Search Results (12)

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

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 vegetation has an essential role in maintaining the hydrological and energy balance. These processes in urban areas have been long overlooked due to the fragmentation and uneven feature of land use and vegetation distribution. Recent advances in remote sensing and the ease of data acquisition have allowed a more precise mapping of vegetation and land cover, making it possible to simulate the above processes at micro scales. This research selects a small typical residential catchment in Japan as the study area and the purpose of this research is to investigate the impact of urban vegetation on mitigating urban runoff and the heat island effect. The remote-sensed Normalized Difference Vegetation Index (NDVI) data were used to represent vegetation spatial distribution and seasonal variation. A single layer canopy model and the Storm Water Management Model were coupled to simulate interception, evapotranspiration, and runoff generation processes. The effects of vegetation amount and landscape patterns on the above processes were also considered. The results showed that the coupled model had a satisfactory performance in the modeling of these processes. When the vegetation amount was set to 1.4 times its original value, the summer total runoff had a 10.7% reduction and the average surface temperature had a 2.5 °C reduction. While the vegetation amount was 0.8 times its original value, the total runoff increased by 6%, and the average surface temperature in summer increased by 1.5 °C. The combination of green roof and dense street trees showed the best mitigation performance among the different landscape patterns. The results of this study could be used as a reference for future green infrastructure development in areas with similar climate and vegetation characteristics. Full article

Street trees are of great importance to urban green spaces. Quick and accurate segmentation of street trees from high-resolution remote sensing images is of great significance in urban green space management. However, traditional segmentation methods can easily miss some targets because of the different sizes of street trees. To solve this problem, we propose the Double-Branch Multi-Scale Contextual Network (DB-MSC Net), which has two branches and a Multi-Scale Contextual (MSC) block in the encoder. The MSC block combines parallel dilated convolutional layers and transformer blocks to enhance the network’s multi-scale feature extraction ability. A channel attention mechanism (CAM) is added to the decoder to assign weights to features from RGB images and the normalized difference vegetation index (NDVI). We proposed a benchmark dataset to test the improvement of our network. Experimental research showed that the DB-MSC Net demonstrated good performance compared with typical methods like Unet, HRnet, SETR and recent methods. The overall accuracy (OA) was improved by at least 0.16% and the mean intersection over union was improved by at least 1.13%. The model’s segmentation accuracy meets the requirements of urban green space management. Full article

At a time of rising urbanisation and climate change, urban green spaces (UGSs) are an essential element to help adapt to extreme weather events. Especially in urban core areas, heat and drought are regarded as human stress factors. The delineation of such areas constitutes an important reference geometry in topographic geodata (urban mask). This article deals with possibilities for investigating UGSs in European cities—based on unified urban masks—by applying city-wide metrics to Copernicus data (Urban Atlas including the Street Tree Layer). Both public and tree-covered urban green spaces are examined in detail. Selected results are presented for 30 European cities that display a wide range of urban structures. The spatial reference to uniformly delineated urban masks places the analytical focus of city-wide metrics onto corresponding core areas. In general, the values of UGS metrics vary considerably between cities, indicating the strong influence of city-specific factors on urban structures in Europe. For the comparative analysis of tree-covered urban areas, the Urban Green Raster Germany and a municipal tree register are used to provide additional data sources. The regular updating of the Copernicus dataset means that green spaces in European cities can be monitored, also using urban masks. Full article

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions. Full article

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees. Full article

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments. Full article

As a green infrastructure component, urban street vegetation is increasingly being utilized to mitigate air pollution, control microclimates, and provide aesthetic and ecological benefits. This study investigated the effect of vegetation configurations on particulate matter (PM) flows for pedestrians in road traffic environments via a computation fluid dynamics analysis based on the road width (four and eight-lane) and vegetation configuration (single-, multi-layer planting, and vegetation barrier). Airflow changes due to vegetation influenced PM inflow into the sidewalk. Vegetation between roadways and sidewalks were effective at reducing PM concentrations. Compared to single-layer planting (trees only), planting structures capable of separating sidewalk and roadway airflows, such as a multi-layer planting vegetation barrier (trees and shrubs), were more effective at minimizing PM on the sidewalk; for wider roads, a multi-layer structure was the most effective. Furthermore, along a four-lane road, the appropriate vegetation volume and width for reducing PM based on the breathing height (1.5 m) were 0.6 m³ and 0.4 m, respectively. The appropriate vegetation volume and width around eight-lane roads, were 1.2–1.4 m³ and 0.8–0.93 m, respectively. The results of this study can provide appropriate standards for street vegetation design to reduce PM concentrations along sidewalks. Full article

An increasing concentration of air pollutants, which negatively affect human health and living environment, present a serious environmental concern around the world. Street trees can help reduce carbon (C) and PM_2.5 in cities that lack sufficient greenspace. This study quantified C uptake and PM_2.5 deposition on street trees in the Republic of Korea and suggested sustainable design guidelines to enhance the effects of C and PM_2.5 reduction. The mean C uptake and the PM_2.5 deposition on street trees per unit area were 0.6 ± 0.1 t/ha/y and 2.0 ± 0.3 kg/ha/y, respectively. The major determining factors of the levels of C uptake and PM_2.5 deposition on street trees were the species, density, size, and layering structure of the planted trees. Street trees in the Republic of Korea annually offset C and PM_2.5 emissions from vehicles by 1.4% and 180%, respectively. Based on these results, design guidelines are suggested that can contribute to sharing the value and the importance of planting street trees for the reduction of C and PM_2.5 levels in greenspaces. Full article

As the intensity of the urban heat island effect increases, the cooling effect of urban trees has become important. Urban trees cool surfaces during the day via shading, increasing albedo and transpiration. Many studies are being conducted to calculate the transpiration rate; however, most approaches are not suitable for urban trees and oversimplify plant physiological processes. We propose a multi-layer model for the transpiration of urban trees, accounting for plant physiological processes and considering the vertical structure of trees and buildings. It has been expanded from an urban canopy model to accurately simulate the photosynthetically active radiation and leaf surface temperature. To evaluate how tree and surrounding building conditions affect transpiration, we simulated the transpiration of trees in different scenarios such as building height (i.e., 1H, 2H and 3H, H = 12 m), tree location (i.e., south tree and north tree in a E-W street), and vertical leaf area density (LAD) (i.e., constant density, high density with few layers, high density in middle layers, and high density in lower layers). The transpiration rate was estimated to be more sensitive to the building height and tree location than the LAD distribution. Transpiration-efficient trees differed depending on the surrounding condition and plant location. This model is a useful tool that provides guidelines on the planting of thermo-efficient trees depending on the structure or environment of the city. Full article

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution. Full article