Spectral features of vineyards in south Brazil from ASTER imaging

Silva, Patrícia Rodrigues da; Ducati, Jorge Ricardo

doi:10.1080/01431160902810612

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…To measure the continuous spatial variability of vine vigour, optical remote sensing provides a synoptic view of grapevine photosyntheticallyactive biomass over entire vineyards and appear to be a management tool of enormous potential with red grape varieties, especially if the canopy architecture can be linked to production of phenolics and colour in ripe grapes (Lamb et al, 2004). Other authors have considered the possibility of substituting the information obtained from remote sensing (satellite or aerial images) by optical proximal sensors computing vegetation indexes and ultrasonic sensors to identify areas presenting critical vegetation conditions (Mazzetto et al, 2010), or have experimented with reflection radiometers to characterize spectral features of vineyards (da Silva & Ducati, 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Martínez‐Casasnovas

Agelet-Fernandez

Arnò

et al. 2012

Span. j. agric. res.

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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...

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Section: Introductionmentioning

confidence: 99%

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

Martínez‐Casasnovas

Agelet-Fernandez

Arnò

et al. 2012

Span. j. agric. res.

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“…5 The links between remotely sensed descriptors of vine canopy vigor at different phenological development stages, and subsequent measurements of total phenolics and color in harvested fruit for Cabernet Sauvignon grapes in a cool climate vineyard in Australia are investigated. 9 IKONOS images were used to derive leaf area on a per vine basis (LA v ) map of wine grape vineyards in conjunction with ground measurements, and show a significant relationship between ground-based and image-based LA v . 8 Advanced spaceborne thermal emission and reflection radiometer (ASTER) imagery and field radiometry are combined to analyze the spectral features of vineyards and to investigate the identification of grape variety.…”

Section: Introductionmentioning

confidence: 99%

Vineyard parcel identification from Worldview-2 images using object-based classification model

Sertel

Yay

2014

J. Appl. Remote Sens

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Accurate identification of spatial distribution and characteristics of vineyard parcels is an important task for the effective management of vineyard areas, precision viticulture, and farmer registries. This study aimed to develop rule sets to be used in object-based classification of Worldview-2 satellite images to accurately delineate the boundaries of vineyards having different plantation styles. Multilevel segmentation was applied to Worldview-2 images to create different sizes of image objects representing different land cover categories with respect to scale parameter. Texture analysis and several new spectral indices were applied to objects at different segmentation levels to accurately classify land cover classes of forest, cultivated areas, harvested areas, impervious, bareland, and vineyards. A specific attention was given to vineyard class to identify vine areas at the parcel level considering their different plantation styles. The results illustrated that the combined usage of a newly developed decision tree and image segmentation during the object-based classification process could provide highly accurate results for the identification of vineyard parcels. Linearly planted vineyards could be classified with 100% producer's accuracy due to their regular textural characteristics, whereas regular gridwise and irregular gridwise (distributed) vineyard parcels could be classified with 94.87% producer's accuracy in this research.

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“…Specific classes have quite different reflectance spectra (Swain and Davis 1978;Lillesand and Kiefer 1987), and even within a class, sub-classes carry characteristic spectra, making reliable identifications possible. This happens, for example, with vegetation reflectance spectra, and for vineyards it was demonstrated that they can be separated from other vegetation (Da Silva and Ducati 2009), while in another study, positive identifications of grape varieties were reported (Cemin and Ducati 2011). This paper was motivated by oral reports from organic vintners in the Loire Valley, made to the first author while in field trips in the region, stating that they could see by the naked eye the color differences between their vineyards and the neighboring conventional vineyards.…”

Section: Introductionmentioning

confidence: 99%

Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley, France

2014

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Aim: To test the use of Remote Sensing imagery and techniques to differentiate between conventional and organic vineyards.Methods and results: Conventional and organic vineyards were identified on three satellite images acquired by the ASTER sensor of the Loire Valley. A sample of 46 conventional and 12 organic plots was used; grape varieties were Chenin Blanc (33 plots) and Cabernet Franc (25 plots). Mean reflectances were extracted from pixels inside each plot for the nine spectral bands (visible and infrared) of ASTER. A statistical discriminant analysis was performed. The vegetation index NDVI was also analysed. Results showed that all 12 organic plots, and 41 out of 46 conventional plots were correctly separated, a 91.4% success rate. Also, 23 out of 25 Cabernet, and 30 out of 33 Chenin plots were also correctly identified, also a 91.4% success rate. Regarding NDVI, there are no differences between conventional and organic vineyards within a 5% significant level. Analyses focused on the influences of chemical treatments on vineyard colors and on the effects of light reflected by inter-row spaces, suggested that both processes introduce spectral changes in conventional vineyards, mainly in short-wave infrared. Results also indicate that infrared information is essential to spectral discrimination.Conclusion: The use of chemicals, typical to conventional viticulture, has an impact on leaf composition and cell structure, being an important factor to imprint a characteristic reflectance pattern to these vineyards; the contribution to the integrated reflectance from inter-row vegetation is probably also a differentiating factor. Both causes act synergistically to build a significant spectral difference between conventional and organic vineyards.Significance and impact of the study: Remote Sensing techniques can be used as a first approach to vineyard monitoring, producing relevant information on viticultural methods, which can be used as early indicators of the need for field inspection or conventional laboratory analysis.

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Spectral features of vineyards in south Brazil from ASTER imaging

Cited by 8 publications

References 8 publications

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

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

Vineyard parcel identification from Worldview-2 images using object-based classification model

Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley, France

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