Surface Heat Island Research Articles

The impacts of landscape structure changes on urban surface temperature and heat islands formation of a growing city in southern Sri Lanka

The impacts of landscape structure changes on urban surface temperature and heat islands formation of a growing city in southern Sri Lanka

A Methodological Framework for High-Resolution Surface Urban Heat Island Mapping: Integration of UAS Remote Sensing, GIS, and the Local Climate Zoning Concept

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.

The interplay between urbanization, vegetation loss and surface heat island in cities: two decadal empirical evidence from Pakistan

The interplay between urbanization, vegetation loss and surface heat island in cities: two decadal empirical evidence from Pakistan

Characterizing Two Types of Urban Air and Surface Heat Island according to Urban Design -Case of Casablanca Neighborhoods-

The urban heat island (UHI) is a recognized signature of the urban climate, however beyond this characterization; it isstill a relevant phenomenon to study in urban design. In this paper, we seek to characterize UHI in differing urbanforms, comparing air and surface temperatures to characterize Air UHI (AUHI) and Surface UHI (SUHI) respectively.The analysis site corresponds to the districts of Casablanca, Morocco's largest city, covering coastal and centralneighborhoods with different urban planning styles. We used land surface temperatures (LST) from the Landsat-8satellite and both observed and simulated air temperatures from the Copernicus reanalysis. We argue that LST isalways higher than air temperature and that there is a shift between SUHI and AUHI that we have quantified fordaytime and nighttime monitoring. We also concluded that UHI depends on four criteria: location, land use, buildingheight, and road width.

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Compared with surface temperature, the near-surface temperature is more related with human health. However, extensive researches have been conducted on the UHI effect globally using surface temperature considering its accessibility. In this study, a comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta Region is investigated. This study first proposed a spatialization method suitable for air temperature in highly urbanized areas with complex land cover. Based on this method, a dataset of 1-km gridded air temperature is developed, and an in-depth analysis of the changes of near-surface and surface heat island is further carried out. Results show that both the near-surface urban heat island intensity (NSUHII) and surface urban heat island intensity (SUHII) are rather strong over the past 20 years, presenting similar spatial distributions as well. However, in the rapidly expanding urban areas especially during summer and winter seasons, the difference in magnitude and time variations (R) between NSUHII and SUHII are pronounced. Hence, adaptions and mitigation strategies on NSUHI and SUHI should be developed and implemented separately in such occasions, which is especially important for developed areas such as Yangtze River Delta Region.

Quantifying of surface urban heat island intensity in Isfahan metropolis using MODIS\Terra\LST data

Heat island characteristics depend on the background climate of the site where the city is located. Therefore, an index was defined for the Isfahan metropolitan area to quantify the surface urban heat island intensity. This new index is based on the representative pixels of urban and non-urban areas. For this purpose, MODIS land cover type product (MCD12Q1) data were used to distinguish between urban and non-urban areas. Also, data from the MODIS/Terra land surface temperature product (MOD11A1) from 2000 to 2018 were utilized for daytime and nighttime to study the surface heat island intensity. Then, the representative pixels of urban and non-urban areas were identified using the spatial correlation method, and the heat island index was calculated for the metropolitan area of Isfahan. The study showed that the frequency distribution of the nighttime heat island index follows a normal distribution and is often 3.5 to 4º K above the temperature of the surrounding areas of the city. The 365-day floating mean of the surface urban heat island reveals that this index has increased in recent years. The research of temporal behavior showed that the intensity of the surface urban heat island reaches its maximum in January and becomes weaker in summer, while the survey of spatial behavior showed that the core of the surface urban heat island extends towards downtown areas, where the oldest part of the city is located.

Remote Sensing and Field Measurements for the Analysis of the Thermal Environment in the “Bosco Verticale” Area in Milan City

The trend of urbanization nowadays has caused serious issues related to climate. One of the most important ones is that of the ‘Urban Heat Island (UHI)’ and it occurs in major cities throughout the world. The most important categories, and therefore the most studied ones, are the canopy urban heat island (CUHI) and surface heat island (SUHI). The aim and the novelty of the current study was to assess different remote sensing approaches to detect the thermal environment of an open area inside a large city. The study was undertaken in an urban area with green spaces, in the Bosco Verticale area in the city of Milan, during the spring and summer period of 2021. The area is characterized by different types of cover materials, which were investigated in terms of surface temperature under shaded and non-shaded conditions. Both field measurements and remote sensing techniques were applied. Remote sensing techniques included downscaling techniques and the usage of different split-window algorithms applied on the Landsat8 satellite sensor data. The land surface temperature (LST) extracted from remote sensing methods was compared with the surface temperature derived from in situ measurements. For the needs of the study, both in situ measurements and the collection of meteorological data from different fixed meteorological stations throughout the city of Milan were carried out. The results revealed the significance of greenery presence inside the urban environment, as a comparison of the meteorological data across the urban area of Milan showed that the areas with a low presence of greenery were found to be warmer than those with a higher presence of green elements. Concerning the field measurements in the study area, the results showed a significant reduction in both surface and air temperature in shaded places. On the other hand, the presence of conventional artificial materials in sunny areas led to relatively high values of both surface and air temperature. The downscaling method showed satisfying results in terms of average LST values; however, some discrepancies appeared in terms of the RMSE index. The application of split-window algorithms has shown that some forms of the ‘Generalized split-window algorithm’ and some forms of the ‘Jimenez-Munoz algorithm’ presented better performance among the studied algorithms. Comparing the LST values derived from the most representative algorithm, the ‘Du, Wan algorithm’, with those derived from downscaling methods, it was found to be quite close. However, under shaded conditions, the results derived from the ‘Split-window algorithm’ were found to be more precise. The application of remote sensing techniques in microscale in urban regions should be further studied in future, as they could be an essential tool for observing microclimatic conditions in urban areas and on building scale.

Quantifying the Impact of Urban Growth on Urban Surface Heat Islands in the Bangkok Metropolitan Region, Thailand

The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration of populations, a global trend, notably exemplified in megacities such as Bangkok, Thailand. The global trend of urbanization has witnessed unprecedented growth in recent decades, with cities transforming into megametropolises that profoundly impact changes in urban temperature, specifically the urban heat island (UHI) phenomenon induced by the rapid growth of urban areas. Elevated urban concentrations lead to increased city density, contributing to higher temperatures within the urban environment compared to the surrounding areas. The evolving land-use surface has assumed heightened significance due to urban development, necessitating accelerated efforts to mitigate urban heat islands. This study aims to quantify the influence of urban growth on urban surface temperature in Bangkok and its surrounding areas. The inverse relationship between urban temperature and land surface temperature (LST), coupled with urban area density, was examined using Landsat 5 and 8 satellite imagery. The analysis revealed a positive correlation between higher temperatures and levels of urban growth. Areas characterized by high-rise structures and economic activities experienced the most pronounced impact of the heat island phenomenon. The city exhibited a notable correlation between high density and high temperatures (high–high), signifying that increased density contributes to elevated temperatures due to heat dissipation (significant correlation of R2 = 0.8582). Conversely, low-temperature, low-density cities (low–low) with a dispersed layout demonstrated effective cooling of the surrounding area, resulting in a significant correlation with lower local temperatures (R2 = 0.7404). These findings provide valuable insights to assist governments and related agencies in expediting planning and policy development aimed at reducing heat in urban areas and steering sustainable urban development.

Bi-Temporal Analysis of Vegetation Index on Land Surface Temperature in Kottayam, Kerala

The impact of NDVI (Normalized Difference Vegetation Index) on the LST (Land Surface Temperature) as well as on the genesis of surface heat islands in urban areas during two different time periods was assessed in Kottayam district, Kerala, India. Landsat TM, Landsat OLI and TIRS imagery from the years 1988 and 2020 were employed to scrutinize the relationship between NDVI and LST. The area covered under different NDVI classes were quantified. The finding indicated that NDVI of the research region decreased from 0.77 in 1988 to 0.59 in 2020, resulting in an increase in LSTmax from 34.46 °C in 1988 to 40.63 °C in 2020. Decrease in NDVI resulted in an increase in the high UHI class from 20.83 km2 in 1988 to 660.59 km2 and from 7.26 km2 to 181.35km2 in the very high UHI class. An inverse relationship was observed between NDVI and LST, with Pearson coefficients of 0.5737 and 0.5199 for 1988 and 2020, respectively, which indicates that NDVI could serve as a crucial metric for evaluating LST and UHI effects. Future research will explore the effect of seasonal variability in LULCC on LST, day and night time UHI and their impacts on human health and energy consumption.

USING REMOTE SENSING TECHNOLOGIES FOR MONITORING URBAN HEAT ISLANDS

Background. The urbanization process is accelerating every day, which entails significant changes in the natural landscape. This leads to microclimatic changes, air pollution, thermal effect, etc. Due to air pollution by man-made emissions in urbanized areas, the thermal regime is changing; the concentration of carbon dioxide and water vapor has now reached 90 % of the total amount of pollutants. As a result, another problem arises that contributes to global warming – the "greenhouse effect". Elevated air temperatures affect human health leading to breathing problems, seizures, heat and sunstroke, heat stress, and increased mortality. Considering the potential danger of elevated air temperatures caused by urban heat islands affecting the lives of residents, an effective and relevant method for surface temperature analysis and heat island location determination should be developed. Methods. During urban heat island monitoring, the main factor of analysis is surface temperature, which was determined in the study using indices such as: Normalized Difference Vegetation Index (NDVI), Urban Thermal Field Variance Index (UTVFI) and Normalized Difference Built-up Index (NDBI). Results. This paper describes a study of the distribution of urban heat islands in three European capitals, including Kyiv, Oslo and Rome, from May 2013 to August 2023. Using the capabilities of the GoogleEarthEngine cloud platform and data from the Landsat 8 OLI/TIRS satellite, the condition of green spaces, the number of impervious surfaces and indices of surface temperatures (LST) were compared, resulting in maps of the distribution of urban heat islands (UHIs) in the areas of selected cities and towns demonstrating quantitative changes. Conclusions. Thus, the study showed a decrease in the number of urban heat islands along with impervious surfaces in the city of Kyiv by 4 %. At the same time, Rome and Oslo experienced an increase in the number of urban heat islands along with impervious surfaces. The data obtained prove the feasibility of using the chosen research method and can be used to assess the environmental condition, identify risk zones, and develop effective measures to further prevent the spread of UHI in megacities.

NDVI and NDBI indexes as indicators of the creation of urban heat islands in the Sarajevo basin

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment.

Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran

The Urban Heat Island phenomenon happens due to the differences in the thermal behavior between urban and rural areas which many factors such as vegetated, water, impervious and built-up areas could affect this phenomenon. Urban Heat Island consists of three types: Canopy heat island, Boundary heat island, and surface heat island. In this study, the surface type of urban heat island is analyzed. In this paper, 13 TM/ETM+ images have been obtained from 1990 to 2015(an image biennially). Urban Heat Islands effects are much more severe in summer; therefore, all images have been taken in summer. NDVI, IBI, albedo, and also land surface temperature were derived from images. Various neural network topologies have been used to identify the best model for predicting the urban heat island intensity. The LST of 2016 has been considered as validation data, thus the best result from fitting structures was obtained from Cascade which the Bayesian Regularization was its training algorithm (R-squared=0.62, RMSE=1.839 K).

Exploring the connection between morphological characteristic of built-up areas and surface heat islands based on MSPA

Spatial pattern of built-up areas can exert an important influence on surface urban heat islands (SUHIs). While previous studies relied heavily on landscape indicators to characterize land use spatial pattern, such quantitative indicators cannot correspond to distinct spatial positions. By comparison, morphological spatial pattern analysis is an appealing alternative for measuring land use pattern as it can spatially visualize various land use elements from the perspective of mathematical morphology. Therefore, this research moves a step forward in understanding the spatio-temporal connection between the morphological characteristic of built-up areas and SUHI intensity. Taking a largely-urbanized city as an example, we first analyzed the SUHI intensity and morphological characteristic of built-up areas according to multi-temporal remote sensing data. Second, we used the Pearson correlation test and random forest regression to explore the linear and non-linear connections between SUHI intensity and various influence factors, respectively. Compared with landscape pattern, land use morphological characteristic plays a more important role in urban thermal environments. Both the correlation test and random forest regression suggested that the proportion of “core” was a dominant influence factor of the SUHI. The SUHI intensity in the study area was positively connected to the proportions of core, edge, and bridge of built-up areas, but negatively connected to the proportion of islet. Our results can help urban planners emphasize the influence of morphological characteristic in an earlier phase of thermal environment design. This assessment workflow can also be reused for various climatic factors in other cities because of its flexibility.

Changes in the surface urban heat island between 1986 and 2021 in the polycentric Górnośląsko-Zagłębiowska Metropolis, southern Poland

Progressing urbanization forces urban infrastructure development and leads to an increase in artificial impervious surfaces that directly contribute to changes in surface urban heat island (SUHI) in cities. This study discusses spatial and temporal changes in the SUHI extent and intensity in the biggest Metropoly in Southern Poland in summer between 1986 and 2021. The study is based on Landsat satellite images, demographic and land cover data from 1990 to 2018 from CORINE Land Cover (CLC) classifications. The comparison of satellite images recorded in different years, on similar dates and under similar meteorological conditions revealed an increase in the SUHI extent from 0.6 to 4.3 % between 1986 and 2021 due to increase in the share of impervious areas. Moreover, regardless of changes in spatial extent, the mean land surface temperature (LST) of all types of land cover increased, which may be related to recent warming. In the research period, permanent surface heat island covered 0.85 % of the GZM, and over 50 % of its area was located only within four cities. Although the mean LST for SUHI increased, the SUHI intensity decreased due to increased LST of the cold island. The SUHI extent was more related to the inhabitant number (correlation: 0.83 to 0.98) than population density (0.39–0.58). SUHI expansion in combination with recent warming of climate is a particularly negative effect where society is ageing, like in GZM.

Diurnal Variation in Urban Heat Island Intensity in Birmingham: The Relationship between Nocturnal Surface and Canopy Heat Islands

Urban heat islands have garnered significant attention due to their potential impact on human life. Previous studies on urban heat islands have focused on characterizing temporal and spatial variations over longer periods of time. In this study, we investigated the urban heat island (UHI) in Birmingham from September 2013 to August 2014 using higher temporal resolution SEVIRI satellite surface temperature data along with data from the Birmingham Urban Climate Laboratory (BUCL) meteorological station and the UK Meteorological Office meteorological station. Our aim was to characterize the diurnal variations in the surface urban heat island intensity (SUHII) and canopy urban heat island intensity (CUHII) and to explore their relationship under the influence of three factors (day/nighttime, season, and wind speed) using regression analysis. Our findings reveal that SUHII and CUHII exhibit relatively stable patterns at night but vary significantly during the day with opposite diurnal trends. In addition, SUHII and CUHII were more variable in spring and summer but less variable in winter. During the nighttime, SUHII represents CUHII with high confidence, especially during spring and summer, but less so during the cold season. In addition, SUHII represents CUHII with greater confidence under low-wind conditions. This study deepens our understanding of the diurnal dynamics of urban heat islands and the influence of atmospheric conditions on the relationship between surface and canopy heat islands in urban areas. The results of this study can be used for heat island studies in cities that lack high-precision observation networks and to guide sustainable urban development.

Insights into heat islands at the regional scale using a data-driven approach

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate.

Spatiotemporal Features of the Surface Urban Heat Island of Bacău City (Romania) during the Warm Season and Local Trends of LST Imposed by Land Use Changes during the Last 20 Years

Using MODIS and Landsat LST images, the present paper advances a series of results on the characteristics of the surface heat island (SUHI) of Bacău City (Romania) during the warm season (April to September) for a period of 20 years (2001–2020). At the same time, given their higher temporal resolution and their availability for both day and night, MODIS LST was used to understand the spatial features of the SUHI in relation to land use. In this way, a total of 946 MODIS Terra and 483 Landsat satellite images were used to outline the main LST characteristics of the days with clear sky in this middle-sized city in northeast Romania. In order to analyze MODIS LST changes in relation to land use changes in the period 2001–2018, we used the standardized CORINE Land Cover datasets. With the help of the Rodionov test, we were able to determine the geometry and intensity of the SUHI. During the day, the spatial extension of the SUHI reaches its maximum level and is delimited by the isotherm of 31.0 °C, which is 1.5–2.0 °C warmer than the neighboring non-urban areas. During the night, the SUHI has a more regulated spatial extension around the central area of the city, delimited by the 15.5 °C isotherm with LST values that are 1.0–1.5 °C warmer than the surrounding non-urban areas. Additionally, from a methodological point of view, we highlight that resampled MODIS and Landsat images at a spatial resolution of 500 m can be used with confidence to understand the detailed spatial features of the SUHI. The results of this study could help the elaboration of future policies meant to mitigate the effects of urbanization on the SUHI in an era of increasing air temperatures during summer.

Effects of Land Use Changes Across Different Urbanization Periods on Summer Rainfall in the Pearl River Delta Core Area

The Pearl River Delta (PRD) is one of the three urban agglomerations in China that have experienced rapid development. For this study, a core area of the PRD was identified, comprising the highly urbanized areas of Guangzhou, Foshan, Zhongshan, Zhuhai, Shenzhen, and Dongguan Cities. The expansion of these urban areas was tracked across three time periods—the year population urbanization rate exceeded 70% (2000), 18 years before (1982), and 18 years after (2018). This study used the Weather Research and Forecasting (WRF) model to explore summer rainfall changes across different urbanization periods in the PRD core area. The results show that urban land expansion mainly occurred in the post urbanization period. Rainfall changes across different urbanization periods were roughly consistent with previously observed spatial and temporal changes accompanying urban expansion in the PRD core area. Extreme rainfall mainly increased in the post urbanization period, shifting rainstorm center towards the PRD core area. Further causal analysis revealed that land use changes affected rainfall by altering thermodynamics and water vapor transfer. The urban expansion changed the surface energy balance, resulting in increased surface heating and heat island effects. The heat island effects thickened the planetary boundary layer and increased vertical wind speeds, which initiated dry island effects, thereby causing more water vapor transportation to the atmosphere. Consequently, rainstorms and extreme rainfall events have become concentrated in urban areas.

Cooling Effect of Trees with Different Attributes and Layouts on the Surface Heat Island of Urban Street Canyons in Summer

In recent years, the impact of surface heat islands in urban street canyons has become increasingly apparent. However, the research on the use of trees to mitigate surface heat islands remains limited. To address this gap, this study combines experiments and simulations to analyze the cooling effect of trees on surface temperatures under varying timeframes and layouts in an east–west street canyon. The results reveal that the temperature of the road decreases by 10–15 °C, which is 2–4 times greater than that on the south side. Moreover, at 5:00 p.m. in the afternoon, the cooling effect on the south side is 10.3 °C, which is twice that of the north side. In practical planning and design, the diameter of the tree canopy should be maximized, and trees with leaf-area densities greater than 1.5 m2/m3 should be selected. Additionally, the layout of trees should be optimized to maximize the tree canopy coverage. These findings have important implications for optimizing plant selection and placement in street canyons.

Spatial Influence of Multifaceted Environmental States on Habitat Quality: A Case Study of the Three Largest Chinese Urban Agglomerations

Habitat structure and quality in the urban agglomeration (UA) are subject to multiple threats and pressures due to ongoing anthropogenic activities and call for comprehensively effective solutions. Many approaches, including cartographic comparison, correlation analysis, the local entropy model, and GeoDetector, were jointly used to clarify the interplay between habitat quality and multiple environmental issues. In response to the overlapped risks of diverse environmental systems, this study presented an integrated research framework to evaluate the spatial influences of multifaceted environmental situations on habitat quality. We conducted the case study in the three largest Chinese UAs: Beijing–Tianjin–Hebei (BTH), Greater Bay Area (GBA), and Yangtze River Delta (YRD). The evaluation results show that the three UAs shared similarities and differences in relationship/impact types and their strengths. In 2015, most of the three UAs’ landscapes delivered low–medium magnitudes of habitat quality (score <0.7) and emerged with unevenly severe consequences over space across different environmental aspects, highlighting the importance of maintaining habitat safety. Overall, habitat quality scores were synergistic with NDVI, but antagonistic to surface heat island intensity (SHII), PM2.5 concentrations, and residential support. However, locally structured relationships exhibited geographical complexity and heterogeneity between habitat quality and environmental systems. Regarding GeoDetector evaluation, PM2.5 concentrations in BTH, SHII in GBA, and NDVI in YRD played a dominant role in single-factor and interaction analysis. More importantly, the synergistic effect of various environmental issues on habitats was manifested as mutually enhanced rather than independent or weakened interactive effects, implying the aggravation of compound effects and the necessity of prioritization schemes. This study could provide beneficial insights into the interconnections between habitats’ sustainability and multifaceted environmental situations in UAs.