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The museum of the wood age
As a material, wood has no equal in strength, resilience, adaptability and availability. It has been our partner in the cultural evolution from woodland foragers to engineers of our own destiny. Tracing that partnership through tools, devices, construction and artistic expression, Max Adams explores the role that wood has played in our own history as an imaginative, curious and resourceful species. Beginning with an investigation of the material properties of various species of wood, The Museum of the Wood Age investigates the influence of six basic devices wedge, inclined plane, screw, lever, wheel, axle and pulley and in so doing reveals the myriad ways in which wood has been worked throughout human history. From the simple bivouacs of hunter-gatherers to sophisticated wooden buildings such as stave churches; from the decorative arts to the humble woodworking of rustic furniture; Max Adams fashions a lattice of interconnected stories and objects that trace a path of human ingenuity across half a million years of history -- Source other than Library of Congress.
Book
Carbon footprint and sustainability assessment of wood utilisation in Hungary
Forest management allows the sustained removal of significant amounts of carbon from the atmosphere. Within different activities in the forest, wood utilisation has the most significant man-made environmental impact which affects the carbon balance, which is important to know, to be able to accurately identify its role in climate change. This study aims to determine the carbon footprint of logging during utilisation based on scenario analysis in national default and theoretical assortment structures (11 additional scenarios for each forest stand) within the entire life cycle of raw wood products. Based on a common functional unit (100 m
3
of cut wood), a comparative environmental life cycle analysis (LCA) for intermediate and final cutting was performed in shortwood forestry work systems in beech (
Fagus spp.
), oak (
Quercus spp.
), spruce (
Picea spp.
), black locust (
Robinia pseudoacacia
), and hybrid poplar (
Populus x euramericana
) stands in Hungary. After obtaining the results, the present study calculated the carbon footprint order for the utilisation life cycle phases and the entire tree utilisation life cycle. The distribution of absolute carbon footprint (ACF: considered emitted CO
2
from fossil and biotic origins together) by final cutting exhibited the following order: hybrid poplar (6%)—spruce (8%)—beech (26%)—oak (27%)—black locust (33%). The ACF ranking for the whole technological life cycle (intermediate and final cutting, 400 m3 of cut wood) was hybrid poplar– spruce—oak—beech–black locust. The carbon footprint rankings of the studied stands were expanded to the national level.
Journal Article
A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects
In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.
Journal Article
Study on Wood in Houses as Carbon Storage to Support Climate Stabilisation: Study in Four Residences around Jakarta Municipal City
Global agreements mandate the international community, including Indonesia, to commit to reducing the risks and impacts of climate change. Indonesia’s Nationally Determined Contributions (NDCs) will contribute to the achievement of the Convention’s goals by reducing greenhouse gas (GHG) emissions and increasing climate resilience. This commitment must be supported by a wide range of actions, including the use of timber. Despite the fact that wood contains carbon, limited information is currently available on the size of the wood utilisation subsector’s contribution to reducing GHG emissions. More research is needed on the magnitude of wood products’ contribution to climate change mitigation. This study assessed the amount of carbon stored in wood used as a building material. Purposive sampling was used to select the cities with rapid housing development surrounding Jakarta’s capital city, i.e., the Bekasi District, East Jakarta City, Depok City, and Bogor District. The amount of carbon stored in wood was calculated according to EN 16449:2014-06 and energy dispersive X-ray spectroscopy (EDS/EDX) analysis. Results show that wood is currently only used in door frames, door leaves, window frames, shutters, and vents. The carbon stored on the components ranges from 450 to 680 kg (average of 554.50 kg) in each housing unit, according to the EN 16449:2014-06 calculation. The weight range is between 130 and 430 kg (average of 400.42 kg) according to EDX/S carbon analysis. With an increase in housing needs of 800,000 units per year, this amount has the potential to store 0.44 million tons of carbon over the lifespan of the products.
Journal Article
Australian Rainforest Woods
Australian Rainforest Woods describes 141 of the most significant Australian rainforest trees and their wood. The introductory sections draw the reader into an understanding of the botanical, evolutionary, environmental, historical and international significance of this beautiful but finite Australian resource. The main section examines the species and their wood with photographs, botanical descriptions and a summary of the characteristics of the wood. A section on wood identification includes fundamental information on tree growth and wood structure, as well as images of the basic characteristics.
With more than 900 colour images, this is the most comprehensive guide ever written on Australian rainforest woods, both for the amateur and the professional wood enthusiast. It is the first time that macrophotographs of the wood have been shown in association with a physical description of wood characteristics, which will aid identification. This technique was developed by Jean-Claude Cerre, France, and his macrophotographs are included in the book.
eBook
Wood defect detection based on the CWB-YOLOv8 algorithm
As an important renewable resource, wood is widely used in various industries. When addressing wood defects that limit the amount of wood used during processing, manual inspection and other technologies are not suitable for automated production scenarios. In this paper, we first establish our own dataset, which includes information about multiple tree species and multiple defects types, to enhance the overall applicability of the proposed model. Second, target detection technology involving deep learning is used for defect detection. The conditional parametric convolution (CondConv), Wise-IoU, and BiFormer modules are used to improve upon the latest YOLOv8 algorithm. Based on the experimental findings, the suggested approach exhibits notable improvements in terms of both the mAP@0.5 index and the mAP@0.5:0.95 index, surpassing the performance of the YOLOv8 algorithm by 3.5% and 5.8%, respectively. It also has advantages over other target detection algorithms. The proposed method can effectively improve wood utilization and automated wood processing technology.
Journal Article
Climate-change mitigation strategies at the level of a forestry company in the light of age-class legacy effects
Key message
We analyzed the future carbon balance of 47,000 ha of forests dominated primarily by Scots pine (
Pinus sylvestris
L.) and managed by the Szombathely Forestry Company in Hungary. Biomass, harvested wood products, and substitution effects were considered. Strong age-class legacy effects predetermine the biomass pool to turn into a carbon source with increased harvest. The highest harvesting intensity scenario proved most favorable for the overall carbon balance up to 2055.
Context
Forests and wood utilization play a key role in climate change mitigation by enhancing carbon sinks, increasing offsite carbon stocks, and promoting resource efficiency through material and energy substitution.
Aims
This case study examines the 47,000 ha forest managed by the Szombathely Forestry Company in western Hungary, dominated by climate-vulnerable coniferous species. Climate projections for the region indicate an inevitable shift to climate-resilient broadleaved species, requiring increased harvesting and regeneration. The study analyzed age-class structure, wood mobilization potential, and future carbon balances to assess the climate change mitigation impacts of intensified harvesting.
Methods
We used the Forest Industry Carbon Model, a yield table-based tool specifically designed to integrate data from the Hungarian Forest Authority’s database and to simulate forest stand-based carbon stock changes, wood product carbon balances, and substitution effects. We examined the future carbon balance under a business-as-usual scenario and scenarios with final harvest areas expanded by 10%, 20%, 30%, and 40%.
Results
Our analysis revealed strong age-class legacy effects, with a large area approaching harvesting age, signaling a key management decision. Our simulations indicated that biomass would become a carbon source if harvesting intensity increased by more than 10%, while a 40% increase was the most favorable scenario for the overall forest industry carbon balance.
Conclusions
We conclude that the company should base its management decisions on the broader carbon balance of the forest-based sector, while adhering to the Forest Authority’s harvesting age prescriptions to ensure long-term sustainability.
Journal Article
The regional economic impacts on the development of wood chip utilization in Maniwa city
This paper reports on an empirical investigation about regional economic impacts by the promotion of wood chip utilization in Maniwa city, Japan. We clarify the estimated potential of regional economy development by using input–output analysis for Maniwa city. The result indicates that 315 million JPY of woody chip products can be estimated to generate roughly 448 million JPY of direct, indirect, and induced economic effects. The value of 448 million JPY was equivalent to about 0.2 % of total production value in Maniwa. It is also expected to create approximately 21 jobs by promotion of wood chip utilization. We examined the regional economic impacts on the woody biomass utilization, and evaluated to estimate direct, indirect and induced economic effects, and job creation to clarify the economic impact on Maniwa. We will leave the negative economic effects and the environmental impacts in the process of woody biomass utilization as a future task.
Journal Article
ODCA-YOLO: An Omni-Dynamic Convolution Coordinate Attention-Based YOLO for Wood Defect Detection
Accurate detection of wood defects plays a crucial role in optimizing wood utilization, minimizing corporate expenses, and safeguarding precious forest resources. To achieve precise identification of surface defects in wood, we present a novel approach called the Omni-dynamic convolution coordinate attention-based YOLO (ODCA-YOLO) model. This model incorporates an Omni-dimensional dynamic convolution-based coordinate attention (ODCA) mechanism, which significantly enhances its ability to detect small target defects and boosts its expressiveness. Furthermore, to reinforce the feature extraction and fusion capabilities of the ODCA-YOLO network, we introduce a highly efficient features extraction network block known as S-HorBlock. By integrating HorBlock into the ShuffleNet network, this design optimizes the overall performance. Our proposed ODCA-YOLO model was rigorously evaluated using an optimized wood surface defect dataset through ablation and comparison experiments. The results demonstrate the effectiveness of our approach, achieving an impressive 78.5% in the mean average precision (mAP) metric and showing a remarkable 9% improvement in mAP compared to the original algorithm. Our proposed model can satisfy the need for accurate detection of wood surface defects.
Journal Article
Life cycle assessment of structural glued laminated timber production with different dimensions and exposure conditions
Glued laminated timber (glulam) is an essential material in modern wooden constructions that offers advantages in terms of strength and versatility. This study conducted a life cycle assessment (LCA) of glulam production in Japan and analyzed the environmental impacts based on different dimensions and exposure conditions. Primary data were collected from 12 factories representing 58.1% of Japan’s production. The assessment followed the ISO 14040/14044 standards and employed mass-based allocation as the primary approach, with economic allocation analyzed for comparison. The results revealed that the major contributors to greenhouse gas (GHG) emissions from glulam production were purchased lamina manufacturing (38%), transportation (27%), and electricity consumption (21%). Large-dimension glulam exhibited the highest environmental impact, largely due to their increased energy consumption. Despite the differences in adhesive types for various exposure conditions, their impact on overall emissions was relatively minor. A sensitivity analysis of the allocation methods revealed significant variations in the reported emissions, emphasizing the importance of methodological choices in LCA studies. This study provides geographically representative LCA data for glulam production in Japan, thereby contributing to improvements in sustainable manufacturing practices. These findings highlight the need to optimize raw material procurement, decarbonize energy, and improve transport efficiency to reduce environmental impacts. Future research should refine the LCA data quality, particularly for lamina production and international supply chains. These insights can support policy development and industrial efforts toward more environmentally sustainable wood utilization.
Journal Article