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.

Crop rotation is an important determining factor of crop productivity. Sustainable agriculture requires correct rules of crop rotation. Failure to comply with these rules can lead to deterioration of soil biochemical characteristics and land degradation. In Ukraine as well as in many other countries, sunflower monocropping is common practice and the impact of this fact should be studied to find the most precise rules to save the economic potential of land and minimize land degradation factors. This research provides an evaluation of the sunflower monocropping effect on the vegetation indices obtained from MODIS vegetation indices datasets for Ukraine as one of the countries with the biggest sunflower export in Europe. The crop rotation schemes are represented by their area proportions at the village level calculated based on the crop classification maps for 2016 to 2020. This representation gives the possibility to use regression models and f-test feature importance analysis to measure the impact of 3-year and 5-year crop rotation sequences. For these purposes, we use several models: a four-year binary representation model (model A1) and a model with all possible three-year crop rotation scheme representations (model B). These models gave the possibility to evaluate crop rotation schemes based on their biophysical impact on the next sunflower plantings and found that sunflower planting with an interval of three or more years is optimal in terms of the sustainability of soil fertility.

The Russian-Ukrainian War, ongoing since 2014, impacts an area containing Emerald Network environmental-protection sites created through the implementation of the Bern Convention on the Conservation of European Wildlife and Natural Habitats. Here we explore the impact of this conflict on institutional links supporting environmental sustainability and conservation efforts. Using satellite data, we analyzed tree cover changes in the Luhansk region’s Emerald Network protected areas from 1996 to 2020. The results reveal that the implementation of Bern Convention conservation policies led to a shift from deforestation (−4% each) to reforestation (+8% and +10%) on both sides of the Emerald Network divided by the demarcation line in 2014. It also shows that despite the war, territories under Ukraine control after 2014 continued reforestation (+9%), while sites under Russian control experienced dramatic forest loss (−25%). These findings emphasize the significant consequences of warfare-induced separation of local institutions on conservation areas and underscore the positive impact of the Emerald Network establishment, both before and after the conflict’s onset.

Wildfires 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.