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Research Article | Open Access

Estimating wood quality attributes from dense airborne LiDAR point clouds

Nicolas Cattaneo( ), Stefano Puliti, Carolin Fischer, Rasmus Astrup
Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, Høgskoleveien 8, 1433, Ås, Norway
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Abstract

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.

Keywords

Machine learningWood qualityUAV laser scanningPoint cloud metrics

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Forest Ecosystems
Volume 11 Issue 2,
April 2024
Article number: 100184
DOI: 10.1016/j.fecs.2024.100184
Cite this article:
Cattaneo N, Puliti S, Fischer C, et al. Estimating wood quality attributes from dense airborne LiDAR point clouds. Forest Ecosystems, 2024, 11(2): 100184. https://doi.org/10.1016/j.fecs.2024.100184

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Received: 30 October 2023
Revised: 05 March 2024
Accepted: 05 March 2024
Published: 16 March 2024
© 2024 The Authors.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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