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Authors = Peter M. van Bodegom

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PETER (2019) , M (17627) , VAN (1604) , BODEGOM (1)

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Open AccessArticle Trait Estimation in Herbaceous Plant Assemblages from in situ Canopy Spectra
Remote Sens. 2013, 5(12), 6323-6345; doi:10.3390/rs5126323
Received: 23 September 2013 / Revised: 7 November 2013 / Accepted: 13 November 2013 / Published: 25 November 2013
Cited by 10 | Viewed by 1822 | PDF Full-text (1218 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities
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Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities of plant traits in a herbaceous setting and how different trait-aggregation methods influence estimation accuracies. Canopy reflectance of 40 herbaceous plant assemblages was measured in situ and biomass was analysed for N, P and C concentration, chlorophyll, lignin, phenol, tannin and specific water concentration, expressed on a mass basis (mg∙g−1). Using Specific Leaf Area (SLA) and Leaf Area Index (LAI), traits were aggregated to two additional expressions: mass per leaf surface (mg∙m−2) and mass per canopy surface (mg∙m−2). All traits were related to reflectance using partial least squares regression. Accuracy of trait estimation varied between traits but was mainly influenced by the trait expression. Chlorophyll and traits expressed on canopy surface were least accurately estimated. Results are attributed to damping or enhancement of the trait signal upon conversion from mass based trait values to leaf and canopy surface expressions. A priori determination of the most appropriate trait expression is viable by considering plant growing strategies. Full article

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