Rooftop Surface Temperature Analysis in an Urban Residential Environment

Remote Sensing

Qunshan Zhao²  Soe W. Myint¹  Elizabeth A. Wentz²  Chao Fan¹  Janet Nichol³  Richard Müller³  Dale A. Quattrochi³ 

[1]School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA

[2] E-Mails:

[3]GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA

[4] E-Mail:

[5]GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA

[6] E-Mail

关键词: rooftop;    UHI;    MASTER;    LIDAR;    OLS regression analysis;    GIS;    urban environment;   

DOI  :  10.3390/rs70912135

来源: mdpi

【 摘 要 】

The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.