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Open AccessArticle

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

AMA Style

Thomson ER, Malhi Y, Bartholomeus H, Oliveras I, Gvozdevaite A, Peprah T, Suomalainen J, Quansah J, Seidu J, Adonteng C, Abraham AJ, Herold M, Adu-Bredu S, Doughty CE. Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. Remote Sensing. 2018; 10(10):1532. https://doi.org/10.3390/rs10101532

Chicago/Turabian Style

Thomson, Eleanor R.; Malhi, Yadvinder; Bartholomeus, Harm; Oliveras, Imma; Gvozdevaite, Agne; Peprah, Theresa; Suomalainen, Juha; Quansah, John; Seidu, John; Adonteng, Christian; Abraham, Andrew J.; Herold, Martin; Adu-Bredu, Stephen; Doughty, Christopher E. 2018. "Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery" Remote Sens. 10, no. 10: 1532. https://doi.org/10.3390/rs10101532

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