Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ∼99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m 3 ha-1 and for live biomass of about 2 t ha-1 over the study area.

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km²) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year⁻¹ and 5.6 Gg C C·year⁻¹ for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.

Some of the highest 90Sr activity concentrations recorded beyond the Chornobyl Exclusion Zone occur in the Ivankiv district of Ukraine, located approximately 50 km south of the power plant, an area which nonetheless remains important for agricultural production. Although characterized by soils with low exchangeable calcium values, which can enhance the bioavailability of certain radionuclides, information on the transfer of 90Sr to food crops and trees in the region has remained limited to date. Analysis of 116 grain samples (wheat, rye, oat, barley or Triticale) collected from fields in 13 settlements in the region between 2011 and 2019 revealed 90Sr and 137Cs activity concentrations above Ukrainian limits in almost half of those samples, with annual averages exceeding this limit in four of those nine years (most recently in 2018) and with no clear evidence for a declining trend over time. Analysis of paired sandy soil samples from the same fields yielded concentration ratios for transfer of 90Sr from soils to grains that were on average 3 times higher than that specified by the IAEA. In addition, three quarters of wood samples collected from the trunks of trees (primarily pines) from 12 locations in the same district between 2015 and 2019 contained 90Sr activity concentrations in excess of the Ukrainian limits for firewood (60 Bq/kg), with levels more than four times that limit at one location and again no evidence for decline over time. A single sample of ash collected from a domestic wood-burning oven in the district contained 90Sr at a level 25 times higher than in the most contaminated wood sample collected in this study. Overall these results reveal additional facets of the ongoing legacy of Chornobyl contamination within the Ivankiv district, and the diversity of pathways by which local residents may be exposed to radionuclides. They also highlight the dangers associated with the current lack of routine and comprehensive environmental and food monitoring programs within the region, especially at a time in which the use of locally-sourced wood for biomass energy generation is set to expand markedly.

For 34 years since the 1986 nuclear disaster, the Chernobyl Exclusion Zone (ChEZ) landscapes have been protected with very limited human interventions. Natural afforestation has largely occurred throughout the abandoned farmlands, while natural disturbance regimes, which dominantly include wildfires, have become more frequent and severe in the last years. Here, we utilize the dense time series of Landsat satellite imagery (1986–2020) processed by using the temporal segmentation algorithm LandTrendr in order to derive a robust land cover and forest mask product for the ChEZ. Additionally, we carried out an analysis of land cover transitions on the former farmlands. The Random Forest classification model developed here has achieved overall accuracies of 80% (using training data for 2017) and 89% on a binary “forest/non-forest” validation (using data from 1988). The total forest cover area within the ChEZ has increased from 41% (in 1986) to 59% (in 2020). This forest gain can be explained by the afforestation that has occurred in abandoned farmlands, which compensates for forest cover losses due to large fire events in 1992, 2015–2016, and 2020. Most transitions from open landscapes to dense forest cover occurred after the year 2000 and are possibly linked to past forest management practices. We conclude that a consistent forest strategy, with the aid of remote monitoring, is required to efficiently manage new forests in the ChEZ in order to retain their ecosystem functions and to ensure sustainable habitats.

The paper reports results of the study of depots and biogenic fluxes of 90Sr and 137Cs in the typical coniferous (Scots pine) and deciduous (Silver birch) forest ecosystems of the Chernobyl Exclusion Zone during 2016 - 2018. Data on activity concentrations and shares of the total activity of the studied radionuclides in the components of aboveground and underground biomass and their vertical distributions by 10 cm layers of the soil profile up to a depth of 1 m are presented. The downward and upward fluxes of 90Sr and 137Cs activity (including the processes of their deposition as a result of growth and formation biomass) are calculated in annual terms. Significantly higher 90Sr mobility in elements of forest ecosystems than 137Cs is confirmed. The estimated flux values for the investigated forest areas indicate a gradual further increase in the share of these radionuclides in the aboveground biomass components (up to 0.9 %·year-1 from the total activity in forest ecosystems) owing to the increase of organic matter stocks.