Abstract
Abstract. Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.
Highlights
Urban areas are places that provide their residents with a relatively high quality of life in terms of housing, services, employment and consumption (Pfeffer et al, 2010)
Urban growth in African cities is often coupled with rising poverty, whose nature is not adequately captured by a simple urban-rural dichotomy (Coulter et al, 2016)
4.1 Accuracy of land cover classification Accuracies of classification derived from Pleiades image are presented in (Table 3)
Summary
Urban areas are places that provide their residents with a relatively high quality of life in terms of housing, services, employment and consumption (Pfeffer et al, 2010). Expansion of the economic base such as, higher per capita income and an increase in number of working persons creates demand for new buildings, infrastructure and housing (Paek, 2006). Such expansion in response to the continued increase need of land utilisation triggered by population agglomeration and societal needs encourages many developers to rapidly construct new houses and infrastructure (Bhatta, 2010). In the face of rapid urbanisation African cities often lack the data and capacity to document the rapid urban change. Use is often made of field-based monitoring or urban change, which is considered accurate, it is generally inefficient, expensive and time consuming for assessment of areas with a large spatial extent
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