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

Published on Feb 1, 2018in Resources Conservation and Recycling7.04
· DOI :10.1016/j.rse.2017.11.026
Ran Goldblatt7
Estimated H-index: 7
(UCSD: University of California, San Diego),
Michelle Stuhlmacher2
Estimated H-index: 2
(ASU: Arizona State University)
+ 8 AuthorsRobert C. Balling29
Estimated H-index: 29
(ASU: Arizona State University)
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.
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