Urban Street Trees Research Articles

A functional trait-based assessment of urban street tree selection for Ethiopia

Urbanization in Ethiopia is rapidly reducing green space and biodiversity, and placing ecological stress on trees. Urban street trees (UST) can provide multiple ecosystem services, but need to cope with challenging street ecology and future climate risks. However, UST selection in Ethiopia is poor, and conventional trials and modern breeding are costly and time consuming to apply in Ethiopia. The main objective of the study was to identify potential UST species for two Ethiopian cities; Addis Ababa, and Arba Minch, by studying interspecific differences in functional traits. For the purpose, we created a database with list of 120 candidate UST gathered from multi-stakeholder workshop, literature review, and senior experts survey, and their ecological with their phenological and morphological features. Then we filtered 25 potential UST through framework analysis considering eco-morphological features of the trees. For the 25 species, we measured three types of plant functional traits: conservative traits (leaf mass per area and leaf dry matter content), acquisitive traits (leaf area, specific leaf area and leaf water content) and drought tolerance traits (leaf turgor pressure loss point, and leaf succulence index). Our results showed a strong trade-off between conservative and acquisitive traits, particularly between leaf dry matter content (LDMC) and specific leaf area (SLA) (r = −0.71), consistent with plant economic spectrum theory. Using PCA analysis, we identified three groups of potential USTs (Type I, II and III) that exhibit different adaptation strategies, consistent with Grime’s trait-based classification of universal plant adaptation strategies (CSR). Compared to species with high acquisitive traits (Type II and Type III), Type I species have high conservative and drought-tolerant traits shows better adaptation to harsh road environments, whereas Type II and Type III species may be more advantageous for urban parks and other resource-rich components of urban ecology. We also found significant variation in conservative, acquisitive and drought-tolerant traits among different species, indicating their different adaptation strategies. Our research advances the knowledge of plant adaptation in urban environments and provides a useful method for UST selection.

Urban street trees as carbon sink: a case study of Naya Raipur, Chhattisgarh

Urban street trees as carbon sink: a case study of Naya Raipur, Chhattisgarh

A new approach to monitor the life cycle of urban street tree canopies

One common measure to mitigate the impacts of rising temperatures within cities is increasing the amount of vegetation, especially urban trees and their canopies. To maximize the associated benefits of urban trees in a given city or neighborhood, it is important that the canopy of newly planted trees matches or exceeds the canopy of the trees that are lost. However, cities often use cross-sectional data to make decisions about the number of trees that should be planted without considering an individual tree’s growth and life span. In this study, individual tree growth and longevity data were used to portray the current and future conditions of street trees in six neighborhoods near Downtown Los Angeles and to identify locations in those neighborhoods that are in urgent need of interventions for tree planting. The results of this study indicate that, under the typical tree mortality scenario, near half of the current tree canopy will disappear by 2050, and on average, approximately one percent of the existing street trees will be lost due to aging each year. The approach used in this study shows how cities can monitor the age and mortality rate of the urban tree canopy over time. This approach informs tree planting campaigns that will preserve the long-term vitality and size of urban tree canopies and contribute to the resiliency of cities in a warmer future climate.

YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network

In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development.

Identification of urban street trees for green belt development for optimizing pollution mitigation in Delhi, India.

The current study evaluated the effects of air pollution on selected street trees in the National Capital Territory during the pre- and post-monsoon seasons to identify the optimally suitable tree for green belt development in Delhi. The identification was performed by measuring the air pollution tolerance index (APTI), anticipated performance index (API), dust-capturing capacity (DCC) and proline content on the trees. The APTI of street trees of Delhi varied significantly among different tree species (F11,88.91 = 47.18, p < 0.05), experimental sites (F3,12.52 = 6.65, p < 0.001) and between seasons (F1,31.12 = 16.51, p < 0.001), emphasizing the relationships between trees and other types of variables such as the climate and level of pollution, among other factors. This variability emphasizes the need to choose trees to use for urban greening in the improvement of air quality in different environments within cities. Ascorbic acid (AA) concentration and relative water content (RWC) had a strong influence on APTI with an extremely significant moderate positive correlation between AA concentration and APTI (r = 0.65, p < 0.001) along with RWC and APTI (r = 0.52, p < 0.001), indicating that higher levels of AA concentration and RWC are linked to increased air pollution tolerance. The PCA bi-plot indicates AA has poor positive loading coefficients with PC1 explaining 29.49% of the total variance in the dataset. The highest APTI was recorded in Azadirachta indica (22.01), Leucaena leucocephala (20.65), Morus alba (20.62), Ficus religiosa (20.61) and Ficus benghalensis (19.61), irrespective of sites and seasons. Similarly, based on API grading, F. religiosa and F. benghalensis were identified as excellent API grade 6 (81-90%), A. indica and Alstonia scholaris as very good API grade 5 (71-80%), M. alba, Pongamia pinnata and Monoon longifolium as good API grade 4 (61-70%) and Plumeria alba as moderate API grade 3 (51-60%) in different streets of Delhi. As these plants are indigenous to the region and hold significant socio-economic and aesthetic significance in Indian societies, they are advisable for avenue plantations as part of various government initiatives to support environmental sustainability.

Social and environmental outcomes of urban street tree bed stewardship

Abstract As projections of exponential urbanization persist, promoting the health of urban populations is one of the most urgent and challenging issues of the 21st century. By taking a planetary health approach and considering the reciprocal relationship between human health and the health of ecosystems, the integration of nature-based solutions into cities can address multiple urban challenges at once. Citizen stewardship of street trees and street tree beds is one such nature-based solution that challenges rapid urbanization while offering a healthier and greener future. Not only does citizen stewardship of street trees play a significant role in tree longevity, but our research suggests that the process of stewardship itself also positively impacts the health and wellbeing of stewards themselves. In this study, we sought to assess the motivations of street tree citizen stewards and the perceived social, environmental, and health effects of stewarding street tree beds in Freiburg, Germany. Based on a questionnaire-based survey and semi-structured interviews with tree stewards, we found that the most common motivations of citizen stewards were modeling positive environmental behavior as well as a desire to help the environment and one’s community. The perceived outcomes included a strong sense of place, a meaningful connection to nature, and the belief that stewarding a street tree bed is good for one’s mental health as well as the environment. Participants also considered street tree beds to be their personal gardens, suggesting that street tree beds may serve as restorative spaces that strengthen public health and social capital while sustaining urban greening. This case study supports and expands the scientific discourse of urban gardening while suggesting that citizen stewardship of street trees and tree beds offers myriad co-benefits for urban ecosystems and public health.

Quantifying City- and Street-Scale Urban Tree Phenology from Landsat-8, Sentinel-2, and PlanetScope Images: A Case Study in Downtown Beijing

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI &gt; 0.5) to high (NDVI &gt; 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution.

The challenges of urban street trees and how to overcome them

The challenges of urban street trees and how to overcome them

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

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).

Tree abundance, species richness, or species mix? Exploring the relationship between features of urban street trees and pedestrian volume in Jinan, China

Accumulating evidence has confirmed that urban greenery, especially street trees, is beneficial to walking behaviors. Existing street greenery exposure-walking behavior studies have focused on the quantity of greenery, which is often measured by normalized difference vegetation index (NDVI) or green view index (GVI). However, some important qualities of street trees, the dominant component of urban greenery, were often overlooked, due to the labor-intensive and expensive nature of on-site surveys. To address this issue, we proposed a cutting-edge deep learning technique to identify street tree species at the individual tree level. We established a citywide dataset of all street trees with species information via 185,831 Baidu Street View images (BSV) in Jinan, China. Population-level walking intensity, measured by pedestrian volume, was retrieved from BSV images using Baidu AI. We further adopted spatial regression models to investigate the association between pedestrian volume and street tree characteristics, including street tree abundance (number of street trees), species richness (number of unique tree species) and species mix (the degree of diversity of tree species). The built environment and urban greenery covariates were adjusted in the models. The results indicate that the street tree abundance and species mix are positively associated with pedestrian volume. Species richness is not associated with it. Besides, spatial mismatch is identified between abundance and species mix of street trees in the study area. Hence, to facilitate walking behavior and deliver related health benefits, it is necessary to develop fine-grained measures of street greenery features.

Lepidoptera species richness and community composition in urban street trees

The triple threats of climate change, habitat loss, and environmental pollution have stimulated discussion on how urban areas can be modified to both mitigate heat increases and provide habitat for wildlife such as insects. The strategy of using trees to reduce temperatures has been adopted by numerous cities. However, the majority of street trees planted around the world are non-native. Studies conducted in non-urban areas have demonstrated in comparison to native plants, non-native plants are less likely to support native insect diversity. Here we use a database approach to quantify the number of native Lepidoptera species associated with 76 of the most common street tree species planted in Vancouver, Canada. We tested the prediction that compared to non-native trees, native street trees will support a higher diversity and unique community of native Lepidoptera. As predicted, native street trees were associated with five times as many native Lepidoptera species, and the Lepidoptera communities supported by native vs. non-native street trees were distinct. There was no difference in native Lepidoptera associations between broadleaf vs. coniferous street trees. These results are consistent with studies that have used active sampling techniques to investigate insect richness on a smaller subset of native and non-native tree species. Collectively, these data provide good evidence that the planting native instead of non-native trees will help stem the loss of insect diversity in urban areas

Records of urban occurrences expand estimates of the climate niches in tree species

AbstractAimQuantifying a species' climate niche is often the first step to determining potential sensitivity to climate change. This process typically relies on native occurrence records, assuming that these reflect the breadth of a species' climatic requirements. Yet, many species survive in non‐native regions with climates beyond their native range. Identifying their characteristics could help to better elucidate responses to climate change. Here, we use an extensive data set of urban street tree and native range records for 566 tree species to assess differences between species native and urban climate niches and whether niche differences are predictable given characteristics of species native ranges.LocationGlobal.Time Period1970–2020.Major Taxa StudiedTree species.MethodsMultivariate niche estimations were undertaken using eight climate variables. Calculations included the size of species' urban and native niches, overlap and direction of shift. Relationships between niches and native range attributes (temperature and precipitation breadth, range size, Köppen Geiger subclasses occupied) were calculated using multiple linear regression.Results83% of tree species studied had significantly larger urban niches compared to native niches. Species with narrow geographic ranges demonstrated greater mismatch between their two niches, and the direction of niche shift was associated with their native range Köppen Geiger zone. For example, species native to the wet tropics tend to be planted in cooler, drier urban spaces; those native to cold regions are planted in warmer urban spaces.Main ConclusionNative range occurrences may poorly reflect the breadth of climatic conditions a species can tolerate—particularly for species with narrow geographic ranges. For some species, Köppen Geiger zones may be a useful proxy to help understand sensitivity to climate change. Accounting for possible plasticity in tree species to climate can improve our understanding of their sensitivity and adaptive capacity to climate change.

Large-scale determinants of street tree growth rates across an urban environment.

Urban street trees offer cities critical environmental and social benefits. In New York City (NYC), a decadal census of every street tree is conducted to help understand and manage the urban forest. However, it has previously been impossible to analyze growth of an individual tree because of uncertainty in tree location. This study overcomes this limitation using a three-step alignment process for identifying individual trees with ZIP Codes, address, and species instead of map coordinates. We estimated individual growth rates for 126,362 street trees (59 species and 19% of 2015 trees) using the difference between diameter at breast height (DBH) from the 2005 and 2015 tree censuses. The tree identification method was verified by locating and measuring the DBH of select trees and measuring a set of trees annually for over 5 years. We examined determinants of tree growth rates and explored their spatial distribution. In our newly created NYC tree growth database, fourteen species have over 1000 unique trees. The three most abundant tree species vary in growth rates; London Planetree (n = 32,056, 0.163 in/yr) grew the slowest compared to Honeylocust (n = 15,967, 0.356 in/yr), and Callery Pear (n = 15,902, 0.334 in/yr). Overall, Silver Linden was the fastest growing species (n = 1,149, 0.510 in/yr). Ordinary least squares regression that incorporated biological factors including size and the local urban form indicated that species was the major factor controlling growth rates, and tree stewardship had only a small effect. Furthermore, tree measurements by volunteer community scientists were as accurate as those made by NYC staff. Examining city wide patterns of tree growth indicates that areas with a higher Social Vulnerability Index have higher than expected growth rates. Continued efforts in street tree planting should utilize known growth rates while incorporating community voices to better provide long-term ecosystem services across NYC.

Street tree risk assessment at the food court of Sultan Muhammad IV Stadium in Kota Bharu, Kelantan

Urban street trees constitute a significant element of the metropolitan ecosystem, but they pose a certain risk when they are present with humans. The extreme effects of trees may manifest as harm and detriment to both communal and individual assets. In addition, the deterioration and death of old and valuable trees can affect the diversity and cultural value of this natural treasure. The purpose of this study was to conduct a tree risk assessment by conducting a visual investigation of the usual nine trees at the Sultan Muhammad IV Stadium food court, Kota Bharu, Kelantan. The evaluation was executed employing the Visual Tree Assessment (VTA) methodology, which was developed by the International Society of Arboriculture (ISA). The findings revealed that one tree was classified as being in the extreme risk group, five trees were classified as being in the high-risk category, and three trees were classified as being in the moderate risk category. The study emphasizes the importance of understanding tree risk and health to prioritize community safety and environmental sustainability in urban areas.

Application of Ground Penetrating Radar in Warning of Urban Street Tree Collapse: A Review

Application of Ground Penetrating Radar in Warning of Urban Street Tree Collapse: A Review

Numerical simulation of the effect of street trees on outdoor mean radiant temperature through decomposing pedestrian experienced thermal radiation: A case study in Guangzhou, China

Street trees have often been proposed to improve the outdoor thermal environment. However, due to the combined effects generated by street trees, the impacts of street trees on the thermal radiation field in a street canyon and mean radiant temperature (Tmrt) reduction have not been systematically analyzed in previous studies. In this paper, we developed a Tmrt decomposition model using first-order Taylor series expansion to decompose the various factors of the street trees on Tmrt reduction and quantify their individual and combined effects in Guangzhou, China. Our results showed that street trees significantly reduce Tmrt and severe heat stress events (Tmrt ≥ 60 °C). For 80% tree crown cover, the maximum decrease of Tmrt by 26.2 °C - 42.1 °C and the severe heat stress events decreased by 197 h - 500 h over the course of a year at different sidewalk locations, compared to the street canyon without trees. During the daytime, the interception of radiation (especially short-wave radiation) by street trees was the primary determinant in reducing Tmrt, with a contribution that mostly exceeded 70%. Additionally, the shading effects of street trees decreased the short-wave radiation reflected from urban surfaces, contributing about 20% to Tmrt reduction. Lastly, our results also showed the leaf area index played an important role in the Tmrt reduction. Our study quantified the individual and combined effects of each factors of reducing Tmrt by street trees, which will better understand the thermal benefits of urban street trees.

Improving the ecological benefits evaluation on urban street trees: Development of a living vegetation volume quantifying framework with multi-source data

The rate of urbanization is increasing, and solutions to the pollution issue are desperately needed. Urban street trees are an essential factor in improving the environmental quality of cities. Quantifying precise ecological benefits of individual trees is important for monitoring urban ecology. However, since street tree surveys are time-consuming and expensive, it is challenging to assess the ecological benefits of street trees. Besides, there is still a need for more investigation into the advantages of multi-source data for ecological benefits estimates. Effective living vegetation volume (eLVV) as an urban green space indicator is beneficial for providing the vertical structure of street trees and offering a possibility for estimating the ecological benefits. The objective of this study is to evaluate the accurate ecological benefits of street trees by calculating eLVV. We developed and validated a framework for evaluating the ecological benefits of urban vegetation using point cloud and image processing techniques. Terrestrial laser scanning and unmanned aerial vehicle data were used to predict indicators at the single tree scale and road scale. Models based on game theory were applied to score all the indicators and assess the ecological benefits. The results showed that: (1) The proposed framework demonstrated that multi-source data allowed for the exact indicators of ecological benefits. (2) The eLVV of the street trees on two roads was less on the north side than on the south side, reflecting the difference in the ecology of the street trees in terms of orientation. This paper explores the advantages of multi-source data in measuring detailed ecological benefits, which support the digital management of street trees and the sustainability of regional ecology.

The ‘civic-transformative’ value of urban street trees

Urban street trees (USTs) have a range of values – some of which are easier to quantify than others. Focusing specifically on the UK context and using the Sheffield Tree Protests (2012–) as a case study, whilst confirming existing research as to the variety of values associated with their specifically ‘cultural’ services, the article argues that USTs have an additional potential form – what I call ‘civic-transformative value’. This form of value has at least three key characteristics. Firstly, it is place-based and communal; second, its form is ‘relational’; and finally, as intrinsically contingent, it is pluralistic in the sense that its civic-transformative potential is dependent on successfully integrating a range of other values. The article emphasises both the possibility and necessity of ‘convergence’ – that is, a pluralistic and pragmatic alliance of values which might help protect not only USTs, but other embattled sites of nature.

Modeling Management-Relevant Urban Forest Stand Characteristics to Optimize Carbon Storage and Sequestration

Urban forests are an essential part of adaptation and mitigation solutions for climate change. To understand the relationship between carbon storage, sequestration, and stand density in the most heavily-managed aspect of the urban forest—street trees—we modified the parameters and algorithms of a rural forest dynamics model, the perfect plasticity approximation (PPA), to reflect urban street tree conditions. The main changes in the new street tree PPA are the maintenance of a prescribed stand density via management of recruitment, the possibility of crown-roof overlap, and increased mortality rates. Using the street tree PPA, we explored overall productivity, crown allometry relative to stem diameter, and mortality rate to test each mechanism’s impact on urban street tree carbon storage and sequestration across a gradient of prescribed stand density, with the goal of finding conditions in which street tree carbon storage and sequestration are optimized. We compared the qualitative trends in storage from the street tree PPA to those found in the U.S. Forest Service’s Urban Forest Inventory Analysis data. We found that carbon storage and sequestration increase with prescribed density up to a point where carbon storage and sequestration saturate. Optimized carbon storage and sequestration result from a stand with high productivity, maximized crown allometry relative to stem diameter, and a low mortality rate. These insights can be used to inform urban street tree maintenance strategies that effectively increase carbon storage and sequestration within a given city, such as focusing afforestation campaigns on adequate areas with the lowest street tree densities.

Assessing urban-heating impact on street tree growth in Berlin with open inventory and environmental data

AbstractExcess heat (i.e., Urban Heat Island; UHI) and other urban conditions affect tree physiology with outcomes from enhanced growth to mortality. Resilient urban forests in the face of climate change require species-specific understanding of growth responses. However, previous studies assessing growth dynamics were primarily based on remote sensing of communities rather than individuals, or relied on labor-intensive methods that can limit the spatial coverage necessary to account for highly variable urban growing conditions. Here, we analyze growth dynamics of common urban street tree species over time and across space for Berlin (Germany) combining dendroecological (temporal) and inventory assessments (spatial). First, we show annual increments increased across the 20th century for early (i.e., young) growth. Second, we use an approach relying on open inventory data to identify growth potential in relation to excess heat while accounting for age, potential management effects, and the urban fabric (i.e., planting area; building density, height; available soil nutrients) with generalized additive models for the ten most abundant species. Our analyses showed that younger trees may benefit from increased temperatures, while older individuals feature lower growth at greater UHI magnitudes. Furthermore, planting area as well as building density modulate growth responses to temperature. Lastly, we discuss management implications in the context of climate change mitigation, considering that younger trees are predominantly located at UHI “hot spots” and will undergo the observed age-dependent shift in temperature-growth sensitivity. By relying on increasingly available open data, our approach here is or will be transferable to other urban regions.