Search Results (207)

The Latin American and Caribbean (LAC) region faces persistent challenges of inequality, climate change vulnerability, and deteriorating air quality. The Aburrá Valley, where Medellín is located, is a narrow tropical valley with complex topography, strong thermal inversions, and unstable atmospheric conditions, all of which exacerbate the accumulation of pollutants. In Medellín, ${\mathrm{NO}}_2$ concentrations have remained nearly unchanged over the past eight years, consistently approaching critical thresholds, despite the implementation of air quality control strategies. These persistent high concentrations are closely linked to the variability of the atmospheric boundary layer (ABL) and are often intensified by prolonged dry periods. This study focuses on a representative street canyon in Medellín that has undergone recent urban interventions, including the construction of new public spaces and pedestrian areas, without explicitly considering their impact on ${\mathrm{NO}}_x$ dispersion. Using Computational Fluid Dynamics (CFD) simulations, this work evaluates the influence of urban morphology on ${\mathrm{NO}}_x$ accumulation. The results reveal that areas with high Aspect Ratios (AR > 0.65) and dense vegetation exhibit reduced wind speeds at the pedestrian level—up to 40% lower compared to open zones—and higher ${\mathrm{NO}}_2$ concentrations, with maximum simulated values exceeding 50 μg/m³. This study demonstrates that the design of pedestrian corridors in complex urban environments like Medellín can unintentionally create pollutant accumulation zones, underscoring the importance of integrating air quality considerations into urban planning. The findings provide actionable insights for policymakers, emphasizing the need for comprehensive modeling and field validation to ensure healthier urban spaces in cities affected by persistent air quality issues. Full article

Street view imagery has become a vital tool for assessing urban street greenery, with the Green View Index (GVI) serving as the predominant metric. However, while GVI effectively quantifies overall greenery, it fails to capture the nuanced, human-scale experience of urban greenery. This study introduces the Front-Facing Green View Index (FFGVI), a metric designed to reflect the perspective of pedestrians traversing urban streets. The FFGVI computation involves three key steps: (1) calculating azimuths for road points, (2) retrieving front-facing street view images, and (3) applying semantic segmentation to identify green pixels in street view imagery. Building on this, this study proposes the Street Canyon Green View Index (SCGVI), a novel approach for identifying boulevards that evoke perceptions of comfort, spaciousness, and aesthetic quality akin to room-like streetscapes. Applying these indices to a case study in Nanjing, China, this study shows that (1) FFGVI exhibited a strong correlation with GVI (R = 0.88), whereas the association between SCGVI and GVI was marginally weaker (R = 0.78). GVI tends to overestimate perceived greenery due to the influence of lateral views dominated by side-facing vegetation; (2) FFGVI provides a more human-centered perspective, mitigating biases introduced by sampling point locations and obstructions such as large vehicles; and (3) SCGVI effectively identifies prominent boulevards that contribute to a positive urban experience. These findings suggest that FFGVI and SCGVI are valuable metrics for informing urban planning, enhancing urban tourism, and supporting greening strategies at the street level. Full article

Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established residential suburbs in Western Sydney, Australia. Established areas featured larger housing lots and mature street trees, while newly developed suburbs had smaller lots and limited vegetation cover. Microclimate data were collected during summer 2021 under both heatwave and non-heatwave conditions in full sun, measuring air temperature, relative humidity, wind speed, and wet-bulb globe temperature (WBGT) as an index of heat stress. Daily maximum air temperatures reached 42.7 °C in new suburbs, compared to 39.3 °C in established ones (p < 0.001). WBGT levels during heatwaves were in the “extreme caution” category in new suburbs, while remaining in the “caution” range in established ones. These findings highlight the benefits of larger green spaces, permeable surfaces, and lighter roof colours in the context of urban heat exposure. Maintaining mature trees and avoiding dark roofs can significantly reduce summer heat and improve outdoor thermal comfort across a range of conditions. Results of this work can inform bottom-up approaches to climate-responsive urban design where informed homeowners can influence development outcomes. Full article

In high-density and land-scarce urban environments such as Taipei—a typical example of compact development in East Asia—informal green spaces (IGSs)—defined as unmanaged or unplanned vegetated urban areas such as vacant lots, street verges, and railway margins—play a growing role in urban environmental and social dynamics. This study explores residents’ perceptions of IGSs and examines how these spaces contribute to urban sustainability and land governance. Using a mixed-methods approach that combines the literature review, field observations, and a structured public opinion survey in Taipei’s Wenshan District, the study identifies key perceived benefits and drawbacks of IGSs. Findings show that residents highly value IGSs for enhancing urban greenery, offering recreational opportunities, and promoting physical and mental health. However, concerns persist regarding safety, sanitation, and maintenance—particularly fears of waste accumulation, mosquito breeding, and risks to children. The results highlight the dual nature of IGSs as both vital ecological assets and potential sources of urban disorder. These insights underscore the need for inclusive, community-based governance models that can transform IGSs into legitimate components of green infrastructure. The study contributes to emerging discussions on adaptive urban land governance by proposing that informal spaces be strategically integrated into urban planning frameworks to enhance environmental equity, resilience, and citizen well-being. Full article

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

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development. Full article

The intensification of extreme temperature events driven by climate change has heightened the vulnerability of urban areas to heatwaves, making it a critical environmental challenge. In this study, we investigate the spatial characteristics of urban heatwave vulnerability in Jungang-dong, Gangneung—a representative mid-sized coastal city in South Korea that experiences a strong urban heat island (UHI) effect due to the prevalent land–sea breeze dynamics, high building density, and low green-space ratio. A representative heatwave day (22 August 2024) was selected using AWS data from the Korea Meteorological Administration (KMA), and hourly meteorological conditions were applied to Computational Fluid Dynamics (CFD) simulations to model the urban microclimates. The thermal stress levels were quantitatively assessed using the Universal Thermal Climate Index (UTCI). The results indicated that, at 13:00, the surface temperatures reached 40 °C and the UTCI values peaked at 43 °C, corresponding to a “Very Strong Heat Stress” level. Approximately 17.4% of the study area was identified as being under extreme thermal stress, particularly in densely built-up zones, roadside corridors with high traffic, and pedestrian commercial areas. Based on these findings, we present spatial analysis results that reflect urban morphological characteristics to guide the optimal allocation of urban cooling strategies, including green (e.g., street trees, urban parks, and vegetated roofs), smart, and engineered infrastructure. These insights are expected to provide a practical foundation for climate adaptation planning and thermal environment improvement in mid-sized urban contexts. Full article

Under the synergy of urban heritage conservation and regional cultural continuity, this study explores the spatial features of “mausoleum–city symbiosis” landscapes in Huangling County’s gully regions. Focusing on Fangzhou Ancient City, we address historical spatial degradation caused by excessive industrialization and disordered urban expansion. A methodological framework is proposed, combining low-altitude UAV-derived high-density point cloud data with RandLA-Net for semi-automatic semantic segmentation of buildings, vegetation, and roads by integrating multispectral and geometric attributes. Key findings reveal: (1) Modern buildings’ abnormal elevation in steep slopes disrupts the plateau–city visual corridor; (2) Statistical analysis shows significant morphological disparities between historical and modern streets; (3) Modern structures exceed traditional height limits, while divergent roof slopes aggravate aesthetic fragmentation. This multi-level spatial analysis offers a paradigm for quantifying historical urban spaces and validates deep learning’s feasibility in heritage spatial analytics, providing insights for balancing conservation and development in ecologically fragile areas. Full article

Background: Equally with other indigenous green leafy vegetables, Solunum nigrum L. has been widely consumed by the VhaVenda tribe found in the Limpopo Province of South Africa since ancient times as a source of food diversification due to its higher-quality nutritional value, sustainability, food security, and medicinal benefits. It is mostly cultivated from seeds in seedling trays and transplanted in the open field, and at the maturity stage, marketing and distribution are mainly conducting through informal markets (i.e., street vendors). However, recently, it can be found in selected supermarkets and commercial grocery stores in South Africa. The leaves and young shoots of S. nigrum are cooked solely and/or as a supplementary vegetable with Brassica rapa L. subsp. chinensis (Chinese cabbage), Spinacia oleracea L. (spinach), Amaranthus graecizans L. (green amaranth), Solanum lycopersicum L. (tomato), and/or cooking oil for flavor. Objective: Contrary to other green leafy vegetables, few studies have been conducted on the metabolites released by S. nigrum and the influence of growing conditions on the metabolites thereof. Method: A ¹H-nuclear magnetic resonance tool was used to identify the untargeted metabolites released by S. nigrum, and spectra were phase-corrected and binned with MestReNova and statistically analyzed with SIMCA 18.0.2. Results: The findings showed that a total of 12 metabolites were detected between the growing conditions. Eleven similar metabolites, such as glycocholate, chlorogenate (human health benefits), caffeine for its bitter taste, choline, 3-Chlorotyrosine (antidiabetic, blood pressure), etc., and a few vital soluble sugars, were detected in S. nigrum samples grown in the open field and greenhouse-cultivated. Glucose was exclusively detected in the S. nigrum grown under greenhouse conditions. Full article

This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications, mobile measurements) were integrated with deep learning-derived street-level GVI through image analysis. A random forest-multiple regression hybrid model evaluated spatiotemporal variations and GVI impacts across time, street orientation, and urban-rural gradients. Key findings include: (1) Urban street Ta prediction model: Daytime model: R² = 0.54, RMSE = 0.33 °C; Nighttime model: R² = 0.71, RMSE = 0.42 °C. (2) GVI shows significant inverse association with temperature, A 0.1 unit increase in GVI reduced temperatures by 0.124°C during the day and 0.020 °C at night. (3) Orientation effects: North–south streets exhibit strongest cooling (1.85 °C daytime reduction), followed by east–west; northeast–southwest layouts show negligible impact; (4) Canyon geometry: Low-aspect canyons (H/W < 1) enhance cooling efficiency, while high-aspect canyons (H/W > 2) retain nocturnal heat despite daytime cooling; (5) Urban-rural gradient: Cooling peaks in urban-fringe zones (10–15 km daytime, 15–20 km nighttime), contrasting with persistent nocturnal warmth in urban cores (0–5 km); (6) LCZ variability: Daytime cooling intensity peaks in LCZ3, nighttime in LCZ6. These findings offer scientific evidence and empirical support for urban thermal environment optimization strategies in urban planning and landscape design. We recommend dynamic coupling of street orientation, three-dimensional morphological characteristics, and vegetation configuration parameters to formulate differentiated thermal environment design guidelines, enabling precise alignment between mitigation measures and spatial context-specific features. Full article

Comprehensive reviews of continuously vegetated areas to determine dispersed locations of invasive species require intensive use of computational resources. Furthermore, effective mechanisms aiding identification of locations of specific invasive species require approaches relying on geospatial indicators and ancillary images. This study develops a two-stage data workflow for the invasive species Kudzu vine (Pueraria montana) often found in small areas along roadsides. The INHABIT database from the United States Geological Survey (USGS) provided geospatial data of Kudzu vines and Google Street View (GSV) a set of images. Stage one built up a set of Kudzu images to be implemented in an object detection technique, You Only Look Once (YOLO v8s), for training, validating, and testing. Stage two defined a dataset of confirmed locations of Kudzu which was followed to retrieve images from GSV and analyzed with YOLO v8s. The effectiveness of the YOLO v8s model was assessed to determine the locations of Kudzu identified from georeferenced GSV images. This data workflow demonstrated that field observations can be virtually conducted by integrating geospatial data and GSV images; however, its potential is confined to the updated periodicity of GSV images or similar services. Full article

Wildfires represent a growing concern worldwide, and their frequency has increased due to climate change and human activities, posing risks to biodiversity and human safety. In the Metropolitan District of Quito (DMQ), the combination of flammable vegetation and steep slopes increases the wildfire susceptibility. Although there are no formally designated firebreaks in these areas, many natural and artificial elements, such as roads, water bodies, and rocky terrain, can effectively function as firebreaks if properly adapted. This study aimed to evaluate the wildfire behavior and assess the effectiveness of both adapted existing barriers and proposed firebreaks using FlamMap simulations. Geospatial and meteorological data were integrated to generate landscape and weather inputs for simulating wildfires in nine high-susceptibility areas within the DMQ. Fuel vegetation models were obtained by matching the national land-cover data with Scott and Burgan fuel models, and OpenStreetMap data were used to identify the firebreak locations. The simulation results show that adapting existing potential firebreaks could reduce the burned area by an average of 42.6%, and the addition of strategically placed firebreaks could further reduce it by up to 70.2%. The findings suggest that implementing a firebreak creation and maintenance program could be an effective tool for wildfire mitigation. Full article

Being an essential issue in global climate warming, the response of urban green spaces to air pollution and climate variability because of rapid urbanization has become an increasing concern at both the local and global levels. This study explored the response of urban vegetation to air pollution and climate variability in the Bucharest metropolis in Romania from a spatiotemporal perspective during 2000–2024, with a focus on the 2020–2024 period. Through the synergy of time series in situ air pollution and climate data, and derived vegetation biophysical variables from MODIS Terra/Aqua satellite data, this study applied statistical regression, correlation, and linear trend analysis to assess linear relationships between variables and their pairwise associations. Green spaces were measured with the MODIS normalized difference vegetation index (NDVI), leaf area index (LAI), photosynthetically active radiation (FPAR), evapotranspiration (ET), and net primary production (NPP), which capture the complex characteristics of urban vegetation systems (gardens, street trees, parks, and forests), periurban forests, and agricultural areas. For both the Bucharest center (6.5 km × 6.5 km) and metropolitan (40.5 km × 40.5 km) test areas, during the five-year investigated period, this study found negative correlations of the NDVI with ground-level concentrations of particulate matter in two size fractions, PM2.5 (city center r = −0.29; p < 0.01, and metropolitan r = −0.39; p < 0.01) and PM10 (city center r = −0.58; p < 0.01, and metropolitan r = −0.56; p < 0.01), as well as between the NDVI and gaseous air pollutants (nitrogen dioxide—NO₂, sulfur dioxide—SO₂, and carbon monoxide—CO. Also, negative correlations between NDVI and climate parameters, air relative humidity (RH), and land surface albedo (LSA) were observed. These results show the potential of urban green to improve air quality through air pollutant deposition, retention, and alteration of vegetation health, particularly during dry seasons and hot summers. For the same period of analysis, positive correlations between the NDVI and solar surface irradiance (SI) and planetary boundary layer height (PBL) were recorded. Because of the summer season’s (June–August) increase in ground-level ozone, significant negative correlations with the NDVI (r = −0.51, p < 0.01) were found for Bucharest city center and (r = −76; p < 0.01) for the metropolitan area, which may explain the degraded or devitalized vegetation under high ozone levels. Also, during hot summer seasons in the 2020–2024 period, this research reported negative correlations between air temperature at 2 m height (TA) and the NDVI for both the Bucharest city center (r = −0.84; p < 0.01) and metropolitan scale (r = −0.90; p < 0.01), as well as negative correlations between the land surface temperature (LST) and the NDVI for Bucharest (city center r = −0.29; p< 0.01) and the metropolitan area (r = −0.68, p < 0.01). During summer seasons, positive correlations between ET and climate parameters TA (r = 0.91; p < 0.01), SI (r = 0.91; p < 0.01), relative humidity RH (r = 0.65; p < 0.01), and NDVI (r = 0.83; p < 0.01) are associated with the cooling effects of urban vegetation, showing that a higher vegetation density is associated with lower air and land surface temperatures. The negative correlation between ET and LST (r = −0.92; p < 0.01) explains the imprint of evapotranspiration in the diurnal variations of LST in contrast with TA. The decreasing trend of NPP over 24 years highlighted the feedback response of vegetation to air pollution and climate warming. For future green cities, the results of this study contribute to the development of advanced strategies for urban vegetation protection and better mitigation of air quality under an increased frequency of extreme climate events. Full article

Constructing visually appealing public spaces has become an important issue in contemporary urban renewal and design. Existing studies mostly focus on single dimensions (e.g., vegetation ratio), lacking a large-scale integrated analysis of urban color and visual elements. To address this gap, this study employs semantic segmentation and color computation on a massive street-view image dataset encompassing 56 cities worldwide, comparing eight machine learning models in predicting Visual Aesthetic Perception Scores (VAPSs). The results indicate that LightGBM achieves the best overall performance. To unpack this “black-box” prediction, we adopt an interpretable ensemble approach by combining LightGBM with Shapley Additive Explanations (SHAPs). SHAP assigns each feature a quantitative contribution to the model’s output, enabling transparent, post hoc explanations of how individual color metrics and visual elements drive VAPS. Our findings suggest that the vegetation ratio contributes the most to VAPS, but once greening surpasses a certain threshold, a “saturation effect” emerges and can no longer continuously enhance visual appeal. Excessive Sky Visibility Ratio can reduce VAPS. Moderate road visibility may increase spatial layering and vibrancy, whereas overly dense building significantly degrades overall aesthetic quality. While keeping the dominant color focused, moderate color saturation and complexity can increase the attractiveness of street views more effectively than overly uniform color schemes. Our research not only offers a comprehensve quantitative basis for urban visual aesthetics, but also underscores the importance of balancing color composition and visual elements, offering practical recommendations for public space planning, design, and color configuration. Full article

Vertical structure monitoring of urban vegetation provides data support for urban green space planning and ecological management, playing a significant role in promoting sustainable urban ecological development. Three-dimensional green volume (3DGV) is a comprehensive index used to characterize the ecological benefit of urban vegetation. As a critical component of urban vegetation, street trees play a key role in urban ecological benefits evaluation, and the quantitative estimation of their 3DGV serves as the foundation for this assessment. However, current methods for measuring 3DGV based on point cloud data often suffer from issues of overestimation or underestimation. To improve the accuracy of the 3DGV for urban street trees, this study proposed a novel approach that used convex hull coupling k-means clustering convex hulls. A new method based on terrestrial laser scanning (TLS) data was proposed, referred to as the Convex Hull Coupling Method (CHCM). This method divides the tree crown into two parts in the vertical direction according to the point cloud density, which better adapts to the lower density of the upper layer of TLS data and obtains a more accurate 3DGV of individual trees. To validate the effectiveness of the CHCM method, 30 sycamore (Platanus × acerifolia (Aiton) Willd.) plants were used as research objects. We used the CHCM and five traditional 3DGV calculation methods (frustum method, convex hull method, k-means clustering convex hulls, alpha-shape algorithm, and voxel-based method) to calculate the 3DGV of individual trees. Additionally, the 3DGV was predicted and analyzed using five fitting models. The results show the following: (1) Compared with the traditional methods, the CHCM improves the estimation accuracy of the 3DGV of individual trees and shows a high consistency in the data verification, which indicates that the CHCM method is stable and reliable, and (2) the fitting results R² of the five models were all above 0.75, with the exponential function model showing the best fitting accuracy (R² = 0.89, RMSE = 74.85 m³). These results indicate that for TLS data, the CHCM can achieve more accurate 3DGV estimates for individual trees, outperforming traditional methods in both applicability and accuracy. The research results not only offer a novel technical approach for 3DGV calculation using TLS data but also establish a reliable quantitative foundation for the scientific assessment of the ecological benefits of urban street trees and green space planning. Full article