Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Year 2024, Volume: 11 Issue: 2, 346 - 371, 29.06.2024

Neel Chaminda Withanage , Shen Jingwei

https://doi.org/10.54287/gujsa.1466745

Cited By: 2

Abstract

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

Keywords

China, Nighttime Light, Prefectures Cities, Remote Sensing, SNPP-VIIRS, Urbanization

Thanks

The authors express their gratitude to the Earth Observation Group and Oak Ridge National Laboratory for providing freely available SNPP-VIIRS data.

References

• Cao, X. (2008). Research on the countermeasures for developing night tourism in Chinese cities. Exploration of Economic Issues, 8, 125-128. (in Chinese) https://doi.org/10.3969/j.issn.1006-2912.2008.08.025
• Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., & Wu, J. (2021). An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data, 13(3), 889-906. https://doi.org/10.5194/essd-13-889-2021
• Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., & Wu, J. (2020). An extended time-series (2000-2023) of global NPP-VIIRS-like nighttime light data [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/YGIVCD
• Dai, J., Dong, J., Yang, S., & Sun, Y., (2021). Identification method of urban fringe area based on spatial mutation characteristics. Journal of Geo-Information Science, 23(8), 1401–1421. https://doi.org/10.12082/dqxxkx.2021.200502
• Delmelle, E. C. (2015). Five decades of neighborhood classifications and their transitions: A comparison of four US cities, 1970–2010. Applied Geography, 57, 1–11. https://doi.org/10.1016/j.apgeog.2014.12.002
• Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., & Bright, E. (2009). A Global Poverty Map Derived from Satellite Data. Computers & Geosciences, 35(8), 1652-1660. https://doi.org/10.1016/j.cageo.2009.01.009
• Ellison, G., Glaeser, E. L., & Kerr, W. R. (2010). What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3), 1195-1213. https://doi.org/10.1257/aer.100.3.1195
• Fan, J., Ma, T., Zhou, C., Zhou, Y., & Xu, T. (2014). Comparative estimation of urban development in China’s cities using socioeconomic and DMSP/OLS night light data. Remote Sensing, 6(8), 7840-7856. https://doi.org/10.3390/rs6087840
• Feng, Z., Peng, J., & Wu, J. (2020). Using DMSP/OLS nighttime light data and K–means method to identify urban–rural fringe of megacities. Habitat International, 103, 102227. https://doi.org/10.1016/j.habitatint.2020.102227
• Grove, J. M., Cadenasso, M. L.,Pickett, S. T. A., Machlis, G. E., & Burch Jr. W. R. (2015). The Baltimore School of Urban Ecology. Yale University Press.
• Habitat, UN., Arctic Public Academy of Sciences, & FGUP “Russian State Scientific Research and Design Institute of Urbanistics”. (2006, April 26-27). ANNOTATION of the concept of the United Nations Human Settlements Programme (UN-HABITAT) project: “Sustainable Development of Cities in Arctic Region through the Improvement of their Engineering and Transportation Infrastructure and Personnel Training for Good Urban Governance”. In: 3rd SAO Meeting, Syktyvkar, Russia. https://hdl.handle.net/11374/681
• He, Z., Xu, S., Shen, W., Long, R., & Chen, H. (2017). Impact of urbanization on energy-related CO2 emission at different development levels: regional difference in China based on panel estimation. Journal of Cleaner Production, 140(Part 3), 1719-1730. https://doi.org/10.1016/j.jclepro.2016.08.155
• Hu, X., Qian, Y., Pickett, S. T. A., & Zhou, W. (2020). Urban mapping needs up-to-date approaches to provide diverse perspectives of current urbanization: A novel attempt to map urban areas with nighttime light data. Landscape Urban Planning, 195, 103709. https://doi.org/10.1016/j.landurbplan.2019.103709
• Huang, Y., Wu, C., Chen, M., Yang, J., & Ren, H., (2020). A Quantile Approach for Retrieving the “Core Urban-Suburban-Rural” (USR) Structure Based on Nighttime Light. Remote Sensing, 12(24), 4179. https://doi.org/10.3390/rs12244179
• Imhoff, M. L., Lawrence, W. T., Stutzer, D. C., & Elvidge, C. D. (1997). A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area. Remote Sensing of Environment, 61(3), 361-370. https://doi.org/10.1016/S0034-4257(97)00046-1
• Jia, W., Zhao, S., Zhang, X., Liu, S., Henebry, G. M., & Liu, L. (2021). Urbanization imprint on land surface phenology: The urban-rural gradient analysis for Chinese cities. Global Change Biology, 27(12), 2895-2904. https://doi.org/10.1111/gcb.15602
• Li, D., & Li, X. (2015). An overview on data mining of nighttime light remote sensing. Acta Geodesy and Cartography Sinica, 44(6), 591-601.
• Li, X., Gong, P., & Liang, L.A. (2015). A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of the Environment, 166, 78-90. https://doi.org/10.1016/j.rse.2015.06.007
• Lin, Z., Xu, H., & Huang, S. (2019). Monitoring of the Urban Expansion Dynamics in China's East Coast Using DMSP/OLS Nighttime Light Imagery. Journal of Geographical Information Science, 21(7), 1074-1085.
• Liu, S., Shi, K., & Wu, Y. (2022). Identifying and evaluating suburbs in China from 2012 to 2020 based on SNPP–VIIRS nighttime light remotely sensed data. International Journal of Applied Earth Observation and Geoinformation, 114, 103041. https://doi.org/10.1016/j.jag.2022.103041
• Liu, Z., He, C., Zhang, Q., Huang, Q., & Yang, Y. (2012). Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106(1), 62-72. https://doi.org/10.1016/j.landurbplan.2012.02.013
• Lu, L., Zhang, Y., & Luo, T. T. (2014). Difficulties and Strategies in the Process of Population Urbanization: A Case Study in Chongqing of China. Open Journal of Social Sciences, 2(12), 90-95. https://doi.org/10.4236/jss.2014.212013
• Luo, T., Zeng, J., Chen, W., Wang, Y., Gu, T., & Huang, C. (2023). Ecosystem services balance and its influencing factors detection in China: A case study in Chengdu-Chongqing urban agglomerations. Ecological Indicators, 151, 110330. https://doi.org/10.1016/j.ecolind.2023.110330
• Ma, M., Lang, Q., Yang, H., Shi, K., & Ge, W. (2020). Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data. Remote Sensing, 12(19), 3248. https://doi.org/10.3390/rs12193248
• Ma, Q., He, C., Wu, J., Liu, Z., Zhang, Q., & Sun, Z. (2014). Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data. Landscape Urban Planning, 130, 36-49. https://doi.org/10.1016/j.landurbplan.2014.06.009
• 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 Sensing of Environment, 124, 99-107. https://doi.org/10.1016/j.rse.2012.04.018
• Ma, T., Zhou, Y., Zhou, C., Haynie, S., Pei, T., & Xu, T. (2015). Night-time light derived estimation of spatiotemporal characteristics of urbanization dynamics using DMSP/OLS satellite data. Remote Sensing of Environment, 158, 453-464. https://doi.org/10.1016/j.rse.2014.11.022
• National Bureau of Statistics. (2000-2020). City Statistical Yearbook. China Statistics Press: Beijing, China (Accessed:17/09/2023). https://data.cnki.net/yearBook/single?id=N2022040095
• Pan, J., & Li, J. (2016). Estimate and Spatio-Temporal Dynamics of Electricity Consumption in China Based on DMSP/OLS Images. Geographical Research, 35(4), 627-638.
• Schneider, A., Friedl, M. A., & Potere. D. (2010). Mapping global urban areas using MODIS 500-m data: New methods and datasets based on urban ecoregions. Remote Sensing of Environment, 114(8), 1733-1746. https://doi.org/10.1016/j.rse.2010.03.003
• Shi, K., Chen, Y., Yu, B., Xu, T., Li, L., Huang, C., Liu, R., Chen, Z., & Wu, J. (2016). Urban Expansion and Agricultural Land Loss in China: A Multi-scale Perspective. Sustainability, 8(8), 790. https://doi.org/10.3390/su8080790
• Shi, K., Wu, Y., Liu, S., Chen, Z., Huang, C., & Cui, Y. (2023). Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data. GIScience & Remote Sensing, 60(1), 2161199. https://doi.org/10.1080/15481603.2022.2161199
• Su, Y, Chen, X., Wang, C., Zhang, H., Liao, J., Ye, 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. GIScience & Remote Sensing, 52(2), 218-238. https://doi.org/10.1080/15481603.2015.1007778
• Sun, Y., & Zhao, S. (2018). Spatiotemporal dynamics of urban expansion in 13 cities across the Jing-Jin-Ji urban agglomeration from 1978 to 2015. Ecological Indicators, 87, 302-313. https://doi.org/10.1016/j.ecolind.2017.12.038
• Thapa, R. B., & Murayama, Y. (2009). Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches. Remote Sensing, 1(3), 534-556. https://doi.org/10.3390/rs1030534
• Tian, Y. (2020). Mapping suburbs based on spatial interactions and effect analysis on ecological landscape change: A case study of Jiangsu province from 1998 to 2018, eastern China. Land, 9(5), 159. https://doi.org/10.3390/land9050159
• Tian Y., & Qian, J. (2021). Suburban identification based on multi-source data and landscape analysis of its construction land: A case study of Jiangsu Province, China. Habitat International, 118, 102459. https://doi.org/10.1016/j.habitatint.2021.102459
• United Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). New York: United Nations. https://population.un.org/wup/publications/Files/WUP2018-Report.pdf
• Wijesinghe, W. M. D. C., & Withanage, W. K. N. C. (2021). Detection of the changes in land use and land cover using remote sensing and GIS in Thalawa DS Division. Prathimana Journal, 14(1), 72–86
• Withanage, W. K. N. C., Mishra, P. K., & Jayasinghe, B. C. (2024). An Assessment of Spatio-temporal Land Use/Land Cover Dynamics Using Landsat Time Series Data (2008-2022) in Kuliyapitiya West Divisional Secretariat Division in Kurunagala District, Sri Lanka. Journal of Geospatial Surveying, 4(1), 12-23. https://doi.org/10.4038/jgs.v4i1.52
• Withanage, N. C., Shi, K., & Shen, J. (2023). Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data. Remote Sensing, 15(18), 4632. https://doi.org/10.3390/rs15184632
• Xiao, P., Wang, X., Feng, X., Zhang, X., & Yang, Y. (2014). Detecting China’s Urban Expansion over the Past Three Decades Using Nighttime Light Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4095-4106. https://doi.org/10.1109/JSTARS.2014.2302855
• Xu, T., Ma, T., Zhou, C., & Zhou, Y. (2014). Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP/OLS Night Light Data. Remote Sensing, 6(8), 7708-7731. https://doi.org/10.3390/rs6087708
• Yang, Y., Ma, M., Tan, C., & Li, W. (2017). Spatial Recognition of the Urban-Rural Fringe of Beijing Using DMSP/OLS Nighttime Light Data. Remote Sensing, 9(11), 1141. https://doi.org/10.3390/rs9111141
• Yu, B., Tang, M., Wu, Q., Yang, C., Deng, S., Shi, K., Peng, C., Wu, J., & Chen, Z. (2018). Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data. IEEE Geoscience and Remote Sensing Letters, 15(8), 1279-1283. https://doi.org/10.1109/LGRS.2018.2830797
• Yuh, Y. G., Tracz, W., Matthews, H. D., Turner, S. E. (2023). Application of machine learning approaches for land cover monitoring in northern Cameroon. Ecological Informatics, 74, 101955. https://doi.org/10.1016/j.ecoinf.2022.101955
• Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320-2329. https://doi.org/10.1016/j.rse.2011.04.032
• Zhang, H., Liang, C., & Pan, Y. (2022). Spatial Expansion of Built-Up Areas in the Beijing-Tianjin-Hebei Urban Agglomeration Based on Nighttime Light Data: 1992-2020. International Journal of Environmental Research and Public Health, 19(7), 3760. https://doi.org/10.3390/ijerph19073760
• Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., & Zhang, B. (2020). Development of a global 30 m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data, 12(3), 1625-1648. https://doi.org/10.5194/essd-12-1625-2020
• Zheng, Y., Tang, L., & Wang, H. (2021). An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328, 129488. https://doi.org/10.1016/j.jclepro.2021.129488
• Zhou, Q., Li, B., & Sun, B. (2008, July 3-11). Modelling spatio-temporal pattern of landuse change using multi-temporal remotely sensed imagery. In: Proceedings of the 21st Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2008, (Vol. 37, Part B7), (pp. 729-734), Beijing, China.
• Zhou, Y., Smith, S. J., Elvidge, C. D., Zhao, K., Thomson, A., & Imhoff, M. (2014). A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147, 173-185. https://doi.org/10.1016/j.rse.2014.03.004