Skip to main content
SpringerLink
Log in

Extraction and Evolution Analysis of Urban Built-Up Areas in Beijing, 1984–2018

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

Accurate extraction of the boundaries of urban built-up areas (UBA) based on remote sensing and GIS technology is extremely important for predicting urban spatial evolution. Therefore, this study proposes a method for extracting urban built-up areas based on the impervious surface aggregation density (ISAD) of an object and uses the long-time series of the built-up area extraction results to analyze the spatial and temporal evolution characteristics of Beijing cities. The results show that the precision for extracting UBA using this method is 91.3%, which is better than that of existing methods based on supervised classification. The extraction results can also accurately depict the urban form and maintain the integrity of the built-up areas. Built-up areas in Beijing expanded from 1984 to 2018, with the most significant expansion in 2014–2018. In these 5 years, the expansion speed and intensity of Beijing respectively reached 270.70 km2/a and 1.65%, which were 2.43 times that of the entire research period. Built-up areas in Beijing have been expanding past their original limits over time in a ‘centralized concentric circle’ expansion model. The focus of the UBA moved northeast by 11.56 km. In the past 35 years, the contours of Beijing’s urban borders have become increasingly complex, and the spatial form of built-up areas has become increasingly discrete. The analysis of the spatial evolution of UBA is beneficial for more sustainable, compact and coordinated urban development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Cai, J., Huang, B., & Song, Y. (2017). Using multi-source geospatial big data to identify the structure of polycentric cities. Remote Sensing of Environment, 202, 210–221. https://doi.org/10.1016/j.rse.2017.06.039.

    Article  Google Scholar 

  • Deng, L., Shen, Z., & Ke, Y. (2018). Built-up area extraction and urban expansion analysis based on remote sensing images. Journal of Geo-Information Science, 20(07), 996–1003.

    Google Scholar 

  • Dong, C., Zhang, J., Niu, L., Li, K., Zhang, B., & Jin, L. (2017). Research on the change of urban development land based on land cover of national geographic conditions: A case study of Lanzhou new area. Science of Surveying and Mapping, 42(02), 28–34.

    Google Scholar 

  • Fei, W., & Zhao, S. (2019). Urban land expansion in China’s six megacities from 1978 to 2015. Science of the Total Environment, 664, 60–71. https://doi.org/10.1016/j.scitotenv.2019.02.008.

  • Guindon, B., Zhang, Y., & Dillabaugh, C. (2004). Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sensing of Environment, 92(2), 218–232. https://doi.org/10.1016/j.rse.2004.06.015.

    Article  Google Scholar 

  • Li, W. (2013). Study on extraction methods of impervious surface information extraction from urban area using remote sensing. Shanxi: North University of China.

  • Li, G., & Li, F. (2019). Urban sprawl in China: Differences and socioeconomic drivers. Science of the Total Environment, 673, 367–377. https://doi.org/10.1016/j.scitotenv.2019.04.080.

    Article  Google Scholar 

  • Li, X., Gong, P., & Liang, L. (2015). A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 166, 78–90. https://doi.org/10.1016/j.rse.2015.06.007.

    Article  Google Scholar 

  • Li, H., Li, L., Zhang, T., & Chen, L. (2019). Mapping and characterizing the Spatio-temporal heterogeneity of impervious surface in Xuzhou urban area. Resources and Environment in the Yangtze Basin, 28(03), 668–680.

    Google Scholar 

  • Liu, Z., Liu, S., Qi, W., & Jin, H. (2018). Urban sprawl among Chinese cities of different population sizes. Habitat International, 79, 89–98. https://doi.org/10.1016/j.habitatint.2018.08.001.

    Article  Google Scholar 

  • Liu, C., Huang, X., Zhu, Z., Chen, H., Tang, X., & Gong, J. (2019). Automatic extraction of built-up area from ZY3 multi-view satellite imagery: Analysis of 45 global cities. Remote Sensing of Environment, 226, 51–73. https://doi.org/10.1016/j.rse.2019.03.033.

    Article  Google Scholar 

  • Martin, D. (1996). An assessment of surface and zonal models of population. International Journal of Geographical Information Science, 10(8), 973–989.

    Google Scholar 

  • Meng, F., & Liu, M. (2013). Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan, China. International Journal of Remote Sensing, 34(24), 8838–8853.

    Article  Google Scholar 

  • Meng, Q., Zhang, L., Sun, Z., Meng, F., Wang, L., & Sun, Y. (2018). Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China. Remote Sensing of Environment, 204, 826–837. https://doi.org/10.1016/j.rse.2017.09.019.

    Article  Google Scholar 

  • Monkkonen, P., Comandon, A., Montejano Escamilla, J. A., & Guerra, E. (2018). Urban sprawl and the growing geographic scale of segregation in Mexico, 1990–2010. Habitat International, 73, 89–95. https://doi.org/10.1016/j.habitatint.2017.12.003.

    Article  Google Scholar 

  • Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, 10.

  • Nengroo, Z. A., Bhat, M. S., & Kuchay, N. A. (2017). Measuring urban sprawl of Srinagar city, Jammu and Kashmir, India. Journal of Urban Management, 6(2), 45–55. https://doi.org/10.1016/j.jum.2017.08.001.

    Article  Google Scholar 

  • Newsreport, U. P. (2002). Urban residential area planning and design specification GB50180-93(implemented on April 1, 2002). Urban Planning Newsreport, 08, 6–11.

    Google Scholar 

  • Nguyen, L. H., Nghiem, S. V., & Henebry, G. M. (2018). Expansion of major urban areas in the US Great Plains from 2000 to 2009 using satellite scatterometer data. Remote Sensing of Environment, 204, 524–533. https://doi.org/10.1016/j.rse.2017.10.004.

    Article  Google Scholar 

  • Ning, X., Wang, H., Zhang, H., Liu, Y., Pang, B., & Hao, M. (2018). High-precision urban boundary extraction and urban sprawl spatial-temporal analysis in China's prefectural cities from 2000 to 2016. Geomatics and Information Science of Wuhan University, 43(12), 1916–1926.

    Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66.

    Article  Google Scholar 

  • Shi, K., Chang, H., Yu, B., Bing, Y., Huang, Y., & Wu, J. (2014). Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4–6), 358–366.

    Article  Google Scholar 

  • Shi, L., Zhang, Z., Liu, F., Zhao, X., Liu, B., Xu, J., et al. (2015). Spatial expansion remote sensing monitoring of special economic zones from 1973 to 2013. Journal of Remote Sensing, 19(06), 1030–1039.

    Google Scholar 

  • Sonde, P., Balamwar, S., & Ochawar, R. S. (2020). Urban sprawl detection and analysis using unsupervised classification of high resolution image data of Jawaharlal Nehru port trust area in India. Remote Sensing Applications: Society and Environment, 17, 100282. https://doi.org/10.1016/j.rsase.2019.100282.

    Article  Google Scholar 

  • Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, 1–13. https://doi.org/10.1016/j.rse.2015.12.027.

    Article  Google Scholar 

  • Tian, L., Li, Y., Yan, Y., & Wang, B. (2017). Measuring urban sprawl and exploring the role planning plays: A shanghai case study. Land Use Policy, 67, 426–435. https://doi.org/10.1016/j.landusepol.2017.06.002.

    Article  Google Scholar 

  • Tong, L., & Hu, S. (2016). Characterizations of urban sprawl in major Chinese cities. Resources Science, 38(01), 50–61.

    Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.

    Article  Google Scholar 

  • Wang, P. P. (2017). National residents’ income increased steadily in 2016, and residents’ consumption further improved. National Bureau of Statistics of China. http://www.stats.gov.cn/tjst/sjjd/201701/t20170120_1456174.html.

  • Wang, S., & Zhang, H. (2019). A method of urban built-up area boundary recognition based on vehicle track data. Bulletin of Surveying and Mapping (01), 56-59+64.

  • Wang, X., Liu, J., Zhuang, D., & Wang, L. (2005). Spatial-temporal changes of urban spatial morphology in China. Acta Geographica Sinica (03), 392–400.

  • Wang, L., Meng, Q., Wu, J., Zhang, J., & Zhang, L. (2015). Monitoring and analyzing spatio-temporal changes of heat island intensity in Beijing main urban construction area from 2005 to 2014. Journal of Geo-information Science, 17(09), 1047–1054.

    Google Scholar 

  • Wei, X., Chen, Z., Zhang, L., Jiang, P., & Wu, F. (2019). Improvement of the evaluation method for ecosystem service value with interference effect of urban expansion. Resources and Environment in the Yangtze Basin, 28(01), 30–38.

    Google Scholar 

  • Xie, W., Huang, Q., He, C., & Zhao, X. (2018). Projecting the impacts of urban expansion on simultaneous losses of ecosystem services: A case study in Beijing, China. Ecological Indicators, 84, 183–193. https://doi.org/10.1016/j.ecolind.2017.08.055.

    Article  Google Scholar 

  • Xiong, C., & Dou, X. (2018). Research on Chang Analysis in Urban Spatial Pattern Based on the Result of Geographical Condition Census. Bulletin of Surveying and Mapping (09), 117–120.

  • Xu, H. (2005). A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing (05), 589-595.

  • Xu, Z., & Gao, X. (2016). A novel method for identifying the boundary of urban built-up areas with POI data. Acta Geographica Sinica, 71(06), 928–939.

    Google Scholar 

  • Yan, M., & Huang, J. (2013). Review on the research of urban spatial expansion. Progress in Geography, 32(07), 1039–1050.

    Google Scholar 

  • Yang, R., & Zhang, X. (1997). A study on the impetus mechanism and models of urban spatial expansion. Areal Research and Development (02), 2-5+22.

  • Yang, T., Kuang, W., Liu, W., Liu, A., & Pan, T. (2017). Optimizing the layout of eco-spatial structure in Guanzhong urban agglomeration based on the ecological security pattern. Geographical Research, 36(03), 441–452.

    Google Scholar 

  • Yin, C. L., Meng, F., Xu, Y. N., Yang, X. Y., Xing, H. Q., & Fu, P. J. (2020). Developing urban built-up area extraction method based on land surface emissivity differences. Infrared Physics & Technology, 110, 103475. https://doi.org/10.1016/j.infrared.2020.103475.

    Article  Google Scholar 

  • Yu, X., Li, Q., & Yang, C. (2018). Remote sensing monitoring of urban space expansion in Chongqing's main urban area. Geospatial Information, 16(01), 68–70+76+68.

  • Zeng, C., Liu, Y., Stein, A., & Jiao, L. (2015). Characterization and spatial modeling of urban sprawl in the Wuhan metropolitan area, China. International Journal of Applied Earth Observation and Geoinformation, 34, 10–24. https://doi.org/10.1016/j.jag.2014.06.012.

    Article  Google Scholar 

  • Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.

    Article  Google Scholar 

Download references

Acknowledgments

This work is founded by Shandong Natural Science Foundation (ZR2018MD008), the Open Research Funding Program of KLGIS (KLGIS2016A01), the National Natural Science Foundation of China [41271413] and the National Natural Science Foundation of China [41801308]. We also would like to thank the Centre for Earth Observation and Digital Earth of the Chinese Academy of Sciences, who kindly provided the Landsat 8 imagery used in this study, within the framework of a sharing program for earth observation data.

Author Contributions Statement

Conceptualization: Chenglong Yin, Fei Meng; Methodology: Chenglong Yin, Fei Meng, Lin Guo, Yuxuan Zhang, Zhan Zhao; Formal analysis and investigation: Chenglong Yin, Fei Meng, Huaqiao Xing, Guobiao Yao; Writing - original draft preparation: Chenglong Yin, Fei Meng, Lin Guo; Writing - review and editing: Chenglong Yin, Fei Meng, Yuxuan Zhang, Guobiao Yao; Funding acquisition: Fei Meng, Huaqiao Xing; Resources: Fei Meng; Supervision: Fei Meng.

Author information

Authors and Affiliations

  1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China

    C. L. Yin, F. Meng, L. Guo, Y. X. Zhang, Z. Zhao, H. Q. Xing & G. B. Yao

Authors
  1. C. L. Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. X. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Z. Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. H. Q. Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. G. B. Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Meng.

Ethics declarations

Disclosures

The authors have no relevant financial interests in the manuscript and no other potential conflicts of interest to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, C.L., Meng, F., Guo, L. et al. Extraction and Evolution Analysis of Urban Built-Up Areas in Beijing, 1984–2018. Appl. Spatial Analysis 14, 731–753 (2021). https://doi.org/10.1007/s12061-021-09374-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-021-09374-7

Keywords

Search

Navigation