Abstract
Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.
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References
Archila Bustos M F, Hall O, Anderson M, 2015. Nighttime lights and population changes in Europe 1992–2012. Ambio, 44(7): 653–665. doi: https://doi.org/10.1007/s13280-015-0646-8
Bennett M M, Smith L C, 2017. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment, 192: 176–197. doi: https://doi.org/10.1016/j.rse.2017.01.005
Cao X, Wang J M, Chen J et al., 2014. Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data. International Journal of Applied Earth Observation and Geoinformation, 28: 193–200. doi: https://doi.org/10.1016/j.jag.2013.12.004
Ceola S, Laio F, Montanari A, 2015. Human-impacted waters: new perspectives from global high-resolution monitoring. Water Resources Research, 51(9): 7064–7079. doi: https://doi.org/10.1002/2015WR017482
Chen M X, Liu W D, Tao X L, 2013. Evolution and assessment on China’s urbanization 1960–2010: under-urbanization or over-urbanization? Habitat International, 38: 25–33. doi: https://doi.org/10.1016/j.habitatint.2012.09.007
Croft T A, 1978. Nighttime images of the earth from space. Scientific American, 239(1): 86–101. doi: https://doi.org/10.1038/scientificamerican0778-86
Elvidge C, Ziskin D, Baugh K et al., 2009. A fifteen year record of global natural gas flaring derived from satellite data. Energies, 2(3): 595–622. doi: https://doi.org/10.3390/en20300595
Fan P L, Qi J G, 2010. Assessing the sustainability of major cities in China. Sustainability Science, 5(1): 51–68. doi: https://doi.org/10.1007/s11625-009-0096-y
Fensholt R, Langanke T, Rasmussen K et al., 2012. Greenness in semi-arid areas across the globe 1981–2007: an Earth Observing Satellite based analysis of trends and drivers. Remote Sensing of Environment, 121: 144–158. doi: https://doi.org/10.1016/j.rse.2012.01.017
Granero M A S, Segovia J E T, Pérez J G, 2008. Some comments on Hurst exponent and the long memory processes on capital markets. Physica A: Statistical Mechanics and its Applications, 387(22): 5543–5551. doi: https://doi.org/10.1016/j.physa.2008.05.053
Gu Y Y, Qiao X N, Xu M J et al., 2019. Assessing the impacts of urban expansion on bundles of ecosystem services by Dmsp-Ols nighttime light data. Sustainability, 11(21): 5888. doi: https://doi.org/10.3390/su11215888
Hamed K H, Rao A R, 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4): 182–196. doi: https://doi.org/10.1016/S0022-1694(97)00125-X
Hsu F C, Baugh K E, Ghosh T et al., 2015. DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. Remote Sensing, 7(2): 1855–1876. doi: https://doi.org/10.3390/rs70201855
Hu Y N, Peng J, Liu Y X et al., 2017. Mapping development pattern in Beijing-Tianjin-Hebei urban agglomeration using DMSP/OLS nighttime light data. Remote Sensing, 9(7): 760. doi: https://doi.org/10.3390/rs9070760
Hurst H E, 1951. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116: 770–799.
Imhoff M L, Lawrence W T, Stutzer D C et al., 1997. A technique for using composite DMSP/OLS ‘city light’ satellite data to map urban area. Remote Sensing of Environment, 61(3): 361–370. doi: https://doi.org/10.1016/S0034-4257(97)00046-1
Jasinski T, 2019. Modeling electricity consumption using nighttime light images and artificial neural networks. Energy, 179: 831–842. doi: https://doi.org/10.1016/j.energy.2019.04.221
Jia T, Chen K, Wang J Y, 2017. Characterizing the growth patterns of 45 major metropolitans in Mainland China using DMSP/OLS data. Remote Sensing, 9(6): 571. doi: https://doi.org/10.3390/rs9060571
Jiang W G, Yuan L H, Wang W J et al., 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51: 117–126. doi: https://doi.org/10.1016/j.ecolind.2014.07.031
Jiapaer G, Liang S L, Yi Q X et al., 2015. Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecological Indicators, 58: 64–76. doi: https://doi.org/10.1016/j.ecolind.2015.05.036
Karmeshu N, 2012. Trend Detection in Annual Temperature and Precipitation Using the Mann-Kendall Test: A Case Study to Assess Climate Change on Select States in the Northeastern United States. Philadelphia, PA: University of Pennsylvania.
Kendall M G, 1975. Rank Correlation Methods (4th ed). London: Charles Griffin.
Li Q T, Lu L L, Weng Q H et al., 2016. Monitoring urban dynamics in the southeast U.S.A. using time-series DMSP/OLS nightlight imagery. Remote Sensing, 8(7): 578. doi: https://doi.org/10.3390/rs8070578
Li X, Li D R, 2014. Can night-time light images play a role in evaluating the Syrian Crisis?.. International Journal of Remote Sensing, 35(18): 6648–6661. doi: https://doi.org/10.1080/01431161.2014.971469
Li X, Ma R Q, Zhang Q L et al., 2019. Anisotropic characteristic of artificial light at night: systematic investigation with VIIRS DNB multi-temporal observations. Remote Sensing of Environment, 233: 111357. doi: https://doi.org/10.1016/j.rse.2019.111357
Li X C, Gong P, 2016. Urban growth models: progress and perspective. Science Bulletin, 61(21): 1637–1650. doi: https://doi.org/10.1007/s11434-016-1111-1
Li X C, Zhou Y Y, 2017. Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing, 38(21): 6030–6046. doi: https://doi.org/10.1080/01431161.2016.1274451
Liang W, Yang M, 2019. Urbanization, economic growth and environmental pollution: evidence from China. Sustainable Computing: Informatics and Systems, 21: 1–9. doi: https://doi.org/10.1016/j.suscom.2018.11.007
Lin G C S, 2007. Reproducing spaces of Chinese urbanisation: new city-based and land-centred urban transformation. Urban Studies, 44(9): 1827–1855. doi: https://doi.org/10.1080/2F00420980701426673
Liu L, Leung Y, 2015. A study of urban expansion of prefectural-level cities in South China using night-time light images. International Journal of Remote Sensing, 36(22): 5557–5575. doi: https://doi.org/10.1080/01431161.2015.1101650
Ma Q, He C Y, Wu J G et al., 2014. Quantifying spatiotemporal patterns of urban impervious surfaces in China: an improved assessment using nighttime light data. Landscape and Urban Planning, 130: 36–19. doi: https://doi.org/10.1016/j.landurbplan.2014.06.009
Ma T, Zhou C H, Pei T et al., 2012. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS night-time light data: a comparative case study from China’s cities. Remote Sensing of Environment, 124: 99–107. doi: https://doi.org/10.1016/j.rse.2012.04.018
Mandelbrot B B, Wallis J R, 1969. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 5(5): 967–988. doi: https://doi.org/10.1029/WR005i005p00967
Mann H B, 1945. Nonparametric tests against trend. Econometrica, 13(3): 245–259. doi: https://doi.org/10.2307/1907187
Milich L, Weiss E, 2000. GAC NDVI interannual coefficient of variation (CoV) images: ground truth sampling of the Sahel along north-south transects. International Journal of Remote Sensing, 21(2): 235–260. doi: https://doi.org/10.1080/014311600210812
Propastin P, Kappas M, 2012. Assessing satellite-observed night-time lights for monitoring socioeconomic parameters in the Republic of Kazakhstan. GIScience & Remote Sensing, 49(4): 538–557. doi: https://doi.org/10.2747/1548-1603.49.4.538
Qian B, Rasheed K, 2004. Hurst exponent and financial market predictability. In: Proceedings of the 2nd IASTED International Conference on Financial Engineering and Applications. Cambridge, MA, USA: MIT, 203–209.
Román M O, Wang Z S, Sun Q S et al., 2018. NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment, 210: 113–143. doi: https://doi.org/10.1016/j.rse.2018.03.017
Sen P K, 1968. Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63(324): 1379–1389. doi: https://doi.org/10.1080/01621459.1968.10480934
Theil H, 1992. A rank-invariant method of linear and polynomial regression analysis. In: Advanced Studies in Theoretical and Applied Econometrics. (Vol. 23). Dordrecht: Springer, 345–381. doi: https://doi.org/10.1007/978-94-011-2546-8_20
Tripathy B R, Tiwari V, Pandey V et al., 2017. Estimation of urban population dynamics using DMSP-OLS night-time lights time series sensors data. IEEE Sensors Journal, 17(4): 1013–1020. doi: https://doi.org/10.1109/JSEN.2016.2640181
Wei Y D, Ye X Y, 2014. Urbanization, urban land expansion and environmental change in China. Stochastic Environmental Research and Risk Assessment, 28(4): 757–765. doi: https://doi.org/10.1007/s00477-013-0840-9
Xin X, Liu B, Di K C et al., 2017. Monitoring urban expansion using time series of night-time light data: a case study in Wuhan, China. International Journal of Remote Sensing, 38(21): 6110–6128. doi: https://doi.org/10.1080/01431161.2017.1312623
Xu P F, Wang Q, Jin J et al., 2019. An increase in nighttime light detected for protected areas in mainland China based on VIIRS DNB data. Ecological Indicators, 107: 105615. doi: https://doi.org/10.1016/j.ecolind.2019.105615
Xu P F, Jin P B, Cheng Q, 2020. Monitoring regional urban dynamics using DMSP/OLS nighttime light data in Zhejiang province. Mathematical Problems in Engineering, 2020: 9652808. doi: https://doi.org/10.1155/2020/9652808
Yang P, Xia J, Zhang Y Y et al., 2017. Temporal and spatial variations of precipitation in Northwest China during 1960–2013. Atmospheric Research, 183: 283–295. doi: https://doi.org/10.1016/j.atmosres.2016.09.014
Yi K P, Tani H, Li Q et al., 2014. Mapping and evaluating the urbanization process in northeast China using DMSP/OLS nighttime light data. Sensors, 14(2): 3207–3226. doi: https://doi.org/10.3390/s140203207
Yi K P, Zeng Y, Wu B F, 2016. Mapping and evaluation the process, pattern and potential of urban growth in China. Applied Geography, 71: 44–55. doi: https://doi.org/10.1016/j.apgeog.2016.04.011
Yin Z M, Li X, Tong F et al., 2020. Mapping urban expansion using night-time light images from Luojia1-01 and International Space Station. International Journal of Remote Sensing, 41(7): 2603–2623. doi: https://doi.org/10.1080/01431161.2019.1693661
Zhang Q, Seto K C, 2013. Can night-time light data identify typologies of urbanization?. A global assessment of successes and failures. Remote Sensing, 5(7): 3476–3494. doi: https://doi.org/10.3390/rs5073476
Zhang Q W, Su S L, 2016. Determinants of urban expansion and their relative importance: a comparative analysis of 30 major metropolitans in China. Habitat International, 58: 89–107. doi: https://doi.org/10.1016/j.habitatint.2016.10.003
Zhao N, Jiao Y M, Ma T et al., 2019. Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China. Science of the Total Environment, 688: 1005–1015. doi: https://doi.org/10.1016/j.scitotenv.2019.06.374
Zheng Q M, Zeng Y, Deng J S et al., 2017. ‘Ghost cities’ identification using multi-source remote sensing datasets: A case study in Yangtze River Delta. Applied Geography, 80: 112–121. Doi: https://doi.org/10.1016/j.apgeog.2017.02.004
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Xu, P., Lin, M. & Jin, P. Spatio-temporal Dynamics of Urbanization in China Using DMSP/OLS Nighttime Light Data from 1992–2013. Chin. Geogr. Sci. 31, 70–80 (2021). https://doi.org/10.1007/s11769-020-1169-1
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DOI: https://doi.org/10.1007/s11769-020-1169-1