| Summary: | <p>Abstract</p> <p>Background</p> <p>The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between <it>Culex quinquefasciatus </it>and <it>Anopheles gambiae </it>s.l. larval habitats and environmental parameters within Internally Displaced People (IDP) campgrounds in Gulu, Uganda. A total of 65 georeferenced aquatic habitats in various IDP camps were studied to compare the larval abundance of <it>Cx. quinquefasciatus </it>and <it>An. gambiae </it>s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m × 150 m grid cells stratified by levels of drainage. The LULC change was estimated over a period of 14 years. Poisson regression analyses and Moran's <it>I </it>statistics were used to model relationships between larval abundance and environmental predictors. Individual larval habitat data were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. Multispectral QuickBird imagery classification and DEM-based GIS methods were generated to evaluate stream flow direction and accumulation for identification of immature <it>Cx. quinquefasciatus </it>and <it>An. gambiae </it>s.l. and abundance.</p> <p>Results</p> <p>The main LULC change in urban Gulu IDP camps was non-urban to urban, which included about 71.5 % of the land cover. The regression models indicate that counts of <it>An. gambiae </it>s.l. larvae were associated with shade while <it>Cx. quinquefasciatus </it>were associated with floating vegetation. Moran's <it>I </it>and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of <it>Anopheles</it>; larvae, however, <it>Culex </it>are not consistently clustered. A stepwise negative binomial regression decomposed the immature <it>An. gambiae </it>s.l. data into empirical orthogonal bases. The data suggest the presence of roughly 11% to 28 % redundant information in the larval count samples. The DEM suggest a positive correlation for <it>Culex </it>(0.24) while for <it>Anopheles </it>there was a negative correlation (-0.23) for a local model distance to stream.</p> <p>Conclusion</p> <p>These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with <it>Culex </it>and <it>Anopheles </it>aquatic habitats.</p>
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