Tetiana Ilienko

Intensive anthropogenic activity exerts substantial pressure on the environment, complicating robust attribution of climate change and its impacts on vegetation. Conventional monitoring approaches, particularly the Normalised Difference Vegetation Index (NDVI), do not always allow a clear separation of natural climatic drivers from anthropogenic influences when analysing large areas. This creates a need for more sensitive and discriminating methods for assessing vegetation condition under ongoing climate change. The article examines the assessment of climate change impacts on the vegetation cover of Ukraine using modern Earth Observation techniques and satellite data. The primary objective was to justify the use of specialised chlorophyll indices (CVI, CARI, MTVI2, MCARI) as a more informative alternative to traditional NDVI for differentiating natural climatic factors from anthropogenic impacts. Using a test site in Vyshhorod district (Kyiv region), we analysed a range of land-cover types — water bodies, agricultural fields, forests, grasslands and urban areas — based on Sentinel-2 imagery with a spatial resolution of 10–20 m. Geospatial processing and visualisation were carried out in a GIS environment using QGIS. For validation, statistical analysis of climatic variables was undertaken using data from the NASA Giovanni platform (AIRS), encompassing CO2 concentrations and temperature dynamics over Ukraine for 2015–2022. The results showed that specialised chlorophyll indices provide a wider range of values than NDVI, allowing a more accurate characterisation of vegetation physiological status and the detection of even subtle changes in canopy condition. NASA data revealed a statistically significant increase in mean annual temperature of 1.2–1.5°C and a rise in CO2 concentration by 15–20 ppm, which correlate with an extension of the growing season by 2–3 weeks and an increase in mean chlorophyll index values by 15–25%. The CARI index exhibited particular sensitivity to species composition, enabling differentiation of forest stand types and the identification of artificial objects. Combined analysis of CVI, CARI and MCARI together with CO2 dynamics made it possible to delineate areas where vegetation change is primarily driven by climatic factors. The findings confirm that the use of specialised chlorophyll indices, in conjunction with climatic indicators, provides a more objective assessment of vegetation status and helps to disentangle climatic signals from confounding anthropogenic influences. The results emphasise the need for continued monitoring of spatio-temporal patterns of change to support the development of adaptive management strategies for natural and agro-ecosystems in Ukraine under global climate pressures.

Climate change intensifies land degradation processes in Ukraine, necessitating the development of specialized information systems to support effective monitoring and management of land resources. This research aims to substantiate and design the structure of a pilot information-analytical web resource “Agrokosmos” for tracking the spatial distribution of desertification and drought phenomena. The methodology employs system analysis of international land degradation monitoring platforms, logical-structural approach to web resource design, semantic analysis of key terms, and investigation of target audience needs in the field of sustainable land management. A hierarchical website structure has been developed, comprising a homepage and ten main sections: institutional and legal framework, general information, methodological and technological approaches, data, spatial distribution of processes, analytical reports, adaptation measures, knowledge base, additional information sources, and news. A semantic core has been formed around twelve thematic clusters, encompassing over one hundred key terms to enhance search-engine visibility. Ten target audience groups have been identified, including scientists, government agencies, agricultural specialists, international organizations, and the general public. The proposed architecture ensures integration of satellite data, geoinformation tools, cartographic materials, and analytical reports within a unified decision support environment. The resulting structure provides a practical foundation for a national desertification monitoring platform, will facilitate Ukraine’s implementation of its obligations under the UN Convention to Combat Desertification, and will enhance drought early warning effectiveness. Future directions include populating the platform with current Earth remote sensing data and developing automated algorithms for identifying areas at risk of degradation

This study examines the impact of climate change on natural and agricultural ecosystems in Ukraine through satellite remote sensing and geospatial technologies. The primary objective was to determine spatiotemporal patterns of ecological transformations induced by climate warming, with emphasis on land surface temperature trends, vegetation dynamics, water availability, and invasive species distribution. The integration of satellite-derived information proves essential for comprehensive monitoring of climate change impacts on agroecosystems, providing unprecedented spatial coverage and temporal continuity for detecting environmental changes at regional scales. The research was conducted using GIS technologies, satellite observation systems, and data collection through Giovanni, Copernicus, and EO BROWSER databases. Temperature data from the AIRS satellite (2010–2025), processed through the NASA Giovanni platform, revealed consistent increases in mean monthly surface temperatures, reduced snow cover duration, and intensified summer heat waves. Vegetation dynamics were assessed using NDVI, NDWI, and NDMI indices derived from Sentinel-2 imagery through EO Browser and Copernicus platforms. Results for Rivne Oblast indicate a notable extension of the growing season by more than one month, demonstrating adaptive shifts in vegetation development to new climatic conditions. In Odesa Oblast, significant decreases in plant moisture content (NDMI) were recorded, alongside reduced precipitation and surface water area, exemplified by the desiccation of Lake Putrene. This indicates growing drought risk and water stress in agricultural landscapes. The study also examined biological responses to climate change, focusing on the invasive freshwater snail Potamopyrgus antipodarum. A clear correlation was identified between regional warming trends and rapid population growth and spatial expansion of this species in Volyn and Odesa oblasts. Data suggest that milder winters and narrower temperature ranges created favorable thermal conditions facilitating successful colonization. Comprehensive analysis of satellite data for the period 2010–2025 unequivocally confirms the presence of significant climate changes across Ukraine’s territory. The satellite-based monitoring approach demonstrates critical importance for early detection and quantification of climate-induced changes in agroecosystems, enabling timely implementation of adaptive management strategies. The identified trends—temperature increases, transformation of vegetation cycles, invasive species expansion, and water resource degradation — require immediate implementation of adaptive measures and comprehensive natural resource management strategies. Overall, the research underscores the potential of satellite monitoring for detecting climate-related ecological changes and highlights the indispensable role of remote sensing technologies in understanding complex interactions between climate variability and ecosystem responses. The study’s findings emphasize the urgent need for continuous environmental monitoring, adaptive ecosystem management, and scientifically-grounded strategies for climate change mitigation in Ukraine.

As a result of the accelerated processes of urbanization and the seasonal variations in the landscape cover, adaptive and precise methods for built-up area identification from space are necessary. The conventional spectral indices, majorly NDBI and NDVI, show inadequate stability over seasonal variations particularly when snow is present and where open soil exists. Within this study, a comparison between NDBI, NDWI, modified NDBIBlue, and EVI-S indices proposed by the authors is made. Seasonal multispectral Sentinel-2 images were chosen to test them. This gives an opportunity to evaluate these indices’ effectiveness in four seasonal periods. According to the three types of categories — building water areas and arable land each index gave out varied sensitivity levels. The optimum results for the discrimination of urbanized areas were obtained during summer, and winter proved to be an inappropriate season for classification since there is a very high number of false positives due to snow coverage. The last step of this work was classification with Maximum Likelihood using three index layers, building a vectorized model of the structures with great fidelity to what is seen in the satellite image. This study can initiate trust in a hybrid approach that takes seasonal sensitivity from spectral indices and hands over to automated methods of classification while it points out that there is constant need that methods should be advanced having errors within classes reduced particularly when open ground or shaded areas are present.

Remote sensing methods are essential for environmental monitoring and assessment. They provide objective data on vegetation cover, enabling the de­tection of changes and evaluation of ecological processes. In Ukraine, located in a temperate climate zone, favorable conditions support vegetation growth and agricultural activities. However, the agricultural sector faces significant environmental challenges, such as climate change, soil degradation, and erosion. Monitoring these processes is crucial for sustainable land use and resource management. To assess changes in vegetation cover and their environmental impact, satellite remote sensing and spectral indices are commonly applied. Widely used indices include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and their modifications. These indices, however, have limitations, including sensitivity to soil background, atmospheric interferen­ce, and saturation at high vegetation densities. To address these limitations, a new index called EVI-S (Enhanced Vegetation Index — Soil Adjusted) has been proposed. It combines the strengths of SAVI and EVI2 while minimizing atmospheric interference. The primary goal is to develop and validate this new index to enhance ecological monitoring. The study area is the Feodosiivska territorial community in Kyiv region, which includes natural ecosystems and agri­cultural lands. The landscape is predominantly flat, typical of the forest-steppe zone. Sentinel-2 satel­lite images, processed using QGIS with the SCP plugin, provided data for calculating NDVI, EVI, EVI2, SAVI, and EVI-S indices. Results indicate that the EVI-S index shows higher maximum and average values compared to conventional indices, suggesting improved sensitivity to dense vegetation. Correlation analysis demonstrated that while traditional indices (EVI, EVI2, SAVI) show strong mutual correlation, NDVI displays slightly lower correlation due to satu­ration issues. Notably, EVI-S exhibits a high correla­tion with NDVI, indicating retained similarity despite improved formulation. EVI-S is particularly effec­tive in analyzing urban and mixed landscapes, where conventional indices may underestimate vegetation presence. Its wider dynamic range and increased sensitivity make it useful for monitoring vegetation growth and biomass, especially in forests and agri­cultural areas. Further studies are needed to assess its application in urban environments, where hetero­ geneous vegetation cover may impact accuracy. The findings demonstrate that EVI-S can complement traditional indices, offering enhanced sensitivity in contexts where accurate vegetation density analysis is necessary. Its practical application is promising for crop monitoring during growth periods and for evaluating dense forest areas.

Agroecosystems of Ukraine are experiencing increased impact from climate change and military actions, leading to growing risks of land degradation, desertification, and soil depletion in all natural-climatic zones of the country. Under these conditions, the system of agroecological monitoring becomes crucial, with satellite observations being an integral component that provides both retrospective analysis and operational tracking of the spatial-temporal distribution of negative phenomena. This creates a foundation for improving information support for managing natural and production processes in agriculture and environmental management. The article, based on the report by Corresponding Member of NAAS O.S. Demyanyuk, presents research results conducted within the framework of the National Academy of Agrarian Sciences program “Agrocosmos” (“Satellite agroecological monitoring, agroresource management, and forecasting the impact of climate change on agroecosystem productivity”), aimed at developing scientific and methodological support for using satellite monitoring data to solve environmental problems. Satellite indicators of climate change risks and military actions on agroecosystems have been substantiated and identified. Through retrospective analysis of these indicators’ dynamics over a 40-year period, clear trends of climate change and their impact on agroecosystems in the eastern part of the Forest-Steppe were established, and these indicators were tested for monitoring negative phenomena in agricultural landscapes due to the destruction of the Kakhovka Hydroelectric Power Plant. Identifiers of military actions’ impact on soil cover based on satellite information were established; corresponding map schemes were created using GIS with an assessment of the degree of impact on soil resources in Kharkiv region. To improve information support for agroecological monitoring, particularly regarding the problem of desertification and land degradation processes, the structure of a database for a corresponding information-analytical website has been developed. Based on published sources and conducted research, the high potential of aerospace monitoring data for agroecosystems has been proven for identification of agricultural crops, their classification and mapping, particularly using the vegetation index NDVI, as well as for performing modern dynamic agroecological zoning of agricultural lands according to their suitability for growing agricultural crops with different biological requirements using aerospace monitoring data.

Aim. To determine the patterns of the climate change impact on the transformation of agricultural production and the ecosystem of Ukrainian Polissia in terms of time and space. Methods. Satellite and statistical data for the last 40 years (1982–2022) were used. The mean temperature for the vegetation period was defined by the sum of radiation temperatures of the terrestrial surface, calculated using the data of the infrared range (10.3–11.3; 11.4–12.4 μm) of the high-precision AVHRR radiometer of artificial meteorological Earth's satellites, NOAA, and the precipitation dynamics – using the data of ERA5 ECMWF/Copernicus Climate Change Service. The impact of climate change on the vegetation state and phenological parameters, including the beginning, end, and duration of the vegetation season, was determined by the normalized difference vegetation index (NDVI), obtained using the data of the infrared (0.72–1.1 μm) and red (0.58–0.68 μm) ranges of the AVHRR radiometer on the website of STAR NESDIS NOAA. Crop yields and the dynamics of sown areas were determined by the data from the State Statistics Service of Ukraine. The information about the dynamics of the forest cover was obtained using the Global Forest Watch satellite data. To evaluate the impact of climate change on Ukraine's forests, the investigation was conducted on burnt forest areas, using the MCD64A1 6.1 index, developed on the basis of the MODIS satellite data. Results. According to the satellite data, the radiation temperature of the terrestrial surface during the growing period increased by 2.2 ºС on average in the territory of Ukrainian Polissia over the last 40 years. There is a regional difference in the temperature regime observed in the direction from west to east. In the territory of western Polissia, the increase in the temperature of the terrestrial surface in recent decades was within the range of 1.2–1.6, and in the central and eastern parts – within 2.3–2.9 ºС. Due to the warming, the duration of the growing period was extended by 21–35 days, mostly because of earlier spring onset. There is a descending trend in the average annual precipitation amount down to 20–30 mm, which is especially notable for central and eastern Polissia. The warming was the reason for the introduction of the crops, new for this region, into the structure of sown areas, especially corn and sunflower, which had a generally positive effect on the vegetation state by the NDVI index, which increased from 0.30 in 1982–1992 to 0.36 in 2012–2022 on average. The crop yield increased accordingly, and according to the statistical data for the recent years, amounted to: corn – 7.0–9.5 t/ha, winter wheat – 4.5–5.0, and sunflower – 1.5–2.0 t/ha, which is close to the level of their yield on chornozem. At the same time, due to climate change and the transformation of agricultural activity, there has been a higher risk of deterioration in the ecological state of typical Polissia landscapes, droughts, and soil degradation. According to the results of the analysis of twenty-two years of the dynamics in forest fires, the largest areas of burnt forests were registered in 2012 (694.30 sq.km), 2015 (1,078.81 sq.km), and 2020 (776.27 sq.km), which demonstrated the increase in fires during the recent decade along with the tendency towards longer fire hazard period. Conclusions. The increase in the temperature of the terrestrial surface of the Ukrainian Polissia during the growing period and the lengthening of the growing period created the conditions for corn and sunflower to be introduced into the arable areas, which had a positive impact on the vegetation state index, NDVI. As a result of these transformations both in the sown area of these crops and their yield, Polissia is becoming a new grain-oil belt of Ukraine. Concurrently, there are higher risks associated with maintaining high performance of agroecosystems and the increase in the risks for soil degradation processes, the deterioration of the ecological state of wetlands and forests, as well as drying-out of small rivers and lakes. Balancing the high yield of modern agroecosystems and safe nature management requires systemic measures in adapting the agricultural activity and nature management to new climatic conditions, which requires implementing the management of soil, water, and bio-resources, achieving optimal parameters of fertility for mineral and peaty-swampy soils. Reconstructing current land reclamation systems to optimize water regimes for the agricultural lands and protect typical Polissia ecosystem is needed.

The spread of bark beetles poses a significant threat to forest ecosystems, causing substantial damage to coniferous forests. The mountain pine beetle (Dendroctonus spp.) is considered to be one of the most harmful bark beetles, infesting pine trees and leading to severe consequences for forest ecosystems. This article aims to detect and analyze the dynamics of mountain pine beetle infestation in a pine forest stand, determine its affected area, and evaluate the effectiveness of sanitary and restoration measures. The study utilized satellite data from the Sentinel-2 mission and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to monitor the spread of mountain pine beetle infestation. The analysis was conducted for a pine forest stand in the Ostriv Forestry of the Vyshchedubechanske Forestry Enterprise in Kyiv Oblast, Ukraine. The results demonstrated the effectiveness of using satellite data and vegetation indices for monitoring mountain pine beetle infestation. The combined application of NDVI and NDMI enabled the detection of infested areas in the early stages by capturing changes in the spectral characteristics of tree crowns and vegetation moisture content. The satellite data analysis confirmed the complete infestation of a 0.01 km2 pine forest stand by the mountain pine beetle in 2017. A link between the activation of beetle spread and climate changes, particularly rising temperatures and decreasing precipitation, was observed, leading to the weakening of pine stands. The study revealed that in 2018, sanitary felling of the infested pine stand was carried out, as evidenced by the decrease in NDVI values. In 2019, a project to restore the forest ecosystem through the establishment of a new pine plantation was initiated. Satellite observations in April 2024 demonstrated successful growth of the young pine forest, with NDVI values indicating an increase from 0.25 in April 2020 to 0.45 in April 2024, reflecting active vegetation growth and successful ecosystem restoration. The obtained results highlight the importance of using satellite data for timely detection of pest outbreaks in forest ecosystems, determining the extent of infestation, monitoring the effectiveness of sanitary measures, and assessing the recovery of forest stands after restoration efforts. The study underscores the potential of satellite remote sensing techniques in forest ecosystem monitoring and supports decision-making processes for sustainable forest management.

Desertification is a major environmental threat that affects the lives of millions of people around the world. In order to effectively prevent this effect in agro-ecosystems at local and regional levels, it is necessary to have accurate information about its spatial distribution. Remote sensing plays a crucial role in this process due to its cost-effectiveness, ability to cover large areas, and use of a variety of sensors and data analysis methods. The combination of GIS, mathematical models and ground observations allows for more accurate data on land degradation. The purpose of this study is to summarize the experience of both Ukrainian and foreign researchers in the use of remote sensing for monitoring and assessment of desertification and land degradation processes at the global and national levels, as well as to identify areas for further improvement of remote sensing methods for integrated monitoring of desertification in agroecosystems and land degradation. This article reviews the experience of using satellite remote sensing data, mathematical models of desertification, and geographic information systems to identify areas prone to degradation in different parts of the world. Special attention is paid to the adaptation of these models to local conditions using additional parameters. The article also highlights the achievements of Ukrainian scientists in the field of satellite monitoring of agricultural landscapes, their classification and assessment based on remote sensing data. Prospects for further research are outlined. The use of an integrated approach combining geographic information systems, satellite data, analysis of environmental indicators, and mathematical models of desertification adapted to regional characteristics proves to be effective. The main directions remain the adaptation of models to regional conditions, the development of forecasting methods using remote sensing data, and the expansion of international data and experience exchange. It is important to continue to improve remote monitoring methods for the early detection of degradation processes and to strengthen research at the national level for the implementation of appropriate measures.

У даній роботі розглядається підхід до прогнозування врожайності кукурудзи на зерно за допомогою нечітких когнітивних карт (FCM) та експертний підхід для опису ступеня впливу одних факторів (концептів) на інші. FCMs – це методологія моделювання, заснована на досвіді. Вона включає в себе основні переваги нечіткої логіки та нейронних мереж. FCM являють собою графічну модель, яка складається із вузлів-концептів, що з’єднані ребрами. Вузли-концепти описують елементи системи, а ребра виражають зв’язки між цими концептами. FCM можна застосовувати в різних сферах, особливо для точного землеробства, моделювання та прогнозування врожайності. FCM також можна застосовувати для моделювання складних систем та використовувати в задачах прогнозування. FCM є ідеальним інструментом для моделювання динамічних систем. Основними перевагами та особливостями запропонованого алгоритму є гнучкість, простота та висока адаптованість до різноманітних умов. У цій роботі для класифікації врожайності кукурудзи було обрано підхід FCM. Ця запропонована методологія може використовувати супутникові та експертні дані для прогнозування врожайності. Розроблена модель FCM складається із вузлів, які представляють основні концепти, що впливають на врожайність, такі як: вміст калію (K), гумусу, фосфору (P), pH, вміст азоту (N), вологи, температура, NDVI (нормалізований диференційний вегетаційний індекс), LAI (індекс листкової поверхні). Калій, фосфор, pH, азот та гумус є експертними даними, а температура, вологість, NDVI і LAI є супутниковими даними. Орієнтовані ребра FCM відображають причинно-наслідкові зв’язки між концептами та врожайністю. Основною метою даного дослідження було визначити рівень врожайності кукурудзи за допомогою нечітких когнітивних карт. Наша модель була застосована для прогнозування класу врожайності між трьома можливими категоріями (низька, середня та висока) для трьох різних експертів. Було показано, що запропонований алгоритм може вирішувати задачу прогнозування врожайності кукурудзи.Слід зазначити, що даний алгоритм можна застосовувати для прогнозу врожайності інших сільськогосподарських культур. Слід зазначити, що даний алгоритм можна застосовувати для прогнозу врожайності інших сільськогосподарських культур.