Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description
AbstractThis paper presents a method for fitting cylinders into a point cloud, derived from a terrestrial laser-scanned tree. Utilizing high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter smaller than a centimeter. The cylinders are stored as a hierarchical tree-like data structure encapsulating parent-child neighbor relations and incorporating the tree’s direction of growth. This structure enables the efficient extraction of tree components, such as the stem or a single branch. The method was validated both by applying a comparison of the resulting cylinder models with ground truth data and by an analysis between the input point clouds and the models. Tree models were accomplished representing more than 99% of the input point cloud, with an average distance from the cylinder model to the point cloud within sub-millimeter accuracy. After validation, the method was applied to build two allometric models based on 24 tree point clouds as an example of the application. Computation terminated successfully within less than 30 min. For the model predicting the total above ground volume, the coefficient of determination was 0.965, showing the high potential of terrestrial laser-scanning for forest inventories. View Full-Text
Share & Cite This Article
Hackenberg, J.; Morhart, C.; Sheppard, J.; Spiecker, H.; Disney, M. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description. Forests 2014, 5, 1069-1105.
Hackenberg J, Morhart C, Sheppard J, Spiecker H, Disney M. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description. Forests. 2014; 5(5):1069-1105.Chicago/Turabian Style
Hackenberg, Jan; Morhart, Christopher; Sheppard, Jonathan; Spiecker, Heinrich; Disney, Mathias. 2014. "Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description." Forests 5, no. 5: 1069-1105.