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.

This study proposes a novel preprocessing approach for improving oil spill detection from Synthetic Aperture Radar (SAR) satellite imagery using deep learning models. A transfer learning approach with the LinkNet segmentation architecture pre-trained on ImageNet is employed. The model is trained on Sentinel-1 SAR data from 2018–2023 using a designed preprocessing pipeline that converts the single-channel SAR input to a 3-channel RGB image. The proposed preprocessing involves transforming the original SAR intensity values to a normal distribution, extracting nonlinear features, and encoding them into the RGB channels. Quantitative results on a test set show the preprocessed model achieves an improvement of 0.038 in F1-score and 0.054 in Intersection over Union compared to the original dB-scale preprocessing approach. Qualitative evaluation on independent SAR scenes from the Mediterranean Sea also demonstrates the model's ability to generalize to new geographic areas after training on data from other regions. The proposed preprocessing technique shows promising performance gains for automatic oil spill segmentation from SAR imagery and potential for integration with other preprocessing methods and task-specific neural network architectures.

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).

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.

Built-up area mapping is essential task for Sustainable Development Goals (SDGs) indicators related to sustainable cities and communities. The calculation of indicator 11.3.1: “Ratio of land consumption rate to population growth rate” can be used by governments and decision makers for efficient smart city growth monitoring and planning. In this work, the built-up area map based on local data were built by using land cover classification approach for smart cities, developed in Horizon-2020 ERA-Planet SMURBS project. These maps were validated and compared with use of Global Human Settlement Layer, ground truth data and Maryland Forest product. The results shows that global products, such as Global Human Settlement Layer are very useful and have good accuracy especially in the case for global indicator 11.3.1 assessment. It can show the full picture of global urbanization changes. But, it is better to use local data for city scale, to provide accurate tracking of urban area development. Local data in this case could be more informative for the decision makers in purpose of the city growth management and proper use of environmental resources.

This paper presents fire monitoring studies based on heterogeneous satellite data. In particular, this paper describes fire monitoring service developed by the Space Research Institute of NASU and SSAU for Ukraine and other existing fire monitoring systems and services. We consider different data sources for automatic fire detection at the national level in Ukraine. For this research were used data acquired by MODIS, Landsat-8, Sentinel-2 and Sentinel-3. The fire detection methodology for the first three satellites is implemented in Google Earth Engine. Workflow for automatic fire detection using Sentinel-3 data and fire detection system for Ukraine is developed within the Horizon-2020 ERA-Planet SMURBS project in Amazon cloud platform.

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.

In this paper we propose method for land degradation indicators identification within the ERA-PLANET project. We also consider data sources that can be used for these purposes and consider the possibility of using high spatial resolution satellite-based classification maps. Project GEOEssential within the framework of the ERA-PLANET Project, using heterogeneous satellite data has the potential to make positive changes in land use. In this paper the ways of forming essential variables are described with specific attention to domain where and how it is possible to improve the state of productivity of land and to identify the non-proper use of land, which leads to desertification, forest degradation and the general degradation of land resources.

Along the season crop classification based on satellite data is challenging task for Ukraine because of a big diversity of different agricultural crops with different phenology (crop calendars). Taking into account the availability for free of high resolution (10 to 30 meter) optical and SAR data from different satellite, the most resource consuming task is ground data collecting. That is why the proper time of ground surveys and crop classification maps developing is very important. In the study we propose to build three crop classification maps for JECAM Ukraine test site in Kyiv region during the vegetation season. The first one is built in the middle of May to classify winter cereals and rapeseeds. The next crop classification map is developing in July to discriminate major summer crops (spring cereals, maize, soybeans, sunflowers). The final crop map is built in autumn to refine summer crops and sugar beet discrimination. Time series of multi-temporal satellite images with restored missing (clouded and shadowed) data are classified using neural network approach, in particular ensemble of multi-layer perceptrons (MLPs). It is shown, that addition of satellite data from the end of previous year to the spring imagery allows to significantly improve the accuracy of winter crops classification. In July it is possible to deliver the map with major summer crops with overall accuracy higher than 87%, and the overall accuracy of final map at the end of the season is 94%.

Many applied Earth observation problems are based on land cover and land use maps, derived from satellite data. That is why it is important to assess their accuracy. We have developed retrospective regional 30 meter resolution land cover maps for Ukraine based on Landsat data for 1990, 2000 and 2010. As there is no reference data for validating retrospective periods, validation of the maps could be done only with photo-interpretation. In this paper we investigate two different sampling schemes for reference samples selection: pseudo-random (purposeful) samples selection (first approach) and systematic on regular grid (second approach). With systematic samples selection we receive the lower accuracy of classification (overall, user and producer), then with pseudo-random Nevertheless we consider the validation results with the systematic sampling scheme (the second approach) to be more reliable comparing to the first one, because the second sampling scheme is less subjective. Moreover, samples proportion within the second approach better corresponds to the statistics.

In this paper we propose a new methodology to automatically generate retrospective high resolution land cover maps on a regular basis for the whole territory of Ukraine. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is used for multi-temporal Landsat-4/5/7 satellites imagery classification with previously restored missing data due to clouds, shadows and non-regular coverage. This methodology was used to obtain land cover maps for the territory of Ukraine for three decades, namely 1990s, 2000s and 2010s, with overall accuracy more than 97%.