Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery
| Autor: | Thomson, E., Malhi, Y., Bartholomeus, H., Oliveras, I., Gvozdevaite, A., Peprah, T., Suomalainen, J., Quansah, J., Seidu, J., Adonteng, C., Abraham, A., Herold, M., Adu-Bredu, S., Doughty, C. |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
leaf traits
tropical forest spectroscopy leaf economic spectrum UAV Science Leaf economic spectrum PE&RC Ghana hyperspectral Laboratory of Geo-information Science and Remote Sensing Hyperspectral Tropical forest PLSR Leaf traits West Africa Laboratorium voor Geo-informatiekunde en Remote Sensing Spectroscopy |
| Zdroj: | Remote Sensing, Vol 10, Iss 10, p 1532 (2018) Remote Sensing 10 (2018) 10 Remote Sensing, 10(10) Remote Sensing |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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