Purpose of the Review: Forest accessibility and road network density are important concerns not only for forest managers, but also for everyone who benefits from forests, whether for professional or personal use. A well-planned forest road network, integrated with the forest ecosystem, is a fundamental element of rational and efficient forest management. This review aims to describe the current state of forest road density and overall forest accessibility in Europe, as well as relevant global examples. Recent Findings: When a forest road network is distributed adequately across an area, it can support many, if not all, tasks outlined in forest management plans, with high efficiency and minimal costs during construction and maintenance. Collecting data on forest accessibility and primary forest road density at the national level in many countries is a complex task. Diverse terrain conditions, economic factors, and forest management practices have led to variability in the data. A common thread, however, is the optimistic outlook on the use of modern technologies for road network planning and data acquisition. Summary: Efficient forest management, especially harvesting operations, relies on a road network with appropriate density, load-bearing capacity, and alignment. Primary forest transport infrastructure helps reduce timber extraction costs by minimising extraction distances and shortening the travel time from the forest stand to the market. As such, forest road density and accessibility are crucial elements in planning forest operations. Despite differences in terrain conditions, harvesting systems, and economic contexts across Europe and globally, research consistently highlights the need to improve the efficiency of road network planning. Future research on forest accessibility should focus on integrating valuable data collection with scientific research and ensuring effective knowledge transfer to forest practitioners.

Processor tower yarders (PTY) represent the current state of yarding technology being extensively used in mountainous conditions such as those from Central Europe where they were also developed and used for the first time. In proper technical conditions which are mostly related to forest road infrastructure such equipment may be introduced by technology transfer in other countries such as Romania where they could replace actual less-efficient forest equipment used in steep terrains. The aim of this study was to evaluate the efficiency of such equipment in conditions of thinning operations by adapting a time study to the general concepts and by using data collection techniques to suit the operational conditions imposed by such equipment. In conditions of a mean tree volume of 0.21 m3 × tree-1, the results of our study indicate net production rates as high as 12.72 m3 × h-1 when processing trees on landing, which could be also improved up to 17.52 m3 × h-1 if the PTY have been be adequately installed on the forest road. Another key aspect which could improve the efficiency of such equipment performing landing operations is the number of planned and realized wood assortments since the time expenditure was affected by their number. Given the reduced impact on forest soils as well as the increased efficiency of tower yarders, our study concludes that there would be a lot of potential in actually using them in the Romanian forests located in steep terrain, if proper transportation infrastructure would exist.

Forest operations are well known in exposing their workers to many risk factors, and they often require ergonomic interventions for improvement. In this regard, evaluation of biomechanical exposure has gained a lot of interest due to the concerning scientific results repeatedly showing the association between poor working postures and the development of work-related musculoskeletal disorders. Due to its simplicity, easy understanding, cost affordability, and the capability to evaluate the whole body, the OWAS method has been commonly used in postural evaluation of forestry work, being able to map the experimental observations in a final action category, in the form of a postural risk index (PRI), which helps designing or taking actions for ergonomic improvement. However, postural comparability is both relevant and important when, for instance, one tries to improve a work method or to introduce a new technology. Unfortunately, the PRI metric holds a rather low capability to characterize the changes brought by such factors in terms of postural dissimilarity or similarity, making it difficult to accurately follow the changes. For this reason, we introduce in the postural analysis, test and discuss herein two commonly used similarity metrics as specific to plant sociology and other ecology-related sciences, namely the Sørensen’s quotient of similarity (hereafter QS) and the Canberra metric (hereafter CM); their selection was based on their mathematical capabilities of dealing with data at two resolutions, namely species and individuals. Three case studies were setup to show the differences between QS, CM, and PRI and their usefulness for postural analysis while, for a better understanding, the results were described and discussed by analogy to the living world. As the technology of automating data collection and processing for postural analysis is in progress, the utility of similarity metrics in postural assessment and comparison could be further expanded so as to map a given work sequence in the time domain against best-fit postural profiles. The main conclusion of this study is that the PRI is useful for action-taking while the similarity metrics are useful for pairwise postural change evaluations and comparison.

Research Highlights: A low-cost experimental system was developed to enable the production monitoring of small-scale wood processing facilities by the means of sensor-collected data and the implementation of artificial intelligence (AI) techniques, which provided accurate results for the most important work operations. Background and Objectives: The manufacturing of wood-based products by small-scale family-held business is commonly affected by a lack of monitoring data that, on the one hand, may prevent the decision-making process and, on the other hand, may lead to less technical efficiency that could result in business failure. Long-term performance of such manufacturing facilities is limited because data collection and analysis require significant resources, thus preventing the approaches that could be pursued for competitivity improvement. Materials and Methods: An external sensor system composed of two dataloggers—a triaxial accelerometer and a sound pressure level meter—was used in combination with a video camera to provide the input signals and meta-documentation for the training and testing of an artificial neural network (ANN) to check the accuracy of automatic classification of the time spent in operations. The study was based on a sample of ca. 90 k observations collected at a frequency of 1 Hz. Results: The approach provided promising results in both the training (ca. 20 k) and testing (ca. 60 k) datasets, with global classification accuracies of ca. 85%. However, the events characterizing the effective sawing, which requires electrical power, were even better recognized, reaching a classification accuracy of 98%. Conclusions: The system requires low-cost devices and freely available software that could enable data feeding on local computers by their direct connection to the devices. As such, it could collect, analyze and plot production data that could be used for maintaining the competitiveness of traditional technologies.