4.7 Article

Assessing the impact of fine-scale structure on predicting wood fibre attributes of boreal conifer trees and forest plots

Journal

FOREST ECOLOGY AND MANAGEMENT
Volume 479, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.foreco.2020.118624

Keywords

Wood fibre attribute; 3D architectural model; Terrestrial LiDAR; Tree structure; Plot structure

Categories

Funding

  1. Atlantic Canada Opportunities Agency [ICF 786-16766-195492]
  2. Centre for Forest Science and Innovation, Canada [221208]
  3. Natural Science and Engineering Council of Canada (NSERC), Canada [CRDPJ-390394, RGPIN-2014-04508]
  4. Newfoundland and Labrador Department of Innovation, Business and Rural Development, Canada [WES2010.06.02.045]
  5. Research and Development Corporation, Canada [5404.1221.102]
  6. AWARE project (NSERC) [CRDPJ-462973-14]
  7. CWFC
  8. FPInnovations
  9. CBPPL

Ask authors/readers for more resources

Information on wood fibre attributes (WFA) is crucial for optimizing forest management and enhancing the competitiveness of the industry. Factors influencing WFA include both plot and tree levels. The use of t-lidar systems and architectural models can lead to improved prediction of WFA, highlighting the importance of precise forest structure characterization in forest inventory.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available