Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality

Abstract

The objective of research was to analyse the potential of Normalized Difference Vegetation Index (NDVI) maps from satellite images, yield maps and grapevine fertility and load variables to delineate zones with different wine grape properties for selective harvesting. Two vineyard blocks located in NE Spain (Cabernet Sauvignon and Syrah) were analysed. The NDVI was computed from a Quickbird-2 multi-spectral image at veraison (July 2005). Yield data was acquired by means of a yield monitor during September 2005. Other variables, such as the number of buds, number of shoots, number of wine grape clusters and weight of 100 berries were sampled in a 10 rows × 5 vines pattern and used as input variables, in combination with the NDVI, to define the clusters as alternative to yield maps. Two days prior to the harvesting, grape samples were taken. The analysed variables were probable alcoholic degree, pH of the juice, total acidity, total phenolics, colour, anthocyanins and tannins. The input variables, alone or in combination, were clustered (2 and 3 Clusters) by using the ISODATA algorithm, and an analysis of variance and a multiple rang test were performed. The results show that the zones derived from the NDVI maps are more effective to differentiate grape maturity and quality variables than the zones derived from the yield maps. The inclusion of other grapevine fertility and load variables did not improve the results.Additional key words: cluster analysis; differential management zones; NDVI; precision viticulture; selective harvesting; yield maps. ResumenAnálisis de zonas de manejo diferencial en viñedo y relación con el desarrollo de la viña, madurez y calidad de la uva El objetivo de la investigación fue analizar el potencial de mapas del índice de vegetación de la diferencia normalizada (NDVI) a partir de imágenes de satélite, mapas de cosecha y variables de fertilidad y carga de las cepas para delinear zonas de manejo con diferentes propiedades de madurez y calidad de la uva. Se estudiaron dos parcelas localizadas en el NE de España (Cabernet Sauvignon y Syrah). El NDVI fue derivado de una imagen multiespectral Quickbird-2 adquirida en el envero (julio 2005). Los datos de cosecha fueron obtenidos por medio de un monitor de rendimiento en septiembre de 2005. Otras variables, tales como el número de yemas, número de sarmientos, número de racimos y peso de 100 bayas fueron muestreados en un marco de 10 filas × 5 cepas. Estas variables fueron usadas, en combinación con el NDVI, para definir los aglomerados (clusters) como alternativa a los derivados de los mapas de cosecha. Dos días antes de la vendimia se muestreó la uva. Las propiedades analizadas fueron el grado alcohólico probable, el pH del mosto, la acidez total, los polifenoles totales, el color, los antocianos y los taninos. Las variables de entrada, solas o en combinación, fueron aglomeradas (2 y 3 aglomerados) por medio del algoritmo ISODATA, llevando a cabo después un análisis de varianza y de rangos múltiples. Los resultados muestran que las zona...

Vine vigor, yield and grape quality assessment by airborne remote sensing over three years: Analysis of unexpected relationships in cv. Tempranillo

“…Relevant grape composition variability was observed, as reported previously by other authors (Bramley, 2005;Fiorillo et al, 2012;Martínez-Casasnovas et al, 2012). Lower values of variables expressing vigor, lower acidity and higher sugars and phenolic compounds, reflected that, generally, 2010 could be considered as an average year while 2011, and especially 2012, were extremely dry.…”

“…In order to avoid the influence of the light intensity reflected by objects (whether they are shadowed or fully exposed), other normalised indexes have been developed, such as the Normalized Difference Vegetation Index (NDVI). Since this ratio diminishes atmospheric and luminous variations, it is very convenient in multi-temporal studies (Fischer, 1994) Several studies demonstrate that NDVI can provide information not only about vine vegetative status, but about yield and grape composition, pH, acidity, sugar content or phenolic compounds (Lamb et al, 2004;Arnó et al, 2011;Fiorillo et al, 2012;Martínez-Casasnovas et al, 2012). Nevertheless, latest technology developments have improved the spectral resolu-3 Vine vigor, yield and grape quality by airborne remote sensing of lens light fall-off and variable pixel response.…”

“…The distinction of three zones was a better adaptation for the requirements of the winemaking and commercial strategies of the winery. This clustering method was preferred since it has been positively assessed by other researchers (Bramley & Hamilton, 2004;Martínez-Casasnovas et al, 2012). Analysis of variance (ANOVA) and LSD-Fisher comparison was performed for all variables in order to verify the differences between the three management zones.…”

An overview of the recent approaches to terroir functional modelling, footprinting and zoning

“…Hall et al, 2003;Johnson et al, 2003;Lamb et al, 2004;Zarco-Tejada et al, 2005;Rodríguez-Pérez et al, 2007;Hall and Wilson, 2013) and grape quality Martínez-Casasnovas et al, 2012;, or more sophisticated image processing aimed at either mapping terroir units (Pedroso et al, 2010;Vaudour et al, 2010;Urretavizcaya et al, 2013) or identifying vineyards (e.g. Wassenaar et al, 2002;Warner and Steinmaus, 2005;Rabatel et al, 2006Rabatel et al, , 2008Delenne et al, 2008Delenne et al, , 2010 ( Tables 1 and 2).…”

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

“…Automatic crop identification methodologies based on the processing of remotely sensed images reduce the cost and accelerate the updating process, making this technique a viable option for detecting changes in AGDB [3]. Different authors have assessed the utility of remotely sensed image processing for automatic identification and management of crops [4][5][6][7] as well as for updating GDBs [8,9].…”