Climate change particularly threatens the xeric limits of temperate-continental forests. In Hungary, annual temperatures have increased by 1.2 °C–1.8 °C in the last 30 years and the frequency of extreme droughts has grown. With the aim to gain stand-level prospects of sustainability, we have used local forest site variables to identify and project effects of recent and expected changes of climate. We have used a climatic descriptor (FAI index) to compare trends estimated from forest datasets with climatological projections; this is likely for the first time such a comparison has been made. Four independent approaches confirmed the near-linear decline of growth and vitality with increasing hot droughts in summer, using sessile oak as model species. The correlation between droughts and the expansion of pest and disease damages was also found to be significant. Projections of expected changes of main site factors predict a dramatic rise of future drought frequency and, consequently, a substantial shift of forest climate classes, especially at low elevation. Excess water-dependent lowland forests may lose supply from groundwater, which may change vegetation cover and soil development processes. The overall change of site conditions not only causes economic losses, but also challenges long-term sustainability of forest cover at the xeric limits.

This study explored the land use and land cover (LULC) changes (1995–2023) in the gold mining hotspots of Mang’onyi, Sambaru, and Londoni in the Singida region of Tanzania. The study integrated remote sensing (RS) to evaluate the LULC transitions with social survey assessments (83 respondents) to determine the resident’s perceptions of the environmental, social, and economic implications of mining bridging technical data with socio-economic realities. Supervised classification of Landsat images was conducted using the random forest (RF) classifier to generate LULC maps with five classes (bareland, agricultural land, forest, built-up, and shrubs and grasses), followed by an analysis to identify LULC change trends. The results showed an overall increase in agricultural land 168.51 km² (587.55%), bareland 7.70 km² (121.45%), and built-up areas 0.55 km² (134.15%), while forest and shrubs and grasses areas declined by 97.67 km² (− 72.59%) and 79.09 km² (− 43.49%), respectively. A social survey assessment revealed residents perceived environmental (deforestation, biodiversity loss, land degradation, water, air, soil pollution), social (occupational hazards, land use conflicts, negative effects on livelihoods and culture, discrimination, child labor, community displacement), and economic (improved housing, infrastructural development, job creation, economy boost, improved access to services) impacts resulting from mining activities. Our findings underscore the importance of balancing the economic benefits of gold mining with the imperative to protect the environment and support sustainable livelihoods in the mining regions.

The Darfur conflict, which emerged in the early 21st century, represents a multifaceted crisis driven by socio-political and environmental factors, with resource scarcity, exacerbated by climate change, playing a pivotal role in intensifying tensions between agricultural and pastoral communities. While climate change is typically associated with adverse environmental outcomes, an analysis of data spanning four decades (1980–2023) reveals a contrasting trend of increased precipitation, enhanced vegetation, and decreased drought frequency in recent years. This research explores the potential of these positive environmental changes to mitigate resource-based conflicts and foster political stability in Darfur as improved environmental conditions are posited to create a foundation for conflict resolution and sustainable peacebuilding. The present study integrates trends in the Enhanced Vegetation Index (EVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to examine these shifts. EVI data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250 m resolution, was used to assess large-scale vegetation patterns in arid and semi-arid landscapes. The Autoregressive Integrated Moving Average (ARIMA) model was employed to forecast future precipitation scenarios up to the year 2034, enhancing the understanding of long-term climatic trends. Data processing and analysis utilized advanced tools, including Google Earth Engine (GEE), ArcGIS Pro (version 3.4), and R software (version 4.3.2). The findings reveal a significant (33.19%) improvement in natural vegetation cover between 2000 and 2023, with degraded and unchanged areas accounting for 1.95% and 64.86%, respectively. This finding aligns with a marked increase in annual precipitation and a reduction in drought intensity over the study period. Historical SPEI analysis showed persistent drought events between 1980 and 2012, followed by a notable decline in drought frequency and severity from 2013 to 2024. Precipitation projections suggest a stable trend, potentially supporting further vegetation recovery in the region. These environmental improvements are preliminarily linked to climate-change-induced increases in precipitation and reductions in drought severity. This study’s findings contribute to a nuanced understanding of the interplay between environmental dynamics and socio-political stability in Darfur, offering actionable insights for policy interventions aimed at fostering sustainable peace and resilience in the region.

Satellite image classification serves a critical function across various applications, from land cover mapping and urban planning to environmental monitoring and disaster management. In recent years, significant advancements in machine learning and computer vision, coupled with increased accessibility to satellite imagery, have driven considerable progress in this field. Classification techniques for satellite imagery can be primarily divided into three key approaches: automatic, manual, and hybrid. Each approach offers unique advantages but also comes with its own set of limitations. While most methodologies gravitate toward automatic techniques, choosing an appropriate method should be a carefully considered decision based on specific needs. This paper provides an exhaustive review of cutting-edge classification algorithms, including Artificial Neural Networks (ANNs), Classification Trees (CTs), and Support Vector Machines (SVMs). It also offers a comparative analysis between these modern methods and traditional techniques, focusing on their respective performance metrics when applied to satellite data. This study examines key factors affecting remote sensing data classification, including classifier parameter adjustments and combining multiple classifiers. It reviews existing literature to enhance feature selection and classifier optimization for better accuracy. However, it also points out the continuous need for research in image processing to improve classification accuracy.

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment.