Issue link: http://winesandvines.uberflip.com/i/998810
July 2018 WINES&VINES 61 WINE EAST GRAPEGROWING They concluded that a combina- tion of remotely sensed data with intimate vineyard knowledge, es- pecially of the soil, is needed to predict grape composition and ultimately wine quality. Remote sensing has proved to be a useful tool for monitoring vineyard vegetative growth, and for making inferences about grape composition from multispectral measurements. In Ontario, NDVI data from remote sensing was as- sociated with numerous variables in Riesling vineyards, including vine water status, yield compo- nents and berry composition. 16 Similar applications were made in Pinot Noir vineyards, and proved to be a good tool to determine color and phenolic potential of grapes, in addition to water status, yield and vine size. 15 These studies were unique in their employment of remote sensing in cover- cropped vineyards and thereafter using protocols for excluding the spectral reflectance contributed by inter-row cover crops. UAV-based remote sensing like- wise has been used for making inferences about grape composi- tion from multispectral measure- ments. 23 However, use of UAVs for remote sensing in vineyards is a relatively new area of research, thus far untested in Canada, and capable of acquiring high-resolu- tion spatial data without the high cost of conventional aircraft. As with proximal sensing, there has been little published, and most of that has confirmed an ability to acquire NDVI and related images. Relationships were found between photosynthesis and chlorophyll fluorescence by hyperspectral im- agery captured via UAVs, as well as between both photosynthesis and chlorophyll fluorescence vs. remote measurements. 35 Other relationships were demonstrated between both chlorophyll a/b and leaf carotenoids vs. several VIs based on multispectral images ac- quired by UAVs. 38 UAVs were used for assessment of vineyard water status by cor- relation of stem ψ with NDVI. 2 Further relationships were ob- served between several Vis, in- cluding NDVI vs. vine water status [leaf water potential ( ψ ) and sto- matal conductance]. 38 Addition- Figure 3: Spatial maps of the Cabernet Franc vineyard at Chateau des Charmes, St. Davids, Ont. in 2016. 3a: UAV- based NDVI; 3b: Yield per vine (kg); 3c: Weight of cane prunings per vine (kg); 3d: Berry weight (g): 3e: Berry soluble solids (Brix): 3f: Berry pH. Scale 1 = 2167. Figure 2: Spatial maps of the Cabernet Franc vineyard at Chateau des Charmes (CDC), St. Davids, Ont. in 2016. 2a: UAV- based NDVI; 2b: GreenSeeker-based NDVI; 2c: UAV-based thermal image; 2d: Soil moisture; 2e: Leaf water potential; 2f: LT 50 (winter hardiness). Scale 1 =2167. a b c d e f a b c d e f