Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 μS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

The resilience-sustainability-quality of life nexus

Social-ecological resilience (SER), sustainability (SUS) and quality of life including wellbeing (QOL) are distinct but highly interconnected goals that are essential for human survival, development, and adaptation to environmental and socioeconomic changes. However, these goals are often addressed in silos or pairs, and a framework explicitly linking all three is currently lacking. In this paper, we present the SER-SUS-QOL nexus framework and discuss how social and ecological changes affect these goals. The main thrust of this nexus is that efforts toward SER and SUS need to be explicitly framed in terms of the ultimate goal, which has to be the QOL of the present and future generations. We contend that critically assessing the dynamic interplay between SER, SUS and QOL, as well as the factors impacting them, can help promote transformative governance and planning in the contemporary era. Understanding the multifaceted interrelationship between these goals is crucial to empower planners and decision-makers to navigate the complexities of our rapidly changing world and address the challenges brought by interrelated social and ecological changes. To deepen our understanding of this nexus, more research is needed on various issues, including, but not limited to, trade-offs and synergies, cascading effects, spatiotemporal dynamics of SER, SUS and QOL outcomes, potential inconsistencies between SER and transformative changes toward SUS, and the role of local/indigenous/traditional ecological knowledge in transformative governance and planning.

Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city

Previous studies have provided valuable insights into the impact of green space (GS) on land surface temperature (LST). However, there is a need for in-depth comparative research on changing landscape patterns in cities and their effects on the urban thermal environment. This study investigates the spatial arrangement of GS and the influence of impervious surfaces on LST in urban areas, examining their cooling and warming effects in the urban landscapes of Beijing and Islamabad. The study aims to assess the impact of the spatial arrangement of GS on LST using a moving window of 1 km2 to analyze the overall effect of landscape patterns on the urban environment. Using Gaofen (GF-2) and Landsat-8 satellite data, we examined the biophysical surface properties of core urban areas. The results indicate a significant difference in the mean LST of 5.44 °C and 3.31 °C between impervious surfaces and GS in Beijing and Islamabad, respectively. The barren land and GS in Islamabad experience a higher LST of 3.39 °C compared to Beijing, which accounts for 1.39 °C. In Beijing, configuration metrics show no significant effect on urban LST, while edge density (ED) exhibits a slightly negative trend. In contrast, in the city of Islamabad, the landscape shape index (LSI), patch density (PD), and number of patches (NP) metrics have a significant influence on LST. The cooling effect of GS patches (0.1-0.5 ha) is more pronounced, while that of GS patches of 15-20 ha shows no significant effect on LST. The temperature difference (TD) of 5.01 °C was observed from the edge of GS in Beijing and 3.3 °C in Islamabad. Considering Islamabad's lush green scape compared to Beijing, this study suggests that Islamabad may experience an increase in LST in the future due to urbanization. This study's findings may assist urban policy-makers in designing sustainable green city layouts that effectively address future planning considerations.

Effects of landscape pattern on land surface temperature in Nanchang, China

The composition and configuration of landscapes are critical important to design effective approaches to mitigate urban thermal environment in the urbanization process. In this research, land use maps and land surface temperature (LST) retrieval were derived in Nanchang city of central China based on product datasets and the thermal infrared band of Landsat. The results showed that the thermal environment of Nanchang had become worse over the past two decades, that is, the proportion of area of the extremely low temperature zone (ELTZ) decreased from 4.39 to 0.77% from 2001 to 2020, and that of medium temperature zone (MTZ) reduced by 20%, whereas those of the high temperature zone (HTZ) and the extremely high temperature zone (EHTZ) increased sharply after 2001, and by 2020, the area ratio increased by 11% and 7.16%, respectively. The agricultural land (AL) area decreased from 68.44 to 49.69%, was gradually replaced by construction land (CL). The CL occupied the largest proportion in EHTZ, HTZ and slight high temperature zone (SHTZ); water landscape (WL) and green land (GL) occupied the largest proportion in ELTZ, low temperature zone (LTZ); and AL occupied the largest proportion in SHTZ, MTZ, and slight low temperature zone (SLTZ). Landscape configuration also obviously impacted on LST. The model fitting was well (R = 0.87) between land use area and LST by multiple regression analysis. The significant correlation between LST and six landscape pattern indices of CL (p < 0.01) indicated that the larger percent (PLANT, R = 0.78) and the more concentrate (LPI, R = 0.73) of CL implied the higher LST, while the more fragment (NP, R = - 0.45), dispersed and complex shape (R = - 0.35) were benefit to relieve LST. Contrastively, the larger percent and the more concentrated and complex shape distribution of AL, GL and WL, the lower LST (p < 0.01). In addition, LST had closely correlation with landscape level indices such as aggregation degree (AI, R = 0.44) and diversity (SHDI, R = - 0.60) (p < 0.01).

Greater local cooling effects of trees across globally distributed urban green spaces

Urban green spaces (UGS) are an effective mitigation strategy for urban heat islands (UHIs) through their evapotranspiration and shading effects. Yet, the extent to which local UGS cooling effects vary across different background climates, plant characteristics and urban settings across global cities is not well understood. This study analysed 265 local air temperature (TA) measurements from 58 published studies across globally distributed sites to infer the potential influence of background climate, plant and urban variables among different UGS types (trees, grass, green roofs and walls). We show that trees were more effective at reducing local TA, with reductions 2-3 times greater than grass and green roofs and walls. We use a hierarchical linear mixed effects model to reveal that background climate (mean annual temperature) and plant characteristics (specific leaf area vegetation index) had the greatest influence on cooling effects across UGS types, while urban characteristics did not significantly influence the cooling effects of UGS. Notably, trees dominated the overall local cooling effects across global cities, indicating that greater tree growth in mild climates with lower mean annual temperatures has the greatest mitigation potential against UHIs. Our findings provide insights for urban heat mitigation using UGS interventions, particularly trees across cities worldwide with diverse climatic and environmental conditions and highlight the essential role of trees in creating healthy urban living environments for citizens under extreme weather conditions.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Effects of large-scale land consolidation projects on ecological environment quality: A case study of a land creation project in Yan'an, China

Large land consolidation projects modify the structures and functions of regional ecosystems through the reshaping of the territorial spatial pattern, thereby affecting the ecological environmental quality (EEQ). To investigate the effects of large-scale land consolidation projects on EEQ, this study takes the major land consolidation project of "bulldoze mountains to create land" (BMCL) in Yan'an City as a research object and evaluates the change of EEQ based on Remote Sensing Ecological Index (RSEI). The consolidated area and the control area were set up to comparatively analyze the EEQ change processes and spatial distribution characteristics of these two areas in the full life cycle of BMCL. According to the results, the mean RSEI of the consolidated area was 0.128 lower than that of the control area, and the EEQ of the consolidated area was always lower than that of the control area. BMCL had a strong negative impact on the EEQ grade of the consolidated area, especially in the early stage. However, the positive effect of BMCL on EEQ gradually emerged in the late stage of the large land consolidation project. The overall EEQ grade of the consolidated area has also improved. The results of the stepwise regression analysis indicated that the wetness component and the normalized differential vegetation index played key roles in improving the EEQ of the BMCL. Overall, the local BMCL strongly affected the EEQ of the consolidated area but would not cause the EEQ of the whole region to experience any dramatic, abrupt change in the short term. This study provided references for the evaluation and analysis of the ecological effects of land consolidation at the regional scale, offering a feasible way to evaluate the spatio-temporal change of EEQ in BMCL.

Study on coupling coordination degree of urbanization and ecological environment in Chengdu-Chongqing economic circle from 2002 to 2018

Under the western development strategy of China, the urbanization process of Chengdu-Chongqing economic circle (CCEC) develops rapidly, but also brings a series of ecological and environmental problems. Understanding the coordination mechanism that links urbanization and the ecological environment (UE) is crucial for promoting environmental conservation and sustainable development. Using the comprehensive nighttime light index (CNLI) and the remote sensing ecological index (RSEI), this study objectively evaluated the urbanization level and ecological environment quality and used the modified coupling coordination degree model to quantify the coupling coordination of UE for the two indexes. The results show that (1) the urbanization level of each city in the CCEC presents an increasing trend year by year, showing a circle distribution pattern with the twin cities as the core. (2) The overall eco-environmental quality presents a fluctuating upward trend, and in recent years, it has been significantly improved, showing a spatial pattern of higher in the surrounding areas and lower in the middle. (3) The overall coupling coordination of the study area is developing, and the upward trend is larger in recent years. The spatial distribution is centered on the "double nucleus," and the distribution of circles is characterized by a gradual decrease from the inside to the outside; the coupling coordination of cities in the northwest and east of the CCEC is decreasing, and that of cities in the northeast and southwest is increasing. During the study period, a total of 5 cities have reached the level of coordinated development, while most other cities are in the stage of uncoordinated development, mainly due to the lagging development of urbanization. CCEC still needs to strengthen the construction of urbanization, in order to improve the degree of coordination between economic development and ecological environment.

Land surface and air temperature dynamics: The role of urban form and seasonality

Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from Ta within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to Ta, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and Ta in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- Ta in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

Projection of bioclimatic patterns via CMIP6 in the Southeast Region of Türkiye: A guidance for adaptation strategies for climate policy

Over the past three decades, global urbanization and climate change have caused significant differences in climate conditions between urban and rural environments. The effects of global warming affect the climatic values in the urban area. The bioclimatic comfort in an area effectively chooses a site regarding the urban quality of life and activities. This study aims to predict the temporal and spatial changes of the bioclimatic comfort zones of Gaziantep province in terms of climate comfort in the context of long-term global scenarios. The future climate simulation maps were produced and analyzed comparing comfort conditions according to Shared Socioeconomic Pathways (SSPs) 245 and 585 scenarios of the Intergovernmental Panel on Climate Change's (IPCC) Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). Spatio-temporal changes in temperature, humidity, and bioclimatic comfort areas were analyzed to inform these efforts according to Thom's discomfort index (DI) and effective temperature-taking wind velocity (ETv). The current situation of bioclimatic comfort areas to examine their synergy under extreme hot weather throughout the province and their possible concerns in 2040, 2060, 2080, and 2100 were modeled using ArcGIS 10.8 software. SSP585/2100 will create hot (84%) areas, according to DI, and warm (29%) areas, according to ETv. The spatial results of the research are discussed, and some strategies are produced in terms of urban planning, design, and engineering.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87 °C and 14.2 °C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and the none UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors.

What impacts ecosystem services in tropical coastal tourism cities? A comparative case study of Haikou and Sanya, China

The ecological environment of tourism-oriented towns is attracting increasing attention. Taking the cities of Haikou and Sanya as examples, we examined changes in six ecosystem services (ES), including water conservation (WC), crop production (CP), soil retention (SR), carbon storage (CS), habitat quality (HQ), and tourism recreation (TR) from 2005 to 2020. From the three perspectives of geographical environment, socioeconomic development, and tourism development force, 14 indicators were chosen to examine the impact on ES. Except for Haikou's TR, the other ES of Haikou and Sanya showed a decreasing trend from 2005 to 2020. The values of six ES were lower in coastal zones than in noncoastal zones, which were more obvious in Sanya. Specifically, the areas of low value in Sanya were concentrated in the coastal region, and the areas with low value in Haikou were primarily distributed in blocks along the coast and in bands or points in the central and southern areas. From the perspective of influencing factors, the natural environmental factors dominate in Haikou, followed by the socio-economic factors and finally the tourism development factors, while the natural environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socio-economic factors. We provided recommendations for sustainable tourism development in Haikou and Sanya. This study has significant implications for both integrated management and scientific decision-making to enhance the ES of tourism destinations.

Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Perceptions of the health risk from hot days and the cooling effect of urban green spaces: a case study in Xi'an, China

Background

Hot days are one of the typical threats to human health and sustainable cities. The exploration of residents' perceptions of thermal environment and its mitigation measures will support the health risk prevention.

Methods

A survey with a combination of closed-ended and open-ended questions was conducted in July 2021 among 13 urban parks in Xi'an City, China. With the help of ANOVA and ordinal logistic regression, this study investigated the influencing factors both on residents' health risk perception of hot days and their perception of the effect of urban ecological landscape on reducing the thermal risk. The relationship between health risk perception and residents' needs of urban ecological construction was also explored.

Results

According to 325 valid questionnaires, the male-female ratio of respondents was found to be 1:0.87, young people aged 18-29 (26.46%), the retirees (27.08%) and the ones with undergraduate education (33.23%) were, relatively, the largest groups. The results show that 92.31% of the respondents believed that their daily lives were under the influence of hot days. Housing types, occupation, cooling equipment at work, and outdoor working hours all had a significant impact on their high temperature perceptions. The proportion of respondents who were under a huge health risk and sought medical treatment due to hot days was 30.16% and 44.92%, respectively. Women were 18.52 and 2.33 times more likely to suffer health threats and experience discomforts than men. Furthermore, 73.23% of the respondents believed that the urban ecological landscapes in Xi'an had an enhanced cooling effect in recent years. Compared with the morphological characteristics, residents' recognition of the restriction of landscape's area on its cooling effect was higher, and the residence duration showed a significant influence.

Conclusion

The cooling effect of green spaces and water effectively resisted urban thermal threats, and residents' needs of the urban ecological landscapes was associated with their health risk perceptions of hot days. In the future, it is necessary to promote the early warning of hot days, meanwhile, the optimization of landscape patterns of green infrastructures should be implemented in urban planning for the purposes of residents' health risk prevention.

Satellite-based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015

Tree planting is a prevalent strategy to mitigate urban heat. Tree cooling efficiency (TCE), defined as the temperature reduction for a 1% tree cover increase, plays an important role in urban climate as it regulates the capacity of trees to alter the surface energy and water budget. However, the spatial variation and more importantly, temporal heterogeneity of TCE in global cities are not fully explored. Here, we used Landsat-based tree cover and land surface temperature (LST) to compare TCEs at a reference air temperature and tree cover level across 806 global cities and to explore their potential drivers with a boosted regression tree (BRT) machine learning model. From the results, we found that TCE is spatially regulated by not only leaf area index (LAI) but climate variables and anthropogenic factors especially city albedo, without a specific variable dominating the others. However, such spatial difference is attenuated by the decrease of TCE with tree cover, most pronounced in midlatitude cities. During the period 2000-2015, more than 90% of analyzed cities showed an increasing trend in TCE, which is likely explained by a combined result of the increase in LAI, intensified solar radiation due to decreased aerosol content, increase in urban vapor pressure deficit (VPD) and decrease of city albedo. Concurrently, significant urban afforestation occurred across many cities showing a global city-scale mean tree cover increase of 5.3 ± 3.8% from 2000 to 2015. Over the growing season, such increases combined with an increasing TCE were estimated to on average yield a midday surface cooling of 1.5 ± 1.3°C in tree-covered urban areas. These results are offering new insights into the use of urban afforestation as an adaptation to global warming and urban planners may leverage them to provide more cooling benefits if trees are primarily planted for this purpose.

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO₂), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO₂ variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO₂ was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Quantifying the spatial nonstationary response of influencing factors on ecosystem health based on the geographical weighted regression (GWR) model: an example in Inner Mongolia, China, from 1995 to 2020

The identification of ecosystem health and its influencing factors is crucial to the sustainable management of ecosystems and ecosystem restoration. Although numerous studies on ecosystem health have been carried out from different perspectives, few studies have systematically investigated the spatiotemporal heterogeneity between ecosystem health and its influencing factors. Considering this gap, the spatial relationships between ecosystem health and its factors concerning climate, socioeconomic, and natural resource endowment at the county level were estimated based on a geographically weighted regression (GWR) model. The spatiotemporal distribution pattern and driving mechanism of ecosystem health were systematically analysed. The results showed the following: (1) the ecosystem health level in Inner Mongolia spatially increases from northwest to southeast, displaying notable global spatial autocorrelation and local spatial aggregation. (2) The factors influencing ecosystem health exhibit significant spatial heterogeneity. Annual average precipitation (AMP) and biodiversity (BI) are positively correlated with ecosystem health, and annual average temperature (AMT) and land use intensity (LUI) are estimated to be negatively correlated with ecosystem health. (3) Annual average precipitation (AMP) significantly improves ecosystem health, whereas annual average temperature (AMT) significantly worsens eco-health in the eastern and northern regions. LUI negatively impacts ecosystem health in western counties (such as Alxa, Ordos, and Baynnur). This study contributes to extending our understanding of ecosystem health depending on spatial scale and can inform decision-makers about how to control various influencing factors to improve the local ecology under local conditions. Finally, this study also proposes some relevant policy suggestions and provides effective ecosystem preservation and management support in Inner Mongolia.

Exploring the relationship between seasonal variations of land surface temperature and urban morphological factors in complex urban areas

The urban heat island effect is an increasingly serious problem in urban areas. Previous studies suggest that spatial variation in the urban land surface temperature (LST) is determined by interactions among urban morphological factors, but few studies have explored the main factors that affect the LST in different seasons in complex urban areas, especially at a fine scale. By considering the central Chinese city of Jinan as an example, we selected 19 parameters related to the architectural morphological factors, ecological basis factors, and humanistic factors and explored their effects on the LST in different seasons. A correlation model was used to identify the key factors and to analyze the main impact thresholds in different seasons. In the four seasons, the 19 factors all had significant correlations with LST. In particular, architectural morphological factors comprising the average building height and high building ratio had significant negative correlations with the LST in the four seasons. The architectural morphological factors comprising the floor area ratio, spatial concentration degree, building volume density, and urban surface pattern index comprising the mean nearest neighbor distance to green land, as well as humanistic factors comprising the point of interest density, nighttime light intensity, and human activity intensity of land surface had significant positive correlations with LST in the summer and autumn. Ecological basis factors made the main contributions to the LST in the spring, summer, and winter, whereas humanistic factors contributed the most in the autumn. The contributions of architectural morphological factors were relatively low in the four seasons. The dominant factors differed in each season but their thresholds had similar characteristics. The results obtained in this study deepen our understanding of the relationships between urban morphology and the urban heat island effect, and provide practical suggestions for improving the urban thermal environment through reasonable building planning and management.

Regulation of water bodies to urban thermal environment: Evidence from Wuhan, China

Exploring how water bodies work on the thermal environment in a real and complex urban context is of great significance to develop urban blue infrastructure (BI) system for climate adaption. Previous studies mainly focused on the water cool island (WCI) effect in the microenvironment, which cannot provide enough evidence for systematic application. This study explored the cooling effect of water bodies on the urban environment in Wuhan, China based on three scenarios in which water bodies was regarded as individual samples, water network system and one landscape category of urban ecosystem, respectively. Results indicate that all detected water bodies expressed WCI effect with the mean WCI intensity of 5.5°C and the range of 1.1°C to 13.3°C. The main factors influencing the cooling effect of urban water body system were Percent of landscape (PLAND_W), Landscape shape index (LSI_W) and Contrast-weighted edge density (CWED_W) of water bodies, which could explain 69.0% of urban LST variation. An urban water body system with relatively larger water area, more regular boundary and simpler surroundings might be more efficient in cooling the urban environment. Nevertheless, when considering the interaction of water bodies with other land covers on thermal environment, the contribution of water bodies to the cooling benefit was depressed. The main factors were Similarity index_mean of Construction land (SIMI_MN_C), Class area of Construction land (CA_C), Total edge contrast index of Water body (TECI_W), Landscape shape index of Water body (LSI_W) and Percent of landscape of Vegetated area (PLAND_V). They combined to explain 71.9% of LST variation in urban areas. Developing an urban BI system by connecting the dispersed water bodies and fragmenting the contiguous construction land in rapidly urbanized areas can be more realistic for mitigating UHI effect and adapting to climate change.

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (&gt; 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

Spatiotemporal analysis of the surface urban heat island (SUHI), air pollution and disease pattern: an applied study on the city of Granada (Spain)

There is worldwide concern about how climate change -which involves rising temperatures- may increase the risk of contracting and developing diseases, reducing the quality of life. This study provides new research that takes into account parameters such as land surface temperature (LST), surface urban heat island (SUHI), urban hotspot (UHS), air pollution (SO₂, NO₂, CO, O₃ and aerosols), the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI) and the proportion of vegetation (PV) that allows evaluating environmental quality and establishes mitigation measures in future urban developments that could improve the quality of life of a given population. With the help of Sentinel 3 and 5P satellite images, we studied these variables in the context of Granada (Spain) during the year 2021 to assess how they may affect the risk of developing diseases (stomach, colorectal, lung, prostate and bladder cancer, dementia, cerebrovascular disease, liver disease and suicide). The results, corroborated by the statistical analysis using the Data Panel technique, indicate that the variables LST, SUHI and daytime UHS, NO₂, SO₂ and NDBI have important positive correlations above 99% (p value: 0.000) with an excess risk of developing these diseases. Hence, the importance of this study for the formulation of healthy policies in cities and future research that minimizes the excess risk of diseases.

The spatial configuration of buildings: A vital consideration impossible to ignore in regulating urban land surface temperature? Evidence from 35 Chinese cities

Land urbanization not only efficiently met the requirements of social development but also brought severe heating effects, especially the changes in Land Surface Temperature (LST). The effects of building density on LST and driving relation of the configuration of buildings remain poorly understood over large areas. Using Landsat 8 satellite imagery acquired from the summer of 2019, this study quantified the heating effects (k) of building density on LST across 35 cities in China, and further analyzed the driving relation of the configuration of buildings such as the size, shape, and spatial distribution on k. Here the regression analysis results showed that the building density had a significant relationship with LST, and the k varied from 1.10 to 7.27 amount of the different cities. The size and aggregation distribution of buildings were the main positive drivers for the effects of building density on LST, and the shape of buildings was not obviously related to the k-value. The results of thermal environment simulation showed that the major reason might be that the close spatial relationship reduced the heat exchange between buildings and the atmosphere, resulting in higher LST. These conclusions will provide an important reference for urban planning and design.

Assessment of the impact of the different settlement patterns on the summer land surface temperature: Elazığ

Currently, cities are at the center of the debate on global warming since land use and land cover change (LULC) in cities are considered to be major contributors to global climate change. In this study, Çarşı and Doğukent Neighborhood areas in the city center of Elazığ province, Turkey, were examined in terms of land use and land cover (LULC). Both areas were chosen because they have different patterns and features, such as different residential densities, street aspect ratios and orientations, impervious surfaces, vegetation, and elevations. The aim is to assess the effect of the different patterns of these settlements on the land surface temperature (LST) using Landsat 8 satellite images in the summertime, July 19, 2021. The results showed that the maximum, minimum, and average LST of the Doğukent Neighborhood, which is characterized by uniform streets with dense vegetation and streets oriented to the NE-SW or NW-SE, were recorded as 44.4, 38.4, and 41.0 °C, respectively, while 45.4, 40.4, and 43.8 °C were recorded in the Çarşı Neighborhood characterized by excessive residential areas and deep streets with lack of vegetation oriented to the E-W direction. However, the average difference is around 2.8 °C, implying that residential areas with mid-building heights and vegetated streets oriented to NE-SW or NW-SE are thermally better than those with high aspect ratio streets and lacking vegetation and oriented to E-W. It was found that small variations in land elevation of these areas do not significantly affect the LST. The results of this study will set an example not only for the city of Elazığ, but also for the determination of urban transformation areas, new housing areas, and climate change in most cities of Turkey and other countries, and will provide support for sustainable and more livable urbanization in most cities. Transferring the data obtained by local governments to the physical plan decisions could also contribute to preventing climate change.

Characteristics of the Thermal Environment and its Guidance to Ecological Restoration in a Resource-Based Area in the Loess Area

The thermal environment is a crucial part of ecological environments. It is vital to study the distribution and generation of thermal environments for regional sustainable development. Mining area, agricultural area and urban area were taken as the research object, and remote sensing data were used to study the spatiotemporal distribution characteristics of the thermal environment. The relationship between the thermal environment and land use types was analyzed, and the effect of mining and reclamation on the thermal environment was emphasized. The main findings were: (1) the thermal effect zone in the study area was dispersed. The area ratio of the thermal effect zone accounted for 69.70%, 68.52%, 65.85%, 74.20% and 74.66% in the year 2000, 2003, 2009, 2013 and 2018, respectively. The contribution to the overall thermal effect was in the order of agricultural area > mining area > urban area. (2) The proportion of forest and the average grid temperature always showed a significant negative correlation in different scales and had the highest correlation and the greatest influence. (3) The land surface temperature (LST) of opencast areas was higher than the surrounding temperature, and the temperature difference was 3-5 °C. The LST of reclaimed sites was lower than the surrounding temperature, and the temperature difference was -7 to 0 °C. The quantitative study found that reclamation mode, shape and spatial location could affect the cooling effect of the reclaimed site. This study can provide a reference for the mitigation of thermal effects and the identification of influences of mining and reclamation on the thermal environment in the coordinated development of similar regions.

Discover the Desirable Landscape Structure of Urban Parks for Mitigating Urban Heat: A High Spatial Resolution Study Using a Forest City, Luoyang, China as a Lens

Urban parks can mitigate the urban heat island (UHI) and effectively improve the urban microclimate. In addition, quantifying the park land surface temperature (LST) and its relationship with park characteristics is crucial for guiding park design in practical urban planning. The study's primary purpose is to investigate the relationship between LST and landscape features in different park categories based on high-resolution data. In this study, we identified the land cover types of 123 parks in Luoyang using WorldView-2 data and selected 26 landscape pattern indicators to quantify the park landscape characteristics. The result shows that the parks can alleviate UHI in most seasons, but some can increase it in winter. While the percentage of bare land, PD, and PAFRAC have a positive impact on LST, AREA_MN has a significant negative impact. However, to deal with the current urban warming trend, a compact, clustered landscape configuration is required. This study provides an understanding of the major factors affecting the mitigation of thermal effects in urban parks (UP) and establishes a practical and feasible urban park renewal method under the idea of climate adaptive design, which provides valuable inspiration for urban park planning and design.

Cooling Effect of Urban Blue and Green Spaces: A Case Study of Changsha, China

The cooling effects of blue-green spaces on the urban heat island effect are complex and different. The purpose of this study is to simulate how the cooling effect of blue-green space changes with its size and shape. The cooling effects of 53 green patches and 28 water bodies in Changsha were extracted based on Landsat images. A surface fitting model was used to quantitatively reveal the relationship between the cooling effect of blue-green space and its size and shape. The results show that the cooling effects of blue-green spaces were enhanced with the increasing size, and then would become stable after a certain range (threshold). Certain thresholds were identified between the blue and green space areas (2.98 ha and 3.15 ha, respectively) and the cooling distance, and between the blue and green space areas (4.84 ha and 4.92 ha, respectively) and the cooling magnitude. In addition, the green space with an area of 9.08 ha and landscape shape index (LSI) of 2.97 could achieve a better cooling distance (413.46 m); and the blue space with an area of 29.4 ha and LSI of 1.75 could achieve a better cooling magnitude (5.17 °C). These findings provide useful guidelines for urban planning and improving urban livability in other regions with terrain and climate conditions similar to Changsha.

Considerations on the impact of "source-sink" landscape pattern changes on urban thermal environment and cooling efficiency: a case study of Nanjing, China

The urban heat island effect caused by rapid urbanization has had a great impact on human health and ecological environment. The evolution of landscape patterns often affects regional thermal characteristics at the local scale. How to rationally allocate land cover types from the perspective of urban planning is a huge challenge. This paper takes Nanjing, a typical "stove city" in China, as an example to study the impact of landscape pattern changes on the urban thermal environment. Firstly, based on the "source" landscape "/sink" landscape identified by the "source-sink" landscape index, on this basis, the contributions of the "source-sink" landscape and its effects are calculated, and the temporal and spatial evolution laws are analyzed. Second, we study the cooling effect of different characteristic landscapes. The results show that the change of landscape pattern is closely related to the urban thermal environment, and the expansion of built-up areas is the main reason for the urban thermal environment. With the development of urbanization, the "source-sink" landscape structure in most districts and counties has aggravated the heat island effect, and the ratios of "sink" and "source" landscape contribution ratios with strong levels are concentrated in the suburban areas. The results of cooling efficiency research show that the cooling range of forest land is larger than that of water bodies. At the same time, we found that the cooling efficiency of the sink landscape is the highest when the area of the sink landscape is 0.18-0.9 hm² and the shape index is between 1 and 4. The research results can provide feasible and practical scientific suggestions for the planning and ecological construction of Nanjing.

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.

Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city

Urban heat islands, where temperatures are elevated relative to non-urban surrounds, are near-ubiquitous in cities globally. Yet, the magnitude and form of urban heat islands in the tropics, where heat has a large morbidity and mortality burden, is not well understood, especially for those of urban informal settlements. We used 29 years of Landsat satellite-derived surface temperature, corroborated by in situ temperature measurements, to provide a detailed spatial and temporal assessment of urban heat islands in Makassar, Indonesia, a city that is representative of rapidly growing urban settlements across the tropics. Our analysis identified surface urban heat islands of up to 9.2 °C in long-urbanised parts of the city and 6.3 °C in informal settlements, the seasonal patterns of which were driven by change in non-urban areas rather than in urban areas themselves. In recently urbanised areas, the majority of urban heat island increase occurred before land became 50% urbanised, whereas the established heat island in long-urbanised areas remained stable in response to urban expansion. Green and blue space protected some informal settlements from the worst urban heat islands observed across the city and maintenance of such space will be essential to mitigate the growing heat burden from urban expansion and anthropogenic climate change. Settlements further than 4 km from the coast and with Normalised Difference Vegetation Index (NDVI) less than 0.2 had higher surface temperatures, with modelled effects of more than 5 °C. Surface temperature measurements were representative of in situ heat exposure, measured in a subset of 12 informal settlements, where mean indoor temperature had the strongest relationship with surface temperature (R² = 0.413, P = 0.001). We advocate for green space to be prioritised in urban planning, redevelopment and informal settlement upgrading programs, with consideration of the unique environmental and socioeconomic context of tropical cities.

Dynamical changes of land use/land cover and their impacts on ecological quality during China's reform periods: A case study of Quanzhou city, China

The rapid growth of China's economy has greatly accelerated the process of urbanization during China's reform periods. Urbanization has significantly caused land use and land cover (LULC) changes and thus has impacts on the local climate and ecosystem. This study chooses Quanzhou, a fast-developing city of southeast China, as an example to detect and quantify the LULC and ecological changes from 1989 to 2018 by using the remotely sensed technique. The LULC of Quanzhou was derived from the four Landsat images taken in 1989, 1999, 2007 and 2018, and the land-use-degree ratio index and land-use-change method were used to estimate the change of land use. The remote sensing based ecological index (RSEI) was used to detect the ecological changes of the city. The built-up land expansion intensity and annual built-up land expansion rate were carried out for seven districts of Quanzhou. The results show that the urban area of Quanzhou has drastically grown by 192.99 km2 at the expense of forest, water, and cropland land during the 1989~2018 period. Moreover, the built-up land of seven districts had expanded at the average rate of 0.027~0.154 per year and the built-up expansion intensity was higher than 0.59. The average RSEI value of Quanzhou city dropped from 0.78 in 1989 to 0.34 in 2018, which suggested an overall decline in ecological quality. The proportion of areas with an RSEI rating good decreased from 30.84% to 11.52% while the proportion of areas with rating bad increased from 4.73% to 19.11% during the past 29 years. This study has shown the built-up land expansion intensity is negatively correlated with the ecological quality change, and the increase in built-up land can greatly accelerate the decline of the ecological quality. Government policies play a profound impact on land use changes, urbanization and eco-environment changes. Therefore, the policy decision-makers should take enough action and consider integrating the concept of ecology to enable the healthy and sustainable development of the city.

Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea

Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment.

Analysing the relationship between human modification and land surface temperature fluctuation in the Ramganga basin, India

In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India. It has been hypothesised that the footprints of anthropogenic activities in the region have been connected to the LST fluctuation for the region, which could indicate environmental degradation. The LST variation between 2000 and 2016 has been estimated to test this hypothesis. The spatio-temporal correlation between human modification and LST has been computed. LST has been calculated with MODIS satellite data in the Google earth engine (GEE) platform, and anthropogenic activities can be visualised using an LU/LC map of the basin created by the Classification and Regression (CART) technique. The statistical parameters (average, maximum and standard deviation) of annual temperature for each pixel in 17 years (2000-2016) have been assessed to establish the links with human modification. The result of this work portrays a positive correlation of 0.705 between maximum LST and human modification. The forest class in the basin region has the lowest average human modification value (0.37), and it also possesses the lowest mean LST of 26.72 °C. Similarly, the settlement class has the highest average human modification value (0.85), and the mean LST temperature of this class has been on the higher side, having a value of 31.07 °C.

Evaluation of seasonal ecological vulnerability using LULC and thermal state dynamics using Landsat and MODIS data: a case study of Prayagraj City, India (1987-2018)

Prayagraj city (India) has been selected as a smart city by the Ministry of Housing and Urban Affairs, Government of India in 2015. However, long-term spatiotemporal seasonal Land Use/Land Cover (LULC) dynamics and Land Surface Temperature (LST) interactions with ecological vulnerability for different seasons are lacking. Accordingly, this research has been carried out to study the seasonal (summer and winter) LULC and its change pattern, thermal dynamics, and their role in exploring the ecological state over Prayagraj city and its surroundings using multi-temporal Landsat (1987-2018) and MODIS Terra data (2007-2018) at both diurnal and nocturnal scenarios. The LULC classification was carried out using Maximum Likelihood Classifier (MLC) by adopting the Anderson classification scheme with more than 85% of overall accuracy. The Landsat data-based LST has been estimated using Mono-Window Algorithm (MWA) for diurnal scenario whereas MODIS-based LST was calculated for nocturnal scenario. Ecological vulnerability state has been evaluated both in day-time and night-time using Urban Thermal Field Variance Index (UTFVI) in summer and winter during 1987-2018 and 2007-2018, respectively. Overall, built-up land increased the most by 18.25% which was responsible for massive urbanization during 1987-2018. In contrast, forest land decreased by 2.22% during 1987-2018. The most vulnerable class was agriculture land followed by forest land irrespective of seasons. Thermal state was intensified by mean LST by 1.25 ℃ in summer and 0.58 ℃ in winter in day-time. However, in night-time, the mean LST intensified by 6.64 ℃ in summer and 1.86 ℃ in winter. The excellent ecological class having no SUHI effects declined in summer during 1988-2018 by 1.59% but surged in winter by 12.33% during 1987-2018 in north-west regions at day-time, whereas in night-time the excellent ecological class having no SUHI effects severely declined in summer as well as in winter during 2007-2018 by 11.1% and 1.32%, respectively. However, the worst ecological class having strongest SUHI effects severely spread in night-times compared to day-time which mainly concentrated in central core part of the city during 2007-2018 by 5.33%. The present study has generated a comprehensive long-term geospatial database which can be used for urban planning to achieve sustainable development to make Prayagraj city a truly smart city in future.

Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE

The development of urbanization has changed the original land cover and exacerbated the urban heat island effect, seriously affecting the sustainable development of the ecological environment. Research on urban heat island characteristics and land cover changes in five major urban agglomerations in China to provide a reference for preventing thermal environmental risks and urban agglomeration construction planning. This paper estimates the surface urban heat island intensity (SUHII) of the five major urban agglomerations in China from 2003 to 2019 based on Google Earth Engine (GEE) through the urban-rural dichotomy, analyzes their trends through the Sen + M-K trend analysis method, and combines the detrending rate matrix to analyze the impact of land cover type shift on urban heat island change. Research shows that (1) the land cover types of the five major urban agglomerations in China have changed considerably from 2003 to 2019, and all five major urban agglomerations in China experienced varying degrees of urban expansion. (2) The annual average value of SUHII decreases in Beijing-Tianjin-Hebei, Yangtze River Delta, and middle reaches of the urban agglomerations, while the annual average value of SUHII increases in Chengdu-Chongqing and Pearl River Delta urban agglomerations. (3) The spatial composition of land cover types in the five major urban agglomerations in China is highly spatially correlated with urban heat islands, with urban land and bare land urban heat islands being the most pronounced. (4) The land cover type shift has the most significant heat island impact on Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengdu-Chongqing urban agglomerations. (5) The land cover change (LCC) with an increasing trend in SUHII is mainly bare land converted to arable land, and water bodies, grassland, forest land, and arable land converted to urban land.

Spatial–temporal characteristics of surface thermal environment and its effect on Lake surface water temperature in Dianchi Lake basin

The surface thermal environment plays an important role in urban sustainable development and ecological environment protection. Existing researches mainly focus on the formation process and mechanism of the surface thermal environment and lack the analysis of its effect on the lake ecological environment under the influence of human activities. Therefore, based on the analysis of the variations in land surface temperature (LST) and lake surface water temperature (LSWT) of Dianchi Lake at multiple spatio-temporal scales, this study evaluated the response of LSWT by using the methods of spatial influence, the center of gravity migration trajectory, trend analysis, and correlation analysis. The results show that: (1) Urbanization has a greater warming effect on LSWT than on LST, and the warming effect at night is greater than that at daytime. From 2001 to 2018, the warming trend of LSWT in daytime and night was 0.01°C/a and 0.02°C/a, respectively, while the cooling trend of LST in daytime was −0.03°C/a and the warming trend of LST in night was 0.01°C/a. (2) Areas with high human activity are warming faster, both in the eastern and northern coastal areas of lake and the heavily urbanized sub-basins. (3) The spatial influence of LST and LSWT are highly correlated, and the response of the outer buffer in the range of 2 km is obvious, and the direction of gravity center migration trajectory is consistent. The results are of great significance for the control and improvement of urban heat island and ecological environment protection of Dianchi Lake in Kunming and can provide data support and decision support for urban planning, promoting the construction of the ecological civilization city in Kunming, and reducing the accumulation of urban surface heat.

Quantifying the spatial pattern of urban heat islands and the associated cooling effect of blue-green landscapes using multisource remote sensing data

Surface urban heat islands (SUHIs) are a global concern. Although their spatial pattern and the cooling effect of blue-green landscapes have been documented, exploring more accurate and quantitative results is still necessary. For Hangzhou, we combined nighttime light (NTL) data with LST images to investigate the spatial morphology of SUHIs and analyze the cooling effect of blue-green landscapes. The radiative transfer equation (RTE) method was used to derive the land surface temperature (LST). Then, based on the unique feature of Luojia1-01 NTL data, the concentric zone model (CZM) was proposed to depict the urban spatial structure. The CZM was applied to construct a number of equal-area concentric belts along the urban-rural gradient to determine the SUHI range and the corresponding blue-green landscape cooling effects. Finally, local Moran's I indices were adopted to identify the cold-hot spots of the SUHI and the relationship with land use. The minimum, average and maximum LSTs were 21.81 °C, 32.79 °C and 44.79 °C, respectively. Additionally, 59.16 % of the study area was affected by the SUHI, and the mean LST inside the SUHI was 36.4 °C, clearly higher than that of the rural area. The SUHI hotpots were clustered in regions with intensive human activities, forming archipelagos. Due to the different blue-green landscape densities, the cooling capacity had spatial heterogeneity in different urban rural belts (URBs), and the cooling capacity of URB₁₆ was approximately 71 times that of URB₁. The cooling efficiency increased with blue-green landscape density in general; hence, blue-green landscape density thresholds of 40 % and 70 % were recommended in the urban planning of different urban function zones. Relating the pattern of NTL data to LST images provide meaningful insight into the spatial pattern of SUHIs and the optimization of urban planning.

Visits to the accident and emergency department in hot season of a city with subtropical climate: association with heat stress and related meteorological variables

BACKGROUND

Literature reporting the association between heat stress defined by universal thermal climate index (UTCI) and emergency department visits is mainly conducted in Europe. This study aimed to investigate the association between heat stress, as defined by the UTCI, and visits to the accident and emergency department (AED) in Hong Kong, which represents a subtropical climate region.

METHODS

A retrospective study involving 13,438,846 AED visits in the public sector from May 2000 to September 2016, excluding 2003 and 2009, was conducted in Hong Kong. Age-sex-specific ANCOVA models of daily AED rates on heat stress and prolonged heat stress, adjusting for air quality, prolonged poor air quality, typhoon, rainstorm, year, day of the week, public holiday, summer vacation, and fee charging, were used.

RESULTS

On a day with strong heat stress (32.1 °C ≤ UTCI ≤ 38.0 °C), the AED visit rate (per 100,000) increased by 0.9 (95% CI: 0.5, 1.3) and 1.7 (95% CI: 1.3, 2.1) for females and males aged 19-64 and 4.1 (95% CI: 2.7, 5.4) and 4.1 (95% CI: 2.6, 5.6) for females and males aged ≥ 65, while keeping other variables constant. On a day with very strong heat stress (38.1 °C ≤ UTCI ≤ 46.0 °C), the corresponding rates increased by 0.6 (95% CI: 0.1, 1.2), 2.2 (95% CI: 1.7, 2.7), 4.9 (95% CI: 3.1, 6.7), and 4.7 (95% CI: 2.7, 6.6), respectively. The effect size of heat stress associated with AED visit rates was negligible among those aged ≤ 18. Heat stress showed the greatest effect size for males aged 19-64 among all subgroups.

CONCLUSION

Biothermal condition from heat stress was associated with the health of the citizens in a city with a subtropical climate and reflected in the increase of daily AED visit. Public health recommendations have been made accordingly for the prevention of heat-related AED visits.

Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km², 15.47 km², and 9.86 km² for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km² of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.

Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study

The role of urban green space (UGS) in mitigating the urban heat island (UHI) effect has been demonstrated in a growing body of literature. However, the potential influence of the spatial equity of UGS distribution on the UHI effect has largely been overlooked. The present study aims to identify this potential influence using the spatial equity of UGS and the land surface temperature (LST) as measures of UGS spatial distribution and UHIs, respectively. A comprehensive spatial distribution map of UGS was generated by combining the UGS coverage fraction data within urban impervious pixels and the green cover data outside urban impervious pixels. Then, the spatial equity of UGS distribution across all urban impervious pixels was determined using the Gini coefficient. In addition, an LST map was derived using the thermal infrared spectral bands of Landsat 8 OLI/TIRS products. A case study of Dongguan, a highly urbanized city in China, showed that (1) the distribution of both UGS and LSTs were spatially aggregated in all the towns of the city, (2) the LST of urban impervious pixels was negatively correlated with the area of surrounding UGS, and (3) the Gini coefficient of UGS was positively correlated with the proportion of hot and cool areas, but negatively correlated with the proportion of medium-hot and medium-cool areas. These findings indicate that increasing the amount of UGS is beneficial to the reduction of urban average LSTs, while promoting the spatial equity of UGS distribution is conducive to reducing the spatial aggregation of LSTs within urban areas, thereby improving the overall urban thermal environment. Therefore, as a nature-based solution, promoting the spatial equity of UGS distribution could enhance the overall cooling effect of UGS more effectively at the city scale, and thus further underpin the sustainable development of the urban environment.

Estimation of Urban-Rural Land Surface Temperature Difference at Different Elevations in the Qinling-Daba Mountains Using MODIS and the Random Forest Model

Land surface temperature (LST) variations are very complex in mountainous areas owing to highly heterogeneous terrain and varied environment, which complicates the surface urban heat island (SUHI) in mountain cities. Previous studies on the urban heat island (UHI) effect mostly focus on the flat terrain areas; there are few studies on the UHI effect in mountainous areas, especially on the influence of elevation on the SUHI effect. To determine the SUHI in the Qinling-Daba mountains (China), MODIS LST data were first preprocessed and converted to the same elevations (1500 m, 2000 m, 2500 m, 3000 m, and 3500 m) using a digital elevation model and the random forest method. Then, the average LSTs in urban land, rural land, and cultivated land were calculated separately based on the ranges of the invariable urban, rural, and cultivated areas during 2010-2018, and the urban, rural, and cultivated land LST difference were estimated for the same elevations. Results showed that the accuracy of LST estimated using the random forest method is very high (R² ≥ 0.9) at elevations of 1500 m, 2000 m, 2500 m, 3000 m and 3500 m. The difference in urban, rural, and cultivated lands' LST has a trend of decrease with increasing elevation, meaning that the SUHI weakens at higher elevations. The average LST of urban areas is 0.52-0.59 °C (0.42-0.57 °C) higher than that of rural and cultivated areas at an elevation of 1500 m (2000 m). The average LST of urban areas is 0.10-1.25 °C lower than that of rural and cultivated areas at elevations of 2500 m, 3000 m, and 3500 m, indicating absence of the SUHI at those elevations.

Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

The emotional health of urban residents is increasingly threatened by high temperatures due to global heating. However, how high temperature affects residents’ emotional health remains unknown. Therefore, this study investigated the spatiotemporal pattern of temperature’s impact on residents’ irritability using data from summer high-temperature measurement and emotional health survey in Beijing, combined with remote sensing images and statistical yearbooks. In detail, this study formulated a multiscale geographically weighted regression (MGWR) model, to study the differentiated and spatial influence of high-temperature factors on emotion. Results show: From 09:00 to 20:00, irritability level rose first then gradually dropped, with a pattern of “aggregation-fragmentation-aggregation.” Irritability is very sensitive to intercept and building density (BD). Other variables all have spatial heterogeneity [except for fraction vegetation coverage (FVC) or road network density (RND) as they are global variables], including normalized difference vegetation index (NDVI), water surface rate (WSR), floor area ratio (FAR), and Modified Normalized Difference Water Index (MNDWI) (sorted from the smallest to the largest in scale). Irritability is negatively correlated with NDVI, WSR, and RND, while positively correlated with intercept, MNDWI, FVC, FAR, and BD. Influence on irritability: WSR &lt; NDVI &lt; BD &lt; MNDWI &lt; RND &lt; intercept &lt; FVC &lt; FAR.

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.

Dynamic and Heterogeneity of Urban Heat Island: A Theoretical Framework in the Context of Urban Ecology

The dynamic and heterogeneity of the urban heat island (UHI) is the result of the interactions between biotic, physical, social, and built components. Urban ecology as a transdisciplinary science can provide a context to understand the complex social–biophysical issues such as the thermal environment in cities. This study aimed at developing a theoretical framework to elucidate the interactions between the social–biophysical patterns and processes mediating UHI. To do it, we conducted a theoretical review to delineate UHI complexity using the concept of dynamic heterogeneity of pattern, process, and function in UHI phenomenon. Furthermore, a hypothetical heterogeneity spiral (i.e., driver-outcome spiral) related to the UHI was conceived as a model template. The adopted theoretical framework can provide a holistic vision of the UHI, contributing to a better understanding of UHI’s spatial variations in long-term studies. Through the developed framework, we can devise appropriate methodological approaches (i.e., statistic-based techniques) to develop prediction models of UHI’s spatial heterogeneity.