Статті (ММАД)

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https://ela.kpi.ua/handle/123456789/43459
У зібранні розміщено матеріали, що опубліковані або готуються до публікації в наукових журналах та збірниках.

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Перегляд Статті (ММАД) за Автор "Kussul, Nataliia"

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  • Ескіз
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    Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment
    (Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia
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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.
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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
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    Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control
    (Remote Sensing, 2021) Makarichev, Victor; Vasilyeva, Irina; Lukin, Vladimir; Vozel, Benoit; Shelestov, Andrii; Kussul, Nataliia
  • Ескіз
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    Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data
    (MDPI, 2023) Kussul, Nataliia; Fedorov, Oleh; Yailymov, Bohdan; Pidgorodetska, Liudmyla; Kolos, Liudmyla; Yailymova, Hanna; Shelestov, Andrii
    Fire is one of the most common disturbances in natural ecosystems. The analysis of various sources of information (official and unofficial) about the fires in Ukraine (2019–2020) showed a lack of timely and reliable information. Satellite observation is of crucial importance to provide accurate, reliable, and timely information. This paper aims to modify the index of fire danger of a forest’s FWI by increasing its precision, based on the use of higher spatial resolution satellite data. A modification of the FWI method involves the utilization of the soil moisture deficit, in addition to the six subindices of the FWI system. In order to calculate the subindices values, weather data from the Copernicus Atmosphere Monitoring Service were used. Soil moisture deficit is calculated using Sentinel-1 radar satellite data on the water saturation degree of the soil surface layer and geospatial parameters from the 3D Soil Hydraulic Database of Europe. The application of the proposed methodology using the specified satellite, weather, and geospatial data makes it possible to assess fire danger on a continental scale with a spatial resolution of 250 m, 1 km, and a daily temporal resolution. Validation of the proposed method for modifying the FWI system demonstrates an improvement in the precision and relevance of fire danger prediction.
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    Generative adversarial network augmentation for solving the training data imbalance problem in crop classification
    (2023) Shumilo, Leonid; Okhrimenko, Anton; Kussul, Nataliia; Drozd, Sofiia; Shkalikov, Oleh
    Deep learning models offer great potential for advancing land monitoring using satellite data. However, they face challenges due to imbalanced real-world data distributions of land cover and crop types, hindering scalability and transferability. This letter presents a novel data augmentation method employing Generative Adversarial Neural Networks (GANs) with pixel-to-pixel transformation (pix2pix). This approach generates realistic synthetic satellite images with artificial ground truth masks, even for rare crop class distributions. It enables the creation of additional minority class samples, enhancing control over training data balance and outperforming traditional augmentation methods. Implementing this method improved the overall map accuracy (OA) and intersection over union (IoU) by 1.5% and 2.1%, while average crop type classes’ user accuracy (UA) and producer accuracies (PA), as well as IoU, were improved by 11.2%, 6.4% and 10.2%.
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    Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?
    (Sustainability, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Skakun, Sergii; Kussul, Nataliia
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    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.