Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

Eleanor Thomson,John Seidu,Christopher Doughty,Martin Herold,Andrew Abraham,Harm Bartholomeus,Juha Suomalainen,Stephen Adu-Bredu,Theresa Peprah,Christian Adonteng,Agne Gvozdevaite,John Quansah,Yadvinder Malhi,Imma Oliveras

doi:10.3390/rs10101532

Abstract

The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.

Highlights

Plant functional traits refer to specific features about a plant’s morphological and physiological characteristics that determine factors such as its preferred environment, growth rate, life strategy, dispersal ability and tolerance of pests and hazards [1]
Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with leaf mass per area (LMA) and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg))
This study demonstrates the application of unmanned aerial vehicle (UAV) to generating leaf economic spectrum (LES) information and advancing the study and monitoring tropical forest functional diversity

Summary

Introduction

Plant functional traits refer to specific features about a plant’s morphological and physiological characteristics that determine factors such as its preferred environment, growth rate, life strategy, dispersal ability and tolerance of pests and hazards [1]. On the other end of the spectrum, species located in lower-precipitation and -nutrient regimes tend to trade rapid growth for persistence and invest in tough, well-protected leaves, with higher LMA values and lower N and P concentrations They produce leaves that can better withstand herbivory and physical hazards, leading to increased leaf longevity and a lower rate of nutrient cycling [14,15]. The LES has been shown to be a good indicator of plant performance [16,17], with a species’ position on the spectrum describing the plant’s overall strategy (quick growing but ephemeral versus slow growing but enduring). This spectrum has been shown to operate across a wide variety of species and biomes, with important implications for global dynamic vegetation and climate models [18]

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