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…

Phenolic composition of Tempranillo Blanco grapes changes after foliar application of urea

Our research aimed to determine the effect and efficiency of foliar application of urea on the phenolic composition of Tempranillo Blanco grapes. The field experiment was carried out in 2019 and 2020 seasons and the plot was located in D.O.Ca Rioja (North of Spain). The vineyard was Vitis vinifera L. Tempranillo Blanco and grafted on Richter-110 rootstock. The treatments were control (C), whose plants were sprayed with water and three doses of urea: plants were sprayed with urea 3 kg N/ha (U3), 6 kg N/ha (U6) and 9 kg N/ha (U9). The applications were performed in two phenological stages, pre-veraison (Pre) and veraison (Ver). Also, each of the treatments was repeated one week later. Control and treatments were performed in triplicate and arranged in a randomised block design. Grapes were harvested at optimum ripening stage. High-performance liquid chromatography was used to analyse the phenolic composition of the grapes. Finally, the results obtained from the analytical determinations – flavonols, flavanols and non-flavonoid (hydroxybenzoic acids, hydroxycinnamic acids and stilbenes) – were studied statistically by analysis of variance. The results showed that, in 2019, U6-Pre and U9-Pre treatments increased the hydroxybenzoic acid content in grapes, and also all foliar treatments applied at Pre enhanced the stilbene concentration. Moreover, U3-Ver was the only treatment that rose flavonol and stilbene contents in the Tempranillo Blanco grapes. In 2020, all treatments applied at Pre enhanced the flavonol concentration in grapes. Furthermore, U3-Pre and U9-Pre treatments increased stilbene content in grapes. Nevertheless, the hydroxybenzoic acid content was improved by U6-Ver and U9-Ver and besides, hydroxycinnamic acid concentration in grapes was increased by all treatments applied at Ver. In conclusion, the lower and highest dose of urea (U3 and U9), applied at pre-veraison, were the best treatments to improve the Tempranillo Blanco grape phenolic composition.

Variations of soil attributes in vineyards influence their reflectance spectra

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.

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.

Mechanisms involved in the heating of the environment by the aerodynamic action of a wind machine to protect a vineyard against spring frost

One of the main consequences of global warming is the rise of the mean temperature. Thus, the heat summation by the plants begins sooner in the early spring, and by cumulating growing degree-days, phenological development tends to happen earlier. However, spring frost is still a recurrent phenomenon causing serious damages to buds and therefore, threatening the harvests of the winegrowers. The wind machine is a solution to protect fruit crops against spring frost that is increasingly used. It is composed of a 10-m mast with a blowing fan at its peak. By tapping into the strength of the nocturnal thermal inversion, it sweeps the crop by propelling warm air above to the ground. Thus, stratification is momentarily suppressed. Furthermore, the continuous action of the machine, alone or in synergy, or the addition of a heater allow the bud to be bathed in a warmer environment. Also, the punctual action of the tower’s warm gust reaches the bud directly at each rotation period. All these actions allow the bud to continuously warm up, but with different intensities and over a different period. Although there is evidence of the effectiveness of the wind machines, the thermal transfers involved in those mechanisms raise questions about their true nature. Field measurements based on ultrasonic anemometers and fast responding thermocouples complemented by laboratory measurements on a reduced scale model allow to characterize both the airflow produced by the wind machine and the local temperature in its vicinity. Those experiments were realized in the vineyard of Quincy, in the framework of the SICTAG project. In the future paper, we will detail the aeraulic characterization of the wind machine and the thermal effects resulting from it and we will focus on how the wind machine warms up the local atmosphere and enables to reduce the freezing risk.

Updating the Winkler index: An analysis of Cabernet sauvignon in Napa Valley’s varied and changing climate

This study aims to create an updated, agile viticultural climate index (similar to the Winkler Index) by performing in-depth analyses of current and historical data from industry partners in several major winegrowing regions. The Winkler Index was developed in the early twentieth century based on analysis of various grape-growing regions in California. The index uses heat accumulation (i.e. Growing Degree Days) throughout the growing season to determine which grape varieties are best suited to each region. As viticultural regions are increasingly subject to the complexity and uncertainty of a changing climate, a more rigorous, agile model is needed to aid grape growers in determining which cultivars to plant where. For the first phase of this study, 21 industry partners throughout Napa Valley shared historical phenology, harvest, viticultural practice, and weather data related to their Cabernet sauvignon vineyard blocks. To complement this data, berry samples were collected throughout the 2021 growing season from 50 vineyard blocks located throughout 16 American Viticultural Areas that were then analyzed for basic berry chemistry and phenolics. These blocks have been mapped using a Geographic Information System (GIS), enabling analysis of altitude, vineyard row orientation, slope, and remotely sensed climate data. Sampling sites were also chosen based on their proximity to a weather station. By analyzing historical data from industry partners and data specifically collected for this study, it is possible to identify key parameters for further analysis. Initial results indicate extreme variability at a high spatial resolution not currently accounted for in modern viticultural climate indices and suggest that viticultural practices play a major role. Using the structure of data collection and analyses developed for the first phase, this project will soon be expanded to other wine regions globally, while continuing data collection in Napa Valley.