Journal of Remote Sensing & GIS

Using GIS and remote sensing to identify environmental variables of malaria vector breeding sites in western Kenya

3^rd World Congress on GIS and Remote Sensing

September 20-21, 2017 Charlotte, USA

Sydney Neeley

Duke University, USA

Posters & Accepted Abstracts: J Remote Sensing & GIS

Abstract:

This study used Landsat 8 satellite imagery to identify environmental variables of households with malaria vector breeding sites in a malaria endemic rural district in Western Kenya. Understanding the influence of environmental variables on the distribution of malaria has been critical in the strengthening of malaria control programs. Satellite imagery has been used to extract environmental variables related to malaria vector habitat and transmission. Using remote sensing and GIS technologies, this study performed a land classification, NDVI, Tasseled Cap Wetness Index, and derived land surface temperature values of the study area and examined the significance of each variable in predicting the probability of a household with a mosquito breeding site with and without larvae. The findings of this study revealed that households with any potential breeding sites were characterized by higher moisture, higher vegetation density (NDVI) and in urban areas or roads. The results of this study also confirmed that land surface temperature was significant in explaining the presence of active mosquito breeding sites (P<0.000). The present study showed that freely available Landsat 8 imagery has limited use in deriving environmental characteristics of malaria vector habitats at the scale of the Bungoma East District in Western Kenya. Further understanding of how environmental variables influence mosquito habitats at a smaller geographic scale is necessary to accurately predict how malaria distributions could shift at village-level in response to climate change.

Biography :

Sydney Neeley holds a Master of Science from the Duke Global Health Institute and a certificate in Geographic Information Systems (GIS). Her research focuses on using a combination of satellite remote sensing technologies and Geographic Information Systems (GIS) to conduct statistical, spatial and geographic analyses of population health data. She continues to use geospatial analysis techniques as a crucial tool to monitor and predict suitable environmental and climatic conditions for disease vectors and conditions.