An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN

•A geometric method for wood-leaves separation from TLS point clouds is proposed.•Classification of wood points is highly accurate in broad leaved non-decidous trees.•The accuracy of classification drops for fine branches and twigs (diameter <3 cm).•Our approach requires only spatial coordinates...

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Vydáno v:Agricultural and forest meteorology Ročník 262; s. 434 - 444
Hlavní autoři: Ferrara, Roberto, Virdis, Salvatore G.P., Ventura, Andrea, Ghisu, Tiziano, Duce, Pierpaolo, Pellizzaro, Grazia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.11.2018
Témata:
Aboveground biomass
algorithms
automation
biomass
branches
Broadleaf forest
forest canopy
forests
geometry
leaf area index
lidar
photosynthesis
Quercus suber
Quercus suber L
scanners
Terrestrial Laser Scanner (TLS)
Tree structure
trees
wood
Broadleaf forest
Tree structure
Terrestrial Laser Scanner (TLS)
Quercus suber L
Aboveground biomass
ISSN:0168-1923, 1873-2240
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Shrnutí:•A geometric method for wood-leaves separation from TLS point clouds is proposed.•Classification of wood points is highly accurate in broad leaved non-decidous trees.•The accuracy of classification drops for fine branches and twigs (diameter <3 cm).•Our approach requires only spatial coordinates of each point, not intensity value.•The separation method could be potentially applied to different TLS systems and tree species. The terrestrial light detection and ranging (LiDAR) technique has been recently used to provide 3D structural information of forest canopy at the individual tree level. However, the operational use of Terrestrial Laser Scanner (TLS) for canopy characterization of broadleaf non-deciduous forests needs further investigations. The estimation of wood volume, above-ground woody biomass, tree canopy characteristics and leaf area index often requires separation of photosynthetically active material and non-photosynthetically active material. This article describes an automated wood-leaves separation method, based on spatial geometric information of TLS point clouds, for broad leaved non-deciduous trees. Scans of seven individuals of Quercus suber L. trees were acquired by using the TLS phase-based Leica HDS6100. Point clouds were partitioned in cubic volumes (voxels) that were used as input to generate clusters through the point density algorithm DBSCAN. The clustering process led to the identification of wood and non-wood voxels. A specific automatic routine was written to process data from the point clouds to the visualization of clustering results. The analysis of results showed good performance for this approach, with the overall accuracy in classifying wood components of trees ranging from 95% to 97%. The largest accuracies were observed for branches larger than 5 cm in diameter whereas the accuracy of classification dropped, as expected, for branches with diameter lower than 3 cm. The results suggest that the proposed method can be conveniently used to extract woody components from point clouds of broad leaved non-deciduous trees.
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ISSN:0168-1923
1873-2240
DOI:10.1016/j.agrformet.2018.04.008