An article of Eleanor Thomson, Yadvinder Malhi, Harm Bartholomeus, Imma Oliveras, Agne Gvozdevaite, Theresa Peprah, Juha Suomalainen, John Quansah, John Seidu, Christian Adonteng, Andrew Abraham, Martin Herold, Stephen Adu-Bredu and Christopher Doughty: Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery, has been published in Remote Sensing, 2018, 10(10), 1532;
In 2016, Harm Bartholomeus and Juha Suomalainen, used the Hyperspectral Mapping System (HYMSY) for a large campaign to map ecosystem carbon cycling and plant traits of the tropical forests in Ghana. This dataset has been one of the main sources for a paper which has been recently published in Remote Sensing entitled: Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery.
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