GiESCO 2019 banner
IVES 9 IVES Conference Series 9 GiESCO 9 Evaluation of intra-vineyard spatial and temporal variability of leaf area index using multispectral images obtained by satellite (Landsat 8, Sentinel-2) and unmanned aerial vehicle platforms

Evaluation of intra-vineyard spatial and temporal variability of leaf area index using multispectral images obtained by satellite (Landsat 8, Sentinel-2) and unmanned aerial vehicle platforms

Abstract

Context and purpose of the study – Estimation of vineyard leaf area index (LAI) is an important aspect for the winegrowers. However, tracking and monitoring are difficult tasks due to time constraints. Satellite and unmanned aerial vehicle (UAV) imaging have become a practical monitoring method for LAI. Nevertheless, for a proper LAI determination, the image’s spatial resolution is a key factor, since low-resolution images are incapable of distinguishing between adjacent vines due to the large area covered in each pixel, this leads to misinterpretation or generalisation of vineyard information. The objective of this study was to evaluate the effect of spatial resolution on the accuracy of LAI estimation using different spatial resolutions: Landsat8 (30 m), Sentinel-2 (10 m) and UAV Multispectral images (0.05 m).

Material and methods – This study was carried out in a dryland vineyard cv. Pinotage situated in Stellenbosch, at the Welgevallen experimental farm (33°57’8” S, 18°52’26” E). The block (1.9 ha) has a North-South orientation and was planted on a West-South-West slope. The vines are trained on a sevenwire (moveable) hedge trellis, VSP (vertical shoot positioning) system. Three sources of remote sensing data, with different spatial resolutions, were chosen: i) Multispectral images acquired by a multi-rotor unmanned aerial vehicle (UAV) (spatial resolution 0.052 m); ii) Landsat 8 images (spatial resolution of 30 m) and iii) Sentinel-2A images (spatial resolution of 30 m). Images from these three sources were used to calculate the normalised difference vegetation index (NDVI) from the experimental site, and these values were compared with field measurements (empirical LAI model).

Results – Results obtained from low-resolution satellite images show a poor accuracy in the estimation of LAI on a plant scale. The image resolution of Landsat 8 and Sentinel-2 was not high enough to differentiate between adjacent groups of vines. The UAV multispectral images obtained the best agreement with the field LAI measurements, due to the high resolution (0.052 m pixel size). It is evident with the results obtained that UAV imaging is the most appropriate and accurate monitoring methodology since this technology providing enough information to estimate LAI per plant.

DOI:

Publication date: September 27, 2023

Issue: GiESCO 2019

Type: Poster

Authors

Yolandi BARNARD1, Guillermo OLMEDO2, Albert STREVER1, Carlos POBLETE-ECHEVERRÍA1*

1 Department of Viticulture and Oenology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
2 EEA Mendoza, Instituto Nacional de Tecnología Agropecuaria, Mendoza M5507EVY, Argentina

Contact the author

Keywords

Normalised Difference Vegetation Index (NDVI), Unmanned Aerial Vehicle (UAV), grid analysis, spatial variability

Tags

GiESCO | GiESCO 2019 | IVES Conference Series

Citation

Related articles…

Correlations between N,S,O-heterocycle levels and age of Champagne base wines

Champagne regulation allows winegrowers to stock small amounts of still wines in order to compensate vintages’ quality shifts mainly due to climate variations. According to their technical requirements and house style some Champagne producers (commonly named “Champagne houses”) use these stored wines in the blend in order to introduce an element of complexity. These wines possess the particularity of being aged on fine lees in thermo-regulated stainless steel tanks. The Champagne house of Veuve Clicquot Ponsardin has several wines stored this way.

Quantification of polysaccharides of variety Pomaces of the D.O.Ca Rioja

Pomace is one of the main residues generated by the wine industry and represents an environmental problem. Currently, there is a growing interest in the revaluation of these products because different bioactive compounds can be obtained from them, such as polyphenols, grape seed oils and polysaccharides. Red grape pomace can be an important source of polysaccharides, but they are currently little studied and even less with viable and environmental extraction processes (green extraction), such as flash extraction. The residual amount of the fraction rich in pectin (residual pulp) and component rich in hemicellulose in the pomace and the strength of association of the pectin with the cellulose-xyloglucan network depend on the degree of extractability of the polysaccharides in red winemaking and on the winemaking conditions.

Antociani ed acidi cinnamici per la caratterizzazione di vitigni in zone diverse della Toscana

The phenolic compounds (cathechins, cynnamic acids, anthocyanidins) in wines made from 6 vine-varieties (Sangiovese, Cabernet S., Nero d’Avola, Foglia Tonda, Pinot N., Mazzese) grown in 4 different pedoclimatic zones of Tuscany (Arezzo, Grosseto, Pisa and Lucca) have been analyzed by HPLC.

The origin and the discovery of “terroir”

Le mot “terroir” dérive du latin “terra”, mais déjà les Romains l’indiquaient comme “locus” ou”loci”, c’est-à-dire un lieu ayant le “genius”destiné à la production d’un produit d’excellente qualité.

Sensory definition of green aroma concept in red French wines. Evidence for the contribution of novel volatile markers

The aromatic complexity of a wine results from the perception of the association of volatile molecules and each aroma can be categorized into different families. The “green” aromas family in red wines has retained our attention by its close link with the fruity perception. In that study, the “green” olfactory concept of red wines was considered through a strategy combining both sensory analysis and hyphenated chromatographic techniques including HPLC and MDGC (Multidimensional Gas Chromatography). The aromatic space of this concept was specified by lexical generation through a free association task on 22 selected wines by a panel of wine experts. Then, 70 French red wines were scored on the basis of the intensity of their “green” and “fruity” attributes.