Effects of building morphology on land surface temperature in Xi'an City – insights based on different settlement types and seasons

Combined impacts of buildings and urban remnant mountains on thermal environment in multi-mountainous city

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development.

Increases in urban temperature affect the urban ecological environment and human health and well-being. In urban morphology, building characteristics are important factors affecting the land surface temperature (LST). Contemporary research focuses mainly on the effects of land use, urban tissue configuration, and street networks on the LST, and the effects of building characteristics on the LST need to be further understood. The mean LST and the urban morphology indicators of a single grid were calculated via a remote sensing inversion and a spatial analysis, and a geographically weighted regression (GWR) model was established to explore the influence of the building coverage ratio (BCR), mean building height (BH_mean), floor area ratio (FAR), and mean sky view factor (SVF_mean) on the LST. The results show that the correlations between the urban morphology indicators and the LST at a scale of 100~500 m are of different degrees, and the correlations are more significant at a scale of 200 m. Therefore, the optimal spatial scale for studying the influence of urban morphology indicators on the LST is 200 m. The fitting effect of the GWR model is significantly better than that of the ordinary least squares (OLS) method, and the effects of each indicator on the thermal environment have spatial non-stationarity. The BCR, BH_mean, FAR, and SVF_mean differ in their ability to raise and lower the temperature in different spatial zones, and the order of influence is as follows: BCR > SVF_mean > FAR > BH_mean. This study will provide a reference for the urban planning of Urumqi.

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

Separate and combined effects of 3D building features and urban green space on land surface temperature

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.

With continuous urban densification, revealing impacts of urban structures on thermal environment is necessary for climate adaptive design. In this study, random forest and partial difference plots were employed to depict the relative importance and interdependent effects of complex building morphology to land surface temperature (LST) variability. The six spatial factors of building density (BD), mean building height (MBH), building height difference (BHD), floor area ratio (FAR), building volume density (BVD) and mean compactness factor (MCF) were calculated at grids of 90, 300, 600 and 900 m. The results showed that BD, MCF and MBH exerted stable and significant impacts on LST with the highest prediction accuracy at 600 m neighborhood scale, and FAR and BVD were the least correlated to LST changes. Meanwhile, the influencing factors presented different correlation patterns with LST. Among them, the increase of BD had a positive linear effect on LST. MCF and MBH were nonlinearly correlated with the LST variation, and their threshold values of cooling effect were also identified. In addition to controlling BD, it also suggested that comprehensively arranging more small-volume buildings as well as increasing building height to enlarge shadow coverage were more conducive to ground heat mitigation.

Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China

Urban overheating significantly affects thermal comfort and livability, making it essential to understand the relationship between urban form and land surface temperature (LST). While the horizontal dimensions of urban form have been widely studied, the vertical structures and their impact on LST remain underexplored. This study investigates the influence of three-dimensional urban form characteristics on LST, using ECOSTRESS sensor data and four machine learning models. Six urban morphology variables—building density (BD), mean building height (MH), building volume (BVD), gross floor area (GFA), floor area ratio (FAR), and sky view factor (SVF)—are analyzed across different seasons and times of day. The results reveal that MH, BD, and FAR are season-stable factors, with higher MH correlated with lower LST ((e.g., an observed reduction of approximately 3 °C in spring), while higher BD is associated with higher LST (e.g., an increase of about 3.5 °C in autumn). In contrast, BVD, GFA, and SVF are season-varying factors with variable impacts depending on the time of year. Higher BVD is generally associated with elevated LST, while GFA and SVF are linked to lower LST. These associations reflect absolute changes in LST, measured directly from ECOSTRESS data. These findings offer valuable insights into the complex interactions between urban morphology and LST, helping to inform strategies for urban heat mitigation and sustainable planning.

Urbanization continues to accelerate, driving global warming change and, at more local scale, land cover changes. In cities, new surface materials, buildings, roads and changes to the surface morphology alter airflow and heat exchange between the urban surface and the atmosphere. As a result, cities are almost always warmer than their surroundings rural area in a phenomenon known as Urban Heat Island (UHI) that could represent a hazard for city inhabitants. Consequently, it is important to evaluate the magnitude of the UHI and understand the urban characteristics involved in its formation process.The aim of the present study is to assess the Surface Urban Heat Island (SUHI) in Bolzano urban area evaluating its correlation with the urban morphology and its biophysical characteristics. The indices considered to describe the urban morphology are Building Coverage Ratio (BRC), Building Volume Density (BVD), Mean Building Height (MBH), Green Space Ratio (GRS), and Sky View Factor (SVF) at 30 m resolution. The biophysical indices considered are albedo, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) at 30 m resolution.The morphological indices were calculated starting from building, green area, land cover data, and DEM, whereas biophysical indices were derived from Landsat 8/9 OLI/TIRS satellite images. Two images, one for the summer season and one for the winter season, were selected based on air temperature and absence of clouds: 07/19/2022 during a 7-days period of very high temperatures and 02/14/2021 during a 7-days period of very low temperatures. Subsequently, a linear model analysis was fitted, setting the Urban Heat Island Intensity (UHII) as the dependent variable and the morphological and biophysical indices as independent variables.Results showed how some indices were positive or negative correlated with the UHII both in summer and winter, whereas other had a different behavior depending on the season.Results regarding summer period highlighted UHII positive correlations with most of the morphological indices and negative correlation with most biophysical indices. In contrast, in winter, all the biophysical indices were positive correlated with the UHII. Moreover, most morphological indices were positive correlated with it.Understanding which urban characteristics impact more in the SUHI formation is crucial for improving city environment and people health and this study set a first step into it.This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) – SPOKE TS 1.

Land use/land cover (LULC) changes exacerbate urban heat island (UHI) effect, leading to ecological and environmental problems. LULC patterns exhibit different driving mechanisms on land surface thermal environments across different seasons. However, so far, quantitative research distinguishing seasonal differences in LULC patterns at the landscape and class levels in relation to land surface temperature (LST) has remained limited. This paper applies the boosted regression trees (BRT) model and structural equation models (SEM) to investigate the seasonal variations in LST response to LULC patterns. The research findings indicated substantial seasonal variations in the response of LST to LULC. Pronounced cropland heat island effect was observed during spring and winter, whereas the heat island effect of built-up areas was pronounced during summer and autumn. The impacts of LULC indices on LST generally showed threshold effects, and clear interactions between different influencing factors were observed. An increase in core area percentage of landscape (CPLAND) or patch cohesion index (COHESION), when interacting with percentage of landscape (PLAND), notably enhanced LST variations. The impacts of landscape patterns across all LULC types on LST were pronounced and predominantly direct throughout all seasons. The variations in LST were dominated by the landscape patterns of built-up areas and forestland during summer and autumn, while the landscape patterns of cropland and forestland predominantly determined LST in spring, and the LST in winter was mainly influenced by the landscape patterns of cropland and water. This study provides insights into how LULC regulates urban thermal environment.

Comprehensive effect of the three-dimensional spatial distribution pattern of buildings on the urban thermal environment

The effects of 3D architectural patterns on the urban surface temperature at a neighborhood scale: Relative contributions and marginal effects

The impact of urban morphology on land surface temperature across urban-rural gradients in the Pearl River Delta, China

The influence of the landscape pattern on the urban land surface temperature varies with the ratio of land components: Insights from 2D/3D building/vegetation metrics