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Linking plant strategies and plant traits derived by radiative transfer modelling

Authors :
John Philip Grime
Teja Kattenborn
Sebastian Schmidtlein
Fabian Ewald Fassnacht
Simon Pierce
Javier Lopatin
Source :
Journal of Vegetation Science. 28:717-727
Publication Year :
2017
Publisher :
Wiley, 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 been shown before that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches were 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 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 close relation to CSR gradients using only our understanding of the relation between traits and reflectance is a step forward towards an operational use of the CSR model in remote sensing. This article is protected by copyright. All rights reserved.

Details

ISSN :
11009233
Volume :
28
Database :
OpenAIRE
Journal :
Journal of Vegetation Science
Accession number :
edsair.doi...........b86ee797dcde35d2f2456f35cb7c961d
Full Text :
https://doi.org/10.1111/jvs.12525