Abstract
Unmanned aerial vehicles (UAV) are a novel and flexible tool in precision viticulture, as they can be used to acquire useful information to evaluate the spatial variation of vegetative growth, yield components and grape quality. In this work, the capability of multispectral imagery acquired by a UAV and the derived spectral information to assess the spatial variability of a Tempranillo (Vitis vinifera L.) vineyard has been explored. The study was conducted in a vertical shoot positioned (VSP) Tempranillo vineyard of 3.5 ha located in Navarra, Spain. With the aim of establishing relationships between field variables and the remotely sensed information, a grid of 74 experimental blocks at 20 m intervals was built. Several variables related to the vegetative growth and yield were measured prior to harvest in each block. Multispectral imagery was acquired at 17 cm spatial resolution with a Mini-MCA 6 (Tetracam inc, USA) camera which contained information on 6 different spectral bands located in the visible and near infrared spectral regions. After geometric and radiometric corrections, the images were combined to generate a mosaic of the whole vineyard and pixel values across bands were extracted for the corresponding experimental blocks in the field. Spectral bands and indices derived from the aerial images were correlated with vine vigor and yield variables and their statistical significance was tested. Overall, the study has shown that, while spectral information and derived indices were effectively correlated to vine vigor and yield parameters, the correlation was lower than expected. We hypothesize that a specific post-processing chain will overcome the inherent systematic and random noise and distortions of the original images thus improving correlation with field data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acevedo-Opazo, C., Tisseyre, B., Guillaume, S. And Ojeda, H. 2008. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precision Agriculture 9 (5) 285–302.
Baluja, J., Diago, M. P., Balda, P., Zorer, R., Franco Meggio, Morales, F. and Tardaguila, J. 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30 511–522.
Berni, J., Zarco-Tejada, P. J., Suárez, L. and Fereres, E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing 47 (3) 722–738.
Bramley, R. 2001. Progress in the development of precision viticulture-Variation in yield, quality and soil properties in contrasting Australian vineyards. www.cse.csiro.au/client_serv/resources/bramley1.pdf.
Bramley, R. G. V. and Hamilton, R. P. 2004. Understanding variability in winegrape production systems. Australian Journal of Grape and Wine Research 10 (1) 32–45.
Chen, J. M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22 (3) 229–242.
Daughtry, C., Walthall, C., Kim, M., De Colstoun, E. B. and McMurtrey, J. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74 (2) 229–239.
Gamon, J. A., Peņuelas, J. and Field, C. B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41 (1) 35–44.
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90 (3) 337–352.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J. and Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81 (2) 416–426.
Hall, A., Lamb, D., Holzapfel, B. and Louis, J. 2002. Optical remote sensing applications in viticulture-a review. Australian Journal of Grape and Wine Research 8 (1) 36–47.
Hijmans, R. J. and Etten, J. v. 2012. Raster: Geographic Analysis and Modeling with Raster Data. http://CRAN.R-project.org/package=raster.
Keitt, T. H., Bivand, R., Pebesma, E. and Rowlingson, B. 2012. Rgdal: Bindings for the Geospatial Data Abstraction Library. http://CRAN.R-project.org/package=rgdal.
Lamb, D. W., Weedon, M. M. and Bramley, R. G. V. 2004. Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution. Australian Journal of Grape and Wine Research 10 46–54.
Lechner, A. M., Fletcher, A., Johansen, K. and Erskine, P. 2012. Characterising Upland Swamps Using Object-based Classification Methods and Hyper-spatial Resolution Imagery Derived from an Unmanned Aerial Vehicle. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-4 101–106.
Lobo, A. 1997. Image Segmentation and Discriminant Analysis for the Identification of Land Cover Units in Ecology. IEEE Transactions on Geoscience and Remote Sensing 35 (5) 1136–1145.
Lobo, A., Chic, O. and Casterad, A. 1996. Multisensorial Classification of Mediterranean Crops: Per-pixel Versus Per-patch Statistics and Image Segmentation. International Journal of Remote Sensing 17 (12) 2385–2400.
Martin, P., Zarco-Tejada, P. J., Gonzalez, M. and Berjón, A. 2007. Using hyperspectral remote sensing to map grape quality inTempranillo’vineyards affected by iron deficiency chlorosis. VITIS-GEILWEILERHOF-46 (1) 7.
Qi, J., Chehbouni, A., Huete, A., Kerr, Y. and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48 (2) 119–126.
R Core Team. 2012. R: A Language and Environment for Statistical Computing. www.R-project.org, R Foundation for Statistical Computing.
Rondeaux, G., Steven, M. and Baret, F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55 (2) 95–107.
Roujean, J. L. and Breon, F. M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51 (3) 375–384.
Rouse, J. and Center, G. S. F. 1974. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation, Texas A & M University, USA, Remote Sensing Center.
Sepulcre-Cantó, G., Diago, M. P., Balda, P., Martínez de Toda, F., Morales, F. and Tardáguila, J. 2009. Monitoring vineyard spatial variability of vegetative growth and physiological status using an unmanned aerial vehicle (UAV). International Symposium of Viticulture GiESCO 2009. UC Davis, USA.
Steven, M. D. 1998. The Sensitivity of the OSAVI Vegetation Index to Observational Parameters. Remote Sensing of Environment 63 (1) 49–60.
Steven, M. D. and Jaggard, K. W. 1995. Advances in crop monitoring by remote sensing. In: (Eds.) F. Danson, S. Plummer, Advances in Environmental Remote Sensing. Wiley, Chichester, UK, pp 143–156.
Acknowledgements
This study was carried out as part of the TELEVITIS project (Ref. ADER-2008-00187) funded by the Agencia de Desarrollo Tecnológico de La Rioja (La Rioja, Spain). Special gratefulness to Airestudio Geoinformation Technologies S. Cop for the acquisition of images with the UAV.
Author information
Authors and Affiliations
Corresponding author
Editor information
Rights and permissions
Copyright information
© 2013 Wageningen Academic Publishers The Netherlands
About this paper
Cite this paper
Rey, C. et al. (2013). Multispectral imagery acquired from a UAV to assess the spatial variability of a Tempranillo vineyard. In: Stafford, J.V. (eds) Precision agriculture ’13. Wageningen Academic Publishers, Wageningen. https://doi.org/10.3920/978-90-8686-778-3_76
Download citation
DOI: https://doi.org/10.3920/978-90-8686-778-3_76
Publisher Name: Wageningen Academic Publishers, Wageningen
Online ISBN: 978-90-8686-778-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)