nerc.ac.uk

Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees

Chavana-Bryant, Cecilia; Malhi, Yadvinder; Anastasiou, Athanasios; Enquist, Brian J.; Cosio, Eric G.; Keenan, Trevor F.; Gerard, France F.. 2019 Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees. Science of the Total Environment, 666. 1301-1315. https://doi.org/10.1016/j.scitotenv.2019.01.379

Before downloading, please read NORA policies.
[img] Text
N513704PP.pdf - Accepted Version
Restricted to NORA staff only until 20 May 2020.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (9MB)

Abstract/Summary

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1016/j.scitotenv.2019.01.379
CEH Sections/Science Areas: Hydro-climate Risks (Science Area 2017-)
ISSN: 0048-9697
Additional Information. Not used in RCUK Gateway to Research.: Contact: ffg@ceh.ac.uk
Additional Keywords: Amazon, tropical evergreen forests, leaf chemistry, chemometrics, PLSR, leaf phenology, leaf traits, leaf age effects, imaging spectroscopy, remote sensing, age effects, leaf physiology, foliar chemistry
NORA Subject Terms: Ecology and Environment
Date made live: 18 Feb 2019 11:02 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/513704

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...