Urban Heat Research Articles

Unraveling the Complexities of Land-Use–Land Cover Transformation and Its Impact on Land Surface Temperature and Urban Heat Island Effect: A Study of Nowshera District, Pakistan

Unraveling the Complexities of Land-Use–Land Cover Transformation and Its Impact on Land Surface Temperature and Urban Heat Island Effect: A Study of Nowshera District, Pakistan

Mitigating urban heat island effects using trees in planters with varied crown shapes

Urban downtown areas are often overheated and contribute to the creation of urban heat island (UHI) phenomenon. Implementation of trees is considered an effective way to mitigate UHI and improve outdoor thermal comfort (OTC) in densely built-up areas.This study aims to investigate the potential of applying additional trees in planters to mitigate UHI effects and improve OTC in open urban areas. Simulation of OTC is performed for hot summer days using Universal Thermal Climate Index (UTCI). In this study, we have devised a method to determine favourable locations for a predetermined number of additional trees, with different crown shapes (cylinder, sphere and cone), to improve OTC conditions. In the proposed method, an evolutionary algorithm based on natural selection was used as it is effective for solving problems that have a large number of combinations. The method considers the geometry of the built environment, the geometry, locations and number of additional trees, weather data and UTCI simulation.The method was applied to three different urban morphologies located in the downtown of Novi Sad, Serbia. The results show that, depending on urban geometries and morphologies, different crown shapes lead to different UTCI reduction. Cylinder-shaped and sphere-shaped tree crowns showed to be the most effective in reducing heat stress. The positioning of additional trees reduced the UTCI values at single manikin locations by up to 6.11 °C, indicating that the process of determining their locations is crucial for mitigating the heat and improving OTC conditions during hot summer days.

Exploring the cooling potential of green roofs for mitigating diurnal heat island intensity by utilizing Lidar and Artificial Neural Network

Urban areas frequently exhibit higher elevated temperatures than their rural counterparts due to the prevalence of structures over natural resources, a phenomenon known as daytime surface urban heat island (DSUHI). This study simulates the cooling effects of green roofs (GR) for mitigating DSUHI by utilizing 2D and 3D urban morphological parameters over downtown Austin, Texas, USA. We estimated spectral indices using Landsat 8, Sentinel-2A, and Lidar data to include built-up, vegetation, waterbodies, daytime land surface temperature (DLST), buildings (height volume and density), sky view factor (SVF), and solar radiation (SR). Finally, we integrated eleven different neural network algorithms for GR simulation, validation, and correlation between DLST and the above urban features- the strongest model generated an R2 of 0.783 and an RMSE of 0.925°F. We found converting 4.2% of the total rooftop area to GR resulted in an average DLST decrease of 2.80°F. The most significant cooling effects occurred with buildings heights between 13–28 meters, high SVFs, SR, and closer proximity to water bodies. Our findings amplify the strategic importance of GRs in urban morphology and planning, guiding green infrastructure development to mitigate and foster urban environment sustainability.

Identifying climate-ready plant for urban environment: Integrating machine learning with traditional plant selection tools

Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning.We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1,070 urban plant species across two periods: the baseline (1981-2010) and the future (2041-2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning.Our findings indicate that nearly half (49.0%) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9%) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.

Influence of the Urban Heat Island Effect of a Large City on the Physiological Stability of Tree Plantations

Influence of the Urban Heat Island Effect of a Large City on the Physiological Stability of Tree Plantations

Ilha de calor de superfície e indicadores geoespaciais: avaliação decadal comparativa por sensoriamento remoto

Abstract Surface Urban Heat Islands (SUHI) are formed through processes that include decreasing vegetation and increasing materials that are more conductive to heat. To investigate this phenomenon, the objective of this work is to comparatively verify the surface temperature and geospatial indicators to analyze environmental variation in the city of São Paulo, Brazil. The study method is remote sensing, with data from the Landsat 8 satellite over a ten-year timeframe. The variables include land cover, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and land surface temperature (LST), thereby obtaining the Urban Thermal Field Variation Index (UTFVI) and estimating SUHI formation. The results allowed for the presentation of a progression of variables, highlighting the recurrent formation of SUHI in the central and eastern regions of the city of São Paulo, Brazil. Conversely, the southern and extreme northern districts exhibited the best NDVI indices and low LST values.

Seasonal urban surface thermal environment analysis based on local climate zones: A case study of Chongqing

The rapid urbanization has exacerbated the heat island effect, impacting city development and residents' health. This study, using Local Climate Zones (LCZ) as a framework, connects spatial structure, resource allocation, and thermal environment research. It investigates the spatiotemporal heterogeneity of the surface thermal environment and its driving forces, crucial for mitigating heat issues. Utilizing various data sources like remote sensing images, road network data, land use data, high-resolution street view data, and building data, the research employs the random forest algorithm to map LCZs in Chongqing's central urban area. Through mathematical statistics, equi-sector analysis, ring-layer analysis, and Pearson correlation analysis, the study examines seasonal variations in spatiotemporal heterogeneity and driving mechanisms of the surface thermal environment. Key findings include: (1) The central urban area of Chongqing is dominated by open-building and vegetation-type LCZs, with building-type LCZs showing a “clustered” distribution, while natural-type LCZs are mainly found in the suburbs with ribbon and block distribution in the urban area. (2) The surface thermal environment in the study area correlates strongly with surface cover and exhibits significant high temperature effects in summer. (3) The surface thermal conditions vary significantly among different LCZs and exhibit seasonal patterns, natural-type LCZs generally have lower temperatures compared to building-type LCZs.(4) The surface thermal characteristics within the same category of LCZs in different locations display distinct differences and seasonal variations. (5) The internal temperatures of LCZs are significantly linked to four surface attributes, each displaying seasonal fluctuations. Greenness, height, and wetness are inversely related to the surface thermal conditions, while brightness shows a positive correlation. Both seasonal variations and LCZ types differences have a noticeable influence on their respective driving mechanisms to some degree.

Changes in morphological and physiological traits of urban trees in response to elevated temperatures within an Urban Heat Islands.

Urban Heat Islands (UHI) are a common phenomenon in metropolitan areas worldwide where the air temperature is significantly higher in urban areas than in surrounding suburban, rural or natural areas. Mitigation strategies to counteract UHI effects include increasing tree cover and green spaces to reduce heat. The successful application of these approaches necessitates a deep understanding of the thermal tolerances in urban trees and their susceptibility to elevated urban temperatures. We evaluated how the photosynthetic thermal optimum (Topt), photosynthetic heat tolerance (T50), and key leaf thermoregulatory morphological traits (leaf area, specific leaf area, leaf width, thickness and LDMC) differ between conspecific trees growing in 'hot [UHI]' vs. 'cool' parts of Montreal, Canada (with a difference of 3.4°C in air temperature), to assess the ability of seven common tree species to acclimation to higher temperatures. We hypothesized that individuals with hotter growing temperatures would exhibit higher Topt and T50, as well as leaf thermoregulatory morphological traits aligned with conservative strategies (e.g., reduced leaf area and increased leaf mass) compared to their counterparts in the cooler parts of the city. Contrary to our a priori hypotheses, leaf area increased with growing temperatures and only four of the seven species had higher T50 and only three had higher Topt values in the hotter area. These results suggest that many tree species cannot acclimate to elevated temperatures and that the important services they provide, such as carbon capture, can be negatively affected by high temperatures caused by climate change and/or the UHI effect. The ability vs inability of tree species to acclimate to high temperatures should be considered when implementing long term tree planting programs in urban areas.

Analyzing the Contribution of Urban Land Uses to the Formation of Urban Heat Islands in Urmia City

An Urban Heat Island (UHI) is an important variable in climate and environmental studies. Nowadays, population growth and urbanization development are the most important factors affecting the temperature increase in urban areas, which cause the creation of heat islands in urban areas. (1) Background: This study explores the influence of major land uses on the creation of Urban Heat Islands in Urmia city, Iran. (2) Methods: To achieve the aim of this study, Landsat satellite data including Landsat 5 and 8 imageries from the time periods of 1990 and 2023 were used. With the series of data processing and analyses on vegetation cover and land surface temperature, the impact of main land uses on the creation of Urban Heat Islands and the intensification of their effects have been investigated. (3) Results: The analysis reveals that barren lands consistently exhibit the highest temperature, while garden lands show the lowest temperature across both periods. In addition, the spatial distribution of Urban Heat Islands demonstrates a clustered pattern throughout the study period, with hot spots mainly located in the northwestern and southwestern areas. (4) Conclusions: This study’s findings can be helpful for urban policymakers and planners to develop practical strategies to mitigate UHIs and improve climate resilience in cities.

Utilizing Machine Learning for Predictive Maintenance of Climate-Resilient Highways through Integration of Advanced Asphalt Binders and Permeable Pavement Systems with IoT Technology

This review provides a comprehensive examination of the application of advanced technologies in enhancing the climate resilience of highway infrastructure. The focus is on the integration of machine learning for predictive maintenance, the use of Internet of Things (IoT) devices for real-time monitoring, and the deployment of advanced materials, specifically permeable pavements and modified asphalt binders. The review explores how these technologies work synergistically to create durable, low-maintenance highway systems capable of withstanding extreme environmental conditions. Advanced asphalt binders, such as those modified with styrene-butadiene-styrene (SBS) and nanomaterials, are discussed for their enhanced flexibility, thermal stability, and load-bearing capacity. Additionally, the environmental and structural benefits of permeable pavements are highlighted, particularly their role in stormwater management and reduction of urban heat island effects. Several case studies highlight the successful implementation of these technologies in diverse geographical and climatic conditions, including North Carolina, Australia, and Raipur, India. These cases illustrate the adaptability and environmental benefits of permeable pavements in managing stormwater, recharging groundwater, and mitigating the urban heat island effect. By synthesizing recent advancements and practical implementations, this review emphasizes the importance of integrating predictive technologies and resilient materials to develop sustainable, climate- adaptive highway systems. These insights offer a foundation for future infrastructure policies and technological innovations aimed at enhancing the durability and environmental sustainability of highway networks.

Analysis of the Availability and Utilization of Green Open Space (GOS) in Kahayan Hilir Subdistrict, Pulang Pisau Regency Using the DPSIR Approach

This study aims to analyze the availability and utilization of Green Open Space (GOS) in Kahayan Hilir Subdistrict, Pulang Pisau Regency, utilizing the DPSIR (Driving Forces, Pressures, States, Impacts, Responses) approach and the Indonesian Green Blue Index (IHBI) model. This research addresses the growing demand for adequate GOS due to rapid urbanization and economic development. The study identifies the driving forces behind GOS changes, pressures arising from land conversion and population growth, the current state of GOS, and the public's perception of GOS utilization. Data collection involved interviews with stakeholders, systematic field observations, and a comprehensive analysis of regional planning documents. The results indicate that the availability of GOS in Kahayan Hilir is still below government standards, resulting in environmental issues such as declining air quality, biodiversity loss, and increased urban heat island effects. Public perception indicates an awareness of the importance of GOS for health and recreation, although accessibility and facilities remain limited. This study recommends increasing both the quantity and quality of GOS through stricter policies, community participation, and public-private partnerships to support environmental quality and the well-being of residents in Kahayan Hilir Subdistrict.

An evaluation of intra-university campus temperature variability under variable synoptic weather conditions using mobile transects.

Intensive observations were collected in a wide range of synoptic weather conditions to evaluate variability in the intra-urban heat island on the campus of the University of North Carolina at Charlotte between February 2023 and June 2023. An easily reproducible bicycle-based mobile transit route around the university was traversed during 20 afternoon and 20 evening periods. The magnitude of observed temperature range from an individual data collection period is defined as the campus urban heat island intensity, with areas having more anthropogenic modification also having higher temperatures. While other papers have examined the relationship between the city-scale urban heat island intensity and the present weather conditions, this paper aims to disentangle the relationship between present weather conditions and the magnitude of thermal variability across a small intra-urban campus with diverse land use and land cover characteristics. This will contribute to a better understanding of intra-urban heat islands, particularly identifying days where conditions will be highly dangerous in more developed areas, and not in more natural environments. When comparing the standardized mobile-transit observations to the regionally present weather conditions it is evident that clear and calm conditions often enhance both city-scale and campus-scale heat islands, increasing temperature disparities. While the spatial distribution of warm and cool areas across campus remains relatively constant, the campus-scale heat island is significantly modulated by the present weather conditions.

A Methodological Approach to the Study of Retroreflective Pavements

Climate change, principally driven by human activities, has led to an increase in global temperature, which is predicted to continue rising in the coming years. This temperature increase is even more pronounced in urban areas due to the heat island effect. This phenomenon is highly influenced by the presence of paved streets made with bituminous mixtures, which are characterised by their high solar radiation absorption capacity. Bituminous mixtures retain and re-emit a large amount of heat that intensifies the urban heat island effect. The novelty of this work is to measure retroreflective properties of bituminous mixtures that present a highly textured surface. In this context, the aim of this study is to evaluate the retroreflectance of different bituminous mixtures for use as pavement surfaces, focusing on the influence of colour and different types of aggregates. For this, total and directional reflectance measurements were conducted to determine the retroreflectance of these mixtures, with the purpose of mitigating the heat island effect in urban environments without affecting users through reflected solar radiation. The results show the retroreflective capacity of the designed mixtures within the visible spectrum, especially those manufactured with light-coloured aggregates and synthetic binders pigmented with titanium dioxide. Thus, the retroreflectance of the lighter mixtures range from 37.9% at a 0° entrance angle to 68.9% at 60°, while the black mixtures exhibit values between 5.1% and 8.4%.

Estimating Cooling Energy Demand from Building Attributes and Environmental Parameters using 3D City Models

Abstract. Most of the global population have shifted to urbanization with advancements in technology. With this transition comes the responsibility of applying these technologies to promote sustainable development practice. Since energy demand is highest in the urbanized areas, it is important that proper assessment and management of energy resource is considered in policy-making and urban planning. This study investigated the estimation of cooling energy demand in Iloilo City Proper, Philippines using a 3D city model integrated with building attributes including building functions and year of construction, while also taking into consideration meteorological factors in the area. The proposed method used the application SimStadt2.0, following the German computation standard DINV18599. Building functions and year of construction were extracted from available building attributes data and satellite images respectively, using the free and open-source software QGIS. A CityGML Level of Detail 1 was generated from 3Dfier using building footprints and LiDAR point cloud data, along with the extracted building attributes of the 5,426 buildings. Meteorological data from INSEL 8 were also considered in the estimation of cooling energy demand in SimStadt2.0. Results showed a monthly energy demand of 12.33 kWh to 313,530.08 kWh in the study area. The estimated energy demand values were higher than the standard mean for different building functions in the country, but within the expected range of values for each season. Urban Heat Islands (UHIs), analyzed using Land Surface Temperature (LST) values, also have significant correlation to areas with higher cooling energy demands. However, inconsistencies can imply the need for further investigation.

Embodied Thermal Insecurity and Counter‐Hegemonic Heat Mapping

AbstractLived experiences with urban heat are often rendered invisible, shrouded under the cloak of neoliberal resilience discourse and sanitised heat mapping and messaging. This is particularly tragic for disadvantaged at‐risk populations in white, settler colonial contexts where heat tolerance is worn as a badge of honour. Here, drawing upon semi‐structured interviews and focus group discussions in Perth, Western Australia, and with feminist emphasis on the embodied, the everyday, and bodily difference, we illustrate how corporeal thermal insecurities among rough sleepers, people with disabilities, and across intersectional disenfranchisement immobilise and dehumanise. By employing the logic of structures of violence to thermal suffering, we reveal how housing and energy precarity exacerbate entrenched racism and normalised discrimination. Our aim is to expand current debates on heat action plans (HAPs) and cool refuges by examining what “better weathering” could mean in practice, via the materialities of heated urban bodies (HUBs) and to demonstrate how such counter‐hegemonic heat mapping serves as a corporeal critique of the neoliberal resilient subject.

Leveraging Spatial Analysis Techniques for Developing a Climate Resilient and Localized Urban Greening Plan for Pasig City

Abstract. A recent study has been conducted to evaluate the current state of green spaces in Pasig City, Philippines, and to propose a Climate Resilient and Localized Urban Greening Plan (CRLUGP) to address urban greening that is essential for creating sustainable cities, especially in the face of climate change and urban population growth. The study utilized Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) to map green spaces, analyze climate risks, and identify necessary corrective measures. The findings revealed that Pasig City currently only has 70.18 hectares of green space, which is significantly below the World Health Organization's recommended minimum of nine (9) square meters of green space per capita. Projections indicate that by 2032, the city will need 925.72 hectares of green space to meet the needs of its growing population. Additionally, the study identified a connection between low canopy cover and increased urban heat island effects, with the top 10 barangays showing a canopy cover of only 27.64%, well below the recommended 40%. Moreover, areas with limited green spaces were found to be at a higher risk of flooding, highlighting the importance of more vegetation for managing stormwater and mitigating climate-related risks. The study recommends a strategic approach to urban greening, leveraging spatial analysis for continuous monitoring and evaluation. By integrating spatial technologies and data-driven decision-making, Pasig City aims to establish a more sustainable and resilient urban environment for its residents.

Investigation into the Mechanism of the Impact of Sunlight Exposure Area of Urban Artificial Structures and Human Activities on Land Surface Temperature Based on Point of Interest Data

With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores the impact of sunlight exposure areas of artificial structures and human activities on land surface temperature (LST) in Hefei and Xuzhou, using Landsat 9 data, Google imagery, nighttime light data, and Point of Interest (POI) data. Building shadow distributions and urban road surface areas were derived, and geospatial analysis methods were applied to assess their impact on LST. The results indicate that the sunlight exposure areas of roofs and roads are the primary factors affecting LST, with a more pronounced effect in Xuzhou, while anthropogenic heat plays a more prominent role in Hefei. The influence of sunlight exposure on building facades is relatively weak, and population density shows a limited impact on LST. The geographical detector model reveals that interactions between roof and road sunlight exposure and anthropogenic heat are key drivers of LST increases. Based on these findings, urban planning should focus on optimizing building layouts and heights, enhancing greening on roofs and roads, and reducing the sunlight exposure areas of artificial structures. Additionally, strategically utilizing building shadows and minimizing anthropogenic heat emissions can help lower local temperatures and improve the urban thermal environment.

Correlation analysis of landscape patterns and urban thermal environment in Kunming based on a panel data model

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment.

An improved machine learning-based model for prediction of diurnal and spatially continuous near surface air temperature

Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013–2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN’s superior performance with a Pearson correlation coefficient (r) of 0.98 and a root mean square error (RMSE) of 0.97 K, compared to r and RMSE of 0.85 and 1.09, respectively, for the linear model. FLANN showed greater improvements for urban stations with r and RMSE differences of 0.19 and − 2.03 K. Application of FLANN to predict Tair in Hong Kong in July 2023 enabled detailed urban heat island (UHI) analysis, revealing dynamic spatial and temporal UHI patterns. This study highlights FLANN’s potential for accurate Tair prediction and UHI analysis, enhancing urban thermal environment management.

Exploring Cool Pavement Technologies: A Lab-Based Experimental Analysis of Temperature and Reflectivity

Urban heat islands (UHI) have become an increasingly pressing issue owing to the global warming trend and a steady growth of urban areas. Revegetation, shading, or the reduction of sealed surfaces is not always possible in cities. Therefore, one way to fight UHIs is through adapting currently used pavements. This study assesses the thermal behavior of 12 different pavement materials in a custom-made test stand. The materials include mastic asphalt ( MA 8, MA 8 white), porous asphalt ( PA 4, PA 4 abraded, PA 4 white, PA 8, PA 8 abraded, PA 8 white), asphalt concrete ( AC 11 transparent, AC 8 yellow), and semi-flexible pavements ( SFP 8, SFP 8 zeolite). The albedo was assessed using pyranometer. Sensors and thermal imaging were used to measure temperatures during two simulated heat cycles. Both the temperatures underneath the test specimens and at the surface were considered, as well as the cooling times of the materials. The results showed high maximum temperatures for PA and MA, as well as low maxima for the white painted materials, with differences in maxima of up to 15°C. Porous asphalt, like PA 8, cools faster than MA 8 from roughly 47°C to 28°C, taking 3.7 h compared with MA 8’s 5.1 h. This highlights the importance of considering cooling times and regional solar phases in experimental setups. Through the wide range of tested materials, a significant linear correlation between the measured albedo and surface temperature was proven. Ultimately, a foundation for thermal comparability between these materials was established.