Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV

Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV 1 Introduction</h5> The mapping of the percentage of green vegetation per unit of ground surface, i.e., the vegetation fraction (VF), is a major issue in remote sensing. Monitoring the temporal and spatial variations in the VF in a specific area has many ecological and agricultural applications, such as the identification of land degradation and desertification ( Xiao and Moody, 2005 ), the estimation of the phenological and physiological status of vegetation ( Yu et al., 2013 ) and the prediction of crop yields ( Yang et al., 2006 ), among others. In precision agriculture (PA), quantifying the distribution of VF within a crop-field is a first and crucial step prior to addressing further objectives. One of these objectives is the detection and mapping of weeds in crop fields, with the ultimate goal of applying site-specific weed management (SSWM) techniques and controlling weed patches according to their coverage at each point of the crop-field. In this context, remote imagery for mapping weeds has been traditionally provided by piloted airborne ( Castro et al., 2012; Peña-Barragán et al., 2011 ) or satellite platforms ( Castro et al., 2013; Martín et al., 2011 ). However, these platforms are limited in their http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV

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Publisher
Elsevier
Copyright
Copyright © 2014 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2014.02.009
Publisher site
See Article on Publisher Site

Abstract

1 Introduction</h5> The mapping of the percentage of green vegetation per unit of ground surface, i.e., the vegetation fraction (VF), is a major issue in remote sensing. Monitoring the temporal and spatial variations in the VF in a specific area has many ecological and agricultural applications, such as the identification of land degradation and desertification ( Xiao and Moody, 2005 ), the estimation of the phenological and physiological status of vegetation ( Yu et al., 2013 ) and the prediction of crop yields ( Yang et al., 2006 ), among others. In precision agriculture (PA), quantifying the distribution of VF within a crop-field is a first and crucial step prior to addressing further objectives. One of these objectives is the detection and mapping of weeds in crop fields, with the ultimate goal of applying site-specific weed management (SSWM) techniques and controlling weed patches according to their coverage at each point of the crop-field. In this context, remote imagery for mapping weeds has been traditionally provided by piloted airborne ( Castro et al., 2012; Peña-Barragán et al., 2011 ) or satellite platforms ( Castro et al., 2013; Martín et al., 2011 ). However, these platforms are limited in their

Journal

Computers and Electronics in AgricultureElsevier

Published: Apr 1, 2014

References

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