Patterns Of Urban Heat Island Research Articles

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.

Surface and canopy urban heat island disparities across 2064 urban clusters in China

The vast majority of urban heat island (UHI) studies are now derived from surface temperatures, substituting for the original air temperature-based definition. The disparities in hourly surface-canopy UHI effects (SUHI, CUHI) and the contrasting mechanisms are currently poorly understood. Here, we use high-resolution hourly LST and air temperature data from 2064 urban clusters in China to estimate SUHI and CUHI intensities and their driving mechanisms during the summer and winter of 2022. Across all urban clusters, we find that SUHI is on average ten times higher than the CUHI in the summer (0.97 VS. 0.09 °C) yet nearly triple that in the winter (0.30 VS. 0.11 °C), with SUHI exceeding CUHI in 91.5 % and 65.7 % of urban clusters, respectively. Seasonal and hourly analyses on SUHI/CUHI confirm typically opposite hysteresis variations (magnitude, peak, and timing of occurrence) and more correlated surface-canopy UHIs patterns during the night. We further demonstrate that SUHI magnitude can be largely explained by biophysical factors, urban attributes, and climate contexts, whereas CUHI interferes with additional constraints linked to ground-air energy transfer and advective dissipation. The improvement of urban greenery aids summer cooling efficiently in equatorial and boreal regions, while albedo measures are relevant in mitigating nocturnal warming in arid regions. Our findings support multiple technologies as ideas for urban three-dimensional UHIs (surface, canopy and boundary) and energy mechanisms, and the urgent need for ambitious urban heat mitigation strategies to minimize future climate change impacts.

Long-term monitoring, predicting and connection between built-up land and urban heat island patterns based on remote sensing data

The alterations observed in urbanized areas have given rise to urban climate change, contributing to the emergence of urban heat islands (UHIs). This study investigates changes and predicts the built-up land/UHIs in Rasht city from 1991 to 2031. Built-up lands were classified using the normalized built-up composite index (NBCI) and their prediction for 2031 was performed. Surface biophysical parameters were then derived for the prediction of land surface temperature (LST) for 2031 using multiple linear regression (MLR) and Markov chain-cellular automata (CA-Markov) modeling. Finally, alterations in both built-up land and UHI within the city were scrutinized across various geographical directions and temporal periods. The study's findings reveal commendable overall classification accuracy for NBCI (ranging from 87% to 91% across different years) and CA-Markov (89%) in 2021. The MLR analysis produced favorable results with a root mean square error of 1.33 K in predicting LST for 2021. The significant correlation (R = 0.89) between changes in built-up lands and UHI indicatesthat built-up land/UHI exhibit a notable degree of freedom and sprawl, resulting in a negative urban degree-of-goodness.These results demonstrate the direct effects of built-up lands on UHI changes. Therefore, by determining the appropriate pattern in the built-up lands, it is possible to control the pattern of UHI. These findings hold practical significance for urban planners, offering valuable insights to mitigate adverse impacts on the urban environment.

Exploring the Configurational Relationships between Urban Heat Island Patterns and the Built Environment: A Case Study of Beijing

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management.

Analysis of LST, NDVI, and UHI patterns for urban climate using Landsat-9 satellite data in Delhi

The present study is based on remote sensing techniques focusing on Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to investigate their influence on land use and land cover dynamics, and the assessment of the Urban Heat Island (UHI) effect in Delhi, India. The objective of this study is to calculate LST, NDVI, and UHI values to understand the changes in LULC patterns, urbanization, and temperature increase within the city. Unlike previous studies conducted with Landsat-8, the present study employs Landsat-9 data, ensuring a higher level of authenticity in the results. Landsat-9, equipped with state-of-the-art sensors and instrumentation, provides superior data quality, enhanced image resolution, and advanced capabilities for precise monitoring and analysis. The methodology encompasses six steps for LST retrieval, enabling the calculation of UHI values and intensity. Ground data from 32 meteorological stations validate the LST results. Pearson correlation coefficients between LST and NDVI exhibit correlations ranging from −0.58 to −0.68 for three dates. On Dec 8, 2023, there is a weak negative correlation of −0.004. The analysis of changing land cover with variation in NDVI and LST unveils a diverse landscape, primarily characterised by green cover (47.34%), followed by built-up area (44.57%), barren land (7.57%), and water (0.52%). The study identifies the minimum value of UHI intensity for Delhi to be 8.13 °C on 26-Feb 2023 and the maximum value of UHI was estimated 10.29 °C on 2-June 2023. The study of Urban Heat Island (UHI) patterns revealed distinctive seasonal trends. The urban areas exhibited relatively cooler temperatures compared to surrounding rural regions on Dec 8, 2023. The conclusion drawn from this comprehensive analysis is that rapid urbanization in Delhi has significantly contributed to the increase in LST and UHI values. This rise can largely be attributed to the extensive use of concrete in construction activities, which exacerbates the UHI effect. Moreover, this analysis signifies the dynamic nature of UHI and emphasizes the urgency for strategic urban planning and climate-sensitive design approaches. Implementing such measures can create more sustainable and resilient urban environments.

Spatiotemporal Patterns of Land Surface Temperature, Urban Heat Island, and Potential Heat Stress Risk Areas Assessment for Tropical Inland City and Coastal City in Thailand

Global warming and rapid urban growth, compounded by the urban heat island (UHI) effect, are posing substantial challenges for urban populations and ecological well-being due to increased heat stress risks. This study aimed to analyze the spatiotemporal patterns of land surface temperature (LST), UHI, and potential heat stress risk areas (PHSRA) in two distinct cities, an inland city, Nakhon Ratchasima, and a coastal city, Si Racha, during the summer. Geoinformatics techniques and Crichton’s Risk Triangle method were employed, using updated demographic data and Sentinel-3 data from 2017 to 2022. The findings indicate that the inland city daytime average LST values were higher than in the coastal city, while the opposite was true at nighttime. Additionally, high daytime UHI patterns in the inland city were observed on the outskirts, and nighttime UHI concentrated in the urban center. The coastal city exhibited a strong daytime and nighttime UHI compared to the inland city, with daytime UHI peaking in the inner area and nighttime UHI near the coastline. The PHSRA was categorized into five risk levels (very high: > 0.8, high: 0.8–0.6, moderate: 0.6–0.4, low: 0.4–0.2 and very low: ≤ 0.2). During the daytime the highest risk areas, the high and very high levels of PHSRA in the inland city (23.84%) were greater than those in the coastal city (16.46%). Conversely, at nighttime, the coastal city (16.46%) exhibited higher levels than the inland city (5.26%). In nighttime conditions, all levels of PHSRA occurred more significantly than during the daytime in these cities. Understanding the spatiotemporal variations in PHSRA is vital for pinpointing places at risk of heat stress accidents in summer. This data is a goldmine for city planners and healthcare authorities, helping them plan interventions and effective measures for the future.

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.

Comprehensive analysis of the impact of elevated metro line infrastructure on the urban heat island effect in Chennai: A multi-model approach

This study is driven by the essential need to understand the dynamic urban heat island patterns in Chennai, particularly in light of the city’s significant expansion which resulted in the establishment of connectivity through the implementation of the elevated metro line infrastructure project. A notable gap in the current research landscape lies in the exploration of the impact of this recently introduced metro infrastructure on urban heat islands. Utilizing a multi-modal approach, the research incorporates remote sensing data, Land Use and Land Cover (LULC) patterns, mobile transect surveys along a segment of the metro stretch, and in situ data logging at three locations. The study reveals a strong correlation between mean Land Surface Temperature (LST) values and landscape metrics. The introduction of the elevated metro line resulted in a noteworthy decrease of 4.26 °C in UHI patterns in the month of May. It was also found that the varying land-use along the metro also plays an impact on average Surface Urban Heat Island Intensity (SUHII). The three elevated corridors demonstrate reductions in SUHII by 2.3 °C, 1.4 °C, and 1.3 °C. Mobile transect surveys indicate that afternoons show an average UHI Intensity (UHII) of 4.7 °C and 4.6 °C for road and metro-road scenarios, with the metro line exhibiting a comparatively lower UHII of 2.8 °C. Data logging reveals that the Metro_Veg (metro-line with vegetation on either side) scenario exhibits a more pronounced UHI effect compared to Metro_Build (metro-line with buildings on either side). Metro_Veg records the highest air temperature at 45.90 °C, followed by Metro_Build and Road_Veg (road with vegetation on either side) at 43.70 °C and 39.70 °C, respectively. The elevated metro structure provides shade on roads, resulting in the lowest surface temperatures in the metro scenario. Overall, the findings suggest that metro line infrastructure holds potential for UHI mitigation. While extending metro lines across every road may not be feasible, the study advocates for shading above roads as a practical mitigative measure. Future research can explore various structures and materials, including dynamic solutions, for mitigating UHI effects along urban roads, contributing to sustainable urban development.

Spatio-temporal analysis of LST, NDVI and SUHI in a coastal temperate city using local climate zone

Extreme heat due to changing climate poses a new challenge for temperate climates. The challenge is further aggravated by inadequate research, policy, or preparedness to effectively respond and recover from its impacts. While urban morphology is crucial in mitigating urban heat, it has received limited attention in urban planning, highlighting the need for further exploration, particularly in temperate regions. To illustrate the challenge and its potential mitigations, we use the example of the coastal temperate city of Cardiff. To establish the interrelations between urban morphology and urban heat island patterns, we explored the spatiotemporal variations in land surface temperature (LST), normalised difference vegetation index (NDVI), and surface urban heat island (SUHI) to local climate zone (LCZ) classification for Cardiff. Results showed a significant variation in SUHI in the LCZ zones. Both LST and NDVI land were found to vary significantly across the LCZ zones demonstrating their association with the urban form and morphology of the locality. For built-up areas, a more compact built-environment with smaller vegetation cover and larger building density was 2.0 °C warmer than the open built-environment when comparing the mean summer LSTs. On average, the natural classes exhibit a LST that is 8.0 °C lower than the compact built-environment and 6.0 °C lower than the open built-environment. Consequently, the high-density, built-up LCZs have a greater SUHI effect compared to the natural classes. Therefore, temperate climate cities will benefit from incorporating an open built-environment that has sufficient greenery and open spaces. These findings help determine the optimal urban form for temperate climates and develop heat mitigation strategies while planning, designing, or improving the new and existing urban areas. In addition, the LCZ map applied in this study for Cardiff will enable international comparison and testing of proven climate change adaptation and mitigation techniques for similar urban areas.

Quantifying surface urban heat island variations and patterns: Comparison of two cities in three-stage dynamic rural–urban transition

Urban heat island (UHI) not only reflects the environmental thermal comfort and energy consumption, but also affects the urban meso‑scale climate. There are many researches related with UHI mainly focusing on urban and rural area, while neglecting dynamic rural–urban transition especially in a rapid urbanization in China. Beijing and Zhengzhou are studied by using city clustering algorithm (CCA) and boundary generation algorithm (BGA) to delineate the urban, peri‑urban and rural boundaries from 2000 to 2023 within three stages. Fourier transform model was used to identify the UHI patterns. Results show: 1) Two cities have undergone obvious expansions in 20 years, with a consistent mean LST decrease from urban to peri‑urban and rural areas in three stages. 2) The distribution of UHII was more consistent in Beijing, while it varied more in Zhengzhou across seasons. 3) The UHI patterns notably differ, with Zhengzhou experiencing variable patterns and Beijing consistently showing oblate patterns. 4) The profiles of UHII and NDVI in two cities varied seasonally and reflected urban expansions in terms of longitude and latitude. Understanding the long-term changes and patterns of urban heat islands in different cities will provide information for formulating adaptive policies for urban sustainability.

Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning

Urban heat island (UHI) has become a worldwide concern under global warming and urbanization waves, which pose significant threats to human health and urban sustainability. Despite Land Use/Land Cover (LULC) being regarded as a crucial factor influencing UHI, few studies simulated the nonlinear influences from LULC compositions and configurations, especially considering the difference between monocentric and polycentric cities. Therefore, we measured the patterns of LULC and UHI from 2006 to 2022 using cases of a monocentric city (Chengdu) and a polycentric city (Chongqing). Further, we simulated future and land surface temperature (LST) using Machine Learning (ML) models, and further calculated the simulated UHI in 2030 by urban-rural LST differences. The results showed that Chengdu had prominent UHI intensity in cores and the surrounding satellite towns. Chongqing's main center and subcenters had high UHI intensity under clustered patterns. ML results indicated that built-up percentage was the most crucial factor driving LST. Configuration factors dominated in impacting Chengdu's LST, while composition factors were more critical in Chongqing. Moreover, future UHI is estimated to decrease in Chongqing's core, compared with the remaining high UHI in Chengdu's core. These results might provide insights for understanding and mitigating future UHI effects.

Urban heat island patterns in garden cities: Putrajaya as a case study

The garden-city model, characterised by sprawling urban expansion and extensive open spaces, features green and water surfaces alongside substantial road networks, large parking lots and unshaded lands. This diverse landscape contributes to uneven air temperature distribution within the city. Our study investigated urban heat island (UHI) patterns in various urban environments within Putrajaya, focusing on rural, business, residential and near-water areas. Atmospheric data were collected over a year. Putrajaya exhibits two distinct UHI patterns: cool-day-hot-night in residential zones and hot-noon-temperate-elsewhere in impermeable areas. Business areas display alternating patterns based on seasonal climatic conditions and anthropogenic factors. The efficacy of artificial water bodies as a UHI countermeasure in garden cities depends on the size of the water body and the nature of the surrounding land cover. Shaded and green areas exhibit UHI intensities limited to 3°C, while impermeable areas, regardless of proximity to water surfaces, experience UHI up to 6°C. Overall, the garden-city model tends to restrict UHI variations within −1 to 3°C compared to −2 to 4°C in nearby Kuala Lumpur.

Urban heat island analysis based on high resolution measurement data: A case study in Beijing

Urban heat island (UHI) is a widely concerned urban climate which impacts billions of residents worldwide. Nevertheless, inadequate rigorous air temperature (Tair) measurement has become an obstacle to UHI research and mitigation, especially in megacities like Beijing. In this study, we applied a 1-km hourly Tair dataset interpolated from records of 521 stations in Beijing. The dataset enables us to analyze UHI patterns on a block scale and the impacts of landscapes combined with the local climate zones (LCZ) scheme. Our results indicate that: (1) Tair and its temporal variation in each LCZ vary significantly, which are highly relevant to their morphology and thermal-radiative properties. (2) LCZ 1 and LCZ 2 have the highest nocturnal UHI intensities (UHII). Discrepancies among LCZs also cause various temporal variations in their UHII. (3) The spatial pattern of LCZs affects regional temperature. Area ratio and aggregation extent of LCZ 1&2 show significant positive correlations with regional temperature, while LCZ A&D&G have the opposite regularity. (4) Distinct mitigation strategies are put forward for each subarea in Beijing with various endowments of population and the LCZ structure. The study provides insight into Beijing UHI and plausible mitigation advice in megacities like Beijing.

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

Analysis of thermal comfort of settlements based on urban humidity index in Yogyakarta City

Increasing urban development has led to an increase in land use, particularly in settlements. The massive and uncontrolled growth of settlements can lead to a decrease in the quality and thermal comfort of the settlements. Yogyakarta is one of the cities in Indonesia with rapid development and population growth. This study aims to analyse the quality and thermal comfort of settlements in Yogyakarta city. The remote sensing data used is Landsat 8 OLI image for the analysis of vegetation index, land surface temperature, and urban heat island (UHI). The results of the model are presented in the form of maps so that the spatial pattern of UHI can be identified and correlated with the comfort level calculated using the temperature humidity index (THI). Based on the results of the data analysis conducted, it is known that the analysis of thermal comfort in the city of Yogyakarta is dominated by the comfortable class (97%), uncomfortable class (3%), and very uncomfortable class (0%).

MAPPING LOCAL CLIMATE ZONES WITH MULTIPLE GEODATA AND THE OPEN DATA CUBE: INSIGHTS OF DOMAIN USER REQUIREMENTS AND OUTLOOKS OF THE LCZ-ODC PROJECT

Abstract. Rapid urbanization and climate change are intensifying the urban heat island (UHI) phenomenon across cities worldwide. There is a pressing need to implement evidence-based mitigation and adaptation strategies as well as to develop tools for effectively measuring the impact of such actions on UHI patterns. In this context, the Local Climate Zone (LCZ) concept is a well-established classification system commonly used for the assessment of UHI. With this in mind, we present here the LCZ-ODC project aiming to develop a methodology for LCZ mapping in the Metropolitan City of Milan (northern Italy) by leveraging multiple geospatial data and cutting-edge software tools, including the Open Data Cube (ODC). A key aim of the project is to develop user-oriented solutions facilitating the exploitation of the generated LCZ maps for different application tasks. In this paper, we first present a brief overview of the methodologies and data sources used in the literature for LCZ mapping. Then, we introduce the LCZ-ODC project, with a focus on the end-user requirements which were gathered through a questionnaire distributed to a sample of potential stakeholders. The primary objective of the survey was to collect insights and consolidate requirements related to the key features of LCZ maps that will be produced within the project. The outcomes of the survey play a pivotal role in guiding the project’s development phase, ensuring that the project outputs will effectively address the identified end-user needs.

Assessment of Mutual Variation of Near-Surface Air Temperature, Land Surface Temperature and Driving Urban Parameters at Urban Microscale

The Urban Heat Island (UHI) effect is of critical concern for cities’ adaptation to climate change. The UHI effect shows substantial intra-urban variation at the city microscale, causing disparities in thermal comfort and energy consumption. Therefore, air temperature assessment should be prioritized for effective heat mitigation and climate adaptation. However, meteorological stations’ spatial distribution is far from meeting the scale that the UHI and its driving parameters operate. This limitation hampers demonstrating the intra-city variability of UHI and its origin of sources; for example, most studies employ Land Surface Temperature (LST), usually without demonstrating the relationship between UHI and LST. The current body of knowledge on urban climate implies a much better understanding and more detailed information on the spatial pattern of UHI and the driving factors to provide decision-makers with tools to develop effective UHI mitigation and adaptation strategies. In an attempt to address the adequacy of the use of LST and UPs in describing the intra-city variability of UHI, this study investigates the relationship between LST daytime and nighttime, and air temperature (Ta) daytime and nighttime, and driving urban parameters (UPs) of UHI together. Although it is well recognized that the intensity of the UHI is characterized by Ta, particularly at night, so-called nocturnal UHI, the use of remotely sensed LST is common, owing to the lack of spatially detailed Ta data in cities. Our findings showed that nocturnal UHI is weakly correlated with nighttime LST with a Pearson correlation (r) of 0.335 at p > 0.05 and that it is not correlated with daytime LST for the case study, highlighting the need for Ta observations for representing the intra-urban variation of nocturnal UHI. Among UPs, Sky View Factor (SVF), Building Volume Density (BVD), and Road Network Density (RND) explained 69% of the variability of Ta nighttime that characterizes nocturnal UHI. Therefore, UPs that performed well in estimating nocturnal UHI may be used in the absence of densely distributed Ta measurements. In a further investigation of the urban cooling phenomenon based on UHI diurnal changes, a particular region with high nighttime temperatures spoiled the Ta daytime and nighttime coherence. This region is characterized by high Mean Building Height (MBH), BFD, and BVD that re-emits heat, low SVF that prevents urban cooling, and high RND that releases extra heat at night. These particular UPs can be of prior interest for urban cooling. The present study, exploring the relationships of LST and Ta in a diurnal context, offers a further understanding of the preference of LST, Ta, or UPs to characterize UHI. Ta, in relation to major causative factors (UPs), provides insights into addressing the localities most vulnerable to the UHI effect and possible strategies targeting heat mitigation for sustainability and climate change resilience.

Spatial Pattern of Urban Heat Island to Enhance Urban Cooling Ecosystem Services in the Post-Pandemic Era

Urbanization, followed by reduced green space, often leads to increased land surface temperatures (LST), which is also known as the urban heat island (UHI) effect. Urban cooling ecosystem services provided by forests and parks play a crucial role in reducing the UHI effect (which is reflected in LST) and improving the liveability and sustainability of cities. The pandemic has changed many people’s views about the importance of ecosystem services to their well-being. The post-pandemic era is a turning point for infrastructure and environmental improvements to increase ecosystem services’ benefits. This study aims to look at the potential for increasing ecosystem services’ benefits through the provision of urban forests and parks based on UHI conditions in Surakarta city. In this study, UHI was identified from LST based on Landsat 8 and Landsat 9 remote sensing images. The spatial pattern of the UHI was analyzed based on block units using road boundaries using cluster and outlier statistical spatial analysis. In formulating alternatives to provide urban forests and parks, we also interviewed stakeholders from a village revitalization program in Kampung Mojo, Surakarta city. The results show that after the pandemic, in a revitalized neighborhood, the surface temperature was still high. The revitalization program did not include the provision of parks because the basic issue of this program is land ownership legality. Nevertheless, the UHI spatial pattern provides evidence that the provision of urban forests and parks is needed on the central and southern sides of Surakarta city to enhance urban cooling ecosystem services.

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively.

City densification and temporal dynamics of traditional inner core of Ibadan, Nigeria

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island