Illés, G., Kovács, G. and Heil, B. 2011. Comparing and evaluating digital soil mapping methods in a Hungarian forest reserve. Can. J. Soil Sci. 91: 615–626. To investigate applications of widespread digital soil mapping methods in forestry management, soil maps for a Hungarian forest reserve were developed using general discriminant and classification tree analysis as predictive tools. Soil samples were collected applying an unaligned semi-systematic grid. Second level units of the World Reference Base of Soil Resources and their yield capacity were determined. Terrain attributes were derived using a digital elevation model, and they were assigned to soil data to be used as predictors for second level units of the World Reference Base for Soil Resources (SLU) maps. A comparison was made of prediction accuracy. Both the discriminant analysis and the classification tree-based prediction were able to derive SLU maps; however, the classification accuracies were uneven. The methods used provided 63–65% average classification accuracy for dominant SLUs, but only 0–18% in the case of less common SLUs. One of the major issues of digital soil mapping that needs to be addressed is that the same inputs may result in different output maps depending on the use of spatial predictions. To overcome this problem we created a new combination of these methods in which the classification accuracies were used to select the most appropriate prediction. For each location, the method that gave higher prediction accuracy was used to extend the soil map to unknown areas. In this way we improved the overall accuracy of output maps as well as the prediction accuracies of individual SLUs.

Coffee ranks high in terms of growth, trade, and consumption across the world. Although there has been an increasing trend in its consumption as a beverage across the globe, its on-farm production and productivity are threatened by climate change in the dominant coffee-growing regions. Smallholder farmers produce about 80% of the world’s coffee and are constrained in adopting climate-smart improved technologies. Climate change and livelihoods are interconnected, and understanding and strengthening their linkages is crucial in generating sustainable coffee supplies. This paper analyzed climate change and mitigation components in the context of livelihoods under smallholder coffee farming systems. An online search of globally published journal articles was done in the Web of Science and Google Scholar databases. Bibliographic and metadata analysis was done using VOSviewer software, while the publication trend of the included articles was analyzed using Mann–Kendall. Overall, this review reveals that livelihood assets owned by the coffee farmer have a strong bearing on the adaptation and mitigation of climate change, while Fairtrade certification has mixed effects on farmers’ income and well-being. Agroforestry is a major climate change adaptation strategy under coffee farming and is linked to the livelihood status of the farmer, gender, and certification. This review echoes the strong linkage between agroforestry, livelihood components, and Fairtrade certification under smallholder farmers based on the empirical researched information available.

Plantation forestry has a long history in Europe and still supports local industry, generating employment, improving environmental quality, and mitigating climate change. As part of these plantations, medium-rotation poplars (5–8 years) provide good quality logs for fiber production, and the branches and tops can be converted into green energy. Finding a cost-effective harvesting system for this plantation is challenging due to the small tree size and the need for log production, which prevents whole-tree chipping. To verify the economic benefit of using small mechanized cut-to-length (CTL) technology, four different CTL chains were tested in western Slovakia. All chains tested consisted of a harvester and a forwarder. Each machine had a skilled operator and was timed while cutting and processing (or forwarding) eight experimental sample plots. Sample plots were randomly assigned to each treatment, and each one covered an area between 0.08 and 0.10 ha (120–170 trees). Harvester productivity ranged from 2.2 to 4.2 bone-dry tons per scheduled machine hour (BDT SMH⁻¹), and harvester cost from EUR 11 to EUR 22 BDT⁻¹. Forwarding productivity and cost ranged from EUR 2.0 to 4.5 BDT SMH⁻¹ and from EUR 9 to 20 BDT⁻¹. Total harvesting costs ranged between EUR 26 and 36 BDT⁻¹. Choosing a smaller harvester is preferable due to the small tree size, which caps productivity regardless of a machine’s intrinsic potential. Furthermore, small harvesters and forwarders are lighter on the ground, which can be a valuable asset on the many wet sites planted with poplar.

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.

We present our carbon stock estimation method developed for mixed coniferous and deciduous forests in the Hungarian hilly region, covering diverse site conditions. The method consists of four complex steps, integrating traditional field surveys with modern remote sensing and GIS. The first step involves comprehensive field data collection at systematically distributed sampling points. The second step is tree species mapping based on satellite image time series. The third step uses Airborne Laser Scanning to estimate aboveground biomass and derive the carbon stock of roots. The final step involves evaluating and spatially extending field and laboratory data on litter and humus from sampling points using geostatistical methods, followed by aggregating the results for the forest block and individual forest sub-compartments. New elements were developed and implemented into the complex methodology, such as aboveground biomass calculation with voxel aggregation and underground carbon stock spatial extension with EBK regression prediction. Additionally, we examined how the accuracy of our method, designed for a 200 m sampling grid, decreases as the distance between sampling points increases.

Sustainable management of land and its ecosystem requires well-documented Land-use and Land Cover changes (LULCCs) that have occurred over time. The objective of this study was to document the LULCCs, determine their relationship with climate and population changes in Western Uganda, and provide evidence to support decision-making in this region. This study utilized satellite images for the years 1992, 2000, 2010, and 2020 obtained from the European Space Agency Climate Change Initiative (ESA CCI), Climatic data from NASA, and Population Data from the Uganda Bureau of Statistics (UBoS). LULCCs and their influencers were characterized in the region, for the period 1992 to 2020. ArcGIS Pro Software Program was used to Filter time series data using Definition Query, while the scatterplot was employed to determine the relationship between changes in climate and population on LULCCs. Results indicate that LULC is dominated by agriculture, covering (66.46%), followed by forests (16.22%), waterbodies (8.0%), grassland, shrubland, wetland, and urban areas at (6.11%), (2.63%), (0.49%) and (0.1%) respectively. From 1992 to 2020, the area under agriculture, forest and urban increased by 0.1%, 0.1%, and 0.08%, respectively, while Grassland, wetland, and shrubland declined by (0.22%), (0.05%), and (0.01%) respectively. LULCCs for areas under wetland, grassland, and urban areas have a stronger relationship with precipitation and population growth. This information can be used by the decision-makers at the local, district, and national levels to better guide land-use practices aimed at sustainable land-use management for the current and future generations.