Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing（2019）
Abstract
Mapping urban dynamics at the global scale becomes a pressing task with the increasing pace of urbanization and its important environmental and ecological impacts. In this study, we proposed a new approach to mapping global urban areas from 2000 to 2012 by applying a region-growing support vector machine classifier and a bidirectional Markov random field model to time-series nighttime light data. ...
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Key words
Urban areas,Indexes,Earth,Support vector machines,Remote sensing,MODIS,Sociology
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