Anomalous climatic conditions are recognized to be linked with outbreaks of various human and livestock diseases in various countries (Nicholls, 1991 [1]).
Vegetation in semi-arid and arid regions responds quickly to rainfall, and anomalous landcover conditions can be quickly mapped over large areas. For example indices such as the normalized difference vegetation index (NDVI) can been obtained rapidly in order to identify regions where conditions are suitable for the development of RVF epizootics.
Refinement in determining the spatial distribution of RVF viral activity, through identification of ideal mosquito habitat, has been possible using higher resolution satellite pictures from Landsat, SPOT5, and air-borne synthetic aperture radar data Pope et al, 1992; Linthicum et al,1994).
Recently, a Medias France study allowed Ponds identification in Barkedji in the Ferlo region (Senegal) by using remote sensing data (high spatial resolution).
However, predictive indicators are needed in order to forecast Rift Valley Fever outbreaks. A combination of several climate indices and satellite vegetation measurements can be used to monitor conditions and map areas that are likely to be hot spots for RVF outbreaks