Distribution Of Urban Heat Island Research Articles

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.

Impact of urban spatial dynamics and blue-green infrastructure on urban heat islands: A case study of Guangzhou using Local Climate Zones and predictive modeling

Amidst mounting concerns regarding health risks associated with excessive carbon emissions, attention has turned to the heat mitigation ability of blue-green infrastructure (BGI). BGI offers heat mitigation through transpiration cooling, effectively countering the urban heat island (UHI) effect while facilitating energy conservation and carbon reduction. This study adopted the InVEST model to assess the spatial pattern of UHI and heat mitigation service based on the Local Climate Zone (LCZ) classification, along with quantifying the energy savings in Guangzhou for 2019. The results revealed that the UHI effect exhibited greater intensity in the southwest built-up area. Compact Lowrise (LCZ-3) and Heavy Industry (LCZ-10) areas demonstrated higher UHI intensity, while Water (LCZ-G) and Dense Trees (LCZ-A) areas showcased the highest heat mitigation index (HMI) and optimal cooling capacities. Three urban development scenarios were devised to simulate the UHI distribution, heat mitigation capacity, and energy savings by 2025, using the CA-Markov model. In comparison to the Baseline scenario, the Environmental Protection scenario showed a modest 3.67 % decrease in the built-up area, but a noteworthy increase in HMI and energy saving, by 21.2 % and 6.6 % respectively. This scenario can serve as a reference pathway for urban energy conservation and sustainable development.

Advancing climate resilience through a geo-design framework: strengthening urban and community forestry for sustainable environmental design

Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban, suburban, and town environments. This field often entails extensive civic involvement and collaborative partnerships with institutions. Its overarching objectives span a spectrum from preserving water quality, habitat, and biodiversity to mitigating the Urban Heat Island (UHI) effect. The UHI phenomenon, characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage, serves as a focal point in this study. The study focuses on developing a methodological framework that integrates Geographically Weighted Regression (GWR), Random Forest (RF), and Suitability Analysis to assess the Urban Heat Island (UHI) effect across different urban zones, aiming to identify areas with varying levels of UHI impact. The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect. Conducted in various London areas, the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI. By mapping the spatial variability of UHI, the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies. The study’s findings are expected to provide valuable insights for urban planners and policymakers, aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management.

Urban heat island distribution observation by integrating remote sensing technology and deep learning

ABSTRACT Using particle swarm optimisation algorithm to optimise support vector machines enhances urban heat island observation methods, while remote sensing technology aids in selecting temperature estimation parameters. Then the two are combined to construct a model for estimating urban near-surface temperature. A contribution study is conducted on the selected parameters. The selected parameters have contributions in the near-surface temperature estimation. The determination coefficient of the constructed urban near-surface temperature estimation model was 0.892. The root mean square error was 0.42°C, the F1 value is 0.82, and the running time is 0.41 seconds, which was superior to other comparison models. Additionally, this model was applied to observe the urban heat island in Xi’an. The overall spatial distribution was low in the south and high in the north, with the central area being higher than the surrounding area, the highest temperature is 23.51°C, and the lowest temperature is 19.05°C. Moreover, the intensity level in the high-temperature area accounted for 16.9%. Based on the above results, the near-surface temperature estimation model constructed in the study has shown high accuracy and efficiency in urban heat island observation. It can be applied in practice, providing theoretical reference for urban planning and ecological environment protection.

Utilization of Multitemporal Landsat Images for Analysis Urban Heat Island Based on Google Earth Engine in Cimahi City

Cimahi City is physically part of the Bandung-Cimahi core city conurbation of the system of cities in the Bandung City Basin Region so has high development activity. This condition causes an increase in the surface temperature of Cimahi City, especially in the downtown area and triggers the Urban heat island phenomenon. This research was conducted to map multi temporal spatial changes in the vegetation index (MSAVI) and land surface temperature (LST) in Cimahi City in 2015, 2019 and 2023 and to analyze their relationship to the phenomenon and the widespread distribution of urban heat island using Landsat 8 imagery by integrating cloud techniques. computing Google Earth Engine. The results of surface temperature (LST) were correlated with vegetation density (MSAVI) using a simple regression test to determine how much influence changes in vegetation density had on surface temperature (LST). The distribution of urban heat islands was obtained from the classification of LST processing with urban heat island threshold values. The results showed that there was an increase in the distribution area of the urban heat island phenomenon with a total area of urban heat island increasing by 1319.94 Ha in 2015, 3389.04 Ha in 2019, and 3634.04 Ha in 2023. The areas that are dominated by urban impacts The heat island occurred in South Cimahi District with an area affected of 1440.43 Ha.

ANALISIS DAMPAK PERUBAHAN PENGGUNAN LAHAN PERKOTAAN TERHADAP URBAN HEAT ISLAND DI KECAMATAN KEDATON

The Urban Heat Island phenomenon is defined as a form of local climate change. In the formation of the Urban Heat Island phenomenon, there are several factors, namely changes in land use, vegetation density, building density, and vehicle emissions. Dense development areas that have caused changes in land use, settlements, and transportation activities have begun to accumulate in Kedaton District. Kedaton District is included in the activity center area so that changes in land use from 2017 to 2022 occur significantly, which can cause an increase in urban temperatures. This study aims to analyze the impact of changes in urban land use on Urban Heat Island in Kedaton District. The variables used are land use, surface temperature, vegetation density, building density, motor vehicle intensity, and motor vehicle emissions. The analytical methods used are LST, NDVI, NDBI, road service level analysis, vehicle emission analysis, and correlation analysis. The results of the analysis show that the impact of changes in land use will result in a 76% decrease in vegetation density in 2022, a 95% increase in building density, and a 99% increase in vehicle emissions. The impact of changes in land use has contributed to the distribution of urban heat islands in Kedaton District. To minimize the impact of changes in use on the Urban Heat Island in Kedaton District, mitigation efforts based on the resilient city concept are needed, namely the addition of private green open spaces, reflective roof paint in densely populated areas, and optimizing public transportation.

ANALISIS PERUBAHAN URBAN HEAT ISLAND DAN PENGARUHNYA TERHADAP INDEKS KENYAMANAN KOTA BANJARBARU

The Banjarbaru administrative status has changed from an Administrative City to a City, and in 2022 become the Provincial Capital. Population growth and infrastructure/building development due to these changes may affect environmental changes. A significant increase in air temperature against its normal due to environmental changes can trigger climate change. Urban Heat Island (UHI) is a phenomenon recognized by the higher temperature conditions in urban areas compared to surrounding areas. UHI identification is known by calculating the difference in Land Surface Temperature (LST) values using remote sensing (satellite data). An increase in the value of LST in an area can trigger the appearance of UHI and make the environment less comfortable. The LST value is also related to population, vegetation, residential areas, and built-up land. Thermal Humidity Index (THI) is an index used to determine comfort level based on temperature and humidity conditions. This study aims to analyze spatiotemporal changes in UHI phenomena in the Banjarbaru area during three administrative status periods (1998 - 2022) and its effect on the comfortable index. This study uses Landsat satellite data (consist Visible, Near Infrared, and Thermal Bands) and Relative Humidity (RH) from 2 weather stations in Banjarbaru. The result shows that the distribution of UHI in the Banjarbaru City area increases around industrial areas, settlements, government centers, and along arterial roads that are open land without trees. In general, the Banjarbaru area experienced an increase in the comfort index. Initial conditions belonging to the comfortable and less comfortable classes changed to become less comfortable to uncomfortable, especially in the North Banjarbaru sub-district and around Syamsudin Noor Airport ( Landasan Ulin sub-district).

The Impact of Urban Green Space on The Urban Heat Island Phenomenon – A Study Case in East Jakarta, Indonesia

The urban heat island (UHI) phenomenon has grown disastrous in many major cities across the world, including Jakarta. The more cities worldwide that suffer this scenario, the faster global warming will occur, making it a global concern that has to be given priority. The goal of this study was to determine the distribution of UHI in each region and how land use planning may mitigate it. East Jakarta was found to have the highest UHI in Jakarta. The method of this study used spatial analysis, which consists of land surface temperature (LST) and normalized vegetation difference index (NDVI) using GIS. The analysis results showed that the area with the highest LST and the widest coverage of UHI in East Jakarta is in Cakung where the temperature has increased to 4oC, while the area with the lowest LST and the narrowest coverage of the UHI area is Cipayung. Meanwhile, Cipayung has the most urban green space and urban forest, making up roughly 5.6% and 1.96% of its area, while Matraman has the least, making up 1.09% of its area without any urban forest. Therefore, there are no areas in East Jakarta that meet the requirements of 10% urban forest and 30% green space. However, the region with the lowest UHI distribution is Cipayung, which has the greenest urban area. It shows how the presence of urban forests, affects the distribution of UHI in metropolitan areas.

USING REMOTE SENSING TECHNOLOGIES FOR MONITORING URBAN HEAT ISLANDS

Background. The urbanization process is accelerating every day, which entails significant changes in the natural landscape. This leads to microclimatic changes, air pollution, thermal effect, etc. Due to air pollution by man-made emissions in urbanized areas, the thermal regime is changing; the concentration of carbon dioxide and water vapor has now reached 90 % of the total amount of pollutants. As a result, another problem arises that contributes to global warming – the "greenhouse effect". Elevated air temperatures affect human health leading to breathing problems, seizures, heat and sunstroke, heat stress, and increased mortality. Considering the potential danger of elevated air temperatures caused by urban heat islands affecting the lives of residents, an effective and relevant method for surface temperature analysis and heat island location determination should be developed. Methods. During urban heat island monitoring, the main factor of analysis is surface temperature, which was determined in the study using indices such as: Normalized Difference Vegetation Index (NDVI), Urban Thermal Field Variance Index (UTVFI) and Normalized Difference Built-up Index (NDBI). Results. This paper describes a study of the distribution of urban heat islands in three European capitals, including Kyiv, Oslo and Rome, from May 2013 to August 2023. Using the capabilities of the GoogleEarthEngine cloud platform and data from the Landsat 8 OLI/TIRS satellite, the condition of green spaces, the number of impervious surfaces and indices of surface temperatures (LST) were compared, resulting in maps of the distribution of urban heat islands (UHIs) in the areas of selected cities and towns demonstrating quantitative changes. Conclusions. Thus, the study showed a decrease in the number of urban heat islands along with impervious surfaces in the city of Kyiv by 4 %. At the same time, Rome and Oslo experienced an increase in the number of urban heat islands along with impervious surfaces. The data obtained prove the feasibility of using the chosen research method and can be used to assess the environmental condition, identify risk zones, and develop effective measures to further prevent the spread of UHI in megacities.

GEOTECHNOLOGIES APPLIED TO THE BEHAVIORAL STUDY OF URBAN HEAT ISLANDS

Many cities are impacted by the effect of heat islands. In this context, the aim of this study was to analyze the spatial and temporal distribution of urban heat islands and the influence of green areas on the formation of urban microclimates in the city of Rio de Janeiro. As a result, it was observed that Rio de Janeiro city is under heat islands influence, in which the oldest and most urbanized areas of the city are the most affected by high temperatures. It was possible to verify the role of vegetation in the formation of urban microclimates through the negative correlation coefficient between surface temperature (Ts) and vegetation indexes (NDVI and EVI), along with the positive correlation coefficient between surface temperature (Ts) and the Index of Areas Built by Normalized Difference (NDBI), what demonstrates the influence of urbanization on the increase of temperature. Keywords: Heat Island, city, Rio de Janeiro

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.

Spatial Association Rules and Thermal Environment Differentiation Evaluation of Local Climate Zone and Urban Functional Zone

Urban heat islands (UHIs) caused by urbanization have become a major issue affecting the sustainable development of the ecological environment. The distribution of UHIs is mainly affected by the reflection and transmission of heat radiation caused by differences in urban spaces, and the anthropogenic heat emissions caused by social activities. At present, the research on the urban thermal environment involves two spatial classification systems: local climate zone (LCZ), based on urban morphology and spatial patterns; and urban functional zone (UFZ), based on socio-economic activities. It is not clear whether there are association rules between these two systems in different cities. Against this background, this study explores the association rules between the UFZ and LCZ classification systems using the selected Chinese cities in different regions as typical examples. Our results confirm that there are common association rules from UFZ to LCZ, as the form of buildings is greatly influenced by the types of functional areas in urban construction. Specifically, the medical zone corresponds to the compact mid-rise zone (LCZ2); the business service area and the office area also correspond to the compact zone (LCZ1-LCZ3); and the industrial area corresponds to the compact low-rise zone (LCZ3). These functional zones have the same association rules in different cities. The cross-regional mining of the relationship between different urban functional systems will help to coordinate different planning departments and carry out the integration of multiple spatial plans. Furthermore, we found that LCZ has a better differentiation effect on the surface temperature through our comparison research, which makes it more suitable as a reference for research on the thermal environment and the heat island effect.

Urban growth and loss of green spaces in the metropolitan areas of São Paulo and Mexico City: effects of land-cover changes on climate and water flow regulation

Urban green spaces provide ecosystem services that directly or indirectly benefit people, however, urban growth (especially in developing countries) generates the loss of these green areas and consequently of their ecosystem services. This paper aims to present the effects of land use and land cover changes in the last 30 years on two basic urban ecosystem services, water flow regulation and local and regional climate in two of main Latin American megacities (São Paulo and Mexico City). The study focuses on urban green spaces because they reduce the urban heat island effects, improve air quality, create habitats for biodiversity conservation, provide cultural services, and contribute to flooding prevention and groundwater conservation. A spatial analysis with the aid of geographic information systems was performed to assess (i) the urbanization process of the Metropolitan Areas of both cities over time; (ii) how this process generated changes in the land cover in both metropolises, and (iii) how these changes caused environmental negative impacts on ecosystem services. The loss of green areas as a consequence of urban expansion in the Metropolitan Areas of São Paulo and Mexico City changed the spatial distribution of urban heat island and increased the surface runoff, generating floods during the rainy periods. Our results suggest the urgent need for implementation of ecosystem-based spatial planning and ecological restoration of urban green areas in both studied cities to prevent further losses in ecosystem services and to improve the quality of life of urban inhabitants.

On-site measurement and numerical simulation study on characteristic of urban heat island in a multi-block region in Beijing, China

The urban heat island (UHI) effect can lead to an increase in winter temperatures in cold regions, which in turn increases energy consumption and air pollution within urban areas. Field measurements of UHI effects can provide high-precision and high-resolution data, making them suitable for investigating the spatial-temporal distribution of UHI. However, there have been relatively few studies on the field testing of UHI effects in multiple blocks, especially those that involve continuous monitoring. Additionally, while many studies have demonstrated the impact factors of UHI intensity, there is a scarcity of research on quantitative and weight analyses of these factors. In this study, on-site measurements of UHI characteristics were carried out in a typical multi-block region in Beijing. The on-site sampling was conducted in both summer and winter seasons for 29 consecutive sampling days with 20 sampling points. The average urban heat island intensity (UHII) during the sampling period was 1.7 ± 1.2 °C in summer and 3.7 ± 1.8 °C in winter. Temperatures measured at different sampling locations varied significantly, with a maximum spatial difference of 7.5 °C in summer and 4.1 °C in winter. Simulation results showed that, in summer, each ±10% change in wind speed, albedo, or solar radiation intensity would cause the UHII to change by ∓0.16 °C, ∓0.21 °C, or ± 0.17 °C, respectively. In winter, the effects of these factors were very limited. The effect weights of wind speed, albedo, and solar radiation intensity on UHII in summer were 31.3%, 36.6%, and 32.1%, respectively. The position of green space in relation to the dominant wind direction of the city would have a greater effect on UHI mitigation in summer than in winter. This study provides a theoretical basis for mitigating UHI effects and is of great significance for sustainable urban design.

STUDY OF URBAN HEAT ISLAND IN YOGYAKARTA CITY USING LOCAL CLIMATE ZONE APPROACH

Yogyakarta urban area has increased significantly for the past ten years and altered ecological features, such as inducing urban heat island (UHI). Our objectives are examining UHI characteristics in Yogyakarta urban with atmospheric variables which include air temperature and relative humidity and analyzing the UHI distribution using the LCZ method. This study uses the LCZ classification to spatially compare thermal characteristic and explain how land use and building geometry affect UHI. The system comprises 17 standard classes at the local scale, using quantitative approach which includes numerical data, such as aspect ratio, building heights, and street canyons to support the classification system. Three LCZ classes found in Yogyakarta urban area are LCZ 3, LCZ 5, and LCZ 6. The LCZ variables which affect temperature and relative humidity are building heights, width of street canyons, and land use. The biggest thermal difference is ΔLCZ3-LCZ5 and ΔLCZ3-LCZ6, which happened during 08.00 – 12.00 and 16.00 – 20.00. Small UHI magnitude (< 2K) is affected by small difference in morphology and fabric. Medium UHI magnitude (2 – 5K) is mostly caused by large difference in fabric and small difference in morphology. In the future, the LCZ should be widely applied for urban planning regarding climatic mitigation.

Integration of Remote Sensing and Big Data to Study Spatial Distribution of Urban Heat Island for Cities with Different Terrain

Urban microclimate has posed a detrimental effect on the life of the urban population. This research drives with an aim of identifying environmentally conscious factor vis-a-vis urban planning which leads to the vicious cycle of urban climate change. The vicious cycle is inclusive of many urban dynamics’ parameters, which are complicated to understand. This research emphasizes on using Remote Sensing Big Data on Google Earth Engine as an advancement to study Climate Vulnerability leading to Urban Climate Gentrification. Temporal data of Landsat for the past 30 years has been taken into consideration for the study. Three cities with diverse geographical and terrain characteristics have been selected for the study, to understand the modern decisive planning is in coherence with the Sustainable Development Goals. Understanding spatial and temporal information of Urban hotspots using High-Resolution Satellite data is just not enough to suffice the need to decrease the temperature by 2- 3°C. The present study is a toll on how the reasons for microclimate change vary along with the terrain, spatial location, and urban growth pattern of the city.

Modeling of Daytime and Nighttime Surface Urban Heat Island Distribution Combined with LCZ in Beijing, China

Along with urbanization, surface urban heat island (SUHI) has attracted more attention. Due to the lack of perspective of spatial heterogeneity in relevant studies, it is difficult to propose specific strategies to alleviate the SUHI. This study discusses the impact of spatial heterogeneity on the day and night SUHI by taking one day and night in Beijing as an example, and uses it to improve the efficiency of SUHI simulation for related planning. This study, based on the local climate zone (LCZ), deeply discusses the relationship between urban morphology and the SUHI. Then, an artificial neural network (ANN) model with the LCZ is developed to predict the distribution of the SUHI. The results show that: (1) In summer, the general SUHI intensity distribution patterns are compact zone > large low-rise zone > open zone and medium floor zone > low floor zone > high floor zone. (2) Building density and albedo in dense areas are higher correlated with the SUHI than open areas. The building height has a significant negative correlation with the SUHI in high-rise zone, but has a positive correlation in middle and low floors. (3) The LCZ improves the overall accuracy of the ANN model, especially the simulation accuracy in the daytime. In terms of regions, LCZ2, LCZ8, and LCZ10 are improved to a higher degree. This study is helpful to formulate the SUHI mitigation strategies of “adapting to the conditions of the LCZ” and provide reference for improving the sustainable development of the urban thermal environment.

Application of Landsat-8 OLI/TIRS to assess the Urban Heat Island (UHI)

Urban Heat Island (UHI) phenomenon is a particular climate change impact in urban areas that can trigger adverse effects on environmental conditions. This effect is usually worsened by increasing anthropogenic activities. While the impact of UHI has mostly been studied in metropolitans around the world, few studies are presented investigating the outcome in mid-sized cities. This study aims to assess spatial distribution of Urban Heat Island (UHI) in Bogor city, Indonesia, by utilizing thermal data (Band-10) from Landsat-8 OLI/TIRS. The Landsat imagery was acquired in May 2021, in the beginning of the dry season. The result of study shows Land Surface Temperature (LST) values ranged from 26.23 to 33.76°C, with an average temperature of 31.29°C, while the range of NDVI was between 0.08 to 0.55. Further, the correlation between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) was calculated to investigate the relationship between vegetation density and surface temperature condition for examining the outlook and possibilities to minimize the impact of UHI through improving vegetal conditions. A negative correlation suggests that expanding vegetation coverage can reduce urban heat island impacts by 35%, and other factors influence the rest. The land surface temperature threshold to determine UHI is 32.01°C, so the UHI area was identified at ca. 13.12 km2 around the city centre.

Urban heat island phenomenon in majalengka regency

The Majalengka Regency considering by The Government of West Java Province is one of the priorities of infrastructure development to sustain the accelerate of development. The conversion function of agricultural land into non-agricultural or industrial will affect land surface temperature, which triggers the urban heat island phenomenon. The purpose of this study was to analyze the spatial and temporal distribution of urban heat island and the assosiation of land cover, building density, and vegetation greenness with land surface temperatures in Majalengka in 2013, 2016, and 2019. Variables used in this study were land cover, vegetation greenness, building density, and land surface temperature obtained from the processing of Landsat Image 8 are validating survey at 98 points selected by random sampling method. The results showed that the phenomenon of urban heat island dominates the north to the Majalengka Regency. The Areas with low land surface temperatures are found in areas with land cover in the form of built land, low greenness, and high building density. The southern of Majalengka Regency of the land surface temperature is getting lower with vegetation land cover, high vegetation greenness, and low building density. Low land surface temperature is associated with high vegetation greenness and low building density while the highest surface temperature is associated with built-up area with high building density. The difference in temperature between 2013-2019 is around 6°C with a reduction in vegetation area around 109 km2.

Climate Justice in the City: Mapping Heat-Related Risk for Climate Change Mitigation of the Urban and Peri-Urban Area of Padua (Italy)

The mitigation of urban heat islands (UHIs) is crucial for promoting the sustainable development of urban areas. Geographic information systems (GISs) together with satellite-derived data are powerful tools for investigating the spatiotemporal distribution of UHIs. Depending on the availability of data and the geographic scale of the analysis, different methodologies can be adopted. Here, we show a complete open source GIS-based methodology based on satellite-driven data for investigating and mapping the impact of the UHI on the heat-related elderly risk (HERI) in the Functional Urban Area of Padua. Thermal anomalies in the territory were mapped by modelling satellite data from Sentinel-3. After a socio-demographic analysis, the HERI was mapped according to five levels of risk. The highest vulnerability levels were localised within the urban area and in three municipalities near Padua, which represent about 20% of the entire territory investigated. In these municipalities, a percentage of elderly people over 20%, a thermal anomaly over 2.4 °C, and a HERI over 0.65 were found. Based on these outputs, it is possible to define nature-based solutions for reducing the UHI phenomenon and promote a sustainable development of cities. Stakeholders can use the results of these investigations to define climate and environmental policies.