Can remotely sensed meteorological data significantly contribute to reducecosts of tsetse surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of gener ating tsetse distribution and abundance maps from remotely sensed data, usi ng a restricted amount of field data. A discriminant analysis model is test ed using contemporary, tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration an d Meteosat platforms, A split sample technique is adopted where a randomly selected part of field measured data (training set) sei? es to predict the other part (predicted set). The obtained results are their compared with fi eld measured data per corresponding grid-square. Depending on the size of t he training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analys is for the prediction, not only, of the tsetse distribution, but more impor tantly of their abundance. This opens lip new avenues because satellite pre dictions and field data may be combined to strengthen or substitute one ano ther and thus reduce costs of field surveys.