Modeling black spruce wood fiber attributes with terrestrial laser scanning

• Published in: Canadian Journal of Forest Research
• Main Authors: Giroud, Guillaume, Schneider, Robert, Fournier, Richard, A, Luther, Joan, Martin-Ducup, Olivier
• Other Authors: Direction de la recherche forestière, Ministère des Ressources naturelles du Québec, Université du Québec à Rimouski (UQAR), Centre d'Applications et de Recherches en TELédétection Sherbrooke (CARTEL), Département de géomatique appliquée Sherbrooke (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Canadian Forest Service – Atlantic Forestry Centre, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2019
• Subjects: Forest inventory; Modeling of wood fiber attributes; Canada - Québec; Nondestructive measurement of trees; Black spruce boreal forest; Terrestrial laser scanning; [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics; Phylogenetics and taxonomy; [SDV.EE.ECO]Life Sciences [q-bio]/Ecology; environment/Ecosystems; [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; Canada; Newfoundland
• Online Access: https://hal.science/hal-02140287; https://doi.org/10.1139/cjfr-2018-0342

International audience A model comparison approach, based on the Akaike’s information criterion, was used to evaluate the contribution of terrestrial laser scanning (TLS) to the estimation of wood fiber attributes at the tree level for black spruce (Picea mariana (Mill.) B.S.P.) trees growing in Newfoundland, Canada. Substantial efforts were made to acquire, process, and develop accurate and detailed metrics of the tree, its crown, and its immediate environment. Based on the resulting data set, significant relationships were found, and models were successfully developed, using only TLS metrics, for predicting wood fiber attributes. The models accounted for 47%, 33%, 51%, 44%, and 52% of variance in wood density, coarseness, fiber length, microfibril angle, and modulus of elasticity respectively, with root mean square error values of 46 kg/m3, 37 µg/m, 0.20 mm, 3.5 º, 2.3 GPa. Our ability to estimate the wood fiber attributes was improved by combining TLS metrics with conventional field measurements. This study demonstrates that the use of TLS metrics improves the estimation of the wood fiber attributes at the tree level beyond that possible with conventional field measurements.