A Satellite-Derived Climatological Analysis of Urban Heat Island over Shanghai during 2000–2013

The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI.

Comparative Analysis of Variations and Patterns between Surface Urban Heat Island Intensity and Frequency across 305 Chinese Cities

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements.

Combined Effects of the Surface Urban Heat Island with Landscape Composition and Configuration Based on Remote Sensing: A Case Study of Shanghai, China

Rapid urbanization leads to changes in surface coverage and landscape patterns. This results in urban heat island (UHI) effects and a series of negative ecological consequences. Considering this concern and taking Shanghai as an example, this paper concentrates on the effects of surface coverage and landscape patterns on urban land surface temperature (LST). The research is based on quantitative retrieval of remote sensing data with consideration of methods in multiple disciplines, including landscape ecology, geographic information systems, and statistical analysis. It concludes that, over time, the thermal environment of Shanghai is becoming critical. The average LST ranking of different surface coverage is as follows: Construction land (CL) > bare land (BL) > green land (GL) > agricultural land (AL) > water body (WB). LST varies significantly with the type of surface coverage. CL contributes the most to the UHI, while WB and GL have obvious mitigation effects on the UHI. The large area, low degree of landscape fragmentation, and complex outlines lead to low LST rankings for GL, WB, and AL and a high LST ranking for CL. The conclusions indicate that CL should be broken down by GL and WB into discrete pieces to effectively mitigate UHI effects. The research reveals UHI features and changes in Shanghai over the years and provides practical advice that can be used by urban planning authorities to mitigate UHI.

Effects of Spatial Pattern of Forest Vegetation on Urban Cooling in a Compact Megacity

Urban forests can be an effective contributor to mitigate the urban heat island (UHI) effect. Understanding the factors that influence the cooling intensity of forest vegetation is essential for creating a more effective urban greenspace network to better counteract the urban warming. The aim of this study was to quantify the effects of spatial patterns of forest vegetation on urban cooling, in the Shanghai metropolitan area of China, using correlation analyses and regression models. Cooling intensity values were calculated based on the land surface temperature (LST) derived from remote sensing imagery and spatial patterns of forest vegetation were quantified by eight landscape metrics, using standard and moving-window approaches. The results suggested that 90 m × 90 m was the optimal spatial scale for studying the cooling effect of forest vegetation in Shanghai’s urban area. It also indicated that woodland performed better than grassland in urban cooling and the size, shape, and spatial distribution of woodland patches had significant impacts on the urban thermal environment. Specifically, the increase of size and the degree of compactness of the patch shape can effectively reduce the LST within the woodland. Areas with a higher percentage of vegetation coverage experienced a greater cooling effect. Moreover, when given a fixed amount of vegetation covers, aggregated distribution provided a stronger cooling effect than fragmented distribution and increasing overall shape complexity of woodlands can enhance the cooling effect on surrounding urban areas. This study provides insights for urban planners and landscape designers to create forest adaptive planning strategies to effectively alleviate the UHI effect.

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

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.

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

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.

Adaptive Analysis of Green Space Network Planning for the Cooling Effect of Residential Blocks in Summer: A Case Study in Shanghai

The effect of Urban Heat Islands (UHIs) is becoming increasingly serious in cities. Research on the adaptive planning policies for microthermal environments at the residential block level of cities is thus becoming of greater significance. Based on the cooling effect of planning control elements in residential block areas, the element effects characteristics of water bodies and vegetation distribution on the thermal environment of residential blocks were analyzed by using ENVI-met software. The simulation data analysis showed that the combination of water bodies and vegetation had a synergistic cooling effect. Based on these results, simulations of five effective adaptive measures were carried out step by step in planning scenarios, that is, improving the water bodies with vegetation corridors, the application of high-albedo material on streets, and increasing the number of green patches, east-west green corridors, and north-south green corridors. The results were as follows. First, although each of the five optimization strategies have a certain degree of cooling effect on the entire block, the superposition of each factor had a synergistic effect. Second, different spatial optimization strategies had different cooling ranges for each subzone. The optimization of the north-south green corridor, green patches, and water features corridors were particularly significant for microclimate cooling. The east-west green corridor has a certain influence on a certain range of downwind zones and had an auxiliary cooling effect. The high-albedo material also had a weak overall decrease function for the thermal environment. Finally, the downwind area of the urban creek network had a great impact on cooling intensity, with distance attenuation characteristics; it was also proposed that the comprehensive cooling effect of the green space network with optimized layout was greater than that of any single green space element. The optimization scenario planning research provided a method for improving the scientific distribution of adaptation measures in urban residential blocks.

Comparative Analysis of Urban Heat Island Intensities in Chinese, Russian, and DPRK Regions across the Transnational Urban Agglomeration of the Tumen River in Northeast Asia

Quantification of the spatial pattern of urban heat island intensities across the transnational urban agglomeration of the Tumen River is important for the promotion of sustainable regional development. This study employed Landsat images and MODIS LST data obtained in 2016 to determine the intensity of urban heat islands in this region, enabling direct comparison of data from the sub-regions of China, Democratic People’s Republic of Korea (DPRK), and Russia. The average urban heat island intensity for the region was found to be 1.0 °C, with the highest intensity of 3.0 °C occurring during the summer time. The intensity of urban heat islands on the Chinese side was higher than on the other two sides, with city size, socio-economic development levels and vegetation coverage significantly affect their intensity. Urban heat island effects in Chinese cities in the region contribute increases in maximum summer temperatures and the number of high-temperature days that pose a threat to the health of their residents. The factors that influence urban heat island intensities in these cities and the impacts of urban heat island effects on the quality of life and health of residents are discussed. Therefore, it is desirable to reduce the impact of urban heat island effects on cities in the region by increasing the area of green spaces they contain, as well as controlling their size and population.

Impacts of Grading Rule on Urban Thermal Landscape Pattern Research

The thermodynamic landscape method is becoming a more popular approach for urban heat island research with the development of remote sensing technology. However, a limited amount of research discusses the theoretical and methodological issues of this method. This paper analyzed the reliability and stability of the results of thermal landscape pattern analysis with six different grading rules through surface temperature retrieval, landscape pattern analysis, and Geographic Information System (GIS) spatial analysis. The results demonstrate the following points. (1) The six grading methods can be categorized into two types: pixel number methods and temperature range methods. The grading results of the two kinds of methods lack comparability, whereas the grading results within one kind of method have high comparability. The temperature range methods have good consistency. The average value of the consistency indices (Si) of thermal landscape levels reaches up to 81.55%. The anomaly temperature method and standard deviation method are recommended for future research. (2) The grading rule significantly affects the stability of landscape indices, and its average variation coefficient reaches up to 22.36%. The authors suggest the use of landscape indices that have strong stability, such as shape index and landscape division index, in future research. (3) The results of the sensitivity analysis show that the change of the temperature range of thermal landscape levels affects landscape indices slightly, whereas the effect of the change of the level number of thermal landscapes on landscape indices is intense. The authors suggest categorizing the thermal landscape into six levels in future research in order to enhance the consistency and comparability among case studies.

Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities

Anthropogenic heat and modified landscapes raise air and surface temperatures in urbanized areas around the globe. This phenomenon is widely known as an urban heat island (UHI). Previous UHI studies, and specifically those based on remote sensing data, have not included cities north of 60°N. A few in situ studies have indicated that even relatively small cities in high latitudes may exhibit significantly amplified UHIs. The UHI characteristics and factors controlling its intensity in high latitudes remain largely unknown. This study attempts to close this knowledge gap for 28 cities in northern West Siberia (NWS). NWS cities are convenient for urban intercomparison studies as they have relatively similar cold continental climates, and flat, rather homogeneous landscapes. We investigated the UHI in NWS cities using the moderate-resolution imaging spectroradiometer (MODIS) MOD 11A2 land surface temperature (LST) product in 8-day composites. The analysis reveals that all 28 NWS cities exhibit a persistent UHI in summer and winter. The LST analysis found differences in summer and winter regarding the UHI effect, and supports the hypothesis of seasonal differences in the causes of UHI formation. Correlation analysis found the strongest relationships between the UHI and population (log P). Regression models using log P alone could explain 65–67% of the variability of UHIs in the region. Additional explanatory power—at least in summer—is provided by the surrounding background temperatures, which themselves are strongly correlated with latitude. The performed regression analysis thus confirms the important role of the surrounding temperature in explaining spatial–temporal variation of UHI intensity. These findings suggest a climatological basis for these phenomena and, given the importance of climatic warming, an aspect that deserves future study.