Abstract
Global warming is intensifying worldwide, and urban heat islands are occurring as urbanization progresses. The cool roof method is one alternative for reducing the urban heat island phenomenon and lowering the heat on building roofs for a comfortable indoor environment. In this study, a cool roof evaluation was performed using an unmanned aerial vehicle (UAV) and a red, green and blue (RGB) camera instead of a laser thermometer and a thermal infrared sensor to evaluate existing cool roofs. When using a UAV, an RGB sensor is used instead of expensive infrared sensor. Various color space techniques, namely light-reflectance value, hue saturation value (HSV), hue saturation lightness, and YUV (luma component (Y) and two chrominance components, called U (blue projection) and V (red projection)) derived from RGB images, are applied to evaluate color space techniques suitable for cool roof evaluation. This case study shows the following quantitative results: among various color space techniques investigated herein, the white roof with lowest temperature (average surface temperature: 44.1 °C; average indoor temperature: 33.3 °C) showed highest HSV, while the black roof with the highest temperature (surface temperature average: 73.4 °C; indoor temperature average: 37.1 °C) depicted the lowest HSV. In addition, the HSV showed the highest correlation in both the Pearson correlation coefficient and the linear regression analyses when the correlation among the brightness, surface temperature, and indoor temperature of the four color space techniques was analyzed. This study is considered a valuable reference for using RGB cameras and HSV color space techniques, instead of expensive thermal infrared cameras, when evaluating cool roof performance.
Highlights
Temperature rises are intensifying with global warming worldwide
The brightness values of the color space techniques for each rooftop color were compared with the surface and indoor temperatures to evaluate the cool roof performance
The technique showed a high correlation of the Pearson correlation coefficient with the surface temperature (−0.95) and linear regression analysis (0.91)
Summary
Temperature rises are intensifying with global warming worldwide. Abnormally high temperatures have occurred in Central Africa, Europe, the Middle East, Alaska, and South America [1,2].with the development of industries, the urban heat island phenomenon has occurred with urbanization, population increase, and vegetation reduction. The phenomenon is mainly caused by asphalt roads, concrete artificial structures, and high-rise buildings. The solar radiation reflected on building roofs raises the external surface temperature by up to 50 to 60 ◦ C [3,4]. Such a structure has a bad effect on the living environment of people because it raises the temperature of the city, thereby causing problems for residential life and the cooling load [5]. In an effort to reduce the urban heat island phenomenon, research is being conducted to lower the surface temperature of building roofs
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