Deriving internal crown geometric features of Douglas-fir from airborne laser scanning in a realized-gain trial. (18th January 2021) - du Toit, Francois; Coops, Nicholas C; Goodbody, Tristan R H; Stoehr, Michael; El-Kassaby, Yousry A
Abstract: Tree improvement programs are critical to establishing high yield seed sources while maintaining genetic diversity and developing sustainable plantation forests. Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) is commonly used in improvement programs due to its superior strength and stiffness properties. Progeny testing trials in British Columbia (BC), Canada aim to increase stem volume without sacrificing wood quality. As genetically improved stock in BC is entering its fourth generation, wood quality and branch attributes are becoming more important as selection criteria evolve. This study investigates the utility of Airborne Laser Scanning (ALS) to produce metrics that describe branch attributes, and test whether these attributes showed differences in trees of three different genetic gain levels (GL); Wild-Stand (WS), Mid-Gain (MG) and Top-Cross (TC), planted at two different spacings (2.9 m and 4.0 m) in a realized-gain trial. New methods were developed to utilize ALS data to estimate metrics such as branch angle, length and volume using a point clustering approach. The relative impact of GL and spacing on branch attributes were assessed. Spacing was significant for branch angle (2.9 m = 73.53°, 4.0 m = 72.46°), whereas GL (WS = 0.861 m, MG = 0.917 m, TC = 0.948 m) and spacing (2.9 m = 0.884, 4.0 m = 0.942) were significant for length. For all metrics, TC trees at 4.0 m spacing were consistently significantly different whenever GL or the interaction effect was significant. This data provide an insight into how ALS can be used to model branch attributes, whereas the ability to analyse trees by plot, individual tree and individual branch attributes further allows researchers and foresters to maximize the value of ALS data. Findings from this research can be integrated into large-scale programs not just for monitoring trees, but also for identifying new trees that can display attributes associated with larger volumes and increased value.