Name
Title | ||
|---|---|---|
Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. |
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: r(2) = 0.42 (LMA), r(2) = 0.43 (N), r(2) = 0.21 (P), r(2) = 0.20 (leaf potassium (K)), r(2) = 0.23 (leaf calcium (Ca)) and r(2) = 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: r(2) = 0.25 (LMA), r(2) = 0.22 (N), r(2) = 0.22 (P), and r(2) = 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10101532 | REMOTE SENSING |
Keywords | Field | DocType |
leaf traits,leaf economic spectrum,UAV,hyperspectral,spectroscopy,tropical forest,PLSR,Ghana,West Africa | VNIR,Functional diversity,Remote sensing,Partial least squares regression,Hyperspectral imaging,Atmospheric sciences,Tropical forest,Reflectivity,Geology,Canopy,Precipitation | Journal |
Volume | Issue | Citations |
10 | 10 | 0 |
PageRank | References | Authors |
0.34 | 5 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleanor R. Thomson | 1 | 0 | 0.34 |
| Yadvinder Malhi | 2 | 18 | 8.29 |
| Harm M. Bartholomeus | 3 | 69 | 11.21 |
| Imma Oliveras | 4 | 0 | 0.34 |
| Agne Gvozdevaite | 5 | 0 | 0.34 |
| Theresa Peprah | 6 | 0 | 0.34 |
| J. Suomalainen | 7 | 156 | 19.01 |
| John Quansah | 8 | 0 | 0.34 |
| John Seidu | 9 | 0 | 0.34 |
| Christian Adonteng | 10 | 0 | 0.34 |
| Andrew J. Abraham | 11 | 0 | 0.34 |
| herold martin | 12 | 101 | 26.05 |
| Stephen Adu-Bredu | 13 | 0 | 0.34 |
| Christopher E. Doughty | 14 | 0 | 0.34 |