Abstract

Many growth and yield models (GYMs) have been developed in order to allow forest managers to predict future yield and explore potential management strategies. Remote sensing provides a potential alternative to field-based inputs to conventional GYMs, in particular, airborne drone laser scanning (DLS) has been used to accurately classify individual tree locations and derive stem size metrics, such as tree height and diameter at breast height (DBH) and competitive neighbourhoods, across entire stands, rather than plot-level samples. We adopted an older GYM, PTAEDA4.0, for use in R, which incorporated spatially explicit individual tree and local neighbourhood calculations, and was intended for use in the south-eastern US. Both field- and DLS-only inputs were used to estimate four-years of growth and yield on two managed loblolly pine (Pinus taeda) sites located in the south-eastern U.S.A. with variable stem density, genotype, and silviculture. All GYM estimates generally under-predicted actual field-measured values for both field- and DLS-derived inputs; however, the estimates produced by 2017 field and DLS metrics were statistically equivalent. For site one, the normalized root mean square (NRMSE) were 21–25% for estimating the tree height and 14–16% for DBH. For site two, NRMSE was 6–8% for estimating tree height and 8–12% for DBH. This implies that the accuracy of inputs was similar. The results demonstrate that the GYM would need re-parametrization to account for the current study site; however, this is beyond the scope of this research. Whilst the DLS was unable to account for all trees (98 to 99 % correctly found), the results demonstrate the potential of DLS as an alternative to traditional field measurements. © 2025 Informa UK Limited, trading as Taylor & Francis Group.