Multitemporal settlement and population mapping from Landsat using Google Earth Engine
Multitemporal settlement and population mapping from Landsat using Google Earth Engine
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.
landsat, multitemporal, population mapping, google earth engine, settlement mapping, urbanization, spatial demography
199-208
Patel, Nirav N.
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Angiuli, Emanuele
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Gamba, Paolo
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Gaughan, Andrea
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Lisini, Gianni
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Stevens, Forrest R.
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Tatem, Andrew J.
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Trianni, Giovanna
5f08e949-1ba0-451b-bbbc-df75e48dfaff
March 2015
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Angiuli, Emanuele
9e1f5e28-3200-4315-878e-7c282f0a847c
Gamba, Paolo
342785e2-aa22-47fe-be4f-346072c3ab7b
Gaughan, Andrea
5925174b-06f5-4434-b945-7c9927ff8215
Lisini, Gianni
c9441233-7959-4e24-8ca8-4632d09ea548
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Trianni, Giovanna
5f08e949-1ba0-451b-bbbc-df75e48dfaff
Patel, Nirav N., Angiuli, Emanuele, Gamba, Paolo, Gaughan, Andrea, Lisini, Gianni, Stevens, Forrest R., Tatem, Andrew J. and Trianni, Giovanna
(2015)
Multitemporal settlement and population mapping from Landsat using Google Earth Engine.
International Journal of Applied Earth Observation and Geoinformation, 35, part B, .
(doi:10.1016/j.jag.2014.09.005).
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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More information
e-pub ahead of print date: 28 September 2014
Published date: March 2015
Keywords:
landsat, multitemporal, population mapping, google earth engine, settlement mapping, urbanization, spatial demography
Organisations:
Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 369520
URI: http://eprints.soton.ac.uk/id/eprint/369520
ISSN: 0303-2434
PURE UUID: a062f4f1-d0aa-481c-bc04-42118d1b682d
Catalogue record
Date deposited: 30 Sep 2014 10:25
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
Nirav N. Patel
Author:
Emanuele Angiuli
Author:
Paolo Gamba
Author:
Andrea Gaughan
Author:
Gianni Lisini
Author:
Forrest R. Stevens
Author:
Giovanna Trianni
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