This 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.

Accurately 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.

In 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).

1stJuly 2021 the law on the creation of land market start effect in Ukraine. As a result, land appraisal became cornerstone task in Ukrainian agriculture sector. The official methodology on land appraisal includes use of soil fertility characteristics combined with coefficients related to the distance to the infrastructure objects or settlements and placing of field in specific functional areas, like recreational, or areas with high level of radiation pollution. In this study we collected open source infostructure geospatial information and characteristics of fields obtained from remote sensing data - crop types and Normalized Difference Vegetation Index to build land price predictive model trained on the official land market information. This work designed to investigate potential of geo-informational technologies and remote sensing in the land appraisal use. We separated all available ground truth land price data into three groups by fields size - very small, small, medium and big. We found different relationships between field characteristics and prices. For very small fields the most important features are area, altitude, slope, bonitet and distances to elevators, villages and roads. For small fields the most important are bonitet, altitude, area and distances to cities and roads. For medium and big field's area, slope, distance to cities, roads and historical NDVI.

This paper analyzes the problem of fire danger assessment and identifies the necessary sources and characteristics of satellite, ground and statistical data for the new approach of fire danger assessment. Modern information systems for fire danger assessment and fire monitoring using satellite and weather data are considered. The fire danger assessment method has been adapted for all types of land cover in Ukraine, which previously was successfully used in the Canadian methodology for determining the Fire Weather Index (FWI).

One of the parts of the Land Monitoring Service is Copernicus Urban Atlas, which provides reliable and comparable land use maps with high accuracy for large number European functional urban areas and their neighbors for every 6 years (2006, 2012, 2018). Unfortunately, there is no available such products for Ukrainian cities and there is no possibility to reproduce the technology by which they are obtained. This is due to the unavailability of sufficient high resolution satellite data information at the cities level, which is an integral part of the European methodology for obtaining the Urban Atlas. That is why we have proposed new approach on the base of open data which can be applicable to any other city. Kyiv (Ukraine) became the first city outside the Europe, for which the methodology by creating Urban Atlas was developed, which is compliant in structure and functionality to the European Copernicus Urban Atlas. The methodology was scaled for Lviv City, as well as applied and tested for other cities, in particular for Rivne, Irpin (Ukraine) and Lublin (Poland). In addition to the main management tasks that the Urban Atlas helps to solve, the obtained products can be used to unify and air quality monitoring in cities, and as a base for assessing the sustainable development goals indicator 11.6.2 “Annual mean levels of fine particulate matter in cities”.

Ukrainian agricultural industry is one of the main sectors of economic growth. Nevertheless, Ukraine is way behind in the development. This is mostly due to the low level of modern technologies utilization by businesses and state entities. To ensure transparency, equity and reliability of Ukrainian land market, objective information on land use and crop state is required. The World Bank program “Supporting Transparent Land Governance in Ukraine” addresses these issues. Within the project, we performed satellite monitoring of land use in Ukraine, analyzed the feasibility of Google's cloud-based technology for processing large amount of data and developed a new platform to analyze the crop state using open and free Sentinel-1/2 satellite data. It is a 5-year project, which is extended for the whole country this year. We plan to make the technology of satellite monitoring operational and deployed in governmental institutions in 2023.

For accurate crop classification, it is necessary to use time-series of high-resolution satellite data to better discriminate among certain crop types. This task brings the following challenges: a large amount of satellite data for download, Big data processing and computational resources for utilization of state-of-the-art classification approaches. For solving these problems, we have developed an automated crop classification workflow, which is based on machine-learning techniques. By deployment of the workflow on the cloud platform, we can overcome challenges of Big data downloading and processing. In this paper, we present the system architecture and describe the experiments on structural and parametric identification of machine learning models utilized in the system.