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Remote Sens. 2015, 7(11), 14458-14481; doi:10.3390/rs71114458

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard

1
Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de La Rioja), Finca La Grajera, Carretera de Burgos Km 6, Logroño 26007, Spain
2
Instituto de Economía, Geografía y Demografía. Centro de Ciencias Humanas y Sociales, CSIC, Albasanz 26–28, Madrid 28037, Spain
3
Institute of Earth Sciences Jaume Almera, ICTJA-CSIC, Lluis Sole Sabaris s/n, Barcelona 08028, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Mutlu Ozdogan, Yoshio Inoue and Prasad S. Thenkabail
Received: 1 September 2015 / Revised: 30 September 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
View Full-Text   |   Download PDF [1385 KB, uploaded 9 November 2015]   |  

Abstract

Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38–0.70) and mean cluster weight (0.40). These results proved the utility of the multi-spectral imagery acquired from a RPAS to delineate homogeneous zones within the vineyard, allowing the grapegrower to carry out a specific management of each subarea. View Full-Text
Keywords: precision viticulture; remote sensing; remotely piloted aerial system; spectral indices; kappa index precision viticulture; remote sensing; remotely piloted aerial system; spectral indices; kappa index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rey-Caramés, C.; Diago, M.P.; Martín, M.P.; Lobo, A.; Tardaguila, J. Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard. Remote Sens. 2015, 7, 14458-14481.

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