Remotely sensed functional diversity and its association with productivity in a subtropical forest

Functional diversity is a critical component driving ecosystem functioning. Spatially explicit data of plant functional traits and are essential for understanding biodiversity effects on Here we retrieved three morphological (95th quantile height, leaf area index, foliage height diversity) physiological (chlorophyll + b content, specific area, equivalent water thickness) from airborne laser scanning multispectral Sentinel-2 data, respectively. We found LiDAR-derived parameters correlated well with in-situ plot-level (R2 ≥ 0.67). For satellite-derived traits, partial least squares regression (PLSR) obtained higher prediction accuracy = 0.26–0.43, cross-validation community-weighted mean (CWM) trait data) than vegetation index (VI) approach. The remotely-sensed were used as input to estimate multi-trait (FD) indices in species-rich subtropical mountainous forest. Finally, investigated the influence single-trait CWMs, FD environmental variables remotely-derived aboveground carbon stocks (aboveground biomass, AGB) primary productivity (kernel normalized difference kNDVI). CWMs all significant predictors AGB kNDVI, suggested by mass-ratio hypothesis. Morphological also important indicating complementarity crown architectures. In best-fit multivariate models, first principal CWM that most richness was additionally selected models kNDVI at landscape scales. Our work highlights potential using assess relationship between functioning across large, contiguous areas.