Search Results (198)

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

The rapid urbanization process has exacerbated the urban heat island (UHI) effect in megacities like Shanghai. Urban green infrastructure (UGI) plays a crucial role in mitigating the UHI effect; however, its cooling capacity is subject to various urban land development patterns. This study examined 39 typical locations in downtown Shanghai to measure how urban land development patterns affect the UGI’s cooling capacity. Using a data-driven framework, we identified 12 key influencing factors and explored 4 interactions for building three regression models: multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR). For each of these models, we considered two variations: a basic model neglecting interactions and an enhanced model including interactions. Results showed that all enhanced models outperformed their basic counterparts. On average, the enhanced models increased their predictive power by 14.59% for training data and 32.15% for testing data. Additionally, among the three enhanced models, the SVR-enhanced models show the best performance, followed by the PLSR-enhanced models. Their mean predictive power increased by 8.33−37.43% for training data and 31.77−43.558% for testing data vs. the MLR-enhanced models. Overall, our findings revealed that impervious surfaces contribute positively to urban warming, while UGI acts as a negative contributor. Moreover, we highlighted how urban land development metrics, particularly the UGI’s percentage and spatial arrangements in relation to adjacent buildings, significantly affect the thermal environment. The findings can offer valuable insights for urban planners and decision-makers involved in managing UGI and developing strategies for UHI mitigation and urban climate adaptation. Full article

Urban heat island conditions increase heat exposure and constrain safe outdoor activities. Urban forests can mitigate thermal loads; however, stand morphology can produce divergent microclimates. We aimed to quantify how stand type (open vs. closed), season (spring, summer, fall), and activity intensity (MET 1.0–6.0) jointly modulate thermal comfort and to identify season-specific optimal MET levels in an urban forest in Daejeon, Republic of Korea. We combined site-specific 3D canopy modeling with hourly Predicted Mean Vote (PMV) simulations driven by AMOS tower data (2023–2024). Comfort was defined as |PMV| ≤ 0.5. Analyses included seasonal means, Cliff’s delta, and generalized estimating equation logistic models to estimate the SITE × SEASON × MET interactions and predict comfort probabilities. Across most seasons and MET levels, C1 was more comfortable than C2. However, at MET 1.0 in summer, the pattern was reversed, which may reflect the canopy shading and associated decreases in mean radiant temperature (MRT) of C2. Comfort peaked at MET 2.0–3.0 and declined sharply at ≥4.5 MET. The three-way SITE × SEASON × MET interaction was significant (p < 0.001). The season-specific optimal MET values under our boundary conditions were 3.0 (spring), 2.0–2.5 (summer), and 3.0 (fall). These simulation-based PMV-centered findings represent model-informed tendencies. Nevertheless, they support actionable guidance: prioritize high-closure stands for low-intensity summer use, leverage open stands for low-to-moderate activities in spring and fall, and avoid high-intensity programs during warm periods. These results inform the programming and design of urban-forest healing and recreation by matching stand type and activity intensity to season to maximize comfortable hours. Full article

The formation, development, and spatial distribution of CUHIs are influenced by urban spatial heterogeneity, yet the scale and seasonal dependencies of the effects of urban morphology modulation on CUHIs have not been fully explored, needing further study. Based on multi-source data for the Yangtze-Huaihe River Valley (YHRV), this study employs the XGBoost model to systematically investigate the effects of two-dimensional (2D)/three-dimensional (3D) urban morphological indicators on CUHIs and their inherent scale–seasonal dependencies. Results show significant provincial heterogeneity in YHRV’s CUHIs: Shanghai exhibits the highest CUHI intensity (CUHII) across all seasons, with a peak of 1.55 °C in winter, followed by Zhejiang and Jiangsu. Seasonally, winter CUHII averages 0.6–0.8 °C (the highest), followed by autumn, while spring and summer have lower values. The effect of the modulation of urban morphology on CUHIs exhibits distinct spatiotemporal dependence: in winter and autumn, CUHII is mainly dominated by the percentage of landscape (PLAND) and largest patch index (LPI) at the 4 km buffer scale (correlation coefficients r = 0.475 and 0.406 for winter); in spring and summer, the 2 km buffer scale shows a more balanced regulatory role of multiple urban morphological indicators. Notably, 2D indicators of urban morphology are consistently more influential in regulating CUHIs than 3D indicators. The Hefei station case effectively validates the model’s sensitivity to changes in urban morphology. This study provides a quantitative basis for season–scale collaborative regulation of urban thermal environments in the YHRV. Future research will integrate climatic factors into XGBoost via screening, reconstruction, and interaction quantification to enhance its predictability for transient heat island processes. Full article

Gamified virtual reality (VR) environments are increasingly used to enhance engagement and learning, yet most designs still adopt a one-size-fits-all approach that overlooks motivational diversity. The HEXAD framework, which classifies users into six player types, provides a promising lens for addressing this gap, but its predictive validity in immersive VR remains contested. This study investigates how HEXAD profiles shape navigation, time allocation, and engagement dynamics in an open-ended gamified VR environment. Thirty-two undergraduate participants, all regular gamers, completed the HEXAD scale before exploring a VR setting with five thematic islands without predefined tasks. System logs and screen recordings captured first island choices, sequential visit patterns, and time spent, and data were analyzed using qualitative pattern analysis alongside nonparametric statistics. The results showed significant associations between player type and initial choices, with Players favoring Game Island, Socialisers choosing Social Island, Philanthropists engaging most with Library, and Achievers and Free Spirits drawn to Experience. Kruskal–Wallis tests of exploration breadth revealed moderate effect sizes across types, though significance was limited by sample size. Three emergent strategies, Focused Explorers, Wanderers, and Strategic Switchers, captured motivational orientations beyond single metrics, while heat map visualizations highlighted clustering around Game and Experience Islands. By situating these findings within flow theory and inclusive–adaptive design principles, this study demonstrates how behavioral traces can link motivational typologies with embodied interaction. Overall, the results advance debates on HEXAD’s robustness and contribute practical pathways for developing adaptive, motivation-sensitive VR environments that support sustained engagement and inclusivity. Full article

Extreme sea surface temperature (SST) events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), severely affect warm water coral reefs. However, further study is required on their historical and future spatiotemporal patterns, driving mechanisms, and impacts in coral reef regions. This study analyzed the spatiotemporal patterns in MHWs/MCSs for the periods 1982–2022 and 2023–2070 using ten indices based on OISSTv2.1 and CMIP6 data, respectively, identified key MHW drivers via four machine learning methods (Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting) and SHAP values (Shapley Additive Explanations), and then examined their relationship with coral coverage across ten global marine regions. Our results revealed that (1) MHWs are not only increasing in their average intensity but also becoming more extreme, while MCSs have declined. More MHW days are observed in regions like the Red Sea, the Persian Gulf, and the South Pacific Islands, with increases of up to 28 days per decade. (2) Higher-latitude coral reefs are experiencing more severe MHWs than equatorial regions, with up to 1.24 times more MHW days, emphasizing the urgent need to protect coral refuges. (3) MHWs are projected to occur nearly year-round by 2070 under scenario SSP5–8.5. The area ratio of MHWs to MCSs is expected to rise sharply from 2040 onward, reaching approximately 100-fold under the SSP2–4.5 scenario and 196-fold under the SSP5–8.5 scenario, particularly in the Marshall Islands and Caribbean Sea regions. (4) The coefficient of variation (CV) of annual temperature, annual ocean heat content, and monthly temperature were the top three factors driving MHW intensity. We emphasize that future MHW predictions should focus more on the CV of forecasting indicators rather than just the climate means. (5) Coral coverage exhibited post-mortality processes following MHWs, showing a strong negative correlation (r = −0.54, p < 0.01) with MHWs while demonstrating a significant positive correlation (r = 0.6, p < 0.01) with MCSs. Our research underscores the sustained efforts to protect and restore coral reefs amid escalating climate-induced stressors. Full article

To examine the integrated influence of urban spatial form on the urban heat island (UHI) effect, this study selects the area within Beijing’s Fifth Ring Road as a case study. A multiscale grid system is established to quantify fourteen two- and three-dimensional morphological indicators. A Random Forest algorithm is employed to assess the relative importance of each factor. The optimal analytical scale for each key variable is then identified, and its nonlinear relationship with land surface temperature (LST) is analyzed at that scale. The main findings are as follows: (1) The Random Forest model achieves the highest predictive accuracy at a 600 m scale, significantly outperforming traditional linear models by effectively addressing multicollinearity. This suggests that machine learning offers robust technical support for UHI research. (2) Form variables exhibit distinct scale dependencies. Two-dimensional indicators dominate at medium to large scales, while three-dimensional indicators are more influential at smaller scales. Specifically, the mean building height is most significant at the 150 m scale, the standard deviation of building height at 300 m, and the impervious surface fraction at 600–1200 m. (3) Strong nonlinear effects are identified. The bare soil fraction below 0.12 intensifies surface warming; the water body fraction between 0.20 and 0.35 provides the strongest cooling; plant coverage offers maximum cooling between 0.25 and 0.45; building density cools below 0.3 buildings/hm² but contributes to warming beyond this threshold; building coverage ratio generates the greatest warming between 0.08 and 0.32; height variability provides optimal cooling between 8 m and 40 m; and mean building height shows a positive correlation with LST below 6 m but a negative one above that height. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable. Full article

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

Rapid population growth and urbanization have greatly impacted the environment, causing a sharp rise in city temperatures—a phenomenon known as the Urban Heat Island (UHI) effect. While previous research has extensively examined the influence of land use characteristics on urban heat islands, their impact on community demographics and UHI severity remains unexplored. Moreover, most previous studies have focused on specific locations, resulting in relatively homogeneous environmental data and limiting understanding of variations across different areas. To address this gap, this paper develops ensemble learning models to predict UHI severity based on demographic, meteorological, and land use/land cover factors in Midwestern United States. Analyzing over 11,000 data points from urban census tracts across more than 12 states in the Midwestern United States, this study developed Random Forest and XGBoost classifiers achieving weighted F1-scores up to 0.76 and excellent discriminatory power (ROC-AUC > 0.90). Feature importance analysis, supported by a detailed SHAP (SHapley Additive exPlanations) interpretation, revealed that the difference in vegetation between urban and rural areas (DelNDVI_summer) and imperviousness were the most critical predictors of UHI severity. This work provides a robust, large-scale predictive tool that helps urban planners and policymakers identify key UHI drivers and develop targeted mitigation strategies. Full article

Ornamental plants play a crucial role in the mitigation of urban heat islands. Recent decades have seen an increased frequency of abnormal climatic events like warm springs, but how these climatic events impact plant phenology in ornamental plants in urban areas is unclear. This study examines how climate fluctuations affect the flowering patterns (1963–2018) and thermal requirements of 28 woody ornamental species in Xi’an, a principal city in Central China. Years were classified as cold (<13.3 °C), normal (between 13.3 and 17.2 °C), or warm (>17.2 °C) based on March–May temperatures. The results show that the first flowering dates (FFDs) advanced by 10.63 days in warm years but were delayed by 6.14 days in cold years compared to normal years. Notably, thermal requirements (5 °C threshold) were 11.3% higher in warm years (343.05 vs. 308.09 °C days) and 9.4% lower in cold years (279.19 °C days), likely due to reduced winter chilling accumulation in warm conditions. While thermal time models accurately predicted FFDs in normal years (error: 0.33–1.37 days), they showed systematic biases in abnormal years—overestimating advancement by 1.56 days in warm years and delays by 3.42 days in cold years. These findings highlight that the current phenological models assuming fixed thermal thresholds may significantly mispredict flowering times under climate variability. Our results emphasize the need to incorporate dynamic thermal requirements and chilling effects when forecasting urban plant responses to climate change, particularly for extreme climate scenarios. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

Urban morphology critically shapes environmental performance, yet few studies integrate multiple sustainability targets within a unified modeling framework for its design optimization. This study proposes a data-driven, multi-scale approach that combines parametric simulation, artificial neural network-based multi-task learning (MTL), SHAP interpretability, and NSGA-II optimization to assess and optimize urban form across 18 districts in Hong Kong. Four key sustainability targets—photovoltaic generation (PVG), accumulated urban heat island intensity (AUHII), indoor overheating degree (IOD), and carbon emission intensity (CEI)—were jointly predicted using an artificial neural network-based MTL model. The prediction results outperform single-task models, achieving R² values of 0.710 (PVG), 0.559 (AUHII), 0.819 (IOD), and 0.405 (CEI), respectively. SHAP analysis identifies building height, density, and orientation as the most important design factors, revealing trade-offs between solar access, thermal stress, and emissions. Urban form design strategies are informed by the multi-objective optimization, with the optimal solution featuring a building height of 72.11 m, building centroid distance of 109.92 m, and east-facing orientation (183°). The optimal configuration yields the highest PVG (55.26 kWh/m²), lowest CEI (359.76 kg/m²/y), and relatively acceptable AUHII (294.13 °C·y) and IOD (92.74 °C·h). This study offers a balanced path toward carbon reduction, thermal resilience, and renewable energy utilization in compact cities for either new town planning or existing district renovation. Full article