Evaluating Effects of Remotely Sensed Neighborhood Crowding and Depth-to-Water on Tree Height Growth

Abstract

Previous research on the effects of neighborhood crowding and soil moisture on tree height growth have been limited by time-consuming and sometimes inaccurate ground-based measurements of tree height. Recent developments in unmanned aerial vehicles (UAVs) allow detailed 3D point clouds of the canopy surface to be generated at relatively low cost. Using UAV-derived point clouds, we obtained height measurements of 4386 trees for the years 2019 and 2021. We also calculated four neighborhood crowding indices and a topography-based moisture index (depth-to-water) for these trees. Using initial tree height, neighborhood crowding indices and the depth-to-water index, we developed Bayesian hierarchical models to predict height growth for three tree species (Picea glauca (white spruce), Populus tremoluides (trembling aspen) and Pinus contorta (lodgepole pine)) across different stands. Bayes-R2 values of the final models were highest for white spruce (35%) followed by trembling aspen (28%) and lodgepole pine (25%). Model outputs showed that the effect of crowding and depth-to-water on height growth are limited and species-dependent, adding a maximum of 7% to the Bayes-R2 metric. Comparing different neighborhood crowding indices revealed that no index is clearly superior to others across all three species, as different neighborhood crowding indices resulted in only minor differences in model performance. While height growth can be partially explained by aerially derived neighborhood crowding indices and the depth-to-water index, future studies should focus on identifying relevant site characteristics to predict tree growth with greater accuracy.