Urban Forestry & Urban Greening最新文献

Effects of planned Highly Intensive Densification (HID) were investigated in southern Eriksberg, a residential area located four km from the city center, mainly built in the 1950s in western Uppsala, Sweden according to the planning ideal Houses-in-Park. Uppsala municipality has proposed a doubling of the number of apartments in the southern part of the residential area, mostly through situating dwellings in-between existing houses and in some of the existing green places and spaces. This way of densifying by inserting an equally large area is a new approach in larger Swedish cities, which we term HID. The research presented in this paper is based on case study methodology. Method triangulation has been used to elucidate the projected consequences on urban greening of the planned HID-process in southern Eriksberg. We have analysed the development plan of Eriksberg and measurable effects but also inventoried attitudes and experiences among residents. The document study showed that an implementation of the HID plan will transform the original concept Houses-in-Park into a compact design with significant smaller green spaces in between the houses. The main result showed that most of the respondents had in general strong opinions and feelings regarding the planned loss of green structure. The courtyard green, the district green and the entrance green were highly valued by the respondents. Further on it was clear that a majority were concerned about the residential area losing its original character, planned according to the idea, Houses-in-Park.

Detailed assessment of the ecosystem services provided by urban green spaces requires data on urban tree species. While many approaches for mapping of urban trees from remotely sensed data have been proposed, the fusion of multi-temporal satellite imagery with very high resolution orthophotos remains relatively underexplored. In this research, we assess the potential of a multimodal deep learning approach with intermediate data fusion for classifying common tree species found in the Brussels Capital Region. Our method combines two image sources: (a) multi-temporal PlanetScope data and (b) high-resolution aerial imagery. To evaluate the contribution of each image source, we separately train and assess each branch of the network. Both image sources demonstrate the ability to predict prevalent tree species with high accuracy. However, the fusion of the two image sources yields the best results, achieving an overall accuracy of 0.88 for the five most common tree species in the region. Our approach is compared to two conventional machine learning methods: a random forest (RF) and a support vector machine classifier (SVM) and outperforms both with a 11 percentage point increase in overall accuracy over RF and a 10 percentage point increase over SVM. Increasing the number of species from five to thirteen, including all species with more than 500 tree samples, results in a marginal decrease in accuracy (from 0.88 to 0.84). Overall, our deep learning approach demonstrates its efficacy in classifying common tree species in urban settings and provides a foundation for a comprehensive quantification of ecosystem services offered by urban trees through remote sensing data.

Tree failure risk assessment involves visually evaluating trees by considering three essential factors: identifying potential targets that may be affected if the tree falls, assessing the potential consequences of the fall, and determining the likelihood of tree failure. This assessment was used to evaluate the safety of trees in a study area at Thammasat University Rangsit Center, Thailand. In two priority-selected areas for tree risk management, 3659 trees representing 139 species were assessed, and to understand the spatial patterns of tree health conditions and risks, the study employed a GIS-based mapping methodology to manage tree inventory and analyze the spatial patterns of tree health conditions and risks. A decision tree protocol based on the chi-squared automatic interaction detector (CHAID) algorithm, which employs machine learning, was used to evaluate the risk of tree failure. Our study successfully identified seven variables that are crucial in assessing the risk of tree failure. According to the findings, the overall accuracy rate of failure risk classification was 87.35 %, and of all the trees evaluated, 280 trees (7.65 % of the total) representing 34 different species were at high risk. It is recommended to start the assessment process by evaluating important variables such as tree cavities, pest infestations, mechanical damage, dead branches, and epicormic growth. Machine learning protocols, integrated with GIS, are shown to be effective, spatially-explicit, decision-support tools for detecting tree failure potential and assessing risk ratings. Application of these tools improves tree risk management practices.

In response to worldwide calls for promoting health and well-being, there is growing interest in creating restorative environments. Although restorative environments can be perceived by multiple sensory modalities, visual studies dominated the majority of restorative research, while olfaction is a frequently overlooked yet important sense. Grounded in Attention Restoration Theory (ART) and cross-modal interaction of olfaction and vision, this research seeks to investigate the independent and interactive effects of vision and olfaction on the perceived restorativeness of a Metasequoia walkway in urban green space. The visual stimulus was a 360-degree virtual reality (VR) tour in a 200-meter-long Metasequoia walkway, and the olfactory stimulus was Metasequoia essential oil extracted from the tree leaves. Based on a stress recovery experiment, 120 participants were randomly assigned to one of four groups: control, vision, olfaction, and combination. Psychological evaluations (semantic differential scale [SDS] and perceived restorativeness scale [PRS]) and physiological measurements (systolic blood pressure [SBP], diastolic blood pressure [DBP], heart rate [HR] and blood oxygen level [BOL]) were used as indicators of restorative effects. The findings reveal that a combination of visual and olfactory stimulation can contribute more to restorative effects compared to a single stimulus, in terms of both physiological and psychological indicators. This calls for a greater focus on the effects of vision and olfaction towards more sustainable landscape design and management.

Landscape components are essential in the design and fruition of any urban green space (UGS). This is particularly relevant considering the growth rate of the urban population worldwide, the increase in the number and intensity of severe weather episodes due to climate change, and the relevance of UGSs for global human well-being, as highlighted by the United Nations in their Sustainable Development Goals. This study examines users’ perceptions of UGSs regarding the importance of landscape components, which can influence users’ preferences for UGS use. The research was conducted in a major urban park subdivided into 10 landscape units in Oporto, Portugal. The paper uses information gathered through face-to-face surveys from stationary park users (n = 500) engaged in diverse activities, through which 13 landscape elements were assessed for their relevance in determining choice of location. The results show significant differences between the assessed landscape units at the user level for all socio-demographic variables, with the exception of the gender variable. Significant differences between landscape units were also identified in terms of the relevance attributed to the different landscape components. Exploratory factor analysis identified five main factors influencing user preferences: comfort and security (including vegetation density, tranquillity of space, availability of shade, good maintenance, and forest coverage), landscape diversity (diversity of open spaces, diversity of flora and fauna, and presence of shrubs), water presence, recreational facilities, and open spaces for activities. The results also highlight an issue related to distributional justice regarding the assessed study area. Our results have relevant implications for the design and management of UGSs. We propose diverse actions, addressing issues related to the balance between open spaces and rich greenery, multifunctional design, shade coverage, maintenance, forest coverage, vegetation diversity, the incorporation of water features, and the provision of recreational facilities.

Gardens are unique ecosystems with the potential to deliver an array of important environmental and health benefits, particularly for urban populations. A large body of research has established that contact with nature and activities within green spaces (including gardening) are beneficial for our physical, mental and social health. An adjacent body of research has explored whether and how pro-environmental behaviours are also linked to positive outcomes for human health and wellbeing. But published research has not yet established whether pro-environmental behaviour in the context of gardening (“sustainable horticulture”) further increases the health and wellbeing benefits that gardeners derive. This paper uses evidence from a nationally representative UK survey (n=2086) conducted in August 2021 to explore this question. We used multiple regression analysis to quantitatively demonstrate that a positive association between gardening more sustainably and perceived health benefits from gardening exists at the population level amongst UK adults. This finding implies the possibility that promoting environmentally-minded gardening techniques may lead to an increase in the health and wellbeing benefits that gardeners can derive from their actions. The paper puts forth a range of potential mechanisms that could explain the identified positive association between sustainable gardening and perceived health benefits and identifies related policy implications.

Climate change-intensified urban warming has brought attention to urban parks’ spatial allocation due to their cooling effects. However, conventional accessibility assessments of park cooling effects consider temperature and size, overlooking critical factors such as thermal comfort and supply and demand differences in thermal environments, which more accurately represent public thermal stress. We developed a multimode Gaussian-based Huff three-step floating catchment area method based on thermal stress differences between population locations and parks. This method integrates thermal comfort and cellular population data to assess the spatial mismatch between the supply and demand for park cooling services in Shanghai. Our findings show that most central and developing urban areas have excellent park cooling accessibility. However, considering population demand, central Shanghai requires improved internal park planning to enhance the cooling supply. In contrast, Shanghai's suburban areas exhibit significant supply-demand imbalances, especially in the south and southeast; they require an enhanced cooling supply through planning interventions. Incorporating thermal comfort differences into calculations shifts the highest per capita cooling supply area from the outer suburbs to the suburbs, substantially reducing areas with high demand but low supply. Our novel analytical approach to assessing park cooling accessibility can assist policymakers in developing precise climate adaptation strategies.

Green roofs are a sustainable strategy for improving the eco-environment in urban areas. However, plants on green roofs are increasingly threatened by extreme heat and drought due to climate change. There is a lack of comprehensive understanding of the physiological water demand of green roof vegetation under extreme heat. Therefore, this exploratory study investigated two commonly used Sedum species for urban green roofs (Sedum spectabile Boreau and Sedum alfredii Hance) under extreme heat, considering two treatments: irrigation and non-irrigation. The results indicated that under extreme heat, if without irrigation, the two Sedum species’ mean daily water consumption by transpiration was approximately 4.68 g per Sedum plant. However, under irrigation, the diurnal variation of stomatal conductance of the two Sedum species allowed the stomata to remain open throughout the day, resulting in an over-twentyfold increase in mean daily water consumption through transpiration (120.34 g per Sedum plant) and twofold increase in leaf area index. A random forest model showed that multi-environmental factors explained 67.53 % of the variability in stomatal behavior. Photosynthetically active radiation and soil moisture are the primary environmental factors that directly affect stomatal conductance. The irrigation induces stomatal opening throughout the diurnal cycle, ensuring the continuation of vegetation growth and the maintenance of physiological functions under extreme heat, which then contributes to keeping its eco-environmental functions and providing sustainable services, such as cooling and carbon sequestration. The findings of this study can guide planning and managing green roofs in urban areas that face extreme heat events.

Various physical environmental factors influence the recreational experience of urban river corridors, and understanding visitors' perceptions of these factors holds significant implications for promoting the health and well-being of urban residents. This study specifically focuses on the Hutuo River corridor in Shijiazhuang, China, and conducted a semi-structured questionnaire survey with 620 participants. The findings reveal that visitors mainly come here to socialize with family and friends in a natural environment and escape the city's hustle and bustle. Safety, management and maintenance, and overall conditions are the primary features visitors are concerned about in their experience. When addressing usage constraints, visitors highlight accessibility issues, including traffic congestion and overcrowding. Additional limitations encompass deficiencies in physical facilities, such as shortages of shared bicycles, convenience amenities, fitness facilities, and services catering to vulnerable groups. Moreover,challenges include unavailable toilet services and insufficient shaded areas along the riverbanks. Sociodemographic attributes such as gender, age, marital status,education level, monthly income, and residential distance significantly influence the recreational experience of urban river corridors, emphasizing the importance of considering diversity in urban planning and design. By comprehensively exploring the dynamics of urban river corridor utilization from a social-ecological perspective, our study's findings offer insights relevant to future design and maintenance endeavors.

In this study, we pilot a novel use of location quotient and proportional comparison methodology paired with mobile device location data. Specifically, we sought to understand visitation patterns in an urban park context based on visitor home locale socio-demographics, using an example from Fairmount Park in Philadelphia, PA, USA. We examined visitors’ home locale demographics (i.e., percent African American and median household income) across a variety of park amenities (e.g., playgrounds, ball fields, splashpads), using location quotients and proportional analyses to compare the home locale demographics of visitors to specific amenities to park visitors as a whole. Findings illustrate amenities with over- or underrepresentation of visitors from certain socio-demographic groups, with bivariate analyses documenting intersectionality between race and income. Results of such analyses can aid park managers in understanding use of particular amenities and service gaps among historically underserved populations, and in turn, support more equitable resource allocation.