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 language | English (US) |
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Article number | 2969 |
Journal | Remote Sensing |
Volume | 11 |
Issue number | 24 |
DOIs | |
State | Published - Dec 1 2019 |
Externally published | Yes |
Keywords
- Climate
- Google earth engine
- Land cover
- Machine learning
- Nightlight
- Remote sensing
- Urban areas
ASJC Scopus subject areas
- General Earth and Planetary Sciences