LecigneDelagrangeTaugourdeau2021

Référence

Lecigne, B., Delagrange, S., Taugourdeau, O. (2021) Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth. Forests, 12(4). (URL )

Résumé

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.

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@ARTICLE { LecigneDelagrangeTaugourdeau2021,
    AUTHOR = { Lecigne, B. and Delagrange, S. and Taugourdeau, O. },
    JOURNAL = { Forests },
    TITLE = { Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth },
    YEAR = { 2021 },
    ISSN = { 1999-4907 },
    NUMBER = { 4 },
    VOLUME = { 12 },
    ABSTRACT = { 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. },
    ARTICLE-NUMBER = { 391 },
    DOI = { 10.3390/f12040391 },
    OWNER = { Luc },
    URL = { https://www.mdpi.com/1999-4907/12/4/391 },
}

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