Перегляд за Автор "Yailymova, Hanna"
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Документ Відкритий доступ A multimodal dataset for forest damage detection and machine learning(Assembled by Conference Management Services, Inc., 2024) Yailymova, Hanna; Yailymov, Bohdan; Salii, Yevhenii; Kuzin, Volodymyr; Shelestov, Andrii; Kussul, NataliiaAccurately recognizing areas of forest damage is crucial for planning, monitoring recovery processes, and evaluating environmental impact following catastrophic events. The widespread accessibility of satellite data, coupled with the ongoing advancement of machine and deep learning techniques, as well as computer vision methods, renders the implementation of these approaches in the automatic detection of damaged forest areas highly difficult. Nevertheless, a significant challenge in this regard is the scarcity of labeled data. The purpose of this article is to provide a useful and reliable dataset for territory of Ukraine for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services. The created dataset contains 18 locations with a time series of satellite images with a resolution of up to 10 m per pixel across Ukraine, as well as weather information. The data was collected from the Copernicus Sentinel-1,2 satellite missions as well as based on ERA-5 weather information.Документ Відкритий доступ Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network(Anhalt University of Applied Sciences, 2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Shumilo, Leonid; Lavreniuk, Mykola; Lavreniuk, Alla; Sylantyev, Sergiy; Kussul, NataliiaДокумент Відкритий доступ Agriculture land appraisal with use of remote sensing and infrastructure data(IEEE, 2022) Kussul, Nataliia; Shelestov, Andrii; Yailymova, Hanna; Shumilo, Leonid; Drozd, SophiaДокумент Відкритий доступ Air Quality as Proxy for Assesment of Economic Activity(2023) Yailymova, Hanna; Kolotii, Andrii; Kussul, Nataliia; Shelestov, AndriiIn Ukraine most of citizens and economic activity are concentrated over urban city centers and city functional areas. Thus, Air Quality and, in particular, levels of fine particulate matter (e.g., PM2.5 and PM 10 ) over cities can be a proxy for assessment of economic activity and density of city populations. Since the russia invasion to Ukraine started on 24 of February 2022 according to UNHCR (the UN Refugee Agency) 8 million refugees from Ukraine have now been registered across the Europe. Almost 7 million more are displaced within the country. On the other hand, there is no official statistics from national statistical service showing current influence of invasion on city economic activity or inhabitants amount. Thus, such a proxy can be used to see current situation by analyzing of particulate matter time series. In this work we compare averaged annual cumulated PM2.5 for 2018–2021 years with values for 2022 and estimate the correlation them with publicly available statistics on migration to see some relations. Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate in cities (population weighted)” is being extended for 2022 and compared with previous years.Документ Відкритий доступ Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment(Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, NataliiaДокумент Відкритий доступ Artificial Intelligence models in solving Ill-posed Inverse problems of Remote Sensing GHG emission(Leaving Planet Symposium, 2022) Sylantyev, Sergiy; Yailymova, Hanna; Shelestov, AndriiДокумент Відкритий доступ 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, KlausThe 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.Документ Відкритий доступ Autoregressive models for air quality investigation(2023-08) Zalieska, Olena; Yailymova, HannaThe aim of the work is to build a forecast of air quality in Kyiv for some period of time. For this purpose we preprocessed and analized data, selected and fitted a model.Документ Відкритий доступ Complex method for land degradation estimation(IOP Conf. Series: Earth and Environmental Science, 2023-01) Kussul, Nataliia; Shumilo, Leonid; Yailymova, Hanna; Shelestov, Andrii; Krasilnikova, TetianaДокумент Відкритий доступ Crop Yield Forecasting for Major Crops in Ukraine(IEEE, 2021) Shelestov, Andrii; Shumilo, Leonid; Yailymova, Hanna; Drozd, SophiaДокумент Відкритий доступ Current Advances on Cloud-Based Distributed Computing for Forest Monitoring(Springer, 2023) Shelestov, Andrii; Salii, Yevhenii; Hordiiko, Nataliia; Yailymova, HannaOne of the most important tasks related to environmental protection is forests monitoring. Meanwhile, specialists deal with the problem of big data and the need to utilize powerful computing resources that are not always available. Cloud solutions (CREODIAS, Google Earth Engine, etc.) provide instant satellite data access and the ability to quickly and conveniently process geospatial data in the cloud and use it to search for information products. Forest monitoring is supported by the European Commission (EU project SWIFTT), the World Wildlife Fund and others. This work analyzes Sentinel-2 satellite spectral channels, which distribution of pixel values was constructed for diseased and healthy forests, and the possibility of separating these two classes was analyzed based on the Bhattacharya distance. The informativeness of time series application of the normalized difference vegetation index (NDVI) was analyzed. The assumption that the average value of NDVI decreases and the standard deviation increases when the forest changes is confirmed. Getting results for large areas will lead to a big data problem. Therefore, the structure of the pilot information system is proposed as the basis for a further cloud solution with the development of a machine (deep) learning model for forest monitoring in any territory (including Ukraine). This system allows monitoring forests dynamics based on time series of satellite data at the country level and worldwide. This will be an important step for Ukraine as a potential member of the EU in the field of providing information services and monitoring the most sensitive natural resources.Документ Відкритий доступ Fire Danger Assessment Based on the Improved Fire Weather Index(IEEE, 2022) Kussul, Nataliia; Yailymov, Bohdan; Shelestov, Andrii; Yailymova, HannaДокумент Відкритий доступ Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data(MDPI, 2023) Kussul, Nataliia; Fedorov, Oleh; Yailymov, Bohdan; Pidgorodetska, Liudmyla; Kolos, Liudmyla; Yailymova, Hanna; Shelestov, AndriiFire 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.Документ Відкритий доступ Flooded areas monitoring below the Kakhovka Dam based on machine learning and satellite data(Assembled by Conference Management Services, Inc., 2024) Yailymov, Bohdan; Yailymova, Hanna; Kussul, Nataliia; Shelestov, AndriiThis study analyzed the flooding under the Kakhovka Dam in Ukraine using satellite remote sensing data after the dam was destroyed on June 6, 2023. Maps of the water bodies were created before and after the flooding disaster using Sentinel-1, Sentinel-2, and Landsat-9 imagery. A random forest classifier was used to map the flooded areas. As of June 9, 2023, the total flooded area below the Kakhovka Dam was 47,330 hectares, impacting agricultural lands, forests, grasslands and human settlements. The flooding also affected areas along the Ingulets River, leading to inundation of croplands located close to the river banks which could impact water quality. The disappearance of water canals that were used for irrigation of croplands is also analyzed, showing the far-reaching agricultural impacts of this flooding event. This study demonstrates the utility of satellite remote sensing for rapid monitoring and quantification of the impacts from dam failure flooding disasters.Документ Відкритий доступ Forecast of Yield of Major Crops in Ukraine in War Conditions 2022 based on MODIS and Sentinel-2 Satellite Data(2023) Kussul, Nataliia; Drozd, Sophia; Yailymova, HannaUkraine was one of the main exporters of plant products. However, as a result of the aggression, the country's agriculture has suffered greatly, export volumes are decreasing, which may provoke a shortage of agricultural products on world markets. It is impossible to assess crop yield and forecast the harvest volume locally, as the collection of information has become difficult due to the active conduct of hostilities and the occupation of a large part of the territories. Therefore, it is necessary to use land remote sensing data to assess crop yield. In this research, we will build regression models based on a random forest for each region of Ukraine to estimate crop yield based on 16-day composites of the NDVI time series during the summer vegetation period from Sentinel-2 (10m) and MODIS (500m) satellites, involving in the calculation NDVI crop maps. The official yield of maize, sunflower, soybean, rapeseed, and wheat for the years 2016-2021 was used as training data. According to the results of the analysis, models based on NDVI from the MODIS satellite showed better accuracy (relative error within 8-18%), but models based on NDVI data from Sentinel-2 better described the variance of the predicted yield. During the research, we found a sharp drop in land productivity indicators compared to the productivity of 2021 for the territories of central, southern and eastern Ukraine. According to our estimates based on MODIS data, the average yield at the country level is expected to be 40.98 t/ha for wheat, 57.66 t/ha for maize, 23.57 t/ha for sunflower, 21.06 t/ha for soybeans, 21.15 t/ha for rapeseed. Estimates based on Sentinel-2 data: 43.22 t/ha for wheat, 71.93 t/ha for maize, 26.86 t/ha for sunflower, 22.94 t/ha for soybeans, 28.23 t/ha for rapeseed.Документ Відкритий доступ Fusion of classification algorithms for landfill detection in Ukraine(2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Mikava, PolinaДокумент Відкритий доступ Geospatial Analysis of Life Quality in Ukrainian Rural Areas(IEEE, 2023) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, AndriiIn this work, the authors developed an initial algorithm for assessing the quality of life in rural areas of Ukraine using the aggregation of heterogeneous geospatial information. The approach consists in a comprehensive assessment of the remoteness of the village from vital infrastructure facilities (hospitals, educational institutions, banks, libraries, shops, roads, power lines, etc.), to natural ecosystems (water bodies, forests or parks), as well as to occupied territories. The obtained results show that the largest number of villages with a depressed quality of life are located in the eastern and southern territories of the country, and with a positive quality - mainly in the west and central Ukraine. This, of course, is partly related to active hostilities, but considering that the proposed algorithm works based on the analysis of distances to various objects, it can be concluded that the war only worsened the condition of life in the villages.Документ Відкритий доступ Geospatial monitoring of sustainable and degraded agricultural land(2023-07) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, Andrii; Shumilo, LeonidIn this study, the assessment of sustainable development goal (SDG) indicator 2.4.1 for Ukraine and Germany is conducted using geospatial and satellite data. The traditional methodology for the SDG indicator 2.4.1 calculation cannot be directly applied to the Ukrainian territory due to the lack of systematic data collection of the essential indicators. Therefore, the authors have developed an integrated approach to estimate land degradation, that uses different schemes for various land cover and crop types at the national scale, utilizing satellite data and employing the WOFOST model for crop growing simulation. The research describes the information sources used for creation crop type classification maps and the necessary data for modeling leaf area index (LAI) based on the WOFOST model. The calculated indicators are determined for each Ukrainian region from 2018 to 2022. Observations in 2022 show a decline in the indicator 2.4.1 across nearly all regions of Ukraine, directly attributed to the military conflicts within the Ukraine. To assess the possibility of applying the developed technology to a large area, the indicator was calculated for a European country (Germany).Документ Відкритий доступ Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine(MDPI, 2023-10) Yailymov, Bohdan; Shelestov, Andrii; Yailymova, Hanna; Shumilo, LeonidWildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important in Ukraine to assess the losses caused by the military actions. This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine. The methodology enables efficient national-scale burned area detection and automated identification of anthropogenic fires in regions with low fire risk. Implemented over Ukraine, 104.229 ha were mapped as burned during July 2022, with fires inconsistently corresponding to high FPI risk, indicating predominantly anthropogenic causes.Документ Відкритий доступ Ground Based Validation of Copernicus Atmosphere Monitoring Service Data for Kyiv(IEEE, 2021) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Samoilenko, Oleg; Shumilo, Leonid