Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher $R^2$ score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.

Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, accurate historical data on groundwater pollution is required to detect and monitor considerable environmental impacts. To collect such data appropriate sampling and assessment methodologies with an optimum spatial distribution augmented should be exploited. Thus, the configuration of water monitoring sampling points and the number of the points required are now considered as a fundamental optimization challenge. The paper offers and tests metaheuristic approaches for optimization of monitoring procedure and multi-factors assessment of water quality in “New Moscow” area. It is shown that the considered algorithms allow us to reduce the size of the training sample set, so that the number of points for monitoring water quality in the area can be halved. Moreover, reducing the dataset size improved the quality of prediction by 20%. The obtained results convincingly demonstrate that the proposed algorithms dramatically decrease the total cost of analysis without dampening the quality of monitoring and could be recommended for optimization purposes.

In a three-month experiment influence of different organic amendments (coal humate, peat-gel, biochar) in comparison with mineral ameliorants (NPK and CaCO₃) on chemical and physical properties of soils at different stages of degradation has been shown. Objects of the research were abrazem and podzol soils of technogenic barrens near Monchegorsk city, Murmansk region (Russia). Festuca rubra was used as a test-culture. According to the obtained data, we can conclude that organic applicants may be suitable for remediation of soils, contaminated by heavy metals. Applicants, selected for the experiment, unequally affected the soil properties and the test-culture growth. For the most disturbed and contaminated soils – abrazems –we suggest coal humate and calcium carbonate as the most promising additives for both toxicants’ immobilization and preventing bioaccumulation. Biochar and peat-gel in the tested concentration showed a more expressed positive effect on podzol soils with a lower level of contamination. In both soils, abrazem and podzol, biochar favors to the test-culture growth more, than other amendments. The potential advantage of used organic amendments, coal humate and biochar, in contrast to ameliorants is their ability to improve the physical soil properties.

The long-term emission impacts of the nickel processing industry in the Kola Peninsula, the largest source of sulfur dioxide and heavy metals emissions in Northern Europe, have created vast technogenic barrens near the mineral industry complexes. The pace of rehabilitation using the improved remediation technologies to enhance sustainable environmental management and regional economic development is of crucial social and economic importance. In a 120-day incubation experiment, we evaluated the prospects for the restoration of two soils at different degradation stages via carbon pool regulation comparing to mineral ameliorants – NPK fertilizer, and liming agent. Organic additives used included a humic preparation based on an alkaline brown coal extract, wood-derived biochar, and peat-derived gel, supplied by mycorrhizae fungi. The results demonstrate that the selected organic amendments are suitable for restoration of acidic metal contaminated soils. Specifically, the treatments provided a measurable increase in soil carbon content, a marked decrease in acidity, a decrease in extractable metal contents, together with an enhanced nutrient uptake and vegetative growth. A stabilization effect increased from biochar to peat-gel, liming agent and humic preparation, with an accompanying increase in soil pH. Although biochar showed a reduced ability to metal stabilization, the associated treatments were the most productive. The most effective amendments in multi-metallic contaminated soils need to be able to stabilize bioavailability of metals, adjust pH to the optimum for plant growth, and regulate nutrient consumption.