Kalle Kärhä

Purpose of Review: Forest roads are vital for the relocation of wood harvesting machinery, secondary transportation, forest management, fire prevention, and recreational activities. Monitoring and maintaining the condition of a large and spatially distributed road network requires resource-efficient methods and new expertise. This review examines methods for measuring and predicting the condition and trafficability of unpaved forest roads, based on 87 publications from 2010 to 2025. The aim was to examine evaluated road features and the methods used to measure them. Recent Findings: Forest road condition is commonly evaluated by structural strength, surface quality, geometry, and drainage. Traditional techniques (e.g., bearing capacity measurements and visual inspections) are the most frequently used. Mobile mapping approaches and remote sensing methods, which include airborne laser scanning, unmanned aerial vehicle surveys, and satellite‑based observations, have been increasingly applied to assess road condition. Machine and deep learning methods have recently been used to identify potholes, rutting, and surface roughness based on image, video, and sensor data. Other approaches employed to predict and evaluate road condition include laboratory testing, traffic monitoring, transportation data, and meteorological records. Summary: Although traditional field methods remain dominant, emerging technologies are increasingly used to assess forest road condition. Reliable prediction of trafficability requires integrating multiple data sources. Despite technological advances, real-time dynamic road condition maps and situation awareness are lacking, and determining the actual condition of a road often requires a field visit. Novel digital solutions and data sources could improve condition forecasting and support more efficient forest road network maintenance. However, the generalizability of these findings may be limited by the scope of the review and language constraints.

Harvester data are a potentially valuable source of detailed information on forest inventory in first-thinning stands. In first-thinning operations performed using modern cut-to-length harvesters, all the trees within the strip roads are removed. The resulting systematic strip road network covers approximately 15–20% of the stand area. We investigated how strip-road trees can be identified from harvested production data to quantify within-stand variation and evaluate how strip-road sample placement affects the representativeness of the sample. Three different strip-road tree identification methods were evaluated: (1) a boom-angle and -length method; (2) a buffer method using reconstructed strip-road centrelines in a global coordinate system; and (3) a boom-angle method. The accuracy of the resulting diameter distributions, stem counts and basal-area estimates was assessed using Reynold’s error index and root mean square error metrics against detailed field reference data. In addition, theoretical offset strip roads were established and systematically shifted across each measurement area to quantify within-stand variation and sampling sensitivity. The most accurate stem count estimates were obtained using the boom-angle and -length method and the buffer method, whereas the boom-angle sector method performed poorly. Offset strip-road tree samples revealed substantial variation in stem count and basal area in some stands, indicating that strip-road alignment and spatial heterogeneity can significantly influence the representativeness of the sample. Our findings show that, in general, harvester-based strip-road sampling is a feasible and cost-efficient approach for estimating stand-level forest variables in first thinnings if tree identification methods and spatial biases are carefully considered.

Mechanical site preparation (MSP) is essential for successful forest regeneration. However, excessively exposed soil may harm the environment and increase the total costs of seedling stand management. A drill-based MSP device (drill), designed to reduce soil exposure, was recently tested in Finland. This study investigated the quality of drill-based MSP and the early performance of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) seedlings planted in different seasons on five mineral soil stands. The findings showed that the mean area of the drilling spots (0.18 m2) was about 20% of that reported for mounding. The drill mostly resulted in good (61%) or satisfactory (34%) planting spot quality (e.g., good spots were covered with mineral soil). Ground and soil obstacles, such as roots, stones, stumps, and logging residues, negatively affected planting spot quality. About 13% of the planting spots did not have a pure mineral soil cover, which is required to mitigate pine weevil (Hylobius abietis L.) damage. Most seedlings were still alive one year after planting, but the proportion of healthy seedlings was 71% for Norway spruce and 48% for Scots pine two years after planting. Overall, drill-based MSP may be most feasible for drier sites with less competitive ground vegetation. However, the productivity and costs, the performance of the planted seedlings, and other potential benefits of drill-based MSP should be further tested under varying operating conditions and compared with those of conventional MSP methods.

In the forest sector, the idle time of machinery has a significant impact on fuel consumption, greenhouse gas (GHG) emissions, and operating costs. This study investigated the idle times of forest machinery and timber trucks in Finland. Study materials were collected through questionnaires from small- and medium-sized logging ( loggers ) (N = 817) and timber-hauling ( timber-haulers ) (N = 339) enterprises (SMEs). The response rate from the loggers was 17.9% (n = 146) and 25.7% (n = 87) from the timber-haulers. The study revealed that only a quarter of the loggers and a half of the timber-haulers were aware of the idle times of their fleets. Of these, 55% of the loggers and 61% of the timber-haulers estimated their idle times at 6–14% of the total operating hours of their fleets. The results also indicated that idle time should be lower compared to current levels: respondents that were aware of idling suggested that 2–5% was a more desirable proportion, while the respondents that were unaware of idling rated desirable idle time at < 2% of total operating time. Respondents who had detected the idle times of their fleet noted significantly more opportunities to reduce idling than those who were unaware of idling. The work phases with the greatest potential to reduce idling times during logging operations were “Planning on the harvesting site” and “Operator breaks”. Correspondingly, “Waiting time at the destination (i.e., timber reception of the mill)” and “Driver breaks” were the phases with the most potential to reduce idling during timber-hauling. This study concluded that there is significant potential to reduce fuel consumption and GHG emissions by improving the awareness of the forest machine operators and truck drivers with regard to the idle times of machinery and vehicles. As such, more research data and operator/driver training are needed.

The advancement of harvester technology increasingly relies on automated forest analysis within machine operational ranges. However, real-world testing remains costly and time-consuming. To address this, we introduced the Tree Classification Framework (TCF), a simulation platform for the cost-effective testing of harvester technologies. TCF accelerates technology development by simulating forest environments and machine operations, leveraging machine-learning and computer vision models. TCF has four components: Synthetic Forest Creation, which generates diverse virtual forests; Point Cloud Generation, which simulates LiDAR scanning; Stem Identification and Classification, which detects and characterises tree stems; and Experimental Evaluation, which assesses algorithm performance under varying conditions. We tested TCF across ten forest scenarios with different tree densities and morphologies, using two-point cloud generation methods: fixed points per stem and LiDAR scanning at three resolutions. Performance was evaluated against ground-truth data using quantitative metrics and heatmaps. TCF bridges the gap between simulation and real-world forestry, enhancing the harvester technology by improving efficiency, accuracy, and sustainability in automated tree assessment. This paper presents a framework built from affordable, standard components for stem identification and classification. TCF enables the systematic testing of classification algorithms against known ground truth under controlled, repeatable conditions. Through diverse evaluations, the framework demonstrates its utility by providing the necessary components, representations, and procedures for reliable stem classification.

Purpose of the Review: This literature review focused on studies on alternative powertrains and fuels of non-road mobile machinery (NRMM) during the last 15 years and investigated their future potential and expectations. The goal was to evaluate different alternative powertrains based on previous research and highlight the possibilities and challenges of each technology. Additionally, the aim was to conduct a comprehensive overview about the technology development phase of alternative powertrains. Recent Findings: This review covered a total of 115 studies consisting of hybrid, full-electric, biofuels, biogas, and hydrogen solutions. The results highlighted that hybrid and full-electric technologies have the greatest potential to replace conventional diesel engines in the future. The main challenges identified were battery reliability and high technology costs. Regarding biofuel, biogas, and hydrogen, the benefits were mainly lower emissions while the challenges were high costs and low production. Full-electric and hydrogen powertrains were found to reach zero local emissions during operations, while compared to diesel, repair and maintenance caused less emissions of 36–46% during the life cycle with full-electric and hydrogen solutions. With hybrid, biofuels, and biogas powertrains, the emission reduction potential ranged from 37 to 81% during operations and 36–66% during the entire life cycle. The highest Technology Readiness Levels (TRLs) were identified for hybrid and full-electric technologies in industrial machinery (6.9–7.4). The lowest measurable TRL (2.5) was with biogas powered construction machinery. The TRLs of biogas and hydrogen of forest machinery were excluded from this review due to the lack of research. Summary: Alternative powertrains can eventually replace diesel engines, if the challenges with implementation, production, and reliability are solved. Furthermore, the benefits of electric and renewable technologies/fuels are unambiguous from the emission reduction and energy efficiency perspectives. Consequently, we recommend that future research focus especially on the implementation of alternative technologies as well as the improvement of the manufacturing infrastructure.

Evaluating the potential of a harvester-mounted LiDAR system in monitoring biodiversity indicators such as low vegetation during forest harvesting could enhance sustainable forest management and habitat conservation including dense forest areas for game. However, there is a lack of understanding on the capabilities and limitations of these systems to detect low vegetation characteristics. To address this knowledge gap, this study investigated the performance of a harvester-mounted LiDAR system for measuring low vegetation (height <5 m) attributes in a boreal forest in Finland, by comparing it with handheld mobile laser scanning (HMLS) and drone laser scanning (DLS) systems. LiDAR point cloud data was collected in September 2023 to quantify the low vegetation height (maximum, mean, and percentiles), volume (voxel-based and mean height-based) and cover (grid method). Depending on the system, LiDAR point cloud data was collected either before (HMLS and DLS), during (harvester LiDAR) or after (HMLS and DLS) harvesting operations. A total of 46 fixed-sized (5 m × 5 m) grid cells were studied and analyzed. Results showed harvester-mounted LiDAR provided consistent estimates with HMLS and DLS for maximum height, 99th height percentile, and volume across various grids (5 cm, 10 cm, 20 cm) and voxel (20 cm) sizes. High correlation was observed between the systems used for these attributes. This study demonstrated that harvester-mounted LiDAR is comparable to HMLS and DLS for assessing low vegetation height and volume. The findings could assist forest harvester operators in identifying potential low vegetation and dense areas for conservation and game management.