Remote Sensing Techniques for Urban Heating Analysis: A Case Study of Sustainable Construction at District Level

Bonafoni, Stefania; Baldinelli, Giorgio; Verducci, Paolo; Presciutti, Andrea

doi:10.3390/su9081308

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…Surface and air UHI differences and the timing of their peaks depend on the urban and rural surface properties (e.g., moisture, aerodynamic roughness, albedo, emissivity, and thermal admittance), season, geography, and prevailing weather conditions [29,192]. Schwarz et al [20] carried out a campaign in Leipzig, Germany, providing LST from airborne flights and air temperature (T air ) from weather stations and a mobile traverse.…”

Section: Relationship Between Surface and Air Uhismentioning

confidence: 99%

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

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Section: Relationship Between Surface and Air Uhismentioning

confidence: 99%

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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“…Factors related to the urban texture at district level that affect the SUHI phenomenon-driving the heat trapping and absorption-rely on surface albedo, material, and color; on building layout, slope, and height; and on greenery cover [51][52][53][54]. At the same time, the urban texture can intensify the warming effect by reducing the wind circulation [55].…”

Section: Yearly Trend Of Suhi Footprint and Magnitudementioning

confidence: 99%

Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand

Keeratikasikorn

Bonafoni

2018

Remote Sensing

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For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand's six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales-diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003-2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 • C and 6 • C; during nighttime, the intensities are rather similar for all the six cities, between 1 • C and 2 • C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture.

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“…The UHI is rather weak in the daytime, but it becomes more pronounced after sunset. Under some specific conditions, studies have found that some relevant factors (e.g., urban geometry, thermal admittance, and moisture) were important control factors for the UHI intensity [18][19][20]. Remote sensors such as the Landsat TM/ETM+/TIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) play important roles in SUHI research [21].…”

Section: Introductionmentioning

confidence: 99%

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

Wang

Yao

et al. 2019

Remote Sensing

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Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to investigate the diurnal, seasonal, and interannual variations in the SUHI intensity (SUHII, the urban LST minus the rural LST) in 44 South American cities in different climate zones and types of rural land. To examine the effects of factors that may influence the SUHII, correlations between the SUHII and the enhanced vegetation index (EVI), urban area, population, altitude, and anthropogenic heat emissions were performed. The results showed that the SUHI effect was obvious in South America. The mean daytime SUHII was higher than the mean night-time SUHII in all areas except for the arid climate zone. In the daytime, the summer displayed a stronger SUHII in the warm temperate climate zone than the other seasons. The night-time SUHII showed less obvious seasonal variations. In addition, the surrounding land cover influenced the SUHII. During the day, the SUHII was therefore stronger in rural areas that were covered by forests than in other types of rural land. Interannually, most cities showed an insignificant temporal trend in the SUHII from 2003 to 2016. The daytime SUHII was significantly and negatively correlated with the ∆EVI (the urban EVI minus the rural EVI) across the 44 cities, but a poor relationship was observed at night. In addition, anthropogenic heat emissions were positively correlated with the night-time SUHII. Urban area, population, and altitude were weakly correlated with the SUHII, which suggested that these factors may not have a significant impact on the spatial variations in the SUHII in South America.

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Remote Sensing Techniques for Urban Heating Analysis: A Case Study of Sustainable Construction at District Level

Cited by 11 publications

References 41 publications

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

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