Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences

Morphological spatial clustering of high-density central areas and their coupling relationship with thermal environment--a case study of the wuyi road hatchback in changsha

Intense urban development and high urban density cause the thermal environment in urban centers to deteriorate continuously, affecting the quality of the living environment. In this study, 707.49 hectares of land in the central area of Changsha were divided into 121 plots. 11 microclimate-related morphological indicators were comprehensively selected, and the K-means method was used for cluster analysis. Then, the relationship between morphological clusters and the thermal environment was explored by simulating the thermal environment of the study area with ENVI-met. First, five spatial types were found to characterize the area: high-level with high floor area ratio, low density, and low greenery; middle-level with high floor area ratio high density; medium-capacity with high density and small volume; low-level with low density and high greenery; and low floor area ratio, low density, and high greenery. Second, the building windward surface density, sky openness, building density, floor area ratio and green space rate affect the thermal environment. Third, Cluster3 had the highest average air temperature (Ta), followed by Cluster5, furthermore Clusters4, 1, and2 had relatively low Ta. The spatial vitality index and green space rate in Cluster1; the area-weighted building shape index, average building volume and sky openness in Cluster2; green space rate in Cluster3; indicators such as the floor area ratio and green space rate in Cluster4; indicators such as the impervious surface rate and green space rate in Cluster5 had greater influences on Ta. Fourthly, simply increasing the area of green space cannot maximize the cooling effect of green spaces. Instead, constructing an equalized greening network can better regulate the thermal environment. Fifthly, the results provide a scientific basis for the design and the regulation of urban centers.

Comprehensive spatiotemporal evaluation of urban growth, surface urban heat island, and urban thermal conditions on Java island of Indonesia and implications for urban planning

Urban heat island (UHI) and thermal comfort conditions are among the impacts of urbanization, which have been extensively studied in most cities around the world. However, the comprehensive studies in Indonesia in the context of urbanization is still lacking. This study aimed to classify land use and land cover (LULC) and analyse urban growth and its effects on surface urban heat islands (SUHIs) and urban thermal conditions as well as contributing factors to SUHI intensity (SUHII) using remote sensing in the western part of Java Island and three focused urban areas: the Jakarta metropolitan area (JMA), the Bandung and Cimahi Municipalities (BC), and the Sukabumi Municipality (SKB). Landsat imagery from three years was used: 2000, 2009, and 2019. Three types of daytime SUHII were quantified, namely the SUHII of urban central area and two SUHIIs of urban sprawl area. In the last two decades, urban areas have grown by more than twice in JMA and SKB and nearly 1.5 times in BC. Along with the growth of the three cities, the SUHII in the urban central area has almost reached a magnitude of 6 °C in the last decade. Rates of land surface temperature change of the unchanged urban pixels have magnitudes of 0.25, 0.15, and 0.14 °C/year in JMA, SKB, and BC, respectively. The urban thermal field variance index (UTFVI) and discomfort index (DI) showed that the strongest SUHI effect was most prevalent in urban pixels and the regions were mostly in the very hot and hot categories. Anthropogenic heat flux and urban ratio have positive contributions to SUHII variation, while vegetation and water ratios are negative contributors to SUHII variation. For each city, the contributing factors have a unique magnitude that can be used to evaluate SUHII mitigation options.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Unveiling the complex networks of urban tree diversity research: A global perspective

Ecosystem services offered by urban forests must be proactively managed to remain diverse and sustainable. Recent research findings deserve a systematic synthesis to elucidate inherent knowledge structures and dynamics. This study focused on the urban tree diversity theme from 2000 to 2022. Web of Science Core Collection database provided bibliometric details on academic publications. The data-driven quantitative analysis explored research quantities, emphasis, trends, patterns, linkages, and impacts by countries, institutions, authors, journals, and citations. Publications and research topics have expanded continually, with accelerated growth in recent years. Research activities, outputs and interactions demonstrated conspicuous spatial clustering. A few countries, institutions and researchers generated a notable proportion of publications. Their scholarly contributions were visualized in knowledge graphs as complex networks of nodes and inter-node links. Keyword analysis generated a network to indicate research hotspots and frontiers to steer and prioritize future studies. Recent findings affirmed that cities can harbor substantial tree diversity due to enhanced habitat heterogeneity and successful species adaptation. Aligning tree traits with environmental conditions and management objectives can improve benefits. Urbanization can filter tree traits to shape community assemblages through stressors: habitat degradation, fragmentation and loss, in conjunction with pollution, climate change, and introduced species. Diversity preservation strategies include protecting remnant natural vegetation, connecting green spaces, and restoring complex canopy geometry and biomass structure. The emerging frontiers are marked by modeling future species distributions, leveraging technologies like remote sensing, linking ecology with human values, and committing to community-based stewardship. Management can be upgraded by interdisciplinary perspectives integrating ecological science and social engagement. The findings highlight the need for biodiversity enrichment anchored by native species, trait-matched assemblages, adaptive policies, and community participation to create livable-green cities. This review synthesizes key advances in urban tree ecology and biodiversity research to inform the planning and stewardship of resilient urban forests.

Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

Spatiotemporal decoupling between impervious surface areas and ecosystem services

Impervious surface area, due to its high energy storage and low permeability, hinders the cycles of material and energy between soil and atmosphere, thus affecting the sustainable supply of ecosystem services. It is of great practical significance to explore the influence of impervious surface areas on ecosystem services for territorial spatial planning and ecological construction projects. Correlation analysis and decoupling are used to explore the spatiotemporal variation and interaction between impervious surface areas and six ecosystem services in Hangzhou Bay from 1996 to 2018. The results show that different abundance levels of impervious surface areas are negatively correlated with ecosystem services, and with the increase of the impervious surface areas, the correlation coefficients with ecosystem services gradually decrease; there are mostly weak decoupling (79.2%) and strong decoupling (11.9%) between impervious surface areas and ecosystem services from 2007 to 2018, and the central areas that have achieved urbanization are mostly dominated by strong and weak decoupling, while the peripheral areas of the central cities are dominated by expansive recoupling and expansive connection. These results indicate that the loss of ecosystem services can be mitigated with the continuous advancement of urbanization and the enhancement of land use intensification. Finally, based on the aforementioned results, differentiated control suggestions are proposed, in order to provide insights for the sustainability of the urbanization of Hangzhou Bay and other cities with similar characteristics around the world.

The impact of anthropogenic transformation of urban soils on ectomycorrhizal fungal communities associated with silver birch (Betula pendula Roth.) growth in natural versus urban soils

Betula pendula Roth. is considered a pioneering plant species important for urban ecosystems. Based on the sequencing of fungal ITS, we characterized the ectomycorrhizal (ECM) communities of twenty silver birch trees growing in a contaminated, highly anthropo-pressured urban environment and in a natural reserve site. We analysed chemical properties of each tree soil samples, focusing on effects of anthropogenic transformation. Three effects of urbanization: high heavy metal content, increased salinity and soil alkalinity, were highly correlated. The examined trees were divided into two forest and two urban clusters according to the level of anthropogenic soil change. The effect of soil transformation on the ECM communities was studied, with the assumption that stronger urban transformation leads to lower ECM vitality and diversity. The results of the study did not confirm the above hypothesis. The ECM colonization was above 80% in all clusters, but the forest clusters had significantly higher share of vital non-ECM root tips than the urban ones. Eleven mycorrhizal fungal species were identified varying from seven to nine and with seven species observed in the most contaminated urban plot. However, the lowest Shannon species diversity index was found in the most natural forest cluster. In conclusion, our findings demonstrate no significant negative effect of the urban stresses on the ECM communities of silver birch suggesting that both forest and urban trees have the potential to generate a similar set of ECM taxa.

The impact of tree species and planting location on outdoor thermal comfort of a semi-outdoor space

Previous studies have shown that tree arrangement provides effective regulation of the outdoor thermal environment and combats the urban heat island (UHI) effect. To further explore semi-outdoor thermal environment improvement using tree arrangement, we selected two common arbor species from Guangdong Province, namely, Lagerstroemia speciosa and Bombax ceiba. We discuss the influence of courtyard tree arrangements on the thermal environment of semi-outdoor spaces (courtyards and overhead spaces) of a teaching building in a hot-humid area. The ENVI-met model was used and verified with field measurements; the universal thermal climate index (UTCI) was used as an index to evaluate the thermal environment of semi-outdoor spaces. We found that (1) adjusting the distance between trees and buildings reduced the UTCI values by 0.4 (overhead spaces) and 0.8 ℃ (courtyards); and (2) when the distance between the arbor and the building is fixed, the UTCI values of arranging Lagerstroemia speciosa can be reduced by up to 0.5 (overhead spaces) and 1.0 ℃ (courtyards) compared to that of Bombax ceiba; this study provides practical suggestions for the layout of trees in semi-outdoor spaces of teaching buildings in the hot-humid areas of China.

Efficacy assessment of green-blue nature-based solutions against environmental heat mitigation

Nature-based solutions (NBS) such as green (vegetation) and blue (waterbodies) infrastructure are being promoted as cost-effective and sustainable strategies for managing the heatwaves risks, but long-term monitoring evidence is needed to support their implementation. This work aims to conduct a comparative assessment of the cooling efficiency of green (woodland and grassland) and blue (waterbody) NBS in contrast to a built-up area. Over a year of continuous fixed monitoring showed that the average daily maximum temperatures at NBS locations were 2-3 °C (up-to 15%) lower than the built-up area. Woodland showed the maximum temperature reduction in almost all seasons, followed by waterbody and grassland. NBS performed the best during the summers, peak sunshine, and heatwave hours (up to ∼ 6 °C cooler than built-up area). Using an e-bike for mobile monitoring, the areas where green-blue NBS were combined showed the highest spatial cooling extent, followed by waterbody, woodland, and grassland areas. The database generated can validate city-scale environmental models and assist city planners to incorporate NBS into urban dwellings based on the opportunity, need and scope, aligning with Sustainable Development Goals 11 (sustainable cities and communities) and 13 (climate action).

Assessment of changes in environmental factors in a tourism-oriented Island

Tourism development has influenced industrial structure changes and has become a major driving force for China's new urbanization. However, the development will negatively impact natural resources and the ecological environment and will become an essential driving factor for land use change. Therefore, understanding the impact of tourism urbanization is crucial for sustainable local development. This study selected the Dachangshan Island in the Changhai County, Dalian, China, as the study area, because it is the only coastal island-type border county in China. During the study period, changes in local environmental factors were analyzed based on land use data, Landsat 5 and Landsat 8 data of 2009, 2014, and 2019. The results showed that: (1) the overall land surface temperature (LST) in the research region shows an increasing trend; the LST in 2014 and 2019 increased by 6.10 and 5.94 °C, respectively, compared with 2009. With respect to specific land types, impervious surfaces maintained a high land surface temperature (25.44, 32.38, and 31.86); however, surface temperatures for cropland, forest, grassland, and water bodies remained stable. (2) The land use land cover (LULC) change analysis from 2009-2019 indicates that impervious surfaces and cropland increased by 0.5653 km2 and 0.9941 km2, while the areas of forest, grassland, and water bodies decreased. The results also showed that forests (-1.3703 km2) are most affected by urbanization. (3) The results of the landscape index calculation showed that the variation at the patch scale is different for different LULC types. The patch density of impervious surfaces decreased, but the aggregation index increased over time, while the patch density of the forest increased continuously. At the landscape scale, overall patch type and distribution remained stable. The purpose of this study is to explore the environmental changes of islands and provide a reference for the sustainable development of islands.

Response Relationship between the Regional Thermal Environment and Urban Forms during Rapid Urbanization (2000–2010–2020): A Case Study of Three Urban Agglomerations in China

Urban agglomerations are currently facing regional thermal environment deterioration. However, the relationship between thermal environment changes in urban agglomerations in response to urban expansion and the underlying urban morphology-driven mechanisms is not clear. This study utilized data from the three largest urban agglomerations in China for 2000, 2010, and 2020 to explore the response of regional heat island changes to urban morphological variations induced by urban expansion through the quantification of urban landscape form, correlation analysis, and relative importance analysis. The results indicate that the distribution of heat source and built-up areas in urban agglomerations has clear spatial and temporal consistency. Moreover, a high regional heat island intensity (RHII) cluster was shown in a “strip-like” form in Beijing–Tianjin–Hebei and the Yangtze River Delta, while the Pearl River Delta, with the most rapid expansion and contiguity of heat source areas, showed a “ring-like” form. RHII was positively correlated with the area of urban clusters and the proportion of built-up areas. However, configuration metrics, such as patch aggregation, also positively affected RHII. Thus, different landscape structures with the same impervious surface area percentage resulted in different RHII values. The relative importance of urban form metrics varied in different urbanization stages; the impervious layer rate was dominant for low and high urban intensity levels, while the shape complexity of urban patches primarily mitigated the thermal environment at the medium urban development level. These results revealed the response relationship between the regional thermal environment and urban morphology, providing insights into how we can improve the regional thermal environment through targeted strategies for optimizing urban form patterns for areas at different urbanization stages.

Urban landcover differentially drives day and nighttime air temperature across a semi-arid city

Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m²) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (-0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to -0.022 °C per % cover, while tree canopy effects are reduced from -0.026 °C during the day to -0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.

Residential Buildings’ Real Estate Values Linked to Summer Surface Thermal Anomaly Patterns and Urban Features: A Florence (Italy) Case Study

Climate-change-related extreme events impact ecosystems, people, economy, and infrastructures, with important consequences on the real estate market as well. This study aims to investigate the variation of residential buildings’ real estate values in a historic Italian city in relation to the summer surface thermal anomaly pattern and urban features surrounding buildings. Open data from remote sensing products and the national database of the Revenue Agency of Italy were used. Real estate values of residential buildings were spatially analyzed in four urban belts, and the association with daytime summer surface hot- and cool-spot zones was studied through odds ratio (OR) statistic. Urban features (impervious area, tree cover, grassland area, and water body) surrounding residential buildings with different real estate values were also analyzed. Considering the whole Florentine municipality, 13.0% of residential buildings fell into hot-spot zones (only 0.6% into cool-spot ones), characterized by very low tree cover surfaces (generally <1%), most of which were in the central belt (37% of all buildings in central belt). Almost 10% of these buildings belonged to the highest market value class revealing a positive association (OR = 1.53) with hot-spot zones. This study provides useful information to plan targeted building interventions to avoid a probable decrease of the value of residential properties in high heat-related risk areas.

A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images

Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Spatiotemporal Evolution of the Urban Thermal Environment Effect and Its Influencing Factors: A Case Study of Beijing, China

Rapid urbanization has led to significant changes in land surface temperature (LST), which in turn affect the urban thermal environment effect and the health of residents. Exploring the causes of the urban thermal environment effect will provide guidance for promoting sustainable urban development. The spatiotemporal evolution of the urban thermal environment effect within the sixth ring road of Beijing was analyzed by inversion of remote sensing data to obtain the LST in 2004, 2009, 2014, and 2019. In addition, based on multivariate spatial data, we applied the standard deviation ellipse (SDE), spatial principal component analysis (PCA), and other methods to analyze and identify the relationships between the urban thermal environment effect and its influencing factors. The results show that from 2004 to 2019, the spatial distribution of urban development and LST within the sixth ring road of Beijing were closely related, the heat island area showed a small increasing trend, and differences in the thermal environment effect between different administrative regions in different periods were obvious. The main factors affecting the urban thermal environment effect were urban construction intensity, vegetation and water bodies, socioeconomic activities, and geomorphology. It is noteworthy that human factors had a greater impact than natural factors. Among them, the positive effect of the normalized difference impervious surface index (NDBBI) and the negative effect of the fractional vegetation cover (FVC) were the most prominent. This study provides theoretical support for mitigating the urban thermal environment effect and promoting sustainable urban development.

Spatiotemporal Evolution and Relationship between Night Time Light and Land Surface Temperature: A Case Study of Beijing, China

Rapid urbanization has triggered significant changes in urban land surface temperature (LST), which in turn affects urban ecosystems and the health of residents. Therefore, exploring the interrelationship between urban development and LST can help optimize the urban thermal environment and promote sustainable development. Based on remote sensing data from 2004–2019 within the sixth ring road of Beijing, this study investigates the spatiotemporal coupling law of night time light (NTL) and LST using an overall coupling model and analyzes the degree of coordination between them using a coordination model. The spatial response law between them was also analyzed using standard deviation ellipses and bivariate spatial autocorrelation. The results show that, from the perspective of spatiotemporal evolution, the spatial distributions of NTL and LST within the sixth ring road of Beijing were closely related from 2004 to 2019, although the overall coupling of NTL and LST was initially decreased and then continuously increased. From the perspective of coordination types, the main types of coordination between NTL and LST deteriorated over time. The increase in LST lagged behind NTL from 2004 to 2009 (heating hysteresis type), while LST increased ahead of NTL from 2014 to 2019 (heating advance type). This suggests that urban development became less efficient, while LST increases became more significant. In terms of correlation, NTL and LST showed significant positive correlation and spatial positive correlation; the correlation coefficient first decreased significantly and then continued to increase. From 2004 to 2009, the temperature increase caused by urbanization was suppressed due to the 2008 Beijing Olympics and related ecological protection policies, resulting in a significant decrease in the correlation coefficient between NTL and LST. From 2009 to 2019, short-term measures taken by Beijing during the Olympic Games were no longer effective, and the opposition between urban development and related policies made the policies increasingly less effective, thereby increasing the correlation coefficient between NTL and LST, and the increase in LST was more significant. This will greatly affect the urban ecological environment and residents’ health and make the previous government investment to suppress the temperature increase all in vain. This study can provide theoretical and practical support for the development of thermal environment optimization schemes and LST mitigation strategies in Beijing and other cities.

The Mitigation Effect of Park Landscape on Thermal Environment in Shanghai City Based on Remote Sensing Retrieval Method

The mitigation effects of park green space on Urban Heat Island (UHI) have been extensively documented. However, the relative effects of the configuration of park components on land surface temperature (LST) inside the park and indicators (i.e., park cooling intensity and distance) surrounding the park is largely unknown. Therefore, the main objective of this study is to explore the quantitative impacts of configuration and morphology features under different urban park scales on the cooling effect. In this study, based on Landsat-8 OLI/TIRS images on 3 August 2015 and 16 August 2020 during summer daytime, the LSTs of Shanghai City were retrieved by atmospheric correction method. Then, the relationships of park landscape features with LSTs in the park and typical indicators representing cooling efficiency of 24 parks on different grades were analyzed. The results showed that the average temperature in urban parks was, respectively, 1.46 °C and 1.66 °C lower than that in the main city of Shanghai in 2015 and 2020, suggesting that urban parks form cold islands in the city. The landscape metrics of park area (PA), park perimeter (PP), green area (GA) and water area (WA), were key characteristics that strong negatively affect the internal park LSTs. However, the park perimeter-to-area ratio (PPAR) had a significant positive power correlation with the park LSTs. Buffer zone analysis showed that LST cools down by about 0.67 °C when the distance from the park increases by 100 m. The Maximum Cooling Distance (MCD) for 2015 and 2020 had a significant correlation with PA, PC, PPAR, GA and WA, and increased sharply within the park area of 20 ha. However, the medium park group had the largest Maximum Cooling Intensity (MCI) in both periods, followed by the small park group. There could be a trade-off relationship between the MCD and MCI in urban parks, which is worth pondering to research. This study could be of great significance for planning and constructing park landscapes, alleviating Urban Heat Island effect and improving urban livability.

A functional seasonal thermal hot-spot classification: Focus on industrial sites

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim to provide a functional spatial thermal anomaly indicator obtained throughout a thermal summer and winter hot-spot detection. The hot-spot analysis was performed by applying Getis-Ord Gi* spatial statistics to Land Surface Temperature (LST) layers, obtained from Landsat 8 remote sensing data during the 2015-2019 daytime summer and winter period, to delimitate summer hot- and cool-spots, and winter warm- and cold-spots. Further, these ones were spatially combined thus obtaining a comprehensive summer-winter Thermal Hot-Spot (THS_SW) spatial indicator. Winter and summer mean daily thermal comfort profiles were provided for the study area assessing the Universal Thermal Climate Index (UTCI) by using meteorological data available from seven local weather stations, located at a maximum distance of 350 m from industrial sites. A specific focus on industrial sites was carried out by analyzing the industrial buildings characteristics and their surrounding areas (50 m buffer), through the following layers: industrial building area (BA), surface albedo of buildings (ALB), impervious area (IA), tree cover (TC), and grassland area (GA). The novel THS_SW classification applied to industrial buildings has shown that about 50% of the buildings were located in areas characterized by summer hot-spots. Increases in BA and IA revealed warming effects on industrial buildings, whereas increases in ALB, TC, and GA disclosed cooling effects. A decrease of about 10% of IA replaced by TC and GA was associated with about 2 °C decrease of LST. Very strong outdoor heat stress conditions were observed during summer daytime, whereas moderate winter outdoor cold stress conditions were recorded during nighttime until the early morning. The thermal spatial hot-spot classification in industrial areas provides a very useful source of information for thermal mitigation strategies aimed to reduce the heat-related health risk for workers.

Effects of tree plantings and aspect ratios on pedestrian visual and thermal comfort using scaled outdoor experiments

Urban trees ameliorate heat stress for urban dwellers. However, it is difficult to quantitatively assess the integrated impacts of tree planting and street layouts on visual and thermal comfort in simulations and urban field experiments. We conducted scaled outdoor experiments in Guangzhou to investigate the influence of tree plantings on pedestrian visual and thermal comfort in street canyons with various aspect ratios (H/W = 1, 2, 3; H = 1.2 m). We considered the effects of tree crown covers (big and small crown) and tree planting densities (ρ = 1, 0.5) on pedestrian illuminance level and two thermal comfort indices (Physiological Equivalent Temperature: PET and Index of Thermal Stress: ITS). When ρ = 1, trees in most cases reduce pedestrian illuminance (maximum 140.0klux) and improve visual comfort. Decreasing ρ from 1 to 0.5 increases the illuminance (maximum 179.5klux) in the streets with big crown trees (H/W = 1, 2) and in the street with small crown trees (H/W = 2). When ρ = 1 (H/W = 1, 2), big crown trees decrease the peak daytime PET (by about 4.0 °C) and ITS (by about 285 W). Small crown trees (ρ = 1, H/W = 1, 2) produce a warming effect on peak daytime PET (2.0-3.0 °C), but a reduction in ITS is observed when H/W = 2, 3. After reducing ρ from 1 to 0.5, big crown trees increase peak daytime thermal stress according to both indices when H/W = 1, 2. Small crown trees exhibit a similar PET cycle between ρ = 0.5 and ρ = 1 across various H/W, but their daytime reduction of ITS is less effective when ρ = 0.5 (H/W = 2). The discrepancies between PET and ITS are attributed to their different approaches to modelling radiation fluxes. The narrower the street, the lower the illuminance, PET, and ITS, while their increases caused by reduced ρ are limited in narrow streets. Our study informs some potential urban tree planting strategies and produces high-quality validation data for numerical simulations and theoretical models.

Performances of Limited Area Models for the WORKLIMATE Heat-Health Warning System to Protect Worker's Health and Productivity in Italy

Outdoor workers are particularly exposed to climate conditions, and in particular, the increase of environmental temperature directly affects their health and productivity. For these reasons, in recent years, heat-health warning systems have been developed for workers generally using heat stress indicators obtained by the combination of meteorological parameters to describe the thermal stress induced by the outdoor environment on the human body. There are several studies on the verification of the parameters predicted by meteorological models, but very few relating to the validation of heat stress indicators. This study aims to verify the performance of two limited area models, with different spatial resolution, potentially applicable in the occupational heat health warning system developed within the WORKLIMATE project for the Italian territory. A comparison between the Wet Bulb Globe Temperature predicted by the models and that obtained by data from 28 weather stations was carried out over about three summer seasons in different daily time slots, using the most common skill of performance. The two meteorological models were overall comparable for much of the Italian explored territory, while major limits have emerged in areas with complex topography. This study demonstrated the applicability of limited area models in occupational heat health warning systems.

Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.

Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.

Assessment of the Urban Heat Island Impact on Building Energy Performance at District Level with the EUReCA Platform

In recent decades, the cooling energy demand in urban areas is increasing ever faster due to the global warming and the growth of developing economies. In this perspective, the urban building energy modelling community is focusing its research activities on innovative tools and policy actions to improve cities’ sustainability. This work aims to present a novel module of the EUReCA (Energy Urban Resistance Capacitance Approach) platform for evaluating the effects of the interaction between district’s buildings in the cooling season. EUReCA predicts the urban energy demand using a bottom-up approach and low computational resources. The new module allows us to evaluate the mutual shading between buildings and the urban heat island effects, and it is well integrated with the calculation of the energy demand of buildings. The analysis was carried out considering a real case study in Padua (Italy). Results show that the urban heat island causes an average increase of 2.2 °C in the external air temperature mainly caused by the waste heat rejected from cooling systems. This involves an increase in urban cooling energy and electricity demand, which can be affected between 6 and 8%. The latter is the most affected by the urban heat island (UHI), due to the degradation it causes on the HVAC systems’ efficiency.

The Challenge of the Urban Compact Form: Three-Dimensional Index Construction and Urban Land Surface Temperature Impacts

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.