Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. Issue 3 (15th February 2016)

• Record Type: Journal Article
• Authors: Chavana‐Bryant, Cecilia; Malhi, Yadvinder; Wu, Jin; Asner, Gregory P.; Anastasiou, Athanasios; Enquist, Brian J.; Cosio Caravasi, Eric G.; Doughty, Christopher E.; Saleska, Scott R.; Martin, Roberta E.; Gerard, France F.
• Is Part Of: New phytologist. Volume 214:Issue 3(2017)
• Page Start: 1049
• Page End: 1063
• Publication Date: 2016-02-15
• Subjects: canopy trees -- leaf age -- leaf lifecycle -- leaf spectral properties -- leaf traits -- phenology -- tropical forests -- vegetation indices (VIs); Botany -- Periodicals; 580
• DOI: 10.1111/nph.13853
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra‐based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree‐specific age responses. We observed large age‐related variation in leaf spectra across trees. A spectra‐based model was more accurate in predicting leaf age ( R 2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait‐based models using single ( R 2 = 0.07–0.73; %RMSE = 7–38) and multiple ( R 2 = 0.76; %RMSE = 28) predictors. Spectra‐ and trait‐based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (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. Abstract : See also the Commentary on this article by McDowell & Xu, 214 : 903–904 .