Estimating Canopy Taxonomic Diversity Using Hyperspectral Data: Insights From Spectral Simulations in a Tropical Forest

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Publication Details

Journal
Applied Vegetation Science
Published
2025-10-01
DOI
https://doi.org/10.1111/avsc.70050
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Estimating Canopy Taxonomic Diversity Using Hyperspectral Data: Insights From Spectral Simulations in a Tropical Forest

Jean‐Baptiste Féret, Raphaël Pélissier, Colette Badourdine, Grégoire Vincent
Applied Vegetation Science
Remote Sensing in Agriculture
article

Estimating Canopy Taxonomic Diversity Using Hyperspectral Data: Insights From Spectral Simulations in a Tropical Forest

Jean‐Baptiste Féret, Raphaël Pélissier, Colette Badourdine, Grégoire Vincent
article en

Abstract

No abstract available for this paper.
Applied Vegetation ScienceVol. 28(4)
Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Université de Montpellier (FR), AgroParisTech (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), Institut Agro Montpelier (FR), Institut de Recherche pour le Développement (FR), Territoires, Environnement, Télédétection et Information Spatiale (FR)
Agence Nationale de la Recherche, Centre National d’Etudes Spatiales
Openalex Percentile: Top 60%
Remote Sensing in Agriculture
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Estimating Canopy Taxonomic Diversity Using Hyperspectral Data: Insights From Spectral Simulations in a Tropical Forest — Jean‐Baptiste Féret, Raphaël Pélissier, et al. · Applied Vegetation Science (2025) | TGRS Research Map | TGRS