Abstract
ABSTRACTWhile 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 sylvestris L.) 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.
Highlights
Detailed information on wood quality is considered essential for the optimization of wood procurement processes (Holopainen et al 2014; Moore and Cown 2015)
An average of 37.25 whorls with the diameter of the largest knot exceeding 10 mm were identified in each sample tree log section using the X-ray data and 22.93 whorls per sample tree using the terrestrial laser scanning (TLS) data (Table 3)
55% of the whorls within log sections with the largest knot diameter exceeding 10 mm identified with X-ray could be detected using TLS point clouds (Tables 3 and 4)
Summary
Detailed information on wood quality is considered essential for the optimization of wood procurement processes (Holopainen et al 2014; Moore and Cown 2015). Sawmills plan their production before the trees are harvested, wood quality information on standing trees would allow harvesting wood of desired quantity and quality at the desired time instead of storing large amounts of wood onsite. Branches have a direct adverse effect on wood quality due to their high compression wood content and the distorted grain orientation around them (Mitsuhashi et al 2008; Donaldson and Singh 2013). Earlier research aimed at developing methods for predicting wood quality using indicators of branchiness, most commonly the height of the lowest dead branch (Hdb) (Kärkkäinen 1980; Uusitalo 1997; Lyhykäinen et al 2009)
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