Distribution Of Urban Vegetation Research Articles

The green divide: A spatial analysis of segregation-based environmental inequality in Vienna

Cities, as the human habitat of the 21st century, will increasingly face climate change-related risks. Extreme weather events, hot spells, and rising air pollution already have widespread impacts on people, affecting their health and well-being. Urban vegetation is a proposed nature-based solution to address these challenges. However, the equitable distribution of urban vegetation is not always ensured. The study applies two innovative segregation-based inequality indices, recently developed by Schaeffer and Tivadar (2019), which reference to the residential segregation literature as a source of insight for the measurement of environmental inequalities within urban areas. Using high-resolution satellite data and demographic data on a 250 m2 grid level, this study is able to deliver robust evidence on the existence of environmental inequalities with respect to ethnic minorities and urban vegetation. It is observed that minorities consistently experience higher levels of segregation from urban vegetation compared to non-minorities, even when accounting for interactions with wider neighbourhoods. Additionally, the application of a Jackknife simulation provides insights for local policy interventions, mapping hotspots of urban inequalities and provides a solid starting point to tackle these issues on the ground.

Socioeconomics explain tree diversity, abundance, and composition in the compact city of Barcelona, Spain

Trees are frequently unevenly distributed in urban areas, and their diversity, abundance, and composition vary spatially. Some studies have shown that the unequal distribution of urban trees can be associated with demographic and socioeconomic factors. However, most studies have been conducted in diffuse urban systems using remotely-sensed tree cover within single or aggregated land-use types. We examined if the relationship between tree diversity, abundance, composition, and socioeconomic factors varied across land-use types (parks, streets, zonal areas, and total) in the compact city of Barcelona, Spain. We calculated tree species richness and abundance across 73 neighborhoods using 229,962 geo-referenced records of individual trees sampled in 2021. Life expectancy positively explained tree richness and abundance across land-use types, reinforcing that access to urban green infrastructure promotes longer life spans. In contrast, income did not positively explain tree richness or abundance, indicating that tree distribution does not directly result in biodiversity and green space inequalities for disadvantaged groups in terms of income. Total tree composition across neighborhoods responded to socioeconomic gradients, evidencing that neighborhood appearances are socially stratified. Street tree abundance increased in densely populated neighborhoods, possibly because intense street tree plantings in these neighborhoods compensate for the lack of other green infrastructure. Urban tree planting, particularly in streets, can be an effective strategy to increase green spaces in compact cities to overcome the disparities resulting from the unequal distribution of urban vegetation. Long-term data on socioeconomic factors and urban tree distribution are necessary to determine how social stratification affects urban diversity.

Wealthy, educated, and… non-millennial? Variable patterns of distributional inequity in 31 Canadian cities

This article assesses the distribution of urban vegetation in 31 Canadian cities to determine whether certain social-economic/demographic factors are associated with increased proximity to greenness. Using spatial lag models, Canadian census data, and urban ‘greenness fractions’ derived from Landsat imagery, we find diversity between cities in the factors associated with increased urban vegetation. This makes it difficult to establish a single overarching narrative about the state of environmental justice and green equity as it pertains to proximity to vegetation in Canadian cities. Factors such as greater household income and educational attainment were positively associated with proximity to vegetation in numerous cities, while the proportion of millennials was often negatively associated. Variables including the proportion of visible minorities, Indigenous people, and recent immigrants infrequently had significant associations with urban vegetation. These results highlight the need for planning and management actions tailored to individual Canadian cities to improve green equity for marginalized and underserved groups. We discuss the need for funding for greening initiatives and maintenance; democratic participation, engagement, and education opportunities for residents; urban planning that considers the challenge of greening dense environments; and consideration of potential negative outcomes of greening, such as gentrification, to improve green equity in Canadian cities.

Experimental Analysis of Geo-spatial Data to Evaluate Urban Greenspace: A Case Study in Dortmund, Germany

Urban greenspace is of high importance for the general public from different points of view. Among others, it is beneficial for recreational activities as well as for cooling effects during summer in urban climate. Many studies investigate urban greenspace from perspectives related to environmental justice or health geography that are usually based on environmental and sociodemographic data of diverse scales or spatial levels of detail and often suffer from high degrees of generalization. To demonstrate the benefit of a higher degree of spatial detail, a representative part of the city of Dortmund (Germany) is selected as study area. A time series of freely available Sentinel-2 images and a freely available digital object height model (DOHM) are used to analyze and describe the spatio-temporal distribution of urban vegetation and its potential effects on the population. Doing so, the Sentinel-2 images are used for the calculation of vegetation indices to identify the active green period of different vegetation land covers. In addition to that, the three-dimensional appearance of vegetation is correlated with the green period to assume the relevance of green land covers for the general public. The results show that an appropriate selection of cloud-free satellite images is of importance for a greenspace analysis. The combination of high resolution 2D and 3D data sets, namely NDVI and plant height information, reveals the spatial distribution of well-equipped greenspace areas with high recreational and health potential as well as poorly-equipped areas in urban neighborhoods, that could be improved.

Spatial distribution of urban vegetation: A case study of a Canadian University Campus using LiDAR-based metrics

Spatial distribution of urban vegetation: A case study of a Canadian University Campus using LiDAR-based metrics

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

Visualising the urban green volume: Exploring LiDAR voxels with tangible technologies and virtual models

The distribution of vegetation within urban zones is well understood to be important for delivery of a range of ecosystem services. While urban planners and human geographers are conversant with methodologies for describing and exploring the volumetric nature of built spaces there is less research that has developed imaginative ways of visualising the complex spatial and volumetric structure of urban vegetation from the treetops to the ground. Using waveform LiDAR data to measure the three-dimensional nature of the urban greenspace, we explore different ways of virtually, and tangibly engaging with volumetric models describing the 3D distribution of urban vegetation. Using waveform LiDAR data processed into voxels (volumetric pixels) and experimenting with a variety of creative approaches to visualise the volumetric nature of the data, we describe the development of new methods for mapping the urban green volume, using a combination of Geographic Information Systems (GIS), Minecraft, 3D printing and Computer Numerical Control (CNC) milling processes. We demonstrate how such methodologies can be used to reveal and explore the complex nature of the urban green volume. We also describe the outcome of using these models to engage diverse audiences with the volumetric data. We explain how the products could be used readily by a range of urban researchers and stakeholders: from town and city councils, to architects and ecologists.

Housing Market Activity is Associated with Disparities in Urban and Metropolitan Vegetation

In urban areas, the consistent and positive association between vegetation density and household income has been explained historically by either the capitalization of larger lawns and lower housing densities or landscaping and lifestyle districts that convey prestige. Yet cities with shrinking populations and rising land burdens often exhibit high vegetation density in declining neighborhoods. Because the observed associations do not directly address the causal connection between measures of social privilege and vegetation in urban landscapes, it is difficult to understand the forces that maintain them. Here, we compare patterns of household income with new measures derived from housing market data and other parcel-level sources—sale prices, tax foreclosures, new housing construction, demolitions, and the balance of construction and demolition. Our aim is to evaluate whether these spatially, temporally and semantically finer measures of neighborhood social conditions are better predictors of the distribution of urban vegetation. Furthermore, we examine how these relationships differ at two scales: within the City of Detroit and across the Detroit metropolitan area. We demonstrate, first, that linear relationships between income or home values and urban vegetation, though evident at broad metropolitan scales, do not explain recent variations in vegetation density within the City of Detroit. Second, we find that the real estate and demolition records demonstrate a stronger relationship with changes in vegetation density than corresponding changes in US Census measures like income, which suggests they hold at least as much interest for understanding how the relationships between biophysical changes and neighborhood change processes come about.

Evapotranspiration of urban landscapes in Los Angeles, California at the municipal scale

AbstractEvapotranspiration (ET), an essential process in biosphere‐atmosphere interactions, is highly uncertain in cities that maintain cultivated and irrigated landscapes. We estimated ET of irrigated landscapes in Los Angeles by combining empirical models of turfgrass ET and tree transpiration derived from in situ measurements with previously developed remotely sensed estimates of vegetation cover and ground‐based vegetation surveys. We modeled irrigated landscapes as a two‐component system comprised of trees and turfgrass to assess annual and spatial patterns of ET. Annual ET from vegetated landscapes (ETveg) was 1110 ± 53 mm/yr and ET from the whole city (vegetated and nonvegetated areas, ETland) was three times smaller, reflecting the fractional vegetation cover. With the exception of May and June, monthly ETland was significantly higher than predicted by the North American Land Data Assimilation System. ETveg was close to potential ET, indicating abundant irrigation inputs. Monthly averaged ETveg varied from 1.5 ± 0.1 mm/d (December) to 4.3 ± 0.2 mm/d (June). Turfgrass was responsible for ∼70% of ETveg. For trees, angiosperm species (71% of all trees) contributed over 90% to total tree transpiration, while coniferous and palm species made very small contributions. ETland was linearly correlated with median household income across the city, confirming the importance of social factors in determining spatial distribution of urban vegetation. These estimates have important implications for constraining the municipal water budget of Los Angeles and improving regional‐scale hydrologic models, as well as for developing water‐saving practices. The methodology used in this study is also transferable to other semiarid regions for quantification of urban landscape ET.

A surface heat island study of Athens using high-resolution satellite imagery and measurements of the optical and thermal properties of commonly used building and paving materials

High-spatial resolution multispectral satellite images collected over the metropolitan Athens area in Greece were used to generate (a) a shortwave albedo map depicting the albedo spatial variations across the metropolitan area, (b) a fractional vegetation cover map showing the spatial distribution of urban vegetation and (c) a daytime and night-time land surface temperature (LST) map depicting the spatial variations of the surface temperature across the city. From LST maps, cooling and heating regions were identified and analysed to reveal relationships between surface heat islands and urban surface characteristics. Based on the data acquired with the use of satellite images and in order to better define the heat island problem and the mitigation measures that need to be taken, the most common building and paving materials used in the urban fabric of Athens were examined. Their optical properties were measured using a UV/VIS/NIR spectrophotometer fitted with an integrating sphere, an emissometer and their thermal performance was evaluated. Furthermore, measurements of the spectral reflectance help explore the possibility of increasing the near-infrared reflectance of materials in order to increase their total solar reflectance. The solar reflectance index of the samples was calculated in order to characterise them as ‘cool’ or ‘warm’. Cool materials, with high albedo and thermal emittance values, attain lower surface temperatures when exposed to solar radiation, reducing the transference of heat to the environmental air.

Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem

Regional climate change induced by rapid urbanization is responsible for and may result from changes in coupled human-ecological systems. Specifically, the distribution of urban vegetation may be an important intermediary between patterns of human settlement and regional climate spatial variability. To test this hypothesis we identified the relationships between surface temperature, one component of regional climate, vegetation, and human settlement patterns in the Phoenix, AZ, USA region. Combining satellite-derived surface temperature and vegetation data from an early summer day with US Census and topographic data, we found substantial surface temperature differences within the city that correlate primarily with an index of vegetation cover. Furthermore, both of these patterns vary systematically with the social characteristics of neighborhoods through the region. Overall, every $10,000 increase in neighborhood annual median household income was associated with a 0.28°C decrease in surface temperature on an early summer day in Phoenix. Temperature variation within a neighborhood was negatively related to population density. A multivariate model generated using path analysis supports our hypothesis that social impacts on surface temperature occur primarily through modifications of vegetation cover. Higher income neighborhoods were associated with increased vegetation cover and higher density neighborhoods were associated with decreased vegetation variability. These results suggest that settlement patterns in the central Arizona region influence regional climate through multiple pathways that are heterogeneously distributed throughout the city.

Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.

EVALUATION OF THE MITIGATION EFFECTS ON SUMMER THERMAL STRESS BY URBAN VEGETATION IN TOKYO METROPOLITAN AREA

This study aims to evaluate the mitigation effects on summer thermal stress by the current distribution of urban vegetation in Tokyo Metropolitan Area using an urban climate simulation. In previous urban climate researches, land use data were used generally to generate physical characteristics of ground surface. In contrast with them, this paper emphasizes on making use of vegetation coverage ratios calculated from satellite remote sensing data, which make it possible to assess the effects of small vegetation such as roadside trees that are unable to discriminate from land use data. The result shows that air temperature is decreased 1.5°C due to vegetation during daytime on a typical summer day in Tokyo. Vertical distribution and diurnal variation of cooling effects, and direction and velocity of wind are also analyzed.

Composition and distribution of urban vegetation in the Tucson basin

Tucson is a rapidly growing city of a half million people in the arid south-western United States. From 1945 to 1975, 62 per cent of the city's growth has been attributed to migration. The horticultural and landscaping activity of new residents replaced native vegetation with ornamental plants and thereby altered the pollen rain in the Tucson basin. Various pollen studies have shown changes in type and magnitude of pollen produced as well as a high degree of variability in local pollen concentrations in the air. This can be directly related to horticultural groups within the urban vegetation zone. Groundcover, trees and shrubs were tallied from the front yards of 1557 homes in the Tucson basin. Homes were selected randomly from six clusters around atmospheric pollen samplers. No significant (0.05 level) differences were found between any of the clusters in the Tucson basin. The sampled clusters of urban vegetation were then subdivided using Principal Component Analysis on a covariance matrix of the 17 most important plant and groundcover groups. The first eigenvector accounts for a minimum of 50 per cent of the variance in five of six clusters and 40 per cent of the variance in the sixth cluster. The first eigenvector represents the influence of human selectivity in landscaping plants. Tucsonans plant a limited number of plants consistently throughout the city. The second and third eigenvectors show the horticultural groups which are of major importance within each cluster and which are responsible for the differences in the pollen rain recorded at sites throughout the city. The first three eigenvectors accounted for 71–95 per cent of the total variance in the six clusters investigated.