The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates. Urbanization has profound social, environmental and epidemiological implications and makes spatial and quantitative estimations of urban change, population density and socio-economic characteristics valuable information for epidemiology and vulnerability assessment. The performance of urban expansion models largely depends on the quality and type of data available, which have so far been limited, and reduced the confidence and the applications of models for Africa. Satellite remote sensing offers an effective solution for mapping settlements and monitoring urbanization at different spatial and temporal scales and allows the link to empirical observations with urban theory. Moreover, remote sensing data have a great potential to map and predict intra-urban variations in population density because they provide information on the morphology of different residential patterns that can be linked to different population densities and socio-economic parameters.
The general objective of the project is to improve our spatial understanding, prediction and forecast of urbanization and urban population in Africa through the use of remote sensing and spatial modelling. The project addresses two specific objectives:
Produce an urban expansion model at moderate spatial resolution for African cities. This objective will be achieved through i) using HRRS and VHRRS (both optical and radar) to delineate urban extent of a set of cities, compare the accuracy and limitations of the obtained features and optimize the method based on HRRS, ii) using of the best methods identified using HRRS to generate a database of land cover change to urban over the last 30 years across a large number of cities in Africa, iii) using this database to build urban expansion models, evaluate their forecasting accuracy, and apply them to forecast the future distribution of the major urban extents in Africa.
Understand and predict intra-urban variations in human population density in Africa. This will be achieved through i) using HRRS and VHRRS to predict human population density within the urban extent of a set of cities, compare the accuracy and limitations of the RS products, and optimize the method based on HRRS, ii) using VHRRS to analyse and to understand the drivers of changes in human population density within cities; iii) using the best methods based on HRRS data to generate a database of human population density within urban extent across a large number of cities in Africa.
The two objectives will be achieved using satellite images at two different spatial resolutions: HR (~30 m) and VHR (< 5m). HR analyses will be automatized in order to be applied to a large set of 50 African cities, whereas 3 cities will be selected as case studies for the detailed VHR analyses. SAR and optical data will be combined to delineate urban extents at different time periods in order to develop urban expansion models and multi-temporal population distribution datasets. Fused SAR and optical data will be used to improve the extraction of the urban segments in different residential patterns and to improve the intra-urban population density maps.
The project brings together four partners, ULB-LUBIES (high resolution RS, human population mapping, urban models), SIC-RMA (SAR processing, data & image fusion), ULB-ANAGEO (VHRRS, urban mapping, geography of urbanization), and the DGE-US (leaders of the WorldPop consortium). In addition to its own set of objectives, the project aims to contribute to the AfriPop/WorldPop project (www.worldpop.org.uk), which should ensure a rapid dissemination of its deliveries. The Afripop/Worldpop project is an international initiative that has developed over the last 5 years to produce 100-m resolution maps of human population at global scale, and linking the University of Southampton (DGE-US), the University of Florida and ULB-LUBIES.