Search Results (164)

Urbanization has profoundly transformed land surface morphology and amplified thermal environmental modifications, culminating in intensified urban heat island (UHI) phenomena. Local climate zones (LCZs) provide a robust methodological framework for quantifying thermal heterogeneity and dynamics at local scales. Our study investigated the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXA) as a case study and systematically examined spatiotemporal patterns of LCZs and land surface temperature (LST) from 2002 to 2019, while elucidating mechanisms influencing urban thermal environments and proposing optimized cooling strategies. Key findings demonstrated that through multi-source remote sensing data integration, long-term LCZ classification was achieved with 1,592 training samples, maintaining an overall accuracy exceeding 70%. Landscape pattern analysis revealed that increased fragmentation, configurational complexity, and diversity indices coupled with diminished spatial connectivity significantly elevate LST. Rapid development of the city in the vertical direction also led to an increase in LST. Among seven urban morphological parameters, impervious surface fraction (ISF) and pervious surface fraction (PSF) demonstrated the strongest correlations with LST, showing Pearson coefficients of 0.82 and −0.82, respectively. Pearson coefficients of mean building height (BH), building surface fraction (BSF), and mean street width (SW) also reached 0.50, 0.55, and 0.66. Redundancy analysis (RDA) results revealed that the connectivity and fragmentation degree of LCZ_8 (COHESION8) was the most critical parameter affecting urban thermal environment, explaining 58.5% of LST. Based on these findings and materiality assessment, the regional cooling model of “cooling resistance surface–cooling source–cooling corridor–cooling node” of CZXA was constructed. In the future, particular attention should be paid to the shape and distribution of buildings, especially large, openly arranged buildings with one to three stories, as well as to controlling building height and density. Moreover, tailored protection strategies should be formulated and implemented for cooling sources, corridors, and nodes based on their hierarchical significance within urban thermal regulation systems. These research outcomes offer a robust scientific foundation for evidence-based decision-making in mitigating UHI effects and promoting sustainable urban ecosystem development across urban agglomerations. Full article

Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. To this aim, the city of Granada, Spain, was used as a case study. The city is classified into different Local Climate Zones (LCZs) to determine the area available for retrofitting GRs in built-up areas. A total of 14 Surface Temperature Collection 2 Level-2 images were acquired through Landsat 8–9, while 14 images for spectral indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Building Index (NDBI), and Proportion Vegetation (PV) were calculated from Sentinel-2 in dates coinciding or close to LST images. Additional factors were considered including the sky view factor (SVF) and water distance (WD). The results suggest that Granada has limited suitable areas for retrofitting GRs, and available areas can reduce LST with a moderate impact, at an average of 1.45 °C; however, vegetation plays an important role in decreasing LST. This study provides a methodological example to identify the benefits of implementing GRs in reducing LST in semi-arid cities and recommends a combination of strategies for LST mitigation. Full article

Local Climate Zone (LCZ) classification is essential for urban microclimate modeling and heat mitigation planning. Traditional methods relying on manual sampling face limitations in scalability, objectivity, and handling spatial heterogeneity. This study presents an automated framework for LCZ sample generation, facilitating efficient large-scale LCZ mapping and LCZ-based urban climate analysis and geospatial applications. To this aim, it proposes a dual-path automated framework integrating GIS-driven sample generation to enhance LCZ classification accuracy: a multi-parameter Synergistic Optimization approach for urban LCZs and a Distance-driven Maximum Coverage method for natural LCZs. Specifically, urban samples are selected via multi-objective optimization and Pareto front screening for quality and representativeness, while the selection of natural samples prioritizes spatial coverage and diversity. Combining urban morphological parameters with Sentinel-2 imagery and a Random Forest classifier yielded a final accuracy of 0.95 in our test site, confirming the framework’s effectiveness. Full article

Based on the rapid development of urbanization and the increasing severity of extreme heat disasters caused by global warming, it has become increasingly important to enhance the assessment of heat risk. In this study, in response to the urgent need for fine-grained assessment of urban heat risk in arid zones in the context of climate change, an analytical method of dividing Local Climate Zones (LCZs) into street blocks combined with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) heat risk assessment framework is used in Urumqi, a representative city of China’s arid zones. In addition, Shapley Additive Explanations (SHAP) was introduced to quantitatively resolve the driving mechanisms of heat risk in different types of LCZs. The results show that the study area has the largest proportion of bare soil (LCZ F) (37.6%), which is distributed around the built-up types of LCZs, while water (LCZ G) has a very small proportion (0.39%) and only exists in the outskirts of the city. Heat risk was significantly higher in the urban core than in the peri-urban areas, but LCZ F had a very high hazard due to the unique surface characteristics of arid zones, which elevated the heat risk in the peri-urban desertification fringe; SHAP analyses demonstrated that in arid zones, land surface temperature (LST) became a determinant of heat risk for all low-density built-up types of LCZs. This study proposes targeted mitigation strategies for heat risk in arid zones based on the LCZ framework. Full article

This study models changes in land cover and land use in the intermediate city of Puerto Montt, Chile, up to 2050. Three distinct time periods—1988, 2005, and 2020—were used to examine Puerto Montt’s urban growth during these years. These periods were described using the Local Climate Zones (LCZ) technique. Urban growth scenarios were simulated using the Patch-generating Land Use Simulation (PLUS) model. Using Machine Learning (ML) techniques, this model has been widely utilized to explain how urban growth patterns have evolved based on the dynamics that drive changes in land use and land cover. Three scenarios were developed for this study: Business-As-Usual (BAU) (S1), Urban-Regional Planning (S2), and Conservationist (S3). According to the findings, Puerto Montt is predicted to undergo morphological changes by 2050, shifting from rural areas primarily composed of woods and agricultural land to open, low-density, low-rise areas outside the municipal limits set by the Communal Regulatory Plans. According to this study, Puerto Montt’s relative entropy level was high, ranging from 0.87 to 0.96, with a maximum value of 1.00 by 2050. These findings are anticipated to provide planners and decision-makers with further knowledge on the territorial design of upcoming urban areas. Full article

Understanding the mechanisms driving the urban heat island (UHI) phenomenon is essential for urban sustainability. This study investigated the spatiotemporal dynamics and underlying factors of surface urban heat island (SUHI) in Xiamen. Utilizing the radiation conduction equation, we calculated surface urban heat island intensity (SUHII) for the summers and winters of 2003, 2005, 2010, 2015, and 2020, followed by spatial distribution analysis. The local climate zone (LCZ) method was employed to assess surface morphology and spatial structure in 2010 and 2020. Urban driving factors, including built-up areas, building height, gross domestic product (GDP) per capita, industrial structure, and population density, were analyzed using the Geodetector model to explore their influence on SUHI across seasons. Based on different LCZ types, a more detailed analysis was conducted on SUHI and the performance of influencing factors using Pearson’s correlation. Key findings indicate that (1) the proportion of SUHI areas in built-up LCZ types always exceeds that of natural LCZ types and is more pronounced in the summer than in the winter. (2) In built-up LCZ types, open mid-rise built (LCZ 5) showed the highest average proportion of SUHI areas in the summer (95.95%), and large low-rise built (LCZ 8) had the highest average proportion in the winter (95.28%). In natural LCZ types, bare rock or paved (LCZ E) had the highest average proportion of SUHI areas in both the summer (61.86%) and winter (51.26%), and water (LCZ G) had the lowest average proportion in the summer (6.16%) and winter (4.92%). (3) Significantly, building height and proportion of the secondary industry intensified the SUHI in the summer, with dynamic changes observed during the winter. This study provides more targeted insights into mitigating SUHI in Xiamen and other similar coastal cities. Full article

The rapid urbanization processes across the world can be considered one of the most influential factors in climate change, particularly in metropolitan areas. In South America, the growing population and recurrent non-sustainable or controlled urban land management plans are even increasing the negative consequences of urban heat islands. As a representative case study, Soledad in northern Colombia is an area with recurrent strong wind events, which have caused significant damage to property and human lives, conditioning urban plans. This research aimed to assess the micrometeorological conditions in areas of Soledad, where cyclonic events are highly frequent, to gather essential data on urban planning to understand microclimate changes. We conducted in situ measurements of air temperature, surface temperature, wind speed, relative humidity, and atmospheric pressure across different Local Climate Zones (LCZs). Data were analyzed to assess the impact of urban form, vegetation, and sky openness on microclimatic variations. Our results demonstrated that urban morphology, vegetation cover, and sky openness significantly influenced local microclimates, with lower Sky View Factor (SVF) and higher Leaf Area Index (LAI) values contributing to reduced temperatures and improved airflow. Areas with denser urban canyons exhibited higher temperatures and lower wind speeds, emphasizing the need for strategic urban planning to mitigate heat stress and enhance ventilation. Full article

This study presents a comprehensive framework for evaluating urban heat resilience, incorporating urban climatology models, their characteristics, and simulation programs. Utilizing the local climate zone (LCZ) classification method, this research explores how urban geomorphology influences the thermal characteristics of the area. This study integrates spatial data at different “levels of detail” (LOD), from the meso- to building scales, emphasizing the significance of detailed LOD 3 models acquired through 3D laser scanning. The results demonstrate the ability of these models to identify urban heat islands (UHIs) and to simulate urban planning scenarios, such as increasing green spaces and optimizing building density, to mitigate the UHI effect. The ST3D 3D model of the city of Split, represented using an LOD 2 object model, is utilized for meso- and local-scale analyses, while LOD 3 models derived from laser scanning provided in-depth insights at the building scale. The case studies included the Faculty of Civil Engineering, Architecture, and Geodesy building on the University of Split campus and the old town hall in the densely built city center. This framework highlights the advantages of integrating GIS and BIM technology with urban climate analyses, offering tools for data-driven decision-making and fostering sustainable, climate-resilient urban planning. Full article

Deep learning techniques have garnered significant attention in remote sensing scene classification. However, obtaining a large volume of labeled data for supervised learning (SL) remains challenging. Additionally, SL methods frequently struggle with limited generalization ability. To address these limitations, self-supervised multi-mode representation learning (SSMMRL) is introduced for local climate zone classification (LCZC). Unlike conventional supervised learning methods, SSMMRL utilizes a novel encoder architecture that exclusively processes augmented positive samples (PSs), eliminating the need for negative samples. An attention-guided fusion mechanism is integrated, using positive samples as a form of regularization. The novel encoder captures informative representations from the unannotated So2Sat-LCZ42 dataset, which are then leveraged to enhance performance in a challenging few-shot classification task with limited labeled samples. Co-registered Synthetic Aperture Radar (SAR) and Multispectral (MS) images are used for evaluation and training. This approach enables the model to exploit extensive unlabeled data, enhancing performance on downstream tasks. Experimental evaluations on the So2Sat-LCZ42 benchmark dataset show the efficacy of the SSMMRL method. Our method for LCZC outperforms state-of-the-art (SOTA) approaches. 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

Accelerated urbanization in China poses significant challenges for developing urban planning strategies that are responsive to diverse climatic conditions. This demands a sophisticated understanding of the complex interactions between 3D urban forms and local climate dynamics. This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. By integrating urban morphological inputs from eight representative Chinese metropolises, the model’s efficacy was assessed both qualitatively and quantitatively, achieving an RMSE of 0.187, an R² of 0.78, and a PSNR of 14.592. In a generalized test of urban morphology prediction through LCZ classification, exemplified by the case of Zhuhai, results indicated the model’s effectiveness in categorizing LCZ types. In conclusion, the integration of urban morphological data from eight representative Chinese metropolises further confirmed the model’s potential in climate-adaptive urban planning. The findings of this study underscore the potential of generative algorithms based on LCZ types in accurately forecasting urban morphological development, thereby making significant contributions to sustainable and climate-responsive urban planning. Full article

The impact of urban morphology characteristics on regional thermal environments is a crucial topic in urban planning and climate adaptation research. However, existing studies are often limited to a single dimension and fail to fully reveal the spatiotemporal impact mechanisms of multi-dimensional urban morphology on thermal environments and their connection to regional planning policies. This study focuses on the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), combining quantitative data from landscape pattern indices, land use expansion patterns, and local climate zones (LCZs) derived from 2000 to 2020. By using geographically weighted regression and spatial autocorrelation analysis, we systematically explore the spatiotemporal effects and mechanisms of multi-dimensional urban morphology characteristics on regional thermal effects. We found the following points. (1) Built-up land patch density is significantly positively correlated with LST, with the urban heat island (UHI) effect spreading from core areas to the periphery; this corroborates the thermal environment differentiation features under the “multi-center, networked” spatial planning pattern of the GBA. (2) Outlying expansion mitigates local LST rise through an ecological isolation effect, and infill expansion significantly exacerbates the UHI effect due to high-intensity development, reflecting the differentiated impacts of various expansion patterns on the thermal environment. (3) LCZ spatial distribution aligns closely with regional planning, with the solar radiation shading effect of high-rise buildings significantly cooling daytime LSTs, whereas the thermal storage properties of traditional building materials and human heat sources cause nighttime LST increases; this reveals the deep influence of urban morphology mechanisms, building materials, and human activities on thermal environments. The findings provide scientific support for achieving a win–win goal of high-quality development and ecological security in the GBA while also offering a theoretical basis and practical insights for thermal environment regulation in high-density urban clusters worldwide. Full article

Vegetation phenology greatly impacts urban development and climate change responses. However, research on phenological characteristics in small-scale urban areas is limited, especially concerning their spatiotemporal variations. This study analyzes the phenological indicators SOS, EOS, and LOS of urban vegetation in Suzhou from 2003 to 2022, utilizing Local Climate Zones (LCZs) and Urban–Rural Gradients (URGs) to explore their spatiotemporal variations and correlations with various LCZs and URGs. Subsequently, one-way ANOVA and the Honest Significant Difference (HSD) test are employed to compare the applicability of the two analytical methods. The results show that in Suzhou, SOS, EOS, and LOS exhibit trends of advancement, delay, and extension, with annual averages of 1.02 days earlier, 0.55 days later, and 1.57 days longer. Compared to land cover types, LCZ built types exhibit earlier SOS, later EOS, and longer LOS. As the urban gradient shifts from the city center to the suburbs, vegetation phenology shows gradually delayed SOS, advanced EOS, and shortened LOS. Additionally, phenological differences associated with LCZs are more significant and statistically relevant than those linked to URGs. The study confirms urbanization’s impact on vegetation phenology and provides new insights for future research. The findings assist in plant management, climate regulation, and living environment improvement, contributing to the sustainable development of resilient cities. Full article

This study assesses the accuracy of Local Climate Zone (LCZ) classification and its impact on land surface temperature (LST) analysis in Mediterranean cities using high-resolution ECOSTRESS data. Two classification methods were compared: a Geographic Information System (GIS)-based approach integrating high-resolution geospatial data and an LCZ map derived from WUDAPT. Discrepancies in LCZ classification influenced the spatial distribution of urban forms, with WUDAPT overestimating LCZ 6 (open low-rise) and LCZ 8 (large low-rise) while underrepresenting more compact urban types. LST analysis revealed distinct thermal responses between Milan and Lecce, underscoring the influence of urban morphology and local climate. Densely built zones (LCZ 2, LCZ 5) exhibited the highest temperatures, especially at night, while LCZ 8 also retained significant heat. Milan’s dense urban areas experienced pronounced nighttime overheating, whereas Lecce showed a clear daytime temperature gradient, with historic districts (LCZ 2) maintaining lower LST the light-colored and high thermal capacity of building materials. A Kruskal–Wallis test confirmed significant differences between the GIS-based and WUDAPT-derived LCZ maps, highlighting the impact of classification methodology and spatial resolution on LST analysis. These findings emphasize the need for multi-scale approaches to urban climate adaptation and mitigation, providing valuable advice for urban planners and policymakers in development of sustainable and climate-resilient cities. This research is also among the first to integrate ECOSTRESS data with LCZ maps to examine LST variations across spatial and temporal scales. Full article

Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques to optimize urban morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures to generate high-fidelity urban morphology models aligned with local climate zones (LCZs), enhancing their applicability to urban climate studies. This research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples to train the generative models. Quantitative evaluations demonstrated that the integration of CycleGAN with Pix2Pix substantially improved structural fidelity and realism in urban morphology synthesis, achieving a peak Structural Similarity Index Measure (SSIM) of 0.918 and a coefficient of determination (R²) of 0.987. The total adversarial loss in Pix2Pix training stabilized at 0.19 after 811 iterations, ensuring high convergence in urban structure generation. Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. By incorporating LCZ data, the proposed framework successfully bridges urban morphology modeling with climate-responsive urban planning, enabling adaptive design strategies for mitigating UHI effects. This study integrates Pix2Pix and CycleGAN architectures to enhance the realism and structural fidelity of urban morphology generation, while incorporating the LCZ classification framework to produce urban forms that align with specific climatological conditions. Compared to the model trained by Pix2Pix coupled with LCZ alone, the approach offers urban planners a more precise tool for designing climate-responsive cities, optimizing urban layouts to mitigate heat island effects, improve energy efficiency, and enhance resilience. Full article