Issue link: http://winesandvines.uberflip.com/i/998810
July 2018 WINES&VINES 65 WINE EAST GRAPEGROWING versely correlated with anthocyanins, A 520 and total phenols in four of five Cabernet Franc vineyards in 2015. This suggests that zones of low NDVI might be associated with grapes of higher color and phenols, and possibly higher wine quality than high-NDVI zones. Applica- tion of PCA of 2016 data revealed similar relationships. This project has produced literally hundreds of maps! With 12 vineyards under study, three seaons of data collection, and a minimum of 14 variables, that means more than 500 maps. Maps for Chateau des Charmes Cabernet Franc vineyard 2016 are depicted in Figure 2 (page 61). The UAV NDVI map showed a low NDVI zone in the north end of the vineyard (Figure 2a). This corresponded closely with maps pro- duced using GreenSeeker (Figure 2b), highest regions from the thermal camera (Figure 2c) and soil moisture (Figure 2d), and lowest re- gions of leaf ψ (Figure 2e), and higher LT 50 (i.e. less winter hardy; Figure 2f). Zones of low UAV NDVI (Figure 3a) (page 61) were associated with low yield (Figure 3b), vine size (Figure 3c), berry weight (Figure 3d), higher Brix (Figure 3e) and higher pH (Figure 3f), once again suggesting enhanced fruit ma- turity in low NDVI zones. Conversely, those high-NDVI zones identified by the UAV were typically low in thermal camera data and high in GreenSeeker NDVI, leaf ψ , soil moisture, vine size, berry weight and TA, and lower in LT 50 (i.e., more winter hardy). Other Cabernet Franc vineyards showed as- sociations in the UAV data between low NDVI and high thermal zones. These corresponded to low GreenSeeker NDVI, soil moisture, leaf ψ , vine size and berry weight, and higher LT 50 zones. There was some spatial correlation with high-TA and low-Brix areas, but pH and overall yield usually were not strongly related spatially. In most vineyard blocks the UAV NDVI maps were comparable to GreenSeeker NDVI maps. In general, there were good direct spa- tial correlations between UAV and GreenS- eeker NDVI vs. leaf ψ , leaf g s , soil moisture, vine size, LT 50 and TA, and inverse ones with Brix and pH. There were also many situations in which maps from thermal data were in- versely correlated spatially with NDVI. Most frequent spatial correlations in Riesling with UAV and GreenSeeker NDVI zones were leaf ψ , gs, soil moisture, vine size, berry weight, yield and TA. Noteworthy inverse spatial cor- relations included Brix, pH, FVT and PVT. The 2016 patterns in leaf ψ , soil moisture, yield components and berry composition were for the most part consistent with those observed in 2015 for both Riesling and Cabernet Franc. GVLRaV3 titer was determined in 2016 in all vineyards for all vines used for leaf ψ . Zones of low GLRaV titer corresponded spatially with zones of low NDVI in two Cabernet Franc vine- yards (Figure 4) (page 62). The same trend was apparent for three Riesling vineyards. However, low NDVI zones also corresponded in some cases with low leaf ψ , low soil moisture and high concentrations of anthocyanins and phenols. This is a major challenge, because we need to identify unique spectral signatures that designate virus-affected areas that do not cor- relate spatially with other variables such as leaf ψ ; otherwise there is a strong possibility of misdiagnosis. Consequently, more research is needed to identify other vegetation indices that are unique and specific to zones of varying virus status. This work is ongoing. Maps also identify management zones within vineyards that might correspond to fruit of different potential wine quality. This is, in essence, the raison d'être of precision viticul- ture. Figure 5 depicts three Ontario vineyards that show clear zones of high and low NDVI determined by UAV. During harvest, it was clear that the high NDVI zones had larger clusters, larger berries, generally higher yields per vine and, in the case of Riesling, more bunch rot than low NDVI zones. Conclusions Unmanned Aerial Vehicles (UAVs) are a valu- able tool to acquire high-resolution aerial images of vineyards. The data within the high-resolution aerial images can be used to determine the spatial distribution of a variety of canopy variables within a vineyard block and between different vineyard blocks. In this way UAVs can be used to measure the spatial distribution of vigor, water stress, nutrient status, disease, yield components and berry composition. One of the major challenges is the massive amount of data that are collected, the time required for post- flight data processing and the need to derive useful vegetation indices that are specific to variables of interest. Andrew G. Reynolds is professor of biological sciences/ viticulture at the Cool Climate Oenology and Viticulture Institute, Brock University, St. Catharines, Ontario, Canada; Hyun-Suk Lee and Briann Dorin are graduate students at CCOVI, Brock University. Marilyne Jollineau is associate professor in the department of geography at Brock University. Ralph Brown is professor of engi- neering at the School of Engineering, University of Guelph, Guelph, Ontario. Mehdi Shabanian is assistant professor and Baozhong Meng is associate professor at the University of Guelph. Adam Shemrock is with Air-Tech Solutions, Inverary, Ontario. 251 Gambee Road, Geneva, NY 14456 Phone: 315-759-2118 Toll free: 888-234-6752 Fax: 315-789-1848 Email: tanks@vancemetal.com Website: www.vancemetal.com Quality Rugged 12 gauge, Custom & Stock Stainless Tanks Catwalk Systems • Pump Over Carts • Custom Hoppers, Chutes & Bins To see references for this article, go to winesandvines.com and search under Magazine › Features › July 2018.