A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl Structure in a Forest Environment Using Terrestrial Laser Scanning
Tekijät: Pyörälä, Jiri; Liang, Xinlian; Vastaranta, Mikko; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppä, Juha
Kustantaja: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Kustannuspaikka: PISCATAWAY
Julkaisuvuosi: 2018
Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Tietokannassa oleva lehden nimi: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Lehden akronyymi: IEEE J-STARS
Vuosikerta: 11
Numero: 10
Aloitussivu: 3598
Lopetussivu: 3607
Sivujen määrä: 10
ISSN: 1939-1404
eISSN: 2151-1535
DOI: https://doi.org/10.1109/JSTARS.2018.2819598
State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter > 15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67 degrees (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2 degrees (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.
