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

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. British Ecological Society is collaborating with JSTOR to digitize, preserve and extend access to Journal of Animal Ecology. Summary 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.

Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results.

“…However, few studies relating vector distribution to satellite data have addressed the problem of spatial correlation in the data sets. A rare exception is a study undertaken by Kitron and others, 21 who undertook a spatial analysis of tsetse fly distribution in the Lambwe Valley of Kenya using Landsat Thematic Mapper (TM) imagery and a GIS. They found that using multiple regression analysis, they could explain 87% of the variance in fly density using several TM bands.…”

Modeling the Spatial Distribution of Mosquito Vectors for West Nile Virus in Connecticut, USA

“…An alternative approach is to use landscape variables derived from remote sensing satellites as predictors, with or without incorporating the effects of spatial dependence. Pertinent examples include vectors of Eastern equine encephalomyelitis (Moncayo et al 2000), tick vectors of Lyme disease (Brownstein et al 2003, Guerra et al 2001, Kitron et al 1996, sand fly vectors of leishmaniasis (Cross et al 1996, Elnaiem et al 2003, Miranda et al 1998, Thomson et al 1999, tse-tse fly vectors of African trypanosomiasis (Kitron et al 1996, Rogers 2000, and mosquito vectors of malaria (Beck et al 1994, Diuk-Wasser et al 2004, Thomson et al 1996, Wood et al 1991a,b, 1992. Of these models, however, only a few have been validated with an independent dataset (Beck et al 1997, Brownstein et al 2004).…”

Localization of kala-azar in the endemic region of Bihar, India based on land use/land cover assessment at different scales

“…Images obtained from these satellites have a relatively small scene sizes (60-176 km 2 ) and are therefore suitable for relatively small study areas. They have proved to be most useful for local landscape studies of vector habitats, breeding and/or resting sites, where all information is derived from a single satellite image (Kitron et al, 1996;Dister et al, 1997).…”