F
Flávio Jorge Ponzoni
Researcher at National Institute for Space Research
Publications - 71
Citations - 4216
Flávio Jorge Ponzoni is an academic researcher from National Institute for Space Research. The author has contributed to research in topics: Normalized Difference Vegetation Index & Leaf area index. The author has an hindex of 17, co-authored 71 publications receiving 3711 citations.
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The Brazilian Atlantic Forest:: how much is left and how is the remaining forest distributed? Implications for conservation
Milton Cezar Ribeiro,Jean Paul Metzger,Alexandre Camargo Martensen,Flávio Jorge Ponzoni,Márcia Makiko Hirota +4 more
TL;DR: In this paper, the authors quantify how much of the Brazilian Atlantic Forest still remains, and analyze its spatial distribution, and suggest some guidelines for conservation: (i) large mature forest fragments should be a conservation priority; (ii) smaller fragments can be managed in order to maintain functionally linked mosaics; (iii) the matrix surrounding fragments, and (iv) restoration actions should be taken, particularly in certain key areas.
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Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations
Guerric Le Maire,Claire Marsden,Wouter Verhoef,Flávio Jorge Ponzoni,Danny Lo Seen,Agnès Bégué,José Luiz Stape,Yann Nouvellon +7 more
TL;DR: In this paper, two methods for estimating the leaf area index (LAI) of Eucalyptus plantations from MODIS 250m resolution red and near-infrared (NIR) reflectance time series were compared.
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Tree and stand light use efficiencies over a full rotation of single- and mixed-species Eucalyptus grandis and Acacia mangium plantations
G. Le Maire,Yann Nouvellon,Mathias Christina,Flávio Jorge Ponzoni,José Leonardo de Moraes Gonçalves,J.-P. Bouillet,Jean-Paul Laclau +6 more
TL;DR: In this paper, a complete randomized block design was set up using Eucalyptus grandis (E) and Acacia mangium (A), which is a N2-fixing species, planted in monospecific stands (100A, 100E), and in additive (25A:100E, 50A: 100E, 100A:50E) mixtures.
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Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning
Jean-Baptiste Féret,G. Le Maire,G. Le Maire,Sylvain Jay,Daniel Berveiller,Ryad Bendoula,Gabriel Hmimina,A. Cheraiet,J.C. Oliveira,Flávio Jorge Ponzoni,Twinkle Solanki,F. de Boissieu,Jérôme Chave,Yann Nouvellon,Yann Nouvellon,Albert Porcar-Castell,Christophe Proisy,Christophe Proisy,Kamel Soudani,Jean-Philippe Gastellu-Etchegorry,M.-J. Lefevre-Fonollosa +20 more
TL;DR: In this paper, the authors compared the performance of a physically-based method (PROSPECT model inversion) and a machine learning algorithm (Support Vector Machine Regression, SVM) for the estimation of EWT and LMA.
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Spectral features associated with nitrogen, phosphorus, and potassium deficiencies in Eucalyptus saligna seedling leaves
TL;DR: In this paper, the leaves of Eucalyptus saligna seedlings were measured radiometricaly in order to characterize spectrally the symptoms of nitrogen, phosphorous and potassium deficiency.