Performance evaluation strategy for cool roof based on pixel dependent variable in multiple spatial regressions

Abstract

Performance evaluation for cool roof has been mainly based on field sampling, which relies on attributes of an area at one point in time, reflecting an emphasis on the small number of in situ data. One of the major disadvantages of traditional field monitoring is that it is costly, laborious and time consuming due to the large number of samples required. Acknowledging these constraints, this research was inspired by the fact that pixel of remotely-sensed imagery could play a key role in presenting rooftop temperature remarkably due to thermal sensitivity channel. The aim of this research is primarily intended to show how to evaluate energy saving performance of color changing roof, based on pixel of remotely-sensed imagery, together with multiple spatial regressions. An empirical study for a case study site was suggested to demonstrate how a standard remote sensing and multiple spatial regression approach can be used to assist in monitoring performance of cool roof based on thermal pixel in terms of various specific settings such as season, sunset, sunrise timing, solar facing and sitting orientation etc. It is expected that the pixel-based analysis would make it possible to identify area-wide patterns of cool roof performance subject to many different type of artificial structures, which cannot be acquired by traditional field sampling. This research will be used as a valuable reference in implementing performance evaluation for color roofing since the quantitative data generated from remote sensing and multiple spatial regressions can present area-wide visual evidences by permanent record.