Urban morphology detection and computation for urban climate research
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AbstractUrban morphology information is an important indicator in urban planning, information management, and urban climatic applications. However, the problem of urban morphological data harmonization, quality, and availability are well-known, especially in developing countries. In this study, a novel satellite-based approach was proposed to extract 3D urban morphology information, and then retrieve and validate typical urban morphological parameters for urban climatic applications. Some most widely used urban morphological parameters—building coverage ratio (BCR), building height (BH), building volume density (BVD), frontal area index (FAI), sky view factor (SVF), and roughness length (RL)—were calculated and validated. Experiments conducted throughout the entire urban environment of Kowloon Peninsula, the most complex and high-density urban area in Hong Kong, demonstrated that all of the retrieved parameters had a high prediction accuracy, compared with the actual data at spatial resolutions of hundreds of meters. In particular, the prediction accuracy of BCR, BH, BVD, and RL was 70–80% while the accuracy of SVF and FAI was 80–90%. This set of urban morphological data is employed to generate a Hong Kong Urban Climatic Map. As an extension of the World Urban Database and Access Portal Tools (WUDAPT) method to provide 2D urban form data for cities worldwide, this newly developed method can quickly and efficiently produce highly accurate 3D urban morphology data for cities where actual urban morphology data are not accessible. This study also expands spatial understanding of urban climatic conditions in a fast and efficient way, enabling urban climatic application in urban planning for sustainable urban living.
All Author(s) ListYong XU, Chao REN, Peifeng MA, Justin HO, Weiwen WANG, Kevin Ka-Lun LAU, Hui LIN, Edward NG
Journal nameLandscape and Urban Planning
Volume Number167
Pages212 - 224
LanguagesEnglish-United States

Last updated on 2021-13-10 at 01:19