The role of city size and urban form in the surface urban heat island

Comparison of Potential Contribution of Typical Pavement Materials to Heat Island Effect

Pavement materials have significant influence on the urban heat island effect (UHIE). This paper presents a study on the potential effects of pavement materials on UHIE in a natural environment. Three typical pavement materials, including cement concrete, dense asphalt concrete and porous asphalt mixture, were selected to evaluate their anti-UHIE properties by testing. In this paper, heat island potential (HIP) is proposed as a new index to analyze the influence of pavement materials on UHIE. It is shown that the temperature inside a pavement distinctly depends on the depth, and varies, but is usually higher than the air temperature. Solar radiation in the daytime significantly contributes to the temperature of pavement surface and the upper part. The correlation becomes weak at the middle and the bottom of pavements. Among the three materials tested in this study, the anti-UHIE performance of cement concrete is significantly higher than that of the other asphalt mixtures, while the porous asphalt mixture is slightly better than the dense asphalt concrete in anti-UHIE.

Rank-size Distribution of Cities and Municipalities in Bangladesh

This paper examines and updates the rank-size distribution of cities and municipalities in Bangladesh between 1990 and 2019 based on two criteria: (1) built-up urban areas; and (2) population. The distribution of built-up urban areas and population are compared to provide a robust theoretical underpinning of Zipf’s law for future urban developmental planning framework. The data on built-up urban areas is extracted from land cover classification using Google Earth Engine and the population data is obtained from the decennial censuses. The comparison of the conformity to Zipf’s law indicated contradictory results. While a greater proportion of the population has been increasingly concentrated in the smaller and midsized cities over the last three decades, built-up urban areas, on the other hand, have been mostly clustered in two largest cities— Dhaka and Chittagong—accounting for 50 to nearly 60 percent of the total built-up urban areas. These results shed light on the magnitude of continued spatial inequalities in urban development amongst cities and municipalities in Bangladesh despite there being an overall increase of evenness in the distribution of population over time. These results imply an unsustainable rate of urban expansion in Bangladesh and reinforce the need for the exploration of policies and regulations targeted at guiding the rate and direction of evenness in urban expansion.

The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis

There have been debates and a lack of understanding about the complex effects of urban-scale urban form on air pollution. Based on the remotely sensed data of 150 cities in the Beijing-Tianjin-Hebei agglomeration in China from 2000 to 2015, we studied the effects of urban form on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations from multiple perspectives. The panel models show that the elastic coefficients of aggregation index and fractal dimension are the highest among all factors for the whole region. Population density, aggregation index, and fractal dimension have stronger influences on air pollution in small cities, while area size demonstrates the opposite effect. Population density has a stronger impact on medium/high-elevation cities, while night light intensity (NLI), fractal dimension, and area size show the opposite effect. Low road network density can enlarge the influence magnitude of NLI and population density. The results of the linear regression model with multiplicative interactions provide evidence of interactions between population density and NLI or aggregation index. The slope of the line that captures the relationship between NLI on PM2.5 is positive at low levels of population density, flat at medium levels of population density, and negative at high levels of population density. The study results also show that when increasing the population density, the air pollution in a city with low economic and low morphological aggregation degrees will be impacted more greatly.

On the influence of density and morphology on the Urban Heat Island intensity

The canopy layer urban heat island (UHI) effect, as manifested by elevated near-surface air temperatures in urban areas, exposes urban dwellers to additional heat stress in many cities, specially during heat waves. We simulate the urban climate of various generated cities under the same weather conditions. For mono-centric cities, we propose a linear combination of logarithmic city area and logarithmic gross building volume, which also captures the influence of building density. By studying various city shapes, we generalise and propose a reduced form to estimate UHI intensities based only on the structure of urban sites, as well as their relative distances. We conclude that in addition to the size, the UHI intensity of a city is directly related to the density and an amplifying effect that urban sites have on each other. Our approach can serve as a UHI rule of thumb for the comparison of urban development scenarios.

How UHI intensity responds to variations of urban structure is unclear. Here the authors proposed a reduced form approach that is able to estimate UHI intensities based only on the number and location of urban sites as well as their distance.

Effects of urban forms on CO2 emissions in China from a multi-perspective analysis

Effectively evaluating the effects of urban forms on CO₂ emissions has become a hot topic in socioeconomic sustainable development; however, few studies have been able to explore the urban form-CO₂ emission relationships from a multi-perspective view. Here, we attempted to analyze the relationships between urban forms and CO₂ emissions in 264 Chinese cities, with explicit consideration of the government policies, urban area size, population size, and economic structure. First, urban forms were calculated using the urban land derived from multiple-source remote sensing data. Second, we collected and processed CO₂ emissions and three control variables. Finally, a correlation analysis was implemented to explore whether and to what extent the spatial patterns of urban forms were associated with CO₂ emissions. The results show that urban form irregularity had a more significant impact on CO₂ emissions in low-carbon pilot cities than in non-pilot cities. The impact of the complexity of urban forms on CO₂ emissions was relatively significant in the small- and large-sized cities than in the medium-sized cities. Moreover, urban form complexity had a significant correlation with CO₂ emissions in all of the cities, the level of which basically increased with the population size. This study provides scientific bases for use in policy-making to prepare effective policies for developing a low-carbon economy with consideration of the associations between urban forms and CO₂ emissions in different scenarios.

The relationship between urban form and heat island intensity along the urban development gradients

There is an increasing demand for urban form optimization to mitigate urban heat island (UHI) effect under the background of global climate change and urbanization. However, there is still a lack of understanding about how the relationship between urban form and UHI intensity changes under diverse urbanization contexts. This study aims to show the change patterns of the relationship between urban form and UHI intensity along the urban development gradient based upon the investigation of a total of 150 urban areas in the Jing-Jin-Ji region in China in 2000, 2005, 2010 and 2015. We defined a comprehensive urban development index taking into account the size of urban area, population density and night light intensity to classify the different levels of urban development. A multi-model comparison was carried out to validate the results. We found that the increase in urban continuity, sprawling, scale and density all enhanced UHI effect at the regional scale. However, the relationship between urban form indicators and UHI intensity demonstrated two opposite patterns along the urban development gradient: population density, geometric complexity and continuity, and general vegetation index of a city were found to be increasingly influential, while night light intensity, geometric elongation and forest coverage presented declining influence. Finally, the study obtained an ascending contribution rate curve for urban geometry indicators, a convex curve for urban size indicators and a declining curve for urban vegetation. These changes along the urban development gradient may be closely related to the changes of the microclimate in cities due to land use, social and economic activities in different urban development stages. The findings can contribute to more appropriate and effective urban planning in countries and regions undergoing rapid urbanization as a valuable reference.

Paradoxical impact of sprawling intra-Urban Heat Islets: Reducing mean surface temperatures while enhancing local extremes

Extreme heat is one of the deadliest health hazards that is projected to increase in intensity and persistence in the near future. Here, we tackle the problem of spatially heterogeneous heat distribution within urban areas. We develop a novel multi-scale metric of identifying emerging heat clusters at various percentile-based thermal thresholds and refer to them collectively as intra-Urban Heat Islets. Using remotely sensed Land Surface Temperatures, we first quantify the spatial organization of heat islets in cities at various degrees of sprawl and densification. We then condense the size, spacing, and intensity information about heterogeneous clusters into probability distributions that can be described using single scaling exponents (denoted by β, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{\Lambda }}}_{{\boldsymbol{s}}{\boldsymbol{c}}{\boldsymbol{o}}{\boldsymbol{r}}{\boldsymbol{e}}}$$\end{document}Λscore, and λ, respectively). This allows for a seamless comparison of the heat islet characteristics across cities at varying spatial scales and improves on the traditional Surface Urban Heat Island (SUHI) Intensity as a bulk metric. Analysis of Heat Islet Size distributions demonstrates the emergence of two classes where the dense cities follow a Pareto distribution, and the sprawling cities show an exponential tempering of Pareto tail. This indicates a significantly reduced probability of encountering large heat islets for sprawling cities. In contrast, analysis of Heat Islet Intensity distributions indicates that while a sprawling configuration is favorable for reducing the mean SUHI Intensity of a city, for the same mean, it also results in higher local thermal extremes. This poses a paradox for urban designers in adopting expansion or densification as a growth trajectory to mitigate the UHI.

Trend Analysis of Urban Heat Island Intensity According to Urban Area Change in Asian Mega Cities

Urban heat island (UHI) is a phenomenon that occurs in cities worldwide. Therefore, there is an increasing need for studies on the changes in UHI intensity and long-term trends based on macroscopic characteristics related to urbanization. In this study, changes in seasonal UHI intensity based on urban area were analyzed for eight Asian mega cities from 1992–2012. The results indicate that the change in pattern of UHI intensity varies for different cities and seasons. UHI intensity increased as the urban area size increased. Furthermore, the dependency of UHI intensity on the economic situation was also demonstrated. With respect to the seasons, significantly increasing trends appeared during the summer. Moreover, depending on urban characteristics such as geography and climate, increasing trends appeared during other seasons. Population was also found to affect UHI intensity by generating anthropogenic heat; however, its effect as an individual factor appeared to be insignificant. This is a macroscale study that analyzes the effect of urban area size on UHI intensity. Future studies on urbanization factors and levels influencing the UHI intensity using higher resolution materials are required

Emergent self-similarity and scaling properties of fractal intra-urban heat islets for diverse global cities

Urban areas experience elevated temperatures due to the urban heat island (UHI) effect. However, temperatures within cities vary considerably and their spatial heterogeneity is not well characterized. Here, we use land surface temperature (LST) of 78 global cities to show that the surface UHI (SUHI) is fractal. We use percentile-based thermal thresholds to identify heat clusters emerging within SUHI and refer to them collectively as intra-urban heat islets. The islets display properties analogous to that of a percolating system as we vary the thermal thresholds. At percolation threshold, the size distribution of these islets in all cities follows a power law, with a scaling exponent (β) of 1.88 (±0.23,95%CI) and an aggregated perimeter fractal dimension (D) of 1.33 (±0.064,95%CI). This commonality indicates that despite the diversity in urban form and function across the world, the urban temperature patterns are different realizations with the same aggregated statistical properties. Furthermore, we observe the convergence of these scaling exponents as the city sizes increase. Therefore, while the effect of diverse urban morphologies is evident in smaller cities, in the mean, the larger cities are alike. Lastly, we calculate the mean islet intensities, i.e., the difference between mean islet temperature and thermal threshold, and show that it follows an exponential distribution, with rate parameter λ, for all cities. λ varied widely across the cities and can be used to quantify the spatial heterogeneity within SUHIs. In conclusion, we present a basis for a unified characterization of urban heat from the spatial scales of an urban block to a megalopolis.

The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data

This study examines the behavior of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime, the rural zone exhibits a higher average LST than the urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (-0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island but an enlargement of its spatial footprint.

The Challenge of Urban Heat Exposure under Climate Change: An Analysis of Cities in the Sustainable Healthy Urban Environments (SHUE) Database

The so far largely unabated emissions of greenhouse gases (GHGs) are expected to increase global temperatures substantially over this century. We quantify the patterns of increases for 246 globally-representative cities in the Sustainable Healthy Urban Environments (SHUE) database. We used an ensemble of 18 global climate models (GCMs) run under a low (RCP2.6) and high (RCP8.5) emissions scenario to estimate the increase in monthly mean temperatures by 2050 and 2100 based on 30-year averages. Model simulations were from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Annual mean temperature increases were 0.93 degrees Celsius by 2050 and 1.10 degrees Celsius by 2100 under RCP2.6, and 1.27 and 4.15 degrees Celsius under RCP8.5, but with substantial city-to-city variation. By 2100, under RCP2.6 no city exceeds an increase in T_mean > 2 degrees Celsius (relative to a 2017 baseline), while all do under RCP8.5, some with increases in T_mean close to, or even greater than, 7 degrees Celsius. The increases were greatest in cities of mid to high latitude, in humid temperate and dry climate regions, and with large seasonal variation in temperature. Cities are likely to experience large increases in hottest month mean temperatures under high GHG emissions trajectories, which will often present substantial challenges to adaptation and health protection.

Impact of urbanization on spring and autumn phenology of deciduous trees in the Seoul Capital Area, South Korea

Urbanization exerts anthropogenic forcing that affects regional climate and ecosystems. With increasing levels of urbanization associated with urban population growth in the near future, understanding of the impact of urbanization on terrestrial ecosystems is important for predicting future environmental changes. This study evaluates the impact of urbanization on spring and autumn phenology by addressing the relationship between population density and phenology at nine stations in the Seoul Capital Area (SCA), South Korea during 1991-2010. We analyze the spring budburst dates for the six species (Prunus mume, Forsythia koreana, Rhododendron mucronulatum, Prunus yedoensis, Prunus persica, and Prunus pyrifolia) and the leaf coloring date for the two species (Ginkgo biloba and Acer palmatum). Regardless of species, the density of the urban population shows significant negative (positive) relationships with spring (autumn) phenology. In the SCA, urban population increases are related to earlier spring budburst up to 13 days and delayed leaf coloring up to 15 days. The most apparent spring budburst sensitivity is observed in Prunus mume, whereas the most dominant autumn leaf coloring sensitivity is observed in Acer palmatum. The relationship between population density and phenology is supported by the difference in nocturnal temperatures between stations which varies with the population density. Our results suggest that, in addition to global warming, future population growth should be considered in ecosystem assessments of human-induced environmental changes.

Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners.

Turning down the heat: An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale

Guiding urban planners on the cooling returns of different configurations of urban vegetation is important to protect urban dwellers from adverse heat impacts. To this end, we estimated statistical models that fused multi-temporal very fine spatial (20 cm) and vertical (1 mm) resolution imagery, that captures the complexity of urban vegetation, with remotely sensed temperature data to assess how urban vegetation configuration influences urban temperatures. Perth, Western Australia, was used as a case-study for this analysis. Panel regression models showed that within a location an increase in tree and shrub cover has a larger cooling effect than grass coverage. On average, holding all else equal, an approximate 1 km² increase in shrub (tree) cover within a location reduces surface temperatures by 12 °C (5 °C). We included a range of robustness checks for the observed relationships between urban vegetation type and temperature. Geographically weighted regression models showed spatial variation in the cooling effect of different vegetation types; this indicates that i) unobserved factors moderate temperature-vegetation relationships across urban landscapes, and ii) that urban vegetation type and temperature relationships are complex. Machine learning models (Random Forests) were used to further explore complex and non-linear relationships between different urban vegetation configurations and temperature. The Random Forests showed that vegetation type explained 31.84% of the out-of-bag variance in summer surface temperatures, that increased cover of large vegetation within a location increases cooling, and that different configurations of urban vegetation structure can lead to cooling gains. The models in this study were trained with vegetation data capturing local detail, multiple time-periods, and entire city coverage. Thus, these models illustrate the potential to develop locally-detailed and spatially explicit tools to guide planning of vegetation configuration to optimise cooling at local- and city-scales.

The footprint of urban heat island effect in 302 Chinese cities: Temporal trends and associated factors

The urban heat island (UHI) effect has been a concern for decades due its adverse influence on energy consumption, air and water quality, and, most importantly, the health of urban dwellers. Researchers have paid much attention to the magnitude of the UHI effect, but ignored its spatial extent (i.e. footprint) which is another important aspect of the UHI effect. In this study, we systematically analyzed the footprint of surface UHI (SUHI) effect in 302 Chinese cities, especially temporal trends of the footprint, by using multi-source remote sensing data. The footprint of SUHI effect (FP) was estimated by the Gaussian surface, and its temporal trend was examined by the Mann-Kendal and the Sen's slope estimator non-parametric tests. We found the FP showed evident diurnal (daytime > nighttime), seasonal (summer > winter) and inter-city (big cities > small and medium-sized cities) differences. During the period 2003-2016, over 80% of the 302 cities exhibited increasing trends of the FP in annual days and summer days, and the increasing trends were statistically significant (p < 0.05) in about half of these cities. In the nights, the FP increased in more than 70% of the cities, and about one-third of the 302 cities experienced significantly increasing trends of the FP. On average, the annual daytime and annual nighttime FPs increased at a rate of 5.0% per year and 3.8% per year, respectively. More importantly, the correlation analysis indicated that the increase of anthropogenic heat emissions and the decrease of vegetation activities and surface albedos should take lead responsibility for the expansion of the FP in the urbanization process. These results reveal that the spatial extent of heat island effect has expanded significantly in numerous Chinese cities, and this increasing trend will be sustained in the coming years if no more effective measures are carried out.

Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data

Confounding factors like urbanization and land-use change could introduce uncertainty to the estimation of global temperature trends related to climate change. In this work, we introduce a new way to investigate the nexus between temporal trends of temperature and urbanization data at the global scale in the period from 1992 to 2013. We analyze air temperature data recorded from more than 5000 weather stations worldwide and nightlight satellite measurements as a proxy for urbanization. By means of a range of statistical methods, our results quantify and outline that the temporal evolution of urbanization affects temperature trends at multiple spatial scales with significant differences at regional and continental scales. A statistically significant agreement in temperature and nightlight trends is detected, especially in low and middle-income regions, where urbanization is rapidly growing. Conversely, in continents such as Europe and North America, increases in temperature trends are typically detected along with non-significant nightlight trends.

Quantifying urban heat island intensity and its physical mechanism using WRF/UCM

Reliable quantification of urban heat island intensity (UHII) is crucial for the evaluation of extreme heat waves and the related heat stress. As a powerful approach for the study of urban climate, numerical models can simulate urban heat island (UHI) in both high spatial and temporal resolutions. However, accurate quantification of UHII using modelling grid data is still a challenge at present, due to the different criterions for the selection of urban/rural grids. This study simulates the high-resolution UHI in the city of Berlin using the Weather Research and Forecasting Model coupled with Urban Canopy Module. A new method to quantify UHII, which is based on the fitted linear functions of simulated 2-m air temperature (T_2m) using the impervious surface area in WRF grids (ISA_WRF), was adopted and evaluated. The simulated T_2m matches the observations well, with a correlation coefficient of 0.95 (P < 0.01) and RMSE of 1.76 °C. The study area shows a strong UHI at nighttime. The simulated nighttime T_2m increases with the increase in the ISA_WRF. The linear functions of simulated nighttime T_2m against ISA_WRF are well fitted. The UHII is calculated as the products of the slopes of fitted functions and the largest ISA_WRF. The derived UHII shows U-shaped diurnal variations, with high values at nighttime. The difference of simulated surface temperature and sensible heat flux between the impervious surface and the vegetation surface jointly determines the derived UHII. The large difference of surface temperature and the small difference of sensible heat flux between the impervious and the vegetation surface generate the high UHII at nighttime and vice versa during the daytime. The method of ISA_WRF-based function of T_2m overcomes the problems of traditional methods in arbitrary selecting urban/rural grids. It can be used easily to quantify UHII and to do the comparison study of UHII between different cities.

Association between heat stroke and ischemic heart disease: A national longitudinal cohort study in Taiwan

The purpose of this study is to determine the relationship between heat stroke and ischemic heart disease (IHD), in a nationwide population using a longitudinal approach. We retrospectively examined the data from the National Health Insurance Research Database (NHIRD) in Taiwan, for patients examined between 2000 and 2013. In total, 628 patients with a heat stroke episode were enrolled and matched with 1256 patients without any history of a heat stroke episode by propensity score matching at a ratio of 1:2. The mean follow-up years of the heat stroke group was 11.89 years and the mean follow up of the control group was 11.51 years. An association between heat stroke episodes and IHD (log-rank p < .001) was found in a univariate cox regression analysis. After multivariate adjustment, age, comorbidities (hypertension, diabetes, stroke), and lower insurance premiums were associated with IHD events in patients who had a heat stroke. IHD was independently associated with heat stroke following cox multivariate regression analysis and patients with a heat stroke episode had a higher incidence of IHD events compared to those without any heat stroke episode (2598.41/10⁵ person-years vs. 1286.14/10⁵ person-years, adjusted hazard ratio 3.527, 95% CI: 2.078-4.032, p < .001). The onset of IHD in patients who suffered a heat stroke was earlier than in those without a heat stroke episode (2.08 ± 3.45 vs. 3.61 ± 3.25 years, p < .001). In conclusion, clinicians should be aware about evaluating the IHD risk following a heat stroke episode in a patient.

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.

Urbanization and Spillover Effect for Three Megaregions in China: Evidence from DMSP/OLS Nighttime Lights

Urbanization drives human social development and natural environmental changes and shows complex implications for sustainability and challenges of future development, particularly in emerging countries. While extensive studies focus on extracting urban areas more precisely, less attention has been devoted to understand megaregion evolution and its related socioeconomic processes, not by socioeconomic statistics, but by comparing remote sensing based spatiotemporal evolution and the related spillover effect. Three main megaregions (with large area, high population and total gross domestic product) in China are selected for the analysis of development changes in an urbanization (magnitude, development)-diagram, of growth pattern changes based on Gravity Center and weighted Standard Deviation Ellipses and of the megaregions’ spillover effect. Employing the spatiotemporally continuous lighted areas (DN ≥ 12) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime signal (1992–2013) to the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) leads to the following results: (i) Developments in the (magnitude, development)-diagram indicate 25.97%, 45.95%, and 39.10% of the first (high urbanization, fast development) class of the BTH, YRD, and PRD megaregions are rapidly developing into highly urbanized regions. The first class may slow down in the future like the second (high urbanization, slow development) class acting from 1992 to 2013, and the third (moderate urbanization, fast development) class shows potential to become the first class in the future. (ii) The original core function zones of YRD and PRD have highly developed till 1992 and expanding out with fast development from 1992 to 2013. Contrarily, BTH indicates more fast development toward the original core function zones while spatial expansion. (iii) The gravity distance evolution of the three megaregions shows a tendency towards the geometric distance 2013. However, YRD and PRD (BTH) indicate a light intensity expansion (concentration). This may relate to a positive spillover effect of YRD and PRD upon their neighbor cities, with the strongest signal in the early 21st Century and thereafter adjusting and followed by another positive spillover.

Modern Compact Cities: How Much Greenery Do We Need?

The modern compact city is identified as a high-density and mixed-use pattern. Its features are believed to contribute to a form of functional urban design that supports sustainability and, restresses, the importance of ecosystem services. Urban green space (UGS) plays a vital role in the design and impact on how compact cities have developed and triggered a scientific discord on the amount of greenery individuals require and to what extent contemporary approaches address the question. Research points to at least 9 m² of green space per individual with an ideal UGS value of 50 m² per capita. An examination on the perception, use, quality, accessibility and health risks of urban green and blue spaces is explored, alongside the availability of novel UGS and greenery-related approaches that investigate compact city design and planning for health and wellbeing. The amount of 'green' and relating UGS availability in cities indicates vital knowledge modern compact cities must consider.

Urban Cold and Heat Island in the City of Bragança (Portugal)

The thermal environment is an important aspect of the urban environment because it affects the quality of life of urban residents and the energy use in buildings. Urban Heat Island (UHI) and Urban Cold Island (UCI) are complementary effects that are the consequence of cities’ structures interference with the local climate. This article presents results from five years of urban climate monitoring (2012–2016) in a small Portuguese city (Bragança) using a dense meteorological network of 23 locations covering a wide array of Local Climate Zones (LCZ), from urban areas to nearby rural areas. Results show the presence of both the UHI effect, from mid-afternoon until sunrise, and the UCI after sunrise, both being more intense under the dense midrise urban context and during the summer. Urban Green Spaces had an impact on both UHI and UCI, with an important role in cooling areas of the city during daytime in the summer. Other LCZs had less impact on local thermal conditions. Despite the small size of this city, both effects (UHI and UCI) had a relevant intensity with an impact on local climate conditions. Both effects tend to decrease in intensity with increasing wind speed and precipitation.

Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China

Increasingly urban agglomeration, representing a group of cities with a compact spatial organization and close economic links, can rise surface temperature in a continuous area due to decreasing distance between cities. Significant progress has been made in elucidating surface urban heat island intensity (SUHII) of a single city or a few big cities, but the SUHII's patterns remain poorly understood in urban agglomeration regions. Using Aqua/Terra MODIS data over 2010-2015, we examined the SUHII variations and their drivers in Yangtze River Delta Urban Agglomeration (YRDUA) of east China. Instead of using the widely-used suburban/rural areas as references, this study predicted the unaffected reference temperature wall-to-wall from natural forests by a simple planar surface model. Results indicated that urbanization warmed the land surface regardless of urban area size in YRDUA, with the SUHII clearly larger in the day (2.6±0.9°C) than night (0.7±0.4°C). The SUHII varied markedly by cities, yet the largest did not happen in the presumed core cities. Also, the SUHII differed greatly in a seasonal cycle, with summer-winter difference of 4.2±0.9°C and 2.0±0.5°C in the day and night, respectively. Particularly, cooling effects of urban areas were observed in winter for the majority of cities at night. These spatiotemporal patterns depend strongly on the background climate (precipitation and air temperature), vegetation activity, surface albedo, and population density, with contrast mechanisms during the day and night. Further, we showed that ignoring urban agglomeration effect (using suburban/rural areas as the unaffected references) would lead to large biases of SUHII estimates in terms of magnitude and spatial distribution. Our results emphasize the necessity of considering cities altogether when assessing the urbanization effects on climate in an urban agglomeration area.

A new method to quantify surface urban heat island intensity

Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISA_KDE). The linear functions of LST are well fitted by the ISA_KDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.

Population changes in residential clusters in Japan

Population dynamics in urban and rural areas are different. Understanding factors that contribute to local population changes has various socioeconomic and political implications. In the present study, we use population census data in Japan to examine contributors to the population growth of residential clusters between years 2005 and 2010. The data set covers the entirety of Japan and has a high spatial resolution of 500 × 500 m2, enabling us to examine population dynamics in various parts of the country (urban and rural) using statistical analysis. We found that, in addition to the area, population density, and age, the shape of the cluster and the spatial distribution of inhabitants within the cluster are significantly related to the population growth rate of a residential cluster. Specifically, the population tends to grow if the cluster is "round" shaped (given the area) and the population is concentrated near the center rather than periphery of the cluster. Combination of the present results and analysis framework with other factors that have been omitted in the present study, such as migration, terrain, and transportation infrastructure, will be fruitful.

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

Land Surface Temperature (LST) is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI). In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2) were used for the analysis of an eighteen-year time series (2001–2017) of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a) positive LST trends in the urban areas especially during nighttime ranging from +0.412 °K in Marseille to +0.923 °K in Cairo and (b) the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 °K) and Barcelona (+0.307 °K).