Multitemporal settlement and population mapping from Landsat using Google Earth Engine |
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| Institution: | 1. Department of Geography and Geoinformation Science, George Mason University, 4400 University Drive, MS 6C3, Fairfax, VA 22030, USA;2. Department of Electrical, Biomedical and Computer Engineering, University of Pavia, Italy;3. Department of Geography and Geosciences, University of Louisville, 213 Lutz Hall, Louisville, KY 40205 USA;4. Department of Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK;5. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA;6. Flowminder Foundation, 17177 Stockholm, Sweden;1. University of Turku, Department of Geography and Geology, 20014 Turku, Finland;1. Remote Sensing Research Centre, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, QLD 4072, Australia;2. WWF-Australia, 129 Margaret Street, Brisbane, QLD 4000, Australia;1. Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA;2. United States Geological Survey, 2255 N Gemini Drive, Suite 316, Flagstaff, AZ 86001, USA;3. Google Inc., 1600 Amphitheater Parkway, Mountain View, CA 94043, USA;4. Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA |
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| Abstract: | As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs. |
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| Keywords: | Landsat Multitemporal Population mapping Google Earth Engine Settlement mapping Urbanization Spatial demography |
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