The high number of forest fires occurring every year constitutes one of the major degradation factors of ecosystems, especially in the Mediterranean region. The ability of satellite sensors to cover wide areas makes them valuable tools for the prevention, detection and mapping of wildfires and fire related properties of the ecosystems. However, increased spatial resolution, coupled with a lower number of available spectral channels, makes image analysis difficult. Thus, users need to employ additional image analysis techniques in order to achieve their objectives. The aim of this work was to assess to what extent potential fuels can be discriminated within different image analysis techniques and satellite imagery products using the indirect mapping method. The main hypothesis was that very high-resolution data, coupled with object-oriented image analysis (OOIA), would improve the ability to differentiate among potential fuels. To test this hypothesis, fuel types were extracted from high and very high spatial resolution satellite imagery using object-oriented and pixel-based image analyses, respectively. The general conclusion of this study was that the use of object-oriented image analysis not only produces more accurate results, but also allows differentiation among the largest number of potential fuels. Object-oriented analysis allows better discrimination of potential fuels using very high-resolution imagery and the indirect mapping method. Six different potential fuels were classified using the object-oriented approach compared with only four when using pixel-based image analysis. The overall accuracy achieved reached approximately 80%.