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Remote Sens. 2015, 7(9), 12135-12159; doi:10.3390/rs70912135

Rooftop Surface Temperature Analysis in an Urban Residential Environment

1
GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA
2
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol, Richard Müller, Dale A. Quattrochi and Prasad S. Thenkabail
Received: 22 July 2015 / Revised: 2 September 2015 / Accepted: 11 September 2015 / Published: 18 September 2015
View Full-Text   |   Download PDF [5680 KB, uploaded 18 September 2015]   |  

Abstract

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. View Full-Text
Keywords: rooftop; UHI; MASTER; LIDAR; OLS regression analysis; GIS; urban environment rooftop; UHI; MASTER; LIDAR; OLS regression analysis; GIS; urban environment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhao, Q.; Myint, S.W.; Wentz, E.A.; Fan, C. Rooftop Surface Temperature Analysis in an Urban Residential Environment. Remote Sens. 2015, 7, 12135-12159.

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