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TitleThe Use of Radar Remote Sensing for Identifying Environmental Factors Associated with Malaria Risk in Coastal Kenya
DownloadDownloads (Preprint)
 
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorKaya, S; Pultz, T J; Mbogo, C M; Beier, J C; Mushinzimana, E
SourceIGARSS 2002, Toronto, Canada, June 24-28; 2002., https://doi.org/10.4095/219902 Open Access logo Open Access
Year2002
Alt SeriesEarth Sciences Sector, Contribution Series 20043100
Documentbook
Lang.English
Mediapaper; on-line; digital
File formatpdf
Released2002 01 01
AbstractMalaria remains one of the greatest killers of human beings, particularly in the developing world. The World Health Organization has estimated that over one million cases of Malaria are reported each year, with more than 80% of these found in Sub-Saharan Africa. The anopheline mosquito transmits malaria, and breeds in areas of shallow surface water that are suitable to the mosquito and parasite development. These environmental factors can be detected with satellite imagery, which provide enhanced spatial and temporal coverage of most of the earth's surface. The combined use of remote sensing and GIS provides an effective tool for monitoring environmental conditions that are conducive to malaria, and mapping the disease risk to human populations.

Since many vector-borne diseases such as malaria are prevalent in tropical areas, persistent cloud cover often presents a challenge to remote sensing operations. Radar remote sensing has the capability of penetrating clouds, providing a solution to the cloud-cover problem often experienced with optical satellite remote sensing. This research investigates the use of RADARSAT-1 data for monitoring and mapping malaria risk in coastal Kenya. An object-oriented approach to image classification is taken in order to circumvent some of the limitations of traditional pixel-based classification of radar imagery. GIS routines are used to assess how classified land cover variables relate to the presence and abundance of malaria-carrying mosquitoes and their proximity to populated areas, in order to generate a malaria risk map.

GEOSCAN ID219902

 
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