Kühmaier Martin, MSc.

Within the fuel wood supply chain, comminution and transport have been identified as processes
with the highest costs, energy consumption and emissions. The coordination of comminution
and transport aimed at avoiding operational delays is also complex. Nevertheless,
the use of forest biomass helps to reduce the effects of climate change and produces an additional
income, especially in rural areas. About 20 years ago, at the beginning of the industrial
forest fuel utilisation, the focus of the research was on developing and analysing adequate
supply chains and machines. Nowadays, as state-of-the-art systems have been established, the
focus is on improving the efficiency of the processes and the quality of the products. This paper
provides a review of research trends of the last ten years focusing on comminution and transport
of forest biomass in Europe.
Comminution should become more efficient by analysing the effects of wood characteristics on
chipper performance and product quality, by tailoring chipper configuration according to those
findings and by introducing mechanical devices for improving the quality of chips. Transport
processes have the potential to become more efficient if the configuration of trucks is adapted
according to operational and legal requirements, and when considering moisture content
management. Finally, economic and environmental assessment of supply chains was made by
several studies. Future research is expected to focus on customizing the product quality according
to user’s requirements and on optimising the coordination of chipper and truck by
simulation and automatization tools.

Introduction: Cable yarding is a technology that enables efficient and sustainable use of timber resources in mountainous areas. Carriages as an integral component of cable yarding systems have undergone significant development in recent decades. In addition to mechanical and functional developments, carriages are increasingly used as carrier platforms for various sensors. The goal of this study was to assess the accuracy of individual standing tree and stand variable estimates obtained by a mobile laser scanning system mounted on a cable yarder carriage.

Methods: Eight cable corridors were scanned across two forest stands. Four different scan variants were conducted, differing in the movement speed of the carriage and the direction of movement during scanning. An algorithm for tree detection, diameter and height estimation was applied to the 3D datasets and evaluated against manual tree measurements.

Results: The analysis of the 3D scans showed that the individual tree parameters strongly depend on the scan variant and the distance of each individual tree to the skyline. This was due to changing 3D point densities and occlusion effects. It turned out that scan variant 1, in which the scan was performed during slow carriage movement downwards and back upwards again, was advantageous. At a distance of 10 m, which is half of the recommended corridor spacing of 20 m for whole tree cable yarding, 95.44% of the trees in stand 1 and 92.16% of the trees in stand 2 could be detected automatically. The corresponding root mean sqare errors of the diameter at breast height estimatimations were 1.59 cm and 2.23 cm, respectively. The root mean square errors of the height measurements were 2.94 m and 4.63 m.

Conclusions: The results of this study can help to further advance the digitization of cable yarding and timber flow from the standing tree to the sawmill. However, this requires further development steps in cable yarder, carriage, and laserscanner technology. Furthermore, there is also a need for more efficient software routines to take the next steps towards precision forestry.

In the past decades, a considerable body of literature has emerged on timber tracking and tracing in the forest and timber industry supply chain. Therefore, a systematic literature review was conducted using an established method (»PRISMA«). To help define the relevance of timber tracking and tracing, this review addresses the subject areas, objectives and characteristics from scientific studies conducted over the past 25 years. In total 213 papers were included in the qualitative synthesis of the subject areas and objectives, with a 160 of those included in the characteristics synthesis to analyse comparable publication contents.

This study demonstrates the rationale behind the research efforts in the field of timber tracking and tracing. The results showed that the main key objectives were to combat illegal logging and trade, provide sustainable forest management, enable tracking and tracing, enhance efficienc, ensure legal compliance, determine the origin of timber and to identify species. The characteristics of the analysis methods used showed that genetic methods, physical chemistry methods, image methods, geomatics, certification, Radio-Frequency Identification (RFID) and smart technologies and software applications were most common. Most research activities were conducted in Asia and Europe. The majority of tracking and tracing methods were found to be highly practical. The application along supply chain dominated because of the high number of publications in genetic methods where a comprehensive application is possible. Furthermore, the forest, harvesting, and manufacturingwere identified as core application areas. Most studies lacked an economic evaluation of the developed solutions, which is a crucial aspect to consider for future successful implementation. The number of tree/wood species involved was notably extensive with a considerable diversity observed across continents. It will be essential that future research incorporates new technologies such as artificial intelligence (AI) that is currently emerging in the field of timber traceability. This can help achieve the identified objectives and address existing and future challenges through the self-learning property of AI.

In the past decades, a considerable body of literature has emerged on timber tracking and tracing in the forest and timber industry supply chain. Therefore, a systematic literature review was conducted using an established method (»PRISMA«). To help define the relevance of timber tracking and tracing, this review addresses the subject areas, objectives and characteristics from scientific studies conducted over the past 25 years. In total 213 papers were included in the qualitative synthesis of the subject areas and objectives, with a 160 of those included in the characteristics synthesis to analyse comparable publication contents.

This study demonstrates the rationale behind the research efforts in the field of timber tracking and tracing. The results showed that the main key objectives were to combat illegal logging and trade, provide sustainable forest management, enable tracking and tracing, enhance efficienc, ensure legal compliance, determine the origin of timber and to identify species. The characteristics of the analysis methods used showed that genetic methods, physical chemistry methods, image methods, geomatics, certification, Radio-Frequency Identification (RFID) and smart technologies and software applications were most common. Most research activities were conducted in Asia and Europe. The majority of tracking and tracing methods were found to be highly practical. The application along supply chain dominated because of the high number of publications in genetic methods where a comprehensive application is possible. Furthermore, the forest, harvesting, and manufacturingwere identified as core application areas. Most studies lacked an economic evaluation of the developed solutions, which is a crucial aspect to consider for future successful implementation. The number of tree/wood species involved was notably extensive with a considerable diversity observed across continents. It will be essential that future research incorporates new technologies such as artificial intelligence (AI) that is currently emerging in the field of timber traceability. This can help achieve the identified objectives and address existing and future challenges through the self-learning property of AI.