Tree Species In Urban Environments Research Articles

Structure, diversity and spatial distribution pattern of significant tree species in Hefei city

<p>In order to further tap the ecological potential of urban trees, improve the diversity of tree species in urban environment, and promote the sustainable development of urban trees, this paper further analyzes the structure, diversity and spatial distribution law of the prominent tree species in Hefei city on the basis of on–the–spot identification of the prominent trees distributed in the main city. The results show that: 1) 528 significant trees are identified in the three districts, belonging to 40 species, 36 genera and 27 families, with 23 trees evaluated by qualitative index and 505 trees by quantitative index. In quantity distribution, the number of significant trees in the three districts is ranked as Yaohai District> firstringroad City> Government Affairs District. The top four tree species in relative abundance are Platanus acerifolia, Cinnamomum camphora, Ginkgo biloba and Cedar. 2) In terms of the tree age structure, most trees are 20~40 years old, and only 22 trees are over 100 years old; the ratio of evergreen to deciduous tree species is 1: 3, and the ratio of plant to tree is about 7: 18; there are 23 native tree species accounting for 57.50%, but only 171 trees. 3) In terms of area distribution, the average tree height and crown area in Yaohai District are the largest, the average DBH, Shannon–Wiener index and Simpson index in the first–ring city area are the highest, and the species evenness index in Government District is the highest. 4) In terms of site types, most significant trees are distributed in urban streets, residential areas and parks, and only 11.74% of them are distributed in government organizations, schools, public facilities, scenic spots and religious land.</p>

Developing a sub-meter phenological spectral feature for mapping poplars and willows in urban environment

Poplar and willow catkins (PWCs) have caused severe impacts on human health and environmental quality, and accurate poplars and willows (PaWs) mapping with remote sensing is essential to monitor and manage the PWCs. However, two major issues have constrained the urban tree species (e.g. PaWs) identification: (1) the urban tree landscapes are highly fragmented and susceptible to the existence of mixed pixels in the remote sensing imagery; (2) the tree species in urban environment are diverse with high spectral similarity. To this end, this study developed a sub-meter phenological spectral feature (Spsf) with multi-scale and multi-temporal remote sensing imagery for monitoring PaWs at the tree species level.Spsf includes three steps: (1) exploring three key phenological periods of PaWs (leafless period, greenleaf period, and senescence period); (2) selecting one or three spectral indexes to characterize each phenological period; (3) stacking the spectral vegetation indexes from Sentinel-2 SR imagery and freely available sub-meter (0.8 m) Google Earth imagery together. Subsequently, Spsf was taken as the input data to train the deep learning DeepLabv3 + model for predicting the PaWs distribution. The Beijing Plain was chosen as the study area, where the distribution of PaWs was extensive and fragmented. Compared with the field survey reference data, the derived PaWs map achieved the overall accuracy higher than 92 % and the Kappa coefficient of 0.83. The Spsf integrated rich spatial information from sub-meter imagery and phenological spectral information from Sentinel-2 imagery, which may alleviate the impacts of mixed pixels and enhance the spectral separability between PaWs and other tree species effectively. The proposed Spsf-based method provides a new paradigm for sub-meter tree species mapping with multi-source free remote sensing data. The PaWs map can serve as reference data for the relevant departments to monitor and manage the PWCs.

Strong variations in urban allergenicity riskscapes due to poor knowledge of tree pollen allergenic potential

Exposure to allergenic tree pollen is an increasing environmental health issue in urban areas. However, reliable, well-documented, peer-reviewed data on the allergenicity of pollen from common tree species in urban environments are lacking. Using the concept of ‘riskscape’, we present and discuss evidence on how different tree pollen allergenicity datasets shape the risk for pollen-allergy sufferers in five cities with different urban forests and population densities: Barcelona, Montreal, New York City, Paris, and Vancouver. We also evaluate how tree diversity can modify the allergenic risk of urban forests. We show that estimates of pollen exposure risk range from 1 to 74% for trees considered to be highly allergenic in the same city. This variation results from differences in the pollen allergenicity datasets, which become more pronounced when a city’s canopy is dominated by only a few species and genera. In an increasingly urbanized world, diverse urban forests offer a potentially safer strategy aimed at diluting sources of allergenic pollen until better allergenicity data is developed. Our findings highlight an urgent need for a science-based approach to guide public health and urban forest planning.

Tree Allergen Pollen-Related Content as Pollution Source in the City of Ourense (NW Spain)

Allergies became a major public health problem, identified as an important global pandemic with a considerable impact on the worldwide economy. In addition, a higher prevalence of pollen Type I sensitization cases in urban environments in comparison with the rural territories was detected. Our survey sought to assess the main biological pollution episodes caused by the aeroallergens of the major allergenic tree species in urban environments. A Hirst-type volumetric device was used for pollen sampling and a Burkard Cyclone sampler for the detection of tree atmospheric allergens over two years. The main allergens of Alnus, Fraxinus, Betula, Platanus and Olea, were detected in the atmosphere. Three peaks of important pollen concentrations were recorded throughout the year. The developed regression equations between pollen counts and allergen proteins registered great R2 values. The number of days with probability of allergenic symptoms was higher when the pollen and allergen data were assessed altogether. Fraxinus allergens in the atmosphere were detected using Ole e 1 antibodies and the Aln g 1 allergens with Bet v 1 antibodies, demonstrating the cross-reaction processes between the principal allergenic proteins of the Oleaceae and Betulaceae families. Long Distance Transport processes (LDT) showed that pollen from Betula populations located in mountainous areas increased the secondary peaks of pollen and allergen concentrations, and air masses from extensive olive orchards of North-Eastern Portugal triggered the highest concentrations in the atmosphere of Olea pollen and Ole e 1 allergens.

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery.

This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

Estimation of the Allergenic Potential of Urban Trees and Urban Parks: Towards the Healthy Design of Urban Green Spaces of the Future.

The impact of allergens emitted by urban green spaces on health is one of the main disservices of ecosystems. The objective of this work is to establish the potential allergenic value of some tree species in urban environments, so that the allergenicity of green spaces can be estimated through application of the Index of Urban Green Zones Allergenicity (IUGZA). Multiple types of green spaces in Mediterranean cities were selected for the estimation of IUGZ. The results show that some of the ornamental species native to the Mediterranean are among the main causative agents of allergy in the population; in particular, Oleaceae, Cupressaceae, Fagaceae, and Platanus hispanica. Variables of the strongest impact on IUGZA were the bioclimatic characteristics of the territory and design aspects, such as the density of trees and the number of species. We concluded that the methodology to assess the allergenicity associated with urban trees and urban areas presented in this work opens new perspectives in the design and planning of urban green spaces, pointing out the need to consider the potential allergenicity of a species when selecting plant material to be used in cities. Only then can urban green areas be inclusive spaces, in terms of public health.

A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs.

A multiple criteria decision-making approach to put forward tree species in urban environment

Trees are vital for improving urban climate in many aspects and dimensions, especially for densely populated environments characterized by high levels of air pollution. This paper presents a decision-making scheme for benchmarking and prioritizing tree species in urban environments. Public authorities in real-life cases put forward trees species based mainly on cost or aesthetics criteria. In contrast to most-real life cases, the proposed approach facilitates the inclusion of multiple criteria to support decision-making i.e. life span, required growth space, planting capability in built environment, aesthetics, tolerance, pollution attenuation, adaptation to local climate, crown density, cost, and potential allergenicity of species. The framework combines two multi-criteria methods to provide an optimal ranking and enhance the robustness of decision-support. The approach is implemented for the center of Thessaloniki, Greece. Mulberry, sweetgum, scholar tree and hackberry are highly ranked and can be considered as excellent alternatives considering their combined performances to all criteria. A thorough sensitivity analysis by varying parameter values demonstrates the robustness of the corresponding ranking. Although there are gaps of knowledge and uncertainties, especially in the quantification of air pollution attenuation, the proposed approach provides a roadmap for decision-makers to put forward tree species in an organized manner.

Diversity and distribution of the urban tree population in ten major Nordic cities

In order to have a healthy and sustainable urban tree population, a high diversity of species and genera is needed. This study examined (1) the diversity and distribution of genera and species of urban trees in the Nordic region; (2) the diversity in different sites of the city, distinguishing between street and park environments; and (3) the presence of native versus non-native tree species in urban environments in the Nordic region. The analysis of tree diversity was based on urban tree databases comprising a total of 190682 trees in 10 Nordic cities – Aarhus and Copenhagen in Denmark; Espoo, Helsinki, Tampere and Turku in Finland; Gothenburg, Malmo and Stockholm in Sweden; and Oslo in Norway. The tree databases for Copenhagen, Espoo, Helsinki, Stockholm and Tampere only record street trees, while the remaining databases also include park trees. Tilia was the most dominant genus in Arhus, Copenhagen, Espoo, Gothenburg, Helsinki, Oslo and Stockholm, while Sorbus was the most dominant in Malmo and Betula in Tampere and Turku. Tilia×europaea was the most common species, comprising 16.0% of the total number of tree species. There was a higher proportion of species in parks than in street environments. The number of non-native species was higher than the number of native species in both street and park environments. However, the number of individuals belonging to native species was higher than the number of non-native individuals in all cities and environments except park environments in Arhus. The concluding recommendation from this study regarding greater diversity of genera and species is to exploit local experiences of rare species from local urban tree databases. After appropriate evaluation, urban tree planners can evaluate these rare species in larger numbers for e.g. street environments, where the need is greatest.