An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data

Key message

We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark.

Abstract

Context Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality.
Aims Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface.
Methods First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator.
Results The defects were detected and classified with a high accuracy with an average F1 score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects.
Conclusion The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods.

Keywords
Tree quality; Roundwood quality; Forest inventory; Wood grading; Machine learning; Bark

Publication
Nguyen, VT., Constant, T. & Colin, F. An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data. Annals of Forest Science 78, 32 (2021). https://doi.org/10.1007/s13595-020-01022-3

For the read-only version of the full text:
https://rdcu.be/ciiD4

Data availability
The datasets generated during and/or analyzed during the current study are available on the Data INRAE repository (https://doi.org/10.15454/EOBUM0).

Handling Editor
John Moore

Guest Editor: Patrick Heuret

Topical Collection
This article is part of the Topical Collection “Epicormics and related botanical features

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