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Journal of Geographical Sciences

, Volume 26, Issue 3, pp 325–338 | Cite as

Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information

  • Yang Cheng
  • Limin ZhaoEmail author
  • Wei Wan
  • Lingling Li
  • Tao Yu
  • Xingfa Gu
Article

Abstract

DMSP/OLS nighttime light (NTL) image is a widely used data source for urbanization studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas (UAD) is limited by the overestimation of the lit areas resulting from the coarse spatial resolution. In view of geographical condition, we integrate NTL with Biophysical Composition Index (BCI) and propose a new spectral index, the BCI Assisted NTL Index (BANI) to capture UAD. Comparisons between BANI approach and NDVI-assisted SVM classification are carried out using UAD extracted from Landsat TM/ETM+ data as reference. Results show that BANI is capable of improving the accuracy of UAD extraction using NTL data. The average overall accuracy (OA) and Kappa coefficient of sample cities increased from 88.53% to 95.10% and from 0.56 to 0.84, respectively. Moreover, with regard to cities with more mixed land covers, the accuracy of extraction results is high and the improvement is obvious. For other cities, the accuracy also increased to varying degrees. Hence, BANI approach could achieve better UAD extraction results compared with NDVI-assisted SVM method, suggesting that the proposed method is a reliable alternative method for a large-scale urbanization study in China’s mainland.

Keywords

urban area distribution DMSP/OLS biophysical composition index BANI China 

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Copyright information

© Institute of Geographic Science and Natural Resources Research (IGSNRR), Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yang Cheng
    • 1
    • 2
    • 3
  • Limin Zhao
    • 1
    • 2
    Email author
  • Wei Wan
    • 4
  • Lingling Li
    • 1
    • 2
  • Tao Yu
    • 1
    • 2
  • Xingfa Gu
    • 1
    • 2
  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthCASBeijingChina
  2. 2.The Center for National Spaceborne DemonstrationBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Department of Hydraulic EngineeringTsinghua UniversityBeijingChina

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