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60 WINES&VINES July 2018 GRAPEGROWING WINE EAST A ttempts have been made, with limited success, to identify unique zones using remote sensing and to associate regions with variables such as vine water status, soil moisture, vine vigor, yield and berry composition. Although less laborious than manual data collection and production of a multitude of maps, use of conventional aircraft can be costly, and remote sensing in agricultural systems is often imprecise. 30 Data must be converted to variables, e.g., normalized difference vegeta- tive index (NDVI) data through environmental software such as Environment for Visualizing Images (ENVI). 9 Moreover, validation of data acquired by remote sensing is still necessary to determine whether ostensibly unique zones are relevant from a standpoint of physiology, yield, and berry composition. One particular challenge involved masking of cover crop NDVI from all images to assess the vine canopy- specific NDVI. 15, 16 In viticultural applications, remote sensing has been used in modelling vegetative growth, and to infer grape composition from those mea- surements. Remotely sensed multispectral data was used to delineate a Chardonnay vineyard into small-lot production zones. 12 Vine size (weight of cane prunings; an estimate of vigor) was related to vigor zones identified by airborne images. Vigor zones also were related to vine water status and grape composition variables. Thus, indirectly, remote sensing was used to predict vineyard status and grape composition, with direct implications for wine quality. Relationships between vegetation indices (VIs) and vegetative growth were explored by Dobrowski et al. 7 There were strong, positive correlations between extracted VIs and vine size over two years. Additionally, relationships established in the first season were able to predict the vine size in the second season. Remote sensing can be used to predict grape composition variables. 14 Re-sampling the image to a final pixel size approximately equal to the distance between rows, effectively combining vine size and density information into a single pixel, resulted in the strongest correlations to color and phenols. The strongest negative cor- relations between NDVI and color and phenols occurred around veraison. In Languedoc (France), Acevedo-Opazo et al. 1 investigated remotely sensed VIs, vine water status, and grape composition on a num- ber of wine grape varieties in non-irrigated vineyards. Temporally stable relationships oc- curred between zones based on the NDVI and vegetative growth, vine water status and yield. These zones also were consistent with soil type. Viticultural Mapping by UAVs, Part 2 Applying unmanned aerial vehicles in viticulture By Andrew G. Reynolds, Ralph Brown, Marilyne Jollineau, Adam Shemrock, Hyun-Suk Lee, Briann Dorin, Mehdi Shabanian, Baozhong Meng KEY POINTS Remote sensing by unmanned aerial vehicles (UAVs) has been useful in monitoring vineyard vegetative growth and to make inferences about grape composition. A study in Ontario used UAVs to examine cor- relative relationships between vegetation indices and vineyard variables including vine size, yield, berry weight, berry composition and winter hardiness. UAVs have significant potential to identify zones of superior fruit composition and potential wine quality. EDITOR'S NOTE This is the second installment of a two-part series on using unmanned aerial vehicles (UAV) to obtain data to help vineyards implement preci- sion viticulture. In the first article, the authors looked at the different UAV platforms, hardware, sensors and image processing. Here, the authors discuss the use of UAVs in viticulture and include the results of a study done in Ontario vineyards. Figure 1: The principal components analysis for two vineyards in 2015. Figure 1a (left): the Buis Riesling vineyard in Niagara-on-the-Lake, Ont. and Figure 1b (right): the George Cabernet Franc vineyard in Vineland, Ont. a b