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27 pages, 28315 KiB  
Article
Morphological Optimization of Low-Density Commercial Streets: A Multi-Objective Study Based on Genetic Algorithm
by Hongchi Zhang, Liangshan You, Hong Yuan and Fei Guo
Sustainability 2025, 17(16), 7541; https://doi.org/10.3390/su17167541 - 21 Aug 2025
Viewed by 166
Abstract
Through their open space layout, rich green configuration and low floor area ratio (FAR), low-density commercial blocks show significant advantages in creating high-quality outdoor thermal comfort (Universal Thermal Climate Index, UTCI) environment, reducing regional energy consumption load (building energy consumption, BEC) potential, providing [...] Read more.
Through their open space layout, rich green configuration and low floor area ratio (FAR), low-density commercial blocks show significant advantages in creating high-quality outdoor thermal comfort (Universal Thermal Climate Index, UTCI) environment, reducing regional energy consumption load (building energy consumption, BEC) potential, providing pleasant public space experience and enhancing environmental resilience, which are different from traditional high-density business models. This study proposes a workflow for morphological design of low-density commercial blocks based on parametric modeling via the Grasshopper platform and the NSGA-II algorithm, which aims to balance environmental benefits (UTCI, BEC) and spatial efficiency (FAR). This study employs EnergyPlus, Wallacei and other relevant tools, along with the NSGA-II algorithm, to perform numerical simulations and multi-objective optimization, thus obtaining the Pareto optimal solution set. It also clarifies the correlation between morphological parameters and target variables. The results show the following: (1) The multi-objective optimization model is effective in optimizing the three objectives for block buildings. When compared to the extreme inferior solution, the optimal solution that is closest to the ideal point brings about a 33.2% reduction in BEC and a 1.3 °C drop in UTCI, while achieving a 102.8% increase in FAR. (2) The impact of design variables varies across the three optimization objectives. Among them, the number of floors of slab buildings has the most significant impact on BEC, UTCI and FAR. (3) There is a significant correlation between urban morphological parameters–energy efficiency correlation index, and BEC, UTCI, and FAR. Full article
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33 pages, 25046 KiB  
Article
Urban Stadiums as Multi-Scale Cool-Island Anchors: A Remote Sensing-Based Thermal Regulation Analysis in Shanghai
by Yusheng Yang and Shuoning Tang
Remote Sens. 2025, 17(16), 2896; https://doi.org/10.3390/rs17162896 - 20 Aug 2025
Viewed by 261
Abstract
The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal [...] Read more.
The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal thermal characteristics of eight representative stadiums in central Shanghai and the Pudong New Area from 2018 to 2023. A dual-framework approach is proposed: the Stadium-based Urban Island Regulation (SUIR) model conceptualizes stadiums as active cooling agents across micro to macro spatial scales, while the Multi-source Thermal Cognition System (MTCS) integrates multi-sensor satellite data—Landsat, MODIS, Sentinel-1/2—with anthropogenic and ecological indicators to diagnose surface temperature dynamics. Remote sensing fusion and machine learning analyses reveal clear intra-stadium thermal heterogeneity: track zones consistently recorded the highest land surface temperatures (up to 37.5 °C), while grass fields exhibited strong cooling effects (as low as 29.8 °C). Buffer analysis shows that cooling effects were most pronounced within 300–500 m, varying with local morphology. A spatial diffusion model further demonstrates that stadiums with large, vegetated buffers or proximity to water bodies exert a broader regional cooling influence. Correlation and Random Forest regression analyses identify the building volume (r = 0.81), NDVI (r = −0.53), nighttime light intensity, and traffic density as key thermal drivers. These findings offer new insight into the role of stadiums in urban heat mitigation and provide practical implications for scale-sensitive, climate-adaptive urban planning strategies. Full article
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26 pages, 5281 KiB  
Article
Spatial Drivers of Urban Industrial Agglomeration Using Street View Imagery and Remote Sensing: A Case Study of Shanghai
by Jiaqi Zhang, Zhen He, Weijing Wang and Ziwen Sun
Land 2025, 14(8), 1650; https://doi.org/10.3390/land14081650 - 15 Aug 2025
Viewed by 305
Abstract
The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become [...] Read more.
The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development. Full article
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31 pages, 16809 KiB  
Article
Exploring Spatial Differences in Habitat Quality and Their Response to Urban Spatial Form, Using Shanghai as an Example
by Rongxiang Chen, Zhiyuan Chen, Mingjing Xie, Rongrong Shi, Xin Lin, Kaida Chen and Shunhe Chen
Forests 2025, 16(8), 1323; https://doi.org/10.3390/f16081323 - 14 Aug 2025
Viewed by 309
Abstract
Rapid urbanisation has exacerbated habitat fragmentation and degradation, necessitating urgent improvements to urban habitat quality. However, most current studies lack an analysis of spatial differences in local ecological quality, particularly a systematic exploration of how different urban spatial characteristics drive such differences. Based [...] Read more.
Rapid urbanisation has exacerbated habitat fragmentation and degradation, necessitating urgent improvements to urban habitat quality. However, most current studies lack an analysis of spatial differences in local ecological quality, particularly a systematic exploration of how different urban spatial characteristics drive such differences. Based on this, we use Shanghai as an example, employing the InVEST model to assess habitat quality, and utilise CatBoost machine learning models and the SHAP model to reveal the specific spatial distribution characteristics of the habitat quality spatial differences from a morphological perspective, as well as its response to changes in urban spatial form factors. The results indicate that (1) urban habitat quality exhibits significant spatial differences, with quality differences persisting even within regions of the same habitat grade, demonstrating complex spatial characteristics; (2) density-related indicators such as building density and population density have a significant negative impact on the habitat quality spatial difference value, while configuration-related indicators such as the water ratio and Normalised Difference Vegetation Index have a significant positive effect, with Population Density contributing the most among all variables (20.74%); and (3) the variables exhibit significant nonlinearity and threshold effects. For example, when building density exceeds 0.05, the positive impact becomes a negative impact. The interactions between variables further reveal the multi-dimensional coupling mechanisms underlying habitat quality performance. This study contributes to a deeper understanding of the spatial differences of urban habitat quality, providing scientific support for urban ecological zoning management, the optimised allocation of green space resources, and differentiated spatial governance while offering methodological and decision-making references for the construction of high-quality ecological cities. Full article
(This article belongs to the Special Issue Forest and Urban Green Space Ecosystem Services and Management)
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18 pages, 5888 KiB  
Article
Incorporating Building Morphology Data to Improve Urban Land Use Mapping: A Case Study of Shenzhen
by Jiapeng Zhang, Fujun Song, Yimin Wang, Tuo Chen, Xuecao Li, Xiayu Tang, Tengyun Hu, Siyao Zhou, Han Liu, Jiaqi Wang and Mo Su
Remote Sens. 2025, 17(16), 2811; https://doi.org/10.3390/rs17162811 - 14 Aug 2025
Viewed by 287
Abstract
Accurate urban land use classification is vital for urban planning, resource allocation, and sustainable management. Traditional remote sensing methods struggle with fine-grained classification and spatial structure identification, while socio-economic data, like points of interest and road networks, face issues of uneven distribution and [...] Read more.
Accurate urban land use classification is vital for urban planning, resource allocation, and sustainable management. Traditional remote sensing methods struggle with fine-grained classification and spatial structure identification, while socio-economic data, like points of interest and road networks, face issues of uneven distribution and outdated updates. To explore the role of building morphology characteristics in enhancing urban land use classification and their potential as a substitute for socio-economic information, this study proposes a method integrating architectural features with multi-source remote sensing data, evaluated through an empirical analysis using a random forest model in Shenzhen. Three models were developed as follows: Model 1, utilizing only remote sensing data; Model 2, combining remote sensing with socio-economic data; and Model 3, integrating building morphology with remote sensing data to evaluate its potential for enhancing classification accuracy and substituting socio-economic data. Experimental results demonstrate that Model 3 achieves an overall accuracy of 80.09% and a Kappa coefficient of 0.77. Compared to this, Model 1 achieves an accuracy of 74.56% and a Kappa coefficient of 0.70, while Model 2 reaches 79.56% accuracy and a Kappa coefficient of 0.76. Model 3 also shows greater stability in complex, smaller parcels. This method offers superior generalization and substitution potential in data-scarce, heterogeneous contexts, providing a scalable approach for fine-grained urban monitoring and dynamic management. Full article
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21 pages, 11748 KiB  
Article
Assessing the Impact of Urban Spatial Form on Land Surface Temperature Using Random Forest—Taking Beijing as a Case Study
by Ruizi He, Jiahui Wang and Dongyun Liu
Land 2025, 14(8), 1639; https://doi.org/10.3390/land14081639 - 13 Aug 2025
Viewed by 357
Abstract
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 [...] Read more.
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
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17 pages, 2037 KiB  
Article
Urban Tree CO2 Compensation by Albedo
by Desirée Muscas, Livia Bonciarelli, Mirko Filipponi, Fabio Orlandi and Marco Fornaciari
Land 2025, 14(8), 1633; https://doi.org/10.3390/land14081633 - 13 Aug 2025
Viewed by 329
Abstract
Urban form and surface properties significantly influence city liveability. Material choices in urban infrastructure affect heat absorption and reflectivity, contributing to the urban heat island (UHI) effect and residents’ thermal comfort. Among UHI mitigation strategies, urban parks play a key role by modifying [...] Read more.
Urban form and surface properties significantly influence city liveability. Material choices in urban infrastructure affect heat absorption and reflectivity, contributing to the urban heat island (UHI) effect and residents’ thermal comfort. Among UHI mitigation strategies, urban parks play a key role by modifying the microclimate through albedo and evapotranspiration. Their effectiveness depends on their composition, such as tree cover, herbaceous layers, and paved surfaces. The selection of tree species affects the radiation dynamics via foliage color, leaf persistence, and plant morphology. Despite their ecological potential, park designs often prioritize aesthetics and cost over environmental performance. This study proposes a novel approach using CO2 compensation as a decision-making criterion for surface allocation. By applying the radiative forcing concept, surface albedo variations were converted into CO2-equivalent emissions to allow for a cross-comparison with different ecosystem services. This method, applied to four parks in two Italian cities, employed reference data, drone surveys, and satellite imagery processed through the Greenpix software v1.0.6. The results showed that adjusting the surface albedo can significantly reduce CO2 emissions. While dark-foliage trees may underperform compared to certain paved surfaces, light-foliage trees and lawns increase the reflectivity. Including evapotranspiration, the CO2 compensation benefits rose by over fifty times, supporting the expansion of vegetated surfaces in urban parks for climate resilience. Full article
(This article belongs to the Special Issue Urban Form and the Urban Heat Island Effect (Second Edition))
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18 pages, 4123 KiB  
Article
Urban Growth and River Course Dynamics: Disconnected Floodplain and Urban Flood Risk in Manohara Watershed, Nepal
by Shobha Shrestha, Prem Sagar Chapagain, Kedar Dahal, Nirisha Adhikari, Prajjwal Shrestha and Laxmi Manandhar
Water 2025, 17(16), 2391; https://doi.org/10.3390/w17162391 - 13 Aug 2025
Viewed by 446
Abstract
Human activities and river course change have a complex reciprocal interaction. The river channel is altered by human activity, and these alterations have an impact on the activities and settlements along the riverbank. Understanding the relationship between urbanization and changes in river morphology [...] Read more.
Human activities and river course change have a complex reciprocal interaction. The river channel is altered by human activity, and these alterations have an impact on the activities and settlements along the riverbank. Understanding the relationship between urbanization and changes in river morphology is crucial for effective river management, safeguarding the urban environment, and mitigating flood hazards. In this context, this study has been conducted to investigate the interrelationship between morphological dynamics, built-up growth, and urban flood risk along the Manohara River in Kathmandu Valley, Nepal. The Sinuosity Index was used to analyze variation in river courses and instability from 1996 to 2023. Built-up change analysis is carried out using supervised maximum likelihood classification method and rate of change is calculated for built-up area growth (2003–2023) and building construction between 2003 and 2021. Flood hazard risk manning was carried out using flood frequency estimation method integrating HEC-GeoRAS modeling. Linear regression and spatial overlay analysis was carried out to examine the interrelationship between river morphology, urban growth, and fold hazed risk. In recent years (2016–2023), the Manohara River has straightened, particularly after 2011. Before 2011, it had significant meandering with pronounced curves and bends, indicating a mature river system. However, the SI value of 1.45 in 2023 and 1.80 in 2003 indicates a significant straightening of high meandering over 20 years. A flood hazard modeling carried out within the active floodplain of the Manohara River shows that 26.4% of the area is under high flood risk and 21% is under moderate risk. Similarly, over 10 years from 2006 to 2016, the rate of built-up change was found to be 9.11, while it was 7.9 between 2011 and 2021. The calculated R2 value of 0.7918 at a significance level of 0.05 (with a p value of 0.0175, and a standard error value of 0.07877) indicates a strong positive relationship between decreasing sinuosity and increasing built-up, which demonstrates the effect of built-up expansion on river morphology, particularly the anthropogenic activities of encroachment and haphazard constructions, mining, dumping wastes, and squatter settlements along the active floodplain, causing instability on the river course and hence, lateral shift. The riverbank and active floodplain are not defined scientifically, which leads to the invasion of the river area. These activities, together with land use alteration in the floodplain, show an increased risk of flood hazards and other natural calamities. Therefore, sustainable protection measures must be prioritized in the active floodplain and flood risk areas, taking into account upstream–downstream linkages and chain effects caused by interaction between natural and adverse anthropogenic activities. Full article
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49 pages, 52465 KiB  
Article
Developing the Urban Diversity Index (UDI): A Global Comparison of Urban Qualitative Aspect and Its Implications for Sustainable Urban Planning Using POI Data
by Yuki Akiyama, Chiaki Mizutani Akiyama, Kotaro Mizutani and Takahito Shimizu
Sustainability 2025, 17(16), 7286; https://doi.org/10.3390/su17167286 - 12 Aug 2025
Viewed by 619
Abstract
Understanding urban diversity is critical to inclusive planning for sustainable urban development. This study introduces a new Urban Diversity Index (UDI) based on global point-of-interest (POI) data for food-related establishments—defined here as facilities that offer food and beverage services, including various kinds of [...] Read more.
Understanding urban diversity is critical to inclusive planning for sustainable urban development. This study introduces a new Urban Diversity Index (UDI) based on global point-of-interest (POI) data for food-related establishments—defined here as facilities that offer food and beverage services, including various kinds of eating and drinking venues —covering 249 cities across 154 countries. The UDI integrates three components: Pielou’s Evenness Index (J′) to capture the balance of establishment types, a Coverage Ratio (C′) to measure global representativeness of establishment categories, and density (ρ′) to reflect spatial concentration. By applying concentric buffer analysis around city centers, we evaluate the spatial profiles of diversity in each city. Results show that while cities like London and Istanbul have similar index components, they exhibit significant differences in the spatial extent and pattern of high-diversity zones, reflecting their unique morphological and regulatory contexts. Furthermore, the analysis of “Peak Distance Buffer Zones”—areas where UDI remains above 95% of its maximum—reveals diverse urban forms, particularly in Asian megacities. Scatterplots of standardized UDI and peak distances identify distinct typologies of urban diversity structures. Notably, urban population size showed no significant correlation with UDI values. Overall, this study demonstrates the utility of globally standardized POI-based metrics in capturing the spatial heterogeneity of urban qualitative diversity and offers new insights into cross-city comparisons of urban complexity. Full article
(This article belongs to the Special Issue Sustainable Urban Planning and Regional Development)
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29 pages, 16186 KiB  
Article
Living with the River: The Role of Bridges in Shaping Valencia’s Urban Form Until 1957
by María-Montiel Durá-Aras, Eric Gielen, José-Sergio Palencia-Jiménez and Stergios-Aristoteles Mitoulis
Land 2025, 14(8), 1625; https://doi.org/10.3390/land14081625 - 11 Aug 2025
Viewed by 400
Abstract
This study offers a novel perspective on the role of bridges as agents of urban transformation by examining their influence on the morphological development of Valencia (Spain) from the 13th century to the catastrophic flood of 1957. Traditionally viewed as mere connective infrastructure, [...] Read more.
This study offers a novel perspective on the role of bridges as agents of urban transformation by examining their influence on the morphological development of Valencia (Spain) from the 13th century to the catastrophic flood of 1957. Traditionally viewed as mere connective infrastructure, bridges are reframed here as key structuring elements that shaped urban expansion, resilience strategies, and socio-spatial dynamics. Through an innovative classification based on stages of bridges, the research integrates historical cartography, cadastral data, and Geographic Information Systems (GIS) to trace how successive waves of bridge construction aligned with distinct socio-political, environmental, and technological contexts. The study demonstrates that bridge development not only facilitated territorial connectivity but also directed urban growth patterns, enabled functional zoning, and responded adaptively to flood risk and demographic pressure. The case of Valencia is particularly significant in light of contemporary challenges in climate adaptation and sustainable urban planning. By unveiling bridges as morphological and functional drivers of urban form, this research offers transferable insights for cities worldwide grappling with the legacy of riverine geographies and the pressures of resilient transformation. Full article
(This article belongs to the Special Issue Urban Morphology: A Perspective from Space (Second Edition))
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31 pages, 13384 KiB  
Article
Physics-Informed and Explainable Graph Neural Networks for Generalizable Urban Building Energy Modeling
by Rudai Shan, Hao Ning, Qianhui Xu, Xuehua Su, Mengjin Guo and Xiaohan Jia
Appl. Sci. 2025, 15(16), 8854; https://doi.org/10.3390/app15168854 - 11 Aug 2025
Viewed by 465
Abstract
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including [...] Read more.
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including graph convolutional network (GCN), GraphSAGE, and several physics-informed graph attention network (GAT) variants—against conventional artificial neural network (ANN) baselines, using both shape coefficient and energy use intensity (EUI) stratification across three distinct residential districts. Extensive ablation and cross-district generalization experiments reveal that models explicitly incorporating interpretable physical edge features, such as inter-building distance and angular relation, achieve significantly improved prediction accuracy and robustness over standard approaches. Among all models, GraphSAGE demonstrates the best overall performance and generalization capability. At the same time, the effectiveness of specific GAT edge features is found to be district-dependent, reflecting variations in local morphology and spatial logic. Furthermore, explainability analysis shows that the integration of domain-relevant spatial features enhances model interpretability and provides actionable insight for urban retrofit and policy intervention. The results highlight the value of physics-informed GNNs (PINN) as a scalable, transferable, and transparent tool for urban energy modeling, supporting evidence-based decision making in the context of aging residential building upgrades and sustainable urban transformation. Full article
(This article belongs to the Special Issue AI-Assisted Building Design and Environment Control)
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23 pages, 8441 KiB  
Article
Enhancing Hyperlocal Wavelength-Resolved Solar Irradiance Estimation Using Remote Sensing and Machine Learning
by Vinu Sooriyaarachchi, Lakitha O. H. Wijeratne, John Waczak, Rittik Patra, David J. Lary and Yichao Zhang
Remote Sens. 2025, 17(16), 2753; https://doi.org/10.3390/rs17162753 - 8 Aug 2025
Viewed by 338
Abstract
Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of [...] Read more.
Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R2 ≈ 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring. Full article
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22 pages, 2637 KiB  
Article
Vegetation-Specific Cooling Responses to Compact Urban Development: Evidence from a Landscape-Based Analysis in Nanjing, China
by Qianyu Sun, Daicong Li, Xiaolan Tang and Yujie Ren
Plants 2025, 14(16), 2457; https://doi.org/10.3390/plants14162457 - 8 Aug 2025
Viewed by 314
Abstract
The urban heat island (UHI) effect has emerged as a growing ecological challenge in compact urban environments. Although urban vegetation plays a vital role in mitigating thermal extremes, its cooling performance varies depending on vegetation type and urban morphological context. This study explores [...] Read more.
The urban heat island (UHI) effect has emerged as a growing ecological challenge in compact urban environments. Although urban vegetation plays a vital role in mitigating thermal extremes, its cooling performance varies depending on vegetation type and urban morphological context. This study explores the extent to which compact urban development—quantified using the Mixed-use and Intensive Development (MIXD) index—modulates the cooling responses of different vegetation types in Nanjing, China. A combination of landscape metrics, regression-based interaction models, and XGBoost with SHAP analysis is employed to uncover vegetation-specific and structure-sensitive cooling effects. The results indicate that densely planted trees exhibit reduced cooling effectiveness in compact areas, where spatial clustering and fragmentation tend to intensify UHI effects, particularly during nighttime. In contrast, scattered trees are found to maintain more stable cooling performance across varying degrees of urban compactness, while low-lying vegetation demonstrates limited thermal regulation capacity. Critical thresholds of MIXD (approximately 28 for UHI area and 37 for UHI intensity) are identified, indicating a nonlinear modulation of green space performance. These findings underscore the importance of vegetation structure and spatial configuration in shaping urban microclimates and offer mechanistic insights into plant–environment interactions under conditions of increasing urban density. Full article
(This article belongs to the Special Issue Plants in Urban Landscapes (Environments))
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45 pages, 54599 KiB  
Article
Reintegrating Marginalized Rural Heritage: The Adaptive Potential of Barn Districts in Central Europe’s Cultural Landscapes
by Elżbieta Komarzyńska-Świeściak and Anna Alicja Wancel
Sustainability 2025, 17(15), 7166; https://doi.org/10.3390/su17157166 - 7 Aug 2025
Viewed by 471
Abstract
Barn districts—ensembles of agricultural buildings situated at the edges of rural settlements—once played a key role in the spatial and economic organization of agrarian communities in Central Europe. Today, many of these structures remain marginalized and underexplored in contemporary landscape and heritage planning. [...] Read more.
Barn districts—ensembles of agricultural buildings situated at the edges of rural settlements—once played a key role in the spatial and economic organization of agrarian communities in Central Europe. Today, many of these structures remain marginalized and underexplored in contemporary landscape and heritage planning. This paper presents a comparative study of six barn districts in Poland’s Kraków-Częstochowa Upland, where vernacular construction, ecological adaptation, and local tradition shaped distinctive rural–urban interfaces. We applied a mixed-methods approach combining cartographic and archival analysis, field surveys, and interviews with residents and experts. The research reveals consistent patterns of landscape transformation, functional decline, and latent adaptive potential across varied morphological and material typologies. Despite differing levels of preservation, barn districts retain symbolic, spatial, and socio-cultural value for communities and local landscapes. The study emphasizes the importance of reintegrating these marginal heritage structures through adaptive reuse strategies rooted in the values of the New European Bauhaus—sustainability, aesthetics, and inclusion. The findings contribute to broader discussions on rural socio-ecological resilience and landscape-based development, highlighting how place-based strategies can bridge past identities with future-oriented spatial planning. Full article
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25 pages, 7359 KiB  
Article
Street Art in the Rain: Evaluating the Durability of Protective Coatings for Contemporary Muralism Through Accelerated Rain Ageing
by Laura Pagnin, Sara Goidanich, Nicolò Guarnieri, Francesca Caterina Izzo, Jaime Jorge Hormida Henriquez and Lucia Toniolo
Coatings 2025, 15(8), 924; https://doi.org/10.3390/coatings15080924 - 7 Aug 2025
Viewed by 348
Abstract
Contemporary muralism has gained increasing cultural and social relevance in recent years, becoming a prominent form of urban artistic expression. However, its outdoor exposure makes it highly vulnerable to environmental degradation, raising significant challenges for long-term preservation. While solar radiation is widely recognized [...] Read more.
Contemporary muralism has gained increasing cultural and social relevance in recent years, becoming a prominent form of urban artistic expression. However, its outdoor exposure makes it highly vulnerable to environmental degradation, raising significant challenges for long-term preservation. While solar radiation is widely recognized as a main agent of deterioration, the impact of rainfall has received comparatively little attention. This study addresses this gap by evaluating the durability of commercial protective coatings applied to modern paints (alkyd, acrylic, and styrene-acrylic) under simulated rain exposure. The ageing protocol replicates approximately 10 years of cumulative rainfall in Central-Southern Europe. A key innovation of this research is the use of a custom-built rain chamber, uniquely designed to expose a large number of samples simultaneously under highly uniform and controlled rain conditions. The system ensures reproducible exposure through a precision-controlled moving platform and programmable rain delivery. A comprehensive set of analytical techniques was employed to assess morphological, chemical, and functional changes in the coatings and paints before and after ageing. Results highlight the limited performance of current protective materials and the need for more effective solutions for the conservation of contemporary outdoor artworks. Full article
(This article belongs to the Section Surface Characterization, Deposition and Modification)
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