Improving AfriPop dataset with settlement extents extracted from RapidEye for the border region comprising South-Africa, Swaziland and Mozambique

  • Julie Deleu | julie.deleu@eurosense.com EUROSENSE, Wemmel, Belgium.
  • Jonas Franke Remote Sensing Solutions GmbH, Baierbrunn, Germany.
  • Michael Gebreslasie School of Agricultural, Earth and Environmental Sciences, University KwaZulu-Natal, Durban, South Africa.
  • Catherine Linard Biological Control and Spatial Ecology Lab, Université Libre de Bruxelles, Belgium.

Abstract

For modelling the spatial distribution of malaria incidence, accurate and detailed information on population size and distribution are of significant importance. Different, global, spatial, standard datasets of population distribution have been developed and are widely used. However, most of them are not up-to-date and the low spatial resolution of the input census data has limitations for contemporary, national- scale analyses. The AfriPop project, launched in July 2009, was initiated with the aim of producing detailed, contemporary and easily updatable population distribution datasets for the whole of Africa. High-resolution satellite sensors can help to further improve this dataset through the generation of high-resolution settlement layers at greater spatial details. In the present study, the settlement extents included in the MALAREO land use classification were used to generate an enhanced and updated version of the AfriPop dataset for the study area covering southern Mozambique, eastern Swaziland and the malarious part of KwaZulu-Natal in South Africa. Results show that it is possible to easily produce a detailed and updated population distribution dataset applying the AfriPop modelling approach with the use of high-resolution settlement layers and population growth rates. The 2007 and 2011 population datasets are freely available as a product of the MALAREO project and can be downloaded from the project website.

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Published
2015-11-04
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Original Articles
Keywords:
Population mapping, Health, Malaria risk, Remote sensing, Southern Africa
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How to Cite
Deleu, J., Franke, J., Gebreslasie, M., & Linard, C. (2015). Improving AfriPop dataset with settlement extents extracted from RapidEye for the border region comprising South-Africa, Swaziland and Mozambique. Geospatial Health, 10(2). https://doi.org/10.4081/gh.2015.336