Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

Li, Long; Huang, Xin; Li, Jiayi; Wen, Dawei

doi:10.3390/rs9060536

Cited by 25 publications

(16 citation statements)

References 58 publications

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“…Surface and air UHIs exhibited exponential decay trends moving away from the urban area of Wuhan, China, with different magnitudes. This was more evident in the summer than in the winter [195]. In the region of Hangzhou, China, the UHIs computed by T air from weather stations and LST from Landsat images were not comparable, which demonstrates the importance of the choice of dataset, acquisition time, and weather conditions for SUHI−CLHI comparison [196].…”

Section: Relationship Between Surface and Air Uhismentioning

confidence: 93%

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: 93%

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

et al. 2018

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“…Concerning the relationship between the distribution of T s and that of T a , thermal remote sensing has often been used. Many studies investigated the relationship between satellite-derived T s and ground-based T a [24][25][26][27][28][29][30]. The correlation between T s and T a differs according to the time of day, season, land cover, spatial geometry, and spatial resolution of T s and T a data.…”

Section: Significance Of Locally Low T S and T A In Urban Climate Stumentioning

confidence: 99%

Airborne and Terrestrial Observations of the Thermal Environment of Urban Areas Surrounding a High-Rise Building during the Japanese Winter

Oshio

Chen

Asawa

2020

Sensors

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We investigated the distribution of air temperature (Ta) and the factors affecting it in low-rise areas surrounding an isolated high-rise building during the Japanese winter. The study site was the central part of a regional city in Japan (36°5′ N, 140°12′ E), lying north-east of the Tokyo metropolitan area. The daytime surface temperature (Ts) in the shade is generally considered to be comparable to Ta; however, according to airborne remote sensing conducted in December 2009 where a multi-spectral scanner was installed on a fixed-wing aircraft, Ts for pavements in the shade of a high-rise building was significantly lower than Ta of sub-urban areas, indicating an influence of cold storage on Ts. Then, we conducted mobile observations using instruments (thermocouple, four component radiometer, and so on) installed on a bicycle in January 2016 to investigate the detailed distribution of Ta and the factors affecting it. The results showed the Ta over the pavements in the shade of the high-rise building was lower than the Ta of sunlit areas in the same urban area by −2 °C and lower than the Ta of sub-urban areas by −1–1.5 °C, although the advection effect was large due to strong winds around the building. In conclusion, a locally lower Ta compared to the surrounding areas can develop during the day in winter, even in spaces that are open to areas beyond the canopy.

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“…The algorithm can avoid the problem of multicollinearity faced by general regression analysis, with the ability to reach rapid convergence and confer strong generalization [35]. It has been applied to good effect in both large-scale and fine-scale SAT mapping [12,34]. Here, we used random forest regression models implemented in R language of version 3.4.3 (https://www.r-project.org/) to map monthly mean daytime (14:00) and nighttime (02:00) SAT based on EVI, nighttime lights, LULC, DEM, and daytime and nighttime LST data [34].…”

Section: Near-surface Air Temperature Estimation and Evaluationmentioning

confidence: 99%

“…Previous studies have used LST differences between urban and rural areas to calculate the SUHII [4]. Generally, this method is more objective in comparison with previous analyses using a point a certain distance away from the urban area as its reference location [15,16], yet it fails to calculate the CLHII due to an obvious CLHI footprint along urban-rural gradients [12]. On this basis, we defined the CLHII and SUHII as the difference between urban temperature (the average temperature over an urban area as a bulk, without considering internal variations in a city) and the median of temperature in the outermost buffer zones (i.e., the background temperature), because it can reduce the possible bias caused by the outliers among the three outermost buffer zones [36].…”

Section: Quantifying Clhii Along Urban-rural Gradientsmentioning

confidence: 99%

“…Typically, two main strands of research, i.e., surface urban heat island (SUHI) and canopy layer heat island (CLHI), are devoted to investigate UHI effects [12]. The SUHI is determined by the difference in temperature at the surface and has a strong relationship with the surface orientation relative to the sun and land-use and land-cover [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Satellite-Based Spatiotemporal Trends of Canopy Urban Heat Islands and Associated Drivers in China’s 32 Major Cities

Zha

2019

Remote Sensing

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The urban heat island (UHI) effect, in which urbanized areas tend to have warmer conditions compared to their rural surroundings, has drawn increasing attention in recent years. Using ground-based and satellite remote sensing data, we present a method to quantify the spatial pattern and diurnal and seasonal variations in canopy layer heat islands (CLHIs) in China’s 32 major cities during 2009 and investigate their relationships with built-up intensity (BI), nighttime lights, vegetation activity, surface albedo, and surface urban heat island intensity (SUHII). The results show that both the annual daytime and nighttime CLHI intensities (CLHIIs) were positive ranging from 0.2 °C to 2.2 °C and from 0.3 °C to 2.4 °C for these major cities, respectively. Higher CLHIIs were observed in the night, especially for northern parts of China. Along urban–rural gradients, the CLHI effect had an exponential decay shape and differed greatly by season. The CLHII distribution correlated positively and significantly to BI and nighttime lights. Vegetation activity was negatively correlated with the CLHII and more strongly in summer. Surface albedo showed an extremely weak correlation with the CLHII. In addition, CLHII had a strong correlation with SUHII. The annual daytime SUHII was 1.2 ± 1.1 °C (mean ± standard deviation) with 0.40 °C (95% confidence interval 0.36 to 0.44 °C) of annual daytime CLHII. The annual nighttime SUHII was 2.0 ± 0.8 °C with 1.04 °C (0.99 to 1.09 °C) of annual nighttime CLHII. Our results suggest that, reducing built-up intensity and anthropogenic heat emissions and increasing urban vegetation provide a co-benefit of mitigating SUHI and CLHI effects.

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Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

Cited by 25 publications

References 58 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

Airborne and Terrestrial Observations of the Thermal Environment of Urban Areas Surrounding a High-Rise Building during the Japanese Winter

Satellite-Based Spatiotemporal Trends of Canopy Urban Heat Islands and Associated Drivers in China’s 32 Major Cities

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