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Article

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

1
Department of Biological Sciences, Centre for Forest Research (CEF) and NSERC/Hydro-Québec Chair on Tree Growth Control, Université du Québec à Montréal, Centre-Ville Station, P.O. Box 8888, Montreal, QC H3C 3P8, Canada
2
Department of Natural Resources, Institute of Temperate Forest Sciences and Centre for Forest Research (CEF), Université du Québec en Outaouais, 58 Rue Principale, Ripon, QC J0V 1V0, Canada
3
Valorhiz, 1900 Boulevard de la Lironde, 34980 Montferrier sur Lez, France
*
Author to whom correspondence should be addressed.
Academic Editor: Eetu Puttonen
Forests 2021, 12(4), 391; https://doi.org/10.3390/f12040391
Received: 27 January 2021 / Revised: 18 March 2021 / Accepted: 19 March 2021 / Published: 26 March 2021
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture. View Full-Text
Keywords: terrestrial laser scaner; LiDAR; tree architecture; annual shoot; axes specialization; physiological age terrestrial laser scaner; LiDAR; tree architecture; annual shoot; axes specialization; physiological age
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MDPI and ACS Style

Lecigne, B.; Delagrange, S.; Taugourdeau, O. Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth. Forests 2021, 12, 391. https://doi.org/10.3390/f12040391

AMA Style

Lecigne B, Delagrange S, Taugourdeau O. Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth. Forests. 2021; 12(4):391. https://doi.org/10.3390/f12040391

Chicago/Turabian Style

Lecigne, Bastien, Sylvain Delagrange, and Olivier Taugourdeau. 2021. "Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth" Forests 12, no. 4: 391. https://doi.org/10.3390/f12040391

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