Forestry, Faculty of
DSpace
UBCV
Ji, Airu
Master of Science - MSc
University of British Columbia
Amabilis fir and western hemlock are an important softwood lumber resource in British Columbia (BC). For several reasons, the available wood volume in BC has consistently decreased since 1990. This decrease suggests that sawmilling processes should become more efficient in utilizing wood. In lumber manufacturing, considering knot characteristics and distribution within lumber pieces in the optimisation of the primary breakdown patterns would significantly impact the product quality or grade. The goal of the project was therefore to investigate the impact of growth history on knot characteristics and how they, in turn, influence the manufacturing of lumber in Coastal and Interior sawmills and build predictive models. Computed tomography (CT) scanning can non-destructively detect knots in wood and is gaining acceptance in the wood industry. Seventy-two amabilis fir and western hemlock trees from three plots located on Vancouver Island, BC were scanned, and images were processed to extract knot characteristics and distribution to reconstruct three-dimensional (3D) log models. The effects of three diameter at breast height (DBH) classes (30, 40 and 50 cm) and three sites on knot characteristics, including knot volume, number of knots, average knot area on CT image sections and knot/tree volume ratio, were investigated. As expected, the knot characteristics of both species increased with the DBH. The difference of knot distribution between amabilis fir and western hemlock suggests that the latter is more sensitive to growth conditions of temperature, precipitation and sunlight. The 3D log models were then processed in Optitek to simulate the sawmill production and assess the impact of the DBH classes and sites (including knots) on the lumber and value recovery in Coastal and Interior sawmills under normal, optimistic, and pessimistic economic cycles. The sawmilling simulations revealed that the Coastal mill produced a lower lumber volume but a higher value due to the type of products manufactured and the primary breakdown patterns being used. The sawmilling simulation results were compared and used in predicting the value of standing amabilis fir and western hemlock trees. Models were developed based on the knot characteristic and tree features to predict the value of a standing tree.
USING X-RAY SCANNED RECONSTRUCTED LOGS TO PREDICT KNOT CHARACTERISTICS AND LOG VALUE by Airu Ji B.Sc., Beijing Forestry University, 2018 B.Sc. (Wood Products Processing), The University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Airu Ji, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Using x-ray scanned reconstructed logs to predict knot characteristics and log value submitted by Airu Ji in partial fulfillment of the requirements for the degree of Master of Science in Forestry Examining Committee: Julie Cool, Forestry, UBC Supervisor Isabelle Duchesne, Canadian Wood Fibre Centre, Natural Resources Canada Supervisory Committee Member Simon Ellis, Forestry, UBC Supervisory Committee Member Peter Marshall, Forestry UBC Additional Examiner iii Abstract Amabilis fir and western hemlock are an important softwood lumber resource in British Columbia (BC). For several reasons, the available wood volume in BC has consistently decreased since 1990. This decrease suggests that sawmilling processes should become more efficient in utilizing wood. In lumber manufacturing, considering knot characteristics and distribution within lumber pieces in the optimisation of the primary breakdown patterns would significantly impact the product quality or grade. The goal of the project was therefore to investigate the impact of growth history on knot characteristics and how they, in turn, influence the manufacturing of lumber in Coastal and Interior sawmills and build predictive models. Computed tomography (CT) scanning can non-destructively detect knots in wood and is gaining acceptance in the wood industry. Seventy-two amabilis fir and western hemlock trees from three plots located on Vancouver Island, BC were scanned, and images were processed to extract knot characteristics and distribution to reconstruct three-dimensional (3D) log models. The effects of three diameter at breast height (DBH) classes (30, 40 and 50 cm) and three sites on knot characteristics, including knot volume, number of knots, average knot area on CT image sections and knot/tree volume ratio, were investigated. As expected, the knot characteristics of both species increased with the DBH. The difference of knot distribution between amabilis fir and western hemlock suggests that the latter is more sensitive to growth conditions of temperature, precipitation and sunlight. The 3D log models were then processed in Optitek to simulate the sawmill production and assess the impact of the DBH classes and sites (including knots) on the lumber and value recovery in Coastal and Interior sawmills under normal, optimistic, and pessimistic economic cycles. The sawmilling simulations revealed that the Coastal mill produced a lower lumber volume but a higher value due to the type of products manufactured and the primary breakdown patterns being used. The sawmilling simulation results were compared and used in predicting the value of standing amabilis fir and western hemlock trees. Models were developed based on the knot characteristic and tree features to predict the value of a standing tree. iv Lay Summary Wood volume in BC has been decreasing over the last century, which puts pressure on the lumber producers to manufacture higher yield or better quality of lumber. To achieve this goal, the optimisation of breakdown patters could be improved by knowing the position of knots and their distribution. Such strategies could also be used to maximize wood utilisation and harvest less trees from forests. Computed tomography (CT) scanning provides a non-destructive method to detect knots and assist sawmill production. Knot characteristics were compared on different diameter classes of amabilis fir and western hemlock trees harvested from three stands. This work utilized the information of knots to build models predicting factors not directly measurable, e.g., knot characteristics, the lumber recovery and the value of a tree. The model could assist in the decision-making process of sawmills and forest managers. v Preface The thesis is an original and unpublished product of the author, Airu Ji under the supervision of Dr. Julie Cool. Parts of the methodology presented in Chapter 3 (i.e., sections 3.1 to 3.3) is based on the work Characterizing the Wood Attributes and Product Potential of 60-Year-Old Hem-fir in Coastal British Columbia done by Middleton & Munro (2013). Authorization to use the computer tomography images, which had never been used in a research context, was given by FPInnovations. Therefore, the core of the project (i.e., sections 3.4 to 3.6) was executed by Airu Ji and none of the text of the thesis is taken directly from previously published articles or technical reports. The software used to extract knot information was developed by Steve Vallerand, who provided software training at the beginning of the project. The software for simulating the sawmilling operations, Optitek, belongs to FPInnovations and was licensed to the student and the supervisor for the duration of the project. FPInnovations, who was a partner in the project, also provided the Optitek layouts for the Coastal and Interior sawmills as well as the sales prices for the normal economic scenario. The simulation of the lumber breakdown was done by Dr. Julie Cool and Airu Ji collectively to reduce the processing time. The development of the statistical models presented in Chapter 4 (i.e., sections 4.2, 4.4 and 4.5) is based on the contribution from Dr. Hyeyoung Woo. Airu Ji was responsible for the interpretation of the models and data analysis. vi Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface..............................................................................................................................................v List of Tables ............................................................................................................................... viii List of Figures ..................................................................................................................................x List of Abbreviations .................................................................................................................... xii Acknowledgements ...................................................................................................................... xiii Dedication .................................................................................................................................... xiv Chapter 1: Introduction ................................................................................................................... 1 Chapter 2: Literature review ........................................................................................................... 5 2.1 Silviculture .......................................................................................................... 5 2.2 Wood quality ....................................................................................................... 9 2.3 Computerized tomography................................................................................ 11 Chapter 3: Methodology ............................................................................................................... 17 3.1 Stand selection .................................................................................................. 17 3.2 Tree selection .................................................................................................... 18 3.3 X-ray CT scanning ............................................................................................ 19 3.4 Data preparation ................................................................................................ 20 3.5 Simulation of tree breakdown ........................................................................... 25 3.6 Statistical analysis ............................................................................................. 30 Chapter 4: Result and discussion .................................................................................................. 35 4.1 Growth history .................................................................................................. 35 4.2 Model 1: predicting knot characteristics ........................................................... 42 4.3 Sawmilling simulation ...................................................................................... 46 4.4 Model 2: predicting individual tree recovery.................................................... 51 4.5 Model 3: predicting individual tree value ......................................................... 52 Chapter 5: Conclusion................................................................................................................... 57 5.1 General conclusions .......................................................................................... 57 vii 5.2 Limitations of the study .................................................................................... 59 5.3 Recommendations for future research and applications ................................... 60 Bibliography ..................................................................................................................................62 Appendices .....................................................................................................................................74 viii List of Tables Table 3.1: Summary of stand characteristics for the three sites where amabilis fir and western hemlock trees were harvested. Stands 1 and 3 were located in Port Alberni, while Stand 2 was located in Port McNeill. ................................................................................................................ 18 Table 3.2: Distribution of trees by diameter at breast height (DBH) classes for both amabilis fir and western hemlock. ............................................................................................................... 18 Table 3.3: Optimization prices for green lumber products manufactured in the generic BC Coastal sawmill. Prices are listed for products having different thickness (T), width (W), lengths (L), and grades. ............................................................................................................................. 28 Table 3.4: Optimization prices for green lumber products manufactured in the generic BC Interior random length sawmill. Prices are listed for products having different thickness (T), width (W), lengths (L), and grades. ........................................................................................................ 29 Table 3.5: Percentages used to calculate the optimist and pessimist economic scenarios based on different grades of lumber. ....................................................................................................... 30 Table 3.6: The stepwise selection of explanatory variables and their interactions based on their significance for each of the four response variables. ............................................................ 32 Table 3.7: Akaike information criterion (AIC) values of ten linear mixed models using up to four knot characteristics and the sawmill type as explanatory variables. ..................................... 33 Table 3.8: Variance inflation factor (VIF) values for models 5 and 10. ............................... 33 Table 4.1: The significance of site and DBH class on total knot volume, number of knots, average knot area and knot/tree ratio. An asterisk indicates a significant P-value at the 95% confidence level. ........................................................................................................................... 35 Table 4.2: Total knot volume, number of knots, average knot area and knot/tree ratio for three DBH classes of amabilis fir and western hemlock trees harvested from three different sites. ..... 36 Table 4.3: Means and standard deviations of tree volume, total knot volume, average tree area and knot/tree ratio for western hemlock trees harvested from 3 different sites. ........................... 40 Table 4.4: Site differences for potential factors on affecting knots growth of western hemlock........................................................................................................................................................ 40 ix Table 4.5: Average temperature and precipitation from weather stations located in Port Alberni and Albert Bay during 1971-2000. Port Alberni A is located close to Sites 1 and 3, while Albert Bay is near Site 2. .............................................................................................................. 40 Table 4.6: Estimations of coefficients for predicting knot characteristics. An asterisk indicates a significant P-value at the 95% confidence level. ....................................................................... 43 Table 4.7: Means and standard deviations (in parenthesis) of lumber recovery (%) based on real CT-scanned log volumes obtained from amabilis fir and western hemlock logs from three DBH classes and virtual lumber volumes obtained by simulation using a generic Interior random sawmill and a generic Coastal sawmill. ........................................................................................ 49 Table 4.8: Means and standard deviations (in parenthesis) of total values of lumber in a normal economic cycle obtained from amabilis fir and western hemlock logs produced by the virtual Interior random and Coastal mills in three DBH groups. .................................................. 50 Table 4.9: Linear mixed model for predicting lumber recovery. One hundred and forty-four observations and 72 groups were used.......................................................................................... 51 Table 4.10: Fixed effect part for model predicting grade distribution in Interior random mill and Coastal mill. ........................................................................................................................... 53 Table 4.11: AIC values for predicted value of logs on Grade 1-4 in Interior mill and Grade 1-2 in Coastal mill. ........................................................................................................................... 54 Table 4.12: Models of value prediction under normal, optimistic and pessimistic economic scenarios in Interior random mill and Coastal mill (standard errors in parenthesis). ................... 55 x List of Figures Figure 2.1: Five types of knots. Adapted from Qu et al. (2020). .......................................... 11 Figure 2.2: Schema showing the difference between the (a) fan beam, (b) multiple fan beam and (c) cone beam tomography. Adapted from Giudiceandrea et al., (2011). .............................. 12 Figure 3.1: Locations of three experimental stands on Vancouver Island, BC. Stands 1 and 3 were situated near Port Alberni, while Stand 2 was located close to Port McNeil. ...................... 17 Figure 3.2: Example of an X-ray CT image of an amabilis fir log cross section. ................ 19 Figure 3.3: Example of an X-ray CT image of an amabilis fir log tangential section. ......... 20 Figure 3.4: An X-ray CT image of an amabilis fir log cross section characterized by sapwood having a higher moisture content than that of heartwood and the presence of four knots. .......... 20 Figure 3.5: CT images of a cross section of an amabilis fir log (a) before and (b) after using the canny edge detector. ................................................................................................................ 21 Figure 3.6: The geometric centre method compares the position between a perfect circle (red circle) and the outer shape of the log (black shape). The geometric centre of the circle (red dot) is first determined and then re-positioned based on the area difference between the two shapes (yellow dot). .................................................................................................................................. 22 Figure 3.7: Pith to bark ring wood density for the three DBH classes studied for (a) amabilis fir and (b) western hemlock (adapted from Middleton and Munro, 2013). .................................. 23 Figure 3.8: An X-ray CT image of extracted knots from an amabilis fir log without removing the pith area. .................................................................................................................................. 23 Figure 3.9: Result comparison of applying an erosion value of (a)15 vs. (b) 5 in an amabilis fir X-ray CT image........................................................................................................................ 23 Figure 3.10: A CT image of a cross section of one an amabilis fir after processing using CT2Opti. ....................................................................................................................................... 24 Figure 3.11: Examples of log models with extracted knots and reconstructed stem models for amabilis fir and western hemlock ................................................................................................. 25 Figure 3.12: Flowchart of an existing BC Coastal sawmill. This sawmill layout was used to simulate Coastal operations. ......................................................................................................... 26 Figure 3.13: Flowchart of an existing BC Interior random sawmill. This layout was used to simulate Interior operations. ......................................................................................................... 27 xi Figure 3.14: Flowchart describing the statistical modelling strategy of three prediction models........................................................................................................................................................ 31 Figure 4.1: Boxplots showing the impact of sites and DBH class for knot volume, number of knots, average knot area and knot/tree ratio for amabilis fir. ....................................................... 38 Figure 4.2: Comparison of average knot area of logs 110.12 and 109.12, which are from the 40-cm and 50-cm DBH classes, respectively. The knot area of log 110.12 is 47.3 cm2, while it is 55.5 cm2 for log 109.12................................................................................................................. 38 Figure 4.3: Boxplots showing the impact of Site 1, 2, and 3, and DBH class for knot volume, number of knots, average knot area and knot/tree ratio for western hemlock. ............................. 42 Figure 4.4: Separate line models for predicting (a) log(knot volume), (b) number of knots, (c) log(average knot area), and (d) log(knot/tree volume ratio) for amabilis fir and western hemlock trees harvested on Vancouver Island. ........................................................................................... 44 Figure 4.5 Examples of cutting patterns from simulation results of amabilis fir and western hemlock in Interior random mill and Coastal mill ........................................................................ 47 Figure 4.6: Boxplot of the lumber recovery of amabilis fir logs sawn in the virtual (a) Interior random and (b) Coastal mills as a function of tree DBH class (cm). ........................................... 48 Figure 4.7: Boxplot of lumber recovery for western hemlock logs sawn in (a) Interior random and (b) Coastal mills as a function of tree DBH class (cm).......................................................... 48 Figure 4.8: Better grade percentages of amabilis fir (AF) and western hemlock (WH) in the generic Coastal and Interior random sawmills. ............................................................................. 51 Figure 4.9: Comparison of (A) observed and (B) predicted grade distribution for Interior random mill and Coastal mill. ....................................................................................................... 53 xii List of Abbreviations AIC: Akaike information criterion BC: British Columbia BGP: Better grade percentage CT: Computed tomography GDP: Gross domestic product Hem-Fir: Western hemlock and amabilis fir MFA: Microfibril angle MOE: Modulus of elasticity MOR: Modulus of rupture PCT: Pre-commercial thinning SP: Species Sph: Stems per hectare VIF: Variance inflation factor 3D: Three-dimensional xiii Acknowledgements I would like to express my sincere gratitude and appreciation to Dr. Julie Cool, my supervisor and mentor throughout my M.Sc. program, for her consistent support, dedicated guidance and persistent encouragement. She taught me to carry out a research and inspired me to face challenges in life and work. Without her, the completion of this project would not be possible. Special thanks also go to my supervision committee, Dr. Isabelle Duchesne and Dr. Simon Ellis, for providing good and instructive suggestions that enlarged my knowledge regarding to the project and improved the quality of the thesis. I would also like to thank the Canadian Wood Fibre Centre, Natural Resources Canada for the financial support via the Forest Innovation Program, and FPInnovations for their technical support. Thanks to Steve Vallerand, Hyeyoung Wood and all my colleagues for their professional knowledge that helped the proceeding of this research. At the end, I want to say thank you to my parents and friends for being a great source of support in all aspects. xiv Dedication I dedicated this thesis to my parents, Shuixia Ma and Leiming Ji, for supporting me with unyielding love and inspiring me to pursue this research. 1 Chapter 1: Introduction The forest sector plays a key role in the economy of British Columbia (BC) and Canada. In 2019, the contribution of the forest sector to Canada’s nominal gross domestic product (GDP) was 1.1%. The value associated with the export of forest products was around $33 billion for Canada (Natural Resources Canada, 2020) and $12 billion for BC (BC Stats, 2020). Wood product manufacturing increasingly contributed to Canada’s GDP from 2009 to 2019 (71.6%), even though the total wood volume has declined by 6.5% since 1990 due to multiple factors, including climate change, forest fires and pest outbreaks (Natural Resources Canada, 2020). The varying stumpage rate, included in the costs of logs (Jannke, 2019), may affect how sawmills are managed and operated, especially in the BC Interior region. The increasing domestic and international demand for lumber and other wood products (Hall, 2018) also motivates sawmills to either produce larger quantities of lumber from the same volume of resources or higher quality products or grades. From the supply aspect, larger and straighter trees with less knots are demanded. These strategies are therefore associated with an improvement of the volume and value recovery, respectively and could mitigate the risk related to fluctuated stumpage rates and market prices. This is clearly a challenging context for the sawmilling industry, which motivates them to enhance their process efficiency and consider how forest management practices could be beneficial. For different types of sawmills, the technologies being implemented might vary according to the type of products manufactured or the market demand. In Interior mills, the primary breakdown process involves technologies that assess the external characteristics of logs to optimize breakdown patterns. Because the primary breakdown is the most important step of the process, logs are precisely rotated according to the optimizer’s decision and conveyed at high-speed (up to 200 m/min) to the sawing equipment that will accurately position (± 0.0254 mm) its saws and/or canter cutterheads. Being able to process logs having little sweep or taper is therefore advantageous. However, in Coastal sawmills the primary breakdown process typically involves little technology and the decision-making process or optimization is mainly done by the equipment (e.g., headrig) operator. Understandably, this process is much slower than that of Interior sawmills. This lower productivity is compensated by the fact that the logs processed in Coastal mills are typically larger and yield products of higher quality or grade. 2 Differences can also be found in the type of customers doing business with Interior or Coastal sawmills. It was reported by Lee et al. (1999) that Interior mills mainly sell their lumbers in Canada and the United States, while Coastal mills mostly export to the United States and Asia. The manufactured products therefore differ. Interior mills produce dimensional lumbers such as 2x4s and 2x6s, while in Coastal mills most products are sawn in custom metric dimensions that vary due to the market demand. In 2018, the BC Interior and Coastal regions respectively harvested about 49.5 and 18 million cubic meters (BC Ministry of Forests, 2018). Compared to most Interior mills aiming for high yield, the goal of Coastal mills is often to produce higher quality of specialty products. Therefore, higher quality logs with less defects are desired. Improving the quality of trees (e.g., volume, straightness, and wood quality) by implementing forest management practices may positively contribute to the lumber volume and/or value recovery of sawmills. For example, several experiments on commercial thinning have been conducted on both Coastal and Interior stands, but mostly in the BC Coastal region, since around 1950 (Government of BC, n.d.). Studies showed that commercial thinning done at a proper frequency, timing and stand density increases the diameter of residual trees (Omule, 1989). Despite the potential benefits of forest management practices, these are seldom implemented in Canada due to the nature of the tenure system. To enhance volume and/or value recovery of all types of sawmills, it is also essential to optimize the breakdown process. As a result, many commercial scanning and optimization software have been developed for sawmills as well as simulation software, typically used in assessing improvement projects or any potential change in the sawmill value chain (e.g., log diet, implementation of a new breakdown equipment, etc.). In Canada, two main simulation software are used: Optitek (FPInnovations, 2013) and SAWSIM (HALCO Software, n.d.). Both Canadian software and those used internationally (Todoroki & Rönnqvist 2002; Pinto et al. 2006), allow for the detailed programming (including the optimization and grading processes) of existing sawmills and are based on the log shape, which is similar to how industrial optimization software work. The decision-making process of these simulators and optimizers could be improved by introducing information provided by internal scanners, such as X-ray computed tomography (CT). This is an exciting development that allows a simulator or optimizer to also consider internal log features (e.g., knots, cracks, etc.) in the decision-making process. In research, this allows the study of the 3 effect of forest management practices on wood quality and manufacturing processes such as sawmilling. In the wood industry, several industrial CT scanner manufacturers (e.g., Microtec, Bintec, RemaControl, and Luxscan) have manufactured practical industrial scanners for lumber grading, breakdown optimization, and log sorting (An, 2013), although the investment cost is somewhat prohibitive and the risk of investment is relatively high (Gergel et al., 2019). There are around 60 million hectares of forests in BC, which accounts for 64% of the overall land (B.C. Ministry of Forests, 2003). Two of the main tree species on the BC Coastal region are amabilis fir [Abies amabilis (Dougl.) Forbes] and western hemlock [Tsuga heterophylla (Raf.) Sarg.], which were found on 46.8% of the harvested area in 2018 (B.C. Ministry of Forests, 2019). Together, they are considered as one commercial group (i.e., Hem-Fir) due to the similarity of their wood properties. Both species are shade-tolerant, although amabilis fir is considered to be more tolerant to shade than western hemlock (Weber et al., 2016). Their mechanical and physical properties are only slightly lower than that of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] (Kennedy, 1965), so Hem-Fir can be used in construction or framing applications. Apart from being transformed into lumber, Hem-Fir could also be utilized in the manufacture of structural laminated veneer lumber (Wang & Dai, 2013) and cross-laminated timber (Wang et al, 2018). This study is an extension of the research done by Middleton & Munro (2013), who assessed the tree attributes of amabilis fir and western hemlock, and investigated their basic wood properties. After simulating the tree growth, the authors determined that the best rotation age would be between 70-80 years from the aspect of mechanical properties and yield. The general objective of this study is to use both external and internal tree characteristics extracted from X-ray CT images to quantify the impact of growth conditions on lumber quality and predict sawmilling recovery. This work should deepen our knowledge of the connection between forest resource characteristics and the performance (or resulting product yields) of wood industries. The specific research objectives of the project are: 1) Investigate the impact of growth conditions and forest management practices on knot characteristics in Coastal amabilis fir and western hemlock trees; 2) Simulate sawing production of two sawmill types and three economic scenarios; and 3) Estimate knot characteristics and log value based on external tree characteristics. The hypotheses are: 1) Forest management practices impact tree growth and internal knot 4 characteristics the same way for both Hem-Fir species; 2) Knot characteristics and log value are predictable from known tree features. 5 Chapter 2: Literature review 2.1 Silviculture Silviculture is the study associated with measuring the consequence of manipulating a forest structure and tree growth to meet the demand for timber production or other purposes (Nyland, 2016, p. 1). This can be accomplished by changing the composition and density of a site or the intensity of treatments (Nyland, 2016, p. 23). For example, to shorten the rotation, forest management practices include reducing the competition between trees or providing more resources to increase access to sunlight, water, and/or nutrients in the soil to each tree. Initial spacing, timely thinning or partial cutting, pruning and fertilizing are the four primary treatments (Shmulsky & Jones, 2011, p. 263-267). 2.1.1 Initial spacing The initial spacing, also called initial stand density, is the distance between trees or seedlings. The wider the distance, the less competition there is between trees, which generally results in a faster growth rate. Wood density is affected by different site spacings (Shmulsky & Jones, 2011, p. 264). Kang et al. (2004) studied the relationship between the initial stand density and several wood properties of planted Jack pines [Pinus banksiana Lamb.] and demonstrated that the spacing significantly affected wood density. Specifically, increasing the initial spacing would cause a decrease in wood density. However, the effect of initial spacing on wood density is not the same for all softwood species (Shmulsky & Jones, 2011, p. 264). Lasserre et al. (2009) compared the wood quality from two planting density (833 and 2500 stems per hectare) and showed that denser spacings would decrease the microfibril angle (MFA) and ring width but increase the modulus of elasticity (MOE), while there was no significant difference on wood density. In addition, spacing would also affect the branch development. Thinner and fewer branches were found in denser initial spacing sites. 2.1.2 Thinning Another way to change the competition and thus the growth rate of trees consists of thinning stands at different growing ages. There are two different strategies: pre-commercial thinning (PCT) 6 and commercial thinning. Functionally the same as commercial thinning, PCT could reduce the competition and help the growth of trees left on site. Pre-commercial thinning consists in eliminating young trees that are not profitable to process into lumber products, while commercial thinning harvests mature trees that can be sold. Pre-commercial thinning is introduced in detail because of its potential use in BC. 2.1.3 Pre-commercial thinning Pre-commercial thinning is one of the strategies, which removes unwanted juvenile trees at an early stage of development (normally 5 to 15 years depending on site index or site quality). The treatment aims at reducing the competition between residual trees. Moderate PCT should improve the growth conditions of trees and therefore enhance the quality of lumber products, which could be associated with economic gains. However, heavy PCT may cause a decrease in wood quality (by lengthening the period of lower-quality juvenile wood production) or tree quality such as taper (Shmulsky & Jones, 2011, p. 266). Beyond the benefits and constraints, the status quo of PCT in BC is due to the financial investments. Pre-commercial thinning has a positive impact on the growth rate of trees. At the early stage of forests growth, removing small, especially unwanted and unhealthy trees, will decrease the competition, so they could grow slightly taller and faster with larger diameters but with lower density and strength depending on the thinning intensity applied. To help trees grow stronger, Jiménez et al. (2008) indicated that juvenile trees could benefit from PCT so the rate of transpiration is accelerated as a result of reduced competition of water in soil. By thinning to a certain stand density, the volume of residual trees could reach merchantable size in a shorter period of time (i.e., reduce rotation time), which would support various forest or wood value chains, for example, lumber, pulp, composites, bioeconomy, etc. Indirect benefits could also be considered. Further evidence showed that fibres from a thinned forest would be easier to process and refine in thermomechanical pulping mills. Although the pulp quality was not significantly different compared with fibres from the un-thinned forest, the energy used for fibre refining was greatly reduced (Bicho et al., 2013). From a macro aspect, PCT could help the local ecological environment. Some disturbances, such as wind, can cause significant damage to a forest. Applying PCT to reduce stand density 7 could make a forest more resistant to wind, which was confirmed by Achim et al. (2005) for a balsam fir [Abies balsamea (L.) Mill.] forest in New Brunswick, Canada. Furthermore, the reduction of disturbances could cause lower mortality in trees by removing unhealthy young trees in dense stands and increase the availability of sunlight and nutrients (Zhang et al., 2009). Another benefit was reported by Chase et al. (2016), who demonstrated that PCT could increase the moisture content in soil, which may reduce the risk of forest fires. Pre-commercial thinning could bring more economic benefits to forest owners. With fewer disturbances, trees would grow straighter and taller, which would eventually intensify the production of quality timbers. Implementing PCT could increase the recovery of premium grade lumber pieces, which may boost their value (Duchesne et al., 2013). Additionally, an early thinning could increase the lumber recovery over a shorter time, resulting in a greater economic gain in the end (Zhang et al. 2009). Plamondon et al. (2013) established that, within a reasonable spacing, less cost and better harvesting productivity would correspond to larger thinning spacings. As a measurement tool of financial analysis, the return on investment is a big concern, considered by both managers and investors of forests. Modest thinning and control of other vegetation in forested lands would be likely to increase economic revenues and potentially long-term benefits on future timber supply (Pitt et al., 2013). 2.1.3.1 Constraints about implementations of pre-commercial thinning Although the economic benefits of PCT are clear, it may decrease lumber quality. With either a moderate or heavy thinning, the modulus of elasticity (MOE) in bending and the modulus of rupture (MOR) are two metrics of wood quality negatively affected (Zhang et al., 2009). Evidence was also shown by Duchesne et al. (2013) that wood density at stump height is lower in balsam fir, which may be a detriment when manufacturing lumber products. Besides, a study in New Brunswick indicated that PCT might cause some negative effects on wood quality before harvesting, such as the presence of decay on tree butts and rot on roots (Warren et al., 2013). 2.1.3.2 Current situation of PCT in BC Why is PCT not commonly used in the forestry industry in BC? One of the reasons is that over 95% of forests in BC are owned by the provincial government (British Columbia’s Forests 8 and Their Management, 2003). The Crown land is managed based on a tenure system, including area- and volume-based tenures. Mostly, timber supply areas are volume based, and for the companies who bought tenures from the government, revenues are the most important thing to consider. To maximize revenues, harvesting companies minimize costs by eliminating all unnecessary expenses. Similar to other forest management approaches, thinning is a long-term management strategy and benefits are also calculated in the long-term. Because companies are not guaranteed to be given access to the same land at the time of the next harvest, the incentive to invest in silviculture is not compelling (De Montigny et al., 2019). 2.1.4 Pruning Pruning is another strategy to reduce the number and size of knots by removing both dead and green branches. Following pruning, less knots are developed and knots are generally smaller (Shmulsky & Jones, 2011, p. 274). This statement is also supported by Makinen et al. (2014), who indicated that pruned Norway spruce [Picea abies (L.) Karst.] trees would grow less and smaller knots. Långström et al. (1991) observed a reduction of heartwood proportion in 25-year old Scots pine (Pinus sylvestris) trees after pruning. However, the effectiveness of various pruning treatments seems to be dependent on the softwood species (Hevia et al. 2016). If thinning is applied in combination with pruning, the decrease in competition for sunlight would result in an increase in the diameter at breast height (DBH), which compensates for the initial loss in wood volume following the treatment (Nyland, 2016, p. 467). 2.1.5 Fertilization Fertilizing consists in adding fertilizers in the field, to supplement the nutrient content present in the soil and support tree growth. The effect of fertilizing on wood density is the same as with other silvicultural treatments (Shmulsky & Jones, 2011. p. 268). For example, Clark et al. (2004) observed that fertilization of 12-year old loblolly pines (Pinus taeda L.) accelerated tree growth but decreased specific gravity of wood. Consequently, the strength of both juvenile and mature wood was lower than that of unfertilized loblolly pine trees. When combining PCT and thinning with fertilizing, Kantavichai et al. (2020) observed that PCT contributed to reduce the decline in 9 specific gravity of 41-year old Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] trees, while fertilization and later thinning increased the specific gravity. The silvicultural practices should be applied collaboratively to find the best compromise between volume growth, wood quality and economic feasibility of silvicultural treatments, when the practices work oppositely (Shmulsky & Jones, 2011. p. 268). According to the forest management objectives, silvicultural practices should be selected and adjusted accordingly. 2.2 Wood quality Wood quality affects all aspects of dimensional lumber, such as its strength, grade, value, etc. There are many indicators to describe wood quality, which is influenced by silvicultural practices as well as site characteristics and climate (Shmulsky & Jones, 2011, p. 253). For example, fibre length and cellular composition are important indicators for pulp quality, while MOR and MOE are two common properties used to express the mechanical strength. Four wood quality traits are presented in this section. When possible, they are related to the two wood species under study: amabilis fir and western hemlock. 2.2.1 Wood density Density is a physical property of wood, which can be used as a predictor of wood strength. It is measured by the mass over the volume of wood. Because the moisture content in wood significantly influences all physical and mechanical properties (Ido et al., 2013; Ugulino et al., 2020), the moisture content has to be mentioned when defining wood density (Shmulsky & Jones, 2011, p. 176). Specific gravity is used to assess the density relative to that of water and known to affect the stiffness of wood (Shmulsky & Jones, 2011, p. 175). As indicated by Shmulsky & Jones (2011, p. 277), specific gravity is a critical factor of wood quality and a low specific gravity is sometimes unwanted. There are many methods to measure the density. In this study, the wood density was measured by a X-ray Densitometer as indicated in Middleton and Munro, (2013). The authors reported that the stem wood density (oven-dry weight divided by the green volume) of amabilis fir and western hemlock was respectively 0.343 and 0.384 g/cm3, and decreased with tree height. 10 2.2.2 Microfibril angle Microfibril angle (MFA) is a microscopic parameter that measures the angle between the microfibril and the tracheid axis and is mostly observed in the secondary cell wall (S2) (Vera et al., 2017). It is known that the MFA could have a great influence on mechanical properties and dimensional stability (Shmulsky & Jones, 2011). According to Donaldson (2008) the MFA would have a stronger influence on softwoods than hardwoods. The mean MFA at breast height for amabilis fir and western hemlock was respectively 13.3 and 14.7, which was significantly different at the 99% confidence level (Middleton & Munro, 2013) . 2.2.3 Juvenile wood Juvenile wood is formed throughout the life of a tree by young cambium cells and its proportion depends on the growth rate (Shmulsky & Jones, 2011, p. 260). The physical variability of juvenile wood is substantial and the mechanical properties are correspondingly affected (Zobel & Sprague, 1998). As a result, a lower percentage of juvenile wood is desired in most wood production. For example, compared to mature wood, the specific gravity of juvenile wood was lower in numerous pine species and Douglas-fir wood (Bendtsen, 1978; Zobel et al., 1998, p. 29). In softwoods, the MFA of juvenile wood is larger than that of mature wood, indicating that the wood tends to be unstable dimensionally (Knigge, 1975). In hardwoods, the moisture content of juvenile wood is higher but the strength is lower (Zobel et al., 1998, p. 24). The amabilis fir and western hemlock wood used in this study were characterized by a respective average proportion of juvenile wood of 27.8% and 29.9%, during the first 15 years for trees belonging in larger DBH classes (Middleton & Munro, 2013). 2.2.4 Knots For veneers and boards, the number and size of knots are critical to their grades or quality. Silvicultural treatments or forest management practices such as initial spacing, thinning, fertilization and pruning could affect branch growth, also known as knot development (Shmulsky & Jones, 2011, p. 261). There are several knot types (Figure 2.1) (Qu et al., 2020), which have 11 diverse effects on wood properties. Therefore, knot classification algorithms were developed to assess knot dimensions in floor board production (Cetiner et al., 2016). Figure 2.1: Five types of knots. Adapted from Qu et al. (2020). Lemieux et al. (2002) studied the impact of knots on lumber quality by simulating the sawing process with several rotational positions. Interestingly, the authors found that the grading results were not much different although the lumber strength was significantly affected because of the impact of knots on the modulus of elasticity. 2.3 Computerized tomography Computerized tomography (CT) scanning is a well-known medical diagnostic tool proposed by Hounsfield (1973). The system consisted of an X-ray source and a detector in a line, which would rotate 180 degrees to receive attenuated X-rays. As the photon absorption of objects varies, the detected attenuated X-ray intensity would be used to calculate the attenuation coefficient (µ): µ𝒎𝒂𝒕𝒆𝒓𝒊𝒂𝒍 = 𝒍𝒐𝒈𝑺𝒐𝒖𝒓𝒄𝒆 𝒊𝒏𝒕𝒆𝒏𝒔𝒊𝒕𝒚𝑫𝒆𝒕𝒆𝒄𝒕𝒆𝒅 𝒊𝒏𝒕𝒆𝒏𝒔𝒊𝒕𝒚 Equation 2.1 CT scanners can be used to measure the density of materials and objects by taking water as a reference to calculate the intensity change of a voxel of material, which corresponds to a three-dimensional pixel. The CT number, also called the Hounsfield scale, is in Hounsfield units (HU) where air is -1000 and water is 0. The equation of CT number is obtained as follow: 𝑪𝑻 𝒏𝒖𝒎𝒃𝒆𝒓 = 𝟏𝟎𝟎𝟎 × (𝝁𝒎𝒂𝒕𝒆𝒓𝒊𝒂𝒍 − 𝝁𝒘𝒂𝒕𝒆𝒓)/(𝝁𝒘𝒂𝒕𝒆𝒓 − 𝝁𝒂𝒊𝒓) Equation 2.2 12 A CT scanner is typically comprised of two parts: 1) a table or surface used for placing target objects, and 2) a circular gantry for the X-ray unit. In most CT scanners, the X-ray unit rotates within the stationary gantry, while the table moves longitudinally. However, the CT scanner used in this study had a stationary table and the X-ray units was able to move vertically as well as rotate horizontally. This design was adopted to process specimens (e.g., trees) of large size (Lister, 2004). After the introduction of the first single-line CT system, generations of scanners having better performances were gradually designed for different applications. For example, the fan-beam (Figure 2.2.a) and cone-beam scanners (Figure 2.2.b) are more energy efficient than single-line scanners, because the increased numbers of detectors allow for an increased acquisition rate and a larger volume of information to be transferred (Buzug, 2008). Compared to cone-beam scanners (Figure 2.2.c), fan-beam scanners are able to create higher-quality images and are safer for medical applications, but have lower spatial resolution (Lechuga & Weidlich, 2016). Since industrial applications perform well with a lower resolution, the cone-beam scanners are preferred to process large specimens quickly. Figure 2.2: Schema showing the difference between the (a) fan beam, (b) multiple fan beam and (c) cone beam tomography. Adapted from Giudiceandrea et al., (2011). De Chiffre et al. (2014) wrote a comprehensive review on various types of CT scanners and their related capability for numerous industrial purposes, such as in mechanical engineering where the purposes are generally non-destructive testing, visualization of materials, defect detection, process quality control, and/or dimensional measurement and assembly. The authors also 13 mentioned applications in the electrical, food and wood industries, where it could be used in quality control and process optimization applications. 2.3.1 Applications in research 2.3.1.1 Wood density measurement Much research has been done in wood science using an X-ray CT scanner. Wood density, often used as a wood quality indicator, can be determined through scanning. Lindgren (1991) proved that the density of both dried and green wood (moisture contents ranging from 6-117%) could be calculated within an acceptable error range from CT numbers (Eq. 2.2). In essence, the author demonstrated the linear relationship between wood density and the CT numbers. Based on this work, Freyburger et al. (2009) examined and validated the relationship of CT numbers and wood density in 16 species. In both cases, the authors emphasized the importance of calibrating the equipment to ensure its accuracy. Later, Manceur et al. (2012) built a 3D model to predict the wood density of a whole tree based on the wood density estimated by CT scanning. Similarly, Longuetaud et al. (2016) used X-ray CT images to map the basic specific gravity and moisture content across a tree’s radius and along its height for green and oven-dried specimens coming from five different wood species. Additionally, the authors found that the basic specific gravity and the relative water content of lumens were different for each species and concluded that these two parameters could be used to distinguish between the studied wood species. Furthermore, a CT scanning approach can be used to monitor and map wood density of wood infected by white-rot fungus, so that problems caused by fungi such as wood degradation and conservation of wooden antiques, could be investigated (Hervé et al., 2014). 2.3.1.2 Internal features Skog et al. (2009) developed methods to detect the width of heartwood for Scots pine since it is more desirable than sapwood for use in the manufacture of window frames. Longuetaud et al. (2007) wrote an algorithm to identify the sapwood-heartwood border, which could also be helpful when differentiating knots from wet wood pieces (Johansson et al., 2013). 14 2.3.1.3 Knot extraction Knot prediction has scarcely been studied. Moberg (2000) modeled knot diameters and dead knot borders in Scots pine wood from various stand and tree characteristics above ground. Both models were non-linear and showed the largest knot per whorl would increase asymptotically with stem height because dead knots would be present in the lower parts of the stems. According to the models, knot diameters would also increase with tree height. Duchateau et al. (2013a) also used stand and tree parameters to predict knot characteristics which they then used to predict the branching attributes (e.g., the angle of branch from horizontal, number of occluded branches, diameter of branch on small end etc.). In terms of image reconstruction, there are three main parts, log shapes, pith and knots, which mainly involves morphology. Pith and outer shapes are relatively easy to detect since they are simple features. Oja et al. (1998) used a thresholding method to detect the outer log shape longitudinally and radially from CT scanning and optical scanning images. The accuracy was similar for both types of scanners. Longuetaud et al. (2004) used a Hough transform to locate the pith by selecting points on the centric circles corresponding to annual rings on cross sections. This method was the most recent approach to detect pith location. They also indicated the location of some knots may have potential impacts on the detection accuracy of the pith. The detection of knots could be done using several methods. Thresholding is often used to discriminate different density groups (i.e., level of intensity) showing on X-ray CT images. For example, knots would have a higher CT number due to higher density than that of the surrounding wood. Ellipse fitting is another way used to automatically detect knots. Johansson et al. (2013) scanned 600 Scots pine logs and 800 Norway spruce logs using a high-speed industrial CT scanner. Although the image quality was slightly lower than that of a medical CT scanner, the authors were still able to detect 88-94% of the knots. Their approach consisted of extracting knots by thresholding them into segments that were then fitted to ellipses. Similarly, Longuetaud et al. (2012) developed a knot thresholding method using pixel intensity to segment into different levels and a three-dimensional distance map, which resulted in a detection rate of 71-100%. Each algorithm that has been developed is associated with its own knot detection accuracy based on species. However, variations such as noise, quality of images, dead knot ends (Duchateau et al., 2013), 15 high moisture content around pith areas and small knot areas (Oja, 2000) could negatively impact the detection rate. Since the detection of knots is not 100% accurate, studies have been done to assess the errors. Oja (2000) virtually reconstructed knots in logs from CT images and simulated sawing to predict lumber quality. The scanned logs were then sawn and the obtained boards used to compare with simulated boards. Results showed most knots could be detected but dead knots in the butt log area have a higher risk of being poorly detected. Although many studies indicated that using knot characteristics in the log rotation decision-making would increase the value recovery (Berglund et al., 2013; Fredriksson et al., 2017; Rais et al., 2017; Belley et al., 2019), Breinig et al. (2013) demonstrated that both systematic and random errors of individual knot characteristics would impact the accuracy of log rotation. The value of products was therefore affected and sensitive to rotational errors. However, when using a high accuracy knot (live and dead) detection algorithm, the simulated lumber recovery in volume or value could be more reliable when positioning error is controlled. Although X-ray CT scanners are now commercially available, reducing the cost of equipment is still a challenge (Beaulieu & Dutilleul, 2019). Also, because most studies were established based on research needs, the computing time was not considered, and the precision of the method not validated in practice. The authors concluded there is still a long way to reduce the cost and the image processing time to match the high-speed production. 2.3.2 Applications in the wood industry Computerized tomography scanning was introduced in the wood industry at the end of the last century. Although the scanning rate of a medical scanner is not adequate for sawmill operations, Taylor et al. (1984) were able to use one with a faster scanning rate. Still, their scanner, able to scan at a rate of ~1 second per slice (100 projections), is much slower than the feed speed at which logs are processed in sawmills (up to 10 mm/sec). Nonetheless, the authors evaluated the precision of the scanner to assess the log shape and knot location by thresholding grey levels. To do so, the CT images were divided into 24×24 arrays. The proposed methodology was relatively coarse but lead to a method for knot extraction. 16 Besides the feasibility analysis, Taylor et al. (1984) pointed out that the high moisture content could have a negative impact on detecting knots in freshly harvested trees. Other challenges associated with the utilization of CT scanning in sawing operations relate to the processing time, size of the data files and the variability of the wood material (Schmoldt et al. 1998). Nonetheless, compared with other non-destructive methods, such as the microwave, ultrasound etc., CT scanning is the most feasible way to visualize logs due to the lower energy requirement to penetrate through larger logs and the resulting image quality of microscopic features (Wei et al., 2011). Owing to the demand for fast operations in the sawmilling industry, research on high-speed and high-capacity CT scanners engineered from medical scanners (Giudiceandrea et al., 2011) were developed and used to advance the field (Rais et al. 2017; Stängle et al. 2015). The benefits of using CT images to optimize the sawmilling breakdown process was demonstrated by Fredriksson et al. (2014). Specifically, the authors reported that the location of knots could be determined before optimizing the sawing process. Logs could then be rotated to an optimal position that would raise sawmill income. Stängle et al. (2015) exemplified this idea by simulating the breakdowns of 33 European beech logs. Their results showed that both volume and value yield could increase by 24%. Similarly, Rais et al., (2017) reported that using an X-ray CT scanner to optimize breakdown could increase product value by 4-20 % by rotating logs compared to the traditional horns-up log position. Another utilisation of X-ray CT scanning in the sawmill industry is non-destructive testing and quality control. For example, lumber strength could be estimated from the percentage of knots detected by scanners (Schajer, 2001). According to the author, this method could be more reliable than the traditional bending test. Although Oja et al. (2005) found that the accuracy of both methods was similar, they recommended combining the X-ray CT scanner and the conventional strength-grading machine to increase the accuracy significantly. More recently, Oh et al. (2010) built a model to predict the strength of dimensional lumber from scanned knot characteristics and obtained a 55-60% accuracy in different species, which is better than the one reported by Schajer (2001) and Oja et al. (2005). 17 Chapter 3: Methodology 3.1 Stand selection Trees were harvested from three stands located on Vancouver Island, British Columbia. Stands 1 (49°2’N 124°40’W) and 3 (48°58’N 124°39’W) were situated in Port Alberni, while Stand 2 (50°29’N 127°6’W) was located in Port McNeill (Figure 3.1). All trees were grown in the western Coastal hemlock (CWH) biogeoclimatic zone (BEC) (Centre for Forest Conservation Genetics, n.d.). Within the CWH zone, western redcedar (Thuja plicata Donn ex D. Don), amabilis fir (Abies amabilis (Dougl.) Forbes), and western hemlock (Tsuga heterophylla (Raf.) Sarg) are the three prevalent tree species. Figure 3.1: Locations of three experimental stands on Vancouver Island, BC. Stands 1 and 3 were situated near Port Alberni, while Stand 2 was located close to Port McNeil. Stand elevation and slope, stand density, the average age at breast height (BH) and average tree height were recorded (Table 3.1). The site index was used to estimate site quality and corresponds, in this project, to the height of trees at age 50 (Middleton & Munro, 2013). 18 Table 3.1: Summary of stand characteristics for the three sites where amabilis fir and western hemlock trees were harvested. Stands 1 and 3 were located in Port Alberni, while Stand 2 was located in Port McNeill. Stand characteristics Stand 1 Stand 2 Stand 3 Latitude N 49° 2’ N 50° 29’ N 48° 58’ Longitude W 124° 40’ W 127° 6’ W 124° 39’ Elevation (m) 390 260 230 Slope (%) Level 25 - 40 0 - 5 Stand density (sph) 1,020 950 758 Average height of sampled trees (m) 36.89 36.10 34.62 Estimated site index at breast height age of 50 Amabilis fir 32.8 33.7 35.0 Western hemlock 33.6 34.3 37.2 3.2 Tree selection In total, 72 trees were selected using a systematic method described in Middleton & Munro (2013). This population consisted of 12 amabilis fir trees and 12 western hemlock trees for each of the 3 DBH classes that were studied (Table 3.2). Many attributes were measured and recorded, but in this study, we focused on DBH classes, tree height, tree volume, and harvested site location. Table 3.2: Distribution of trees by diameter at breast height (DBH) classes for both amabilis fir and western hemlock. DBH classes (cm) Number of trees Midpoint Range Amabilis fir Western hemlock 30 27.5 – 32.4 12 12 40 37.5 – 42.4 12 12 50 47.5 – 52.4 12 12 After harvesting, trees were slashed into logs having a length ranging between 2.5 m and 5.5 m for further machining and testing. A total of 192 amabilis fir and 182 western hemlock logs were produced. Because the height of the trees was variable, the number of logs and their lengths varied between trees. Over 97% of the logs had a length in the 4.5-5.5 m range and 2-6 logs were slashed per tree. 19 3.3 X-ray CT scanning X-ray CT images were obtained for all logs. Cross section scans were performed every centimeter along the longitudinal axis (Figure 3.2) with an industrial CT scanner manufactured by American Science and Engineering Inc (AS&E) and located at FPInnovations. The scanned logs were radiated by an X-ray beam source having a fan angle of 20˚, a voltage of 4 MeV and a maximum frequency of 133 Hz. The X-rays were detected by a matrix of 512 detectors, which had a length of 706 mm. Figure 3.2: Example of an X-ray CT image of an amabilis fir log cross section. The AS&E CT scanner is large and allows the scanning of logs having up to a 1 m diameter and a 5 m length. Prior to scanning, each log was positioned upright, centered in the CT scanner enclosure and pinned to the floor. During sampling, the log was rotated to make measurements from different angles and ensure all internal characteristics were detected. In this study, each log was rotated twice around its longitudinal axis, which required approximately 4 minutes. The physical pixel size for each cross-section was 0.605 mm/pixel. Fine-resolution CT images having a pixel resolution of 512×512 were therefore generated. Although the CT scanner is significantly more powerful than the coarse-resolution one developed by An & Schajer (2014a, 2014b), its design principles are similar. Approximately 500 CT scans were recorded per log, and a scan of the tangential section was also extracted (Figure 3.3). All CT images were saved as an encodable computer file format (TIFF or TIF). 20 Figure 3.3: Example of an X-ray CT image of an amabilis fir log tangential section. 3.4 Data preparation Due to a different ability to absorb X-rays, wood density of various internal log features can be measured by correlating density with a grayscale (Lindgren, 1991). For example, sap- and heartwood (Longuetaud et al. 2007), knots (Andreu and Rinnhofer, 2003; Bhandarkar et al. 1999; Johansson et al. 2013; Longuetaud et al., 2012), checks (Andreu and Rinnhofer, 2003; Bhandarkar et al., 1999; Wehrhausen et al. 2012), decay (Schmoldt et al. 1996) and resin pockets (Oja and Temnerud, 1999) can be detected in X-ray CT images. Because the density of knots is higher than that of wood (Boutelje, 1966), knots appear brighter in CT images (Figure 3.4). Similarly, areas having a higher moisture content (e.g., sapwood) are brighter, which can negatively affect the automatic knot detection (Figure 3.4). However, two algorithms were recently developed to facilitate the detection of knots in sapwood (Fredriksson et al., 2017; Krähenbühl et al., 2014; Roussel et al., 2014). Figure 3.4: An X-ray CT image of an amabilis fir log cross section characterized by sapwood having a higher moisture content than that of heartwood and the presence of four knots. In this project, the CT images contained information on the external (small- and large-end diameters, length, sweep and taper) and internal characteristics (e.g., knots, sap- and heartwood) of the logs. The CT images were grayscale based on density from black (0) to white (1600) as 21 references, where lighter pixels were associated with higher values of density. During image processing, lower and upper thresholds were set at 1000 and 1500, respectively. Any feature detected outside of these threshold values was ignored. This was done to discard foreign high-density elements like nails. 3.4.1 Image processing To extract the average knot area, minimum and maximum knot areas, average knot volume, total knot volume, and the number of knots from the CT images, the software CT2Opti (Vallerand et al., 2011), was used. The extraction process consisted of three main steps: 1) detecting the outer shape of the log, 2) positioning the pith, and 3) detecting knots. When detecting the outer shape of a log, a slight error could be introduced by the presence of left-over bark or foreign elements that were not filtered during the thresholding step (Figure 3.5a). To validate a log’s external shape, which corresponds to the green line in Figure 3.5b, a canny edge detector algorithm was used to identify unattached objects and reject them from the final log shape. The canny edge detector algorithm is commonly used in computational geometry to detect the edge of an object and ensure the edge-line is only one pixel in width (Vallerand, 2018). Figure 3.5: CT images of a cross section of an amabilis fir log (a) before and (b) after using the canny edge detector. To position the pith, there were two different methods: 1) using the geometric centre or 2) manually locating a pith line. In the first method, the geometric centre would be determined as the centre of a circle drawn close to the outer shape of a log. The difference between the geometric 22 shape of the circle and the real outer shape of the log was then assessed, and the pith was positioned based on the distance of the real outer shape (Figure 3.6). For example, if the real shape went beyond the geometric circle, the exceeded area would be weighed and the pith point would then be adjusted accordingly. In the second method, several log cross sections were selected and pith coordinates were determined so a pith line could be formed. Because there was a larger proportion of errors occurring in the first method, the second one was mainly used in this study. Figure 3.6: The geometric centre method compares the position between a perfect circle (red circle) and the outer shape of the log (black shape). The geometric centre of the circle (red dot) is first determined and then re-positioned based on the area difference between the two shapes (yellow dot). The knots were extracted according to a threshold (1300-1600) determined in previous studies (Belley 2014). The logs were processed in two steps: 1) the area close to the pith and 2) the rest of the log. The area close to the pith was processed separately because the logs were characterized by a wood density above species average (Figure 3.7) that made it difficult to locate the origin of knots (Figure 3.8). After discussion with the CT2Opti software developer and multiple preliminary tests, it was decided not to account for the area close to the pith but include a volume compensation once the growth direction of knots was identified in the rest of the log. Because the area around detected knots is characterized by a larger wood density than that of the surrounding wood (Boutelje, 1966), an erosion function was used to shrink the oversized area (Figure 3.9). This also had the benefit of reducing the error associated with the knot size assessment. The closing function was then used to remove small holes in knots. Through adjusting the parameters of erosion and closing, we could get different results, so a few iterations were needed. 23 Figure 3.7: Pith to bark ring wood density for the three DBH classes studied for (a) amabilis fir and (b) western hemlock (adapted from Middleton and Munro, 2013). Figure 3.8: An X-ray CT image of extracted knots from an amabilis fir log without removing the pith area. Figure 3.9: Result comparison of applying an erosion value of (a)15 vs. (b) 5 in an amabilis fir X-ray CT image. 24 Wet pockets were a common problem for both species that caused false knots detection. This issue was resolved by filtering the knot length along the log and in its growing direction, which facilitated differentiation between wet pockets and knots. In addition, a manual discrimination was needed to ensure knots were correctly detected. Figure 3.10 shows an example of an amabilis fir cross section processed in CT2Opti. Figure 3.10: A CT image of a cross section of one an amabilis fir after processing using CT2Opti. Most logs comprised of approximately 500 sectional grayscale X-ray CT images. After processing, every X-ray CT image would be connected by the z-axis (i.e., pith) to build a complete log. Finally, the logs were combined into trees (Figure 3.11) and converted in a format compatible with the sawmilling simulation software Optitek. 25 Figure 3.11: Examples of log models with extracted knots and reconstructed stem models for amabilis fir and western hemlock 3.5 Simulation of tree breakdown Trees were virtually sawn in the sawmill simulation software Optitek. Optitek is an application developed by FPInnovations, which simulates various log breakdown processes and generates a report about the volume and value of final products. Two types of sawmill were simulated in Optitek: 1) a BC Coastal sawmill (Figure 3.12) and 2) a BC Interior random sawmill (Figure 3.13). These mill layouts correspond to actual BC sawmills currently in operations. Along with the mill layouts, FPInnovations provided the list of products that could be manufactured by each sawmills and their corresponding optimization values (Table 3.3 and Table 3.4). New products were added to both lists to account for the larger trees that were processed and maximize lumber recovery, similar to what sawmills would do assuming their equipment can handle these dimensions. Every piece of lumber virtually produced was graded according to the National Lumber Grading Association (Standard Grading Rules For Canadian Lumber, 2017). It was then possible to assess the volume and value recovery for both sawmill types. 26 Figure 3.12: Flowchart of an existing BC Coastal sawmill. This sawmill layout was used to simulate Coastal operations. 27 Figure 3.13: Flowchart of an existing BC Interior random sawmill. This layout was used to simulate Interior operations. 28 Table 3.3: Optimization prices for green lumber products manufactured in the generic BC Coastal sawmill. Prices are listed for products having different thickness (T), width (W), lengths (L), and grades. Actual dimensions Lumber grades Actual dimensions Lumber grades T W L Utility Economy T W L Utility Economy (in) (in) (in) ($/Board) ($/Board) (in) (in) (in) ($/Board) ($/Board) 1.25 3.94 97 1.17 0.67 1.5 11.81 121 5.28 3.50 103 1.27 0.72 145 7.08 4.62 121 1.47 0.84 157 7.45 5.00 145 1.93 1.10 193 8.94 6.00 157 2.09 1.20 1.5 13.78 121 7.08 4.62 1.5 3.46 97 1.22 0.80 145 8.50 5.54 103 1.26 0.90 157 8.94 6.00 121 1.27 0.99 193 10.73 7.20 145 1.66 1.19 1.97 3.94 97 2.53 1.46 157 1.87 1.29 109 2.55 1.57 1.5 6.93 97 3.45 1.97 121 3.07 2.03 103 3.99 2.14 1.97 8.19 97 5.35 3.05 121 4.06 2.69 109 5.85 3.60 145 4.88 3.23 121 6.31 4.18 157 5.14 3.50 1.97 11.81 121 3.38 2.23 1.5 9.45 97 4.56 2.54 145 6.75 4.46 103 4.98 3.15 157 6.75 4.46 121 5.37 3.50 193 13.51 8.93 145 6.44 4.20 1.97 13.78 121 4.39 2.90 157 6.77 4.55 145 8.78 5.80 1.97 13.78 157 8.78 5.80 2.95 9.84 145 13.19 13.76 193 17.56 11.60 193 23.22 25.00 1.97 15.75 145 11.41 7.54 2.95 11.81 145 18.46 19.26 157 11.41 7.54 157 25.54 27.50 193 22.82 15.08 193 28.10 30.25 1.97 19.69 145 14.84 9.80 2.95 13.78 145 17.58 18.34 157 14.84 9.80 157 24.33 26.19 193 29.67 - 193 26.76 28.81 29 Table 3.4: Optimization prices for green lumber products manufactured in the generic BC Interior random length sawmill. Prices are listed for products having different thickness (T), width (W), lengths (L), and grades. Actual dimensions Lumber grades T W L SQ #2 #3 Economy (in) (in) (in) ($/Board) ($/Board) ($/Board) ($/Board) 1.67 3.77 86 - 1.63 1.10 - 98 2.61 2.13 1.52 1.33 122 3.16 2.84 2.09 1.71 146 3.80 3.41 2.51 2.05 170 4.24 3.96 2.93 2.39 194 5.29 4.83 3.35 a2.71 1.67 5.8 98 3.98 3.40 1.94 2.27 122 4.69 4.18 3.11 2.51 146 5.63 5.01 3.73 3.01 170 6.20 5.78 4.35 3.51 194 7.82 7.11 4.98 4.01 1.67 7.6 146 7.51 6.68 4.98 4.01 170 8.27 7.70 5.81 4.68 194 10.43 9.48 6.63 5.35 1.67 9.65 98 6.43 5.46 3.58 3.03 122 7.57 6.71 4.93 3.93 146 9.09 8.05 5.92 4.71 170 9.99 9.28 6.91 5.50 194 12.64 11.45 7.89 6.28 1.67 11.7 122 11.93 10.89 8.76 7.55 146 16.18 14.94 12.38 10.93 170 17.53 16.68 13.83 12.14 194 26.05 24.62 20.35 18.42 A sensitivity analysis was conducted for the sales price assigned to the various products for each sawmill. Specifically, three scenarios were compared: 1) an average market scenario, 2) a pessimistic market scenario representative of the 2008 economic crisis, and 3) an optimistic market scenario representative of the early 2019 market. The latter two price scenarios were calculated 30 based on certain percentages of the normal economic cycle (Table 3.5) and were provided by FPInnovations. In total, all trees were virtually sawn according to 6 different scenarios. Table 3.5: Percentages used to calculate the optimist and pessimist economic scenarios based on different grades of lumber. Economic cycle Premium # 2 # 3 Stud Econ Optimist +25% +30% +10% +5% +5% Pessimist -15% -25% -20% -10% -10% 3.6 Statistical analysis The statistical impact of growth history on the knot characteristics was analyzed and, based on the result, three predicting models were built. Data management and statistical analyses were performed with R version 3.4.0 (R Core Team 2013). For all models, assumptions were checked using diagnostic plots and only those who met the assumptions were selected for further analysis. 3.6.1 Growth history A randomized complete block design with mixed-effect factors was applied to assess the impact of DBH classes and sites on knot characteristics (knot volume, number of knots, average knot area and knot/tree ratio). A type III analysis of variance (ANOVA) was performed. The three stands were regarded as blocks because of the heterogeneity between the three sites. The three DBH classes and two tree species were considered as independent variables. The interaction between blocks and the independent variables was evaluated at a 95% confidence interval. Because log lengths varied, the restricted estimated maximum likelihood was used to correct for the unbalanced data. Comparisons were presented by boxplot and p-values between groups calculated by Tukey multiple comparisons. 3.6.2 Prediction models The flowchart (Figure 3.14) shows the modelling strategy and the relationship between models. Three models were developed to predict: 1) knot characteristics from known tree attributes 31 measured by Middleton and Munro (2013), 2) lumber recovery from knot characteristics and sawmill type, and 3) lumber value for different price scenarios. Figure 3.14: Flowchart describing the statistical modelling strategy of three prediction models. 3.6.2.1 Model 1: predicting knot characteristics A Pearson correlation matrix was generated to assess the relationship between the knot characteristics extracted with CT2Opti and tree external shape measurements such as DBH, height and volume (Appendix B). From this result, linear models were constructed for predicting knot characteristics. Three of the response variables (knot volume, average knot area and knot/tree ratio) were log-transformed. Explanatory variables include attributes that are related to tree size and growth condition such as DBH (cm), site (factor), species (SP, factor), height (m), and number of stems per hectare (sph, factor). The interactions between response variables and explanatory variables are presented in Table 3.6. In this study, only sites would be considered as random effects. The first step was to choose the best model based on the AIC (Akaike information criterion) values through a stepwise selection that initially included all possible independent variables. Then the random effect (i.e., site) was included in the selected model, and the AIC value used to decide whether it should be considered. 32 Table 3.6: The stepwise selection of explanatory variables and their interactions based on their significance for each of the four response variables. Explanatory variables Knot volume Number of knots Average knot area Knot/tree ratio DBH1     Site SP2    Height  Sph3   DBH*SP   Site*SP Height*SP Sph*SP    1 DBH: Diameter at breast height. 2 SP: Species. 3 Sph: Stems per hectare. Stems per hectare was not significant to predict any knot characteristics based on the Type III ANOVA results. Height was not significant to predict knot volume, knot area, and number of knots, and was barely significant for knot/tree ratio. Due to negligible differences in AIC values, height was excluded from the model considering the cost of doing measurements in the field. 3.6.2.2 Model 2: predicting individual tree lumber recovery Linear mixed models fitted by maximum likelihood were used to predict the recovery of each tree using predicted knot characteristics and sawmill type. Ten models including different variables were tested and one of them was selected first based on the AIC values (Table 3.7) and second, on the variance inflation factor (VIF) values. The two lowest AIC values corresponded to models 5 and 10. The VIF values associated with each explanatory variable were then compared (Table 3.8). They were larger for Model 5 due to multicollinearity issues between knot volume and average knot area. Model 10 was consequently selected. 33 Table 3.7: Akaike information criterion (AIC) values of ten linear mixed models using up to four knot characteristics and the sawmill type as explanatory variables. Model Predicted knot characteristics Sawmill type AIC Knot volume Knot area Number of knots Knot/tree ratio 1      719.078 2     717.422 3     717.078 4    716.231 5    715.323 6    716.619 7   721.726 8   716.642 9   720.495 10    715.458 Table 3.8: Variance inflation factor (VIF) values for models 5 and 10. Model Predicted knot characteristics Sawmill type Knot volume Knot area Number of knots 5 8.800 8.800 - 1.000 10 - 2.476 2.476 1.000 3.6.2.3 Model 3: predicting individual tree value Normally, the value that sawmills can recover out of one tree equals the sum of each piece of lumber multiplied by its price, which is dependent on the lumber’s grade. In sawmills, the revenues associated with the sale of by-products (e.g., chips and sawdust) would also be included. In this study, the number of pieces of lumber produced per tree was challenging to predict, unlike lumber recovery. Therefore, Equation 3.1 was used. 𝐕𝐚𝐥𝐮𝐞 𝐨𝐟 𝐞𝐚𝐜𝐡 𝐭𝐫𝐞𝐞 = 𝐓𝐫𝐞𝐞 𝐯𝐨𝐥𝐮𝐦𝐞 × 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 × 𝐆𝐫𝐚𝐝𝐞 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 Equation 3.1 34 Here the recovery was determined using the linear mixed model built in section 3.6.2.2. Similarly, the lumber distribution for each sawmill type could be predicted by using tree attributes and knot characteristics. A linear mixed effect model was therefore constructed with the random effect of sites. Variables that were not significant (p > 0.05 with Type III ANOVA) were removed from the model through a stepwise selection resembling the approach described in section 3.6.2.1. The value prediction model was therefore developed based on the predicted grade distribution, tree attributes, and different knot characteristics based on model. 35 Chapter 4: Results and discussion 4.1 Growth history Knot characteristics (i.e., knot volume, number of knots, average knot area and knot/tree ratio) were extracted with CT2Opti and calculated based on the log model built in Optitek. The knot/tree ratio was defined as the proportion of knot volume over the total volume of the reconstructed tree (without crowns), which provided an index of between tree variations. The site independent variable did not have a significant impact for amabilis fir trees (Table 4.1). This finding could be due to trees having been harvested at a relatively young age (55-72 years old) and the range of stand densities was maybe too narrow (758, 950 and 1020 stem per hectare for site 3, 2, 1 respectively) to detect any significant differences between sites, given the high tree-to-tree variability. Alternatively, amabilis fir trees are known to be tolerant to shade (Mori et al. 2008), and since no thinning was applied to the stands, the three stand densities were not sufficiently contrasted to detect differences in knot characteristics, meaning that competition influencing branching and knottiness was similar for all sites. It can therefore be speculated that under the CWH BEC zone, the growth conditions over the relatively narrow range of the stand densities studied were somewhat uniform and did not have a significant impact on amabilis fir. Table 4.1: The significance of site and DBH class on total knot volume, number of knots, average knot area and knot/tree ratio. An asterisk indicates a significant P-value at the 95% confidence level. Independent variables Total knot volume Number of knots Average knot area Knot/tree ratio Amabilis fir Site 0.18000 0.09930 0.17300 0.17030 DBH class 0.00000* 0.00001* 0.00000* 0.00055* Western hemlock Site 0.00611* 0.40000 0.02670* 0.00163* DBH class 0.00000* 0.29400 0.00000* 0.41454 The DBH class had a significant effect on all four knot characteristics (Table 4.1) for amabilis fir and only total knot volume and average knot area for western hemlocks. Amabilis fir trees from the 40-cm DBH class were characterized by more knots than those of the 30-cm DBH class (416 36 knots vs. 248 knots for Site 1) (Table 4.2). This significant increase in the number of knots explains the rise in total knot volume and average knot area that were detected (Table 4.2 and Figure 4.1). The number of knots remained statistically similar for the 40- and 50-cm DBH class (475 knots for Site 1), although a significant increase of the total knot volume and average knot area was observed (Table 4.2). This could be due to branches that did not naturally self-prune in the 50-cm DBH class. To visually notice the difference, two representative log sections are shown in Figure 4.2. For amabilis fir, the increase proportion of knot area from 30 to 40 cm DBH and 40 to 50 cm DBH groups are separately around 59% and 47%; for western hemlock, the rates are 51% and 30%. Table 4.2: Total knot volume, number of knots, average knot area and knot/tree ratio for three DBH classes of amabilis fir and western hemlock trees harvested from three different sites. Site DBH class (cm) Total knot volume (cm3) Number of knots Average knot area (cm2) Knot/tree ratio (/103) Amabilis fir 1 30 11 755.31 a3 248 a 23.2 a 4.562 a (6 217.6)2 (71) (6.7) (1.478) 40 27 176.8 b 416 b 33.0 b 6.439 b (7 815.2) (44) (7.6) (2.234) 50 42 253.1 c 475 b 43.0 c 6.032 b (13 406.4) (20) (12.1) (1.686) 2 30 8 115.8 a 254 a 19.0 a 3.110 a (4 010.3) (46) (2.1) (1.683) 40 30 414.4 b 382 b 36.2 b 6.311 b (12 820.2) (58) (14.9) (2.494) 50 47 942.5 c 375 b 64.9 c 7.308 b (8 694.9) (52) (5.1) (2.315) 3 30 14 191.4 a 354 a 23.5 a 5.339 a (4 636.8) (90) (1.6) (0.940) 40 29 182.7 b 375 b 35.2 b 6.527 b (12 588.7) (65) (3.0) (2.475) 50 64 347.1 c 441 b 45.5 c 9.224 b (21 585.5) (18) (8.3) (1.728) 37 Table 4.2: Continued. Site DBH class (cm) Total knot volume (cm3) Number of knots Average knot area (cm2) Knot/tree ratio (/103) Western hemlock 1 30 17 341.4 A 3654 22.8 A 7.279 A (4 296.2) (61) (4.1) (2.829) 40 33 372.2 B 502 29.6 B 7.700 A (9 797.8) (40) (7.1) (2.346) 50 55 357.9 C 496 41.1 C 8.518 A (22 469.4) (127) (2.8) (3.289) 2 30 26 789.8 D 448 25.6 D 11.121 B (6 173.7) (147) (2.9) (2.075) 40 42 088.5 E 432 39.2 E 10.162 B (8 086.9) (77) (5.3) (2.237) 50 67 634.3 F 470 50.9 F 11.337 B (14 665.7) (144) (10.8) (0.411) 3 30 15 069.1 A 372 20.7 A 5.795 A (6 018.1) (155) (2.6) (3.006) 40 29 681.4 B 420 35.1 B 7.207 A (10 074.5) (113) (3.3) (1.959) 50 48 854.5 C 410 43.3 C 7.509 A (3 976.2) (51) (13.3) (1.406) 1 Mean of 12 trees. 2 Standard deviation of the mean. 3 Means followed by the same letter are not significantly different at a 95% confidence interval. 4 Number of knots is not significant for western hemlock 38 Figure 4.1: Boxplots showing the impact of sites and DBH class for knot volume, number of knots, average knot area and knot/tree ratio for amabilis fir. Figure 4.2: Comparison of average knot area of logs 110.12 and 109.12, which are from the 40-cm and 50-cm DBH classes, respectively. The knot area of log 110.12 is 47.3 cm2, while it is 55.5 cm2 for log 109.12. The results for western hemlock differed from those of amabilis fir. The ANOVA revealed that the total knot volume and the average knot area were significantly impacted by the site and DBH class in western hemlock trees, while the knot/tree ratio was only affected by the site independent variable (Table 4.1). It should be noted that the interaction between sites and DBH 39 classes was not tested, because the former was considered as blocks due to their heterogeneity. In general, Site 2 yielded western hemlock trees having larger total knot volumes and average knot areas than the other two sites (Table 4.2), but a smaller overall tree volume (Table 4.3). Because the number of knots was not significantly impacted by the site, this suggests that larger branches (i.e., knots) were grown on Site 2. This could be due to the specific location of each site as well as their respective stand density, slope and aspect (Table 4.4). Sites 1 and 3 are geographically close, while Site 2 is located at a higher latitude and is characterized by a steeper slope that faces North East and certainly increased soil drainage and thus access to water and sunlight. These site differences could have had an impact on mean temperature and precipitation, which could in turn influence the growth response of western hemlock trees. Data from two weather stations located within a 30-km range of the harvesting sites were extracted (Table 4.5) (Canadian Climate Normals 1971-2000 Station Data, 2019) and confirm that the temperature and precipitation were lower for Site 2 in comparison to those of Sites 1 and 3. Way and Oren (2010), who studied the effect of warming on both conifer and broad-leaf species, indicated that elevated temperatures could have a positive effect on tree growth. Similarly, Hember et al. (2019) indicated that the growth rate of black spruce, which has increased noticeably during the last century in Canada, was significantly affected by climate change. Jochner et al. (2018) also demonstrated that warmer temperatures could increase the basal area increments in the Swiss Alps. In this study, the slightly lower temperature and lower precipitation could have slowed the growth of western hemlock significantly. This result suggests that western hemlock trees are more site sensitive compared to amabilis fir. 40 Table 4.3: Means and standard deviations of tree volume, total knot volume, average tree area and knot/tree ratio for western hemlock trees harvested from 3 different sites. Site 1 2 3 Tree volume (m3) 4.4421 4.188 4.409 (1.748)2 (1.604) (1.754) Total knot volume (cm3) 35 357.1 45 504.2 31 201.7 (20 828.6) (19 914.2) (15 832.5) Average knot area (cm2) 31.1 38.6 33.0 (9.1) (12.6) (12.2) Knot/tree ratio (/103) 7.833 10.873 6.837 (2.631) (2.082) (1.787) 1 Mean of 72 trees. 2 Standard deviation of the mean. Table 4.4: Site differences for potential factors on affecting knots growth of western hemlock. Site 1 2 3 Latitude (N) 49°2’ 50°29’ 48°58’ Longitude (W) 124°40’ 127°6’ 124°39’ Stand density (sph) 1020 950 758 Elevation (m) 390 260 230 Slope (%) 0 25-40 0-5 Aspect - NE S Table 4.5: Average temperature and precipitation from weather stations located in Port Alberni and Albert Bay during 1971-2000. Port Alberni A is located close to Sites 1 and 3, while Albert Bay is near Site 2. Port Alberni A (Sites 1 and 3) Alert Bay (Site 2) Latitude 49°2’ N 50°4’ N Longitude 124°5’ W 126°6’ W Elevation 2.40 m 59.40 m Average temperature (°C) 9.5 8.5 Average precipitation (mm) 1910.7 1591.5 41 The stand density (Table 4.4) could also have had an impact on the dimension of branches (i.e., knots). According to the results of this study, Site 2 had an intermediate stand density (950 stems/ha), but yielded western hemlock trees having the largest knots (Table 4.3 and Table 4.4). This result is surprising since a higher stand density was found to reduce the diameter (Grace et al. 2015) and number of branches in Douglas-fir trees (Newton et al., 2012). In this study, it could be hypothesized that the effect of the environmental conditions (e.g., average temperature and precipitation) and the species own crown light requirements on knot characteristics preceded those of stand density for western hemlock. As indicated by Getzin & Wiegand (2007), crown patterns would be affected not only by the solar radiation but also by the mechanic properties in a dense stand. Similar to the results obtained for amabilis fir, the total knot volume and average knot area of western hemlock trees significantly increased with the DBH class (Table 4.2). Such a result was expected and is in agreement with Moberg (1999), who reported that larger diameter trees generated bigger knots. In this study, the relationship between the DBH class and the total knot volume and average knot area appeared to be linear (Figure 4.3). Because the number of knots did not significantly change with the DBH class, it could be concluded that the branches grew at a similar rate to that of the trees and that stand density was too low to promote self pruning in the lower whorls of the trees. 42 Figure 4.3: Boxplots showing the impact of Site 1, 2, and 3, and DBH class for knot volume, number of knots, average knot area and knot/tree ratio for western hemlock. 4.2 Model 1: predicting knot characteristics In the final model (Equations 4.1-4.4) predicting knot characteristics of individual trees, DBH was significant for all response variables, while species was only significant for the average knot area. The interaction between DBH and species was significant for knot volume and knot/tree ratio. The coefficients and standard deviations are listed in Table 4.6. 𝐥𝐨𝐠(𝒌𝒏𝒐𝒕 𝒗𝒐𝒍𝒖𝒎𝒆𝒊) = 𝒃𝟎 + 𝒃𝟏 ∙ 𝑫𝑩𝑯𝒊 + 𝒃𝟐 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊 + 𝒃𝟑 ∙ 𝑫𝑩𝑯𝒊 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊 + 𝒆𝒊 Equation 4.1 𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒌𝒏𝒐𝒕𝒔𝒊 = 𝒃𝟎 + 𝒃𝟏 ∙ 𝑫𝑩𝑯𝒊 + 𝒃𝟐 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊 + 𝒃𝟑 ∙ 𝑫𝑩𝑯𝒊 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊+𝒆𝒊 Equation 4.2 𝐥𝐨𝐠(𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒌𝒏𝒐𝒕 𝒂𝒓𝒆𝒂𝒊) = 𝒃𝟎 + 𝒃𝟏 ∙ 𝑫𝑩𝑯𝒊 + 𝒆𝒊 Equation 4.3 𝐥𝐨𝐠 (𝒌𝒏𝒐𝒕𝒕𝒓𝒆𝒆 𝒗𝒐𝒍𝒖𝒎𝒆 𝒓𝒂𝒕𝒊𝒐𝒊) = 𝒃𝟎 + 𝒃𝟏 ∙ 𝑫𝑩𝑯𝒊 + 𝒃𝟐 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊 + 𝒃𝟑 ∙ 𝑫𝑩𝑯𝒊 ∙ 𝑺𝒑𝒆𝒄𝒊𝒆𝒔𝒊 + 𝒆𝒊 Equation 4.4 43 Where 𝑆𝑝𝑒𝑐𝑖𝑒𝑠𝑖 = {0 𝑤ℎ𝑒𝑛 𝑆𝑃 = 𝐴𝑚𝑎𝑏𝑖𝑙𝑖𝑠 𝑓𝑖𝑟1 𝑤ℎ𝑒𝑛 𝑆𝑃 = 𝑊𝑒𝑠𝑡𝑒𝑟𝑛 ℎ𝑒𝑚𝑙𝑜𝑐𝑘 for 𝑖𝑡ℎ tree (𝑖 = 1, … ,72) Table 4.6: Estimations of coefficients for predicting knot characteristics. An asterisk indicates a significant P-value at the 95% confidence level. Log (knot volume) Number of knots Log (average knot area) Log (knot/tree ratio) 𝑏0 6.6658 (0.3035)1 50.3042 (74.5801) 1.7293 (0.1850) 0.4389 (0.2913) 𝑏1 0.008555* (0.0007460) 0.7978* (0.1833) 0.004429* (0.0004547) 0.003232* (0.000716) 𝑏2 1.4179* (0.4402) 243.5674* (108.1680) - 1.3801* (0.4226) 𝑏3 -0.002805* (0.001081) -0.44576 (0.26562) - -0.002565* (0.001038) 1 Standard deviations of the estimated coefficients. According to the model, each 10-unit increment of DBH (cm) would be associated with a 135% and 78% increase in knot volume for amabilis fir and western hemlock, respectively. Based on the data, the increase in knot volume corresponded to an overall 155% from 30 cm to 40 cm and 78% from 40 cm to 50 cm for amabilis fir (site data pooled) (Table 4.2). The discrepancy could be due to the larger knot volume variation present in the linear model when the DBH is close to 30 cm (Figure 4.4). A similar observation can be made for western hemlock although the field measurements seem to fit the model better since the calculated knot volume increased by 78% from 30 cm to 40 cm and 63% from 40 cm to 50 cm (site data pooled) (Table 4.2). For this model, the adjusted R2 is 0.73, which indicates a good fit. The variation between the models and the raw data could therefore be due to the error terms (ei) that accounts for the site factor, which was not included in the models due to its low VIC value. Alternatively, these discrepancies suggest other factors such as temperature and precipitation could be considered in future iteration of the models to improve their accuracy. 44 Figure 4.4: Separate line models for predicting (a) log(knot volume), (b) number of knots, (c) log(average knot area), and (d) log(knot/tree volume ratio) for amabilis fir and western hemlock trees harvested on Vancouver Island. The number of knots is a mixed-effect model and it was chosen among different models for its lowest AIC value (854.6), which indicates its accuracy. According to the model, each increment of DBH (from 30 cm to 50 cm) would be associated with an increase of 160 and 70 in the number of knots for amabilis fir and western hemlock trees, respectively. This is similar to the values obtained by CT2Opti for both amabilis fir (145) and western hemlock (64) (Table 4.2). The discrepancy being less than 10%, this suggests the model is a good fit for the data in this study. The models for the average knot area are linear fixed-effect models and the adjusted R2 is 0.673. According to the model, every 10 cm increase in DBH should result in a 56% rise in the average knot area for both species, as species was not significant. According to Table 4.2, there is a respective increase of 59% and 50% for amabilis fir and western hemlock when the DBH grows from a 30-cm class to a 40-cm class. The measured change in average knot area was 47% and 30% for amabilis fir and western hemlock, respectively when the DBH class further increased to 50 cm. The model therefore seemed to perform better for amabilis fir trees over the three DBH classes included in this study. For western hemlock, the model was a good fit at lower DBH. The lost in 45 accuracy could be due to the somewhat larger variation when the DBH is close to 50 cm (Figure 4.4). In future studies, different models could be developed for the larger diameter class trees, which could be, as suggested by Middleton and Munro (2013), around 70 to 80 year old Hem-firs, to include other factors important to knot growth, such as soil quality. Negrave et al. (2007) indicated that the volume increase of western hemlock on different nutrition levels of soil can be significant. For other species, Hassegawa et al. (2015) evaluated the site impact on yellow birch and sugar maple trees and reported that the former grows better in shallow and dry soils, while the latter produces higher quality timbers in deep soils rich with nutrients. The knot/tree ratio model is a mixed-effect model characterized by an AIC of 54.1. According to the model, the knot/tree ratio should increase by 91% and 14% respectively for amabilis fir and western hemlock, but was 74% and 12% according to the collected data (Table 4.2). This suggest a relatively good fit for the western hemlock trees. This could be due to the knot/tree ratio of western hemlock trees that was relatively constant over the three DBH classes (Figure 4.4), while this was not the case for the amabilis fir trees (Figure 4.1). This difference caused challenges in accurately modelling both species. Although there are few papers focusing on characterizing knots for individual trees, some researchers have studied the properties of individual knots, such as knot diameter, which could refer to the average knot area in our study, and the angle of knots. For example, Tong et al., (2013) studied the number, diameter, angle and other distribution of knots on a young plantation white spruce and suggested the importance of knot information on forest management decision-making and sawmilling breakdown. Lemieux et al. (2001) studied the knot characteristics and distribution of black spruce by slicing logs into sections, and reported that knot diameters could be predicted from the measurements of branches, which can be by tree measurements including diameter and crown height (Mäkinen et al. 1998). Additionally, Moberg (2006) modeled knots of Scots pine and Norway spruce for features including knot diameter, number of knots per whorl, live and dead knot diameters and the length of knots in the tree longitudinally. Predicting individual knot characteristics was also investigated by Duchateau et al. (2013), who built two nonlinear models for black spruce and Jack pine using tree and branch parameters such as the height of trees, stem diameters, insertion angle of branches, branch diameters and the branch position in the tree, and the equation functioned in both species. Predictors such as DBH, tree height, tree age, and various 46 crown parameters were used in models from previous studies to predict individual knots, but since tree and branching characteristics can be affected by stand features and weather conditions, those predictors can be adopted or discarded depending on the situation. In future studies, parameters associated with tree volume (Appendix 2) such as tree height, tree age and selected crown parameters could be implemented in the models developed in this study to improve the accuracy in predicting knot characteristics and maybe even estimate their position. More recently, Manso et al. (2020) developed a methodology to calculate the knot volume from branch characteristics for Sitka spruce. Instead of directly calculating knot volume from the connected volume of neighbouring knot area sections or CT images, the authors determined the branch diameter and insertion angle to estimate the knot volume. The proposed method may help in estimating knot volume without X-ray scanning trees or logs, but dead knots could be overlooked and knot volume underestimated. 4.3 Sawmilling simulation Moberg and Nordmark (2006) indicated that lumber product recovery should increase with greater DBH. In this study, the larger-sized products were assigned a higher optimization price, which favoured the sawing of those products. The average recovery in the generic Interior random length and Coastal mills were 68% and 60%, respectively and the average value per tree produced during a normal economy cycle was $903.40 and $584.60 per log. A detailed list of product information was included in Appendix B from sawing simulation results. The average lumber volume and value recoveries in the Interior random mill was higher than that of the Coastal mill for both species. This could be caused by the product structure, that is simpler for the Interior random sawmill. In comparison, the Coastal mill generated a product basket having a wide range of dimensions with almost twice as many different lumber sizes than that in Interior random mill. Examples are showed in Figure 4.5. 47 Figure 4.5 Examples of cutting patterns from simulation results of amabilis fir and western hemlock in Interior random mill and Coastal mill 4.3.1 Lumber recovery Lumber recovery was extracted from the sawing simulations as a percentage of the total volume of lumbers over the total tree volume. Overall, the sites did not have a significant effect on the lumber recovery, whereas DBH classes had a significant impact only in the Coastal mill for both species (Figures 4.6 and 4.7). Compared to the Interior mill, there were twice as many product types that mostly varied in length in the Coastal mill and only two grade categories, resulting in shorter products being cut from similar log volumes based on their optimization prices. The 50-cm DBH class was significantly different from the other two DBH groups and produced less pieces of lumber (Table 4.7). This could be due to the 50-cm DBH class being characterized by larger average knot areas (Table 4.2). The presence of large knots could downgrade lumber pieces that would then be edged or trimmed, which lowered the lumber recovery factor, improved the grade turnout, and increased the volume of chips. A decreased ratio of height to diameter or an increased stem taper could be an indicator of a lower lumber recovery factor, which was used to explain the relatively lower yield coming from the lower portion of the tree (e.g., butt log) (Lyhykäinen et al., 48 2009). In this project, those two factors, height to diameter ratio and recovery, showed a weak to moderate correlation (0.3-0.5) only in the Coastal mill for both species. Figure 4.6: Boxplot of the lumber recovery of amabilis fir logs sawn in the virtual (a) Interior random and (b) Coastal mills as a function of tree DBH class (cm). Figure 4.7: Boxplot of lumber recovery for western hemlock logs sawn in (a) Interior random and (b) Coastal mills as a function of tree DBH class (cm). 49 Table 4.7: Means and standard deviations (in parenthesis) of lumber recovery (%) based on real CT-scanned log volumes obtained from amabilis fir and western hemlock logs from three DBH classes and virtual lumber volumes obtained by simulation using a generic Interior random sawmill and a generic Coastal sawmill. DBH class (cm) Interior random sawmill Coastal sawmill Amabilis fir Western hemlock Amabilis fir Western hemlock 30 69.15 65.33 62.21 a1 58.60 c (3.61) (2.75) (3.01) (5.05) 40 70.13 67.86 63.30 a 61.99 c (2.01) (2.15) (1.90) (2.94) 50 68.52 66.43 58.11 b 56.87 d (1.87) (2.50) (3.14) (3.80) Total 69.26 66.54 61.20 59.15 (2.59) (2.59) (3.44) (4.40) 1 Means followed by the same letter are not significantly different at a 95% confidence level 4.3.2 Value recovery Table 4.8 shows the average values of amabilis fir and western hemlock in two mill types. As expected, the value increased with the DBH because of the increasing total tree volume, while the site did not have a significant impact. The 50-cm DBH class was significantly different from the other two DBH groups and produced lower recovery of lumber but generated more value for both species. This is explained by the trimming and/or edging of knots done to improve lumber quality. As the lumber recovery was lower in the Coastal mill, the total value of products was also lower. Same as with the recovery, western hemlock trees yielded a slightly lower value than amabilis fir trees (Table 4.8). This difference could result from the average total log volume of amabilis fir (1.576 m3) that was higher than that of western hemlock (1.390 m3), which was also reported by Middleton and Munro (2013). Another reason could be that although the volume of trees increased with DBH class, the number of knots and knot/tree ratio remained constant or grew only slightly from the 30- to 40-cm DBH groups. Interestingly, this observation suggests that the presence of knots in larger DBH classes may be less important than the total tree volume in influencing the final value. According to Liu et al. (2007), the impact of DBH on lumber value was significant in black spruce. Overall, the lumber value in both sawmills was notably raised when the DBH was larger. 50 Table 4.8: Means and standard deviations (in parenthesis) of total values of lumber in a normal economic cycle obtained from amabilis fir and western hemlock logs produced by the virtual Interior random and Coastal mills in three DBH groups. DBH class (cm) Interior random sawmill Coastal sawmill Amabilis fir Western hemlock Amabilis fir Western hemlock 30 485.52 a 460.35 A 341.46 d 321.58 D (118.12) (110.49) (72.65) (74.52) 40 1004.16 b 899.64 B 638.63 e 590.82 E (133.00) (117.32) (75.53) (68.04) 50 1331.40 c 1239.50 C 836.29 f 778.74 F (200.47) (101.49) (79.04) (72.60) Total 940.36 866.50 605.46 563.71 (383.83) (340.64) (218.99) (202.63) 1 Means followed by the same letter are not significantly different at a 95% confidence level Complementary to the volume recovery and value, the better grade percentage (BGP) was calculated for both mills. In the Interior random sawmill, the BGP represents the proportion of #2 and better grades, while it consists of the Utility grade for the Coastal mill. The Coastal mill BGP ranged from 90% to 100% and is higher than that of the Interior random mill, and the tree species processed in the sawmills was not significant (Figure 4.8). Although there is no evidence that knot characteristics had an impact on BGP, which could be caused by the larger effect of knots on lumber grades in smaller size trees (Jozsa & Middleton, 1994), the DBH and tree height should significantly impact the BGP (Lyhykäinen et al. 2009). The authors developed a model predicting the proportion of different grades in Scots pine using three factors: the DBH, the height of the living crown, and the height of the first dead knots above ground. Similarly, Uusitalo et al. (2018a) built a model predicting the BGP for Scots pine. In that model, the authors used the DBH, the above-ground height of knots and the angle of knots as factors. The prediction accuracy was ~70% and results were sensitive to the length of logs, which is influenced by the bucking process (Nordmark, 2005). 51 Figure 4.8: Better grade percentages of amabilis fir (AF) and western hemlock (WH) in the generic Coastal and Interior random sawmills. 4.4 Model 2: predicting individual tree recovery As indicated in the previous subsection, the mill types significantly impacted the lumber recovery, although it was similar for the two species under study. A linear mixed-effect model (Table 4.9) was fit using the maximum likelihood. The model having a lowest AIC of 715.5 among several models was selected. Each tree was processed by two different sawmill types therefore the groups of trees consist of random effect with two levels. Since knot characteristics were highly correlated with DBH and height, these two variables are not directly shown in this model. Table 4.9: Linear mixed model for predicting lumber recovery. One hundred and forty-four observations and 72 groups were used. Random effects (Intercept) Residual Standard deviation 2.6653 1.8458 Fixed effects Estimate Standard Error Intercept 63.4231 4.0805 Predicted knot area 3.1673 1.7805 Predicted number of knots -0.03557 0.009551 Random Interior mill 7.7216 0.3120 52 Such models (Table 4.9) could be used to predict lumber volume or yield and potentially replace log scale rules. Conventional log scales often misestimate the expected lumber volume or yield (Li et al. 2016). The authors therefore recommended using computer simulations over empirical methods, which was validated by Thomas et al. (2017) who reported a large discrepancy between the lumber volume estimated by log rules and the one determined from laser scanners. For a more accurate estimation of product volume and value, prediction models could be applied. For example, Zhang and Tong (2005) and Tong and Zhang (2009) demonstrated a 64% accuracy in predicting lumber recovery when using Jack pine tree characteristics such as DBH and tree height. Similarly, Liu et al. (2009) developed four prediction models for balsam fir trees from reconstructing trees using DBH and tree height and obtained a R2 above 0.9. The authors even accounted for the difference between real and simulated volumes which they used to reduce the error. However, stem defects such as sweep and rot, were not considered in their study and could have resulted in an overestimation of volume recovery. In this project, the prediction of the recovery is limited to known knot characteristics, which positively contribute in accurately estimating the economic output of logs (Zhang et al. 2005; Liu et al. 2009; Rais et al. 2017; Fredriksson et al. 2019). Further study could be made on transferring those variables to DBH and tree height for better log scaling estimation before scanning. 4.5 Model 3: predicting individual tree value To predict the value of a tree, the lumber grade distribution was first assessed. As showed in Figure 4.9A, grade 1, the best lumber grade, in both sawmills accounted for the largest proportions for higher values, which is in agreement with results obtained by Moberg et al. (2006), Lyhykäinen et al. (2009) and Belley et al. (2019). Additionally, Uusitalo et al. (2018) observed that increasing height of a tree would result in a lower percentage of better grade lumbers, which is similar to the model developed in this study (Table 4.10). Uusitalo et al. (2018) hypothesized this observation could be due to an increased uncertainty associated with optimizing longer logs. 53 Figure 4.9: Comparison of (A) observed and (B) predicted grade distribution for Interior random mill and Coastal mill. Table 4.10: Fixed effect part for model predicting grade distribution in Interior random mill and Coastal mill. Sawmill Interior random Coastal Grade Grade 1 Grade 2 Grade 3 Grade 4 Grade 1 Grade 2 Intercept -57.54 (4.94) -15.82 (2.88) -42.05 (10.91) -3.60 (2.38) -27.71 (11.48) -9.97 (4.54) DBH 0.24 (0.01) 0.10 (0.01) -0.05 (0.02) 0.10 (0.01) -0.03 (0.01) Height 0.01 (0.00) 0.0018 (0.0007) 0.01 (0.00) Species -4.53 (1.84) -0.85 (0.40) -5.53 (1.75) Predicted knot area 16.03 (5.20) 6.70 (2.36) 54 The predicted grade distribution is illustrated in Table 4.10 and the AIC values are showed in Table 4.11. The predictor variables include the DBH, species, height, predicted knot area and sites. Similar to the simulation results, the better grade lumbers predicted accounted for most products for both sawmill types. Sites were considered as a random effect in the model. The parameters of intercepts and slopes for fixed effect factors are listed in Table 4.10. Table 4.11: AIC values for predicted value of logs on Grade 1-4 in Interior mill and Grade 1-2 in Coastal mill. Sawmill Interior random Coastal Grade Grade 1 Grade 2 Grade 3 Grade 4 Grade 1 Grade 2 AIC 524.0 430.0 360.7 282.8 422.1 246.4 In Table 4.12, intercepts and slopes are listed for each model under three economic cycles for both sawmill types. In the Coastal mill, the grade distribution was insignificant in predicting tree value, so only tree attributes (e.g., DBH and height) and knot characteristics are used. 55 Table 4.12: Models of value prediction under normal, optimistic and pessimistic economic scenarios in Interior random mill and Coastal mill (standard errors in parenthesis). Economic cycle Normal Optimistic Pessimistic Sawmill type Interior Coastal Interior Coastal Interior Coastal Intercept -2030.55 (336.22) -1405.87 (215.16) -2551.86 (418.61) -1726.30 (265.35) -1684.13 (281.11) -1184.65 (181.66) DBH 3.89 (0.26) 1.27 (0.43) 4.87 (0.32) 1.63 (0.53) 3.25 (0.22) 1.095 (0.36) Height 0.18 (0.068) 0.19 (0.031) 0.23 (0.085) 0.23 (0.038) 0.145 (0.057) 0.16 (0.026) SP -103.26 (26.57) -127.73 (33.08) -86.63 (22.21) Predicted knotArea 335.46 (102.96) 405.99 (126.98) 280.70 (86.93) Predicted numOfKnots -0.89 (0.19) -1.096 (0.23) -0.75 (0.16) Predicted Grade 1 -19.80 (9.00) -25.10 (11.22) -16.26 (7.53) Predicted Grade 2 33.13 (15.90) 42.42 (19.79) 26.91 (13.29) Predicted Grade 3 26.59 (10.61) 31.66 (13.21) 22.39 (8.87) Tong et al. (2006) predicted lumber value recovery using DBH and tree height in Jack pine. Liu et al. (2007) developed a model from tree properties including DBH, height and taper, to predict lumber value in black spruce. This study also included both DBH and height because these measurements can easily be made. The DBH is a standard measurement of trees and height could be assessed using clinometers or estimated by equations. With these tree characteristics, the value could be predicted using the model for amabilis fir and western hemlock within the harvested area. This prediction could provide foresters with an important input when determining the rotation age. Nevertheless, it should be noted that height of the tree in this study is only the stem height, which 56 could introduce a slight bias. If trees could be scanned using X-ray CT, the height and knot characteristics would be more accurate on predicting the recovery and value. Our models predict knot characteristics, log volume recovery and value with only two easy measurements, DBH and stem heights, which could well-estimate the production results from harvesting or even earlier planification stage. In addition, species would affect model parameters, but the principle could be applied to other species. Although a comparison could not be done with the real-sawn data, a study indicated that including knot attributed in optimization rules would increase lumber values by 23% in jack pine and 15% in white spruce (Belley et al., 2019). Further studies could be done on the validation of the prediction models. 57 Chapter 5: Conclusion 5.1 General conclusions The research project focused on predicting the value of sawmill products that could be manufactured in Coastal or Interior mills from 60-year-old standing amabilis fir and western hemlock trees. The specific objectives were 1) to evaluate how site and DBH influenced the knot characteristics using X-ray CT images; 2) to simulate the sawmilling process to assess how knot characteristics impact the lumber volume and value recovery for two types of sawmill designs common in the province of BC; and 3) to develop statistical models to predict knot attributes as well as lumber recovery and the corresponding product basket in pessimistic, normal or optimistic economic scenarios from tree DBH and height. Four knot characteristics were extracted from the X-ray CT images from each amabilis fir and western hemlock tree. Based on the ANOVA, only DBH had a significant impact on all knot characteristics of amabilis fir, suggesting this species is less sensitive to the site on which is was grown. In western hemlock trees, both site and DBH had a significant effect on total knot volume and average knot area, whereas the knot/tree volume was only significantly impacted by the site. Western hemlock trees could therefore be more susceptible to variations in temperature (Jochner et al., 2018; Way & Oren, 2010) and precipitation than amabilis fir. For both species, the larger the tree, in terms of DBH, the bigger the knots. To minimize the size of knots, amabilis fir and western hemlock trees should be pruned or harvested at a smaller DBH, which may not be desirable from the perspective of sawmills. Alternatively, trees could also be bred with the goal to reduce the occurrence and dimensions of knots using genomic-assisted approaches. From the analysis, it can be concluded that although these two species are often considered as one commercial group (e.g., Hem-Fir), their individual response to similar growth conditions contrasts, which affects the growth of branches and consequently the knot distribution. For example, the volume of individual western hemlock trees grown on Site 2 was the lowest amongst the three stands, but the knots were the largest. As a result, the lumber quality also differed. From the sawmilling simulation results of the Interior sawmill layout, it can be concluded that site did not significantly affect the lumber recovery and value. Surprisingly, the DBH did not have a statistically significant effect on the lumber recovery, although an increase was observed with 58 larger DBH. However, trees from larger DBH classes resulted in an increase in the value recovery. This observation confirms the hypothesis proposed by Middleton and Munro (2013) that larger DBH classes to those included in the study could create more value. From the simulation results, it was possible to quantify that the average lumber recovery and value of amabilis fir could be slightly higher than that of western hemlock. As reported by Middleton and Munro (2013), the structural properties of western hemlock are higher, which further supports the classification of amabilis fir and western hemlock as one commercial group. Alternatively, the sawmilling simulations indicated that only DBH would affect the lumber recovery in Coastal mill, which suggest this type of sawmill is more sensitive to the diameter of logs. This finding was expected given the larger potential product basket and more strict grading rules that are used. The sawmilling simulation results showed that processing Hem-Fir in random sawmills, prevalent in the BC Interior, are associated with higher volume and value recovery than when transformed into Coastal sawmills. This is mainly due to the sawmill’s different product baskets. The random sawmills typically produce dimensional lumber such as 2x4s and 2x6s, which means that the primary breakdown patterns are more uniform. This allows for more lumber pieces, which translates in a larger produced lumber volume, to be sawn in Interior random mills from each log or tree because products are smaller and more uniform than in the Coastal mills. Additionally, the price distribution used to optimize the breakdown process is more favorable in maximizing the number of lumber pieces or the volume of produced lumber in order to enhance the value recovery from each tree. This contrast with the product basket and optimization prices of the Coastal sawmill that are much more variable. In Coastal sawmills, products of higher value are generally prioritized over volume recovery. Furthermore, the dimensions of the lumber products manufactured are more variable in Coastal mills and products are of a higher grade or quality so less products are usually extracted from each tree, resulting in a lower volume recovery than that of Interior random mills. This is combined with the primary breakdown strategy in Coastal mills that often involve little or no optimization and a greater input by the operator. From the results of the study, it can be inferred that the product basket of Hem-Fir sawmills significantly impacts the volume and value recoveries. Models were developed to estimate knot characteristics and the value of 60-year-old standing amabilis fir and western hemlock trees from their tree attributes (e.g., DBH and height) on three harvesting stands located in Vancouver Island, BC. The model provides an insight into the link 59 between forest management practices and the increase of sawmill production based on lumber quality. With the combined use of X-ray CT images and sawmill simulation tools, it was possible to estimate the recovery and value of relatively young amabilis fir and western hemlock trees. If the results could be combined with Hem-Fir growth models (Middleton & Munro, 2013; Province of British Columbia, n.d.), the last step of economic assessment could then be analyzed. For sawmills, the value could be estimated at harvesting based on the current market situation, therefore the revenue could be estimated at an early stage in the decision-making process. For BC Coastal mills, the implementing of X-ray technology would eliminate the uncertainty from empirical primary breakdown strategies that normally only depend on the operator. Along with the model, the estimation of lumber value could also be helped in deciding the harvesting age. 5.2 Limitations of the study There are four limitations to the study. 1. The normal economic scenario corresponds to the middle point of the fluctuating market. For various reasons, there was limited access to economic data from the industry partner in the project. As a result, the optimistic and pessimistic scenarios were defined according to a certain percentage of the normal one for different grades of lumber. This approach reduced the sensitivity of the analysis to evaluate the impact not only on value recovery, but also on the type of product baskets produced by both virtual sawmills. 2. Different results could be expected if the harvested trees are bigger. The studied Hem-Fir trees were 60-year-old and had not reached their recommended rotation age (Middleton and Munro 2013). Consequently, there is uncertainty in using the models developed in this project to estimate the value of older stands grown on the same or similar sites on Vancouver Island. However, since some knot characteristics did not change significantly from the 40-cm to 50-cm DBH class, it might be assumed that the impact of larger diameter classes might also be insignificant on those knot characteristics due to branches being naturally pruned. In parallel, the study provides data on the potential associated with harvesting Hem-Fir trees at a younger stage, which could be beneficial for both the forest and wood industries in establishing strategies to deal with the reduction of forest resources. 60 3. The models were not validated by a real production dataset, but only sawmilling simulation. Although the simulations are based on existing BC sawmills, it is uncertain that the exact same results would have been obtained. This is especially true for the Coastal sawmill since the product basket is more market-driven than for the random Interior sawmill. It is therefore possible that a change in the demand would have a greater impact on the Coastal sawmill. Furthermore, the primary breakdown of Coastal sawmills often involves human interpretation in the decision-making process of the optimal breakdown pattern, which is challenging to predict. Using an industrial dataset or tracking the trees from the forest through the sawmill could also improve the correction term, since it could have been affected by the accuracy of the simulation software. 4. Limited investigation of silvicultural treatment on knot characteristics for Hem-Fir. Because the stands come from a naturally regenerated second-growth forest and not a plantation, it was only possible to infer the impact of past growth conditions based on the characteristics of the stands at the time of harvest. The conclusions of the study can be extrapolated to various forest management practices such as initial stand density, but with caution. Similarly, the developed models were not validated with a new population of amabilis fir and western hemlock trees harvested from the same sites and following the same experimental design. However, the sampled trees were randomly selected so our conclusion could still be inferred to the same plot. 5.3 Recommendations for future research and applications First, the models developed in this study could be further improved by developing a dynamic prediction model based on a continuously evolving economic market dataset. This suggestion assumes that a large dataset would be available, and the analysis would involve big dataset mining or machine learning approaches. Similarly, now that the impact of growth history is better understood, a single model predicting value of standing tree could be developed to reduce the error term and potentially improve the accuracy. Second, to build on the results obtained from the growth history part of the study, it would be interesting to X-ray CT scan a larger range of log sizes to assess the impact of different DBH on knot characteristics, which was suggested by Middleton and Munro (2013). This work would also allow for the models developed in the study to be validated, especially if the logs could be harvested from the same plots or ones located near so comparisons could be made. In parallel, the 61 impact of forest management on knot characteristics could be studied on plantation forests in lieu of second-growth or natural forests. This study would provide a more accurate assessment of silvicultural practices. If the real process recovery and the value of final product could be tracked, the validation of the models could also be done. Third, as for the relationship between silvicultural practices and wood quality, experiments could be done on plantations to control variables. The sampled species could not be restricted to Hem-Fir so that the difference between species could be investigated. If that was the case, Hem-Fir was considered the same commercial group and not compared statistically. Finally, improvement can be also done on the image processing software. After processing a very high number of CT images, a future project could explore methods to reduce the image processing time. The knot extraction software used in this study, CT2Opti, works best with dried or partially dried logs. From my experience and understanding from the literature, I believe the image processing time of the current methodology could be reduced by developing a three-dimension knot modeling software. This approach would allow for a retention of the knot information from the CT images and a decrease in the number of sections to process. For example, three sessions could be sufficient for a three-dimension software to automatically reconstruct a knot by connecting the knot shape from each session. In doing so, vectors of the knot growing direction would be known and a cone-like object representing the knot could be built. Although some small knots could be overlooked, their effect on lumber value is minor (An and Schajer, 2014a) according to the National Lumber Grading Association (National Lumber Grades Authority., 2008). 62 Bibliography Achim, A., Ruel, J. C., & Gardiner, B. A. (2005). Evaluating the effect of precommercial thinning on the resistance of balsam fir to windthrow through experimentation, modelling, and development of simple indices. Canadian Journal of Forest Research, 35(8), 1844–1853. https://doi.org/10.1139/x05-130 An, Y. (2013). Coarse-Resolution CT Scanning for Sawmill Logs Sorting and Grading (Issue October). University of British Columbia. An, Y., & Schajer, G. S. (2014a). Coarse-resolution cone-beam scanning of logs using eulerian CT reconstruction. Part I: Discretization and algorithm. Conference Proceedings of the Society for Experimental Mechanics Series, 8, 9–19. https://doi.org/10.1007/978-3-319-00876-9_2 An, Y., & Schajer, G. S. (2014b). Coarse-resolution cone-beam scanning of logs using eulerian CT reconstruction. Part II: Hardware design and demonstration. Conference Proceedings of the Society for Experimental Mechanics Series, 8, 21–29. https://doi.org/10.1007/978-3-319-00876-9_3 Andreu, J.-P., & Rinnhofer, A. (2003). Modeling Knot Geometry in Norway Spruce from Industrial CT Images (pp. 786–791). https://doi.org/10.1007/3-540-45103-X_104 BC Stats. (2020). Annual B.C. Origin Exports. Beaulieu, J., & Dutilleul, P. (2019). Applications of computed tomography (CT) scanning technology in forest research: a timely update and review. Canadian Journal of Forest Research, 49(10), 1173–1188. https://doi.org/10.1139/cjfr-2018-0537 Belley, D., Duchesne, I., Vallerand, S., Barrette, J., & Beaudoin, M. (2019). Computed tomography (CT) scanning of internal log attributes prior to sawing increases lumber value in white spruce ( Picea glauca ) and jack pine ( Pinus banksiana ). Canadian Journal of Forest Research, 49(12), 1516–1524. https://doi.org/10.1139/cjfr-2018-0409 Bendtsen, B. A. (1978). Properties of wood from improved and intensively managed trees. Forest Products Journal, 28(10), 61–72. https://agris.fao.org/agris-search/search.do?recordID=US19790392295 63 Berglund, A., Broman, O., Grönlund, A., & Fredriksson, M. (2013). Improved log rotation using information from a computed tomography scanner. Computers and Electronics in Agriculture, 90, 152–158. https://doi.org/10.1016/j.compag.2012.09.012 Bhandarkar, S. M., Faust, T. D., & Tang, M. (1999). Machine Vision and Applications CATALOG: a system for detection and rendering of internal log defects using computer tomography. In Machine Vision and Applications (Vol. 11). Bicho, P., Portillo, E., Yuen, B., Yan, D., & Pitt, D. G. (2013). Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 5 - Kraft and Thermomechanical pulping and pulp quality. Forestry Chronicle, 89(4), 490–501. https://doi.org/10.5558/tfc2013-090 Boutelje, J. B. (1966). On the anatomical structure, moisture content, density, shrinkage and resin content of the wood in and around knots in Swedish pine (Pinus sylvestris L, and in Swedish spruce (Picea abies Karst.). Svensk Papperstidn. Breinig, L., Berglund, A., Grönlund, A., Brüchert, F., & Sauter, U. H. (2013). Effect of Knot Detection Errors When Using a Computed Tomography Log Scanner for Sawing Control. Forest Products Journal, 63(7–8), 263–274. https://doi.org/10.13073/FPJ-D-13-00068 British Columbia’s Forests and their Management. (2003). B.C. Ministry of Forests. https://www.for.gov.bc.ca/hfd/pubs/Docs/Mr/Mr113/BC_Forest_Management.pdf Buzug, T. M. (2008). Computed Tomography. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-39408-2 Canadian Climate Normals 1971-2000 Station Data. (2019). https://climate.weather.gc.ca/climate_normals/ Centre for Forest Conservation Genetics. (n.d.). Coastal Western Hemlock Zone. https://cfcg.forestry.ubc.ca/resources/cataloguing-in-situ-genetic-resources/cwh-zone/ Cetiner, I., Var, A. A., & Cetiner, H. (2016). Classification of knot defect types using wavelets and KNN. Elektronika Ir Elektrotechnika, 22(6), 67–72. https://doi.org/10.5755/j01.eie.22.6.17227 Chase, C. W., Kimsey, M. J., Shaw, T. M., & Coleman, M. D. (2016). The response of light, water, and nutrient availability to pre-commercial thinning in dry inland Douglas-fir forests. Forest Ecology and Management, 363, 98–109. https://doi.org/10.1016/j.foreco.2015.12.014 64 Clark, A., Borders, B. E., & Daniels, R. F. (2004). Impact of vegetation control and annual fertilization on properties of loblolly pine wood at age 12. Forest Products Journal, 54(12), 90–96. Commercial Thinning Research - Province of British Columbia. (n.d.). Retrieved January 24, 2021, from https://www2.gov.bc.ca/gov/content/industry/forestry/managing-our-forest-resources/silviculture/silviculture-research/commercial-thinning De Chiffre, L., Carmignato, S., Kruth, J. P., Schmitt, R., & Weckenmann, A. (2014). Industrial applications of computed tomography. CIRP Annals - Manufacturing Technology, 63(2), 655–677. https://doi.org/10.1016/j.cirp.2014.05.011 De Montigny, L., Asante, P., Le Noble, S., & Nigh, G. (2019). The Economic Implications of Pre-commercial Thinning Treatments in Mixed Western Hemlock and Amabilis Fir Stands in Coastal British Columbia under Varying Cost and Benefit Assumptions and Minimum Harvest Criteria (EP1211). www.crownpub.bc.ca Donaldson, L. (2008). Microfibril angle: Measurement, variation and relationships - A review. IAWA Journal, 29(4), 345–386. https://doi.org/10.1163/22941932-90000192 Duchateau, E., Longuetaud, F., Mothe, F., Ung, C., Auty, D., & Achim, A. (2013). Modelling knot morphology as a function of external tree and branch attributes. Canadian Journal of Forest Research, 43(3), 266–277. https://doi.org/10.1139/cjfr-2012-0365 Duchesne, I., Pitt, D. G., & Tanguay, F. (2013). Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 4 – Lumber production, quality and value. The Forestry Chronicle, 89(04), 474–489. https://doi.org/10.5558/tfc2013-089 Dutilleul, P., Han, L., Valladares, F., & Messier, C. (2015). Crown traits of coniferous trees and their relation to shade tolerance can differ with leaf type: a biophysical demonstration using computed tomography scanning data. Frontiers in Plant Science, 6, 172. https://doi.org/10.3389/FPLS.2015.00172 Forests, B. M. of. (2003). British Columbia’s Forests. Forests, B. M. of. (2018). Ministry of Forests, Lands, Natural Resource Operations and Rural Development 2018/19 - 2020/21 Service Plan (Issue February). Fredriksson, M., Broman, O., & Sandberg, D. (2017). The use of CT-scanning technology in wood value-chain research and in wood industry: A state of the art. 13, 533–539. www.proligno.ro 65 Fredriksson, M., Cool, J., Duchesne, I., & Belley, D. (2017). Knot detection in computed tomography images of partially dried jack pine ( Pinus banksiana ) and white spruce ( Picea glauca ) logs from a Nelder type plantation. Canadian Journal of Forest Research, 47(7), 910–915. https://doi.org/10.1139/cjfr-2016-0423 Freyburger, C., Longuetaud, F., Mothe, F., Constant, T., & Leban, J.-M. (2009). Measuring wood density by means of X-ray computer tomography. Ann. For. Sci, 66, 804. https://doi.org/10.1051/forest/2009071 Gergel, T., Bucha, T., Gejdoš, M., & Vyhnáliková, Z. (2019). Computed tomography log scanning - High technology for forestry and forest based industry. Central European Forestry Journal, 65(1), 51–59. https://doi.org/10.2478/forj-2019-0003 Getzin, S., & Wiegand, K. (2007). Asymmetric tree growth at the stand level: Random crown patterns and the response to slope. Forest Ecology and Management, 242(2–3), 165–174. https://doi.org/10.1016/j.foreco.2007.01.009 Giudiceandrea, F., Ursella, E., & Enrico, V. (2011). A high speed CT scanner for the sawmill industry. January, 22. HALCO Software. (n.d.). Retrieved January 13, 2021, from https://www.halcosoftware.com/software-1-sawsim Hall, M. (2018). Strategic Support for Small Business, Corporations and the Forest Industry BC Coastal Fibre Supply. Hassegawa, M., Havreljuk, F., Ouimet, R., Auty, D., Pothier, D., & Achim, A. (2015). Large-Scale Variations in Lumber Value Recovery of Yellow Birch and Sugar Maple in Quebec, Canada. https://doi.org/10.1371/journal.pone.0136674 Hember, R. A., Kurz, W. A., & Girardin, M. P. (2019). Tree Ring Reconstructions of Stemwood Biomass Indicate Increases in the Growth Rate of Black Spruce Trees Across Boreal Forests of Canada. Journal of Geophysical Research: Biogeosciences, 124(8), 2460–2480. https://doi.org/10.1029/2018JG004573 Hervé, V., Mothe, F., Freyburger, C., Gelhaye, E., & Frey-Klett, P. (2014). Density mapping of decaying wood using X-ray computed tomography. International Biodeterioration and Biodegradation, 86, 358–363. https://doi.org/10.1016/j.ibiod.2013.10.009 66 Hevia, A., Álvarez-González, J. G., & Majada, J. (2016). Comparison of pruning effects on tree growth, productivity and dominance of two major timber conifer species. Forest Ecology and Management, 374, 82–92. https://doi.org/10.1016/j.foreco.2016.05.001 Hounsfield, G. N. (1973). Computerized transverse axial scanning (tomography): I. Description of system. British Journal of Radiology, 46(552), 1016–1022. https://doi.org/10.1259/0007-1285-46-552-1016 Ido, H., Nagao, H., Kato, H., & Miura, S. (2013). Strength properties and effect of moisture content on the bending and compressive strength parallel to the grain of sugi (Cryptomeria japonica) round timber. Journal of Wood Science, 59(1), 67–72. https://doi.org/10.1007/s10086-012-1297-z Innovations, F. (2013). Optitek: User ’ s Manual. Jannke, P. F. (2019). British Columbia Interior Forest Sector Competitiveness Study. In BC Council of Forest Industries (Vol. 2019, Issue August). https://www.cofi.org/wp-content/uploads/FEA-BC-Interior-Public-Final-August-2019.pdf Jianhe Wang, B., & Dai, C. (2013). Development of structural laminated veneer lumber from stress graded short-rotation hem-fir veneer. https://doi.org/10.1016/j.conbuildmat.2013.05.096 Jiménez, E., Vega, J. A., Pérez-Gorostiaga, P., Cuiñas, P., Fonturbel, T., Fernández, C., Madrigal, J., Hernando, C., & Guijarro, M. (2008). Effects of pre-commercial thinning on transpiration in young post-fire maritime pine stands. Forestry, 81(4), 543–557. https://doi.org/10.1093/forestry/cpn032 Jochner, M., Bugmann, H., Nötzli, M., & Bigler, C. (2018). Tree growth responses to changing temperatures across space and time: a fine-scale analysis at the treeline in the Swiss Alps. 32, 645–660. https://doi.org/10.1007/s00468-017-1648-x Johansson, E., Johansson, D., Skog, J., & Fredriksson, M. (2013). Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces. Computers and Electronics in Agriculture, 96, 238–245. https://doi.org/10.1016/j.compag.2013.06.003 Jozsa, L. A., & Middleton, G. R. (1994). The effect of knots on lumber grades may be larger in smaller size trees. Forintek Canada Corp. 67 Kang, K.-Y., Zhang, S. Y., & Mansfield, S. D. (2004). The effects of initial spacing on wood density, fibre and pulp properties in jack pine (Pinus banksiana Lamb.). Holzforschung, 58(5), 455–463. https://doi.org/10.1515/HF.2004.069 Kantavichai, R., Turnblom, E. C., & Lowell, E. C. (2020). Effects of density control and fertilization on log wood quality from a douglas-fir stand in western oregon, usa. Forest Science, 66(2), 191–200. https://doi.org/10.1093/forsci/fxz069 Kennedy, E. I. (1965). Strength and related properties of wood grown in Canada. Knigge, W. (1975). Utilization of the southern pines. Wood Science and Technology, 9(1), 80–80. https://doi.org/10.1007/bf00351917 Krähenbühl, A., Kerautret, B., Debled-Rennesson, I., Mothe, F., & Longuetaud, F. (2014). Knot segmentation in 3D CT images of wet wood. Pattern Recognition, 47(12), 3852–3869. https://doi.org/10.1016/j.patcog.2014.05.015 Långström, B., & Hellqvist, C. (1991). Effects of different pruning regimes on growth and sapwood area of Scots pine. Forest Ecology and Management, 44(2–4), 239–254. https://doi.org/10.1016/0378-1127(91)90011-J Lasserre, J. P., Mason, E. G., Watt, M. S., & Moore, J. R. (2009). Influence of initial planting spacing and genotype on microfibril angle, wood density, fibre properties and modulus of elasticity in Pinus radiata D. Don corewood. Forest Ecology and Management, 258(9), 1924–1931. https://doi.org/10.1016/j.foreco.2009.07.028 Lechuga, L., & Weidlich, G. A. (2016). Cone Beam CT vs. Fan Beam CT: A Comparison of Image Quality and Dose Delivered Between Two Differing CT Imaging Modalities. Cureus, 8(9). https://doi.org/10.7759/cureus.778 Lee, L., Cohen, D., & Maness, T. (1999). Markets and technology in western Canadian sawmills. Forest Products Journal, 49(7), 36–42. Lemieux, H., Beaudoin, M., & Zhang, S. Y. (2001). Characterization and modeling of knots in black spruce (Picea mariana) logs. Wood and Fiber Science, 33(3), 465–475. Lemieux, H., Beaudoin, M., Zhang, S. Y., & Grondin, F. (2002). Improving structural lumber quality in a sample of Picea Mariana logs sawn according to the knots. Wood and Fiber Science, 34(2), 266–275. https://wfs.swst.org/index.php/wfs/article/view/770 Li, C., Huang, S., Barclay, H., & Sidders, D. (2016). Modeling lumber yield of white spruce in Alberta, Canada: a comparative approach. https://doi.org/10.1007/s10310-016-0544-3 68 Lindgren, L. O. (1991). Medical CAT-scanning: X-ray absorption coefficients, CT-numbers and their relation to wood density. Wood Science and Technology, 25(5), 341–349. https://doi.org/10.1007/BF00226173 Lister, P. F. (2004). Applications of X-ray computed tomography in the wood products industry. 16th WCNDT 2004 - World Conference on NDT Aug 30 - Sep 3, 2004 - Montreal, Canada. Liu, C. ;, Zhang, Cloutier, Y. ;, & Rycabel, A. ; (2007). Modeling lumber value recovery in relation to selected tree. In Forest Products Journal (Vol. 57). Liu, C., Ruel, J.-C., Groot, A., & Zhang, S. Y. (2009). Model development for lumber volume recovery of natural balsam fir trees in Quebec, Canada. In THE FORESTRY CHRONICLE (Vol. 85, Issue 6). Longuetaud, F., Leban, J.-M., Mothe, F., Kerrien, E., Berger, M.-O., & Leban, J.-M. (2004). Automatic detection of pith on CT images of spruce logs. Computers and Electronics in Agriculture, 44, 107–119. https://doi.org/10.1016/j.compag.2004.03.005 Longuetaud, F., Mothe, F., Fournier, M., Dlouha, J., Santenoise, P., & Deleuze, C. (2016). Within-stem maps of wood density and water content for characterization of species: a case study on three hardwood and two softwood species. 73(3), 601–614. https://link.springer.com/article/10.1007/s13595-016-0555-4 Longuetaud, F., Mothe, F., Kerautret, B., Krähenbühl, A., Hory, L., Leban, J. M., & Debled-Rennesson, I. (2012). Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples. Computers and Electronics in Agriculture, 85, 77–89. https://doi.org/10.1016/j.compag.2012.03.013 Longuetaud, F., Mothe, F., & Leban, J.-M. M. (2007). Automatic detection of the heartwood/sapwood boundary within Norway spruce (Picea abies (L.) Karst.) logs by means of CT images. Computers and Electronics in Agriculture, 58(2), 100–111. https://doi.org/10.1016/j.compag.2007.03.010 Lyhykäinen, H. T., Mäkinen, H., Mäkelä, A., Pastila, S., Heikkilä, A., & Usenius, A. (2009). Predicting lumber grade and by-product yields for Scots pine trees. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2009.03.054 69 Mäkinen, H., & Colin, F. (1998). Predicting branch angle and branch diameter of Scots pine from usual tree measurements and stand structural information. Canadian Journal of Forest Research, 28(11), 1686–1696. https://doi.org/10.1139/x98-141 Makinen, H., Verkasalo, E., & Tuimala, A. (2014). Effects of pruning in Norway spruce on tree growth and grading of sawn boards in Finland. Forestry, 87(3), 417–424. https://doi.org/10.1093/forestry/cpt062 Manceur, A. M., Beaulieu, J., Han, L., & Dutilleul, P. (2012). A multidimensional statistical model for wood data analysis, with density estimated from CT scanning data as an example. Canadian Journal of Forest Research, 42(6), 1038–1049. https://doi.org/10.1139/X2012-053 Manso, R., McLean, J. P., Ash, A., & Achim, A. (2020). Estimation of individual knot volumes by mixed-effects modelling. Canadian Journal of Forest Research, 50(2), 81–88. https://doi.org/10.1139/cjfr-2019-0038 Middleton, G., & Munro, D. (2013). Characterizing the Wood Attributes and Product Potential of 60-Year-Old Hem-fir in Coastal British Columbia. Moberg, L. (1999). Variation in knot size of Pinus sylvestris in two initial spacing trials. Silva Fennica, 33(2), 131–144. https://doi.org/10.14214/sf.663 Moberg, L. (2000). Models of Internal Knot Diameter for Pinus sylvestris. Scandinavian Journal of Forest Research, 15(2), 177–187. https://doi.org/10.1080/028275800750014984 Moberg, L. (2006). Predicting knot properties of Picea abies and Pinus sylvestris from generic tree descriptors. Scandinavian Journal of Forest Research, 21(S7), 49–62. https://doi.org/10.1080/14004080500487011 Moberg, L., & Nordmark, U. (2006). Predicting lumber volume and grade recovery for Scots pine stems using tree models and sawmill conversion simulation. In Forest Products Journal (Vol. 56, Issue 4, pp. 68–74). National Lumber Grades Authority. (2008). Standard Grading Rules for Canadian Lumber. www.nlga.org Natural Resources Canada. (2020). Canada’s forests : Adapting to change The State of Canada’s Forests. https://cfs.nrcan.gc.ca/publications?id=40219&lang=en_CA Negrave, R. W., Prescott, C. E., & Barker, J. E. (2007). Growth and foliar nutrition of juvenile western hemlock and western redcedar plantations on low- and medium-productivity sites 70 on northern Vancouver Island: Response to fertilization and planting density. Canadian Journal of Forest Research, 37(12), 2587–2599. https://doi.org/10.1139/X07-089 Newton, M., Lachenbruch, B., Robbins, J. M., & Cole, E. C. (2012). Branch diameter and longevity linked to plantation spacing and rectangularity in young Douglas-fir. Forest Ecology and Management, 266, 75–82. https://doi.org/10.1016/j.foreco.2011.11.009 Nordmark, U. (2005). Value recovery and production control in bucking, log sorting, and log breakdown. Forest Products Journal. Nyland, R. D. (2016). Silviculture: Concepts and Applications (Third). Oh, J. K., Kim, K. M., & Lee, J. J. (2010). Use of adjacent knot data in predicting bending strength of dimension lumber by X-ray. Wood and Fiber Science, 42(1), 10–20. Oja, J. (2000). Evaluation of knot parameters measured automatically in CT-images of Norway spruce (Picea abies (L.) Karst.). Holz Als Roh - Und Werkstoff, 58(5), 375–379. https://doi.org/10.1007/s001070050448 Oja, J, & Temnerud, E. (1999). The appearance of resin pockets in CT-images of Norway spruce ( Picea abies (L.) Karst.). Holz Als Roh- Und Werkstoff, 57(5), 400–406. https://doi.org/10.1007/s001070050368 Oja, Johan, Grundberg, S., & Grönlund, A. (1998). Measuring the outer shape of Pinus sylvestris saw logs with an X-ray LogScanner. Scandinavian Journal of Forest Research, 13(1–4), 340–347. https://doi.org/10.1080/02827589809382993 Oja, Johan, Källsner, B., & Grundberg, S. (2005). Predicting the strength of sawn wood products: A comparison between x-ray scanning of logs and machine strength grading of lumber. Forest Products Journal, 55(9), 55–60. Omule, S. A. Y. (1989). Growth and yield 32 years after commercially thinning 56-year-old western hemlock. Pitt, D., Lanteigne, L., Hoepting, M. K., Plamondon, J., Duchesne, I., Bicho, P., & Warren, G. (2013). Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 6 - Estimating the economic benefits. Forestry Chronicle, 89(4), 502–511. https://doi.org/10.5558/tfc2013-091 Plamondon, J., & Pitt, D. G. (2013). Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 2 - Efficiency gains in cut-to-length harvesting. Forestry Chronicle, 89(4), 458–463. https://doi.org/10.5558/tfc2013-087 71 Province of British Columbia. (n.d.). Tree & Stand Simulator (TASS) - Province of British Columbia. Retrieved February 19, 2021, from https://www2.gov.bc.ca/gov/content/industry/forestry/managing-our-forest-resources/forest-inventory/growth-and-yield-modelling/tree-and-stand-simulator-tass Qu, H., Chen, M., Hu, Y., & Lyu, J. (2020). Effect of trees knot defects on wood quality: A review. IOP Conference Series: Materials Science and Engineering, 738(1), 012027. https://doi.org/10.1088/1757-899X/738/1/012027 Rais, A., Ursella, E., Vicario, E., & Giudiceandrea, F. (2017). The use of the first industrial X-ray CT scanner increases the lumber recovery value: case study on visually strength-graded Douglas-fir timber. Annals of Forest Science, 74(2). https://doi.org/10.1007/s13595-017-0630-5 Roussel, J. R., Mothe, F., Krähenbühl, A., Kerautret, B., Debled-Rennesson, I., & Longuetaud, F. (2014). Automatic knot segmentation in CT images of wet softwood logs using a tangential approach. Computers and Electronics in Agriculture, 104, 46–56. https://doi.org/10.1016/j.compag.2014.03.004 Schajer, G. S. (2001). Lumber strength grading using x-ray scanning. Forest Products Journal, 51(1), 43–50. Schmoldt, D. L., Li, P., & Abbott, A. L. (1996). A New Approach to Automated Labeling of Internal Features of Hardwood Logs Using CT Images. In Review of Progress in Quantitative Nondestructive Evaluation (pp. 1883–1890). Springer US. https://doi.org/10.1007/978-1-4613-0383-1_246 Schmoldt, D. L., Occeña, L. G., Lynn abbott, A., & Gupta, N. K. (1998). Nondestructive Evaluation of Hardwood Logs: Ct Scanning, Machine Vision and Data Utilization. Nondestructive Testing and Evaluation, 15(5), 279–309. https://doi.org/10.1080/10589759908952876 Shmulsky, R., & Jones, P. D. (2011). Forest Products and Wood Science An Introduction. In Forest Products and Wood Science An Introduction: Sixth Edition. Wiley-Blackwell. https://doi.org/10.1002/9780470960035 Skog, J., & Oja, J. (2009). Heartwood diameter measurements in Pinus sylvestris sawlogs combining X-ray and three-dimensional scanning. Scandinavian Journal of Forest Research, 24(2), 182–188. https://doi.org/10.1080/02827580902806585 72 Stängle, S. M., Brüchert, F., Heikkila, A., Usenius, T., Usenius, A., & Sauter, U. H. (2015). Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection. Annals of Forest Science, 72(1), 57–65. https://doi.org/10.1007/s13595-014-0385-1 Taylor, F. W., Wagner, F. G., Mcmillin, C. W., & Hopkins, F. F. (1984). Locating knots by industrial tomography- A feasibility study. Forest Products Journal, 34(September 1983), 42–46. Thomas, R. E., & Bennett, N. D. (2017). An analysis of the differences among log scaling methods and actual log volume. Forest Products Journal, 67(3–4), 250–257. https://doi.org/10.13073/FPJ-D-16-00039 Todoroki, C., & Rönnqvist, M. (2002). Dynamic Control of Timber Production at a Sawmill with Log Sawing Optimization. Scandinavian Journal of Forest Research, 17(1), 79–89. https://doi.org/10.1080/028275802317221118 Tong, Q. J., & Zhang, S. Y. (2006). Modelling jack pine lumber value recovery in relation to tree characteristics using Optitek simulation. Forest Products Journal, 56(1), 66–72. Tong, Q., & Zhang, S. (2009). Development of lumber recovery correction models for plantation-grown Pinus banksiana trees. Scandinavian Journal of Forest Research, 24(5), 417–424. https://doi.org/10.1080/02827580903140687 Tong, Que Ju, Duchesne, I., Belley, D., Beaudoin, M., & Swift, E. (2013). Characterization of knots in plantation white spruce. Wood and Fiber Science, 45(1), 84–97. Ugulino, B., Cáceres, C. B., Hernández, R. E., & Blais, C. (2020). Influence of temperature and moisture content on bark/wood shear strength of black spruce and balsam fir logs. Wood Science and Technology, 54(4), 963–979. https://doi.org/10.1007/s00226-020-01198-x Uusitalo, J., Ylhäisi, O., Rummukainen, H., & Makkonen, M. (2018). Predicting probability of A-quality lumber of Scots pine (Pinus sylvestris L.) prior to or concurrently with logging operation. Scandinavian Journal of Forest Research, 33(5), 475–483. https://doi.org/10.1080/02827581.2018.1461922 Vallerand, S.; Belley, D.; Duchesne, I.; Beaudoin, M. (2011). Utilisation d’images CT pour la modélisation 3D de billes réelles avec caractéristiques internes. Note de Recherche No. 2 - Décembre 2011. 2 P. http://www.forvaluenet-foretvaleur.ca/index.php?id=23&lg=fr.%0A 73 Vera, H., Pavel, K., Jiri, C., & Jan, V. (2017). Microfiber Angle and its Effect on Wood Cell Behavior. https://doi.org/10.4028/www.scientific.net/AMR.1144.88 Wang, J. B., Wei, P., Gao, Z., & Dai, · Chunping. (2018). The evaluation of panel bond quality and durability of hem-fir cross-laminated timber (CLT). 76, 833–841. https://doi.org/10.1007/s00107-017-1283-7 Warren, G., Baines, P., Plamondon, J., & Pitt, D. G. (2013). Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 3 - Incidence of root and butt decay. Forestry Chronicle, 89(4), 464–473. https://doi.org/10.5558/tfc2013-088 Way, D. A., & Oren, R. (2010). Differential responses to changes in growth temperature between trees from different functional groups and biomes: a review and synthesis of data. In Tree Physiology. https://doi.org/10.1093/treephys/tpq015 Weber, A., Leckie, S., Kimmins, H., Gilbert, B., Blanco, J. A., & Lo, Y.-H. (2016). Survival and growth as measures of shade tolerance of planted western redcedar, western hemlock and amabilis fir seedlings in hemlock-fir forests of northern Vancouver Island. https://doi.org/10.1016/j.foreco.2016.11.019 Wehrhausen, M., Laudon, N., Brüchert, F., & Sauter, U. H. (2012). Crack Detection in Computer Tomographic Scans of Softwood Tree Discs. Forest Products Journal, 62(6), 434–442. https://doi.org/10.13073/FPJ-D-12-00079.1 Zhang, S., Chauret, G., & Tong, Q. (2009). Impact of precommercial thinning on tree growth, lumber recovery and lumber quality in Abies balsamea. Scandinavian Journal of Forest Research, 24(5), 425–433. https://doi.org/10.1080/02827580903124392 Zhang, S. Y., & Tong, Q. J. (2005). Modeling lumber recovery in relation to selected tree characteristics in jack pine using sawing simulator Optitek. Annals of Forest Science, 62(3), 219–228. https://doi.org/10.1051/forest:2005013 Zobel, B. J., & Sprague, J. R. (1998). Characteristics of Juvenile Wood (pp. 21–55). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72126-7_2 74 Appendices Appendix A Knot characteristics - original data 75 Table A.1 Four knot characteristics for each amabilis fir trees studied in the project. Tree name ID Total knot volume Number of knots Average knot area Knot/tree ratio (mm3) (mm2) (/103) 101 13 059.7 244 29.8 4.986 104 44 277.6 498 49.3 6.115 107 19 731.0 321 26.7 6.246 108 24 032.1 449 27.5 4.120 109 44 354.4 481 40.0 5.685 110 25 152.8 364 34.1 5.034 113 30 598.5 395 28.4 7.602 114 35 600.6 443 43.4 8.970 116 56 348.5 472 55.2 8.210 118 17 355.2 460 26.3 4.151 123 9 064.9 274 14.4 4.320 124 5 165.5 153 21.8 2.697 202 27 678.1 451 31.0 5.123 203 48 359.7 390 57.3 10.003 204 7 758.5 289 18.3 2.525 205 17 916. 375 22.7 4.747 207 13 874.3 287 21.7 4.446 209 56 523.4 415 67.6 7.597 210 36 211.4 384 58.6 5.351 211 27 703.5 310 33.6 5.369 214 5 848.6 249 16.6 3.078 218 51 818.8 300 70.4 9.493 219 47 216.3 402 63.2 6.792 221 4 982.1 191 19.6 2.389 301 44 863.5 417 45.7 6.594 302 22 872.6 337 38.9 5.414 303 13 899.5 434 22.5 4.984 309 17 515.5 393 25.8 7.081 310 35 464.2 414 36.6 8.497 311 43 270.9 444 32.6 8.666 312 73 025.0 442 48.6 11.399 314 90 779.8 445 53.6 10.923 315 15 123.1 306 32.9 3.534 316 48 720.3 460 34.0 7.982 322 17 613.3 361 23.4 6.125 323 7 737.2 226 22.4 3.166 76 Table A.2 Four knot characteristics for each western hemlock trees studied in the project. Tree name ID Total knot volume Number of knots Average knot area Knot/tree ratio (mm3) (mm2) (/103) 101 11 245.3 300 17.9 3.886 104 52 466.2 549 37.0 9.153 107 46 873.8 553 35.0 10.997 108 24 485.0 460 21.8 6.061 109 17 500.1 328 26.6 6.507 110 33 899.2 512 25.5 7.761 113 28 230.6 482 36.2 5.983 114 64 070.1 551 41.9 8.900 116 20 838.8 402 21.1 10.631 118 79 031.7 577 42.4 11.970 123 25 863.8 306 43.0 4.047 124 19 781.2 430 25.5 8.094 202 63 309.8 337 65.9 11.582 203 40 827.3 449 44.8 8.504 204 36 075.5 321 33.5 9.771 205 37 602.0 461 36.1 9.382 207 18 881.8 314 27.2 9.253 209 53 849.4 498 42.3 12.990 210 31 452.1 612 22.4 10.188 211 88 152.9 669 40.1 13.143 214 65 637.0 473 50.2 9.809 218 53 437.6 402 47.5 10.816 219 31 934.3 532 28.7 15.589 221 24 891.2 334 23.9 9.452 301 22 907.9 591 18.9 8.452 302 36 908.7 504 39.6 7.762 303 39 620.4 526 34.9 10.139 309 11 992.0 260 23.3 6.114 310 16 321.5 371 22.5 5.122 311 22 848.7 356 34.0 5.589 312 52 636.8 405 44.2 7.193 314 19 347.7 293 31.8 5.338 315 43 869.8 369 61.7 7.190 316 9 055.0 265 18.0 3.493 322 47 512.2 381 33.5 8.055 323 51 399.3 483 33.5 7.596 77 Appendix B Pearson correlation matrices for 17 tree characteristics for amabilis fir and western hemlock trees. 78 Table B.1: Pearson correlation coefficients and p-values of 17 tree characteristics for amabilis fir. The four knot characteristics extracted in the project are correlated with those of Middleton and Munroe (2013). LCH H DBH AMOEc S_ID UV TV AUCSc AV L_sph AMORb AMOEb AD AKA NK TKV K/T_R LCH* 1 H 0.426 1 0.010 DBH 0.039 0.733 1 0.823 0.000 AMOEc -0.073 -0.456 -0.465 1 0.671 0.005 0.004 S_ID -0.600 -0.295 -0.011 0.153 1 0.000 0.081 0.948 0.374 UV 0.143 0.829 0.980 -0.490 -0.103 1 0.405 0.000 0.000 0.002 0.548 TV 0.142 0.828 0.981 -0.491 -0.103 1.000 1 0.410 0.000 0.000 0.002 0.552 0.000 AUCSc -0.188 -0.402 -0.332 0.902 0.173 -0.368 -0.368 1 0.273 0.015 0.048 0.000 0.314 0.027 0.027 AV 0.248 -0.159 -0.419 0.425 -0.166 -0.383 -0.384 0.224 1 0.145 0.354 0.011 0.010 0.334 0.021 0.021 0.189 L_sph 0.304 -0.104 -0.361 0.142 -0.316 -0.314 -0.315 0.048 0.187 1 0.071 0.545 0.031 0.408 0.060 0.062 0.062 0.782 0.275 AMORb 0.160 -0.068 -0.270 0.688 -0.264 -0.237 -0.237 0.733 0.301 0.320 1 0.351 0.693 0.111 0.000 0.120 0.165 0.164 0.000 0.074 0.057 AMOEb 0.208 -0.105 -0.381 0.678 -0.423 -0.323 -0.324 0.640 0.311 0.286 0.767 1 0.225 0.543 0.022 0.000 0.010 0.054 0.054 0.000 0.065 0.091 0.000 AD -0.106 -0.291 -0.270 0.753 0.089 -0.300 -0.300 0.813 0.193 0.125 0.813 0.690 1 0.537 0.085 0.111 0.000 0.605 0.075 0.075 0.000 0.258 0.467 0.000 0.000 AKA -0.034 0.547 0.802 -0.337 0.045 0.782 0.783 -0.248 -0.155 -0.385 -0.140 -0.327 -0.120 1 0.842 0.001 0.000 0.044 0.793 0.000 0.000 0.145 0.366 0.020 0.416 0.052 0.485 NK 0.111 0.549 0.723 -0.275 0.049 0.696 0.696 -0.133 -0.437 -0.373 -0.136 -0.139 -0.013 0.444 1 0.519 0.001 0.000 0.105 0.777 0.000 0.000 0.439 0.008 0.025 0.428 0.420 0.938 0.007 TKV -0.203 0.460 0.864 -0.418 0.180 0.805 0.806 -0.277 -0.377 -0.474 -0.315 -0.467 -0.167 0.809 0.648 1 0.234 0.005 0.000 0.011 0.292 0.000 0.000 0.102 0.024 0.004 0.061 0.004 0.330 0.000 0.000 K/T_R -0.251 0.179 0.606 -0.294 0.236 0.506 0.507 -0.172 -0.281 -0.468 -0.262 -0.393 0.041 0.676 0.582 0.864 1 0.141 0.296 0.000 0.081 0.166 0.002 0.002 0.316 0.097 0.004 0.123 0.018 0.811 0.000 0.000 0.000 *LCH = Living crown height; H = Height; DBH = Diameter at breast height; AMOEc = Average modulus of elasticity in bending at 12% moisture content; S_ID = Site ID; UV = Utilizable volume; TV = Total volume; AUCSc = Average ultimate compression stress (compression perpendicular to grain) at 12% moisture content; AV = Acoustic velocity; L_sph = Latest stem per hectare with DBH >= 175 mm; AMORb = Average modulus of rupture in bending at 12% moisture content; AMOEb = Average modulus of elasticity in bending at 12% moisture content; AD = Average density; AKA = Average knot area; NK = Number of knots; TKV = Total knot volume; K/T_R = Knot/tree ratio. 79 Table B.2: Pearson correlation coefficients and p-values of 17 tree characteristics for western hemlock. The four knot characteristics extracted in the project are correlated with those of Middleton and Munroe (2013). LCH H DBH AMOEc S_ID UV TV AUCSc AV L_sph AMORb AMOEb AD AKA NK TKV K/T_R LCH* 1 H 0.142 1 0.408 DBH -0.128 0.601 1 0.458 0.000 AMOEc -0.303 0.096 -0.196 1 0.072 0.577 0.251 S_ID -0.125 -0.349 0.000 0.149 1 0.466 0.037 0.998 0.386 UV -0.088 0.725 0.980 -0.140 -0.097 1 0.612 0.000 0.000 0.415 0.572 TV -0.088 0.724 0.981 -0.140 -0.096 1.000 1 0.608 0.000 0.000 0.414 0.576 0.000 AUCSc -0.147 0.135 -0.284 0.891 0.001 -0.207 -0.207 1 0.392 0.433 0.093 0.000 0.993 0.226 0.225 AV 0.269 0.268 -0.015 0.021 -0.415 0.033 0.032 0.080 1 0.112 0.114 0.933 0.902 0.012 0.850 0.853 0.641 L_sph 0.399 0.156 -0.115 -0.128 -0.507 -0.056 -0.056 0.041 0.226 1 0.016 0.365 0.503 0.457 0.002 0.747 0.745 0.812 0.185 AMORb -0.062 0.250 -0.268 0.714 -0.382 -0.152 -0.154 0.861 0.248 0.200 1 0.721 0.141 0.114 0.000 0.021 0.375 0.371 0.000 0.144 0.242 AMOEb -0.153 0.387 -0.096 0.800 -0.295 0.015 0.013 0.776 0.238 0.181 0.830 1 0.373 0.020 0.579 0.000 0.081 0.933 0.938 0.000 0.163 0.292 0.000 AD -0.139 0.273 -0.099 0.703 -0.249 -0.007 -0.008 0.776 0.096 -0.006 0.873 0.763 1 0.419 0.107 0.567 0.000 0.143 0.970 0.965 0.000 0.579 0.973 0.000 0.000 AKA 0.015 0.367 0.779 -0.345 0.067 0.720 0.721 -0.411 0.122 -0.119 -0.416 -0.288 -0.323 1 0.930 0.028 0.000 0.039 0.699 0.000 0.000 0.013 0.480 0.490 0.012 0.089 0.055 NK 0.144 0.292 0.249 -0.238 -0.207 0.270 0.270 -0.077 0.066 0.281 -0.082 -0.056 -0.026 0.095 1 0.403 0.084 0.143 0.163 0.225 0.111 0.111 0.654 0.701 0.097 0.636 0.747 0.882 0.581 TKV 0.025 0.505 0.790 -0.390 -0.089 0.776 0.776 -0.307 0.150 0.001 -0.270 -0.245 -0.135 0.702 0.590 1 0.886 0.002 0.000 0.019 0.607 0.000 0.000 0.068 0.383 0.993 0.111 0.149 0.434 0.000 0.000 K/T_R 0.226 -0.022 0.113 -0.463 -0.150 0.086 0.087 -0.270 0.310 0.192 -0.222 -0.393 -0.274 0.305 0.588 0.615 1 0.185 0.900 0.511 0.004 0.383 0.617 0.616 0.111 0.066 0.263 0.193 0.018 0.105 0.071 0.000 0.000 *LCH = Living crown height; H = Height; DBH = Diameter at breast height; AMOE_12c = Average modulus of elasticity in bending at 12% moisture content; S_ID = Site ID; UV = Utilizable volume; TV = Total volume; AUCS_12c = Average ultimate compression stress (compression perpendicular to grain) at 12% moisture content; AV = Acoustic velocity; L_sph = Latest stem per hectare with DBH >= 175 mm; AMOR_12b = Average modulus of rupture in bending at 12% moisture content; AMOE_12b = Average modulus of elasticity in bending at 12% moisture content; AD = Average density; AKA = Average knot area; NK = Number of knots; TKV = Total knot volume; K/T_R = Knot/tree ratio. 80 Appendix C Lumber output data (grade distribution and lumber production) for amabilis fir and western hemlock trees simulated in the virtual Interior random and Coastal sawmills. 81 Table C.1: Grade distribution, lumber production and recovery for amabilis fir trees processed in the virtual Interior random mill. The revenues associated with three price distribution (or scenarios) are also included. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 3 4 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 101 23 11 8 5 47 994.0 1 900.7 2 622.2 72.5 2.638 549.31 681.06 452.74 104 85 30 12 3 130 2 532.0 4 871.5 7 249.8 67.2 2.863 1,332.17 1,665.25 1,105.78 107 25 11 10 5 51 1 230.7 2 364.2 3 196.9 74.0 2.598 668.14 829.45 551.51 108 60 28 11 3 102 2 222.0 4 278.8 6 064.3 70.6 2.729 1,306.15 1,629.22 1,081.89 109 72 32 8 6 118 2 613.2 5 056.1 7 810.5 64.7 2.989 1,522.08 1,901.63 1,265.81 110 31 28 11 3 73 1 826.7 3 537.0 5 004.8 70.7 2.740 1,162.60 1,450.35 951.04 113 28 24 10 1 63 1 470.0 2 843.2 4 031.3 70.5 2.742 890.64 1,111.32 731.38 114 32 20 8 5 65 1 490.7 2 871.6 3 973.8 72.3 2.666 920.82 1,149.01 760.92 116 66 34 18 2 120 2 154.0 4 136.0 5 951.7 69.5 2.763 1,058.30 1,322.80 873.76 118 30 23 11 6 70 1 590.7 3 061.4 4 185.3 73.2 2.631 934.94 1,161.25 770.08 123 17 14 9 2 42 744.7 1 424.0 2 101.7 67.8 2.822 367.45 452.18 297.24 124 13 12 7 2 34 714.0 1 366.3 1 918.0 71.2 2.686 363.12 449.62 296.16 202 34 32 13 5 84 1 980.7 3 823.2 5 407.9 70.7 2.730 1,196.57 1,492.65 979.39 203 36 16 18 1 71 1 713.3 3 308.3 4 840.6 68.4 2.825 1,023.46 1,262.23 847.58 204 20 17 12 5 54 1 155.3 2 214.4 3 073.9 72.0 2.661 617.99 761.50 509.47 205 25 15 12 2 54 1 382.7 2 665.0 3 778.2 70.5 2.733 848.91 1,048.65 700.85 207 27 22 8 2 59 1 031.3 1 979.0 3 122.1 63.4 3.027 520.85 650.68 423.23 209 67 33 15 3 118 2 622.0 5 072.3 7 448.2 68.1 2.841 1,528.21 1,900.53 1,262.59 210 70 33 23 3 129 2 467.3 4 732.3 6 769.9 69.9 2.744 1,284.11 1,594.39 1,061.69 211 38 28 14 2 82 1 932.0 3 731.6 5 164.7 72.3 2.673 1,225.99 1,524.85 1,011.28 214 17 17 7 0 41 660.7 1 256.4 1 903.1 66.0 2.881 286.16 359.90 228.99 218 51 23 13 4 91 2 003.3 3 865.3 5 464.8 70.7 2.728 1,109.19 1,380.64 917.23 219 72 30 19 5 126 2 514.6 4 830.3 6 961.9 69.4 2.769 1,263.92 1,563.56 1,048.78 221 17 10 8 2 37 808.0 1 546.4 2 088.4 74.1 2.585 403.16 499.17 332.32 301 66 31 14 4 115 2 444.0 4 715.2 6 810.6 69.2 2.787 1,388.66 1,733.17 1,150.68 302 32 19 10 4 65 1 516.7 2 932.2 4 229.8 69.3 2.789 920.84 1,146.60 762.92 303 15 23 5 1 44 995.3 1 917.0 2 793.0 68.6 2.806 568.33 717.93 457.62 309 17 16 9 1 43 828.7 1 598.3 2 476.8 64.5 2.989 411.28 510.74 332.90 310 30 21 10 2 63 1 458.0 2 817.1 4 180.1 67.4 2.867 890.19 1,106.59 734.09 311 43 21 7 1 72 1 710.7 3 317.4 4 998.2 66.4 2.922 1,100.66 1,374.10 912.33 82 Table C.1: Continued. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 3 4 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 312 60 45 12 0 117 2 252.0 4 334.0 6 415.7 67.6 2.849 1,161.51 1,456.68 951.88 314 88 31 9 6 134 2 834.0 5 476.6 8 316.3 65.9 2.934 1,774.64 2,219.84 1,477.99 315 31 19 15 2 67 1 551.3 2 996.7 4 283.2 70.0 2.761 934.24 1,158.48 771.105 316 55 36 7 5 103 2 200.0 4 247.8 6 110.9 69.5 2.778 1,247.87 1,559.40 1,030.16 322 26 18 11 0 55 1 030.7 1 988.7 2 878.9 69.1 2.793 582.64 718.33 475.16 323 20 14 5 1 40 846.7 1 630.2 2 446.4 66.6 2.889 487.76 609.95 397.10 83 Table C.2: Grade distribution, lumber production and recovery for amabilis fir trees processed in the virtual Coastal mill. The revenues associated with three price distribution (or scenarios) are also included. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 101 49 4 53 710.9 1 681.5 2 622.2 64.1 3.688 360.21 448.57 305.61 104 75 2 77 1 732.4 4 111.0 7 249.8 56.7 4.185 894.09 1,113.39 758.57 107 59 0 59 874.7 2 069.6 3 196.9 64.7 3.655 449.50 561.88 382.08 108 71 2 73 1 625.2 3 855.9 6 064.3 63.6 3.731 830.50 1,037.91 705.86 109 84 0 84 1 783.0 4 232.3 7 810.5 54.2 4.381 902.90 1,128.63 767.47 110 55 2 57 1 284.4 3 049.5 5 004.8 60.9 3.897 682.04 852.30 579.65 113 54 3 57 1 036.8 2 457.5 4 031.3 61.0 3.888 546.32 682.40 464.20 114 58 2 60 1 107.7 2 624.2 3 973.8 66.0 3.587 578.36 718.43 490.10 116 55 0 55 1 397.6 3 315.3 5 951.7 55.7 4.258 746.15 932.69 634.23 118 60 2 62 1 148.3 2 716.1 4 185.3 64.9 3.645 609.30 757.52 516.54 123 43 0 43 548.7 1 294.6 2 101.7 61.6 3.830 277.09 346.36 235.52 124 43 1 44 504.8 1 194.1 1 918.0 62.3 3.800 246.15 307.49 209.16 202 60 3 63 1 450.3 3 434.6 5 407.9 63.5 3.729 774.88 960.25 655.86 203 62 3 65 1 268.0 3 002.9 4 840.6 62.0 3.818 661.08 821.17 560.19 204 58 3 61 842.9 1 997.0 3 073.9 65.0 3.647 431.50 538.59 366.52 205 46 3 49 1 046.4 2 477.8 3 778.2 65.6 3.611 577.32 716.97 489.16 207 48 2 50 771.6 1 828.8 3 122.1 58.6 4.046 400.57 500.08 340.27 209 81 1 82 1 835.6 4 355.8 7 448.2 58.5 4.058 925.08 1,156.23 786.28 210 64 2 66 1 749.3 4 146.0 6 769.9 61.2 3.870 895.76 1,111.45 758.65 211 73 4 77 1 438.9 3 408.2 5 164.7 66.0 3.589 765.85 952.52 649.38 214 44 2 46 476.5 1 127.1 1 903.1 59.2 3.994 226.13 282.27 192.08 218 54 2 56 1 417.8 3 360.3 5 464.7 61.5 3.854 694.22 867.26 589.92 219 80 5 85 1 756.9 4 166.3 6 961.9 59.8 3.963 868.32 1,072.31 733.71 221 36 2 38 595.4 1 412.5 2 088.4 67.6 3.508 317.88 396.85 270.03 301 87 0 87 1 687.9 4 006.3 6 810.6 58.8 4.035 841.93 1,052.41 715.64 302 67 3 70 1 124.7 2 670.7 4 229.8 63.1 3.761 585.80 728.01 496.52 303 55 2 57 748.0 1 772.5 2 793.0 63.5 3.734 378.43 472.19 321.38 309 51 1 52 598.6 1 419.1 2 476.8 57.3 4.137 294.32 367.75 250.12 310 63 2 65 1 090.9 2 590.5 4 180.1 62.0 3.832 582.45 723.96 493.72 311 68 2 70 1 294.4 3 081.7 4 998.2 61.7 3.861 686.36 857.67 583.31 84 Table C.2: Continued. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 312 65 1 66 1 545.0 3 663.4 6 415.7 57.1 4.153 769.40 957.82 652.68 314 77 3 80 1 849.0 4 384.4 8 316.3 52.7 4.498 921.95 1,147.88 782.14 315 65 1 66 1 135.4 2 692.6 4 283.2 62.9 3.772 613.78 763.10 520.34 316 78 1 79 1 480.8 3 512.2 6 110.9 57.5 4.127 745.18 931.34 633.36 322 52 0 52 757.9 1 796.9 2 878.9 62.4 3.799 398.01 497.52 338.31 323 43 3 46 619.45 1 472.7 2 446.4 60.2 3.949 317.78 396.78 269.96 85 Table C.3: Grade distribution, lumber production and recovery for western hemlock trees processed in the virtual Interior random mill. The revenues associated with three price distribution (or scenarios) are also included. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 3 4 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 102 24 13 11 2 50 1042.0 2002.6 2897.0 69.1 2.780 563.15 694.58 465.39 103 45 25 8 2 80 1936.0 3752.0 5715.5 65.6 2.952 1223.04 1532.16 1007.79 105 27 21 11 6 65 1521.3 2939.8 4267.0 68.9 2.805 918.34 1136.78 757.16 106 32 16 14 2 64 1444.0 2795.1 4046.4 69.1 2.802 870.10 1079.50 718.15 111 18 15 11 1 45 939.3 1809.8 2692.8 67.2 2.867 516.91 640.81 421.62 112 29 26 6 3 64 1530.0 2954.9 4373.2 67.6 2.858 907.82 1141.63 738.97 115 36 17 14 4 71 1780.0 3435.0 4725.5 72.7 2.655 1127.57 1405.75 932.12 117 72 33 18 1 124 2460.7 4743.4 7203.4 65.8 2.927 1324.52 1657.03 1091.15 119 18 12 7 1 38 603.3 1157.6 1953.4 59.3 3.238 253.13 312.88 207.49 120 73 35 14 1 123 2045.3 3955.8 6613.2 59.8 3.233 1155.87 1450.53 947.49 121 58 36 11 2 107 2274.7 4392.3 6404.1 68.6 2.815 1314.81 1647.16 1085.26 122 16 17 6 2 41 844.0 1620.1 2447.5 66.2 2.900 430.79 537.79 350.02 201 38 21 8 5 72 1838.7 3565.6 5470.7 65.2 2.975 1196.21 1490.95 989.05 206 36 15 14 4 69 1710.0 3301.0 4808.4 68.7 2.812 1077.63 1330.30 897.98 208 25 19 9 3 56 1250.7 2423.5 3696.9 65.6 2.956 781.45 973.94 642.68 212 26 13 13 6 58 1429.3 2761.6 4015.9 68.8 2.810 871.41 1076.81 719.72 213 13 10 6 1 30 704.7 1357.8 2043.2 66.5 2.899 424.38 526.00 348.33 215 27 24 13 4 68 1453.3 2813.5 4154.4 67.7 2.859 841.94 1041.09 690.69 216 23 17 12 2 54 1068.0 2049.1 3092.2 66.3 2.895 501.01 617.37 411.31 217 61 31 14 3 109 2392.0 4625.6 6955.9 66.5 2.908 1378.14 1713.10 1139.49 220 76 34 17 0 127 2405.3 4622.2 6701.6 69.0 2.786 1264.54 1583.53 1040.13 222 54 24 10 3 91 1782.7 3429.3 4981.8 68.8 2.795 1008.67 1251.90 841.23 223 13 14 9 3 39 746.7 1429.7 2198.2 65.0 2.944 359.25 447.06 289.28 224 18 12 11 5 46 944.7 1810.2 2637.6 68.6 2.792 510.30 629.35 419.03 304 19 17 11 0 47 892.7 1717.5 2712.2 63.3 3.038 505.35 629.28 411.56 305 32 26 9 1 68 1563.3 3032.6 4757.7 63.7 3.043 983.41 1237.22 806.03 306 27 29 7 1 64 1358.7 2626.1 3912.1 67.1 2.879 831.22 1043.31 678.28 307 12 11 10 1 34 642.0 1233.1 1963.0 62.8 3.058 320.41 392.00 260.68 308 20 17 7 2 46 1093.3 2109.8 3187.3 66.2 2.915 657.79 819.19 540.02 313 29 21 12 2 64 1426.7 2762.2 4094.6 67.5 2.870 875.12 1082.20 723.97 86 Table C.3: Continued. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 3 4 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 317 65 47 15 3 130 2488.0 4789.8 7324.3 65.4 2.944 1268.29 1589.53 1036.99 318 26 16 9 2 53 1258.7 2433.6 3629.7 67.0 2.884 709.66 884.72 586.17 319 56 34 9 2 101 2134.7 4128.2 6110.8 67.6 2.863 1228.84 1539.92 1010.13 320 22 16 6 0 44 852.0 1645.3 2595.2 63.4 3.046 481.77 605.67 393.36 321 60 31 9 2 102 2084.0 4027.6 5908.7 68.2 2.835 1164.21 1456.51 963.14 324 62 38 12 0 112 2336.7 4508.4 6758.0 66.7 2.892 1346.81 1689.98 1105.62 87 Table C.4: Grade distribution, lumber production and recovery for western hemlock trees processed in the virtual Coastal mill. The revenues associated with three price distribution (or scenarios) are also included. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 102 49 3 52 742.5 1765.8 2897.0 61.0 3.902 385.34 481.20 327.38 103 71 2 73 1498.8 3554.2 5715.5 62.2 3.813 786.73 978.98 667.24 105 53 2 55 1083.3 2564.4 4267.0 60.1 3.939 584.08 725.43 494.91 106 61 3 64 1103.6 2614.7 4046.4 64.6 3.666 592.93 732.01 500.94 111 54 2 56 706.9 1679.7 2692.8 62.4 3.809 357.30 445.99 303.49 112 58 2 60 1108.4 2627.6 4373.2 60.1 3.946 601.08 750.94 510.78 115 64 1 65 1345.2 3182.1 4725.5 67.3 3.513 708.75 881.62 601.00 117 78 0 78 1667.5 3954.7 7203.3 54.9 4.32 822.61 1028.26 699.22 119 34 0 34 373.8 886.7 1953.4 45.4 5.225 180.32 225.39 153.27 120 76 0 76 1341.4 3186.0 6613.2 48.2 4.93 705.85 882.31 599.97 121 73 0 73 1613.0 3825.7 6404.1 59.7 3.97 848.14 1060.18 720.92 122 45 1 46 613.2 1451.0 2447.8 59.3 3.992 314.04 392.15 266.80 201 57 0 57 1335.6 3167.1 5470.7 57.9 4.096 711.58 889.48 604.85 206 57 2 59 1285. 3047.3 4808.4 63.4 3.741 717.20 888.64 607.00 208 44 1 45 954.2 2265.4 3696.9 61.3 3.875 514.52 643.05 437.31 212 56 0 56 1095.9 2599.5 4015.9 64.7 3.664 594.21 742.76 505.07 213 31 2 33 512.8 1214.3 2043.2 59.4 3.984 270.13 336.81 229.33 215 62 2 64 1117.8 2659.5 4154.4 64.0 3.717 594.62 743.06 505.36 216 52 5 57 769.1 1822.0 3092.2 58.9 4.021 379.15 472.67 321.85 217 70 2 72 1709.3 4047.7 6956.0 58.2 4.07 883.04 1095.16 747.70 220 75 0 75 1596.6 3791.4 6701.6 56.6 4.197 825.41 1031.76 701.60 222 54 4 58 1286.6 3055.1 4981.8 61.3 3.872 656.85 816.77 556.89 223 37 0 37 549.1 1299.1 2198.2 59.1 4.003 280.08 350.10 238.07 224 52 1 53 715.3 1690.2 2637.6 64.1 3.688 367.77 459.52 312.54 304 48 1 49 656.0 1556.9 2712.2 57.4 4.135 330.29 412.68 280.68 305 56 2 58 1135.1 2693.9 4757.7 56.6 4.191 604.68 751.74 512.61 306 66 0 66 973.7 2311.1 3912.1 59.1 4.018 494.47 618.09 420.30 307 37 1 38 455.6 1079.8 1963.0 55.0 4.309 226.76 283.33 192.71 308 58 5 63 867.8 2061.6 3187.3 64.7 3.673 451.30 563.22 383.30 313 55 2 57 1053.0 2503.5 4094.6 61.1 3.889 560.73 700.20 476.38 88 Table C.4: Continued. Tree name ID Grade Lumber volume (nominal) Lumber volume (actual) Total log volume Recovery Yield Simulation scenario 1 2 Total Normal Optimist Pessimist (Number of lumber pieces) (fbm) (dm3) (dm3) (%) (m³/Mfbm) ($) ($) ($) 317 71 0 71 1622.4 3850.6 7324.3 52.6 4.515 842.30 1052.88 715.96 318 45 0 45 941.6 2233.2 3629.7 61.5 3.855 522.57 653.21 444.18 319 75 0 75 1475.2 3499.6 6110.8 57.3 4.143 730.01 912.51 620.51 320 45 1 46 618.8 1469.6 2595.2 56.6 4.194 316.57 395.42 268.99 321 63 0 63 1392.6 3305.0 5908.7 55.9 4.243 708.30 885.37 602.05 324 78 2 80 1642.7 3896.4 6758.0 57.7 4.114 824.03 1029.48 700.24
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Using x-ray scanned reconstructed logs to predict knot characteristics and log value
Text