Please use this identifier to cite or link to this item:
Title: Modeling of urban wind ventilation using high resolution airborne LiDAR data
Authors: Peng, F
Wong, MS 
Wan, Y
Nichol, JE 
Keywords: Airborne LiDAR
Computer fluid dynamics
Frontal area index
Least cost path
Ventilation corridor
Issue Date: 2017
Publisher: Pergamon Press
Source: Computers, environment and urban systems, 2017, v. 64, p. 81-90 How to cite?
Journal: Computers, environment and urban systems 
Abstract: Accurate mapping of wind ventilation in an urban environment is challenging when large spatial coverage is required. This study has developed a GIS-based model for estimating the frontal area index (FAI) of buildings, infrastructure, and trees using very high resolution airborne light detection and ranging (LiDAR) data, which can also be used to investigate the “wall effect” caused by high-rise buildings at a finer spatial scale along the coasts in the Kowloon Peninsula of Hong Kong. New algorithms were created by improving previous algorithms utilizing airborne LiDAR data in raster unit, as well as considering the backward flow coefficient between windward and leeward buildings. The ventilation corridors estimated by FAI and least cost path (LCP) analysis were analyzed. The optimal ventilation corridors passing through the Kowloon peninsula were observed in the east-west and west-east directions. In addition, these ventilation paths were validated with a computer fluid dynamics (CFD) model i.e. Airflow Analysis in ESRI. The newly developed model calculates finer FAI with greater accuracy when compared with vector-based building polygons. This model further depicts buildings, infrastructure, and trees which are considered as obstacles to wind ventilation. The results can be used by environmental and planning authorities to identify ventilation corridors, and for scenario analysis in urban redevelopment.
ISSN: 0198-9715
DOI: 10.1016/j.compenvurbsys.2017.01.003
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Oct 5, 2018


Last Week
Last month
Citations as of Oct 12, 2018

Page view(s)

Last Week
Last month
Citations as of Oct 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.