Search Results (193)

This study investigates the nonlinear, interactive, and temporally dynamic effects of urban form on street vitality within community-oriented metro station areas (MSAs) in Beijing. It offers potential reference value for other cities facing comparable challenges in MSA implementation and increasing motorization. This research addresses gaps in prior studies concerning the integration of multi-source data, nonlinearity, and diurnal variation. Utilizing an extended node-place-design framework, urban form is conceptualized through network, interface, and functional dimensions. The empirical analysis employs multi-source datasets, including 128,199 mobile device trips recorded in April 2024, OpenStreetMap for network data, Baidu points of interest for functional data, and Grasshopper for interface metrics, covering 183 street samples within a 1000 m radius of metro stations. Traditional regression models—such as ordinary least squares and spatial autocorrelation and cross-correlation—are used as baselines, while a novel gradient-boosting decision tree with latitude and longitude features is applied to enhance predictive performance. The results indicate that key contributors include road network density (16.89%), road intersections (10.56%), and point-of-interest density (9.74%), with Shapley Additive Explanations dependence plots demonstrating nonlinear thresholds. The analyses reveal synergistic or antagonistic interactions among features. Temporal fluctuations in feature importance further support the presence of diurnal dynamics. The study provides insights for time-sensitive urban planning aimed at enhancing MSA vitality, sustainability, and resident quality of life, while acknowledging that the conclusions are context-specific to Beijing and require additional validation in other urban environments. Full article

Street greening is widely recognized as influencing resident well-being and housing prices, and street-view imagery provides a fine-grained data source for quantifying urban microenvironments. However, existing research predominantly relies on single indicators such as the Green View Index (GVI) and overall green coverage/volume lacking a systematic analysis of how the hierarchical structure of trees, shrubs, and grass relates to housing prices. This study examines the high-density block context of Tianjin’s six urban districts. Using the Street Greening Space Structure (SGSS) dataset to construct greening structure configurations, we integrate housing-price data, neighborhood attributes, and 13,280 street-view images from the study area. We quantify how “visibility and hierarchical ratios” are capitalized on in the housing market and identify auditable threshold ranges and contextual gating. We propose an urban–forest structural system centered on visibility and hierarchical ratios that links street-level observability to ecosystem services. Employing an integrated framework combining Geographical-XGBoost (G-XGBoost) and SHapley Additive exPlanations (SHAP), we move beyond average effects to reveal structural detail and contextual heterogeneity in capitalization. Our findings indicate that tree visibility G_TVI is the most robust and readily capitalized price signal: when G_TVI increases from approximately 0.06 to 0.12–0.16, housing prices rise by about 8%–10%. Hierarchical structure is crucial: balanced tree–shrub ratios and moderate shrub–grass ratios translate “visible green” into functional green. Capitalization effects are environmentally conditioned—more pronounced along corridors with high centrality and accessibility—and are likewise common in dense East Asian metropolises (e.g., Beijing, Shanghai, Seoul, and Tokyo) and rapidly motorizing cities (e.g., Bangkok and Jakarta). These patterns suggest parametric prescriptions that prioritize canopy-corridor continuity and keep ratios within actionable threshold bands. We translate these findings into urban greening strategies that prioritize canopy continuity, under-canopy permeability, and maintainability, providing sustainability-oriented, parameterized guidance for converting urban greening structure into ecological capital for sustainable cities. Full article

Tree-covered urban green spaces, including streets, parks, and other public areas, are vital for urban sustainability and people’s well-being. However, such trees face threats from the occurrence of extreme weather. In this study, we investigated wind damage to urban trees in the city of Debrecen, Hungary, during two severe windstorms in July 2025. Field surveys were conducted across three distinct urban zones, covering approximately 515,000 m² in total. We assessed 201 damaged and 325 undamaged trees and recorded the species, size, damage type, and contextual landscape features associated with them being damaged or not. Damage type to trees consisted primarily of broken branches, whilst uprooting and trunk breakage were recorded less often. Most tree characteristics (trunk circumference, height, systematic position, nativity) and the proximity and height of buildings upwind of focal trees were significant predictors of their vulnerability to windstorms. In addition, we surveyed 150 residents in person and received comments from 54 people via online questionnaires and explored their perceptions of storm frequency, the causes of storms, and mitigation measures. Most respondents noted increased storm frequency and attributed that to climate change, and they suggested mitigation measures focused on urban tree management and environmental protection. Some people expressed scepticism about the presence of climate change and/or their ability to address such damage on an individual basis. Our study is the first to integrate assessments of storm-related impacts on urban trees with the opinions of residents living in proximity to them. Our findings highlight the need for climate-adaptive and mechanically robust urban forestry planning and offer insights that guide the management of trees in urban areas globally. Specifically, we propose to undertake the following: (1) Prioritise structurally resilient, stress-tolerant tree species adapted to extreme weather conditions when planting new trees. (2) Integrate wind dynamics, microclimatic effects and artificial stabilisation techniques into urban design processes to optimise tree placement and their long-term stability. Urban planners, builders, developers, and homeowners should be informed about these stabilising practices and incorporate the needs of trees early in the design process, rather than as decorative additions. (3) Develop regionally calibrated risk models and early-warning systems to support proactive and data-driven tree management and public safety. (4) Promote climate literacy and public participation to strengthen collective stewardship and resilience of urban trees. Full article

PM_2.5 pollution is a significant environmental problem in global urbanization. However, traditional macro-scale studies are constrained by data resolution limitations, failing to accurately characterize the microscale built environment or thoroughly investigate its spatially heterogeneous effects on PM_2.5 concentrations. To address this gap, this study constructs a multidisciplinary framework of “Street View Imagery element extraction–spatial heterogeneity modeling–planning strategy optimization” with Xi’an as the case. Leveraging machine learning techniques, the study employs the ResNet50 deep learning model and the ADE20K dataset to precisely extract ten microscale built environment factors from tens of thousands of street view images. Combined with the High-resolution and High-quality Ground-level PM_2.5 Dataset for China, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models were used to systematically reveal the impacts of the microscale built environment on PM_2.5 concentrations. Ten built environment factors were identified with varying spatial heterogeneity in their effects on the PM_2.5 concentrations, as follows: (1) factors with positive effects, in descending order of strength, include building, wall, fence, tree, sky, and grass; (2) factors with negative effects, in descending order of strength, include sidewalk, plant, and car; (3) compared with other factors, the road factor showed a relatively weaker effect. This research provides decision-making support for targeted urban planning and environmental protection, while offering valuable references for air pollution control in other cities. Full article

This study examines the impact of canopy structural characteristics on pedestrians’ visual perception and psychophysiological responses along four roads in the subtropical city of Guangzhou: Huadi Avenue, Jixiang Road, Yuejiang Middle Road, and Huan Dao Road. A Canopy Structural Index (CSI) was innovatively developed by integrating tree height, crown width, diffuse non-interceptance, and leaf area index, establishing a five-tier quantitative grading system. The study used multimodal data fusion techniques combined with heart rate variability (HRV) analysis and eye-tracking experiments to quantitatively decipher the patterns of autonomic nervous regulation and visual attention allocation under different levels of CSI. The results demonstrate that CSI levels are significantly correlated with psychological relaxation states: as CSI levels increase, time-domain HRV metrics (SDNN and RMSSD) rise by 15%–43%, while the frequency-domain metric (LF/HF) decreases by 31%, indicating enhanced parasympathetic activity and a transition from stress to relaxation. Concurrently, the allocation of visual attention toward canopies intensifies. The proportion of fixation duration increases to nearly 50%, and the duration of the first fixation extends by 0.3–0.8 s. The study proposes CSI ≤ 0.15 as an optimization threshold, offering scientific guidance for designing and pruning subtropical urban street tree canopies. Full article

This study evaluates potential routes for electric bicycles (E-Bikes) in Valparaíso, Chile, using street audits performed by both humans and artificial intelligence (AI). Audit methods were compared to identify routes connecting the Puerto metro station with Avenida Alemania (a strategic city avenue), prioritizing criteria such as street infrastructure, habitability, and street coexistence. The results show that the human audit gives higher scores in subjective variables, such as the perception of security and urban dynamism, while AI penalizes infrastructure deficiencies more severely, especially in areas with steep slopes and low tree cover. Despite these differences, both methods highlight the inadequacy of current infrastructure to promote the use of E-Bikes in the city. This work provides a novel perspective by evaluating human and AI-assisted methodologies, suggesting that an integration between the two could improve accuracy and reduce subjectivity in urban audits. In addition, the results underline the need for public policies that prioritize accessibility, safety, and equity in urban mobility, especially in vulnerable areas. Future research should explore training AI algorithms with human audit data to strengthen AI’s ability to interpret contextual variables and dynamics in complex urban environments. Full article

Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of urban spatial perception. Using the central urban area of Shanghai as a case study, we applied machine learning techniques to analyze 67,252 street view images from 2013 and 2019, aiming to quantify the spatiotemporal dynamics of urban perception. The results reveal the following: temporally, the average perception scores in 2019 increased by 4.85% compared to 2013; spatially, for every 1.5 km increase in distance from the city center, perception scores increased by an average of 0.0241; among all sampling points, 65.79% experienced an increase in perception, while 34.21% showed a decrease; and in terms of visual elements, natural features such as trees, vegetation, and roads were positively correlated with perception scores, whereas artificial elements like buildings, the sky, sidewalks, walls, and fences were negatively correlated. The analytical framework developed in this study offers a scalable method for measuring and interpreting changes in urban perception and can be extended to other cities. The findings provide valuable time-sensitive insights for urban planners and policymakers, supporting the development of more livable, efficient, and equitable urban environments. Full article

Urban expansion alters environmental conditions, influencing tree physiology and performance. Urban trees provide cooling, sequester carbon, support biodiversity, filter contaminants, and enhance human health. This study examines how two common urban trees—Norway Maple (Acer platanoides L.) and Little-leaved Linden (Tilia cordata Mill.)—respond to urban site conditions by assessing leaf morphology, stomatal, and gas exchange traits across street and urban park sites in Chicago, IL. Street trees exhibited structural trait adjustments, including smaller leaf area, reduced specific leaf area, and increased stomatal density, potentially reflecting acclimation to more compact and impervious conditions. Norway Maple showed stable photosynthetic assimilation (A), stomatal conductance (g_s), and transpiration (E) across sites, alongside higher intrinsic water-use efficiency (_iWUE), indicating a conservative water-use strategy. In contrast, Little-leaved Linden maintained A and g_s but showed elevated E and _iWUE at street sites, suggesting adaptive shifts in water-use dynamics under street microenvironments. These findings highlight how species-specific physiological strategies and local site conditions interact to shape tree function in cities and underscore the importance of incorporating functional traits into urban forestry planning to improve ecosystem services and climate resilience. Full article

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article

Urban tree diversity plays a crucial role in enhancing the resilience of cities by contributing to ecosystem services such as mitigating the effects of land degradation, combating urban heat islands, improving air quality, and fostering biodiversity habitats. A diverse tree population enhances resilience to vulnerabilities related to climatic stress, disease, and habitat loss by promoting stability, adaptability, and efficiency within the ecosystem. Little is known about urban tree diversity in Serbia; therefore, this study examines the diversity of tree species in the City of Niš, Serbia, to assess its implications for urban resilience and biodiversity preservation in the context of land-use change. Using the Shannon Diversity Index, we quantify species richness and evenness across both central and suburban zones of the city. The results are benchmarked against similar indices in five other European cities to assess how patterns of urban tree distribution vary under different urbanisation pressures. The study reveals that tree diversity is markedly lower in the city centre than in peripheral areas, highlighting spatial inequalities in green infrastructure that may accelerate biodiversity loss due to compact urban development. These findings demonstrate how urban expansion and infrastructure density contribute to ecological fragmentation, potentially leading to long-term effects on ecosystem services. This study emphasises the strategic importance of integrating greenery diversity into urban and landscape planning, particularly in rapidly growing urban centres in Southeastern Europe. This research contributes to the existing body of literature, providing a deeper understanding of the interdependencies between urban tree diversity, land degradation, and biodiversity loss, offering data-driven insights. This enables urban planners, landscape architects, and policy advisors to make informed decisions about street tree diversity and green city infrastructure, contributing to the development of sustainable cities. Full article

Rapid urban growth leads to an extension of artificial surfaces and inefficient energy management, an increase in urban heat islands, and local climate change. This has increased the need for green infrastructure and urban trees are playing an important role. It is important to ensure that tree groups can withstand climate warming and disturbances. This study investigated the physiological parameters of Tilia tomentosa ‘Seleste’ trees situated in a medium-sized Hungarian city, examining their relationship with microclimatic differences observed on opposing sides of a street. Instruments placed on 10 trees recorded air temperature and humidity, revealing a significant difference in total insolation, which resulted in higher maximum daily temperatures on the sunny side. These microclimatic variations were found to significantly affect physiological attributes, particularly pigment content. Trees on the sunny side exhibited a higher relative water content and a higher ratio of chlorophyll a/b, indicative of light acclimatisation. Trees on the sunny side exhibited a higher relative water content and a higher ratio of chlorophyll a/b, indicating an acclimatisation to light. Furthermore, a positive correlation was observed between pigment content, total insolation, and growing degree days. The findings demonstrate how fine-scale microclimate differences influence tree physiology, providing crucial physiological indicators that inform the capacity of urban trees to provide vital ecosystem services, such as local climate regulation. This emphasises the importance of climate-conscious urban planning, as even small-scale climate change can have a broader impact. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article

Flowering street trees are important carriers of urban landscapes and ecological functions, as well as a significant boost to the construction of “Shanghai Flower City”. Most existing studies focus on the ornamental value or single ecological benefits, and there are insufficient systematic evaluations of the landscape–ecology synergistic effect, especially as there are few quantitative studies on the landscape value during the flowering period and long-term ecological benefits. Scientific assessment of multiple benefits is of great significance for optimizing tree species allocation and enhancing the sustainability of road landscapes. Taking flowering street trees in Shanghai’s central urban area as a case study, this paper verifies the feasibility of using digital technology to evaluate their landscape and ecological benefits and explores ways to enhance these aspects. Landscape, ecological, and comprehensive benefits were quantitatively assessed using digital images, the i-Tree model, and the entropy-weighted method. Influencing factors for each aspect were also analyzed. The results showed the following: (1) Eleven species or cultivars of flowering street trees from six families and ten genera were identified, with the majority flowering in spring, fewer in summer and autumn, and none in winter. (2) The landscape benefits model was: Scenic Beauty Estimation (SBE) = −0.99 + 0.133 × Flowering branches+ 0.183 × Degree of flower display + 0.064 × Plant growth + 0.032 × Artistic conception + 0.091 × Visual harmony with surrounding elements. Melia azedarach L., Prunus × yedoensis ‘Somei-yoshino’, and Paulownia tomentosa (Thunb.) Steud. ranked highest in landscape benefits. (3) Catalpa bungei C. A. Mey., Koelreuteria bipinnata Franch., and Koelreuteria bipinnata ‘integrifoliola’ (Merr.) T.Chen had the highest plant height, diameter at breast height (DBH), and crown width among the studied trees, and ranked top in ecological benefits. (4) Koelreuteria bipinnata, Catalpa bungei, and Melia azedarach showed the best overall performance. The comprehensive benefits model was: Comprehensive Benefits = 0.6889 × Ecological benefits + 0.3111 × Landscape benefits. This study constructs a digital evaluation framework for flowering street trees, quantifies their landscape and ecological benefits, and provides optimization strategies for the selection and application of flowering trees in urban streets. Full article

Due to rising temperatures, energy use, and thermal discomfort, urban heat islands (UHIs) pose a serious environmental threat to urban sustainability. This systematic review synthesizes peer-reviewed literature on various forms of green infrastructure and their mechanisms for mitigating UHI effects, and the function of urban green spaces (UGSs) in reducing the impact of UHI. In connection with urban parks, green roofs, street trees, vertical greenery systems, and community gardens, important mechanisms, including shade, evapotranspiration, albedo change, and ventilation, are investigated. This study emphasizes how well these strategies work to lower city temperatures, enhance air quality, and encourage thermal comfort. For instance, the findings show that green areas, including parks, green roofs, and street trees, can lower air and surface temperatures by as much as 5 °C. However, the efficiency of cooling varies depending on plant density and spatial distribution. While green roofs and vertical greenery systems offer localized cooling in high-density urban settings, urban forests and green corridors offer thermal benefits on a larger scale. To maximize their cooling capacity and improve urban resilience to climate change, the assessment emphasizes the necessity of integrating UGS solutions into urban planning. To improve the implementation and efficacy of green spaces, future research should concentrate on policy frameworks and cutting-edge technology such as remote sensing. Full article