کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
3421497 | 1594047 | 2006 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
ایمنی شناسی و میکروب شناسی
میکروبیولوژی و بیوتکنولوژی کاربردی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Geographic information systems (GIS) and remote sensing were used to identify villages at high risk for sleeping sickness, as defined by reported incidence. Landsat Enhanced Thematic Mapper (ETM) satellite data were classified to obtain a map of land cover, and the Normalised Difference Vegetation Index (NDVI) and Landsat band 5 were derived as unclassified measures of vegetation density and soil moisture, respectively. GIS functions were used to determine the areas of land cover types and mean NDVI and band 5 values within 1.5Â km radii of 389 villages where sleeping sickness incidence had been estimated. Analysis using backward binary logistic regression found proximity to swampland and low population density to be predictive of reported sleeping sickness presence, with distance to the sleeping sickness hospital as an important confounding variable. These findings demonstrate the potential of remote sensing and GIS to characterize village-level risk of sleeping sickness in endemic regions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Transactions of the Royal Society of Tropical Medicine and Hygiene - Volume 100, Issue 4, April 2006, Pages 354-362
Journal: Transactions of the Royal Society of Tropical Medicine and Hygiene - Volume 100, Issue 4, April 2006, Pages 354-362
نویسندگان
Martin Odiit, Paul R. Bessell, Eric M. Fèvre, Tim Robinson, Jennifer Kinoti, Paul G. Coleman, Susan C. Welburn, John McDermott, Mark E.J. Woolhouse,