Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS

Abstract

Coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is one of the most commercially important softwood species in North America. In British Columbia, Canada, breeding has increased volume gains between 20 and 30%, while 97% of seedlings come from improved seed sources. Branching traits in particular, have a strong influence on strength and stiffness of Douglas-fir wood; however, they are rarely measured. Remotely Piloted Aerial Systems and Airborne Laser Scanning Systems (RPAS-LS) produce high-density three-dimensional point clouds that can be used for the creation of internal geometric features describing individual tree branching structures. We analyzed a Coastal Douglas-fir progeny test trial located in British Columbia, Canada, and developed a new method to estimate branch attributes from RPAS-LS data for inclusion as selection criteria in tree improvement programs. Branch length, angle, width, and volume were estimated for each tree. Narrow-sense heritability (the proportion of variation due to genetics) and genetic correlations were also estimated. The method extracted branch length with a correlation (r) of 0.93 compared to manual measurements. Using these branch attributes, results then show that branch angle had the highest heritability (0.277), while tree height and branch length had the highest genetic correlation (0.668). These findings are encouraging for forest managers as they indicate that branch level metrics should be considered when selecting trees in breeding programs.