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arXiv · 2311.00480

Comparative analysis of taper models for Pinus nigra: A study across parametric, semi-parametric, and non-parametric models using terrestrial laser scanner acquired data

Issam Boukhris·Nicola Puletti·Christian Vonderach·Matteo Guasti·Said Lahssini·Monia Santini·Riccardo Valentini
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Abstract

Taper equations are indispensable tools for characterizing the stem profile of trees, providing valuable insights for forest management, timber inventory, and optimal assortments allocation. The recent progress in Terrestrial Laser Scanning (TLS) has revolutionized forest inventory practices by enabling non-destructive data collection. In this study, four taper models from three different model categories were established based on point cloud data of 219 Pinus nigra trees. The taper equations fitted with TLS data were used to predict the diameter at specific stem heights and the total stem volume. The results show that among fitted models, the Max and Burkhart segmented model calibrated by the means of a mixed-effects approach provided the best estimate of the diameter at different heights and the total stem volume evaluated for different diameter at breast height (DBH) classes. In numerical terms, this model estimated the diameter and the volume with a respective overall error of 0.781 cm and 0.021 m3. The predicted profile also shows that above a relative height of 0.7, the diameter error tends to increase due to the low reliability of data collected beyond the base of the crown primarily caused by interference from branches and leaves. Nevertheless, this study shows that TLS technology presents a compelling opportunity and a promising non-destructive alternative for generating taper profiles and estimating tree volume.

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Issam Boukhris, Nicola Puletti, Christian Vonderach, Matteo Guasti, Said Lahssini, Monia Santini, Riccardo Valentini. 2023-10-31. Comparative analysis of taper models for Pinus nigra: A study across parametric, semi-parametric, and non-parametric models using terrestrial laser scanner acquired data. https://arxiv.org/abs/2311.00480

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