Abstract
Accurate extraction of urban built-up areas is an essential prerequisite of urbanization research. This paper proposes a local optimal threshold method based on NPP-VIIRS nighttime light images to extract urban built-up areas in mainland China. First, according to the regional economic differences, the image is divided into eight regions. Second, the maximum variance ratio threshold method and particle swarm optimization algorithm are combined to extract the optimal threshold for segmenting the night light images in each region. The optimal threshold for each region is used to extract the urban built-up areas from the NPP-VIIRS images of each region. Finally, eight urban built-up areas are merged. In order to verify the extraction accuracy, one city-level city and one county-level city are extracted in each region, and the boundaries of urban built-up areas are compared with a Google Earth historical image from June 15, 2015. The experimental results show that the kappa for the eight selected city-level cities (Shanghai, Chengdu, Lanzhou, Changsha, Changzhi, Tianjin, Shenzhen, Harbin) is approximately 0.80. The kappa for the eight selected county-level cities (Baoying, Luoping, Fuhai, Dongxiang, Dongsheng, Miyun, Longchuan, Shuangyang) is approximately 0.75. The method exhibits high precision for large-scale urban built-up area extraction without the aid of auxiliary data. The experimental results indicate that light intensity can reflect the development level of a city.
Similar content being viewed by others
Availability of Data and Material
All data generated or analyzed during this study are included in this published article.
Code Availability
Not applicable.
References
Cao, X., Chen, J., Imura, H., & Higashi, O. (2009). A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sensing of Environment, 113(10), 2205–2209. https://doi.org/10.1016/j.rse.2009.06.001.
Elvidge, C., Ziskin, D., Baugh, K., Tuttle, B., Ghosh, T., Pack, D., et al. (2009). A fifteen year record of global natural gas flaring derived from satellite data. Energies, 2(3), 595–622. https://doi.org/10.3390/en20300595.
Han, X., Zhou, Y., Wang, S., Liu, R., & Yao, Y. (2012). GDP spatialization in China based on nighttime imagery. Geo Information Science, 14(1), 128–136. https://doi.org/10.3724/SP.J.1047.2012.00128.
He, C., Shi, P., Li, J., Chen, J., Pan, Y., Li, J., et al. (2006). Restoring urbanization process in China in the 1990s by using non-radiance-calibrated DMSP/OLS nighttime light imagery and statistical data. Chinese Science Bulletin, 51(13), 1614–1620. https://doi.org/10.1007/s11434-006-2006-3.
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.
Jiang, S., Li, J., Duan, P., & Wei, Y. (2019). An image layer difference index method to extract light area from NPP/VIIRS nighttime light monthly data. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2019.1574993.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—international conference on neural networks, Perth, WA, Australia, pp. 1942–1948 vol. 4. https://doi.org/10.1109/icnn.1995.488968.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310.
Li, K., & Chen, Y. (2018). A genetic algorithm-based urban cluster automatic threshold method by combining VIIRS DNB, NDVI, and NDBI to monitor urbanization. Remote Sensing, 10(2), 277. https://doi.org/10.3390/rs10020277.
Liu, C., Frazier, P., & Kumar, L. (2007). Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107(4), 606–616. https://doi.org/10.1016/j.rse.2006.10.010.
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.
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2014). Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 5(2), 165–174. https://doi.org/10.1080/2150704x.2014.890758.
Milesi, C., Elvidge, C. D., Nemani, R. R., & Running, S. W. (2003). Assessing the impact of urban land development on net primary productivity in the southeastern United States. Remote Sensing of Environment, 86(3), 401–410. https://doi.org/10.1016/s0034-4257(03)00081-6.
National Bureau of Statistics of China. Retrieved July 10, 2020. http://www.stats.gov.cn/.
National Oceanic and Atmospheric Administration. Retrieved September 20, 2018. https://www.noaa.gov/.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/tsmc.1979.4310076.
Sharma, R. C., Tateishi, R., Hara, K., Gharechelou, S., & Iizuka, K. (2016). Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data. International Journal of Digital Earth, 9(10), 1004–1020. https://doi.org/10.1080/17538947.2016.1168879.
Shi, K., Huang, C., Yu, B., Yin, B., Huang, Y., & Wu, J. (2014a). Evaluation of NPP-VIIRS nighttime light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4), 358–366. https://doi.org/10.1080/2150704x.2014.905728.
Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., et al. (2014b). Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of china at multiple scales: A comparison with DMSP-OLS data. Remote Sensing, 6(2), 1705–1724. https://doi.org/10.3390/rs6021705.
Yi-Ren, Z. (2005). Reflections on the regional division of the eleventh five-year plan. Areal Research and Development, (03), 3–7(Chinese).
Yu, B., Tang, M., Wu, Q., Yang, C., Deng, S., Shi, K., et al. (2018). Urban built-up area extraction from log-transformed NPP-VIIRS nighttime light composite data. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2018.2830797.
Acknowledgements
Great thanks to the editor and anonymous reviewers for their valuable comments to improve our manuscript.
Funding
This paper is supported by the National Natural Science Foundation of China (No. 41561048) and National Key R&D Program of China (No.2018YFE0184300).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Chen Li conducted experiments, analysis, and paper write-up. Ping Duan and Jia Li conceived and designed the experiments and offered guidance on the entire work. Mingguo Wang and Birong Zhang advised and modified the write-up. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Li, C., Duan, P., Wang, M. et al. The Extraction of Built-up Areas in Chinese Mainland Cities Based on the Local Optimal Threshold Method Using NPP-VIIRS Images. J Indian Soc Remote Sens 49, 233–248 (2021). https://doi.org/10.1007/s12524-020-01209-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-020-01209-1