@article{01HHYMJEJ20PG1DN85PTY6S98J,
  abstract     = {{Terrestrial laser scanning (TLS) has been adopted as a feasible technique to characterize tree stems while the characterization of trees’ branching architecture has remained less explored. In general, branching architecture refers to the spatial arrangement of branches and their characteristics that are important when exploring the eco-physiological functioning of trees or assessing tree biomass and wood quality. Our aim was to develop a point cloud processing method for identifying and segmenting individual branches from TLS point clouds. We applied a Cartesian-to-cylinder coordinate transformation and a simple morphological filtering for stem surface reconstruction and stem-branch separation. Then branch origins were identified as their intersections with the stem surface, and individual branches were segmented based on their connectivity with the branch origins. The method, implemented in MATLAB and openly available, was validated on a 0.4-ha mature and managed southern boreal forest stand. The branch identification performance was assessed based on visual interpretation of 364 randomly sampled stem sections from 100 Scots pine (Pinus sylvestris (L.)) trees that were inspected for branch identification accuracy. The results showed that the branches could only be identified up to the height where the stem could be reconstructed. For 90% of the trees, this threshold ranged between 59.3% and 81.2% relative tree heights. Branches located below this threshold were identified with a recall of 75%, a precision of 92%, and an F1-score of 0.82. Based on our study, it appears that in a managed Scots pine stand, most of the branches can be identified with the developed method for the most valuable stem part eligible for logwood. The findings obtained in this study promote the feasibility of using TLS in applications requiring detailed characterization of trees. The developed method can be further used in quantifying the characteristics of individual branches, which could be useful for biomass and wood quality assessment, for example.}},
  author       = {{Yrttimaa, Tuomas and Kankare, Ville and Luoma, Ville and Junttila, Samuli and Saarinen, Ninni and Calders, Kim and Holopainen, Markus and Hyyppä, Juha and Vastaranta, Mikko}},
  issn         = {{0015-752X}},
  journal      = {{FORESTRY}},
  keywords     = {{cavelab,close-range sensing,semantic segmentation,point cloud classification,alogrithm development,digital forestry,BIOMASS ESTIMATION,FOREST,MODELS,TLS,LIDAR,WOOD,RECONSTRUCTION,ARCHITECTURE,HEIGHT,VOLUME}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{531--545}},
  title        = {{A method for identifying and segmenting branches of Scots pine (Pinus sylvestris L.) trees using terrestrial laser scanning}},
  url          = {{http://doi.org/10.1093/forestry/cpad062}},
  volume       = {{97}},
  year         = {{2024}},
}

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