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

• Published in: Remote sensing of environment 2018-02, Vol.205, p.253-275
• Main Authors: Goldblatt, Ran, Stuhlmacher, Michelle F., Tellman, Beth, Clinton, Nicholas, Hanson, Gordon, Georgescu, Matei, Wang, Chuyuan, Serrano-Candela, Fidel, Khandelwal, Amit K., Cheng, Wan-Hwa, Balling, Robert C.
• Format: Article
• Language: English
• Subjects: Built-up land cover; Cities; Classification; Data integration; Fusion; Google Earth Engine; Image classification; Land; Land cover; Land use; Landsat; Landsat satellites; Learning algorithms; Multisensor fusion; Night; Nighttime; Nighttime light; Pixels; Remote sensing; Studies; Urban areas; Urban environments; Urbanization
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2017.11.026

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 30m 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. •An approach is proposed to map built-up land cover at a large geographical scale.•Our data fusion approach utilizes nighttime-lights data and Landsat imagery.•The approach overcomes the lack of extensive ground-reference data for urban research.•Hexagonal tessellation partition improves classification of heterogeneous land cover.•High quality maps of built-up LC are produced for 3 geographically diverse countries.