A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data

A raster or grid-based Geographic Information System with data on tsetse, t rypanosomiasis, animal production, agriculture and land use has recently be en developed in Togo. The area-wide sampling of tsetse fly, aided by satell ite imagery, is the subject of two separate papers. This paper follows on a first paper, published in this journal, describing the generation of digit al tsetse distribution and abundance maps and how these accord with the loc al climatic and agro-ecological setting. Such maps when combined with data on the disease, the hosts and their owners, should contribute to the knowle dge of the spatial epidemiology of trypanosomiasis and assist planning of i ntegrated control operations. Here we address the problem of generating tse tse distribution and abundance maps from remotely sensed data, using a rest ricted amount of field data. Different discriminant analysis models have be en applied using contemporary tsetse data and remotely sensed, low resoluti on data acquired from the National Oceanographic and Atmospheric Administra tion (NOAA) and Meteosat platforms. The results confirm the potential of sa tellite data application and multivariate analysis for the prediction of th e tsetse distribution and abundance. This opens up new avenues because sate llite predictions and field data may be combined to strengthen and/or subst itute one another. The analysis shows how the strategic incorporation of sa tellite imagery may minimize field collection of data. Field surveys may be modified and conducted in two stages, first concentrating on the expected fly distribution limits and thereafter on fly abundance. The study also sho ws that when applying satellite data, care should be taken in selecting the optimal number of predictor variables because this number varies with the amount of training data for predicting abundance and on the homogeneity of the distribution limits for predicting fly presence. Finally, it is suggest ed that in addition to the use of contemporary training data and predictor variables, training and predicted data sets should refer to the same eco-ge ographic zone.