Delineation of Central Business Districts in mega city regions using remotely sensed data
Delineation of Central Business Districts in mega city regions using remotely sensed data
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DOI：
10.1016/j.rse.2013.05.019
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发表时间：
2013-09
影响因子：
13.5
通讯作者：
H. Taubenböck;M. Klotz;M. Wurm;J. Schmieder;B. Wagner;M. Wooster;T. Esch;S. Dech
中科院分区：
文献类型：
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作者：
H. Taubenböck;M. Klotz;M. Wurm;J. Schmieder;B. Wagner;M. Wooster;T. Esch;S. Dech
中央商务区（CBD）是一种重要的城市结构类型（UST），也是许多大城市的明显结构特征。 CBD区位对于城市功能和曝光的空间布局起着决定性作用。然而，虽然过去的研究强调了空间检测、描绘和制图表示的重要性，但所使用的定义大多是功能性和定性的。基于定量方法的 CBD 半自动空间分类的客观预定义方法/阈值尚不存在。本文提出了一个使用物理和形态参数定义 CBD 的概念框架，并使用三个欧洲测试地点（伦敦金丝雀码头、巴黎拉德芳斯和伊斯坦布尔莱文特）的 3D 城市模型测试了该方法。根据这些案例研究，我们开发了一种可转移的方法，通过结合 Cartosat-1 数字表面模型和多光谱 Landsat ETM+ 图像来检测和描绘更大区域的 CBD。将这种广域方法应用于伦敦、巴黎和伊斯坦布尔这三个欧洲大城市的整个范围，我们以 75.7% 的用户准确度检测 CBD，并以 82.9% 的总体准确度在空间上描绘它们。最后，我们应用空间指标来分析和比较三个大城市的 CBD 位置和分布，发现伦敦和巴黎之间有许多相似之处，但表明伊斯坦布尔具有更复杂的城市足迹和不同的空间 CBD 格局。
Central Business Districts (CBDs) are an important urban structural type (UST), and an apparent structural feature of many large cities. CBD locations play a decisive role in the spatial arrangement of functions and exposures within cities. However, while past research underscores the importance of their spatial detection, delineation and cartographic representation, the definitions used are mostly functional and qualitative. Objective pre-defined methods/thresholds for the semi-automated spatial classification of CBDs, based on a quantitative approach, do not yet exist. This paper presents a conceptual framework to define the CBD using physical and morphological parameters, and tests the approach using 3-D city models of three European test sites (Canary Wharf in London, La Defense in Paris, and Levent in Istanbul). From these case studies, we develop a transferable method to detect and delineate CBDs over larger areas from a combination of Cartosat-1 digital surface models and multispectral Landsat ETM+ imagery. Applying this wide-area method to the entire extents of the three European megacities of London, Paris and Istanbul, we detect CBDs with a user accuracy of 75.7% and spatially delineate them with overall accuracies of 82.9%. Finally, we apply spatial metrics to analyze and compare the location and distribution of CBDs across the three mega cities, finding many similarities between London and Paris, but showing that Istanbul features a more complex urban footprint, and a different spatial CBD pattern.