This analysis concerns an estimation of burned area and fire severity levels in an area affected by a large wildfire that took place in the south of Spain in July 2004. Fire severity is defined in this work as the impact of fire on the vegetation. The objective was to find an efficient method for quick fire severity mapping based on remote sensing techniques that can be useful for postfire forest management. Several methods for image analysis (Linear Spectral Unmixing, Matched Filtering and Normalized Burn Ratio Index) were applied to postfire Landsat 5-TM, Envisat-MERIS, and Terra-MODIS images. Maps depicting fire severity of three levels of an acceptable reliability were obtained using a small amount of field data and following a simple method of processing. Linear spectral unmixing produced the best classifications for MERIS and MODIS images, while the matched filtering technique produced the most accurate classification for the TM image. These preliminary results show that short-term fire severity maps can be obtained by means of high- to medium-resolution postfire remote sensing data, in order to evaluate the situation after a forest fire and plan forest restoration works.