Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model
The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7. Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used.
Main Authors: | , , , , , , , , , , |
---|---|
Format: | article biblioteca |
Language: | eng |
Subjects: | F62 - Physiologie végétale - Croissance et développement, U30 - Méthodes de recherche, télédétection, modèle de simulation, mesure (activité), modélisation des cultures, couvert, modèle mathématique, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_4668, http://aims.fao.org/aos/agrovoc/c_9000024, http://aims.fao.org/aos/agrovoc/c_1262, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_3081, |
Online Access: | http://agritrop.cirad.fr/585105/ http://agritrop.cirad.fr/585105/1/1-s2.0-S016819231730223X-main.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cirad-fr-585105 |
---|---|
record_format |
koha |
spelling |
dig-cirad-fr-5851052024-01-29T05:39:49Z http://agritrop.cirad.fr/585105/ http://agritrop.cirad.fr/585105/ Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Liu Shouyang, Baret Frédéric, Abichou Mariem, Boudon Frédéric, Thomas Samuel, Zhao Kaiguang, Fournier Christian, Andrieu Bruno, Irfan Kamran, Hemmerlé Matthieu, De Solan Benoit. 2017. Agricultural and Forest Meteorology, 247 : 12-20.https://doi.org/10.1016/j.agrformet.2017.07.007 <https://doi.org/10.1016/j.agrformet.2017.07.007> Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model Liu, Shouyang Baret, Frédéric Abichou, Mariem Boudon, Frédéric Thomas, Samuel Zhao, Kaiguang Fournier, Christian Andrieu, Bruno Irfan, Kamran Hemmerlé, Matthieu De Solan, Benoit eng 2017 Agricultural and Forest Meteorology F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 France http://aims.fao.org/aos/agrovoc/c_3081 The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7. Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/585105/1/1-s2.0-S016819231730223X-main.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.agrformet.2017.07.007 10.1016/j.agrformet.2017.07.007 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.agrformet.2017.07.007 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.agrformet.2017.07.007 |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3081 F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3081 F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3081 Liu, Shouyang Baret, Frédéric Abichou, Mariem Boudon, Frédéric Thomas, Samuel Zhao, Kaiguang Fournier, Christian Andrieu, Bruno Irfan, Kamran Hemmerlé, Matthieu De Solan, Benoit Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
description |
The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7. Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used. |
format |
article |
topic_facet |
F62 - Physiologie végétale - Croissance et développement U30 - Méthodes de recherche télédétection modèle de simulation mesure (activité) modélisation des cultures couvert modèle mathématique http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Liu, Shouyang Baret, Frédéric Abichou, Mariem Boudon, Frédéric Thomas, Samuel Zhao, Kaiguang Fournier, Christian Andrieu, Bruno Irfan, Kamran Hemmerlé, Matthieu De Solan, Benoit |
author_facet |
Liu, Shouyang Baret, Frédéric Abichou, Mariem Boudon, Frédéric Thomas, Samuel Zhao, Kaiguang Fournier, Christian Andrieu, Bruno Irfan, Kamran Hemmerlé, Matthieu De Solan, Benoit |
author_sort |
Liu, Shouyang |
title |
Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
title_short |
Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
title_full |
Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
title_fullStr |
Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
title_full_unstemmed |
Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model |
title_sort |
estimating wheat green area index from ground-based lidar measurement using a 3d canopy structure model |
url |
http://agritrop.cirad.fr/585105/ http://agritrop.cirad.fr/585105/1/1-s2.0-S016819231730223X-main.pdf |
work_keys_str_mv |
AT liushouyang estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT baretfrederic estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT abichoumariem estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT boudonfrederic estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT thomassamuel estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT zhaokaiguang estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT fournierchristian estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT andrieubruno estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT irfankamran estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT hemmerlematthieu estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel AT desolanbenoit estimatingwheatgreenareaindexfromgroundbasedlidarmeasurementusinga3dcanopystructuremodel |
_version_ |
1792499323205844992 |