Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity.

Conservation of forest resources is a prerequisite for sustainable development of human society, both in the context of preventing negative climate change and for economic growth. The study aims to establish or refute the co-dependence between the level of forest cover in European countries and the production of gross domestic product. The study object is the socio-economic systems of the national economies of European countries in relation to the totality of forest resources of the continent. Studying the dynamics of forest cover indicators (the share of forests in the total area of the country and forest area per capita), weighted within the internationally recognized regions of Europe, it is confirmed that the level of forest cover of European countries is gradually increasing. The analysis of forest fire area maps identifies three main groups by the level of vulnerability to forest fires: safe (Northern European countries), conditionally safe (Western European countries), and dangerous (Eastern and Southern European countries).Denmark, Finland, France, Norway, and Finland show a direct correlation between the level of forest cover of a country’s territory and gross domestic product. The results of cluster analyses based on the data from 2000, 2010, 2015, and 2020 confirm the existence of a stable cluster of European countries (34 countries) in which there is one type of relationship between the production of gross domestic product and the level of forest cover of the territory. AcknowledgmentThis study was funded by the grant support from the Philipp Schwarz Initiative of the Alexander von Humboldt Foundation.

The complex action of environmental factors often triggers the biomass formation in forest plantations, which is crucial for carbon balance and environmental monitoring, especially in the context of climate change. In this article, we present data on the aboveground biomass accumulation for black locust and common pine (Pinus sylvestris and Robinia pseudo­acacia) as the two most common forest-forming species in the steppe zone. For this purpose, we propose a reliable approach to monitoring of aboveground forest biomass with combining Sentinel-2 multispectral imaging techniques (with L-band) and biometric processing data from coniferous and deciduous stands obtained from field surveys. We represent the results of field surveys with established indicators of aboveground biomass of forest plantations in the field experiment, which averaged 159.9 ± 9.0 t/ha in the studied region. The biometric indexes obtained from the field experiments were used to develop models for predicting biomass using the remote method. Based on the processing of satellite image data, forest vegetation indices were analysed, among which the NDVI (normalized difference vegetation index) was the best predictor to assess biomass. The multiple regression method was found to be the best for predicting and mapping the aboveground biomass in P. sylvestris and R. pseudoacacia within the studied area (RMSE – 23.46 t/ha). Based on the results obtained, we created a map of the aboveground biomass distribution in black locust and common pine stands within the studied region. We established reliable correlations between biometric parameters (mean diameter at breast height, mean height) and aboveground biomass of stands with indicators of spectral bands in satellite images. This enables us to use the constructed models to estimate the overall productivity of coniferous and deciduous forest stands for large areas.

As a component of urban parks, soil is an essential part of urban green infrastructure; soil influences the green plantings in megacities, being affected by environmentally unfavorable factors. To reveal relationships between soil buffering capacity and ecological microbial groups of park soils, soil acid-base buffering capacity (pHBC) was determined with the Arrhenius method. We counted the soil microorganisms using differential diagnostic nutrient media with serial dilutions. Soil microbial biomass was determined by thermal rehydration. In statistical data processing, we used the methods of descriptive statistics, univariate analysis of variance, etc. We established strong positive correlations between pH values and pHBC in the acid and acid-base range, and a negative correlation between pH values and buffering capacity in the alkaline range; a strong correlation between the abundancies of amylolytic and pedotrophic soil microorganisms. We determined the effect of the three following soil sustainability-associated latent factors: Factor 1 – effect correlated with total pHBC, pHBC in the acid range and soil acidity, Factor 2 and Factor 3 – effects characterized by the direction of microbial processes and which formed a balanced system between the soil humus decomposition and the entry of plant litter. We revealed a beneficial impact of subcrown space on the abundancies of soil microorganisms. We recommend using acid and acid-base buffering capacity and microbiological characteristics as soil quality indicators. The abundance of soil microorganisms increased in the forested sites.

The aim of this work is the comparative analysis of the productivity of Scots pines in pure and mixed plantations with main and accompanying species in the Northern Steppe of Ukraine We analysed the taxation parameters such as height, diameter, stand density, which vary with the age of the stand and composition of the plantation. Within the study area pines grow mainly in pure stands (81.9%). The share of mixed forest stands in the study area is only 18.1%. The analysis of representation of age structure showed the presence of the same proportion of the young age group in pure and mixed stands, the dominant share being taken up by middle-aged monoculture stands, and thus a smaller area taken up by mature and overmature pines. The average stand density per 1 ha in pure stands of all age groups is 17% higher than in mixed stands. The dynamics of variations of such biometric data as average height, diameter and density for the different compositions of Scots pine stands were measured for 14 age classes. The mean heights of the pure and mixed stands are vere close, while the average diameter gradually increased with age, reaching its maximum in overmature plantations. The research results showed that there is an excess of this parameter in the mixed compared with the pure stands. We did not find significant differences between middle age pure and mixed stands in accumulation of stand density. But pure pine stands show a clear tendency to dominate in wood accumulation in all age classes through the period of growth. The greatest difference between pure and mixed stands in the mean stand density was observed for those of middle-age. The average stand density in the pure stands was up to 32% higher than the mixed. The area distribution of Scots pine stands according to productivity was as follows. The largest area for both pure and mixed stands was occupied by trees of quality class I, which took up 54.3% of the total pure pine acreage and 41.9% of the mixed stand acreage. The second position was occupied by quality class II, which occupied 27.6% of total pure pine acreage and 36.6% of mixed stand acreage with the perecentage of total stand density at 23.6% and 35.1% respectively. In general, the value of the average stand density falls as the quality class decreases, and essentially depends on the composition of plantations: in pure stands, it is higher than in mixed. The investigated values of the biometric parameters are statistically significant for both the pure and the mixed stands.

The most important disease for plants of the Aesculus genus is leaf spot caused by the leaf miner Cameraria ohridella (Lepidoptera , Gracillariidae) , which limits the use of Aesculus in landscaping in urban areas. The leaf miner C. ohridella is an insect widely spread in most European countries. The study was carried out in an urban area with six species of the genus Aesculus L. ( А e . hippocastanum L., A e . pavia L., A e . parviflora Walt., A e . flava Sol., A e . glabra Willd. and hybrid Aesculus × carnea ). The article reflects the results of research on the influence of the leaf miner C. ohridella on the peculiarities of changes in the chemical composition of chestnut leaf extracts. The component composition of the leaves of different ches t nut species was determined using chromatographic analysis. The study shows the results of the phytochemical screening of the leaves of different species from genus Aesculus , uninfested and infested with C. ohridella . A t otal of 186 chemical co m ponents were identified from the leaf extract s of Aesculus plants with six main groups of chemical components – carboh y drates, polyphenols, steroids, hydrocarbons, fatty acids and miscellaneous. The carbohydrates group is represented in the leaves of all studied Aesculus species in the maximum degree. The obtained data demonstrate significant change in the number of all chemical components under the influence of C. ohridella . The studied species showed an increase in carboh y drates content with the influence of leaf miner C. ohridella , except for such species as A e . flava and A e . parviflora . The qualitative composition of polyphenolic compounds was species-specific. The analysis of the composition of extracts of uninfested leaves established that four components were contained in extracts of Ae. hippocastanum and Ae. pavia , but they were different in their composition. In comparison with the composition of polyphenols in leaves without damage and leaves infected with C. ordidella , the absence of (2R-trans)-catechine in extracts from infected leaves of Ae. hippocastanum and Ae. glabra was found. U sing principal component analys i s , it was established that Ae. glabra shows a clear separation between chemical composition of the infested and uninfested samples. We identified two components of a phenolic nature only in extracts of infested leaves of Ae. flava , (trimethylsilyl catechollactate tris(trimethylsilyl) ether and epigallocatechin (6TMS)) , and quercetin was identified only in infected leaf extracts of Ae. glabra . Th ese results can indicate a greater pote n tial resistance of these Aesculus species to damage by the studied herbivory.