Abstract
This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions.
Highlights
IntroductionThe United Nations Department of Economic and Social Affairs [1] reported that 55.3% of the world population lives in urban areas
Urban areas are the hub of economic activities and, of fossil fuel emissions [2]
The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concluded that urban areas generate the majority of carbon emissions from final energy use [5,6]
Summary
The United Nations Department of Economic and Social Affairs [1] reported that 55.3% of the world population lives in urban areas. Bettencourt and West, (2010) [3] found a relationship between urbanisation and the economy They stated that it requires only 85% more infrastructure when one doubles the population in an urban area. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concluded that urban areas generate the majority of carbon emissions from final energy use [5,6]. It is not well understood how carbon footprints are distributed among cities. Any global study of such relationships requires a consistent definition of urban extent, which is the task of this paper
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