Developing nondestructive species‐specific tree allometry with terrestrial laser scanning

Abstract

Abstract Allometric equations predict organism attributes from simple measurements and underlie many global‐scale estimates, from plant productivity to ecosystem carbon stocks. In forests, destructive harvesting of trees in common groups (e.g. plant functional type) or at the species level is necessary to develop allometry but, since sampling is extremely difficult, predictions from these equations have high uncertainty due to low sample size, spatial bias and unrepresentative sampling of tree size. Terrestrial laser scanning (TLS) is a promising remote sensing technology that enables efficient and nondestructive estimates of tree‐level structure for developing allometric equations. Here, we nondestructively estimated component biomass of three coniferous tree species (Pinus ponderosa, Pinus contorta and Pseudotsuga menziesii) in Colorado, USA using TLS. We evaluated the suitability for this nondestructive data to be supplanted for destructive data in the development of species‐specific allometric equations and compared the prediction accuracy against a commonly used national‐scale allometry. We found TLS biomass estimates were consistently more precise across species (RMSE = ~19%) than nation‐scale allometry (RMSE = ~39%). Nondestructive biomass estimates from TLS are a suitable addition to or replacement for traditional sampling methods, with indistinguishable biomass predictions across most of the tested diameter range. We further show how TLS can be used to develop allometric equations compatible with airborne LiDAR and other remote sensing variables (e.g. height and crown area), developing generalized biomass predictions from crown area and tree height (R2 = 0.87). The ability for TLS to support the development of nondestructive allometry at a global scale will enable a more nuanced understanding of the drivers of individual tree architecture, while supporting the next generation of biomass remote sensing.

Keywords

• 3D
• allometry
• carbon
• LiDAR
• metabolic scaling theory
• Pinus contorta