We compared two methods of land use classification. The first one i s similar to that described by Teina (2009), based on spectral analysis and watershed segmentation, which we simplified by using the NDVI vegetation index. The second one is the semi - automatic classification based on texture analysis (PAPRI method of Borne, 1990). These methods were tested in two different environments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) and Ivory Coast (Marc Delorme Research Station, monoculture, regular spacing, oil palm plantations); and their results were evaluated against manually digitized photo-interpretation maps. In both situations, the PAPRI method produced better results than that of Teina (global kappa of 0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coco nut and do not differentiate it from oil palm, despite their different NDVI signatures. The PAPRI method differentiates productive coconut from mixed plantations and other vegetation, either high or low (70% accuracy). In both situations, Teina's method al lows counting 65% of the coconut trees when they are well spaced. To increase the method accuracy, we suggest (1) field surveys (for small scale studies) and/or finer image resolution, allowing a high precision in manual mapping with a better discriminatio n between coconut and oil palm, thus limiting the proportion of mixed pixels. (2) A phenological monitoring could improve the distinction between coconut and oil palm agrosystems. (3) Hyper-spectral images should allow extracting more precisely the respective signatures of both species. Another possibility would be (4) an object - oriented analysis as proposed by the eCognition software. Finally, (5) coupling the Lidar system with watershed analysis would allow a better characterization of coconut varietal types.