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

Autor: Philippe Burger, Bruno Roux, Guillaume Jubelin, Sylvain Labbé, Frédéric Baret, Camille Lelong
Přispěvatelé: Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National de la Recherche Agronomique (INRA), NEV@NTROPIS CAYENNE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), AVION JAUNE MONTPELLIER, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2008
Předmět:
Engineering
010504 meteorology & atmospheric sciences
Multispectral image
0211 other engineering and technologies
F62 - Physiologie végétale - Croissance et développement
02 engineering and technology
lcsh:Chemical technology
01 natural sciences
Biochemistry
Analytical Chemistry
lcsh:TP1-1185
Instrumentation
imagery
multispectral
precision farming
uav
2. Zero hunger
Expérimentation
Vignetting
Indice de surface foliaire
Vegetation
Spectral bands
Métrologie
Atomic and Molecular Physics
and Optics

Relevé aérien
[SDE]Environmental Sciences
Développement biologique
Méthodologie
Télédétection
Triticum aestivum
Article
Normalized Difference Vegetation Index
Imagery
Multispectral
Precision Farming
UAV
Mesure
Electrical and Electronic Engineering
Leaf area index
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Métabolisme de l'azote
business.industry
Glider
Autre (Sciences de l'ingénieur)
Precision agriculture
U30 - Méthodes de recherche
business
Zdroj: Sensors; Volume 8; Issue 5; Pages: 3557-3585
Sensors
Sensors, MDPI, 2008, 8 (5), pp.3557-3585. ⟨10.3390/s8053557⟩
Sensors 5 (8), 3557-3585. (2008)
Sensors, Vol 8, Iss 5, Pp 3557-3585 (2008)
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s8053557
Popis: International audience; 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.
Databáze: OpenAIRE