Advertisement

Inter-calibration and Urban Light Index of DMSP-OLS Night-Time Data for Evaluating the Urbanization Process in Australian Capital Territory

  • Christopher D. Elvidge
  • Himangshu Kalita
  • Upasana Choudhury
  • Sufia Rehman
  • Bismay Ranjan Tripathy
  • Pavan Kumar
Chapter

Abstract

The magnification mechanism of human settlement is called urbanization, involving various other activities such as population transition, resource consumption, etc. leading to various growing patterns of urban augmentation. The assessment of such spatial pattern is crucial in developing a sustainable urban agglomeration. Night-time light (NTL) data is an important machination for such assessment and detailed monitoring. In this paper, DMSP-OLS (the Defence Meteorological Satellite Program/Operational Linescan Program) datasets have been used to assess the urban straggle of Australian Capital Territory (ACT) and to delineate the urban extent from 1992 to 2012, at an interval of 3 years. DMSP-OLS has the unique capability to detect synthetic lights from cities, towns, industrial sites, ports, etc. Moreover, using ARC GIS calligraphies, 20 random points were selected from the extracted area of interest (AOI). Pixels values of those 20 random points are derived from the given time series dataset (1992–2012). A regression value was extracted from each year by using a second-order polynomial equation. A polynomial regression model is also constructed by taking the regression values and the time series as the two variables, respectively. Urban light index (ULI) is also constructed for analysing the progression of urbanization in ACT from 1992 to 2012 with the help of a derived formula. Furthermore, a unit circle buffer zone having a radius of 20 km is established by taking the centre of each built-up zone as the focal point of the constructed buffer, to compare easily the rate of expansion of urbanization. The present paper indicates the growing potential of the DMSP-OLS night-time satellite data to define the urban light space information, which truly describes the attributes of urban sprawl, and to delineate the evolution of urban morphology and urban extension.

Keywords

Australian Capital Territory DMSP-OLS night-time data Inter-calibration Urban light index Urbanization 

References

  1. Australian Bureau of Statistics (ABS) (2017) Population projections, Australia, 2016 (base) to 2101. ABS cat. no. 3222.0. Canberra: ABSGoogle Scholar
  2. Cinzano P, Falchi F, Elvidge CD, Baugh KE (2001) The artificial sky brightness in Europe derived from DMSP satellite data. Preserving the astronomical sky, pp 95–102Google Scholar
  3. Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29:390–401CrossRefGoogle Scholar
  4. Elvidge CD, Baugh KE, Kihn EA, Kroehl HW, Davis ER (1997) Mapping city lights with nighttime data from the DMSP operational linescan system. Photogramm Eng Remote Sens 63(6):727–734Google Scholar
  5. Elvidge CD, Baugh KE, Dietz JB, Bland T, Sutton PC, Kroehl HW (1999) Radiance calibration of Dmsp-Ols low-light imaging data of human settlements. Remote Sens Environ 68(1):77–88CrossRefGoogle Scholar
  6. Elvidge CD, Tuttle BT, Sutton PC, Baugh KE, Howard AT, Milesi C, Bhaduri B, Nemani R (2007) Global distribution and density of constructed impervious surfaces. Sensors 7(9):1962–1979CrossRefGoogle Scholar
  7. Elvidge C, Ziskin D, Baugh K, Tuttle B, Ghosh T, Pack D, Erwin E, Zhizhin M (2009) A fifteen year record of global natural gas flaring derived from satellite data. Energies 2:595–622CrossRefGoogle Scholar
  8. Feng Y, Kugler J, Zak PJ (2002) Population growth, urbanisation and the role of government in China: a political economic model of demographic change. Urban Stud 39:2329–2343CrossRefGoogle Scholar
  9. Fujita M, Krugman P, Mori T (1999) On the evolution of hierarchical urban systems. Eur Econ Rev 43(2):209–251CrossRefGoogle Scholar
  10. Han J, Hayashi Y, Cao X, Imura H (2009) Application of an integrated system dynamics and cellular automata model for urban growth assessment: a case study of Shanghai, China. Landsc Urban Plan 91:133–141CrossRefGoogle Scholar
  11. He CY, Shi PJ, Li JG, Chen J, Pan YZ, Li J, Zhuo L, Toshiaki I (2006) Restoring urbanization process in China in the 1990s by using non-radiance calibrated DMSP/OLS nighttime light imagery and statistical data. Chin Sci Bull 51:1614–1620CrossRefGoogle Scholar
  12. He CY, Ma Q, Li T, Yang Y, Liu ZF (2012) Spatiotemporal dynamics of electric power consumption in Chinese Mainland from 1995 to 2008 modeled using DMSP/OLS stable nighttime lights data. J Geogr Sci 22:125–136CrossRefGoogle Scholar
  13. Henderson M, Yeh ET, Gong P, Elvidge C, Baugh K (2003) Validation of urban boundaries derived from global night-time satellite imagery. Int J Remote Sens 24(3):595–609CrossRefGoogle Scholar
  14. Huang X, Schneider A, Friedl MA (2016) Mapping sub-pixel urban expansion in China using Modis and Dmsp/Ols nighttime lights. Remote Sens Environ 175:92–108CrossRefGoogle Scholar
  15. Imhoff ML, Lawrence WT, Stutzer DC, Elvidge CD (1997) A technique for using composite DMSP/OLS ‘City Lights’ satellite data to map urban area. Remote Sens Environ 61:361–370CrossRefGoogle Scholar
  16. Letu H, Hara M, Tana G, Nishio F (2012) A saturated light correction method for Dmsp/Ols nighttime satellite imagery. IEEE Trans Geosci Remote Sens 50(2):389–396CrossRefGoogle Scholar
  17. Li SM (2004) Population migration and urbanization in China: a comparative analysis of the 1990 population census and the 1995 national one percent sample population survey. Int Migr Rev 38:655–685CrossRefGoogle Scholar
  18. Li Q, Linlin L, Weng Q, Xie Y, Guo H (2016) Monitoring urban dynamics in the Southeast U.S.A. using time-series Dmsp/Ols nightlight imagery. Remote Sens 8(7):578CrossRefGoogle Scholar
  19. Liu ZF, He CY, Zhang QF, Huang QX, Yang Y (2012) Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landsc Urban Plan 106:62–72CrossRefGoogle Scholar
  20. Liu X, Guohua H, Bin A, Xia L, Shi Q (2015) A normalized urban areas composite index (Nuaci) based on combination of Dmsp-Ols and Modis for mapping impervious surface area. Remote Sens 7(12):17168–17189CrossRefGoogle Scholar
  21. Lo CP (2001) Modelling the population of China using DMSP operational line scan system nighttime data. Photogramm Eng Remote Sens 67:1037–1047Google Scholar
  22. Lo CP (2002) Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime images. Ann Assoc Am Geogr 92:225–240CrossRefGoogle Scholar
  23. Lo CP (2010) Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime images. Ann Assoc Am Geogr 92(2):225–240CrossRefGoogle Scholar
  24. Lu D, Tian H, Zhou G, Ge H (2008) Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens Environ 112(9):3668–3679CrossRefGoogle Scholar
  25. Ma T, Zhou C, Pei T, Haynie S, Fan J (2012) 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 124:99–107CrossRefGoogle Scholar
  26. Mertes CM, Schneider A, Sulla-Menashe D, Tatem AJ, Tan B (2015) Detecting change in urban areas at continental scales with Modis data. Remote Sens Environ 158(0):331–347CrossRefGoogle Scholar
  27. Milesi C, Elvidge CD, Nemani RR, Running SW (2003) Assessing the impact of urban land development on net primary productivity in the southeastern United States. Remote Sens Environ 86:401–410CrossRefGoogle Scholar
  28. Miller JD, Knapp EE, Key CH, Skinner CN, Isbell CJ, Creasy RM, Sherlock JW (2009) Calibration and validation of the Relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens Environ 113:645–656CrossRefGoogle Scholar
  29. Owen TW (1998) Using DMSP-OLS light frequency data to categorize urban environments associated with US climate observing stations. Int J Remote Sens 19:3451–3456CrossRefGoogle Scholar
  30. Pandey B, Joshi PK, Seto KC (2013) Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. Int J Appl Earth Obs Geoinf 23:49–61CrossRefGoogle Scholar
  31. Runkui L, Zhipeng L, Wenju G, Wenjun D, Qun X, Xianfeng S (2014) Diurnal, seasonal, and spatial variation of PM2.5 in Beijing. Sci Bull 60(3):387–395Google Scholar
  32. Schneider A, Friedl MA, Potere D (2010) Mapping global urban areas using Modis 500-M data: new methods and datasets based on ‘urban ecoregions’. Remote Sens Environ 114(8):1733–1746CrossRefGoogle Scholar
  33. Shi K, Huang C, Yu B, Yin B, Huang Y, Wu J (2014) Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sens Lett 5(4):358–366CrossRefGoogle Scholar
  34. Shu S, Yu B, Wu J, Liu H (2011) Methods for deriving urban built-up area using night- light data: assessment and application. Remote Sens Technol Appl 26:169–176Google Scholar
  35. Small C (2002) A global analysis of urban reflectance. In: D.M.a.F.S.-E.C. Jurgens (ed) Proceedings of the third international symposium on remote sensing of urban areas. Istanbul, TurkeyGoogle Scholar
  36. Small, C. (2003). High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens Environ, 88(1–2), 170–186CrossRefGoogle Scholar
  37. Small C (2005) A global analysis of urban reflectance. Int J Remote Sens 26(4):661–681CrossRefGoogle Scholar
  38. Small C, Pozzi F, Elvidge CD (2005) Spatial analysis of global urban extent from DMSPOLS night lights. Remote Sens Environ 96:277–291CrossRefGoogle Scholar
  39. Small C, Elvidge CD, Balk D, Montgomery M (2011) Spatial scaling of stable night lights. Remote Sens Environ 115:269–280CrossRefGoogle Scholar
  40. Su Y, Chen X, Wang C, Zhang H, Liao J, Yuyao Y, Wang C (2015) A new method for extracting built-up urban areas using Dmsp-Ols nighttime stable lights: a case study in the Pearl River Delta, Southern China. Gisci Remote Sens 52(2):218–238CrossRefGoogle Scholar
  41. Sutton PC (2003) A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery. Remote Sens Environ 86:353–369CrossRefGoogle Scholar
  42. Sutton P, Roberts D, Elvidge C, Baugh K (2001) Census from Heaven: an estimate of the global human population using night-time satellite imagery. Int J Remote Sens 22(16):3061–3076CrossRefGoogle Scholar
  43. Tan L (2017) Australian states and territories: New South Wales, 2017, Geography & Environment/Geo/Env: Countries/Continents/Geo/Env: CC – Australia/Oceani. ISBN 9780994624710Google Scholar
  44. Welch R (1980) Monitoring urban-population and energy-utilization patterns from satellite data. Remote Sens Environ 9(1):1–9CrossRefGoogle Scholar
  45. Welch R, Zupko S (1980) Urbanized area energy-utilization patterns from Dmsp data. Photogramm Eng Remote Sens 46(2):201–207Google Scholar
  46. Wigmore L (1971) Canberra: history of Australia’s national capital. Dalton Publishing Company. ISBN 0-909906-06-8
  47. Yi K, Tani H, Li Q, Zhang J, Guo M, Bao Y, Wang X, Li J (2014) Mapping and evaluating the urbanization process in Northeast China using DMSP/OLS nighttime light data. Sensors 14(2):3207–3226CrossRefGoogle Scholar
  48. Zhang J, Zhu T, Kipen H, Wang G, Huang W, Rich D et al (2013) Cardiorespiratory biomarker responses in healthy young adults to drastic air quality changes surrounding the 2008 Beijing Olympics. Res Rep Health Eff Inst 174:5–174Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christopher D. Elvidge
    • 1
  • Himangshu Kalita
    • 2
  • Upasana Choudhury
    • 2
  • Sufia Rehman
    • 3
  • Bismay Ranjan Tripathy
    • 2
  • Pavan Kumar
    • 3
  1. 1.Applied Earth Sciences, National Oceanic and Atmospheric AdministrationStanford UniversityBoulderUSA
  2. 2.Remote Sensing and GISKumaun UniversityAlmoraIndia
  3. 3.Department of GeographyJamia Millia IslamiaNew DelhiIndia

Personalised recommendations