4.7 Article

Detecting China's Urban Expansion Over the Past Three Decades Using Nighttime Light Data

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2014.2302855

关键词

Change detection algorithms; land surface; remote sensing; support vector machines (SVMs); urban areas

资金

  1. National Key Basic Research Program on Global Change [2011CB952001]
  2. Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions

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China has experienced a rapid urban expansion over the past three decades because of its accelerated economic growth. In this study, we detected and analyzed the urban expansion of China during this period using multi-temporal Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and multi-source Normalized Difference Vegetation Index (NDVI) data. First, an intercalibration was performed to improve the continuity and comparability of the nighttime light data from 1992 to 2010. The nighttime light and NDVI data were then subjected to a local support vector machine (SVM) based region-growing method to extract the urban areas from 1992 to 2010. The urban areas from 1981 to 1991 were identified using the areas in 1992 and NDVI data, based on the hypothesis that China's urban expansion continued during this period. Finally, the extracted time-series urban maps were validated with Landsat images. The proposed local SVM-based region-growing method performed better than a local thresholding method and a global SVM-based region-growing method according to visual and quantitative comparisons of the urban boundaries and areas. We also analyzed the expansion rates to understand the dynamics of the urban areas in China and in its seven economic regions. In particular, the urban expansion patterns were investigated in three typical urban agglomerations, i.e., Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The proposed urban expansion direction, urban expansion intensity, and relative ratio of urban expansion demonstrated the regional variation among the three urban agglomerations.

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