Кафедра математичного моделювання та аналізу даних (ММАД)

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https://ela.kpi.ua/handle/123456789/43446
Сайт кафедри: http://mmda.ipt.kpi.ua/

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Перегляд Кафедра математичного моделювання та аналізу даних (ММАД) за Автор "Deininger, Klaus"

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  • Ескіз
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    Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning
    (2023) Kussul, Nataliia; Drozd, Sofiia; Yailymova, Hanna; Shelestov, Andrii; Lemoine, Guido; Deininger, Klaus
    The 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.
  • Ескіз
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    Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields
    (Sustainability, 2022) Kussul, Nataliia; Deininger, Klaus; Shumilo, Leonid; Lavreniuk, Mykola; Ayalew Ali, Daniel; Nivievskyi, Oleg
  • Ескіз
    ДокументВідкритий доступ
    Quantifying War-Induced Crop Losses in Ukraine in Near Real Time to Strengthen Local and Global Food Security
    (Elsevier Ltd, 2023) Deininger, Klaus; Ali, Daniel Ayalew; Kussul, Nataliia; Shelestov, Andrii; Lemoine, Guido
    We use a 4-year panel (2019–2022) of 10,125 village councils in Ukraine to estimate effects of the war started by Russia on area and expected yield of winter crops aggregated up from the field level. Satellite imagery is used to provide information on direct damage to agricultural fields; classify crop cover using machine learning; and compute the Normalized Difference Vegetation Index (NDVI) for winter cereal fields as a proxy for yield. Without conflict, winter crop area would have been 9.35 rather than 8.38 million ha, a 0.97 million ha reduction, only 14% of which can be attributed to direct conflict effects. The estimated drop associated with the conflict in NDVI for winter wheat, which is particularly pronounced for small farms, translates into an additional reduction of output by about 1.9 million tons for a total of 4.84 million tons. Taking area and yield reduction together suggests a war-induced loss of winter wheat output of up to 17% assuming the 2022 winter wheat crop was fully harvested.