Canadian Forest Service Publications
Crown-fibre attribute relationships for enhanced forest inventory: progress and prospects. 2015. Groot, A; Cortini, F.; Wulder, M.A. The Forestry Chronicle. 91(3):266-279.
Issued by: Great Lakes Forestry Centre
Catalog ID: 36239
Availability: PDF (request by e-mail)
A five-year project, Crown-Fibre Attribute Relationships (CFAR), was completed by the Canadian Wood Fibre Centre (Natural Resources Canada) to explore the relationships between tree crown characteristics and wood fibre attributes. The CFAR project used a number of data sets distributed across Canada (e.g., silvicultural experiments), providing a range of crown conditions following spacing and thinning treatments. The approaches developed under the CFAR project, along with other relevant research, indicate that is possible to enhance current forest inventories by providing estimates of fibre attributes from crown characteristics of individual trees that can, or are poised to be, captured using remotely sensed data. Predictability was related to the dimensionality of the fibre attributes with models for zero-dimensionality fibre attributes (e.g., DBH) showing RMSE of 10% to 15% of mean values. Also, one-dimensional (e.g., sapwood area) and two-dimensional (i.e., ring area) models quantified longitudinal patterns with low bias. Current values of wood density were not related to crown characteristics; instead, recent research suggests that wood density is regulated by hydraulic and biomechanical constraints. Further evolution of remote sensing technology and related research will help to address the temporal problem posed by two- and three-dimensional fibre attributes.
Plain Language Summary
We examined the relationships between tree crown characteristics and wood fibre attributes. We used a number of data sets from across Canada, based on experiments of various spacing and thinning treatments. Crown characteristics of individual trees can be, or are poised to be, captured using remotely sensed data. This data could enhance current forest inventories if models could predict fibre attributes of interest. The information could be used to improve timber management planning and operations and could also help to diversify and add value to manufactured forest products. Our results showed that some fibre attributes can be predicted from crown characteristics, but attributes of wood close to the pith and wood density currently cannot be predicted.