RavagliaFournierBacEtAl2019

Reference

Ravaglia, J., Fournier, R.A., Bac, A., Véga, C., Côté, J.-F., Piboule, A., Rémillard, U. (2019) Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data. Forests, 10(7). (Scopus )

Abstract

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. © 2019 by the author.

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@ARTICLE { RavagliaFournierBacEtAl2019,
    AUTHOR = { Ravaglia, J. and Fournier, R.A. and Bac, A. and Véga, C. and Côté, J.-F. and Piboule, A. and Rémillard, U. },
    TITLE = { Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data },
    JOURNAL = { Forests },
    YEAR = { 2019 },
    VOLUME = { 10 },
    NUMBER = { 7 },
    NOTE = { cited By 0 },
    ABSTRACT = { Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. © 2019 by the author. },
    AFFILIATION = { G-Mod, Laboratoire d'Informatique et des Systèmes UMR CNRS 7020, Aix-Marseille Université, Marseille CEDEX 09, 13288, France; Department of Applied Geomatics, Centre d'Applications et de Recherches en Télédétection, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; Institut National de l'information Géographique et Forestière, Laboratoire d'Inventaire Forestier, Nancy, 54000, France; Canadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Québec, QC G1V 4C7, Canada; Pôle Recherche Développement Innovation, Office National des Forêts, Villiers-lès-Nancy, 54500, France; LIS UMR CNRS 7020, Aix Marseille Université, Campus de Luminy, 163 Avenue de Luminy, Case 901, BP 5, Marseille CEDEX 9, 13288, France },
    ART_NUMBER = { 599 },
    AUTHOR_KEYWORDS = { CompuTree; Diameter at breast height (DBH); Forest inventory; LiDAR; SimpleTree; Stem diameter; STEP algorithm; Terrestrial laser scanner },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/f10070599 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073891595&doi=10.3390%2ff10070599&partnerID=40&md5=70091c6d69baeeb45f5b082e75a1f4dc },
}

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