Abstract

We showed that a mobile handheld laser scanner (HHLS) provides useful features concerning the wood quality-influencing external structures of trees. When linked with wood properties measured at a sawmill utilizing state-of-the-art X-ray scanners, these data enable the training of various wood quality models for use in targeting and planning future wood procurement. A total of 457 Norway spruce sample trees (Picea abies (L.) H. Karst.) from 13 spruce-dominated stands in southeastern Finland were used in the study. All test sites were recorded with a ZEB Horizon HHLS, and the sample trees were tracked to a sawmill and subjected to X-rays. Two branch extraction techniques were applied to the HHLS point clouds: 1) a method developed in this study that was based on the density-based spatial clustering of applications with noise (DBSCAN) and 2) segmentation-based quantitative structure model (treeQSM). On average, the treeQSM method detected 46% more branches per tree than the DBSCAN did. However, compared with the X-rayed references, some of the branches detected by the treeQSM may either be false positives or so small in size that the X-rays are unable to detect them as knots, as the method overestimated the whorl count by 19% when compared with the X-rays. On the other hand, the DBSCAN method only detected larger branches and showed a −11% bias in whorl count. Overall, the DBSCAN underestimated knot volumes within trees by 6%, while the treeQSM overestimated them by 25%. When we input the HHLS features into a Random Forest model, the knottiness variables measured at the sawmill were predicted with R2s of 0.47–0.64. The results were comparable with previous results derived with the static terrestrial laser scanners. The obtained stem branching data are relevant for predicting wood quality attributes but do not provide data that are directly comparable with the X-ray features. Future work should combine terrestrial point clouds with dense above-canopy point clouds to overcome the limitations related to vertical coverage.

Published in

ISPRS Open Journal of Photogrammetry and Remote Sensing
2023, Volume: 9, article number: 100040
Publisher: Elsevier B.V.

SLU Authors

• Holmgren, Johan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science
Remote Sensing


Publication identifier

DOI: https://doi.org/10.1016/j.ophoto.2023.100040

Permanent link to this page (URI)

https://res.slu.se/id/publ/129597