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The aim of this study was to investigate the effects of different initial spacings on the quality of boards sawn from logs from unthinned lodgepole pine stands in northern Sweden. The study was conducted in the 44 years-old experiment that had been storm damaged in January 2014. Logs from six unthinned plots planted in four initial spacings (1.1 × 1.1 m, 2.0 × 2.0 m, 2.85 × 2.85 m and 4.0 × 4.0 m) were extracted and sawn into boards. Knot position and size on each board were measured and classified into pre-defined knot types. Mean knot size increased with wider spacing, and the difference between the two spacing extremes was 0.65 cm. Mean knot size also increased with height, showing the highest values around a height of 3.0 m. The number of knots did not depend on the initial spacing. There was an average of 40 knots per board regardless of spacing. This study gives an indication about the knot properties of lodgepole pine timber and identifies a possibility for decreasing the number of seedlings used in regeneration without compromising wood-quality. [ABSTRACT FROM AUTHOR]

Knot properties have a profound influence on the suitability of wood for many wood products leading to significant value differences between different quality grades. It would therefore be rather advantageous to maximise the volume of good quality timber attained from the logs. The objective of this study was to assess how well A-quality lumber of Scots pine derived from log tomography features can be predicted with characteristics measured prior to or concurrently with the logging operation. The study is based on field experiments and X-ray scanning of 204 stems from southern Finland in 2014. We employed mixed logistic regression techniques to model the relationship between the main stem characteristics and probability of A-quality lumber. From the tree characteristics that can be measured or detected from standing trees, the height from the ground level to the lowest dead branch was found to be the best predictor of A-quality lumber. From the characteristics that could, at least in theory, be detected and measured at the moment of harvest, early growth rate and size of tree were found to be the best combination for predicting the probability of A-class quality. [ABSTRACT FROM AUTHOR]

While X-ray scanning is increasingly used to measure the interior quality of logs, terrestrial laser scanning (TLS) could be used to collect information on external tree characteristics. As branches are one key indicator of wood quality, we compared TLS and X-ray scanning data in deriving whorl locations and each whorl’s maximum branch and knot diameters for 162 Scots pine (Pinus sylvestrisL.) log sections. The mean number of identified whorls per tree was 37.25 and 22.93 using X-ray and TLS data, respectively. The lowest TLS-derived whorl in each sample tree was an average 5.56 m higher than that of the X-ray data. Whorl-to-whorl mean distances and the means of the maximum branch and knot diameters in a whorl measured for each sample tree using TLS and X-ray data had mean differences of −0.12 m and −6.5 mm, respectively. One of the most utilized wood quality indicators, tree-specific maximum knot diameter measured by X-ray, had no statistically significant difference to the tree-specific maximum branch diameter measured from the TLS point cloud. It appears challenging to directly derive comparative branch structure information using TLS and X-ray. However, some features that are extractable from TLS point clouds are potential wood quality indicators. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a method for reconstruction of parametrically described whorls and knots from data possible to extract from industrial scanning of logs, using X-ray scanners. The method was conceived using the logs in the Swedish Pine Stem Bank as a foundation, and was based on a few predictor features extracted from these logs; namely whorl volume, distance between whorls and distance between pith and surface. These features were not measured in images but calculated from existing parameterised knots. Simulated test sawing shows that the reconstruction method results in a representative model of the knot structure in the log, when considering the grade distribution of the sawn timber produced by the simulation program. The results of this study could, for instance, be used for improved online quality predictions at sawmills. One step in this direction is to use industrial X-ray data to enlarge the amount of log data available for sawing simulation research. Future work can, therefore, focus on developing a practical application of the results presented here. [ABSTRACT FROM AUTHOR]

The value of solid wood products is to a large extent determined by the sizes, types and distribution of the knots in the products. Hence there is a great interest in describing the internal knot structure of individual logs. The Swedish Stem Bank has been extensively used for modelling the interior knot structure of Scots pine (Pinus sylvestris L.) and for simulating the outcome of sawing operations. The stem bank holds parametric descriptions, extracted from computer tomography (CT) imagery, of mature trees. To enlarge the stem bank with trees from younger stands, a better method for extracting the knot properties from the CT images is needed. In this study, artificial neural networks were used for segmenting and classifying knots in transverse CT images of a 30-year-old Scots pine. The cross-validated prediction rate of correctly classified pixels was 95.9%±1.2%. Classified knots were distinctly separated. Misclassifications were mainly located in the border areas between knots and clear. [ABSTRACT FROM AUTHOR]