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Remote Sensing 2013

Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures

DOI: 10.3390/rs5073476

Qian Zhang,Karen C. Seto

Keywords: DMSP/OLS, urban growth, urbanization, accuracy assessment

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Abstract:

The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights (NTL) can identify different types of urbanization processes. Although data from DMSP/OLS NTL are increasingly used for the study of urban areas, to date there is no systematic assessment of how well these data identify different types of urban change. Here, we randomly select 240 sample locations distributed across all world regions to generate urbanization typologies with the DMSP/OLS NTL data and use Google Earth imagery to assess the validity of the NTL results. Our results indicate that where urbanization occurred, NTL have a high accuracy (93%) of characterizing these changes. There is also a relatively high error of commission (42%), where NTL identified urban change when no change occurred. This leads to an overestimation of urbanization by NTL. Our analysis shows that time series NTL data more accurately identifies urbanization in developed countries, but is less accurate in developing countries, suggesting the need to exert caution when using or interpreting NTL in developing countries.

References

[1]	Batty, M. The size, scale, and shape of cities. Science 2008, 319, 769–771.
[2]	Lankao, P.R.; Nychka, D.; Tribbia, J.L. Development and greenhouse gas emissions deviate from the “modernization” theory and “convergence” hypothesis. Clim. Res 2008, 38, 17–29.
[3]	Mumford, L. The City in History: Its Origins, Its Transformations, and Its Prospects; Harcourt, Brace & World: New York, NY, USA, 1961; p. 794.
[4]	Montgomery, M.R.; Stren, R.; Cohen, B.; Reed, H.E. Cities Transformed: Demographic Change and Its Implications in the Developing World; The National Academies Press: Washington, DC, USA, 2003; p. 552.
[5]	Weber, M. The City; Free Press: New York, NY, USA, 1966; p. 252.
[6]	Davis, J.C.; Henderson, J.V. Evidence on the political economy of the urbanization process. J. Urban Econ 2003, 53, 98–125.
[7]	Henderson, V. The urbanization process and economic growth: The so-what question. J. Econ. Growth 2003, 8, 47–71.
[8]	Schneider, A.; Friedl, M.A.; Potere, D. A new map of global urban extent from modis satellite data. Environ. Res. Lett 2009, 4, 044003.
[9]	Seto, K.C.; Fragkias, M.; Güneralp, B.; Reilly, M.K. A meta-analysis of global urban land expansion. PLoS One 2011, 6, e23777.
[10]	Baugh, K.; Elvidge, C.; Ghosh, T.; Ziskin, D. Development of a 2009 Stable Lights Product Using DMSP-OLS Data. Proceedings of the 30th Asia-Pacific Advanced Network Meeting, Hanoi, Vietnam, 9–10 August 2010; pp. 114–130.
[11]	Chen, X.; Nordhaus, W.D. Using luminosity data as a proxy for economic statistics. Proc. Natl. Acad. Sci. USA 2011, 108, 8589–8594.
[12]	Henderson, J.V.; Storeygard, A.; Weil, D.N. Measuring economic growth from outer space. Am. Econ. Rev 2012, 102, 994–1028.
[13]	Sutton, P.C.; Elvidge, C.D.; Ghosh, T. Estimation of gross domestic product at sub-national scales using nighttime satellite imagery. Int. J. Ecol. Econ. Stat 2007, 8, 5–21.
[14]	Sutton, P.; Roberts, D.; Elvidge, C.; Baugh, K. Census from heaven: An estimate of the global human population using night-time satellite imagery. Int. J. Remote Sens 2001, 22, 3061–3076.
[15]	Sutton, P.C.; Elvidge, C.; Obremski, T. Building and evaluating models to estimate ambient population density. Photogramm. Eng. Remote Sensing 2003, 69, 545–553.
[16]	Zhuo, L.; Ichinose, T.; Zheng, J.; Chen, J.; Shi, P.J.; Li, X. Modelling the population density of china at the pixel level based on DMSP/OLS non–radiance–calibrated night-time light images. Int. J. Remote Sens 2009, 30, 1003–1018.
[17]	Elvidge, C.D.; Imhoff, M.L.; Baugh, K.E.; Hobson, V.R.; Nelson, I.; Safran, J.; Dietz, J.B.; Tuttle, B.T. Night-time lights of the world: 1994–1995. ISPRS J. Photogramm 2001, 56, 81–99.
[18]	Elvidge, C.D.; Tuttle, B.T.; Sutton, P.C.; Baugh, K.E.; Howard, A.T.; Milesi, C.; Bhaduri, B.; Nemani, R. Global distribution and density of constructed impervious surfaces. Sensors 2007, 7, 1962–1979.
[19]	Lu, D.; Tian, H.; Zhou, G.; Ge, H. Regional mapping of human settlements in Southeastern China with multisensor remotely sensed data. Remote Sens. Environ 2008, 112, 3668–3679.
[20]	Ma, T.; Zhou, C.; Pei, T.; Haynie, S.; Fan, J. Quantitative estimation of urbanization dynamics using time series of dmsp/ols nighttime light data: A comparative case study from China’s cities. Remote Sens. Environ 2012, 124, 99–107.
[21]	Elvidge, C.; Safran, J.; Nelson, I.; Tuttle, B.; Ruth Hobson, V.; Baugh, K.; Dietz, J.; Erwin, E. Area and Positional Accuracy of Dmsp Nighttime Lights Data. In Remote Sensing and GIS Accuracy Assessment; Lunetta, R., Lyon, J., Eds.; CRC Press: Boca Raton, FL, USA, 2004; pp. 281–292.
[22]	Henderson, M.; Yeh, E.T.; Gong, P.; Elvidge, C.; Baugh, K. Validation of urban boundaries derived from global night-time satellite imagery. Int. J. Remote Sens 2003, 24, 595–609.
[23]	Levin, N.; Duke, Y. High spatial resolution night-time light images for demographic and socio-economic studies. Remote Sens. Environ 2012, 119, 1–10.
[24]	Small, C.; Elvidge, C.D.; Balk, D.; Montgomery, M. Spatial scaling of stable night lights. Remote Sens. Environ 2011, 115, 269–280.
[25]	Small, C.; Pozzi, F.; Elvidge, C.D. Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sens. Environ 2005, 96, 277–291.
[26]	Tuttle, B.T.; Anderson, S.J.; Sutton, P.C.; Elvidge, C.D.; Kim, B. It used to be dark here: Geolocation calibration of the defense meteorological satellite program operational linescan system. Photogramm. Eng. Remote Sensing 2013, 79, 287–297.
[27]	Liu, Z.; He, C.; Zhang, Q.; Huang, Q.; Yang, Y. Extracting the dynamics of urban expansion in china using DMSP-OLS nighttime light data from 1992 to 2008. Landsc. Urban Plan 2012, 106, 62–72.
[28]	Small, C.; Elvidge, C.D. Night on earth: Mapping decadal changes of anthropogenic night light in asia. Int. J. Appl. Earth Observ. Geoinf 2013, 22, 40–52.
[29]	Zhang, Q.; Seto, K.C. Mapping urbanization dynamics at regional and global scales using multi-temporal dmsp/ols nighttime light data. Remote Sens. Environ 2011, 115, 2320–2329.
[30]	Sutton, P.C.; Taylor, M.J.; Anderson, S.; Elvidge, C.D. Sociodemographic Characterization of Urban Areas Using Nighttime Imagery, Google Earth, Landsat, and “Social” Ground Truthing in Urban Remote Sensing. In Urban Remote Sensing; Weng, Q., Quattrochi, D.A., Eds.; CRC Press: Boca Raton, FL, USA, 2007; pp. 291–310.
[31]	Version 4 DMSP-OLS Nighttime Lights Time Series. Available online: http://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html (accessed on 29 March 2013).
[32]	Elvidge, C.D.; Ziskin, D.; Baugh, K.E.; Tuttle, B.T.; Ghosh, T.; Pack, D.W.; Erwin, E.H.; Zhizhin, M. A fifteen year record of global natural gas flaring derived from satellite data. Energies 2009, 2, 595–622.
[33]	Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 109, 16083–16088.
[34]	Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ 1991, 37, 35–46.
[35]	Bland, M. An Introduction to Medical Statistics, 3rd ed. ed.; Oxford University Press: Oxford, UK, 2000; p. 405.
[36]	Lalkhen, A.G.; McCluskey, A. Clinical tests: Sensitivity and specificity. Contin. Educ. Anaesth. Crit. Care Pain 2008, 8, 221–223.
[37]	Hastie, T.; Tibshirani, R.; Friedman, J.H. The Elements of Statistical Learning; Springer: New York, NY, USA, 2003; p. 552.
[38]	Liu, J.; Liu, M.; Zhuang, D.; Zhang, Z.; Deng, X. Study on spatial pattern of land-use change in china during 1995–2000. Sci. China Ser. D-Earth Sci 2003, 46, 373–384.
[39]	Liu, J.; Tian, H.; Liu, M.; Zhuang, D.; Melillo, J.M.; Zhang, Z. China’s changing landscape during the 1990s: Large-scale land transformations estimated with satellite data. Geophys. Res. Lett 2005, 32, L02405.
[40]	Elvidge, C.D.; Baugh, K.E.; Sutton, P.C.; Bhaduri, B.; Tuttle, B.T.; Ghosh, T.; Ziskin, D.; Erwin, E.H. Who’s in the Dark—Satellite Based Estimates of Electrification Rates. In Urban Remote Sensing; Yang, X., Ed.; John Wiley & Sons, Ltd: Chichester, UK, 2011; pp. 211–224.
[41]	Yep, E. Power problems threaten growth in India. The Wall Street Journal, 3, January, 2012.
[42]	Agency, I.E. World Energy Outlook 2011: Energy for All; International Energy Agency (IEA): Paris, France, 2011.
[43]	Remme, U.; Trudeau, N.; Graczyk, D.; Taylor, P. Technology Development Prospects for the Indian Power Sector; International Energy Agency (IEA): Paris, France, 2011.

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