Characterizing the influence of varying functional traits from remotely sensed data on forest productivity acquired from selected NEON sites

Abstract

Gross primary productivity (GPP) describes total photosynthesis (carbon fixation) in an ecosystem and is key to the global land carbon budget. To reduce uncertainties in carbon accounting for different forest ecosystems, it is crucial to analyze the health and productivity of forested ecosystems. Plant functional traits, which are a combination of morphological, physiological, and environmental characteristics, have been shown to be predictive of forest ecosystem carbon dynamics. This study aimed to assess how well GPP can be predicted by remotely quantified functional traits across varying forested ecosystems. Airborne remote sensing observations and in situ flux tower measurements used in this analysis were acquired from selected forested sites from the National Ecological Observatory Network (NEON) data portal. We investigated hyperspectral indices and lidar derived products as proxies of remotely sensed plant functional traits. Average midday GPP around the date of flight was calculated by developing a relationship between night respiration and temperature and removing that component from the net surface-atmosphere CO₂ exchange (NSAE). We applied multiple linear regression with a best subset approach for three trait classes: morphological and environmental traits from lidar, physiological traits from hyperspectral data, and a combined functional trait model. The best-performing model, using lidar and hyperspectral traits, included CHM mean, DSM standard deviation, PRI standard deviation, and WBI mean producing a R² of 0.87, an adjusted R² of 0.84, a PRESS R² of 0.75 and RMSE of 3.48 μmol CO₂/m²/s. Results show that a combination of plant functional traits are important predictors of forest productivity.