Adeola, Abiodun MorakinyoOlwoch, Jane MukarugwizaBotai, Joel OngegoRautenbach, Cornelis Johannes de WetKalumba, Ahmed M.Tsela, Philemon LehlohonoloAdisa, O.M. (Omolola)Nsubuga, Francis Wasswa Nkugwa2016-06-202017A.M. Adeola, J.M. Olwoch, J.O. Botai, C.J. deW Rautenbach, A.M. Kalumba, P.L. Tsela, O.M. Adisa & F.W.N. Nsubuga (2015): Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa, South African Geographical Journal, 99(1): 14-28. DOI: 10.1080/03736245.2015.1117012.0373-6245 (print)2151-2418 (online)10.1080/03736245.2015.1117012http://hdl.handle.net/2263/53266The advancement, availability and high level of accuracy of satellite data provide a unique opportunity to conduct environmental and epidemiological studies using remotely sensed measurements. In this study, information derived from remote sensing data is used to determine breeding habitats for Anopheles arabiensis which is the prevalent mosquito species over Nkomazi municipality. In particular, we have utilized the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) coupled with land surface temperature (LST) derived from Landsat 5 TM satellite data. NDVI, NDWI and LST are considered as key environmental factors that influence the mosquito habitation. The breeding habitat was derived using multi-criteria evaluation (MCE) within ArcGIS using the derived environmental metric with appropriate weight assigned to them. Additionally, notified malaria cases were analysed and spatial data layers of water bodies, including rivers and dams, were buffered to further illustrate areas at risk of malaria. The output map from the MCE was then classified into three classes which are low, medium and high areas. The resulting malaria risk map depicts that areas of Komatieport, Malelane, Madadeni and Tonga of the district are subjected to high malaria incidence. The time series analysis of environmental metrics and malaria cases can help to provide an adequate mechanism for monitoring, control and early warning for malaria incidence.en© 2015 Society of South African Geographers. This is an electronic version of an article published in South African Geographical Journal, vol. 99, no. 1, pp. 14-28, 2017. doi : 10.1080/03736245.2015.1117012. South African Geographical Journal is available online at : http://www.tandfonline.com/loi/rsag20.Remote sensingGeographic information system (GIS)EnvironmentalMalariaNormalized difference vegetation index (NDVI)Normalized difference water index (NDWI)Land surface temperature (LST)Multi-criteria evaluation (MCE)Postprint Article