Search Results (208)

Surface urban heat island (SUHI) behavior in dryland cities can reverse sign when hot, bare peripheral surfaces exceed urban-core temperatures, yet long monthly records across contrasting climates remain scarce. This study reconstructs land surface temperature (LST) and standardized urban-core–periphery thermal contrasts for five Moroccan cities from 1995 to 2024 using Landsat, ERA5-Land, NDVI, and the annual MODIS MCD12Q1 land-cover product. The diagnostic analysis of SUHI and Urban Heat Sink (UHS) occurrence is explicitly separated from the forecasting component: SARIMA, Random Forest, XGBoost, and LSTM models predict monthly LST rather than UHI intensity. Tangier exhibits a persistent positive SUHI (mean: 4.6 °C; UHS frequency: 3.0%), whereas Béni Mellal, Ifrane, Laayoune, and Taza have negative mean contrasts of −1.4, −2.4, −1.0, and −0.7 °C, respectively; Ifrane records the highest UHS frequency (90.5%). No city shows a statistically significant monotonic UHI trend, and Sen’s slopes remain close to zero. Forecasting skill is city-dependent: SARIMA performs best in Ifrane and Tangier (R² = 0.94 and 0.93), while Random Forest performs best in Taza and Laayoune (R² = 0.90 and 0.79). Approximate 95% empirical uncertainty half-widths derived from held-out RMSE range from ±3.78 to ±15.48 °C, indicating substantial model- and city-specific uncertainty. The results support the hypothesis that local climate and peripheral land-cover context can outweigh urban fraction as controls on SUHI sign and amplitude. Generalization is limited by fixed reference distances, possible urbanization of the outer ring, clear-sky satellite sampling, the distinction between LST and air temperature, and the 36-month recursive forecasting chain used to display the 2026–2027 outlook. Full article

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation. Full article

Warsaw experiences significant urban heat island (UHI) effects driven by low-albedo surfaces and urban geometry, which pose ongoing challenges for public health and climate adaptation. This study evaluates daytime SUHI intensity at satellite acquisition time across the entire city to provide a comparison of two acquisition dates during heatwaves. Utilizing Landsat 7 and Landsat 9 satellite imagery from July 2015 and July 2022, the research compares six distinct SUHII calculation methods, including spectral indices, statistical normalizations, and area-based temperature differences, as minimum SUHII values differed significantly between the two observations (shifting from approximately −13.8 °C to −7.7 °C). This indicates that suburban areas can become thermally similar to the city due to rapid land conversion and decreased evaporative cooling of vegetation during severe heat. Average intensities calculated via the SUHII 4 method reached 5.80 °C in 2015 and 2.07 °C in 2022. Under the criteria considered in this case study—interpretability, explicit physical units, treatment of water bodies, data requirements, and spatial consistency—SUHII 4 was the most suitable of the six tested formulations for the Warsaw analysis. Conversely, dimensionless spectral indices and purely statistical approaches are not recommended due to interpretative limitations. Full article

The urban heat island (UHI) refers to higher air temperatures (T_air) in urban areas than in surrounding rural environments, while the surface urban heat island (SUHI) describes analogous differences in land surface temperature (LST). This study presents a long-term assessment of UHI and SUHI in Modena, Italy, combining meteorological T_air observations with Landsat-derived LST from 188 daytime and 19 nighttime summer scenes (1985–2023). Four indicators—magnitude and range 1 of overall thermal variability and magnitude and range 2 of urban–rural thermal excess—were applied in parallel to LST and T_air to characterize the intensity and spatial variability of thermal conditions within a consistent daytime/nighttime framework. Results indicate significant long-term increases in summer LST, with daytime warming rates of 0.26 °C yr⁻¹ (urban) and 0.27 °C yr⁻¹ (rural). Daytime urban–rural LST differences ranged from 4 to 6 °C; nighttime differences were smaller (1–3 °C). Daytime T_air urban–rural differences were weak and not statistically significant, whereas nighttime T_air showed a clearer urban warming signal. Nighttime LST correlated more closely with T_air (r = 0.49–0.52 across indicators) than daytime LST, and nighttime LST showed strong correlations with T_air in both urban and rural areas (r = 0.96–0.98). Daytime imagery better captures SUHI spatial intensity, whereas nighttime observations provide a more consistent surface-to-atmosphere thermal link, highlighting the value of integrating satellite LST with in situ T_air for integrated UHI and SUHI assessment in medium-sized cities. Full article

The escalating climate crisis presents a profound challenge to global environmental equilibrium, with rapid urbanization acting as a primary catalyst for land-use transformation. This study investigates the intricate relationship between land use and land cover changes (LULC) and the intensification of the Surface Urban Heat Island (SUHI) effect within the Shillong Planning Area (SPA). By integrating remote sensing data with advanced geospatial modeling and machine learning architectures which include Random Forest (RF), Support Vector Machine (SVM), and XGBoost, the research provides a comprehensive analysis of environmental shifts from 2000 to 2024, with predictive projections extending to 2034 and 2044. The analysis reveals a significant expansion in the built environment, with the Normalized Difference Built-up Index (NDBI) rising from 0.17 to 0.26. This urban growth has come at the expense of ecological health, as evidenced by a decline in the Normalized Difference Vegetation Index (NDVI) from a peak of 0.87 down to 0.74. A strong negative correlation between vegetative density and Land Surface Temperature (LST) underscores the critical role of green infrastructure in regional climate regulation. SUHI projections using the RF model, which achieved an Area Under the Curve (AUC) of 0.868, estimate SUHI values of 6.02 °C for 2034 and 6.66 °C for 2044. Predicted LULC scenarios for 2034 and 2044 suggest continued urban expansion, likely intensifying thermal stress. The application of predictive modeling through machine learning provides a robust framework to inform climate-resilient urban planning and sustainable land management. Full article

The intensification of heat waves and the increase in average temperatures, particularly evident in the Mediterranean context, underline the urgency of strengthening sustainable spatial planning, introducing concrete tools to understand and manage how urban form, land-use and local microclimate interact with each other. In this direction, the paper proposes an integrated methodological path to map territorial exposure to surface heat stress in densely urbanised contexts, applying it to the case study of the Caserta conurbation. The approach uses several levels of analysis: (i) the estimation of the Normalised Difference Vegetation Index (NDVI) and the calculation of Land Surface Temperature (LST) from the Landsat series (1987–2022), using radiance/reflectance measurements and deriving LST from the Top-of-Atmosphere Brightness Temperature (BT) and from the Land Surface Emissivity (LSE); (ii) the consistency check of satellite-derived surface temperature using meteorological station data from the archive of the Multi-Risk Functional Center of the Civil Protection of the Campania Region, fed by the measurements of the sensors installed in the area by the Campania Regional Environmental Protection Agency (ARPAC); (iii) the evaluation of the intensity of the Surface Urban Heat Island (SUHI) as the difference between LST values extracted from paired urban and non-urbanised reference areas; (iv) the verification of the relationship between NDVI and LST by linear regression, with NDVI as the explanatory variable and LST as the dependent variable, showing a statistically consistent inverse relationship. The results support a multi-scalar reading of the intervention priorities, highlighting that the potential relevance of nature-based solutions (NBS) and cooling strategies varies according to local urban morphology, geographical configuration, and degree of soil sealing, requiring context-specific planning evaluations. Full article

Surface urban heat island intensity is shaped not only by land-cover composition but also by the spatial heterogeneity of urban surfaces. This study evaluates whether Shannon entropy derived from Dynamic World class probabilities can serve as a robust indicator of pointwise SUHI intensity across seven major Romanian cities. Summer daytime Landsat 8/9 observations for 2021–2025 were harmonized into multi-year median land surface temperature composites, while Dynamic World probabilities were used to compute normalized Shannon entropy at 90, 150, 300, and 600 m aggregation windows. SUHI was defined relative to a rural reference whose delineation was examined through a multi-parameter sensitivity analysis, after which entropy–SUHI relationships were modeled using generalized additive models with and without an additional spatial smooth. Across all seven cities, the entropy–SUHI relationship was consistently negative, with higher entropy values tending to be associated with lower local thermal excess. The best-supported models were usually obtained at 150 m and more broadly within the 150–300 m range, while very coarse aggregation weakened performance. Spatially adjusted models explained 57.2–82.4% of SUHI deviance, showing that entropy is consistently associated with a stable but partial component of intra-urban thermal variability. Alternative tied-best rural delineations mainly shifted the SUHI baseline and left the fitted entropy response essentially unchanged. Our findings support probability-based entropy as a reliable, scale-sensitive descriptor of urban surface mixture relevant to intra-urban thermal patterning across diverse geographical and climatic settings. Full article

Rapid urbanization in tropical Southeast Asia is transforming pervious land into impervious surfaces, intensifying the surface urban heat island (SUHI) effect and increasing the need for consistent urban thermal monitoring. This study assesses how impervious surface area (ISA) expansion relates to the urban thermal environment across five tropical megacities (Bangkok, Jakarta, Manila, Kuala Lumpur, and Ho Chi Minh City). AlphaEarth geospatial foundation model embeddings were used to reduce observation gaps caused by persistent cloud-cover, while MODIS land surface temperature (LST) was used to quantify the thermal response. We compared AlphaEarth classification against conventional Sentinel-2/NDVI approaches and an additional fairer annual Sentinel-2 full-band-plus-index Random Forest baseline, quantified ISA expansion for 2017–2024, and related ISA fraction to dry-season LST at 1 km resolution. Repeated random-holdout tests based on Google Earth Engine samples showed AlphaEarth mean IoU = 0.866 (95% CI: 0.857–0.875), compared with 0.758 (0.749–0.767) for the annual Sentinel-2 full-band-plus-index baseline and 0.686 (0.674–0.698) for the best single-date 5-index baseline. Spatial-block holdout tests gave similar but slightly lower values (AlphaEarth IoU = 0.859; annual Sentinel-2 baseline = 0.747; best single-date baseline = 0.673). Ho Chi Minh City experienced the fastest ISA expansion (+11.0 percentage points; slope = 1.48 pp yr⁻¹, 95% CI: 1.06–1.91), whereas Bangkok reached the highest ISA fraction (65.1%). ISA fraction and LST were consistently and positively associated across cities and years (Pearson r = 0.748–0.900), and mean SUHI intensity during 2017–2024 ranged from 4.01 °C in Bangkok to 8.51 °C in Manila. These results indicate that foundation model embeddings can support cloud-resilient mapping of impervious surface change and thereby improve assessment of tropical urban thermal environments, while also highlighting the need for independent ground-truth validation. Full article

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning. Full article

Nocturnal urban heat presents significant but understudied risks within tropical megacities, where high humidity and heat storage in built-up areas prevent nighttime thermal recovery and intensify chronic heat stress. This study investigates the nocturnal surface urban heat island (SUHI) dynamics in the Bangkok Metropolitan Region (BMR) over two decades (2003–2023) with a daytime SUHI comparative baseline. We examined long-term thermal variations using MODIS land surface temperature data and Landsat urban–rural classification. The results demonstrate an increase in nighttime land surface temperature (LST) of 0.109, with nocturnal SUHI proving more persistent than its daytime counterpart with a temperature difference as high as 2.0 °C between urban and rural areas during the night. While daytime SUHI peaked at 6.3 °C in April 2011, with the strongest effects during April–May, nocturnal SUHI exhibited less seasonal variability but sustained elevated values throughout the year. Heat-retaining nocturnal hotspots have expanded from central Bangkok to newly developed urban areas. Cross-correlation analysis suggests that El Niño–Southern Oscillation (ENSO) strongly modulates SUHI anomalies, with maximum cross-correlations for a time lag of 3 months. These results suggest the need for urban adaptation strategies that specifically address nocturnal heat, as well as design strategies such as improved ventilation, high-emissivity materials, green infrastructure allowing evapotranspiration, and cooling centers for vulnerable populations to enhance thermal resilience across the BMR. Full article

Rapid urbanization has intensified the Surface Urban Heat Island (SUHI) effect, which poses particular challenges for coastal cities where marine environments, climatic regulation, and distinctive urban morphology interact in complex ways. Current research on coastal SUHI remains limited, especially in terms of systematic analyses using the Local Climate Zone (LCZ) framework. Key gaps include insufficient cross-climate comparisons and limited understanding of spatial differentiation patterns linked to LCZ-based SUHI dynamics. This study employs LCZ classification to analyze coastal cities across diverse climatic backgrounds, integrating Pearson’s correlation analysis and coastal distance gradient zoning to investigate the spatio-temporal distribution and influencing factors of Surface Urban Heat Island Intensity (SUHII). The findings reveal that: (1) SUHII exhibits a distinct spatial pattern, with elevated intensities in built-up areas and reduced values in natural zones, alongside seasonally differentiated variations across climate zones. (2) The normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) emerge as dominant drivers, exerting heating and cooling effects, respectively. Elevation alleviates SUHII, whereas anthropogenic factors dominate during summer. (3) Coastal SUHII is governed by dual regulatory mechanisms: land–sea interactions modulate spatial patterns, with NDVI cooling and NDBI heating effects amplifying with distance from the coastline, while nearshore marine regulation suppresses heat accumulation. Additionally, cities across different climatic zones exhibit distinct thermal responses, with vegetation cooling efficiency and building-induced heating intensity showing clear latitudinal gradients. These findings advance understanding of multi-scale drivers of coastal SUHI and provide a scientific basis for climate-adaptive urban planning strategies that optimize coastal morphology. Full article

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies. Full article

Regional management of the urban thermal environment is essential for sustainable development. However, both the surface urban heat island (SUHI) spatiotemporal patterns and driving mechanisms across humid–arid regions remain uncertain. Therefore, 329 cities from various humid–arid regions were selected to investigate the interannual, seasonal, and diurnal distribution characteristics of SUHIs across regions. By constructing six-dimensional influencing factors and using CatBoost-SHAP and SEM methods, the contributions and action pathways of these factors to SUHIs were analyzed across humid–arid regions. The influence mechanisms, differences in feature importance, and similarities and discrepancies in action pathways were thoroughly examined. The findings are as follows: 1. During the day, higher SUHII values occur in humid and semihumid regions, exceeding those in arid and semiarid regions by 1.521 and 0.921, respectively. At night, arid and semiarid regions exhibit UHI effects (SUHII > 0). The SUHI distribution across humid–arid regions demonstrates seasonal variations. 2. ΔSA and ΔNDVI are stable dominant influencing factors across all regions. The contribution rank varies along the humid–arid region: Pollution factors are more important in arid and semiarid regions, whereas surface features and 2D/3D dominate in humid and semihumid regions at night. 3. SUHI regulation by influencing factors across humid–arid regions follows both similar paths and regional variations. This study reveals the SUHI distribution across humid–arid regions and provides reference data for regional thermal environment management. Full article

Rapid urban growth in South Indian coastal cities such as Chennai has intensified the Urban Heat Island (UHI) effect, with paved parking lots, walkways, and open spaces acting as major heat reservoirs. This study specifically compares conventional construction materials with natural and low-thermal-inertia alternatives to evaluate their relative ability to mitigate Surface Urban Heat Island (SUHI) effects. Unlike previous studies that examine isolated materials or single seasons, this pilot provides a unified, multi-season comparison of nine urban surfaces, offering new evidence on their comparative cooling performance. To assess practical mitigation strategies, a field pilot was conducted using nine surface types commonly employed in the region—concrete, interlocking tiles, parking tiles, white cooling tiles, white-painted concrete, natural grass, synthetic turf, barren soil, and a novel 10% coconut-shell biochar concrete. The rationale of this comparison is to evaluate how conventional, reflective, vegetated, and low-thermal-inertia surfaces differ in their capacity to reduce surface heating, thereby identifying practical, material-based strategies for SUHI mitigation in tropical cities. Surface temperatures were measured at four times of day (pre-dawn, noon, sunset, night) across three months (winter, transition, summer). Results revealed sharp noon-time contrasts: synthetic turf and barren soil peaked above 45–70 °C in summer, while reflective coatings and natural grass remained 25–35 °C cooler. High thermal-mass materials such as concrete and interlocked tiles retained heat into the evening, whereas grass and reflective tiles cooled rapidly, lowering late-day and nocturnal heat loads. Biochar concrete performed thermally similarly to conventional concrete but offered co-benefits of ~10% cement reduction, carbon sequestration, and sustainable reuse of locally abundant coconut shell waste. Full article

The COVID-19 pandemic presented an unprecedented opportunity to assess the environmental effects of reduced anthropogenic activity on urban climates. This study investigates the impact of COVID-19-induced lockdowns on land surface temperature (LST) and the intensity of the surface urban heat island (SUHI) in Nowshera District, Khyber Pakhtunkhwa Province, Pakistan, which is experiencing rapid urbanization. Using Landsat 8/9 imagery, we assessed thermal changes across three periods: pre-lockdown (April 2019), during lockdown (April 2020), and post-lockdown (April 2021). Remote sensing indices, including NDVI and NDBI, were applied to evaluate the relationship between land cover and LST. Our results show a significant reduction in average LST during lockdown, from 31.38 °C in 2019 to 25.34 °C in 2020, a 6 °C decrease. Urban–rural LST differences narrowed from 9 °C to 6 °C. A one-way ANOVA confirmed significant differences in LST across the three periods (F (2, 3) = 3691.46, p < 0.001), with Tukey HSD tests indicating that the lockdown period differed significantly from both the pre- and post-lockdown periods (p < 0.001). SUHI intensity fell from 35.10 °C to 28.89 °C during lockdown, then rebounded to 35.37 °C post-lockdown. The indices analysis shows that built-up and rangeland areas consistently recorded the highest LST (e.g., 35.36 °C and 37.09 °C in 2021, respectively), while vegetation and water bodies maintained lower temperatures (34.68 °C and 32.69 °C in 2021). NDVI confirmed the cooling effect of green areas, while high NDBI values correlated with increased LST in urban areas. These findings underscore the impact of human activity on urban heat dynamics and highlight the role of sustainable urban planning and green infrastructure in enhancing climate resilience. By exploring the relationships among land cover, anthropogenic activity, and urban climate resilience, this research offers policymakers and urban planners’ valuable insights for developing adaptive, low-emission cities amid rapid urbanization and climate change. Full article