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.

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.

Mechanized timber harvesting is cost efficient and highly productive. However, mechanized harvesting operations are often associated with several environmental problems, including soil compaction and disturbance. Soil compaction impedes air circulation between the soil and atmosphere, which in turn results in increased concentrations of CO₂ within soil pores. In this study, we investigated the impact of forest machine traffic on soil conditions to determine soil CO₂ efflux (F_c), and soil CO₂ concentrations (S_c). Field measurements included soil bulk density (BD), soil temperature (ST), soil water content (SWC), F_c, and S_c over a 3-year period at a specific thinning operation site (Hwacheon-gun) in the Gangwon Province of Korea. To assess the soil impacts associated with machine traffic, we established four machine-treatment plots (MT) characterized by different geographical and traffic conditions. The results revealed that BD, S_c, and SWC increased significantly on the disturbed track areas (p < 0.05). Furthermore, reduced soil F_c values were measured on the soil-compacted (machine disturbed) tracks in comparison with undisturbed (control) areas. Variations in BD, SWC, and S_c were significantly different among the four MT plots. Additionally, in comparison with undisturbed areas, lower F_c and higher S_c values were obtained in compacted areas with high soil temperatures.

Cable yarding technology remains the most effective operation in steep terrain harvesting systems; however, it has limitations and challenges. Using cable yarders (tractor-, truck-, and excavator-based) to extract tree lengths and whole trees has been common since the late 20th century in South Korea, and cable yarding operations were developed in the late 1800s in the United States and Europe. Machine potential and limitations must be understood to ensure the widespread use of technology, strong cooperation, and optimal selection of machinery size. We reviewed the literature on tower yarder performances from 1990–2021 to determine the alteration of yarders and its productivity pattern and obtained 23 papers; <2 publications per year discussed the determination of cable yarding productivity. We selected independent variables (e.g., silvicultural treatment, harvesting method, and cycle log volume) for cable yarding that would likely affect productivity. Data were analyzed to compare productivities under silvicultural treatment, the harvesting method, and yarding direction and identify the interaction mechanical power (i.e., lifting capacity and machine power), yarding distance, and slope. Cable yarder productivity rates generally depended on the silvicultural treatment, harvesting method, and yarding direction, particularly in clear-cut, tree-length, and uphill yarding operation activities. The lifting capacity, machine power, and slope had no significant correlation with yarders’ productivity, particularly in thinning operations, whereas, in clear-cut productivity, it was influenced by these variables. The results contribute to improving operation activities for cable yarding systems and towards future research directions.

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.