Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy

Foliar functional traits are key drivers of ecological processes in forests. Despite progress in forest trait mapping from imaging spectroscopy, there is a need to build environment-specific, spectra-trait models trained from tree-level measurements to improve the accuracy of local trait maps. We mapped 12 foliar functional traits and their uncertainties in a mixed temperate forest using airborne imaging spectroscopy. Top-of-canopy foliar samples from tree crowns were collected using a drone platform to measure foliar traits for individual trees, from which tree-level crown spectra were determined. Partial least squares regression (PLSR) models were used to predict foliar traits from tree-level reflectance spectra (400–2400 nm). These models predicted leaf mass per area (LMA), specific leaf area (SLA) and equivalent water thickness (EWT) with high accuracy (R2 > 0.8, %RMSE < 15). Models for pigment, nitrogen and cellulose showed a moderate performance (R2 = 0.53–0.68, %RMSE = 17.24–21.31). Poorest performance was observed for lignin, carbon, leaf dry mass content (LDMC) and hemicellulose (R2 = 0.24–0.44, %RMSE = 20.67–26.13). High-resolution (1.25 m pixel−1) trait maps and uncertainties were produced for the entire 16-km2 area. Our study provides models that capture intraspecific variation across tree species from a mixed temperate forest in eastern Canada.