Vine-plot mapping and monitoring are crucial issues in land management, particularly for areas where vineyards are dominant like in some French regions. In this context, the availability of an automatic tool for vineyard detection and characterization would be very useful. The objective of the study is to compare two different approaches to meet this need. The first one uses directional variations of the contrast feature computed from Haralick's co-occurrence matrices and the second one is based on a local Fourier transform. For each pixel, a "vine index" is computed on a sliding window. To foster large-scale applications, test and validation were carried out on standard very high spatial resolution remote sensing data. 70.8% and 86% of the 271 plots of the study area were correctly classified using the co-occurrence and the frequency method, respectively. Moreover, the latter enabled an accurate determination (less than 3% error) of inter-row width and row orientation.