Combining measurements of built-up area, nighttime light, and travel time distance for detecting changes in urban boundaries: Introducing the BUNTUS algorithm

Muhammad Luqman, Peter J. Rayner, Kevin R. Gurney

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Article number2969
JournalRemote Sensing
Volume11
Issue number24
DOIs
StatePublished - Dec 1 2019

Keywords

  • Climate
  • Google earth engine
  • Land cover
  • Machine learning
  • Nightlight
  • Remote sensing
  • Urban areas

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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