Evaluation of environmental data for identification of Anopheles (Diptera: Culicidae) aquatic larval habitats in Kisumu and Malindi, Kenya.

This research evaluates the extent to which use of environmental data acquired from field and satellite surveys enhances predictions of urban mosquito counts. Mosquito larval habitats were sampled, and multispectral thermal imager (MTI) satellite data in the visible spectrum at 5-m resolution were acquired for Kisumu and Malindi, Kenya, during February and March 2001. All entomological parameters were collected from January to May 2001, June to August 2002, and June to August 2003. In a Poisson model specification, for Anopheles funestus Giles, shade was the best predictor, whereas substrate was the best predictor for Anopheles gambiae, and vegetation for Anopheles arabensis Patton. The top predictors found with a logistic regression model specification were habitat size for An. gambiae Giles, pollution for An. arabensis, and shade for An. funestus. All other coefficients for canopy, debris, habitat nature, permanency, emergent plants, algae, pollution, turbidity, organic materials, all MTI waveband frequencies, distance to the nearest house, distance to the nearest domestic animal, and all land use land cover changes were nonsignificant. MTI data at 5-m spatial resolution do not have an additional predictive value for mosquito counts when adjusted for field-based ecological data.