surface urban heat island
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Urban Climate ◽  
2022 ◽  
Vol 41 ◽  
pp. 101056
Author(s):  
Sorin Cheval ◽  
Alexandru Dumitrescu ◽  
Adrian Irașoc ◽  
Monica-Gabriela Paraschiv ◽  
Michael Perry ◽  
...  

Urban Climate ◽  
2022 ◽  
Vol 41 ◽  
pp. 101052
Author(s):  
Shahfahad ◽  
Mohd Waseem Naikoo ◽  
Abu Reza Md. Towfiqul Islam ◽  
Javed Mallick ◽  
Atiqur Rahman

2021 ◽  
Author(s):  
Yanhua Chen ◽  
Wendy Y. Chen ◽  
Raffaele Lafortezza

Abstract Context Surface urban heat island intensity (SUHII) is a classical measure, which is sensitive to the selection of pixels/measurements representative of urban and rural areas, and overlooks pixel-level SUHII variation and thermodynamics of heterogeneous urban landscape. Accounting inter-pixel landscape heterogeneity in SUHII would capture inter-pixel thermodynamics and reveal complicated micro-thermal situations, contribute to assessment of potential heat risks at micro-pixel scale. Objectives This study develops [[EQUATION]] using pixel-based sharpening enhancement method. It integrates a pixel’s LST magnitude that reflects a city’s thermal context with local SUHII considering landscape variations and cognate thermal interactions of neighboring pixels. Methods [[EQUATION]] is constructed using MODIS LST product for Guangzhou (south China) in the summer season of 2015 through cloud-based GEE platform. Its effectiveness is tested using a bivariate choropleth map and Gaussian density curve with stepwise increments of the thermal influence from neighboring pixels. Results We found that (1) local SUHII variations are sensitive to the spatial configuration of a center pixel’s land use and that of its neighbors; (2) [[EQUATION]] makes more pronounced those spots that are heat per se (with higher original LST), but also receive additional heat load from adjacent pixels due to land-use homogeneity; (3) the effectiveness of [[EQUATION]] could be demonstrated by Gaussian density curve. Conclusions This paper proposed a new SHUII indicator, [[EQUATION]] , which models inter-pixel spatial variation of SHUI and highlights how neighboring pixels’ homogenous/heterogeneous land-use and associated thermal properties could affect center pixels’ thermal characteristics via either reinforcement or mitigation of heat load.


2021 ◽  
Vol 12 (3) ◽  
pp. 113-129
Author(s):  
Albert Berila ◽  
◽  
Florim Isufi ◽  

Urban areas, compared to peripheral and rural areas, have higher temperatures which are caused by a series of unplanned activities that are undertaken by humans. Such a thing leads to the emergence of the Surface Urban Heat Island (SUHI) phenomenon. In this paper, summer SUHI is determined through the calculation of LST for the Municipality of Prishtina using GIS and Remote Sensing techniques. To make this calculation, the Landsat 8 satellite image with 0% cloud cover was used. From the calculations made it turns out that the pixels with the highest value of LST are found in those parts where the urban area appears, where there are numerous constructions with impermeable materials, as well as in those areas where there are bare surfaces. Whereas, the pixels with lower values of LST appear in those parts where there are vegetation and water bodies, making these areas fresher. The SUHI phenomenon makes the lives of citizens difficult, therefore, such information is very important for the leaders and urban planners of the city of Prishtina, so that they take a series of steps towards minimizing such an effect in order to the life of the citizens to be as healthy as possible.


2021 ◽  
Vol 13 (21) ◽  
pp. 4469
Author(s):  
Faezeh Najafzadeh ◽  
Ali Mohammadzadeh ◽  
Arsalan Ghorbanian ◽  
Sadegh Jamali

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.


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