Comparison of Terrestrial Laser Scanning and X-ray Scanning in Measuring Scots Pine (Pinus sylvestris L.) Branch Structure

Jiri Pyörälä, Ville Kankare, Mikko Vastaranta, Juha Rikala, Markus Holopainen, Marketta Sipi, Juha Hyyppä, Jori Uusitalo

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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 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.
Original languageEnglish
JournalScandinavian Journal of Forest Research
Volume33
Issue number3
Pages (from-to)291-298
Number of pages8
ISSN0282-7581
DOIs
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 4112 Forestry
  • Ground-based LiDAR
  • wood quality
  • knots
  • wood procurement
  • STANDING TREES
  • LOG SCANNER
  • CLEAR WOOD
  • STEM
  • TIMBER
  • QUALITY
  • LIDAR
  • RECONSTRUCTION
  • ATTRIBUTES
  • PREDICTION

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