Browsing by Author "Kavats, Olena O."

Now showing 1 - 4 of 4
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    Analysis of Methodologies for Carbon Stock Estimation in Forests
    (Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Kavats, Olena O.; Khramov, Dmitriy A.; Sergieeiva, Kateryna L.; Vasyliev, Volodymyr V.
    ENG: Current approaches to carbon stock estimation in forest ecosystems are discussed. Datasets containing biomass and carbon stock estimates that can be used for training/validation in machine learning are described. Examples of applying the remote approach to assessing forest biomass over large areas are analyzed. To estimate the forest carbon stocks in Ukraine, the most promising is the remote approach, which combines ground-based and satellite measurements for forest classification and statistical modeling of carbon stocks. For training and validation of machine learning algorithms, it is proposed to use the GEDI Biomass Map covering most of the territory of Ukraine — from the southern borders to the latitude of Chernihiv in the north. A prototype of forest biomass estimating product in Ukraine can be based on publicly available MODIS NBAR data, SRTM DEM, ECMWF climate data and use the Random Forest machine learning method.
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    Machine Learning-Based Monitoring of War-Damaged Water Bodies in Ukraine Using Satellite Images
    (CEUR-WS Team, Aachen, Germany, 2024) Sergieieva, K.; Kavats, Olena O.; Vasyliev, Volodymyr; Kavats, Yurii V.; Kovrov, Oleksandr
    ENG: Water resources are Ukraine's strategic environmental asset. As a result of the destruction caused by the Russo-Ukrainian War, critical water infrastructure has been severely damaged. This makes it essential to effectively manage and conserve water resources in the face of increasing anthropogenic impact. The use of machine learning methods to monitor water bodies' conditions based on optical and Synthetic Aperture Radar (SAR) satellite images allows for automating analysis processes and providing more accurate and timely results, which is important for making reasonable management decisions. In this study, information tools for mapping and assessing the dynamics of surface water body changes were developed based on Sentinel-1 and Sentinel-2 data using a convolutional neural network. They were used for the mapping of surface water bodies in the Lower Dnipro sub-basin affected by the destruction of the Kakhovka Hydropower Plant dam. To improve the result of satellite image mixed pixels classification in shallow areas of swampy water bodies at the bottom of the destroyed Kakhovka Reservoir, it is proposed to use a block data model and a probabilistic approach to assess the presence of "water" and "ground" class objects in the images, which allows to achieve mapping accuracy of up to 96%.
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    Open Access Satellite Data for Global Greenhouse Gas Monitoring
    (Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», 2022) Kavats, Olena O.; Khramov, Dmitriy A.; Sergieieva, Kateryna L.; Vasyliev, Volodymyr V.
    ENG: Open satellite concentration data for the main greenhouse gases (CO2, CH4, N2O) are considered in terms of their possible use for local, regional, and global monitoring. The main data characteristics are provided. The satellite products most suitable for global moni-toring of greenhouse gas concentrations are specified. The disadvantages of existing satellite data are analyzed.
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    Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask
    (MDPI, 2022) Kavats, Olena O.; Khramov, Dmitriy; Sergieieva, Kateryna
    ENG: Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (s0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate s0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the s0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.