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

Global vegetation, moisture, thermal and climate interactions intensify compound extreme events

Compound extreme events, encompassing drought, vegetation stress, wildfire severity, and heatwave intensity (CDVWHS), pose significant threats to societal, environmental, and health systems. Understanding the intricate relationships governing CDVWHS evolution and their interaction with climate teleconnections is crucial for effective climate adaptation strategies. This study leverages remote sensing, reanalysis data, and climate models to analyze CDVWHS during historical (1982-2014), near-future (2028-2060), and far-future (2068-2100) periods under two Shared Socioeconomic Pathways (SSP; 245 and 585). Our results show that reduced vegetation health, unfavorable temperature conditions, and low moisture conditions have negligible effects on vegetation density. However, they worsen the intensity of heatwaves and increase the risk of wildfires. Wildfires can persist when thermal conditions are poor despite favorable moisture levels. For example, despite adequate moisture availability, we link the 2012 Siberian wildfire in the Ob basin to anomalous negative thermal conditions and concurrent unfavorable thermal-moisture conditions. In contrast, the Amazon experiences extreme and exceptional drought associated with unfavorable moisture conditions in the same year. A comparative analysis of Siberian and North American fires reveals distinct burned area anomalies due to variations in vegetation density and wildfire fuel. The North American fires have lower positive anomalies in burned areas because of negative anomalous vegetation density, which reduced the amount of wildfire fuel. Furthermore, we examine basin-specific variability in climate teleconnections related to compound CDVWHS, revealing the primary modes of variability and evolution of CDVWHS through climate teleconnection patterns. Moreover, a substantial increase in the magnitude of heatwave severity emerges between the near and far future under SSP 585. This study underscores the urgency for targeted actions to enhance ecosystem resilience and safeguard vulnerable communities from CDVWHS impacts. Identifying CDVWHS hotspots and comprehending their complex relationships with environmental factors are essential for developing effective adaptation strategies in a changing climate.

Association between preschooler outdoor play and home yard vegetation as measured by high resolution imagery: Findings from the PLAYCE study

Outdoor play in the home yard is an important source of physical activity for many preschoolers. This study investigated if home yard size and vegetation are related to preschooler outdoor play time. High-resolution remotely sensed data were used to distinguish between types of vegetation coverage in the home yard. Shrub and tree cover, and yard size, were positively associated with outdoor play. Following stratification by socio-economic status (SES - parent education), only tree cover was positively associated with preschooler outdoor play in low SES households. All types of vegetation cover were positively associated with preschooler outdoor play in higher SES households. This study highlights the importance of larger yard sizes and higher levels of vegetation for facilitating outdoor play in preschoolers.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

Association of greenness with the disease burden of lower respiratory infections and mediation effects of air pollution and heat: a global ecological study

Exposure to greenness is increasingly linked to beneficial health outcomes, but the associations between greenness and the disease burden of lower respiratory infections (LRIs) are unclear. We used the normalized difference vegetation index (NDVI) and the leaf area index (LAI) to measure greenness and incidence, death, and disability-adjusted life years (DALYs) due to LRIs to represent the disease burden of LRIs. We applied a generalized linear mixed model to evaluate the association between greenness and LRI disease burden and performed a stratified analysis, after adjusting for covariates. Additionally, we assessed the potential mediating effects of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and heat on the association between greenness and the disease burden of LRIs. In the adjusted model, one 0.1 unit increase of NDVI and 0.5 increase in LAI were significantly inversely associated with incidence, death, and DALYs due to LRIs, respectively. Greenness was negatively correlated with the disease burden of LRIs across 15-65 age group, both sexes, and low SDI groups. PM2.5, O3, and heat mediated the effects of greenness on the disease burden of LRIs. Greenness was significantly negatively associated with the disease burden of LRIs, possibly by reducing exposure to air pollution and heat.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km² during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m^-2·a^-1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m^-2·a^-1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

Interactions among spatial configuration aspects of urban tree canopy significantly affect its cooling effects

Increasing urban tree canopy (UTC) has been widely recognized as an effective means for urban heat mitigation and adaptation. While numerous studies have shown that both percent cover of UTC and its spatial configuration can significantly affect urban temperature, the pathways governing these relationships are largely unexplored. Here we present a cross-city comparison aiming to fill this gap by explicitly quantifying the pathways on which percent cover of UTC and its spatial configuration affect land surface temperature (LST) using structural equation modeling (SEM), based on UTC mapped from high resolution imagery and LST derived from Landsat thermal bands. We found: 1) Although both the direct and indirect pathways significantly affected LST regardless of scales and cities, the direct pathway played a more important role in affecting LST in Baltimore, Beijing, and Shenzhen. In contrast, an opposite result was found in Sacramento, likely due to the effects of buildings and their interactions with UTC. 2) Similarly, the direct pathway of mean patch size (MPS) and mean shape index (MSI) played a more important role in affecting LST than their indirect effects via altering edge density (ED). Our results highlighted the necessity for discomposing the effects of different spatial configuration variables on LST. Understanding the pathways through which UTC affects LST can provide insights into urban heat mitigation and adaptation.

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Urban cemeteries are increasingly surrounded by areas of high residential density as urbanization continues world-wide. With increasing rates of mortality caused by the novel coronavirus, SARS-CoV-2, urban vertical cemeteries are experiencing interments at an unprecedented rate. Corpses interred in the 3rd to 5th layer of vertical urban cemeteries have the potential to contaminate large adjacent regions. The general objective of this manuscript is to analyze the reflectance of altimetry, normalized difference vegetation index (NDVI) and land surface temperature (LST) in the urban cemeteries and neighbouring areas of the City of Passo Fundo, Rio Grande do Sul, Brazil. It is assumed that the population residing in the vicinity of these cemeteries may be exposed to SARS-CoV-2 contamination through the displacement of microparticles carried by the wind as a corpse is placed in the burial niche or during the first several days of subsequent fluid and gas release through the process of decomposition. The reflectance analyses were performed utilizing Landsat 8 satellite images applied to altimetry, NDVI and LST, for hypothetical examination of possible displacement, transport and subsequent deposition of the SARS-CoV-2 virus. The results showed that two cemeteries within the city, cemeteries A and B could potentially transport SARS-CoV-2 of nanometric structure to neighboring residential areas through wind action. These two cemeteries are located at high relative altitudes in more densely populated regions of the city. The NDVI, which has been shown to control the proliferation of contaminants, proved to be insufficient in these areas, contributing to high LST values. Based on the results of this study, the formation and implementation of public policies that monitor urban cemeteries is suggested in areas that utilize vertical urban cemeteries in order to reduce the further spread of the SARS-CoV-2 virus.

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.

Combining GOES-R and ECOSTRESS land surface temperature data to investigate diurnal variations of surface urban heat island

The surface urban heat island (SUHI) phenomenon is characterized by both high spatial and temporal variability, while its diurnal (i.e., diel) variations have rarely been investigated because traditional satellites and sensors flying on polar orbits (e.g., Landsat, MODIS) have no diurnal sampling capability. Here we combined land surface temperature (LST) data from the Geostationary Operational Environmental Satellites (GOES-R) and the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to explore the diurnal variations of SUHI and thermal differentiation among various land covers over the Boston Metropolitan Area. With the combined use of the LST data from GOES-R and ECOSTRESS, we took advantage of the strengths of both GOES-R (i.e., high frequency in each day and night) and ECOSTRESS (i.e., much finer spatial resolution). The SUHI intensity of the urban-core and suburban areas both exhibited clear diurnal patterns for different seasons: a continuous increase in the SUHI intensity from sunrise to noon and a decrease thereafter to sunset, followed by a relatively low and constant intensity during nighttime. The LST contrasts among different land cover types were clearly larger in the daytime than at nighttime and peaked around midday. At noon in summer, the LST of 'Developed, High Intensity' was 2.6 °C higher than that of 'Developed, Medium Intensity', and about 4.6 °C higher than that of "Developed, Open Space" and "Developed, Low Intensity". Controlling the percent impervious surface in construction land at a relatively low level (e.g., below ~49%) could effectively alleviate the impacts of SUHI. Compared with GOES-R data, ECOSTRESS LST is suitable for monitoring the diurnal variations of intracity thermal environment at the subdistrict (or neighborhood) scale. Our study highlights the value of the combined use of geostationary satellite and ECOSTRESS LST in exploring the diurnal cycling of the SUHI, and can help inform urban planning and land-based climate mitigation policies in the context of climate change.

Synchronization, Decoupling, and Regime Shift of Urban Thermal Conditions in Xi’an, an Ancient City in China under Rapid Expansion

Urbanization has profound impacts on economic development and environmental quality. Some of the serious consequences of urbanization are the changes in the thermal environment, which directly affect the greater environment and quality of life. Although many studies have been performed on urban heat islands, few have specifically examined the thermal evolution of rapidly expanding ancient cities and the impacts of urbanization on the thermal environments of important heritage sites. In this study, we analyzed the temporal and spatial patterns of the thermal environment quantified as the surface urban heat island (SUHI) and land surface temperature (LST) values from 2000 to 2018 in Xi’an, an ancient city with rich cultural heritage in China. Specifically, we analyzed the temporal evolution of the thermal environments of the functional zones and heritage sites and explore their coupling relationships with the overall temperature of the study area using a statistical analysis approach. Furthermore, we revealed time-sensitive changes in temperature regimes using the newly proposed double temperature curve approach (DTCA). The results showed that the heat island phenomenon has been intensifying in Xi’an, as evidenced by the summer daytime mean SUHI values being greater than 7 °C continuously since 2010 and the increased frequency of high-intensity SUHI effects. Extreme heat conditions were more frequent in the old urban area (built-up and in existence before 2000) than in the new urban area, while SUHI values in the new area deteriorated more rapidly. The changes in temperature in the functional zones were strongly synchronized with the overall temperature changes in Xi’an, and the temperature differences increased linearly with the overall temperature. The LST values in the four major historical heritage sites investigated in this study were 2–8 °C higher than the background temperature and were decoupled from background temperature changes. From the DTCA, we found the time periods of the thermal environment regime changes for each functional zone or heritage site, which were largely the result of policy guidance. Regional synchronization, site decoupling, and regime shifts in LST suggest opportunities for regional planning and urban landscape optimization to reduce adverse effects of urbanization on the urban environment, particularly in cities with rich historical heritage sites.

Developing a Framework for Closed-Loop Supply Chain and Its Impact on Sustainability in the Petrochemicals Industry

Companies rely on formulating, implementing, and monitoring strategies in social, environmental and economic aspects to ensure that they achieve their goals and keep abreast of developments related to sustainability requirements. Therefore, our study develops a system to integrate the closed-loop supply chain approach in the petrochemical sector. The research follows the quantitative-based approach by collecting data through a questionnaire directed to employees in the supply chain departments, including 230 questionnaires that were collected. Correlation and structural equation models (SEM) were used. This technique consists of multiple regression analysis and factor analysis and analyses the structural relationship between the underlying structures and the measured variables. The results indicated a significant relationship between the supply chains that have a loop from the following perspectives: economic motivations, customer awareness, environmental legislation, and sustainability. By increasing financial reasons, customer awareness and environmental legislation, sustainability will increase as they all move in the same direction. Therefore, the overall effect of a closed-loop supply chain is positive and significant.

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO2 to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO2, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO2 were the major factors that affected FVC change. The average contributions of climate factors, CO2, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development.

Monitoring of Spatiotemporal Change of Green Spaces in Relation to the Land Surface Temperature: A Case Study of Belgrade, Serbia

Understanding the relationship between land use and land cover and thermal environment has recently become an emerging issue for urban planners and policy makers. We chose Belgrade, as a case study, to present a cost- and time-effective framework for monitoring spatiotemporal changes of green spaces in relation to the land surface temperature (LST). Time series analysis was performed using Landsat 5 TM and Landsat 8 OLI/TIRS imagery from 1991 to 2019 with an approximate 5-year interval (18 images in total). Spectral vegetation indices and supervised land cover classifications were used to examine changes of green spaces. The results showed a fluctuating trend of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The highest values were recorded in 2019, indicating vegetation recovery in the last decade. A significant positive correlation was determined between the spectral vegetation indices and the amount of precipitation during growing season. The land cover classification showed that the share of vegetated and bare land decreased by 11.74% during the study period. The most intensive conversion of green and bare land into built-up land cover occurred in the first decade (1991–2000). To assess spatiotemporal changes in the LST, Landsat Collection 2 Surface Temperature products were used. We found a negative correlation between change in the spectral vegetation indices and change in the LST. This indicates that the reduction in vegetation was associated with an increase in the LST. The municipalities that were the most affected in each decade were also identified with our framework. The findings of this study are of great relevance for actions targeting an improvement in urban thermal comfort and climate resilience.

Evaluation of the Equity and Regional Management of Some Urban Green Space Ecosystem Services: A Case Study of Main Urban Area of Xi’an City

Urban green spaces can provide many types of ecosystem services for residents. An imbalance in the pattern of green spaces leads to an inequality of the benefits of such spaces. Given the current situation of environmental problems and the basic geographical conditions of Xi’an City, this study evaluated and mapped four kinds of ecosystem services from the perspective of equity: biodiversity, carbon sequestration, air purification, and climate regulation. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) was used to obtain the partition groups of ecosystem services. The results indicate that first, the complexity of the urban green space community is low, and the level of biodiversity needs to be improved. The dry deposition flux of particulate matter (PM2.5) decreases from north to south, and green spaces enhance the adsorption of PM2.5. Carbon sequestration in the south and east is higher than that in the north and west, respectively. The average surface temperature in green spaces is lower than that in other urban areas. Second, urban green space resources in the study area are unevenly distributed. Therefore, ecosystem services in different areas are inequitable. Finally, based on the regionalization of integrated ecosystem services, an ecosystem services cluster was developed. This included 913 grid spaces, 12 partitions, and 5 clusters, which can provide a reference for distinct levels of ecosystem services management. This can assist urban managers who can use these indicators of ecosystem service levels for planning and guiding the overall development pattern of green spaces. The benefits would be a maximization of the ecological functions of green spaces, an improvement of the sustainable development of the city, and an improvement of people’s well-being.

Dynamic Changes of Local Climate Zones in the Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal Impacts on the Surface Urban Heat Island Effect between 2005 and 2015

Local climate zones (LCZs) emphasize the influence of representative geometric properties and surface cover characteristics on the local climate. In this paper, we propose a multi-temporal LCZ mapping method, which was used to obtain LCZ maps for 2005 and 2015 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), and we analyze the effects of LCZ changes in the GBA on land surface temperature (LST) changes. The results reveal that: (1) The accuracy of the LCZ mapping of the GBA for 2005 and 2015 is 85.03% and 85.28%, respectively. (2) The built type category showing the largest increase in area from 2005 to 2015 is LCZ8 (large low-rise), with a 1.01% increase. The changes of the LCZs also vary among the cities due to the different factors, such as the economic development level and local policies. (3) The area showing a warming trend is larger than the area showing a cooling trend in all the cities in the GBA study area. The main reasons for the warming are the increase of built types, the enhancement of human activities, and the heat radiation from surrounding high-temperature areas. (4) The spatial morphology changes of the built type categories are positively correlated with the LST changes, and the morphological changes of the LCZ4 (open high-rise) and LCZ5 (open midrise) built types exert the most significant influence. These findings will provide important insights for urban heat mitigation via rational landscape design in urban planning management.

A human-centred assessment framework to prioritise heat mitigation efforts for active travel at city scale

Hot weather not only impacts upon human physical comfort and health, but also impacts the way that people access and experience active travel options such as walking and cycling. By evaluating the street thermal environment of a city alongside an assessment of those communities that are the most vulnerable to the effects of heat, we can prioritise areas in which heat mitigation interventions are most needed. In this paper, we propose a new approach for policy makers to determine where to delegate limited resources for heat mitigation with most effective outcomes for the communities. We use eye-level street panorama images and community profiles to provide a bottom-up, human-centred perspective of the city scale assessment, highlighting the situation of urban tree shade provision throughout the streets in comparison with environmental and social-economic status. The approach leverages multiple sources of spatial data including satellite thermal images, Google street view (GSV) images, land use and demographic census data. A deep learning model was developed to automate the classification of streetscape types and percentages at the street- and eye-view level. The methodology is metrics based and scalable which provides a data driven assessment of heat-related vulnerability. The findings of this study first contribute to sustainable development by developing a method to identify geographical areas or neighbourhoods that require heat mitigation; and enforce policies improving tree shade on routes, as a heat adaptation strategy, which will lead to increasing active travel and produce significant health benefits for residents. The approach can be also used to guide post COVID-19 city planning and design.

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.

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

Land surface temperature (LST) predictors, such as impervious and vegetated surfaces, strongly influence the urban landscape mosaic, also changing microclimate conditions and exacerbating the surface urban heat island (SUHI) phenomenon. The aim of this study was to investigate the summer daytime SUHI phenomenon and the role played by impervious and tree cover surfaces in the 10 Italian peninsular metropolitan cities. Summer daytime LST values were assessed by using MODIS data referred to the months of June, July and August from 2016 to 2018. High spatial resolution (10 m) of impervious surface and tree cover layers was calculated based on open-data developed by the Italian National Institute for Environmental Protection and Research. A novel informative urban surface landscape layer was developed combining impervious surfaces and tree cover densities and its mapping for metropolitan cities was performed. Summer daytime SUHI rose significantly, increased especially in inland cities, by increasing the size of areas with low tree cover densities in the metropolitan core (or decreasing areas with low tree cover densities outside the metropolitan core), further increasing its intensity when the impervious density grew. A mitigating effect of the sea on daytime LST and SUHI was observed on coastal cities. The most intense SUHI phenomenon was observed in Turin (the largest Italian metropolitan city): for every 10% increase in areas with highly impervious surfaces and low tree cover densities in the metropolitan core, the SUHI significantly (p < 0.001) increased by 4.0 °C. Increased impervious surfaces combined with low tree cover densities represented the main driving process to increase the summer daytime SUHI intensity in most studied cities. These findings are useful to identify summer daytime LST critical areas and to implement the most efficient urban-heat-island mitigation strategies in order to safeguard the vulnerable urban environment and enhance quality of life for the population.

Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

Statistical Review of Quality Parameters of Blue-Green Infrastructure Elements Important in Mitigating the Effect of the Urban Heat Island in the Temperate Climate (C) Zone

Urban Heat Island (UHI) effect relates to the occurrence of a positive heat balance, compared to suburban and extra-urban areas in a high degree of urbanized cities. It is necessary to develop effective UHI prevention and mitigation strategies, one of which is blue-green infrastructure (BGI). Most research work comparing impact of BGI parameters on UHI mitigation is based on data measured in different climate zones. This makes the implication of nature-based solutions difficult in cities with different climate zones due to the differences in the vegetation time of plants. The aim of our research was to select the most statistically significant quality parameters of BGI elements in terms of preventing UHI. The normative four-step data delimitation procedure in systematic reviews related to UHI literature was used, and temperate climate (C) zone was determined as the UHI crisis area. As a result of delimitation, 173 publications qualified for literature review were obtained (488 rejected). We prepared a detailed literature data analysis and the CVA model—a canonical variation of Fisher’s linear discriminant analysis (LDA). Our research has indicated that the BGI object parameters are essential for UHI mitigation, which are the following: area of water objects and green areas, street greenery leaf size (LAI), green roofs hydration degree, and green walls location. Data obtained from the statistical analysis will be used to create the dynamic BGI modeling algorithm, which is the main goal of the series of articles in the future.

Tree Ecosystem Services, for Everyone? A Compositional Analysis Approach to Assess the Distribution of Urban Trees as an Indicator of Environmental Justice

Trees provide a broad amount of ecosystem services in urban areas. Although it is well documented that trees are essential for the well-being and livability of cities, trees are often not evenly distributed. Studies have found that urban residents with a deprived socioeconomic status are associated with a lower coverage and access to urban trees in their communities, yet a fair distribution of trees contributes to the sustainability and resilience of cities. In this context, the environmental justice movement seeks to ensure equal distribution of green infrastructure and its benefits throughout a territory. The objective of this study is threefold: (i) to determine whether urban trees in Guadalajara, Mexico, are distributed equally; (ii) to assess the association between urban trees and socioeconomic status; and (iii) to introduce compositional data analysis to the existing literature. Due to the compositional nature of the data, compositional analysis techniques are applied. We believe this novel approach will help define the proper management of data used in the literature. The outcomes provide insights for urban planners working towards the Sustainable Development Goals to help eradicate the uneven distribution of urban trees in cities.

Analysis of Spatiotemporal Urban Temperature Characteristics by Urban Spatial Patterns in Changwon City, South Korea

Spatiotemporal air and land surface temperature (LST) characteristics were analyzed based on urban spatial patterns for Changwon City, South Korea. Twelve ASTER (Advanced spaceborne thermal emission and reflection radiometer) Thermal infrared radiance (TIR) images during the daytime and nighttime from June to September, 2012–2014 were used for LST analysis. Air temperature was measured at five meteorological stations. The landcover type, elevation, and location of the meteorological measurement stations were the spatial patterns. The differences among the mean LST for each landcover material were the maximum of 8 °C and 1 °C during the daytime and nighttime, respectively. The LST decreased with increasing built-up area ratio, most prominently in July, but less so with increasing forest area for the same area ratios. The changes of urban temperature according to the spatial pattern were found to be different in each period, and there were some differences from previous studies. This is because the thermal characteristics differ depending on the geographical location, climatic conditions, and building environment of the cities. Therefore, to mitigate the urban heat island continuously, it should be applied to urban planning considering the relationship between spatial patterns and urban temperature, and the urban environment should be considered rather than directly using the results of previous studies.

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.

Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia

Due to the intensity of urban development around the world, there is an increasing body of studies attempting to investigate urban heat island (UHI) in various spatial and temporal scales. In surface heat urban island (SUHI) studies, extended periods of time, broader regions and local government area (LGA) level have become more crucial and will shed light on causes of UHI. Moreover, the spatial pattern and structure of SUHI will be useful for policy-makers to develop mitigation strategies. This study focused on three objectives. Firstly, analyzing land surface temperature (LST), normalized difference built-up (NDBI) and vegetation (NDVI) indices. Secondly, investigating interrelationships among LST, NDVI, and NDBI. Thirdly, identifying LST patterns in the Melbourne metropolitan area. These objectives were achieved through three different methods. The modified automatic mapping method for the first objective, the correlation analysis for the second, and spatial statistical methods for the third. The methodological innovations of this study were considering LGA in interrelationship analysis among LST, NDBI and NDVI, and calculation of NDVI for each acquisition date. The results indicated that the clustering pattern of LST expanded toward the north-west and south-east during the period of the study. Furthermore, the north-west part of the city has the highest positive (0.6) correlation between NDBI and LST, and the south-east part of the city has the lowest negative (-0.8) correlation between NDVI and LST. The most significant increase and decrease in mean LST happened respectively from January 6th to 22nd 2017, and January 14th to 30th January 2014. The temperature degree altered from 19.61 °C to 27.86 °C in inner western suburbs, and from 35.49 °C to 26.88 °C in most LGA's. These findings are critical for planners to localize UHI mitigation action plans, target hot spots in LGA's and allocate resources to respond to the adverse effect of UHI.