School of Geography, South China Normal University, Guangzhou 510631, China.
College of the Environment & Ecology, Xiamen University, South Xiangan Road, Xiangan District, Xiamen, Fujian 361102, China; Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA.
Sci Total Environ. 2019 May 20;666:274-284. doi: 10.1016/j.scitotenv.2019.02.178. Epub 2019 Feb 12.
Previous studies of urbanization have largely focused on the irreversible urban growth process and the conversion of non-urban lands into impervious surfaces, but less on the conversion from impervious surfaces to green space, also referred to as deurbanization. However, urbanization and deurbanization are both typical urban renewal process, which may happen simultaneously during the urban renewal. In this study, we proposed a new method to retrieve and map annual impervious surface percentage (ISP) and to characterize urban growth patterns using time series medium- resolution images. The method is implemented by employing the Cubist tree model for annual ISP inversion (AoCubist), optimizing multi-temporal Landsat composite images to minimize the impact of phenology and inter-year climate variation, and developing the C5.0 decision tree algorithm with temporal-spatial filtering rules to improve the space-time continuity and separability of patterns derived by unsupervised K-means classification. The method was applied to investigate the urban renewal in Guangzhou, China, between 2000 and 2010. The results demonstrate that the use of ISP slope series can capture the spatial variations and temporal trends of urban growth. Validation by fieldwork and comparing with Google Earth imagery indicates that our classification yielded a reasonable overall accuracy, ranging from 88.32% to 90.85%. Annual urban expansion rate remained between 4% and 10%, while annual deurbanization rate varied from 1% to 5%. In addition, the total pixels of rapid deurbanization surpassed that of rapid urban expansion. This finding suggests that various change directions occurred in the urban renewal process and that deurbanization was a way to counter-balance the rapid urbanization. This study provides a solid methodology for ISP change detection and fresh insight into the characteristics of urban growth in terms of timing, duration, and magnitude.
先前的城市化研究主要集中在不可逆转的城市增长过程和将非城市土地转化为不透水面上，但较少关注从不透水面到绿地的转化，也称为去城市化。然而，城市化和去城市化都是典型的城市更新过程，在城市更新过程中可能同时发生。在本研究中，我们提出了一种新的方法，用于检索和绘制年度不透水面百分比（ISP），并使用时间序列中分辨率图像来描述城市增长模式。该方法通过使用 Cubist 树模型进行年度 ISP 反演（AoCubist）、优化多时相 Landsat 合成图像以最小化物候和年际气候变化的影响，以及开发具有时空滤波规则的 C5.0 决策树算法来提高由无监督 K-means 分类得出的模式的时空连续性和可分离性来实现。该方法应用于 2000 年至 2010 年期间中国广州的城市更新研究。结果表明，ISP 斜率序列的使用可以捕捉城市增长的空间变化和时间趋势。实地调查和与谷歌地球图像的比较验证表明，我们的分类具有合理的总体精度，范围在 88.32%到 90.85%之间。年城市扩张率保持在 4%到 10%之间，而年去城市化率从 1%到 5%不等。此外，快速去城市化的总像素数超过了快速城市化的总像素数。这一发现表明，城市更新过程中存在各种变化方向，去城市化是平衡快速城市化的一种方式。本研究为 ISP 变化检测提供了一种可靠的方法，并从时间、持续时间和幅度等方面深入了解城市增长的特征。
