Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure... Agricultural and Forest Meteorology 247 (2017) 12–20 Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet Estimating wheat green area index from ground-based LiDAR measurement MARK using a 3D canopy structure model a, a b c d Shouyang Liu , Fred Baret , Mariem Abichou , Fred Boudon , Samuel Thomas , e f b a g Kaiguang Zhao , Christian Fournier , Bruno Andrieu , Kamran Irfan , Matthieu Hemmerlé , Benoit de Solan INRA EMMAH, UMR 1114 Domaine Saint-Paul, Site Agroparc, 84914 Avignon Cedex 9, France INRA, AgroParisTech, UMR ECOSYS, 78850 Thiverval-Grignon, France CIRAD, EPI INRIA Virtual Plants & UMR AGAP, Campus St Priest, 34095 Montpellier Cedex 5, France ARVALIS-Institut du végétal, Station expérimentale, 91720 Boigneville, France School of Environment and Natural Resources, Ohio Agricultural and Research Development Center, The Ohio State University, Wooster, OH 44691, USA INRA-SupAgro, UMR 759, Place Viala, 34060 Montpellier Cedex 1, France HIPHEN, 84914, Avignon, France ARTICLE I NFO ABSTRACT Keywords: The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here GAI to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development ADEL-Wheat stage http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agricultural and Forest Meteorology Elsevier

Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0168-1923
D.O.I.
10.1016/j.agrformet.2017.07.007
Publisher site
See Article on Publisher Site

Abstract

Agricultural and Forest Meteorology 247 (2017) 12–20 Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet Estimating wheat green area index from ground-based LiDAR measurement MARK using a 3D canopy structure model a, a b c d Shouyang Liu , Fred Baret , Mariem Abichou , Fred Boudon , Samuel Thomas , e f b a g Kaiguang Zhao , Christian Fournier , Bruno Andrieu , Kamran Irfan , Matthieu Hemmerlé , Benoit de Solan INRA EMMAH, UMR 1114 Domaine Saint-Paul, Site Agroparc, 84914 Avignon Cedex 9, France INRA, AgroParisTech, UMR ECOSYS, 78850 Thiverval-Grignon, France CIRAD, EPI INRIA Virtual Plants & UMR AGAP, Campus St Priest, 34095 Montpellier Cedex 5, France ARVALIS-Institut du végétal, Station expérimentale, 91720 Boigneville, France School of Environment and Natural Resources, Ohio Agricultural and Research Development Center, The Ohio State University, Wooster, OH 44691, USA INRA-SupAgro, UMR 759, Place Viala, 34060 Montpellier Cedex 1, France HIPHEN, 84914, Avignon, France ARTICLE I NFO ABSTRACT Keywords: The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here GAI to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development ADEL-Wheat stage

Journal

Agricultural and Forest MeteorologyElsevier

Published: Dec 15, 2017

References

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