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

[Display omitted] •Developed a LiDAR simulator to mimic LiDAR scanning on virtual 3D canopy.•Coupling the LiDAR simulator with 3D wheat structure model enables to learn the GAI predication model without in-situ measurements.•Validation using in-situ measurements demonstrates that the height profile...

Full description

Saved in:
Bibliographic Details
Published in:Agricultural and forest meteorology 2017-12, Vol.247, p.12-20
Main Authors: Liu, Shouyang, Baret, Fred, Abichou, Mariem, Boudon, Fred, Thomas, Samuel, Zhao, Kaiguang, Fournier, Christian, Andrieu, Bruno, Irfan, Kamran, Hemmerlé, Matthieu, Solan, Benoit de
Format: Article
Language:English
Subjects:
3D canopy structure model
ADEL-Wheat
Computer Science
GAI
LiDAR
Modeling and Simulation
Neural networks
Phenotyping
Poisson model
Wheat
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] •Developed a LiDAR simulator to mimic LiDAR scanning on virtual 3D canopy.•Coupling the LiDAR simulator with 3D wheat structure model enables to learn the GAI predication model without in-situ measurements.•Validation using in-situ measurements demonstrates that the height profile of point cloud betters GAI estimation under high values. 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
ISSN:0168-1923
1873-2240
DOI:10.1016/j.agrformet.2017.07.007