Urban Index Research Articles

Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?

Urban green spaces (UGS) provide ecological and habitat benefits such as carbon sequestration, oxygen production, humidity increase, noise reduction, and pollution absorption. UGS maps derived from remote sensing images serve as the fundamental data for urban planning and carbon sequestration assessments. However, the spatial resolution of remote sensing image and the pattern of urban structures significantly influence UGS mapping, making it challenging to obtain accurate UGS maps. To investigate the impact of spatial resolution on UGS mapping, this study utilized five different spatial resolution datasets: Gaofen2 (1 m, 4 m), Sentinel2 (10 m), and Landsat8 (15 m, 30 m). Random forest, LightGBM, and support vector machine were employed to map UGS, and the accuracies of UGS maps at different spatial resolutions were compared. Subsequently, the spatial distribution patterns of uncertainties in UGS maps were analyzed from both overall and urban functional zone perspectives. Furthermore, the uncertainty analysis of UGS mapping was conducted considering different landscape patterns in urban functional zones. The results indicate: (1) UGS map varies at different spatial resolution. Higher uncertainties associated with coarser spatial resolutions. Medium and coarse spatial resolution images inadequately capture the fine-grained distribution of urban green spaces. (2) Uncertainty in UGS mapping at different spatial resolutions is generally consistent in spatial distribution. From a functional zoning perspective, the accuracy of green space mapping over non-natural zones is sensitive to spatial resolution. (3) The distribution pattern of UGS patches affects the accuracy of UGS mapping. Uncertainty can be reduced in UGS mapping at medium and coarse spatial resolutions based on UGS landscape pattern indices by multiple linear regression, random forest and LightGBM model. This study comprehensively reveals that uncertainties in mapping UGS from multi-spatial resolution remote sensing images vary across urban functional zones and landscape pattern indices, and it is the first attempt to propose methods for UGS area correction based on landscape pattern indices. The results of this study will facilitate the application of remote sensing data at different spatial resolutions in urban areas.

Research on the Collaborative Regeneration of Cohousing Under Spatial Densification Mode

Urban renewal is a necessary process for the transformation from incremental space to stock space, and old communities, as an important part of the urban space system, play a non-negligible role in the reconstruction of community spatial order. From the perspective of community remodeling, From the perspective of community remodeling, this paper summarizes the latest research progress through the analysis tool CiteSpace, explores and researches the design of old residential areas in Hangzhou based on the urban air index oriented residential renewal design based on the planning concepts of "co-living community", "urban spatial morphology" and "community microclimate system". On the basis of combining 3D wind field simulation and fluid mechanics software calculation, this paper explores the collaborative regeneration path of old residential areas under spatial densification mode, this paper studies how to combine scientific planning with resident sharing in a new mode, and on this basis, through the self-regulation function of the environment, let the residential area spontaneously form a complete shared living system, and carry out detailed exploration in the protection and reconstruction of the living environment of the old community, and how to promote the spiritual development of modern neighborhoods through transformation. In order to improve the quality of life of urban residents in Hangzhou, the renewal and regeneration design of old residential areas and the transformation and upgrading of future communities have brought new thinking.

Integrating prospect theory and hesitant fuzzy linguistic preferences for enhanced urban flood resilience assessment: A case study of the tuojiang river Basin in western China

The intricate interplay between extreme climatic events and rapid urbanization has compounded the difficulties associated with urban flood resilience, prompting urgent improvements in cities' disaster preparedness and early warning systems. Existing assessment methodologies overlook human factors like decision-makers' psychological biases, hindering precise evaluations of flood defense capabilities. Motivated by the practical need for innovative disaster assessment tools, this study integrates hesitant fuzzy theory with prospect theory. This pioneering approach introduces interval-type indicators into the urban flood resilience assessment framework, establishing a quantitative model to gauge the correlation between urban resilience and various influencing factors. Using the Tuojiang River Basin in western China as a focal point, the research calculates the comprehensive urban flood resilience index for six cities along the Tuojiang River from 2002 to 2021. The findings indicate a consistent upward trajectory in the urban flood resilience index, mirroring government management practices. Notably, Chengdu boasts the highest urban flood resilience index at 1.4288, while Luzhou's index stands at −0.4376, reflecting a shortfall in the expected or desired level within the assessment framework, though without compromising index comparability. Moreover, both cities surpass the regional average of 0.36, categorizing them as high-resilience areas. Significantly, the efficacy of flood defense strategies in these areas correlates positively with economic growth (5.2 %). The model proposed in this study offers a thorough quantification of urban flood defense capabilities, assisting managers in formulating precise disaster mitigation strategies and providing scientific backing for integrated flood disaster management.

Spatiotemporal evolution of land use efficiency in 357 cities across mainland China from 2000 to 2020 based on SDG 11.3.1

High-efficiency land use facilitates the maximization of land utilization, lowers urban construction costs, and optimizes urban functional patterns. The Sustainable Development Goal 11.3.1 (SDG 11.3.1) can be used to assess land use efficiency (LUE), understand the current state of land use, and identify the potential for optimization. This study combines SDG 11.3.1 with other supplementary indicators to establish a land use efficiency evaluation system. This system provides a more precise understanding of internal city changes and enables a scientific assessment of urban LUE in Mainland China. The results showed that: (1) A significant number of cities were growing cities, particularly in the eastern region, with the population of built-up areas increased by 2.92 times from 2000 to 2020; (2) From 2000 to 2020, cities in China underwent rapid urban expansion, with the most significant urban expansion index in 2015–2020; (3) The coordination between population growth rate (PGR) and land consumption rate (LCR) worsened in the western region, while the central and eastern regions showed better coordination. (4) As the urban expansion index increased, the compactness index of the cities in the above three regions decreased and were at lower levels. This study establishes an evaluation system to assess the LUE and reveals the spatial and temporal characteristics of urban and population change. It holds paramount significance in enhancing LUE and encouraging sustainable development in Mainland China and serves as a valuable reference for global urban management.

Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China

The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Spatial and Temporal Characteristics of Fractional Vegetation Cover and Its Response to Urbanization in Beijing

Exploration of the spatiotemporal changes in fractional vegetation cover （FVC） and its response characteristics to urbanization is of great significance for urban ecological protection and planning in Beijing. This study analyzed the spatiotemporal characteristics of vegetation cover changes in Beijing from 2000 to 2020 using the Theil-Sen Median and Mann-Kendall methods based on a long-term time series vegetation cover dataset. Then, this study used the urbanization index as a key indicator of spatial urbanization and utilized the transect line and global grid analysis methods to investigate the response characteristics of FVC to different urbanization gradients. The results indicated that： ① FVC changes showed spatial and temporal heterogeneity. From 2000 to 2020, Beijing was predominantly covered by high vegetation, accounting for 65.22% of the total area, which was mainly distributed in ecological conservation areas consistent with the Jundu, Xishan, and Yaji Mountain ranges. The FVC presented an overall positive development trend, with a decreasing trend of areas with low FVC. The increase in FVC was significant （by 28.68%）, mainly distributed in ecological conservation areas and within a range of 10-12 km in concentric circles centered around Tiananmen Square. The urbanization index and FVC change rate were relatively high in Haidian District, Chaoyang District, Fengtai District, Shijingshan District, and Changping District. ② The artificial land surface in 2000, 2010, and 2020 was 9.69%, 13.64%, and 21.19%, respectively, with significant spatial agglomeration and strong spatial heterogeneity. During the urbanization process in Beijing, the increase in artificial land surface reached 11.5%, with the conversion from arable land to artificial land surface accounting for 53.83% of the total land use conversion area. ③ There was a significant negative correlation between FVC and the urbanization index, indicating that urbanization had a negative impact on regional FVC. However, as the urbanization process stabilized, this negative correlation tended to gradually weaken. Although the central urban areas were mainly characterized by low FVC, there was a significant increasing trend in the FVC, indicating a positive development in the FVC and an improvement in regional ecological quality, which was closely related to the governance of the mountain-water-forest-field-lake-grass-sand system. The results of the study can provide a basis for the development of vegetation restoration programs and ecological management measures in Beijing.

Spatiotemporal characterization of heatwave exposure across historically vulnerable communities

Heatwaves pose a serious threat and are projected to amplify with changing climate and social demographics. A comprehensive understanding of heatwave exposure to the communities is imperative for the development of effective strategies and mitigation plans. This study explores spatiotemporal characterization of heatwaves across the historically vulnerable communities in Mississippi, United States. We derive multiple heatwave metrics including frequency, duration, and magnitude based on temperature data for urban-specific daytime, nighttime, and day–night combined conditions. Our analysis depicts a rising heatwave trend across all counties, with the most extreme shifts observed in prolonged day–night events lacking overnight relief. We integrate physical heatwave hazards with a socioeconomic vulnerability index to develop an integrated urban heatwave risk index. Integrated metric identifies the counties in northwest Mississippi as heat-prone areas, exhibiting an urgent need to prioritize heat resilience and adaptive strategies in these regions. The compounding urban heatwave and vulnerability risks in these communities highlights an environmental justice imperative to implement equitable policies that protect disadvantaged populations. Although this study is focused on Mississippi, our framework is scalable and can be employed to urban regions globally. This study provides a solid foundation for developing timely heatwave preparedness and mitigation to avert preventable heat-related tragedies as extremes intensify with climate change.

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas. Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

The urban air quality nexus: Assessing the interplay of land cover change and air pollution in emerging South Asian cities

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period. Urban expansion was most pronounced in Sylhet (22–254%), while Khulna experienced the largest increase in sparse vegetation (2–124%). Dense vegetation loss was highest in Dhaka (20–77%) and water bodies (9–59%) over this period. Concentrations of six major air pollutants (APTs) - aerosol index, CO, HCHO, NO2, O3, and SO2 - were quantified, showing alarmingly high levels in densely populated industrial and commercial zones. Pearson's correlation indicates strong positive associations between APTs and urban land indices (R > 0.8), while negative correlations exist with vegetation indices. Geographically weighted regression modeling identifies city centers with dense urban built-up as pollution hotspots, where APTs exhibited stronger impacts on land cover changes (R2 > 0.8) compared to other land classes. The highest daily emissions were observed for O3 (1031 tons) and CO (356 tons) at Chittagong in 2021. In contrast, areas with substantial green cover displayed weaker pollutant-land cover associations. These findings underscore how unplanned urbanization drives AP by replacing natural land cover with emission sources, providing crucial insights to guide sustainable urban planning strategies integrating pollution mitigation and environmental resilience.

Measuring urban walkability index in Surakarta historic district to promote sustainable mobility

Measuring urban walkability index in Surakarta historic district to promote sustainable mobility

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin, China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment. Industrialization and urbanization promote social-economic development, but they also have generated a series of environmental and ecological issues in this basin. Previous researches have evaluated urban resilience at the national, regional, urban agglomeration, city, and prefecture levels, but not at the watershed level. To address this research gap and elevate the Yellow River Basin’s urban resilience level, we constructed an urban resilience evaluation index system from five dimensions: industrial resilience, social resilience, environmental resilience, technological resilience, and organizational resilience. The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin. The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010, 2015, and 2020. Furthermore, the grey correlation analysis method was utilized to explore the influencing factors of these differences. The results of this study are as follows: (1) the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015, and significant spatial distribution differences were observed, with a higher resilience level in the eastern region and a low-medium resilience level in the western region; (2) the differences in urban resilience were noticeable, with industrial resilience and social resilience being relatively highly developed, whereas organizational resilience and environmental resilience were relatively weak; and (3) the correlation ranking of resilience influencing factors was as follows: science and technology level>administrative power>openness>market forces. This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Analyzing the spatio-temporal pattern of urban growth and its influence on urban heat islands in the Sekondi-Takoradi metropolis, Ghana

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

An Age-Friendly Neighbourhood Index as a Long-Term Urban Planning Decision-Making Tool

People responsible for shaping the future of cities often seek valuable tools to assist in their decision-making processes. Using objective, quantified, and analysed data proves highly beneficial when determining where to focus interventions at the city level. Various urban indexes have been established to measure different aspects of urban life, ranging from sustainability to liveability. These indexes encompass multiple dimensions of a city, including mobility and walkability, among others. The age-friendly cities initiative developed indicators for assessing the age-friendliness of cities. Some researchers further refined these indicators to focus on urban planning competencies. Building on this foundation, this article aims to present an Age-Friendly Neighbourhood Index (AFNI) validated by a panel of experts using the Delphi method. This index can serve as a valuable tool for urban planners when they need to prioritise interventions to enhance age-friendliness at neighbourhood scale. The article also outlines the necessary data and measurement techniques for these indicators. The AFNI has been applied to a real case study in the city of Santander (Spain). This application assesses the age-friendliness of various neighbourhoods in Santander, demonstrating the challenges in acquiring sub-local quality data and emphasising the need for data-driven urban management.

Measuring ecological well-being based on the degree of ecosystem service supply and demand matching in Shenzhen, China

Measuring urban ecological well-being and achieving a comprehensive understanding of its spatial differentiation characteristics are important foundations for meeting the growing health needs of residents and improving the insufficient and unbalanced development of the urban ecological environment. However, the current framework for defining urban ecological well-being that considers the supply and demand of ecosystem services (ESs) is unclear. The key to enhancing urban ecological well-being lies in measuring the level and patterns of ecological well-being in urban spaces through the degree of ES supply–demand matching; therefore, the present study aims to construct a measurement framework for urban ecological well-being based on this matching. The developed framework was applied to assess ecological well-being in Shenzhen in 2015 and 2020. This study summarizes the spatial patterns and analyzes their temporal and spatial distributions and influencing factors, showing that: (1) the spatial distribution of the comprehensive supply and demand of ESs in Shenzhen both have strong spatial heterogeneity; (2) the area of four types of relatively low ecological well-being patterns in Shenzhen was greater than that of five types of relatively low ecological well-being patterns in 2020, and the proportions of the areas of low, relatively low, and relatively sufficient well-being were the largest; (3) the overall trend of ecological well-being change is one of decline. The area in which Shenzhen’s ecological well-being has declined is far greater than the area in which it has improved. The main reasons for this are the increase in the urban vitality index, an increase in the demand for ESs, and an increase in impermeable surfaces, which have led to a decrease in the supply capacity of the ecosystem in some areas.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

In the context of increasing urban pollution and its adverse health effects, this study focuses on enhancing the air quality prediction model by forecasting future concentrations of various Air Quality Index (AQI) levels to support the development of green smart cities. The primary objective of this study is to develop a robust multivariate time series forecasting model using a combination of Convolutional Neural Networks (CNN) and Customized Stacked-based Long Short-Term Memory (CSLSTM) networks, namely C2SLSTM. The proposed methodology involved preprocessing AQI and meteorological data, scaling the features, and employing a hybrid CNN-LSTM architecture to capture spatial and temporal dependencies in the data. Dynamic AQI pattern changes are computed using online learning. The proposed model was trained on a big dataset containing AQIs such as particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2), along with meteorological factors like weather count, temperature, wind, humidity, and pressure. Experimental results demonstrate that the model achieved a mean absolute error (MAE) of 0.25, mean square error (MSE) of 0.083, root mean square error (RMSE) of 0.289 and a R2-Score of 0.84 on the test set. Also, compared to Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Stacked Long Short-Term Memory (SLSTM), and Multilayer Perceptron (MLP) models, the C2SLSTM model showcases a performance enhancement of 35 %, 50 %, 30 %, and 32 %. Notably, the CNN component effectively extracted spatial features, while the LSTM layers captured temporal patterns, leading to precise predictions. These results indicate that the proposed approach effectively captures temporal dependencies and improves forecasting performance, with R2-Score values tending towards 1 for 12-hour prediction intervals. This research demonstrates the potential of advanced SLSTM architectures in accurately predicting AQI metrics, which can aid in better environmental monitoring and management. Also, the spatiotemporal correlation analysis is done through the designed ir metric.

Urban meteorological drought comprehensive index based on a composite fuzzy matter element-moment estimation weighting model

Due to rapid urbanization and climate change, cities face hidden drought risks. A single drought index may inadequately reflect urban meteorological drought. The indicator weight combination method does not fully consider index correlation and weight. This study constructed an urban meteorological drought evaluation index system and developed the Composite Fuzzy Matter Element Meteorological drought Comprehensive Index (CFEMCI) by combining the moment estimation weighting model. Analyzing Zhengzhou City from 2000 to 2019, CFEMCI effectively captured meteorological drought events, with a probability of detection (POD)>0.78, critical success index (CSI)>0.70, false alarm rate (FAR)<0.13 and failure ratio (FR)<0.22. Most meteorological droughts were classified as Grade I (no drought), with 26% being light and moderate (Grades II-III). Droughts mainly occurred in spring, and the summer drought showed a more significantly aggravating trend. This index provides reliable urban drought monitoring and supports disaster prevention and mitigation efforts.

Does a more compact urban center layout matter in reducing household carbon emissions? Evidence from Chinese cities

In recent years, compact urban development and carbon emissions reduction have been considered essential approaches for achieving sustainable development goals worldwide. Existing research has focused on the correlation between urban spatial structure and carbon emissions with inconsistent results. This study explores the correlation between urban compactness and household CO₂ emissions. Two indices of urban compactness and four categories of household CO₂ emissions are constructed. We utilize Spatial Durbin Models (SDM) and Spatial Autoregressive Models (SAC) on panel data from over 284 cities at the prefecture level and above in China, spanning 2008–2018. The results indicate that, except for heating consumption, household CO₂ emissions exhibit positive associations with two urban compactness indices. These findings suggest that a city with evenly developed urban cores within a relatively smaller urban area may have better household CO₂ emissions efficiency both locally and regionally. Our study contributes to the existing literature on the sustainability of compact city development with new evidence emphasizing a condensed but balanced urban structure.

Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices. Design/methodology/approach The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation. Findings The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker. Originality/value This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

Intra-annual variations and determinants of canopy layer urban heat island in China using remotely sensed air temperature and apparent temperature

Canopy layer urban heat island (CLUHI) is closely related to about 4.7 billion urban residents’ well-being. Compared with sparse observation stations, remote sensing can obtain spatially continuous temperature, which is conducive to intra-annual variation and associated factors of CLUHI over large regions. Such research is scarce, especially from apparent temperature perspective. Thus, the intra-annual spatio-temporal variations and twelve determinants of CLUHI intensities (CLUHIIs) were investigated for 917 urban region agglomerations in China’s five ecological regions, using remotely sensed near-surface air temperature, apparent temperature and other multi-source data, and several spatial analysis and mathematical statistic methods. The findings revealed that annual CLUHIIs by mean temperatures were most between 0.5 and 1.0 ℃, higher in the summer and spring, and larger in North China than South China in the summer. The annual CLUHIIs by maximum temperatures were negative in several cities, especially in rainless West China. North China generally had stronger CLUHIIs by minimum temperatures in all seasons compared to South China. The CLUHIIs by mean apparent temperatures were larger in the summer (most between 0.5 and 2.0 ℃) and varied across ecological regions. Significant positive correlations generally existed among CLUHIIs by various air temperature and land surface temperature indices (p < 0.05). Nevertheless, obvious differences existed among them. The CLUHII was significantly negatively partially correlated with total precipitation, urban–rural difference in enhanced vegetation index and albedo, positive with urban–rural difference in nighttime light intensity, population density, sulfur dioxide concentration, insignificant with total population, urban area size and landscape shape index in different degree. These findings provided valuable insights into CLUHI research.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.