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Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.

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Abstract.A simulation experiment was carried out to analyse the e ects of the modulation transfer function on our ability to estimate the proportions of land cover within a pixel by linear mixture modelling. In the simulated landscape the proportion of each land cover type in every pixel was known exactly. The standard error of the estimate (SEE) between percentages derived from mixture modelling and the actual land cover percentages was 11%. Substantial improvements in estimating the percentages can be obtained simply by deriving estimates for pixels of twice the original dimensions, the SEE dropping to 4.16%, though this is with the obvious consequence of a nal product with a coarser spatial resolution. Alternatively by deconvolving the input bands using a linear approximation of the point spread function the SEE can be reduced by almost as much, namely to 5.11%. If we combine the two approaches, by rst doconvolving the bands, estimating the percentages and then aggregating resultant pixels to twice their original linear dimensions, the SEE drops to 2.24%.

Abstract. An operational atmospheric correction algorithm for Thematic Mapper (TM) imagery has been developed for both sequential and parallel computer environments considering both aerosol and molecular scattering and absorption. The aerosol optical depth is estimated from the image itself using the dark object approach on a movingwindow basis, and the surface reflectance is then retrieved by searching lookup tables that are created using a numerical radiative transfer code. The dark object pixels are identified and their surface reflectance estimated using TM channel 7 (2.1 /am Atmospheric effects include molecular and aerosol scattering and absorption by gases, such as water vapor, ozone, oxygen, and aerosols. Molecular scattering and absorption by ozone and oxygen are relatively easy to correct because their concentrations are quite stable over both time and space. The effect of water vapor absorption is significant for the TM nearinfrared (IR) channels, but there is insufficient information to allow us to estimate water vapor content from TM imagery unless other information is provided. The most disturbing and difficult component of atmospheric correction is to eliminate the effect of aerosols. Aerosol scattering increases apparent surface reflectance over dark objects and it (i.e., scattering) reduces the apparent surface reflectance over bright objects Algorithm DescriptionThe basic principle of this atmospheric correction method is based on radiative transfer theory. For a plane-parallel atmosphere bounded by a Lambertian surface, the radiance at the top of the atmosphere can be expressed as pFaT Zm -Zo + ,r(1 -sp)where L o is the upward radiance of the atmosphere for zero surface reflectance, often called path radiance, p is the surface reflectance, F a is the downward flux at the ground, T is the 17,173