Abstract
The hypothesis of this research was that the maps based on remotely-sensed images would create zones of different vigor, yield, water status, winter hardiness and berry composition and the wines from the unique zones would show different chemical and sensorial profiles. A second hypothesis was that titer of grapevine leafroll-associated virus (GLRaV) could be correlated spatially to NDVI and other spectral indices. To determine zonation, unmanned aerial vehicles (UAVs) with multispectral and thermal sensors were flown over six Cabernet Franc vineyard blocks in Ontario, Canada. Zonation was based on NDVI values, and spatial correlations were examined between the NDVI and leaf water potential (Ψ), soil water content (SWC), stomatal conductance (gs), winter hardiness (LT50), vine size, yield, and berry composition. Additional NDVI data were acquired using GreenSeeker (proximal sensing), and both NDVI data sets produced maps of similar configuration. Several direct correlations were found between UAV-based NDVI and vine size, berry weight, yield, titratable acidity, SWC, leaf Ψ, gs, and NDVI from GreenSeeker. Inverse correlations included thermal data, Brix, color/ anthocyanins/ phenols, and LT50. The pattern of UAV-based NDVI and other variables corresponded to the PCA results. Thermal scan and GreenSeeker were useful tools for mapping variability in water status, yield components, and berry composition. In 2016, zoned maps were created based on UAV NDVI data, and grapes were harvested according to the separate zones. Additionally, spatial correlations between GLRaV titer and NDVI were observed. Use of UAVs may be able to delineate zones of differing vine size, yield components, and berry composition, as well as areas of different virus status and winter hardiness.
Highlights
The Ontario wine industry produces ≈ 80,000 t of grapes and consists of cultivars such as Riesling, Chardonnay, and Cabernet franc as well as Cabernet Sauvignon, Merlot, Pinot noir, and many others
normalized difference vegetative index (NDVI) data from both GreenSeeker and unmanned aerial vehicles (UAVs) flights were closely correlated in Principal components analysis (PCA) (Fig. 1A-D)
In most circumstances NDVI data were inversely correlated to leaf temperature and thermal data obtained from UAV flights
Summary
The Ontario wine industry produces ≈ 80,000 t of grapes and consists of cultivars such as Riesling, Chardonnay, and Cabernet franc as well as Cabernet Sauvignon, Merlot, Pinot noir, and many others (www.grapegrowersofontario.com). Soils are variable due to widespread glacial activity >10,000 years ago, and many vineyards are situated on soils that range widely in texture, depth of solum, and water-holding capacity [1] This soil variability impacts vine vigor, yield, and water status. GreenSeeker and other ground-based (proximal) sensing technologies might allow grapegrowers to identify unique zones without use of aircraft through continuous compilation of NDVI data from vine canopies [2,3,4]. Sensed multispectral data were used to delineate a Chardonnay vineyard into small-lot production zones based on vine size (pruning weights; vigor), which were related to vine water status and grape composition [16]. In Languedoc, temporally stable relationships occurred between NDVI-delineated zones and vegetative growth, vine water status, and yield [19]
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