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Remotely sensed imagery has been used to update and improve the spatial resolution of malaria transmission intensity maps in Tanzania, Uganda, and Kenya. Discriminant analysis achieved statistically robust agreements between historical maps of the intensity of malaria transmission and predictions based on multitemporal meteorological satellite sensor data processed using temporal Fourier analysis. The study identified land surface temperature as the best predictor of transmission intensity. Rainfall and moisture availability as inferred by cold cloud duration (ccd) and the normalized difference vegetation index (ndvi), respectively, were identified as secondary predictors of transmission intensity. Information on altitude derived from a digital elevation model significantly improved the predictions. "Malaria-free" areas were predicted with an accuracy of 96 percent while areas where transmission occurs only near water, moderate malaria areas, and intense malaria transmission areas were predicted with accuracies of 90 percent, 72 percent, and 87 percent, respectively. The importance of such maps for rationalizing malaria control is discussed, as is the potential contribution of the next generation of satellite sensors to these mapping efforts.



Photogrammetric engineering and remote sensing

Publication Date





161 - 166


Trypanosomiasis and Land-Use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, United Kingdom and with the Kenya Medical Research Institute/Wellcome Trust collaborative programme, P.O. Box 43640, Nairobi, Kenya.