Urban Forestry & Urban Greening最新文献

Incentive policies to promote new technologies is a strategy often employed by policymakers and governments. In some cities worldwide, mechanisms to encourage the adoption of Green Roofs (GR) have been implemented over the years. Although GR is not a new technology, the use of incentives, such as subsidies and tax abatements and/or exemptions, is still a recent strategy in many countries. The study covered in this paper proposes to explore the potential of combined incentive mechanisms for green roofs, using direct incentives (financial subsidies) and indirect incentives (tax/fee rebates). The granting incentives were verified by the feasibility assessment from both private (saved money) and public perspectives (reduction in municipal revenue) using scenarios of abatement percentages for fees and taxes and combining direct and indirect incentives. In addition, priority intervention areas for the installation of green roofs were defined based on three parameters: (i) the proportion of existing green spaces, (ii) urban heat islands and (iii) the potential of the building stock for green roof retrofit. The results show that granting a combined solution can be an effective option for facilitating the implementation of green roof projects. Moreover, the study serves as a decision support guide for politicians, urban planners, and public managers to formulate incentive proposals and make well-informed decisions regarding the incentive policies for GR.

Greenways are planned for various goals including nature conservation and promoting mobility by foot or bike in urban areas. A challenge associated with designed greenways pertains to their underutilization for mobility. Due to their multi-functionality greenways are usually not designed with a focus on active mobility, and thus, do not necessarily correspond with daily mobility patterns. This study addresses this issue by devising a methodology for the identification of optimal locations for active urban greenways within a metropolitan expanse, strategically aligned with the day-to-day pursuits of its inhabitants. Distinguished from prior greenway research, this investigation emphasizes individual mobility and adds a novel perspective to greenway planning. The method was tested in the city of Mashhad in Iran. By employing the Analytic Hierarchy Process methodology in conjunction with a Geographic Information System (GIS) and a gravity formula, a meticulous selection process yielded 19 nodes (sites) deemed most suitable for greenway implementation, selected from a pool of 93 prospective nodes. These findings offer pragmatic insights into the expansion of greenways tailored to the daily activities of urban inhabitants, thus complementing established greenway paradigms and strengthening the mobility dimension and active travel corridors in urban greenway planning.

Studies of associations between green space and overweight/obesity are often agnostic to green space type and potential contingencies by gender and household relocation. We investigated associations between total green space, tree canopy, and open grass within 1.6 km road network buffers, on body mass index (BMI) at baseline (n = 110,234; 2005–2009) and follow-up (n=50,672; 2012–2015) using data from the Sax’s Institute’s 45 and Up Study (NSW, Australia). Analyses considered potential non-linearities in these associations and the possibility for effect modification by gender and household relocation. Models were adjusted for age, couple status, region of birth, household income, employment status, educational attainment and housing status. Results revealed that 20 % or more tree canopy, compared to 0–10 % tree canopy was associated with lower BMI. Comparable findings were not observed for open grass or total green space. Nonlinear associations were observed, with a rapid decrease in odds of being overweight/obese at baseline with 20 % tree canopy, that became more stable by 30 % tree canopy. This non-linearity was stronger in females than males. By follow-up, associations indicated potential protective effects with 20 % or more tree canopy for overweight/obesity in females. Logistic regression models examined the odds of becoming a healthy weight at follow-up in overweight/obese individuals at baseline. Results indicated a potentially protective influence of tree canopy on overweight/obesity for people who did not move home (odds ratio (OR) = 1.06, 95 % confidence interval (CI) = 1.01–1.12), but not those who relocated (OR = 0.97, 95 % CI = 0.83–1.11). In sum, conserving and restoring urban tree canopy to at least 20% to 30% of nearby land-use may help to reduce levels of overweight and obesity in the community These benefits may be particularly potent for females and for people who are residentially stable. More open grass, or green space in general, may not have the same benefits for adult overweight/obesity as tree canopy.

Microclimate (e.g., temperature and humidity) in urban green space (UGS) patches changes nonlinearly from the edge to the interior, displaying a microclimatic edge effect (MEE). Reducing the MEE width is an effective and practical strategy for UGS planning and management to enhance climatic benefits (e.g., cooling). However, cost-effective approaches are not available to quantify the MEE width, let alone explore its driving factors. This study proposes a novel and cost-effective method to quantify the MEE width, based on the relationship between climatic variables (e.g., temperature and humidity) and the percentage of remaining UGS after successive edge removals. The method was tested in the subtropical city of Changsha, China, considering air temperature (AT) and humidity. Its effectiveness was also tested using widely available land surface temperature (LST) datasets while considering different spatial resolutions of the UGS map (i.e., 1–10 m) and different analytical units (i.e., 1 km² grid and township census tract). Finally, the generalizability of this method was validated with data on six other representative Chinese cities. The results show that: (1) The estimated MEE widths for temperature and humidity in Changsha are about 8 m and 6 m, respectively. (2) The approach obtains consistent MEE widths irrespective of temperature types and analytical units. (3) A high spatial resolution UGS map (i.e., 1 m) is recommended for higher accuracy. (4) This approach effectively identifies MEE width in six other representative Chinese cities (Beijing, Chengdu, Shanghai, Shenyang, Wuhan, Xi’an), demonstrating its generalizability. This novel approach provides an easy and fast method to identify the MEE width at the landscape scale, which can help (1) better understand the relationship between UGS fragmentation and the urban microclimate, and (2) better plan and manage UGS to improve the urban microclimate.

Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an ${\text{R}}_{\text{adj}}^2$ of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (${\text{R}}_{\text{adj}}^2$ of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (${\text{R}}_{\text{adj}}^2$of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an ${\text{R}}_{\text{adj}}^2$ of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.

Parks have evolved into essential urban sanctuaries, especially amid the COVID-19 pandemic. While previous studies have investigated the impact of lockdown measures on park visitation, limited attention has been given to the impacts of sudden easing of containment measures, especially the “unfencing” of parks in densely populated cities. Employing a regression discontinuity design (RDD), this study analyzes changes in park visitation behaviors among Shanghai residents (N=563) following the policy relaxation. We focus on three key aspects: visitation behavior, choice of park types, and activities undertaken within public parks compared to gated community greenspaces. The results reveal a short-term decrease in visits to nearby parks, reduced public transit use, and shorter travel times. This trend was accompanied by a reduction in visitation to various park types, especially larger ones, with waterfront greenspaces being an exception. However, visit frequency and duration remained unchanged. A significant decrease in exercise and enjoying weather was observed in both parks and community greenspaces. Additionally, there was a noticeable reduction in activities like playing with children, nature appreciation, specifically within community greenspaces. These findings extend our understanding of urban residents' engagement with greenspaces in a transitioning post-pandemic context, providing valuable insights in managing greenspaces during and after public health crises.

As households are increasingly aware that traditional lawns of a grass monoculture have environmental and biodiversity drawbacks, a movement has formed to advocate for alternative lawns. An example is the No Mow May movement, where households do not mow their lawns in May and allow dicots to grow and flower. The purpose of this study is to assess the willingness of U.S. households to join the No Mow May movement, and the extent to which they desire an alternative to the traditional lawn. A total of 908 U.S. households who actively maintain a lawn completed an online survey in May of 2023. When presented with information about the No Mow May movement, 56 % indicated they would participate if some of their neighbors did also. A variety of traditional and alternative lawn illustrations were then provided, where subjects were asked to rate the desirability of each. Most respondents preferred the traditional lawn, even if they looked upon alternatives favorably, while 15–20 percent favored an alternative lawn. These results suggest conventional lawns are still the preferred lawn, but alternatives lawns may become increasingly popular over time.

This study investigates the potential of utilizing volcanic ash (VA), classified as special waste, as an inorganic soil amendment to enhance tree growth and resilience in urban areas near volcanic regions. Lime trees were transplanted into pots filled with urban soil (Cnt) or amended with 10 % VA. Tree’s physiological traits were monitored over the growing season. Notably, VA-treated trees showed improved net CO₂ assimilation (P_n) and stomatal conductance (g_s) during the leaf senescent stage. The analysis of ash, and Cnt and VA soils, showed low concentrations of trace elements. In addition, trace element accumulation in the leaves of VA trees was not observed. In summer, during a 12-day drought stress test, VA-stressed trees exhibited enhanced water absorption, reduced lipid peroxidation, and higher P_n and g_s values in the initial days compared to control-stressed trees. Importantly, the VA also promoted a 33 % larger tree root system, potentially enhancing drought resilience. This could offer an important advantage for trees, especially during the tree's critical establishment phase. Thus, VA could be a promising amendment for urban soils to bolster tree tolerance to drought.

City populations are growing rapidly, with built-up areas occupying ever-larger areas within the cities. One of the last green places in a city is urban lawns. Our study aimed to investigate how lawn management changes can contribute to improving insect pollinator biodiversity at the city scale. Over two years, we studied dozens of urban lawns in Prague (Czech Republic) that were partially left unmown, and we selected three major groups of pollinators: Lepidoptera, Coleoptera and Hymenoptera. This study showed that leaving unmown lawn areas has a strong positive effect on insect biodiversity. The high intensity of lawn flowering through the vegetation season was also crucial. Our research showed that leaving unmown parts of urban lawns is one of the key amendments to maintain or even increase pollinator biodiversity in cities. It also appears to be the best way to attract red-listed species. A significant reducing mowing frequency to achieve a higher flowering intensity should be a positive solution. So-called insect gardening by reducing urban lawn mowing frequency or leaving parts of lawns completely unmown can help not only pollinators but also other animals that find food or shelter in them. This management will also enable the propagation of flowering plants. Additionally, biodiversity measures coincide with reducing evapotranspiration and thus cooling urban heat islands.

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R²: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R²: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R²: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.