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

Cecilia Chavana-Bryant; Yadvinder Malhi; Athanasios Anastasiou; Brian J Enquist; Eric G Cosio; Trevor F Keenan; France F Gerard

doi:10.1016/j.scitotenv.2019.01.379

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

Sci Total Environ. 2019 May 20:666:1301-1315. doi: 10.1016/j.scitotenv.2019.01.379. Epub 2019 Feb 16.

Authors

Cecilia Chavana-Bryant¹, Yadvinder Malhi², Athanasios Anastasiou³, Brian J Enquist⁴, Eric G Cosio⁵, Trevor F Keenan⁶, France F Gerard⁷

Affiliations

¹ Earth & Environmental Sciences, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA; Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA 94720, USA; Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK. Electronic address: cchb@lbl.gov.
² Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK.
³ College of Medicine, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK.
⁴ Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA.
⁵ Sección Química, Pontificia Universidad Católica del Perú, Avenida Universitaria 1801, San Miguel, Lima 32, Peru.
⁶ Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA 94720, USA; Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK.
⁷ Centre for Ecology and Hydrology, Wallingford, Oxfordshire OX10 8BB, UK.

PMID: 30970495
DOI: 10.1016/j.scitotenv.2019.01.379

Abstract

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 (C_area) 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 P_area and N_area. 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.

Keywords: Age effects; Foliar chemistry; Leaf physiology; Mass- vs area-based PLSR models; Phenology; Remote sensing.

MeSH terms

Carbon / analysis
Least-Squares Analysis
Models, Biological
Peru
Phenotype*
Plant Leaves / growth & development
Plant Leaves / physiology*
Seasons
Spectrum Analysis

Substances

Carbon