofiia Drozd, Hanna Yailymova, Andrii Shelestov, Guido Lemoine, Klaus Deininger // International Journal of Applied Earth Observation and Geoinformation, Dec. 2023. - Vol 125, 103562. - P. 1-21.uk
dc.identifier.doihttps://doi.org/10.1016/j.jag.2023.103562
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62216
dc.language.isoenuk
dc.relation.ispartofInternational Journal of Applied Earth Observation and Geoinformationuk
dc.subjectMachine learninguk
dc.subjectRemote sensinguk
dc.subjectWar damaged fieldsuk
dc.subjectStatistical analysisuk
dc.subjectSpectral bandsuk
dc.subjectVegetation indicesuk
dc.subjectClassificationuk
dc.subjectRandom forest classifieruk
dc.subjectSentinel-2uk
dc.titleAssessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learninguk
dc.typeArticleuk

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