Understanding local variability in malaria transmission risk is critically important when designing intervention or vaccine trials. Using a combination of field data, satellite image analysis, and GIS modeling, we developed a high-resolution map of malaria entomological inoculation rates (EIR) in The Gambia, West Africa. The analyses are based on the variation in exposure to malaria parasites experienced in 48 villages in 1996 and 21 villages in 1997. The entomological inoculation rate (EIR) varied from 0 to 166 infective bites per person per rainy season. Detailed field surveys identified the major Anopheles gambiae s.l. breeding habitats. These habitats were mapped by classification of a LANDSAT TM satellite image with an overall accuracy of 85%. Village EIRs decreased as a power function based on the breeding areas size and proximity. We use this relationship and the breeding habitats to map the variation in EIR over the entire 2500-km(2) study area.

The Centre for Ecology and Hydrology (CEH) has recently developed a per-parcel classification procedure which integrates remotely sensed imagery with digital cartography. The aim of the work reported here was to compare the per-parcel approach with a conventional per-pixel classification through examination of maximum likelihood class probabilities. An Airborne Thematic Mapper (ATM) image with 1.25 m spatial resolution was used as a source of training data. The ATM image was spatially degraded to 10 m resolution to form the remotely sensed input and the study area was subdivided into land parcels by manual digitizing. The per-parcel approach extracted raster data from a core area described by a shrunken version of a land parcel, thus eliminating mixed boundary pixels. Boundary pixels were shown to be more variable than the core pixels and their removal improved classification confidence. The per-parcel approach calculated the mean spectral response of the extracted core pixels. This was shown to remove within-parcel variation and improve classification confidence in comparison to per-pixel class allocations. A parcel-based representation was shown to be most appropriate for mapping agricultural land cover in comparison to seminatural areas, because agricultural landscapes have an inherent parcel structure. When land cover is heterogeneous, as in many semi-natural areas, a per-pixel classification would appear to be more appropriate. A hybrid classifier, which could switch between per-parcel and per-pixel mapping, was suggested as a powerful land cover mapping tool.

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