Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are probably the ones with the most severe economic consequences. UAV multispectral images could be a powerful tool for the automatic detection of symptomatic vines. But a missing key step consists of discriminating different kinds of diseases leading to similar leaf discoloration as it is the case with FD and GTD for red vine varieties. In this work we evaluate the potential of several indices to separate three types of vines in UAV multispectral images: asymptomatic, FD symptomatic and GTD (ESCA and Black Dead Arm) symptomatic. Ground truth data and UAV multispectral images have been acquired in 2016 over 21 selected vineyards covering 13 different varieties (red and white) over two areas in South of France. Fifteen vegetation indices and four biophysical parameters, among which three are linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin), were computed from UAV images. The potential of discrimination of these variables were evaluated using univariate classification approaches. Results are presented for red varieties vineyards. Best results were achieved with RGI and GRVI (based on the green and red spectral bands) and the Carotenoid biophysical parameter, which showed the best performance to discriminate healthy and symptomatic foliage. Nevertheless, some problems of FD and GTD pixels misclassification remain for all varieties, which limits the operationality of the method.