Search Results (46)

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

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 areas, while providing numerous benefits, can also produce negative impacts, often referred to as “ecosystem disservices”. While fallen fruits, leaves, and branches may pose tripping hazards, falling trees present a more significant threat to the safety of citizens and buildings. A study was conducted to identify the factors that most influence tree falls, aiming to enhance monitoring and maintenance in high-risk areas and develop preventive felling plans. The analysis was carried out in the city of Venice (Italy) using data from 2019 to 2022. Key variables included daily rainfall and cumulative rainfall over the four days preceding tree falls, minimum temperature, average wind speed and direction, and maximum gust speed on the day of the event and two days prior, as well as detailed information on the affected trees from the municipal GreenSpaces application database (R3GIS). The distribution of fallen trees was assessed in relation to these parameters, and a spatial autocorrelation analysis was performed. The results revealed that tree falls were more frequent during the summer season, coinciding with more intense weather events, especially those characterized by gusts of strong wind (>15 m/s). Street trees and trees in groups, particularly those in parks and densely populated urban areas, were most affected. Tree falls during a single event often occurred in clusters within a radius of approximately 1.5 km. Species analysis indicated that maintaining a diverse mix of tree species could reduce the number of fallen trees, as different species exhibit varying levels of resistance to wind pressure and adaptability to urban conditions. Addressing these findings can help to create more sustainable and livable urban environments, maximizing the benefits of green spaces while mitigating their ecosystem disservices. 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

Urban green spaces (UGSs) are vital in enhancing environmental quality, social well-being, and climate resilience, yet their distribution and accessibility remain uneven in many rapidly urbanizing cities. The 3–30–300 rule offers a structured guideline with which to assess urban greenness, emphasizing tree visibility, canopy cover, and green space proximity. However, its applicability in dense and resource-constrained urban environments has not been sufficiently examined. This study evaluates the feasibility of the 3–30–300 rule in Nagpur, India, using survey-based visibility assessments, NDVI-derived vegetation cover analysis, and QGIS-based accessibility evaluation. The study also introduces the Urban Greenness Exposure Index (UGEI), a composite metric that refines greenness assessment by capturing intra-zone variations beyond broad classifications. The findings reveal significant variations in urban greenness exposure across Nagpur’s ten municipal zones. Low-greenness zones report the highest tree visibility deprivation (below two trees), limited canopy cover (~7%), and restricted green space access (over 80% of residents lacking access within 300 m). The correlation analysis shows that higher canopy cover does not necessarily correspond to better visibility or accessibility, highlighting the need for integrated planning strategies. The study concludes that applying the 3–30–300 rule in high-density Indian cities requires localized adaptations, such as incentivizing street tree planting, integrating vertical greenery, and repurposing vacant lots for public parks. The UGEI framework offers a practical tool for identifying priority zones and guiding equitable greening interventions, based on insights drawn from the Nagpur case study. Full article

Efficient pedestrian detection plays an important role in many practical daily life applications, such as autonomous cars, video surveillance, and intelligent driving assistance systems. The main goal of pedestrian detection systems, especially in vehicles, is to prevent accidents. By recognizing pedestrians in real time, these systems can alert drivers or even autonomously apply brakes, minimizing the possibility of collisions. However, occlusion is a major obstacle to pedestrian detection. Pedestrians are typically occluded by trees, street poles, cars, and other pedestrians. State-of-the-art detection methods are based on fully visible or little-occluded pedestrians; hence, their performance declines with increasing occlusion level. To meet this challenge, a pedestrian detector capable of handling occlusion is preferred. To increase the detection accuracy for occluded pedestrians, we propose a new method called the Discriminative Deformable Part Model (DDPM), which uses the concept of breaking human image into deformable parts via machine learning. In existing works, human image breaking into deformable parts has been performed by human intuition. In our novel approach, machine learning is used for deformable objects such as humans, combining the benefits and removing the drawbacks of the previous works. We also propose a new pedestrian dataset based on Eastern clothes to accommodate the detector’s evaluation under different intra-class variations of pedestrians. The proposed method achieves a higher detection accuracy on Pascal VOC and VisDrone Detection datasets when compared with other popular detection methods. Full article

Transport areas in urban environments typically cover 10–20% of a city’s area. Due to their hierarchical structure and network layout, they present a unique opportunity to integrate Nature-based Solutions (NbSs) within cities strategically. In Poland, however, the current use of NbSs in streetscapes tends to be sporadic, localized, and often resulting from grassroots initiatives. This study aimed to assess how much the provision of ecosystem services (ESs) in cities depends on and can be enhanced by NbSs. To explore this, simulations were conducted using six NbSs scenarios, selected based on an analysis of solutions specifically designed for streets and their characteristics. This research focused on a densely built and populated district of Warsaw. The findings revealed that applying NbSs can significantly reduce stormwater runoff, increase carbon sequestration, and improve air quality. The level of ES provision depends on the solutions used, with the introduction of woody vegetation, particularly tall shrubs and trees, proving most effective. The results show that the scenario-based approach allows for flexible streetscape design, enabling the application of individually selected NbSs. Moreover, the approach helps to select optimal elements that enhance the provision of ES crucial to adapting cities to climate change. Full article

Due to ever-accelerating urbanization in recent decades, exploring the contributions of trees in mitigating atmospheric carbon in urban areas has become one of the paramount concerns. Remote sensing-based approaches have been primarily implemented to estimate the tree-stand atmospheric carbon stock (CS) for the trees in parks and streets. However, a convenient yet high-accuracy computation methodology is hardly available. This study introduces an approach that has been tested for a small urban area. A data fusion approach based on a three-dimensional (3D) computation methodology was applied to calibrate the individual tree CS. This photogrammetry-based technique employed an unmanned aerial vehicle (UAV) and spherical image data to compute the total height (H) and diameter at breast height (DBH) for each tree, consequently estimating the tree-stand CS. A regression analysis was conducted to compare the results with the ones obtained with high-cost laser scanner data. Our study demonstrates the applicability of this method, highlighting its advantages even for large city areas in contrast to other approaches that are often more expensive. This approach could serve as an efficient tool for assisting urban planners in ensuring the proper utilization of the available green space, especially in a complex urban environment. Full article

The intensification of the urban heat island effect, characterized by persistent high temperatures in Chinese cities during summer, has led to notable shifts in urban residents’ activity patterns and travel preferences. Given that street trees, as fundamental components of urban road networks, have significant interaction with residents, it is imperative to investigate their thermal comfort impact. This study aims to enhance the comfortable summer travel experience for urban dwellers. Fujian Agriculture and Forestry University (FAFU) was selected as the case study site, with eight street tree species identified as measurement points. The summer solstice (21 June 2023) served as the representative weather condition. Through monitoring temperature and humidity, the study explored the correlation between street tree species, their characteristic factors, and thermal comfort. Utilizing ENVI-met and ArcGIS, the thermal comfort of campus travel routes was assessed, leading to the development of a summer travel guide based on thermal comfort considerations. The research novelty lies in applying a combined ENVI-met 5.0.2 and ArcGIS 10.8 software approach for modelling and visualizing the microclimate, which enables a more precise analysis of the thermal comfort variations of different campus paths, thus improving the accuracy and applicability of the results in urban planning. The findings reveal several points. (1) Different street trees possess varying capacities to enhance human comfort, with Falcataria falcata and Mangifera indica exhibiting the strongest cooling and humidifying effects, whereas Bauhinia purpurea and Amygdalus persica perform the poorest. Additionally, the research confirms ENVI-met’s scientific accuracy and practicality for microclimate studies. (2) The contribution of street trees to the comfort of campus road travel is primarily determined by the Sky View Factor (SVF), which negatively correlates with cooling and humidifying intensity and positively with thermal comfort. (3) During midday, travel comfort conditions on campus roads are better. Based on the thermal comfort assessment, a summer roadmap was created for the campus. In this case, the campus roads indicated by road A are considered the best travel routes in summer, and the roads indicated by roads B and C are considered alternatives for travelling. This practical application demonstrates how theoretical research results can be translated into practical tools for daily commuting and urban planning. It provides data references and empirical cases for the scientific optimization and enhancement of urban roads. Full article

High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them. Full article

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O₂ production and CO₂ absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m³), followed by the sycamore tree species (557.907 m³), and privet’s was the lowest (16.681 m³). Full article

Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R² = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns. Full article

Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F₁ score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects. Full article

By constructing a rural digital design ecosystem, this paper develops ecological villages through design empowerment, enhances the carbon sequestration benefits of plants in rural areas, and strengthens rural vitality. Combined with the carbon sequestration benefits of street trees in Bulao Village in Nanjing, the feasibility of the digital design ecosystem in rural planning was verified, and the ways and methods of rural environmental renewal were explored. Through the existing literature, the possibility of constructing a digital design ecosystem was deduced, the theoretical framework was derived, field research was carried out in the village of Bulao, the carbon sequestration benefit of street trees was quantified by the i-Tree model, and a structure chart of street trees, including breast diameter, tree height, type, etc., was formed. There were 35 species of street trees in Bulao Village, belonging to 33 genera in 22 families, including 19 species of trees, a total of 312 trees, and 16 species of shrubs. The street trees’ total carbon sink benefit was equivalent to RMB 30,327.47, a single street tree’s average carbon sequestration benefit was RMB 96.86, and the average CO₂ absorption was 164.64 kg. The average CO₂ absorption and single benefit of elm trees were the highest, reaching 465.48 kg·plant⁻¹ and 186.81 RMB·plant⁻¹, respectively. The CO₂ absorption (185.13 kg) and the average benefit per plant (RMB 109.48) of the camphor tree were lower than those of the elm. However, because their number far exceeded that of elms, their total carbon sequestration benefit contribution was the highest, reaching RMB 25,837.28, accounting for 85.19% of the total benefit. In addition, the contribution rates of elm and willow’s total annual carbon sequestration benefits were also relatively high, reaching RMB 747.24 and RMB 710.04, respectively, accounting for 2.46% and 2.34% of the total benefits. This paper uses the digital design ecosystem’s theoretical framework to quantify street trees’ carbon sequestration benefits through field research. It optimizes and improves the plant allocation of parking lots in Bulao Village from the ecology and carbon sink perspectives. Practice shows that inheriting the connotation values of rural culture, improving the quality of the rural environment, and increasing residents’ and tourists’ sense of belonging and identity to the countryside are conducive to jointly promoting sustainable rural development against the background of “dual carbon”. Combining art design with quantitative scientific methods of ecological environment indicators provides a reference for future rural development. Full article

Ginkgo (Ginkgo biloba L.) is a cherished relic among plants, commonly planted as a street tree. However, it faces cultivation challenges due to escalating soil salinization and widespread snowmelt application. Therefore, this study used 4-year-old Ginkgo seedlings to investigate how exogenous melatonin at varying concentrations affects seedling growth and physiology under salinity stress. The results revealed that appropriate melatonin concentrations (0.02, 0.1 mmol·L⁻¹) significantly mitigated leaf yellowing under different NaCl stress levels. Furthermore, they increased ground diameter, current-year branch growth, relative water concentration, free proline, and soluble sugars in leaves. Melatonin also reduced electrolyte exudation rates, flavonoids, and malonic dialdehyde concentration, while enhancing peroxidase and superoxide dismutase activities. This led to reduced chlorophyll content, photosynthetic rate, stomatal conductance, and transpiration rate, stabilizing intercellular CO₂ concentration, preserving photosynthetic structures, and enhancing photosynthetic rates. Additionally, the decline in the photosynthetic electron transport rate, the effective photochemical quantum yield of PSII, and the potential efficiency of primary conversion of light energy of PSII was alleviated. Minimal fluorescence and the non-photochemical quenching coefficient also improved. However, high melatonin concentration (0.5 mmol·L⁻¹) exacerbated salinity stress. After analyzing composite scores, the 0.02 mmol·L⁻¹ melatonin treatment was most effective in alleviating NaCl stress, while the 0.5 mmol·L⁻¹ treatment intensified physiological stress under 200 mmol·L⁻¹ NaCl stress. Principal component analysis and correlation analysis identified seven physiological indicators (photosynthetic rate, transpiration rate, photosynthetic electron transport rate, minimal fluorescence, superoxide dismutase, free proline, and chlorophyll a) and three growth indicators (ground diameter, branch length, and current-year branch thickness) as key markers for rapid salinity stress assessment in Ginkgo. These findings are crucial for addressing challenges associated with snowmelt’s impact on roadside Ginkgo trees, expanding planting areas, and breeding exceptional salt-tolerant Ginkgo varieties. Full article