Prioritizing commercial thinning: quantification of growth and competition with high-density drone laser scanning

Abstract

Abstract Laser scanning sensors mounted on drones enable on-demand quantification of forest structure through the collection of high-density point clouds (500+ points m−2). These point clouds facilitate the detection of individual trees enabling the quantification of growth-related variables within a stand that can inform precision management. We present a methodology to link incremental growth data obtained from tree cores with crown models derived from drone laser scanning, quantifying the relative growth condition of individual trees and their neighbours. We stem-mapped 815 trees across five stands in north-central British Columbia, Canada of which 16% were cored to quantify recent basal area growth. Point clouds from drone laser scanning and orthomosaic imagery were used to locate trees, model three-dimensional crown features, and derive competition metrics describing the relative distribution of crown sizes. Local access to water and light were simulated using topographic wetness and potential solar irradiance indices derived from high-resolution terrain and surface models. Wall-to-wall predictions of recent basal area growth were produced from the best-performing model and summarized across a grid alongside a tree-level competition index. Overall, crown volume was most strongly correlated with observed differences in 5-year basal area increment (R2 = 0.70, P < .001). Competition and solar irradiance metrics were significant as univariate predictors (P < .001) but nonsignificant when included in multivariate models with crown volume. Using predictions from the best-performing model and laser-scanning-derived competition metrics, we present a newly developed growth competition index to assess variability and inform commercial thinning prescription prioritization. Growth predictions, competition metrics, and the growth competition index are summarized into maps that could be used in an operational workflow. Our methodology presents a new capacity to capture and quantify intra-stand variation in growth by combining competition metrics and measures of recent growth with high-density drone laser scanning data.