Question: Do spatial gradients of plant strategies correspond to patterns of plant traits obtained from a physically based model and hyperspectral imagery? It has previously been shown that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches have been based on empirical links and lacked transferability. Therefore, we test if physically based derivations of plant traits may help in finding gradients in traits that are linked to strategies. Location: A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. Methods: Spatial distributions of plant traits were modelled by adopting an inversion of the PROSAIL radiative transfer model (RTM) on airborne hyperspectral imagery. The traits are derived from reflectance without making use of field data but only of known links between reflectance and traits. We tested whether previously found patterns in CSR plant strategies were related to the modelled traits. Results: The results confirm close relationships between modelled plant traits and C, S and R strategies that were previously found in the field. The modelled plant traits explained different dimensions of the CSR space. Leaf area index (LAI) and the reciprocal of specific leaf area appeared to be good candidates for reproducing CSR scores as community traits using remote sensing. LAI has not been used in previous studies to allocate plant strategies. Conclusions: Combining RTMs and the CSR model is a promising approach for establishing a robust link between airborne or spaceborne imagery and plant functioning. The demonstrated potential to map traits with a close relationship to CSR gradients using only our understanding of the relationship between traits and reflectance is a step forward towards operational use of the CSR model in remote sensing.

Linking plant strategies and plant traits derived by radiative transfer modelling / T. Kattenborn, F..E. Fassnacht, S. Pierce, J. Lopatin, J..P. Grime, S. Schmidtlein. - In: JOURNAL OF VEGETATION SCIENCE. - ISSN 1100-9233. - 28:4(2017), pp. 717-727.

Linking plant strategies and plant traits derived by radiative transfer modelling

S. Pierce;
2017

Abstract

Question: Do spatial gradients of plant strategies correspond to patterns of plant traits obtained from a physically based model and hyperspectral imagery? It has previously been shown that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches have been based on empirical links and lacked transferability. Therefore, we test if physically based derivations of plant traits may help in finding gradients in traits that are linked to strategies. Location: A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. Methods: Spatial distributions of plant traits were modelled by adopting an inversion of the PROSAIL radiative transfer model (RTM) on airborne hyperspectral imagery. The traits are derived from reflectance without making use of field data but only of known links between reflectance and traits. We tested whether previously found patterns in CSR plant strategies were related to the modelled traits. Results: The results confirm close relationships between modelled plant traits and C, S and R strategies that were previously found in the field. The modelled plant traits explained different dimensions of the CSR space. Leaf area index (LAI) and the reciprocal of specific leaf area appeared to be good candidates for reproducing CSR scores as community traits using remote sensing. LAI has not been used in previous studies to allocate plant strategies. Conclusions: Combining RTMs and the CSR model is a promising approach for establishing a robust link between airborne or spaceborne imagery and plant functioning. The demonstrated potential to map traits with a close relationship to CSR gradients using only our understanding of the relationship between traits and reflectance is a step forward towards operational use of the CSR model in remote sensing.
CSR; hyperspectral; imaging spectroscopy; leaf economic spectrum; plant functional type; remote sensing; vegetation mapping; vegetation monitoring; ecology; plant science
Settore BIO/03 - Botanica Ambientale e Applicata
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/525160
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