Capturing the Urban Divide in Nighttime Light Images From the International Space Station

Earlier studies utilizing coarse resolution DMSP-OLS nighttime light (NTL) imagery suggest a negative correlation between the amount of NTL and urban deprivation. The International Space Station (ISS) NTL images offer higher resolution images compared to DMSP-OLS or VIIRS images, allowing an analysis of intraurban NTL variations. The aim of this study is to examine the capacity of ISS images for analyzing the intraurban divide. NTL images of four cities (one African, two Asian, and one South American) have been processed and analyzed. The results show that deprived areas are generally the darker spots of built-up areas within cities, illustrating the urban divide in terms of access to street lighting. However, differences exist between cities: Deprived areas in the African city (Dar es Salaam) generally feature lower NTL emissions compared to the examined cities in South America (Belo Horizonte) and Asia (Mumbai and Ahmedabad). Beyond, variations exist in NTL emissions across deprived areas within cities. Deprived areas at the periphery show less NTL compared to central areas. Edges of deprived areas have higher NTL emissions compared to internal areas. NTL emission differences between types of deprived areas were detected. The correlation between ISS NTL images and population densities is weak; this can be explained by densely built-up deprived areas having less NTL compared to lower density formal areas. Our findings show ISS data complement other data to capture the urban divide between deprived and better-off areas and the need to consider socioeconomic conditions in estimating populations.

[1]  Luuk Boelens,et al.  Self-organization in urban development: towards a new perspective on spatial planning , 2011 .

[2]  Michael Wurm,et al.  Ich weiß, dass ich nichts weiß – Bevölkerungsschätzung in der Megacity Mumbai , 2015 .

[3]  Karin Pfeffer,et al.  Matching deprivation mapping to urban governance in three Indian mega-cities , 2009 .

[4]  Yang Yang,et al.  Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review , 2014, Remote. Sens..

[5]  H. Taubenböck,et al.  The similar size of slums , 2018 .

[6]  Statistical Office,et al.  Population Census 2011 , 2011 .

[7]  Christopher D. Elvidge,et al.  Dark Times: nighttime satellite imagery as a detector of regional disparity and the geography of conflict , 2017 .

[8]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[9]  Monika Kuffer,et al.  The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery , 2014, Comput. Environ. Urban Syst..

[10]  Monika Kuffer,et al.  Slums from Space - 15 Years of Slum Mapping Using Remote Sensing , 2016, Remote. Sens..

[11]  Chenghu Zhou,et al.  GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery , 2017, Remote. Sens..

[12]  David Zhang,et al.  Two-stage image denoising by principal component analysis with local pixel grouping , 2010, Pattern Recognit..

[13]  H. Taubenböck,et al.  The physical face of slums: a structural comparison of slums in Mumbai, India, based on remotely sensed data , 2014 .

[14]  Alfred Stein,et al.  Uncertainty analysis for image interpretations of urban slums , 2016, Comput. Environ. Urban Syst..

[15]  Richard Sliuzas,et al.  The risk of impoverishment in urban development-induced displacement and resettlement in Ahmedabad , 2015 .

[16]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[17]  Un-Habitat.  State of the World's Cities 2010/11 : Cities for All: Bridging the Urban Divide , 2010 .

[18]  Jerome P. Baggett.  Habitat For Humanity , 2000 .

[19]  Jonathan Hersh,et al.  Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[20]  P Horstman,et al.  Habitat for Humanity. , 2001, The American journal of nursing.

[21]  Jürgen Fischer,et al.  High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges , 2014, Remote. Sens..

[22]  Zhenfeng Shao,et al.  The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012 , 2017, Remote. Sens..

[23]  Yuyu Zhou,et al.  Urban mapping using DMSP/OLS stable night-time light: a review , 2017, Remote Sensing of Night-time Light.

[24]  Hannes Taubenböck,et al.  Slum mapping in polarimetric SAR data using spatial features , 2017 .

[25]  Jinpei Ou,et al.  Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data , 2015, PloS one.

[26]  Y. Yamagata,et al.  Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data , 2015 .

[27]  Zhe Zhu,et al.  Monitoring urban expansion using time series of night-time light data: a case study in Wuhan, China , 2017, Remote Sensing of Night-time Light.

[28]  Monika Kuffer,et al.  Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia , 2017, Remote. Sens..

[29]  Jianping Wu,et al.  Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Monika Kuffer,et al.  Extraction of Slum Areas From VHR Imagery Using GLCM Variance , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  H. Taubenböck,et al.  The morphology of the Arrival City - A global categorization based on literature surveys and remotely sensed data , 2018 .

[32]  Christopher Doll,et al.  Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery , 2010 .

[33]  Hannes Taubenböck,et al.  Digital deserts on the ground and from space , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[34]  H. Taubenböck,et al.  Detecting social groups from space – Assessment of remote sensing-based mapped morphological slums using income data , 2018 .

[35]  John S. Gulliver,et al.  Dasymetric modelling of small-area population distribution using land cover and light emissions data , 2007 .

[36]  Noam Levin,et al.  High spatial resolution night-time light images for demographic and socio-economic studies , 2012 .

[37]  Christopher Small,et al.  Night on Earth: Mapping decadal changes of anthropogenic night light in Asia , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Monika Kuffer,et al.  Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai , 2017, Remote. Sens..

[39]  Masanao Hara,et al.  A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[40]  B. Portnov,et al.  Outdoor light and breast cancer incidence: a comparative analysis of DMSP and VIIRS-DNB satellite data , 2017, Remote Sensing of Night-time Light.

[41]  C. Elvidge,et al.  Why VIIRS data are superior to DMSP for mapping nighttime lights , 2013 .

[42]  Sebastian Aleksandrowicz,et al.  Impervious surface detection with nighttime photography from the International Space Station , 2016 .

[43]  Andrew J. Tatem,et al.  Identifying residential neighbourhood types from settlement points in a machine learning approach , 2018, Comput. Environ. Urban Syst..