Search Results (87)

Urban roadside forests are vital components of green infrastructure that provide multiple ecosystem services, contributing to climate regulation, environmental quality, and urban resilience. This study assessed the multifunctional ecosystem services of roadside tree communities along four representative road types—Coastal Scenic, Commercial Arterial, Residential Secondary, and Industrial Park Roads—in Weihai, a coastal city in eastern China. Based on a complete tree inventory (6742 individuals from 38 species) integrated with the i-Tree Eco model, we quantified three key ecosystem services, carbon storage and annual sequestration, air-pollutant removal, and stormwater interception, and monetized their benefits. Results indicate that roadside forests stored approximately 1120 tons of carbon and sequestered 78 tons annually (≈USD 0.53 million; CNY 3.85 million), removed 1.28 tons of air pollutants per year (≈USD 9370; CNY 68,400), and intercepted 1560 m³ of stormwater (≈USD 5560; CNY 40,600). Commercial Arterial and Coastal Scenic Roads yielded the highest total ecosystem-service values, while Residential Secondary Roads achieved the greatest per-area efficiency. These findings highlight the significant contribution of urban roadside forests to sustainable and climate-resilient city development and underscore their potential role in urban forest planning and management. Full article

Plant growth regulators (PGRs) are natural or synthetic substances that control and manipulate plant physiological processes, controlling branching and vegetative growth. Maintaining roadside vegetation through frequent mowing is costly, dangerous, and unsustainable. This narrative literature review proposes a revolution in this management by conducting a systematic literature review on the strategic application of PGRs on roadsides. Practices such as the application of plant growth regulators, the use of native cover crops, and bioengineering techniques with stabilizing species were analyzed. Previous studies have shown that the use of regulators such as mepiquat chloride and paclobutrazol reduces plant height and aboveground biomass, favoring growth control and compacting the plant architecture. The environmental and operational impacts related to vegetation control on roadside strips were also considered. Integrated with LiDAR technology for precise monitoring, this model establishes a new paradigm: smart, safe, and sustainable. Therefore, it is hoped that this compendium will fill a gap in national guidelines by offering an evidence-based protocol guideline for the use of PGR as an alternative to traditional management methods, thus reducing the number of mowing and weeding operations in highway right-of-way areas. Full article

Despite the well-established ability of urban green belts to reduce traffic noise, a comprehensive analysis of the specific role played by mixed coniferous trees and shrubs in noise mitigation remains lacking. This study aimed to clarify how different planting patterns and the characteristics of plants affect their noise-reduction performance. To achieve this, noise reduction was measured at 18 roadside green spaces comprising mixed coniferous trees and shrubs in Harbin, China, and Moscow, Russia. The results indicate that in lanes 5–15 m wide, the ‘Abreast’ planting pattern consistently offered greater noise reduction than the ‘Taffy’ configuration at all measured distances (5, 10 and 15 m). In addition, in winter the effectiveness of noise reduction improved due to snow cover, which enhanced the sound-absorbing properties of the vegetation. In our analysis, key factors such as diameter at breast height, minimum height under branches and road width emerged as crucial predictors of traffic noise reduction. Among these, carriageway width and sidewalk width exhibited the strongest correlations with noise attenuation. Finally, we developed a quantitative model for roadside green spaces that incorporates plant characteristics, planting schemes and road features. This model allows us to assess the contribution of each factor to overall noise reduction. The results of this study provide a scientific basis for designing and optimising vegetation-based noise-mitigation strategies to enhance the urban acoustic environment while also offering an analytical framework to support evidence-based urban forestry planning and policy. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

Root tensile strength (T_r) is a fundamental root mechanical trait and serves as a key parameter for assessing the contribution of vegetation to slope stability. T_r is known to exhibit high intraspecific variability, but whether T_r varies with season remains unclear. Here, we investigated the seasonal variation in T_r in two commonly seen herbaceous species, i.e., Artemisia argyi and Cirsium setosum, both of which can be future candidates for revegetating species along roadsides in temperate and subtropical regions. We examined the T_r of their first- (closest to the stem base) and third-order lateral roots sampled in the southwest of Henan, China, in two distinct periods: September (late growing season) and December (dormant season). We found that the T_r of the thicker, first-order roots in September was significantly greater than that in December. However, such seasonal variation was not found for the thinner third-order roots. When fitting the relationship between T_r and root diameter using a two-parameter power law equation, the calibrated equation using the data collected in September led to a marked predictive bias to the data collected in December. All the above patterns were consistent for both species. Soil moisture, which exhibited strong seasonal variation in the study area, might be the key cause of variation in T_r. Our study is among the first to demonstrate seasonal variation in root mechanical traits, indicating that season potentially plays a non-negligible role in impacting soil reinforcement and slope stability by modifying roots’ mechanical quality. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article

Roadside vegetation composition assessment is essential to maintain ecological stability, control invasive species, and ensure the adoption of environmental regulations in areas surrounding active roadside construction zones. Traditional monitoring methods involving visual inspections are time-consuming, labor-intensive, and not scalable. Remote sensing offers a valuable alternative to automating large-scale vegetation assessment tasks efficiently. The study compares the performance of proximal RGB imagery processed using deep learning (DL) techniques against the vegetation indices (VIs) extracted at higher altitudes, establishing a foundation to use the prior in performing vegetation analysis using unmanned aerial vehicles (UAVs) for broader scalability. A pixel-wise annotated dataset for eight roadside vegetation species was curated to evaluate the performance of multiple semantic segmentation models in this context. The best-performing MAnet DL achieved a mean intersection over union of 0.90, highlighting the model’s capability in composition assessment tasks. Additionally, in predicting the vegetation cover—the DL model achieved an R² of 0.996, an MAE of 1.225, an RMSE of 1.761, and an MAPE of 3.003% and outperformed the top VI method of SAVI, which achieved an R² of 0.491, an MAE of 20.830, an RMSE of 23.473, and an MAPE of 59.057%. The strong performance of DL models on proximal RGB imagery underscores the potential of UAV-mounted high-resolution RGB sensors for automated roadside vegetation monitoring and management tasks at construction sites. Full article

While urban greenery is known to regulate microclimates and reduce air pollution, its integrated effects remain insufficiently quantified. Through field monitoring and ENVI-met 5.1 modeling of high-rise residential areas in Jinan, the results demonstrate that: (1) vegetation exhibits distinct spatial impacts in air-quality impacts, reducing roadside PM_2.5 by 26.63 μg/m³ while increasing building-adjacent levels by 17.5 μg/m³; (2) shrubs outperformed trees in PM_2.5 reduction (up to 65.34%), particularly when planted in inner rows, whereas tree crown morphology and spacing showed negligible effects; (3) densely spaced columnar trees optimize cooling, reducing T_a by 3–4.8 °C and the physiological equivalent temperature (PET*) by 8–12.8 °C, while planting trees on the outer row and shrubs on the inner row best balanced thermal and air-quality improvements; (4) each 1 m²/m³ leaf area density (LAD) increase yields thermal benefits (ΔT_a = −1.07 °C, ΔPET* = −1.93 °C) but elevates PM_2.5 by 4.32 μg/m³. These findings provide evidence-based vegetation design strategies for sustainable urban planning. 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

Although it has its advantages for the development of urban areas, road construction is among the greatest threats to biodiversity, due to fragmentation, habitat loss, and changes in landscape structure. This study investigated the effects of different traffic intensities on the understory spider assemblage in the Brazilian Atlantic Forest. Understory spiders were collected between 09:00 h–16:00 h using beating tray samples on roadside vegetation on roads with and without traffic. In total, 1616 spiders belonging to 24 families and 317 morphospecies were collected. The families Araneidae and Theridiidae were more abundant and showed a higher number of morphospecies on both roads. Understory spiders were classified into seven guilds. However, no significant differences were found in functional and taxonomic richness and abundance between the roads. These results indicate that understory spider assemblages showed no significant response to traffic intensity, suggesting potential resilience to this disturbance in the studied context. Additionally, the proximity between locations may result in the founder effect, with spiders migrating from the preserved site to the impacted site. Overall, this study indicates that traffic presence does not significantly impact the diversity and composition of understory spider assemblages in the studied region. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. 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

Gazania is a genus of herbaceous plants from the Asteraceae (daisy) family. Native to southern Africa, several species of this genus have been introduced to different countries as ornamental garden plants due to their beautiful flowers. In the wild, Gazania species have been observed with flowers of different shades of pink, red, yellow, orange and combination of these colours. Some species of Gazania have escaped the gardens and become highly invasive weeds in their introduced range. Invasive, drought-tolerant and prolific seed-producing Gazania plants are found in Australia, New Zealand, Algeria, Egypt, Europe and California. In particular, two perennial species, Gazania linearis and Gazania rigens, commonly known as gazania, have become a major problem in Australia. They have naturalized and are widespread in a range of environments, such as roadsides, pasture/grassland systems, coastal sand dunes, and natural and managed ecosystems. Their seeds and underground reproductive structures are carried along roadsides by slashers, machinery, wind and water, and spread into native vegetation, pastures, horticultural crops and broadacre agronomic crop production systems. Gazania causes significant environmental, production and economic losses in the infested ecosystems. While limited research has been conducted on their biology and invasion ecology, anecdotal evidence suggests that the ability of gazania plants to produce a large number of seeds form thick, dense populations, and tolerate harsh environments, including drought, heat and sub-optimal soil pH, making them persistent, problematic weed species. In addition, perennial growth habit, high genetic diversity and allelopathic potential have also been suggested to facilitate their invasion success, but no research has been conducted on these aspects. Gazania is very difficult to manage, and currently, there are no effective control options available, including chemical herbicides. The lack of knowledge on their biology, invasion pathways and management is hindering the effective management of gazanias. This review compiles and synthesizes currently available information on the distribution, biology, ecology and management of weedy gazania species, with a particular focus on Australia. We also highlight the key knowledge gaps for future research. We believe this information provides researchers and practitioners with an up-to-date account on the weedy aspects of these popular ornamental plants and will help improve management efforts. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article