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Remote Sens. 2017, 9(7), 713; https://doi.org/10.3390/rs9070713

Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013

1
Malaria Elimination Initiative, Global Health Group, UCSF, San Francisco, CA 94158, USA
2
Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
*
Author to whom correspondence should be addressed.
Received: 7 February 2017 / Revised: 3 May 2017 / Accepted: 5 July 2017 / Published: 11 July 2017
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

Abstract

Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by the National Oceanic and Atmospheric Administration, offers a unique vantage point for studying trends in urbanization. A well-documented deficiency of this dataset is the lack of intra- and inter-annual calibration between satellites, which makes the imagery unsuitable for temporal analysis in their raw format. Here we have generated an ‘intercalibrated’ time series of annual NTL images for Africa (2000–2013) by building on the widely used invariant region and quadratic regression method (IRQR). Gaussian process methods (GP) were used to identify NTL latent functions independent from the temporal noise signals in the annual datasets. The corrected time series was used to explore the positive association of NTL with Gross Domestic Product (GDP) and urban population (UP). Additionally, the proportion of change in ‘lit area’ occurring in urban areas was measured by defining urban agglomerations as contiguously lit pixels of >250 km2, with all other pixels being rural. For validation, the IRQR and GP time series were compared as predictors of the invariant region dataset. Root mean square error values for the GP smoothed dataset were substantially lower. Correlation of NTL with GDP and UP using GP smoothing showed significant increases in R2 over the IRQR method on both continental and national scales. Urban growth results suggested that the majority of growth in lit pixels between 2000 and 2013 occurred in rural areas. With this study, we demonstrated the effectiveness of GP to improve conventional intercalibration, used NTL to describe temporal patterns of urbanization in Africa, and detected NTL responses to environmental and humanitarian crises. The smoothed datasets are freely available for further use. View Full-Text
Keywords: Nighttime lights; urbanization; socio-economic indicators; DMSP-OLS; NPP-VIIRS; global public health; Africa Nighttime lights; urbanization; socio-economic indicators; DMSP-OLS; NPP-VIIRS; global public health; Africa
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Savory, D.J.; Andrade-Pacheco, R.; Gething, P.W.; Midekisa, A.; Bennett, A.; Sturrock, H.J.W. Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013. Remote Sens. 2017, 9, 713.

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