Search Results (412)

Urban blue-green infrastructure (UBGI) is a comprehensive solution that balances environmental, social, and economic development objectives and has emerged as a critical approach for fostering urban resilience and sustainable development. This paper conducts a systematic bibliometric analysis of 975 academic articles published between 2000 and 2023 in the Web of Science Core Collection, focusing specifically on the ecosystem services associated with UBGI. Employing CiteSpace visualization technology, this study elucidates the major research trends, thematic clusters, and international collaboration patterns shaping this field. The research delves into the diverse range of ecosystem services provided by blue-green infrastructure and analyzes their contributions to urban well-being. Findings indicate that regulatory services—particularly climate regulation, biodiversity enhancement, and water resource management—have become central research foci within the contexts of urban green infrastructure (UGI), urban blue infrastructure (UBI), and UBGI. Co-citation and keyword analyses reveal that nature-based solutions, hybrid green–gray infrastructure, and the application of urban resilience frameworks are gaining increasing scholarly attention. By summarizing the evolutionary trajectory and priority directions of UBGI research, this study provides significant insights for future interdisciplinary research aimed at enhancing the supply of urban environmental ecosystem services. Full article

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article

The diffusion effect of river cooling is critical for mitigating the urban heat island effect in riverside areas and for establishing an urban cooling network. River cooling effect diffusion is influenced by the two-dimensional (2D) and three-dimensional (3D) urban morphology of surrounding areas. However, the characteristics of 2D/3D urban morphology that facilitate efficient river cooling effect diffusion remain unclear. This study establishes a technical framework to analyze river cooling effect diffusion resistance (RCDR) across different urban morphologies, using the Huangpu River waterside area in Shanghai as a case study. Seven urban morphology indicators, derived from both 2D and 3D dimensions, were developed to characterize the river cooling effect diffusion resistance. The relative contributions and marginal effects were analyzed using the Boosted Regression Tree (BRT) model. The study found that (1) river cooling effect diffusion was heterogeneous, with four typical patterns; (2) the Landscape Shape Index (LSI) and Blue-green Space Ratio (BGR) significantly impacted cooling effect diffusion; and (3) optimal cooling effect diffusion occurred when the blue-green space occupancy ratio exceeded 20% and building density ranged from 0.1 to 0.3. This study’s technical framework offers a new perspective on river cooling effect diffusion and heat island mitigation in riverside spaces, with significant practical value and potential for broader application. Full article

The increasing frequency of extreme weather events, combined with the historic degradation of urban water systems, has prompted cities worldwide to reconsider the role of water in urban planning. This study examines the restoration and integration of historical watercourses into contemporary urban environments through blue and green infrastructure (BGI). Focusing on three case study cities—Poznań (Poland), Milan (Italy), and Beijing (China)—this research explores both spatial and regulatory conditions for reintroducing surface water into cityscapes. Utilizing historical maps, contemporary land use data, and spatial planning documents, this study applies a GIS-based multi-criteria decision analysis (GIS-MCDA) to assess restoration potential. The selected case studies, including the redesign of Park Rataje in Poznań, canal daylighting projects in Milan, and the multifunctional design of Beijing’s Olympic Forest Park, illustrate diverse approaches to ecological revitalization. The findings emphasize that restoring or recreating urban water systems can enhance urban resilience, ecological connectivity, and the quality of public space. Full article

Visual perception of screen elements depends on their color, font, and position in the user interface design. Objects in the central part of the screen are perceived more easily than those in the peripheral areas. However, the peripheral space is valuable for applications like advertising and promotion and should not be overlooked. Optimizing the design of elements in this area can improve user attention to peripheral visual stimuli during focused tasks. This study aims to evaluate how different combinations of text and background color affect the visibility of moving textual stimuli in the peripheral areas of the screen, while attention is focused on a central task. This study investigates how background color, combined with white or black text, affects the attention of participants. It also identifies which background color makes a specific word most noticeable in the peripheral part of the screen. We designed quizzes to present stimuli with black or white text on various background colors in the peripheral regions of the screen. The background colors tested were blue, red, yellow, green, white, and black. While saturation and brightness were kept constant, the color tone was varied. Among ten combinations of background and text color, we aimed to determine the most noticeable combination in the peripheral part of the screen. The combination of white text on a blue background resulted in the shortest detection time (1.376 s), while black text on a white background achieved the highest accuracy rate at 79%. The results offer valuable insights for improving peripheral text visibility in user interfaces across various visual communication domains such as video games, television content, and websites, where peripheral information must remain noticeable despite centrally focused user attention and complex viewing conditions. 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

Blue–green infrastructure is widely recognized for mitigating the urban heat island effect. However, most existing ENVI-met 5.6.1 studies focus on average thermal conditions and overlook fine-scale spatial gradients. This study investigates the urban park in Luoyang City by integrating high-resolution 3D ENVI-met simulations, multi-source data, and field measurements to quantify thermal gradients between park interiors and surrounding built-up areas. A midline cut-off approach was applied to extract horizontal and vertical thermal profiles. The results show that (1) temperature and physiological equivalent temperature (PET) differences are most pronounced at park edges and transition zones, where vegetation and water bodies serve as natural cooling buffers; (2) urban form indicators, especially the building coverage and open space ratio, significantly impact wind speed and the PET, with greenery improving thermal comfort via shading and evapotranspiration, while impervious surfaces intensify heat stress; (3) the park exhibits a distinct cold island effect, with the average PET in the core area up to 12.3 °C lower than in adjacent built-up zones. The effective cooling distance, which is identified through buffer-based zonal statistics, rapidly attenuates within approximately 200 m from the park boundary. These findings offer a novel spatial perspective on thermal regulation mechanisms of urban landscapes and provide quantitative evidence to guide the design of climate-resilient green infrastructure. Full article

While the existing research has extensively explored the impact of urban green spaces on residents’ well-being, studies specifically focusing on waterfront sports buildings remain scarce. This study examines how the combined effects of built environment characteristics in waterfront sports facilities enhance user satisfaction through psychological mechanisms. Based on survey data from 721 users across nine major waterfront sports complexes in China, we find that (1) four social function dimensions (social interaction, accessibility, safety, and multifunctionality) show significant positive correlations with satisfaction; (2) place attachment mediates these relationships. These findings validate the importance of integrating water-oriented design principles with community needs, offering both theoretical contributions to human–water interaction studies and practical implications for urban blue space (defined as visible water features including rivers, lakes, and coastal areas) development. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article

As important urban green spaces, rivers enhance cooling island effects significantly by leveraging environmental factors. This study selected Suzhou River in Shanghai as the subject to explore how to improve blue–green–gray infrastructure to optimize the river cooling island effect on the riparian zone for outdoor activities in summer. A total of 77 samples, including 36 control groups and 41 experimental groups, were categorized into 12 types of blue–green–gray infrastructure composite features. ENVI-met was used to simulate summer thermal comfort, while redundancy analysis and boosted regression trees were used to identify significant factors and thresholds influencing the river’s cooling island effect. The results showed that for Suzhou River, the green–blue–green–gray–green composition most effectively optimizes the river cooling island effect. It is recommended to select construction sites where the river width is 55 m and the percentage of green infrastructure exceeds 40% and keep the distance between green infrastructure and the water body to within 3 m. Additionally, limiting gray infrastructure to less than 10%, with an average building height of 37 m and a building undulation of 25 m, is recommended to achieve the optimal cooling effect. This study finally proposes optimization strategies to maximize the cooling island effect of urban rivers, offering insights for the development of climate-adaptive urban riparian zones. Full article

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

Humans and nature have always been connected. Meanwhile, with the industrial revolution, landscapes have become more artificial, reducing the human–nature relationship. Urban design should follow biophilic principles to reconnect people with nature, mitigate climate change, improve air quality, restore biodiversity loss, and solve social problems. Poor air quality affects people’s health, and vegetation plays a crucial role in purifying the air. Similarly, contact with nature benefits physical and mental health and well-being. However, there is no consensus on how urban design can be beneficial for improving air quality and human health. This review paper aims to provide a comprehensive evaluation of evidence linking nature-based solutions (NBSs), air quality, carbon neutrality, and human health and well-being. Five hundred articles published between 2000 and 2024 were analysed. A number of publications studied the benefits of green infrastructure in improving air quality, carbon sequestration, or the influence of green spaces on human health. The topic of NBSs has recently emerged related to air quality, health, and promoting physical activity, as has accessibility to green spaces and mental health, also associated with blue spaces and residential gardens. The results revealed the gaps in the literature on how to design green and blue spaces to tackle environmental and public health crises simultaneously. Full article

Most image encryption methods based on Rubik’s cube scrambling adopt the idea of cyclic shift or map the image pixels to the cube surface, not fully considering the cube’s three-dimensional (3D) properties. In response to this defect, we propose a mixed chaotic color image encryption method based on parallel rotation scrambling in 3D Rubik’s cube space. First, a seven-dimensional hyperchaotic system is introduced to generate chaotic pseudo-random integer sequences. Then, a proven lemma is applied to preprocess the red (R), green (G), and blue (B) channels of the plain image to realize the first diffusion. Next, the chaotic integer sequence is employed to control Arnold transformation, and the scrambled two-dimensional (2D) pixel matrix is converted into a 3D matrix. Then, the 3D cube is scrambled by dynamically selecting the rotating axis, layer number, and angle through the chaotic integer sequence. The scrambled 3D matrix is converted into a 2D matrix, realizing the second diffusion via exclusive OR with the chaotic matrix generated by logistic mapping. Finally, the matrices of the R, G, and B channels are combined into an encrypted image. By performing the encryption algorithm in reverse, the encrypted image can be decrypted into the plain image. A simulation analysis shows that the proposed method has a larger key space and exhibits stronger key sensitivity than some existing methods. Full article

The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs. Full article

The implementation of a decentralized blue–green infrastructure (BGI) is a key strategy in climate adaptation and stormwater management. However, the integration of urban trees into the multifunctional infrastructure remains insufficiently addressed, particularly regarding rooting space in dense urban environments. Addressing this gap, the BoRSiS project developed the soil-pipe system (SPS), which repurposes the existing underground pipe trenches and roadway space to provide trees with significantly larger root zones without competing for additional urban space. This enhances tree-related ecosystem services, such as cooling, air purification, and runoff reduction. The SPS serves as a stormwater retention system by capturing excess rainwater during heavy precipitation events of up to 180 min, reducing the pressure on drainage systems. System evaluations show that, on average, each SPS module (20 m trench length) can store 1028–1285 L of water, enabling a moisture supply to trees for 3.4 to 25.7 days depending on the species and site conditions. This capacity allows the system to buffer short-term drought periods, which, according to climate data, recur with frequencies of 9 (7-day) and 2 (14-day) events per year. Geotechnical and economic assessments confirm the system stability and cost-efficiency. These findings position the SPS as a scalable, multifunctional solution for urban climate adaptation, tree vitality, and a resilient infrastructure. Full article