Efficient roadside vegetation management is essential for reducing infrastructure maintenance costs and enhancing bioenergy recovery. This study evaluates a modified excavator-based feller equipped with a specific arm to extend its operational reach. Field trials were conducted across three distinct site conditions highway embankment, overpass embankment, and riverbank to assess key performance parameters including cycle times (encompassing moving, positioning, felling, processing, and stacking), fuel consumption, and ground pressure. Compared to the conventional configuration, the arm modification yielded a 66% reduction in moving time and a 34% reduction in stacking time, albeit with a 73% increase in positioning time due to the extended reach. Overall, the modified machine demonstrated enhanced operational efficiency, reduced fuel usage by up to 7%, and decreased soil disturbance through a substantial reduction in the tracked surface area. These findings underscore the potential of telescopic boom technology to improve the versatility and sustainability of mechanised roadside biomass harvesting practices.

Soil conservation during forestry operations is an increasing concern. This study proposes a theoretical model to forecast the depth of ruts associated with harvester trail systems and organisational boundary conditions, with the aim of reducing the harmful impacts of heavy logging machinery. The research combines empirical data on soil penetration resistance and soil water content of six soil types with theoretical rut depth models, emphasising the organisational dimensions of forest operations. Empirical data were gathered from sixteen soil sample plots in Slovenia. Based on this data and existing litterature, six models were developed to show the relationship between soil penetration resistance and soil water content. The maximum harvester trail length was then calculated for each soil type, based on harvest intensity and rut depth models. The findings indicate that modelled rut dept is less than 0.1 m if the soil can withstand a nominal ground pressure of 849 kPa in all analysed soil types. However, modelled rut depth of any soil type will consistently exceed 0.1 m when the soil cannot withstand a nominal ground pressure of 456 kPa. To avoid deeper ruts, it is advisable to plan and use harvester trail lengths of no more than 100 metres at 40% volumetric soil water content when the soil type is unknown. Incorporating soil type data into rut models could be an effective means of predicting rut depth at a given time, thereby boosting the efficiency of forestry operations.

This paper examines the workload of the choker setter in timber extraction operations using the Konrad Mounty 4000 cable yarder. A time study was conducted, measuring the choker setter's heart rate and productivity at varying extraction distances for both »uphill« and »downhill« extraction directions. Heart rate data collected during work was analyzed to evaluate the choker setter's workload in relation to both extraction distance and direction. A total of 257 cycles were recorded across four worksites with similar harvesting performance characteristics. This heart rate data facilitated an analysis of the choker setter’s workload as influenced by field and technological factors.The results indicate that the workload comprises both physical and mental components and generally decreases with increasing extraction distance. The analysis of the measurements indicates that heart rate variability is significantly greater on »downhill« worksites compared to »uphill« worksites. This is a consequence of the different structure of working time at worksites with different directions of timber extraction, as well as the specific characteristics of the work process that affect the workload of the choker setter. Consequently the study has highlighted some insights that can guide the optimization of the choker setter’s workload as an essential factor in designing an efficient work process.

Fully mechanized hardwood operations usually rely on manual bucking, which may decrease potential volume and value recovery as compared to automatic bucking. This study aimed at evaluating bucking done in cut-to-length (CTL) operations, hypothesizing a possible increase of value recovery by using a bucking optimizer. Evaluation of bucking was done by a comparison of actual bucking with a mathematical solution – BuckR – a bucking optimizer based on dynamic programming. Results showed that the mathematical approach significantly outperformed manual bucking when quality was not considered, and only main stem products were included. Throughout 315 study trees (Acer saccharum, Acer rubrum, and Betula alleghaniensis) located in the Acadian forests of Eastern Canada, a mean increase of 112% in value and 84% in volume recovery per tree was reached through mathematical optimization. Contributions of mathematical bucking can be summarized by 1) a significantly higher number of sawlogs and a greater mean log length of sawlogs and pulp logs, 2) a two-fold increase in processed height, and 3) a considerable reduction in minimal diameters as compared to actual bucking. Those results illustrate the possibility to increase value and volume recovery in mechanized CTL operations and therefore to utilize the wooden resource more efficiently.

Mechanical site preparation (MSP) is widely performed around the globe to enhance the success of forest plantations. However, MSP can cause severe soil disturbance whose magnitude depends on various factors such as soil characteristics, soil moisture conditions and the type of machinery. In an experiment conducted in Northeastern France, we analyzed the combined effects of machine size and soil water content on the soil structural quality following MSP performed with subsoilers mounted on excavators. MSP was carried out at two dates with different soil moisture contents (average soil moisture content at 10 cm depth: 40–54% during the wet period, 32–34% during the dry period), using two excavators that differed in weight (2.7 T and 9 T) and engine power, in two forest stands that differed in their soil characteristics (a sandy loam and a clay loam) and that both showed compacted soils. We used a profile wall method (i.e. a visual soil estimation method) to describe the soil structural quality, 2 to 6 months after MSP was performed. All instances of MSP resulted in an increase in the volume of soil favorable to root growth, i.e., soil with a friable structure, compared to the unprepared control (+60%). No evidence of soil degradation due to MSP (compaction, smearing, puddling, voids) was found in comparison to the unprepared control. The larger excavator prepared the soil to a greater depth due to its larger MSP tool and its higher engine capacity (depth of 40 and 53 cm, for the small and large excavator, respectively). MSP performed during the dry period resulted in a greater volume of soil favorable to root growth than MSP performed during the wet period (+60%). Our results indicate that MSP provides better results when conducted during the dry period. Therefore, it is strongly recommended that forest operators carefully consider the timing of MSP operations and prioritize dry soil conditions to execute them.

Black locust (Robinia pseudoacacia L.) is a key tree species globally and in Hungary, valued for its economic benefits, adaptability, and ecosystem services. Despite its invasiveness and susceptibility to frost damage, its high-quality timber and significant nectar production make it economically important. This research, conducted as a collaboration between the Hungarian Forest Research Institute and the University of Debrecen, aimed to evaluate the applicability of remote sensing technologies in supporting black locust (Robinia pseudoacacia L.) research and monitoring efforts. A clonal trial established in 2020 in eastern Hungary aimed to assess the performance of newly bred black locust clones. Tree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches. Tree vitality was evaluated through UAS-based multispectral analysis using vegetation indices, including NDVI, GNDVI, NDRE, and LCI. Our findings revealed no significant differences (p>0.05) between UAS-based and traditional height measurements, confirming UAS as a reliable tool. Clones »NK2« and »PL251« showed superior growth (height of 7.6 m and 7.4 m) and health, while »Üllői« cultivar performed the weakest (5.3 m). Strong correlations were found between some vegetation indices (NDRE and LCI) and tree heights (r=0.593 and r=0.587), emphasizing the potential of remote sensing in efficient forest management. This study highlights the value of integrating UAS technology in forestry, offering cost-effective, accurate and comprehensive data for improving black locust cultivation practices.

Extracting key features from remote sensing imagery for the purpose of identifying tree species holds significant importance. To assess the importance of the newly proposed polygon area index (PAI) and other features in classifying tree species, this research employed WorldView-3/2 (WV-3/2) as the experimental dataset and constructed four distinct feature sets: spectral bands, HSVs (hue, saturation, and value), textures and PAIs. Then, these feature sets and their combinations were utilized, and the random forest method was applied to classify tree species and ascertain the importance of the features involved in the classification process. The experimental results demonstrate that all texture features play a crucial role in the identification of tree species, with eight features being selected. Additionally, numerous HSV features (WV-3: 29, WV-2: 21) and PAI features (WV-3: 7, WV-2: 4) also make significant contributions. However, the spectral bands did not show a notable positive impact, as none were selected. When compared with the use of single-type features, the integration of multiple features from WorldView-3/2 resulted in more effective tree species identification, achieving an overall accuracy of 90.45% for WV-3 and 84.60% for WV-2, surpassing the highest overall accuracy achieved by single-type features, which was 76.64% for WV-3 and 78.27% for WV-2. The experimental results indicate that multi-type features should be used for tree species classification. Additionally, the newly proposed PAI feature demonstrates a positive contribution and is recommended for active use in tree species classification.

Chainsaws are still a commonly used tool in the tending of young forests. The small volume of felled trees makes the use of powerful multi-operational machines economically unjustifiable. Moreover, the use of traditional petrol chainsaws carries a number of problems for the operator, such as noise, vibration, and exhaust fumes. Battery-powered tools can be an alternative to traditional petrol chainsaws. The purpose of this study was to determine the possibilities of using a battery-powered chainsaw during late thinning in terms of productivity and ergonomics. For the study, an ECHO ECCS-58V battery-powered chainsaw and a DOLMAR PS 5000 petrol chainsaw were used. Measurements were conducted in a 14-year-old pine stand. All measured noise level parameters were significantly higher for the petrol chainsaw. The average operating efficiency was 0.15 ha/hfor the battery-powered chainsaw and 0.16 ha/h for the petrol chainsaw. The petrol chainsaw ran for an average of 42.0 minutes on a single tank of fuel, while the battery-powered chainsaw ran for an average of 41.0 minutes on a single battery. It was found that the battery-powered chainsaw achieved very similar productivity. With the availability of a sufficient number of batteries, it can successfully replace traditional petrol chainsaws during the tending of young forests. This will significantly reduce the negative impact of noise, vibrations, and exhaust fumes on the operator, while maintaining satisfactory productivity.

In today's modern forestry, which is founded on sustainable management and development, it is essential to carry out the design phase of forest roads with quality, both for financial and environmental reasons. The basis for this is high-quality field data. This research tested available and potentially suitable methods for field surveys in the process of designing a forest road, including the classical survey method, GNSS and total station survey, as well as structure from motion and airbone lidar scanning survey. A total of 23 forest road designs were created through 3 design methods. Four important forest road parameters were tested: cut and fill volume, carriageway value and roadway width. No statistically significant difference was found for any of the tested parameters between the methods. Total station and ALS-based designs showed the lowest values of cut and fill volumes, 2.41 m3 and 2.54 m3 for the total station design, and 3.20 m3 and 2.47 m3 for the ALS design. Although some deviations were found between SfM designs based on different flight parameters, they were not significant. The results indicate the possibility of using all tested methods in forest areas with steep terrain, after a salvage logging has been performed. Also, by using three design methods and testing their results provides guidance how to test different measurement systems in the future when designing forest roads.

Forest road planning is a key component of sustainable forest management, as it ensures access to harvesting, transport, and protection activities while minimizing environmental impacts. Recent advances in digital terrain models (DTMs) and automated design tools have opened new possibilities for improving the efficiency of road alignment planning. This study evaluates the potential of an AI-driven corridor planning system (Path Explorer) integrated into RoadEng software compared with the traditional zero-line method in designing the forest road »Osmača–Compartment 56« in Bosnia and Herzegovina. Two DTMs with resolutions of 20×20 m and 90×90 m were used to generate alternative alignments, which were then refined in the Location module to produce preliminary projects and compared with the operational (field-designed) alignment. The results indicate that AI-driven preliminary designs achieved shorter alignments and lower estimated construction costs and earthwork volumes, suggesting potential cost savings under the tested conditions. The operational design, based on field measurements, remained the most accurate and suitable for final implementation in complex terrain. The study emphasizes that these conclusions are limited to the tested DTM resolutions and the specific case study area. Nevertheless, the integration of AI-driven corridor planning systems into early design phases can enhance the efficiency and objectivity of forest road planning, supporting more informed and sustainable engineering decisions.