Abstract
Africa has been experiencing a rapid urbanization process, which may lead to an increase in unsustainable land use and urban poverty. Assessing the spatiotemporal characteristics of urbanization dynamics is especially important and needed for the sustainable development of Africa. Satellite-based nighttime light (NTL) data are widely used to monitor the dynamics of urban growth from global to local scales. In this study, urban growth patterns across Africa were analyzed and discussed using stable nighttime light datasets obtained from DMSP/OLS (the Defense Meteorological Satellite Program’s Operational Line-scan System) spanning from 1992 to 2013. We partitioned the nighttime lighting areas into three types (low, medium, and high) using thresholds derived from the Brightness Gradient (BG) method. Our results indicated that built-up areas in Africa have increased rapidly, particularly those areas with low nighttime lighting types. Countries with higher urbanization levels in Africa, like South Africa, Algeria, Egypt, Nigeria, and Libya, were leading the brightening trend. The distribution of nighttime lighting types was consistent with the characteristics of urban development, with high nighttime lighting types showed up at the urban center, whereas medium and low nighttime lighting types appeared in the urban-rural transition zone and rural areas respectively. The impacts of these findings on the future of African cities will be further proposed.
Highlights
Urbanization is often accompanied by growing populations, aggravating socioeconomic activities, and expanding built-up areas and infrastructures [1,2]
Results from this study provide an insight into the dynamics of urban growth across Africa and the problems of the rapid urbanization
As NOAA only released the Defense Meteorological Satellite Program (DMSP)/Operational Line-scan System (OLS) nighttime light (NTL) data from 1992 to 2013, we only studied the spatiotemporal characteristics of African urbanization in this period
Summary
Urbanization is often accompanied by growing populations, aggravating socioeconomic activities, and expanding built-up areas and infrastructures [1,2]. To measure and monitor urbanization dynamics from the local to the global scale, it is necessary to obtain a comprehensive understanding of landscape changes and their environmental effects. Statistical variables, such as socioeconomic and census data, are useful and auxiliary in urbanization studies [5,6]. Such data are usually absent or unavailable in many less-developed countries to be of help in monitoring urbanization dynamics [7]. Satellite data, with various resolutions at multiple spatiotemporal scales, has been extensively applied to study urbanization and its impacts on ecosystems [7,8,9]
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