Next Article in Journal
Remote Detection of the Fluorescence Spectrum of Natural Pollens Floating in the Atmosphere Using a Laser-Induced-Fluorescence Spectrum (LIFS) Lidar
Next Article in Special Issue
Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing
Previous Article in Journal
Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review
Previous Article in Special Issue
Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(10), 1532; https://doi.org/10.3390/rs10101532

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

1
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
2
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
3
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
4
Forestry Research Institute of Ghana, Council for Scientific and Industrial Research, P.O. Box UP 63 KNUST, Kumasi, Ghana
5
Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland
*
Author to whom correspondence should be addressed.
Received: 15 August 2018 / Revised: 10 September 2018 / Accepted: 13 September 2018 / Published: 24 September 2018
(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)
Full-Text   |   PDF [12218 KB, uploaded 11 October 2018]   |  

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. View Full-Text
Keywords: leaf traits; leaf economic spectrum; UAV; hyperspectral; spectroscopy; tropical forest; PLSR; Ghana; West Africa leaf traits; leaf economic spectrum; UAV; hyperspectral; spectroscopy; tropical forest; PLSR; Ghana; West Africa
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Thomson, E.R.; Malhi, Y.; Bartholomeus, H.; Oliveras, I.; Gvozdevaite, A.; Peprah, T.; Suomalainen, J.; Quansah, J.; Seidu, J.; Adonteng, C.; Abraham, A.J.; Herold, M.; Adu-Bredu, S.; Doughty, C.E. Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. Remote Sens. 2018, 10, 1532.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top