Local climate zone classification with different source data in Xi'an, China. Issue 9 (November 2019)

• Record Type: Journal Article
• Authors: He, Shan; Zhang, Yunwei; Gu, Zhaolin; Su, Junwei
• Other Names: Gu Zhaolin guest-editor.; Zhang Yunwei guest-editor.; Zhou Dian guest-editor.
• Is Part Of: Indoor + built environment. Volume 28:Issue 9(2019)
• Page Start: 1190
• Page End: 1199
• Publication Date: 2019-11
• Subjects: Local climate zone -- Urban climatic map -- High-rise buildings -- Urban morphology -- Urban canopy; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Buildings -- Environmental engineering -- Periodicals; Environmental health -- Periodicals; 613.5
• DOI: 10.1177/1420326X18796545
• Languages: English
• ISSNs: 1420-326X

Abstract

The city's local climate zone (LCZ) has been considered as a useful guiding tool for urban climate mapping and classification of urban canopy in cities. However, the LCZ classification methods and results are different in different cities in different countries and regions. Therefore, it is necessary to conduct a targeted research that would take into account local urban morphology and canopy characteristics. In the current work, a LCZ classification method is proposed. This would provide a useful guide for city planning and operation. In the newly proposed method, the LCZ types were first adjusted, according to the characteristics and urban morphology of Chinese cities, in relation to existence of high- and low-rise buildings and high density residential population and land cover of greeneries. Based on investigations of two LCZ classifications of the City of Xi'an, a coupling method was proposed. This method couples the World Urban Database and Access Portal Tools method based on remote sensing data with the classification method based on road network divided by land parcels. The proposed method was validated in this study against the actual land use and construction data that were surveyed in Xi'an, which showed acceptable accuracy. Our results have illustrated the feasibility of our proposed classification for urban LCZ mapping in China.