Search Results (121)

Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape composition and land surface temperature (LST) in Phoenix and Tucson, two rapidly growing cities located in the Sonoran Desert of the southwestern United States. Landsat-9 OLI-2/TIRS-2 satellite imagery was used to derive the LST value and calculate spectral indices. A multi-resolution grid-based approach was applied to assess spatial correlations between land cover and mean LST across varying spatial scales. The strongest positive correlations were observed with barren land, followed by impervious surfaces, while green space showed a negative correlation. Furthermore, the Urban Thermal Field Variation Index (UTFVI) and the Ecological Evaluation Index (EEI) assessments indicated that over one-third of both cities are exposed to strong SUHI effects and poor ecological quality. The findings highlight the critical need for ecologically sensitive urban planning, emphasizing the importance of the morphological structure of cities, the necessity of planning holistic blue–green infrastructure systems, and the importance of reducing impervious surfaces to decrease LST, mitigate SUHI and SUHI impacts, and increase urban resilience in desert environments. These results provide evidence-based guidance for landscape planning and climate adaptation in hyper-arid urban environments. Full article

Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones. Full article

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience. Full article

Intensifying global warming has disrupted natural ecosystems and altered energy consumption patterns. Understanding the impact of land use and cover change on surface urban heat islands (SUHIs) and energy use is critical for sustainable development. In this study, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), and SUHI data were derived using GIS and remote sensing (RS) technology, and quantitative analysis was performed in combination with energy consumption data. The results revealed the following key findings. In summer, the NDVI exhibited a significant negative correlation with total urban building energy consumption (r = −0.52), whereas the NDBI and SUHI showed significant positive correlations (r = 0.72 and r = 0.67, respectively). Moreover, the SUHI served as a mediating role between land use/cover change and electricity consumption, with the direct effect accounting for 36% and the indirect effect accounting for 64% of the total effect. In contrast, the NDBI was significantly positively correlated with energy consumption in winter (r = 0.53). Spline regression analysis further revealed that every one-unit increase in this index corresponded to an increase of approximately 22 million kWh in summer EC and an increase of approximately 1.16 billion kWh in winter EC. Full article

In the context of climate change, surface urban heat islands (SUHIs) have become critical factors affecting the quality of the urban built environment. However, low-precision satellite thermal infrared remote sensing is suitable for urban scales but is insufficient to reveal the spatiotemporal distribution roles of surface heat islands at the neighborhood scale. This research takes the Sipailou Campus of Southeast University as an example and employs UAV thermal infrared imaging to acquire high-precision surface temperature data. It then systematically investigates the relationship and association mechanism between the surface urban heat island intensity (SUHII) and campus 2D/3D landscape configuration. The results indicate that the campus has a cooling effect during the daytime, with an average SUHII of −0.90 °C. It demonstrates the SUHII characteristics for campus land use types are as follows: SUHII_BD > SUHII_IS > SUHII_GS > SUHII_WB. Furthermore, the campus landscape has a significant hierarchical driving effect on SUHII, with the configuration of campus buildings and the impervious surface driving the strong heat island (SHI) and the 3D configuration and structure of greenspace dominantly strengthening the strong cool island (SCI). The overall design strategy of “two-dimensional priority, three-dimensional optimization” enables us to effectively mitigate the campus SUHII. This study reveals the spatiotemporal distribution characteristics of campus SUHII and the key influencing factors, and it also broadens the application of UAV thermal infrared imaging technology in the meso–micro-scale urban heat island assessment, providing suggestions for constructing a climate-adaptive urban landscape. Full article

Utilizing 58 Landsat-7 images taken over 10 years, the current study investigated the relationship between the mitigation of surface urban heat islands (SUHIs) by NbSs (Nature-based Solutions) and influential variables such as physical variables of NbSs, environmental variables of the streets, and meteorological variables. Parks and permeable pavements are the two types of NbS devices under examination. Reference (i.e., unaffected by any NbS) and experimental (i.e., affected by only one NbS) areas were selected to perform the analysis. Areas affected by large water bodies or more than one NbS device were excluded. The cooling effect caused by NbS was linked to the influential variables by multiple regression models. Key findings included the following: Firstly, the distance to an NbS is more important than the area of an individual NbS, implying that small and evenly distributed NbS devices might have better overall cooling effects than large but sparsely placed NbS devices. Secondly, NbSs do not significantly contribute to cooling in districts with grid-type streets, while exhibiting significant cooling for districts with complex street patterns. Older districts with complex street patterns should be the focus of NbS implementation, not newer, modern districts. However, NbS cooling is sensitive to several variables in districts with complex patterns. NbS installation in those districts requires careful planning to maximize engineering investment. Lastly, maintenance can be essential to sustain the cooling capacity of NbSs over time. Full article

The urban heat island (UHI) effect significantly impacts urban environments and quality of life, yet research comparing coastal and inland cities is relatively lacking. This study, using the MYD11A2 dataset, analyzes the spatiotemporal evolution of land surface temperature (LST) and the surface urban heat island intensity index (SUHIII) in Beijing (inland) and Dalian (coastal) from 2003 to 2023, exploring the driving factors from 2003 to 2018 and proposing mitigation strategies for similar cities. Key findings: (1) Beijing’s SUHIII is 0.45 °C higher than Dalian’s during summer days, while Dalian’s SUHIII is 0.24 °C stronger than Beijing’s during winter nights, likely due to Dalian’s maritime climate, which raises nighttime LSTs and intensifies the winter SUHIII. (2) Both cities show similar trends in LST and SUHIII, with fluctuations until 2010, an increase after 2011, and a peak in 2023, with the expansion of heat island areas occurring mainly in suburban regions. (3) From 2003 to 2018, TEMP is the primary factor promoting SUHIII, followed by ET and POP, with EVI as the main mitigating factor. Beijing’s PREP weakens SUHI, while Dalian’s PREP promotes it. Coastal cities should focus on water bodies and wetland planning to mitigate heat islands, especially in areas like Dalian which are affected by PREP. Full article

The surface urban heat island (SUHI) effect, driven by human activities and land cover changes, leads to elevated temperatures in urban areas, posing challenges to sustainability, public health, and environmental quality. While SUHI drivers at large scales are well-studied, finer-scale thermal variations remain underexplored. This study employed the Local Climate Zones (LCZs) framework to analyze land surface temperature (LST) dynamics in Zhengzhou, China. Using 2022 mean LST data derived from a single-channel algorithm, combined with field surveys and remote sensing techniques, we examined 30 potential driving factors spanning natural and anthropogenic conditions. Results show that built-type LCZs had higher average LSTs (31.10 °C) compared with non-built LCZs (28.91 °C), with non-built LCZs showing greater variability (10.48 °C vs. 6.76 °C). Among five major driving factor categories, landscape pattern indices dominated built-type LCZs, accounting for 44.5% of LST variation, while Tasseled Cap Transformation indices, particularly brightness, drove 42.8% of the variation in non-built-type LCZs. Partial dependence analysis revealed that wetness and landscape fragmentation reduce LST in built-type LCZs, whereas GDP, imperviousness, and landscape cohesion increase it. In non-built LCZs, population density, connectivity, and brightness raise LST, while wetness and atmospheric dryness provide cooling effects. These findings highlight the need for LCZ-specific SUHI mitigation strategies. Built-type LCZs require urban form optimization, enhanced landscape connectivity, and expanded green infrastructure to reduce heat accumulation. Non-built LCZs benefit from maintaining soil moisture, addressing atmospheric dryness, and optimizing vegetation configurations. This study provides actionable insights for sustainable thermal environment management and urban resilience. Full article

Rapid urbanization and climate impacts have raised concerns about the emergence and aggravation of urban heat island effects. In Africa, studies have focused more on big cities due to their growing populations and high climate impact, while mid-sized cities remain under-studied, with limited comparative insights into their distinct characteristics. This study therefore provided a spatiotemporal analysis of land use land cover change (LULCC) and surface urban heat islands (SUHI) effects in the Nigerian mid-sized cities of Akure and Osogbo from 2014 to 2023. This study used Landsat 8 and 9 imagery (2014 and 2023) and analyzed data via Google Earth Engine and ArcGIS Pro 3.4. Results showed that Akure’s built areas increased significantly from 164.026 km² to 224.191 km² while Osogbo witnessed a smaller expansion from 41.808 km² to 58.315 km² in built areas. This study identified Normalized Difference Vegetation Index (NDVI) and emissivity patterns associated with vegetation and thermal emissions and a positive association between LST and urbanization. The findings across Akure and Osogbo cities established that LULCC has different impacts on SUHI effects. As a result, evidence from a mid-sized city might not be extended to other cities of similar size and socioeconomic characteristics without caution. Full article

The land surface temperature (LST) in the central urban area has shown a consistent upward trend over the years, exacerbating the surface urban heat island (SUHI) effect. Therefore, this study focuses on the central urban area of Chengdu, using blocks as the research scale. The Gradient Boosting Decision Tree (GBDT) model and SHAP values are employed to explore the nonlinear effects of human settlements (HS) on LST across different seasons. The results show that (1) At the block scale, the overall impact of HS on LST across all four seasons tracks the following order: built environment (BE) > landscape pattern (LP) > socio-economic development (SED). (2) LP is the most important factor affecting LST in summer, while the BE has the greatest influence on LST during spring, autumn, and winter. (3) Most HS indicators exhibit seasonal variations in their impact on LST. The impervious surface area (ISA) exhibits a significant positive impact on LST during spring, summer, and autumn. In contrast, the nighttime light index (NTL) and functional mix degree (FMD) exert a significant negative influence on LST in spring, autumn, and winter. Additionally, the normalized difference vegetation index (NDVI) negatively affects LST in both spring and summer. Moreover, connectivity (CNT) and functional density (FPD) demonstrate notable threshold effects in their influence on LST. (4) Certain HS indicators exhibit interaction effects, and some combinations of these indicators can effectively reduce LST. This study reveals HS–LST interactions through multidimensional analysis, offering block-scale seasonal planning strategies for sustainable urban thermal optimization. Full article

Urbanization promotes urban infrastructure development and increases artificial impervious surfaces, leading to rising temperatures and urban climate alterations, contributing to the appearance and intensification of the Urban Heat Island (UHI). In this study, a 40-year time series of Landsat images of the city of Oran was used to generate two biophysical indices. The Normalized Difference Built-up Index (NDBI) distinguished built-up areas from non-built-up areas, while a semi-automatic classification produced Land Use/Land Cover (LULC) maps, for a precise analysis of urban sprawl. The results revealed a significant expansion of urban areas, with an increase of 65.28 km² between 1984 and 2024. The Normalized Difference Vegetation Index (NDVI) was used to estimate Land Surface Temperature (LST) by applying the “Mono Window” algorithm for Thematic Mapper (TM) images and the “Split Window” algorithm for Enhanced Thematic Mapper (ETM+) and Operational Land Imager–Thermal Infrared Sensor (OLI–TIRS) images. The surface temperature difference between urban and rural areas increased from 0.36 °C in 1984 to 4.5 °C in 2024, highlighting the intensification of the Surface UHI (SUHI) effect. LST maps also helped to identify the areas most vulnerable to UHI, as well as those where this effect is persistent, corresponding to the Permanent UHI (PUHI). Full article

Urban heat islands (UHIs) constitute one of the most conspicuous anthropogenic impacts on local climates, characterized by elevated land surface temperatures in urban areas compared to surrounding rural regions. This study represents a novel and comprehensive effort to characterize the spectral signature of SUHI through the lens of the two-dimensional (2D) turbulence theory, with a particular focus on identifying energy cascade regimes and their climatic modulation. The theory of two-dimensional (2D) turbulence, first described by Kraichnan and Batchelor, predicts two distinct energy cascade regimes: an inverse energy cascade at larger scales (low wavenumbers) and a direct enstrophy cascade at smaller scales (high wavenumbers). These cascades can be detected and characterized through spatial power spectra analysis, offering a scale-dependent understanding of the SUHI phenomenon. Despite the theoretical appeal, empirical validation of the 2D turbulence hypothesis in urban thermal landscapes remains scarce. This study aims to fill this gap by analyzing the spatial power spectra of land surface temperatures across 14 cities representing diverse climatic zones, capturing varied urban morphologies, structures, and materials. We analyzed multi-decadal LST datasets to compute spatial power spectra across summer and winter seasons, identifying spectral breakpoints that separate large-scale energy retention from small-scale dissipative processes. The findings reveal systematic deviations from classical turbulence scaling laws, with spectral slopes before the breakpoint ranging from ~K^−1.6 to ~K^−2.7 in winter and ~K^−1.5 to ~K^−2.4 in summer, while post-breakpoint slopes steepened significantly to ~K^−3.5 to ~K^−4.6 in winter and ~K^−3.3 to ~K^−4.3 in summer. These deviations suggest that urban heat turbulence is modulated by anisotropic surface heterogeneities, mesoscale instabilities, and seasonally dependent energy dissipation mechanisms. Notably, desert and Mediterranean climates exhibited the most pronounced small-scale dissipation, whereas oceanic and humid subtropical cities showed more gradual spectral transitions, likely due to differences in moisture availability and convective mixing. These results underscore the necessity of incorporating turbulence theory into urban climate models to better capture the scale-dependent nature of urban heat exchange. The observed spectral breakpoints offer a diagnostic tool for identifying critical scales at which urban heat mitigation strategies—such as green infrastructure, optimized urban ventilation, and reflective materials—can be most effective. Furthermore, our findings highlight the importance of regional climatic context in shaping urban spectral energy distributions, necessitating climate-specific urban design interventions. By advancing our understanding of urban thermal turbulence, this research contributes to the broader discourse on sustainable urban development and resilience in a warming world. Full article

Compared with atmospheric urban heat islands, surface urban heat islands (SUHIs) are easily monitored by the thermal sensors on satellites and have a more stable spatial pattern resembling the urban and built-up lands across single cities, large metropolitans, and urban agglomerations; hence, they are gaining more attention from scholars and urban planners worldwide in the search for reasonable urban spatial patterns and scales to guide future urban development. Traditional urban–rural dichotomies, being sensitive to the representative urban and rural areas and the diurnal and seasonal variations in the land surface temperature (LST), obtain inflated and varying SUHI spatial footprints of approximately 1.0–6.5 times the urban size from different satellite-retrieved LST datasets in many cities and metropolitan areas, which are not conducive to urban planners in developing reasonable strategies to mitigate SUHIs. Taking the Yangtze River Delta urban agglomeration of China as an example, we proposed an improved structural similarity index to quantify more reasonable spatial patterns and footprints of SUHIs from multiple LST datasets at an annual interval. We identified gridded LST anomalies (LSTAs) related to urbanization by adopting random forest models with climate, urbanization, geographical, biophysical, and topographical parameters. Using a structural similarity index of the LSTA annual cycle at a grid point relative to the urban reference LSTA annual cycle in terms of average values, variances, and shapes to characterize the SUHIs, cross-validated SUHI footprints ~1.06–2.45 × 10⁴ km² smaller than the urban size and clear transition zones between urban areas and the SUHI zone were obtained from multiple LST datasets for 2000–2022. Hence, urban planners can balance urbanization’s benefits with the adverse effects of SUHIs by enhancing the transition zone between urban areas and the SUHI zone in future urban design. Considering that urban areas rapidly transformed into SUHIs, with the ratio of the SUHI extent to the urban size increasing from 0.43 to 0.62 during 2000–2022, urban planners should also take measures to prevent the rapid expansion of high-density urban areas with an ISA density above 65% in future urban development. Full article

Surface urban heat island intensity (SUHII) is a critical indicator of the urban heat island (UHI) effect. However, discrepancies in estimation methods may introduce uncertainty in SUHII values. While previous studies have examined the responses of SUHII to different methods at large scales, further analysis is needed for plateau cities in southwestern China, which have complex geographical features. This study investigates the spatiotemporal patterns and influencing factors of SUHII in 200 plateau cities across southwestern China via nine estimation methods that incorporate rural ranges and elevation-based conditions. The results show that: (1) The annual average daytime and nighttime SUHII for these cities were 0.97 ± 0.78 °C (mean ± std) and 0.21 ± 0.87 °C, respectively. For 22% of the cities during the day and 26% at night, the choice of different SUHII estimation methods resulted in the transformation between a surface urban heat island (SUHI) and a surface urban cold island (SUCI) due to the exclusion of rural pixels more than ±50 m from the median urban elevation. Compared with other regions, high-altitude plateau cities exhibited a slightly lower daytime SUHII but a significantly higher nighttime SUHII because of the lower atmospheric pressure in plateau areas, which limits the conduction and retention of heat. Consequently, heat dissipates more quickly at night, increasing SUHII values. (2) The mean ΔSUHII_AD (absolute difference in SUHII values across methods) was 0.51 ± 0.01 °C during the day and 0.44 ± 0.02 °C at night. (3) In high-altitude plateau cities, for all methods, the correlation of the SUHII with influencing factors was stronger, highlighting their sensitivity to both environmental and anthropogenic influences. These results enhance our understanding of plateau UHI dynamics and highlight the importance of considering appropriate rural definitions for cities with varying geographical characteristics. Full article

The demands for growth and prosperity in developing countries have prompted Ogun State to initiate six economic development clusters oriented around its urban areas. However, little attention has been given to the environmental impact of these clusters in relation to temperature change and thermal consequences. Serving as a baseline study for the Abeokuta Cluster, whose implementation is still ongoing, this study analysed the surface urban heat island (SUHI) effects for 2003, 2013, and 2023 to determine whether variations in these effects exist over time. The study utilised satellite imagery from Landsat sensors and the cloud computing power of Google Earth Engine for data collection and analysis. Findings revealed that Abeokuta City experienced varying degrees of high SUHI effects, while the surrounding areas proposed for residential and industrial development in the Abeokuta Cluster showed low SUHI effects. The differences in SUHI effects within Abeokuta City across the years were found to be statistically significant (F_within = 3.158, p = 0.044; F_between = 5.065, p = 0.025), though this was not the case for the Abeokuta cluster as a whole. This study recommends urban planning strategies and policy interventions to combat SUHI effects in Abeokuta City, along with precautionary measures for the Abeokuta Cluster. Full article