Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa

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dc.contributor.author Adeola, Abiodun Morakinyo
dc.contributor.author Olwoch, Jane Mukarugwiza
dc.contributor.author Botai, Joel Ongego
dc.contributor.author Rautenbach, Cornelis Johannes de Wet
dc.contributor.author Kalumba, Ahmed M.
dc.contributor.author Tsela, Philemon Lehlohonolo
dc.contributor.author Adisa, O.M. (Omolola)
dc.contributor.author Nsubuga, Francis Wasswa Nkugwa
dc.date.accessioned 2016-06-20T08:52:16Z
dc.date.issued 2017
dc.description.abstract The 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_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.embargo 2017-12-30
dc.description.librarian hb2016 en_ZA
dc.description.sponsorship The EU project QWeCI (Quantifying Weather and Climate Impacts on health in developing countries) and the European Commission’s Seventh Framework Research Programme under the [grant number 243964]). en_ZA
dc.description.uri http://www.tandfonline.com/loi/rsag20 en_ZA
dc.identifier.citation A.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. en_ZA
dc.identifier.issn 0373-6245 (print)
dc.identifier.issn 2151-2418 (online)
dc.identifier.other 10.1080/03736245.2015.1117012
dc.identifier.uri http://hdl.handle.net/2263/53266
dc.language.iso en en_ZA
dc.publisher Routledge en_ZA
dc.rights © 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. en_ZA
dc.subject Remote sensing en_ZA
dc.subject Geographic information system (GIS) en_ZA
dc.subject Environmental en_ZA
dc.subject Malaria en_ZA
dc.subject Normalized difference vegetation index (NDVI) en_ZA
dc.subject Normalized difference water index (NDWI) en_ZA
dc.subject Land surface temperature (LST) en_ZA
dc.subject Multi-criteria evaluation (MCE) en_ZA
dc.title Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa en_ZA
dc.type Postprint Article en_ZA


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