Leaf Aging of Amazonian Canopy Trees: Insights to Tropical Ecological Processes and Satellited Detected Canopy Dynamics

Abstract:

Leaf aging is a fundamental driver of changes in leaf traits, thereby, regulating ecosystem processes and remotely-sensed canopy dynamics. Leaf age is particularly important for carbon-rich tropical evergreen forests, as leaf demography (leaf age distribution) has been proposed as a major driver of seasonal productivity in these forests.

We explore leaf reflectance as a tool to monitor leaf age and develop a novel spectra-based (PLSR) model to predict age using data from a phenological study of 1,072 leaves from 12 lowland Amazonian canopy tree species in southern Peru.

Our results demonstrate monotonic decreases in LWC and P_mass and increase in LMA with age across species; N_mass and C_massshowed monotonic but species-specific age responses. Spectrally, we observed large age-related variation across species, with the most age-sensitive spectral domains found to be: green peak (550nm), red edge (680–750 nm), NIR (700-850 nm), and around the main water absorption features (~1450 and ~1940 nm). A spectra-based model was more accurate in predicting leaf age (R²= 0.86; %RMSE= 33) compared to trait-based models using single (R²=0.07 to 0.73; %RMSE=7 to 38) and multiple predictors (step-wise analysis; R²=0.76; %RMSE=28). Spectral and trait-based models established a physiochemical basis for the spectral age model. The relative importance of the traits modifying the leaf spectra of aging leaves was: LWC>LMA>N_mass>P_mass,&C_mass. Vegetation indices (VIs), including NDVI, EVI2, NDWI and PRI were all age-dependent.

This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity, and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.