Determination of species-specific leaf angle distribution and plant area index in a cool-temperate mixed forest from UAV and upward-pointing digital photography

Abstract

Leaf angle distribution (LAD) is an essential canopy element that controls the foliage projection function (G-function), which is a parameter for estimating the plant area index (PAI). In recent studies, digital cover photography (DCP) has been applied to estimate the PAI (PAIDCP) in many terrestrial ecosystems. However, few studies related to this topic have been performed in mixed forests, where LAD features differ among multiple species. This study sought to evaluate the PAIDCP in a cool temperate mixed deciduous-coniferous forest in western Japan using a field-based G-function that was estimated from digital images captured by using a small uncrewed aerial vehicle (UAV; a quadcopter). The intensive UAV observations showed that measured throughout a vertical canopy profile of the deciduous broadleaved tree species, LAD was plagiophile-planophile for Fagus crenata, but was planophile throughout the canopy for Carpinus tschonoskii. Meanwhile, for the other dominant coniferous Abies firma species, we assumed either plagiophile or spherical for LAD and the G-function based on previous studies instead of measuring the parameters directly using the small UAV. With the species-specific LAD and G-function of dominant tree species, we compared the four-year PAIDCP time series with those based on a direct method using litter-trap inventories (PAIlit). The results indicate that PAIDCP provided more suitable representation of the canopy than that obtained assuming the spherical LAD for all tree species in the forest, although estimations of PAIlit includes uncertainties, mainly associating with measurements of specific leaf area (SLA). Our study highlights that in mixed forests with a tall canopy and multiple species it is important to ascertain species-specific LAD values for accurate estimation of the PAI using digital cover photography. Overall, we argue that the combined UAV–DCP photographic approach serves as a powerful tool for estimating the PAI.