Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover

Ran Goldblatt, Michelle F. Stuhlmacher, Beth Tellman, Nicholas Clinton, Gordon Hanson, Matei Georgescu, Chuyuan Wang, Fidel Serrano-Candela, Amit K. Khandelwal, Wan Hwa Cheng, Robert Balling

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.

Original languageEnglish (US)
Pages (from-to)253-275
Number of pages23
JournalRemote Sensing of Environment
Volume205
DOIs
StatePublished - Feb 2018

Keywords

  • Built-up land cover
  • Google Earth Engine
  • Image classification
  • Nighttime light
  • Urbanization

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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