G. Hendrickx et al., A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data, B ENT RES, 91(5), 2001, pp. 333-346
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.