Journal of Geographic Information System, 2015, 7, 226-257 Published Online April 2015 in SciRes. http://www.scirp.org/journal/jgis http://dx.doi.org/10.4236/jgis.2015.72019 How to cite this paper: Ahmed, K.M.S., Hamid, A.A. and Doka, A. (2015) Investigation of Spatial Risk Factors for RVF Dis- ease Occurrence Using Remote Sensing & GIS—A Case Study: Sinnar State, Sudan. Journal of Geographic Information Sys- tem, 7, 226-257. http://dx.doi.org/10.4236/jgis.2015.72019 Investigation of Spatial Risk Factors for RVF Disease Occurrence Using Remote Sensing & GIS—A Case Study: Sinnar State, Sudan Kowther Mohamed Saeed Ahmed 1 , Amna Ahmed Hamid 2 , Abbas Doka 3 1 Remote Sensing & Geographical Information System Master Program, Sudan Academic of Sciences (SAS), Khartoum, Sudan 2 Remote Sensing & Geographical Information System Program, Remote Sensing Authority-National Center for Research, Khartoum, Sudan 3 College of Agricultural Studies, Sudan University of Science & Technology, Khartoum, Sudan Email: kowthersaeed@yahoo.com , amnaah71@gmail.com , adoka21@gmail.com Received 4 February 2015; accepted 18 April 2015; published 22 April 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and nega- tive implications on human and animal health. This study was conducted to verify the factors which influenced the spatial pattern of Rift Valley Fever occurrence and identified the high risk areas for the occurrence of the disease at Sinner State, Sudan. The normalized difference vegeta- tion index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall data in addition to the point data of RVF clinical cases in humans were used in this study. In order to identify the RVF high risk areas, remote sensing data and rainfall data were in- tegrated in a GIS with other information including, soil type, water body, DEM (Digital Elevation Model), and animal routes and analyzed using Spatial Analysis tools. The information on clinical cases was used for verification. The Normalized Difference Vegetation Index (NDVI) was used to describe vegetation patterns of the study area by calculating the mean NDVI. The results of the study showed that, RVF risk increased with the increase in vegetation cover (high NDVI values), and increase in rainfall, which both provided suitable conditions for disease vectors breeding and a good indicator for RVF epizootics. The study concluded that, identification of high risk area for RVF disease improved the understanding of the spatial distribution of the disease and helped in locating the areas where disease was likely to be endemic and therefore preparedness measures should be taken. The identification represents the first step of prospective predictions of RVF out- breaks and provides a baseline for improved early warning, control, response planning, and miti- gation. Further detailed studies are recommended in this domain.