Remote sensing and geographic information systems to predict the density of ruminants, hosts of Rift Valley fever virus in the Sahel.
Rift Valley fever (RVF) is an acute arboviral disease of domestic ungulates and humans in Africa and the Middle East. Since the first epidemic in 1987, Senegal has been confronted with recurrent episodes of the disease. This study aimed to model spatial distribution of ruminants in the agropastoral area of Barkedji (Senegal) where the disease is enzootic. In this Sahelian ecosystem, livestock distribution mainly depends on the availability of resources. Accordingly, remote sensing and geographic information systems (GIS) were used to seek environmental indicators of livestock density. A high-resolution Landsat image was associated with landscape field data to describe the land-cover. A series of normalized difference vegetation index values gave an estimation of the phytomass. In addition the compounds of herders in the study zone were located and sampled. Three surveys were conducted during the rainy season to record the number of herds in each compound of the sample. All these data were overlaid in the GIS. A discriminant analysis was performed to associate the observed herd density with environmental data and to develop a predictive model for the entire study zone. The final result was a 1-km resolution raster map of herd density during a normal rainy season.
Pin-Diop
Raphaëlle
R
Institut de Recherche pour Développement/US140, Maison de Télédétection, 500 avenue Jean-François Breton, 34093 Montpellier Cedex 5, France. raphaellepin@yahoo.fr
Touré
Ibra
I
Lancelot
Renaud
R
Ndiaye
Magatte
M
Chavernac
David
D