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The lack of information on the vegetation status before the use of fire as a management tool in protected areas leads to drastic destruction of the natural vegetation and biodiversity. This paper describes the use of spectral indices and simulation of savanna burning to assess risk of intensive fire propagation in a National Park (Niokolo Koba, Senegal, West Africa). Spectral parameters corresponding to thematic information (wetness, brightness, and greenness) were retrieved using an orthogonal transformation, the Tasseled Cap approach on LANDSAT-ETM images. Wetness and brightness indices were normalized ([sigma]=1 and MEAN=0) and then combined in a simple semi-empirical algorithm of fire risk levels discrimination. These two indices are proven to reflect qualitatively both fuel moisture and its distribution, which constitute the most foreseen determinants of fire propagation in savanna areas. The fire risk assessment algorithm (FIRA algorithm) was used to produce a fire risk map at the beginning of the management fire implement period. In parallel, a fire area simulator (FARSITE) developed by USDA was used with randomly spaced fire sources to determine areas which can be potentially burned in the study site. These simulated burned areas and the FIRA algorithm results were cross-compared to a real fire scars dated at the end of the same burning period and to land cover map. A great consistency was found between the different sources of information. More than 85% of fire prone areas identified by the FIRA algorithm or simulated by FARSITE were located in trees-shrub, woodland, and shrub savannas. These cover types included also 95% of real fire scars. Almost 88% and 84% of real fire scars were found in the risk zones determined by the FIRA algorithm and the simulated burned areas by FARSITE, respectively. The method used is simple and suited for an operational use for management fire implementation in savanna ecosystems.