Terroir 2006 banner
IVES 9 IVES Conference Series 9 High resolution remote sensing for mapping intra-block vine vigour heterogeneity

High resolution remote sensing for mapping intra-block vine vigour heterogeneity

Abstract

In vineyard management, the block is considered today as the technical work unit. However, considerable variability can exist inside a block with regard to physiological parameters, such as vigour, particularly because of soil heterogeneity. To represent this variability spatially, many measurements have to be taken, which is costly in both time and money. High resolution remote sensing appears to be an efficient tool for mapping intra-block heterogeneity. A vegetation index, the Normalized Difference Vegetative Index (NDVI), calculated with red and near infrared leaf reflectance can be used as a vine vigour indicator. Because of the cultivation of vines in rows, a specific image treatment is needed. Only high resolution remote sensing (pixels less than 20 cm per side) allows the discrimination between row pixels and inter-row ones. The significant correlation between NDVI and pruning weight and the possibility to map the vigour with the NDVI by means of high resolution remote sensing, show the ability of NDVI to assess intra-block variations of vine vigour.

DOI:

Publication date: December 22, 2021

Issue: Terroir 2006

Type: Article

Authors

Elisa MARGUERIT (1), Anne-Marie COSTA FERREIRA (1), Cloé AZAÏS, Jean-Philippe ROBY (1), Jean-Pascal GOUTOULY (2), Christian GERMAIN (1), Saeid HOMAYOUNI (1) and Cornelis Van LEEUWEN (1)

(1) ENITA de Bordeaux, 1 cours du Général de Gaulle, CS 40201, 33175 Gradignan cedex, France
(2) INRA de Bordeaux, Domaine de la Grande Ferrade, 71, avenue Édouard Bourlaux B.P. 81, 33 883 Villenave d’Ornon cedex, France

Contact the author

Keywords

vine, Vitis vinifera L., remote sensing, high resolution, pruning weight, NDVI

Tags

IVES Conference Series | Terroir 2006

Citation

Related articles…

Geospatial trends of bioclimatic indexes in the topographically complex region of Barolo DOCG

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.

Multivariate strategies for red wines classification using stilbenes and flavonols content

Bioactive polyphenols from grapes and wines, like stilbenes and flavonols (SaF), are often determined to nutritional evaluation, but also for many other purposes. The objective of this study was to quantify SaF in red wines from “Campanha Gaúcha”, a large and young viticultural region from South Brazil. Moreover, through statistical analysis, evaluate the influence of these compounds according to varieties, production process, harvest years and micro-regions of cultivation. A total of 58 samples of red wines were analyzed by high-performance liquid chromatography coupled to diode array detector (HPLC-DAD) for determination of trans-resveratrol (R), quercetin (Q), myricetin (M), kaempferol (K), trans-e-viniferin (V) and their precursor, cinnamic acid (C).

Hplc-ms analysis of carotenoids as potential precursors for 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) in riesling grapes

In recent years, an undesirable premature “aged” character has been noticed in a growing number of young Riesling wines, associated with extreme weather conditions leading to increased radiation intensity and/ or sun exposure of grapes.

Revealing the aroma profile of Greek wines from indigenous grape cultivars

The indigenous Greek grape varieties Assyrtiko, Malagousia, Moschofilero and Roditis are used to produce white wines that are attracting the interest of wine producers and consumers due to their aromatic characteristics [1]. In addition, the Agiorgitiko and Xinomavro varieties are Greece’s most prominent red grape varieties.

Monitoring vineyard canopy structure by aerial and ground-based RGB and multispectral imagery analysis

Unmanned Aerial Vehicles (UAVs) are increasingly used to monitor canopy structure and vineyard performance. Compared with traditional remote sensing platforms (e.g. aircraft and satellite), UAVs offer a higher operational flexibility and can acquire ultra-high resolution images in formats such as true color red, green and blue (RGB) and multispectral. Using photogrammetry, 3D vineyard models and normalized difference vegetation index (NDVI) maps can be created from UAV images and used to study the structure and health of grapevine canopies. However, there is a lack of comparison between UAV-based images and ground-based measurements, such as leaf area index (LAI) and canopy porosity.