Thinning is a silvicultural process in which trees are both harvested selectively and systematically removed from a stand to enhance forest management and ecosystem dynamics. However, this practice is challenged by the mechanical damage to residual trees, and the nature of this damage, particularly on stand damage during mechanized row-thinning and manual selective thinning, in South Korea is unknown. Therefore, objectives of this study were to compare stand damage levels and wound characteristics between three different thinning operations: manual selective, manual row-, and mechanized row-thinning. After thinning, 12%, 15%, and 10% of the residual damaged trees were observed in manual selective, manual row-, and mechanized row-thinning, respectively. Both types of row-thinning predominantly demonstrated damages at the stem and butt region, whereas in manual selective thinning, the most of the damages occurred on the roots. Manual selective thinning exhibited a slightly larger average wound size (207 cm²) compared to mechanized row- (181 cm²) and manual row-thinning (165 cm²). The wound sizes on the stem in mechanized row-thinning were significantly higher than manual selective (p < 0.05). These results may be helpful in understanding exposure to damage among different thinning types and in managing its occurrence in future practices.

Forest biomass has recently been highlighted as a renewable energy source in South Korea. As the Renewable Energy Certificate weight has been revised to encourage the use of forest biomass for energy production, forest operations have focused on the development of new technologies and harvesting methods for the extraction and utilization of forest biomass. This study examined the application of a drum chipper to forest biomass and analyzed the effects of screen size (40, 50, 65, and 80 mm) on productivity, cost, and particle size distribution. When the screen size was 40 mm to 65 mm, the chipping productivity improved by up to 3.6 times, the fuel consumption decreased by up to 29.7%, and the chipping cost was reduced by up to 31.9%. The fines content tended to decrease with an increase in screen size. The size of woodchips was less than 100 mm in length and ranged from 10 to 63 mm. The 65 mm screen was the most effective (e.g., producing optimal wood chip size at an acceptable production rate) for the chipping operation. Harvesting methods and drying time are also important factors that determine the utilization efficiency of forest biomass. Therefore, further research should be focused on the effect of moisture content on chipping productivity, power required, and fine particle size in forest biomass productions.

Interest in the production of renewable energy using forest biomass is increasing in South Korea, and improved knowledge on operations logistics to lower biomass harvesting costs is needed. This study aimed to build a low-cost forest-biomass harvesting system by analyzing the costs of two integrated (cut-to-length and whole-tree) harvesting systems for logs and logging residues. Two integrated harvesting systems were carried out in the clear-cut mixed forest on a steep slope. Compared to the cut-to-length system that separately extracts logs and logging residues in a forest, the cable whole-tree harvesting system can save $8.8/green weight ton (Gwt) because it requires no additional yarding operation cost of logging residues. Moreover, a breakeven analysis shows that the required machine utilization rates that favor whole-tree harvesting systems over cut-to-length harvesting were more than 70% for cable harvesting systems. The introduction of the whole-tree harvesting system is, therefore, required to produce forest biomass at a low cost. In the future, studies on forest-biomass processing and transportation systems will be needed to provide a biomass feedstock supply cost from stump to biomass power plant.

This study develops forest road recognition technology using deep learning-based image processing to support the advancement of autonomous driving technology for forestry machinery. Images were collected while driving a tracked forwarder along approximately 1.2 km of forest roads. A total of 633 images were acquired, with 533 used for the training and validation sets, and the remaining 100 for the test set. The YOLOv8 segmentation technique was employed as the deep learning model, leveraging transfer learning to reduce training time and improve model performance. The evaluation demonstrates strong model performance with a precision of 0.966, a recall of 0.917, an F1 score of 0.941, and a mean average precision (mAP) of 0.963. Additionally, an image-based algorithm is developed to extract the center from the forest road areas detected by YOLOv8 segmentation. This algorithm detects the coordinates of the road edges through RGB filtering, grayscale conversion, binarization, and histogram analysis, subsequently calculating the center of the road from these coordinates. This study demonstrates the feasibility of autonomous forestry machines and emphasizes the critical need to develop forest road recognition technology that functions in diverse environments. The results can serve as important foundational data for the future development of image processing-based autonomous forestry machines.

Simulator training offers a safe and cost-effective approach to providing new operators opportunities to become familiar with operating modern machinery. However, in Korea, the current programs are insufficient in training skilled operators capable of handling advanced forestry machinery. Consequently, these programs fall short of developing the required technical expertise, leading to difficulties in workforce employment. We compared the performance of simulator-trained participants with that of machine-trained participants by testing operators on real equipment and assessing their stress levels. Participants were categorized as those with and without excavator certificates. Within each category, participants were further divided into those receiving training via simulators or those who were trained using actual equipment. Although we detected no significant differences in the overall performance of simulator- and machine-trained participants, compared with real machine training, simulator training promoted better performance, lower levels of frustration, and a reduced mental workload due to the safer and more controlled virtual environment. These findings can be used to develop more effective training programs by incorporating simulator-based modules that enhance skill acquisition whilst reducing risks. They can also inform policy decisions to improve workforce training in industries dependent on the operation of advanced machinery, thereby ensuring that operators achieve higher levels of competence and safety.

Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations.