Canadian Forest Service Publications
Assessing the impact of fine-scale structure on predicting wood fibre attributes of boreal conifer trees and forest plots. 2021. Côté, J-F.; Luther, J.E.; Lenz, P.; Fournier, R.A.; van Lier, O.R.; Forest Ecology and Management 479: 118624
Issued by: Canadian Wood Fibre Centre
Catalog ID: 40302
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Information about wood fibre attributes (WFA) is important for optimizing forest resource management and increasing the competitiveness of the sector. Many factors influence WFA at both the plot (e.g., age, stand density, climate, and disturbance) and tree (e.g., crown development, stem shape, branchiness) levels. Recently, the use of terrestrial lidar (t-lidar) systems in forest inventory has enabled the measurement of forest structural attributes, which were almost impossible to acquire with traditional field measurements. Using t-lidar scans of individual trees and the architectural model L-Architect, we reconstructed the structure of trees and plots comprising balsam fir and black spruce in insular Newfoundland, Canada. Core samples extracted from concomitant trees were analyzed for a series of nine WFA. The impact of fine-scale structure on predictive models of WFA was assessed with parametric and non-parametric approaches. A variable importance analysis demonstrated that structural attributes derived from L-Architect describing the tree crown geometry, branching structure, stem form, spatial competition and canopy material distribution were highly important in the resulting models. The cross-validated percentage of variance explained for the WFA predictive models ranged from 12–56% and 5–80% at tree- and plot-levels respectively. The addition of fine-scale structure improved the models by 10–31% and 0–53% when compared to models developed using only in situ measurements at tree and plot-levels respectively. Information on species (at tree level) and composition (at plot level) did not improve the predictive capability of models developed with L-Architect fine-scale structure. The results indicate that better characterisation of forest structure using t-lidar and an architectural model can lead to improved WFA prediction and their combination opens opportunities to significantly enhance forest inventory.