Next Article in Journal
Estimation of Actual Evapotranspiration by Remote Sensing: Application in Thessaly Plain, Greece
Next Article in Special Issue
Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification
Previous Article in Journal
Remote Sensing and Wetland Ecology: a South African Case Study
Previous Article in Special Issue
Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval
Article Menu

Export Article

Open AccessArticle

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

CIRAD, UMR TETIS, 500 Rue J.-F. Breton, 34093 Montpellier Cedex 5, France
INRA, UMR 1248 AGIR, Chemin de Borde Rouge, BP52627, 31326 Castanet Tolosan Cedex, France
[email protected], 16 bis Avenue du Quatorze Juillet, 97300 Cayenne, France
L’Avion Jaune, Minéa Incubation, 361 Rue J.-F. Breton, BP5095, 34196 Montpellier Cedex 5, France
Cemagref, UMR TETIS, 500 Rue J.-F. Breton, 34093 Montpellier Cedex 5, France
INRA, UMR 1114 EMMAH, Domaine St Paul, Site Agroparc, 84914 Avignon Cedex 9, France
Author to whom correspondence should be addressed.
Sensors 2008, 8(5), 3557-3585;
Received: 29 April 2008 / Accepted: 23 May 2008 / Published: 26 May 2008
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
PDF [1262 KB, uploaded 21 June 2014]


This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. View Full-Text
Keywords: Imagery; Multispectral; Precision Farming; UAV Imagery; Multispectral; Precision Farming; UAV
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Lelong, C.C.D.; Burger, P.; Jubelin, G.; Roux, B.; Labbé, S.; Baret, F. Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots. Sensors 2008, 8, 3557-3585.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top