School of Information Engineering, China University of Geosciences, Wuhan, Hubei province, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei province, China.
PLoS One. 2018 May 24;13(5):e0198189. doi: 10.1371/journal.pone.0198189. eCollection 2018.
Urban extent data play an important role in urban management and urban studies, such as monitoring the process of urbanization and changes in the spatial configuration of urban areas. Traditional methods of extracting urban-extent information are primarily based on manual investigations and classifications using remote sensing images, and these methods have such problems as large costs in labor and time and low precision. This study proposes an improved, simplified and flexible method for extracting urban extents over multiple scales and the construction of spatiotemporal models using DMSP/OLS nighttime light (NTL) for practical situations. This method eliminates the regional temporal and spatial inconsistency of thresholding NTL in large-scale and multi-temporal scenes. Using this method, we have extracted the urban extents and calculated the corresponding areas on the county, municipal and provincial scales in China from 2000 to 2012. In addition, validation with the data of reference data shows that the overall accuracy (OA), Kappa and F1 Scores were 0.996, 0.793, and 0.782, respectively. We increased the spatial resolution of the urban extent to 500 m (approximately four times finer than the results of previous studies). Based on the urban extent dataset proposed above, we analyzed changes in urban extents over time and observed that urban sprawl has grown in all of the counties of China. We also identified three patterns of urban sprawl: Early Urban Growth, Constant Urban Growth and Recent Urban Growth. In addition, these trends of urban sprawl are consistent with the western, eastern and central cities of China, respectively, in terms of their spatial distribution, socioeconomic characteristics and historical background. Additionally, the urban extents display the spatial configurations of urban areas intuitively. The proposed urban extent dataset is available for download and can provide reference data and support for future studies of urbanization and urban planning.
城市扩展数据在城市管理和城市研究中起着重要作用，例如监测城市化进程和城市区域空间结构的变化。传统的城市扩展信息提取方法主要基于使用遥感图像进行手动调查和分类，这些方法存在劳动力和时间成本大、精度低等问题。本研究提出了一种改进的、简化的、灵活的方法，用于在实际情况下提取多尺度的城市扩展信息并构建时空模型，使用 DMSP/OLS 夜间灯光（NTL）。该方法消除了大尺度和多时相场景中阈值 NTL 的区域时空不一致性。使用该方法，我们从 2000 年到 2012 年在中国的县、市和省尺度上提取了城市扩展并计算了相应的面积。此外，与参考数据的验证表明，总体精度（OA）、Kappa 和 F1 分数分别为 0.996、0.793 和 0.782。我们将城市扩展的空间分辨率提高到 500 米（大约是以前研究结果的四倍精细）。基于上述城市扩展数据集，我们分析了城市扩展随时间的变化，观察到中国所有县的城市扩张都在增长。我们还确定了三种城市扩张模式：早期城市增长、持续城市增长和近期城市增长。此外，这些城市扩张趋势与中国的西部、东部和中部城市在空间分布、社会经济特征和历史背景方面分别一致。此外，城市扩展直观地显示了城市区域的空间配置。所提出的城市扩展数据集可用于下载，并可为未来的城市化和城市规划研究提供参考数据和支持。
