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Spatial Analysis of the Distribution of Tsetse Flies in the Lambwe Valley, Kenya, Using Landsat TM Satellite Imagery and GIS

U. Kitron, L. H. Otieno, L. L. Hungerford, A. Odulaja, W. U. Brigham, O. O. Okello, M. Joselyn, M. M. Mohamed-Ahmed and E. Cook
Journal of Animal Ecology
Vol. 65, No. 3 (May, 1996), pp. 371-380
DOI: 10.2307/5883
Stable URL: http://www.jstor.org/stable/5883
Page Count: 10
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Spatial Analysis of the Distribution of Tsetse Flies in the Lambwe Valley, Kenya, Using Landsat TM Satellite Imagery and GIS
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Abstract

1. Satellite imagery, geographic information systems (GIS) and spatial statistics provide tools for studies of population dynamics of disease vectors in association with habitat features on multiple spatial scales. 2. Tsetse flies were collected during 1988-90 in biconical traps located along transects in Ruma National Park in the Lambwe Valley, western Kenya. Fine spatial resolution data collected by Landsat Thematic Mapper (TM) satellite and reference ground environmental data were integrated in a GIS to identify factors associated with local variations of fly density. 3. Statistical methods of spatial autocorrelation and spatial filtering were applied to determine spatial components of these associations. Strong positive spatial associations among traps occurred within transects and within the two ends of the park. 4. From satellite data, TM band 7, which is associated with moisture content of soil and vegetation, emerged as being consistently highly correlated with fly density. Using several spectral bands in a multiple regression, as much as 87% of the variance in fly catch values could be explained. 5. When spatial filtering was applied, a large component of the association between fly density and spectral data was shown to be the result of other determinants underlying the spatial distributions of both fly density and spectral values. Further field studies are needed to identify these determinants. 6. The incorporation of remotely sensed data imagery into a GIS with ground data on fly density and environnmental conditions can be used to predict favourable fly habitats in inaccessible sites, and to determine number and location of fly suppression traps in a local control programme.

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