Combining biophysical and socioeconomic suitability models for urban forest planning

Urban Forestry & Urban Greening 38 (2019) 371–382

Contents lists available at ScienceDirect

Urban Forestry & Urban Greening journal homepage: www.elsevier.com/locate/ufug

Original article

Combining biophysical and socioeconomic suitability models for urban forest planning

T

⁎

Christopher K. Sass , R. Andrew Lodder, Brian D. Lee University of Kentucky Department of Landscape Architecture S305 Agricultural Science Building Lexington, KY, 40546, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: GIS Forest fragmentation Overlay analysis Restoration Social justice Urban forest Urban heat island

Urban forests are landscape mechanisms that provide ecosystem services and positive health benefits. However, many urban areas lack canopy coverage and continue to lose coverage through land cover change and tree mortality. In the United States, neighborhoods dominated by lower socioeconomic incomes (< US$24,000 median household income) are largely devoid of mature tree canopy cover, which compounds existing socioeconomic issues such as preventable adverse health conditions. This project demonstrates how geospatially combining biophysical attributes with socioeconomic conditions can be used to preliminarily identify opportunities for tree canopy enhancement and establishment, specifically in Lexington, Kentucky (USA). Using ESRI’s ArcGIS and publicly available data, a raster weighted overlay suitability modeling approach was used to combine ten biophysical and socioeconomic factors to identify first-pass planting site opportunities. A visual accuracy assessment using aerial imagery and field inspection of model results indicated that the approach was identifying sites conducive to planting trees. In general, the suitability approach often identified relatively small areas for planting opportunities. This approach provides a starting point for community involvement regarding municipality specific model development and implementation. With this flexible suitability modeling approach, we demonstrate how a community can combine biophysical and socioeconomic data in a planting site selection approach to meet local conditions and desires for tree planting strategies.

1. Introduction

disparity of benefits for those residents (e.g., Pham et al., 2012; Kabisch and Haase, 2014; Wolch et al., 2014). A disparity in location and quality of urban forest canopy cover leads to an inequitable distribution of preventable health issues. Disparity is defined simply as a difference, or as being distinct in quality or character (Mish, 2004). In this work, we borrow the concept of disparity from the health community, particularly as a preventable difference in urban tree canopy coverage determined by socioeconomic status (e.g., Whitehead, 1991, 1992; Braveman, 2006). Neighborhood environments that contain a heathy tree canopy cover are typically located in more affluent neighborhoods, creating a satisfying, healthy space to live (Maas et al., 2006; Wolch et al., 2014). Gentrified or gentrifying neighborhoods demand civic greening projects that maintain or increase property values, whereas low income neighborhoods have less agency and desire to make change (Merse et al., 2009; Conway et al., 2011; Carmichael and McDonough, 2019) increasing disparity of urban tree canopy. Due to the disparity in canopy coverage and population health linkage, a new approach to planting assessment is required.

By the year 2040, 65% of the Earth’s human population is expected to be living in urban regions (Ruther et al., 2016), creating a high need for well-maintained and well-developed urban forests that support human health and well-being (Villeneuve et al., 2012; Lovell and Taylor, 2013). Urban forests can mitigate urban impacts such as increased pollution levels, increased ground-level ozone, stormwater quality and quantity, increased heat island effects, increased habitat fragmentation, and decreased biodiversity (Asaeda et al., 1996; Alvey, 2006; Nowak and Dwyer, 2007; Lovell and Taylor, 2013). Urban forests have been defined as the total pockets of green space and overhead canopy, both public and private, in the urban landscape that might otherwise be dominated by hardscape infrastructure (Escobedo et al., 2011; Brandt et al., 2017). These complex forest pockets intertwined within the urban landscape matrix provide multiple social, human health, and ecological benefits (Escobedo et al., 2011; Elmqvist et al., 2015). Unfortunately, such ecosystem services are often absent in lower socioeconomic neighborhoods due to inadequate urban tree canopy including green spaces such as parks and greenways, exacerbating the

⁎

Corresponding author. E-mail address: [email protected] (C.K. Sass).

https://doi.org/10.1016/j.ufug.2019.01.019 Received 26 March 2018; Received in revised form 29 January 2019; Accepted 29 January 2019 Available online 30 January 2019 1618-8667/ © 2019 Elsevier GmbH. All rights reserved.

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

more self-disciplined (Taylor et al., 2002; Taylor and Kuo, 2006; Li and Sullivan, 2016). In the United States, trees in urban parks store 68,000 tonnes of carbon per year and benefit human health by a monetary value of US $500 million per year (Nowak and Heisler, 2010). Urban forests occurring in designated parks have a greater potential to effect environmental and human health conditions, which are accentuated when accompanied and connected through greenways. Urban greenways play an important role in human and environmental functions and have relevance to community well-being (Gobster and Westphal, 2004), and are therefore important in community engagement and economic development by connecting users throughout a larger community to commercial zones, parks, and one another. Unfortunately, greenways and associated tree canopy cover is unevenly distributed throughout urban areas and tends to be highest in affluent neighborhoods with high academic attainment (Perkins et al., 2004; Wolch et al., 2005, 2014). This disparity often results in urban heat islands and can be remedied through planning using socioeconomic factors. Lack of urban canopy coverage results in the formation of urban heat islands. Urban heat islands are defined as areas in the urban fabric that re-radiate heat from paving for a longer time than the surrounding countryside and adversely affect a community’s physical health (Bornstein, 1968; Knight et al., 2016). Heat islands can increase the temperature of an area by up to 10 °C during daytime and overnight hours (Norton et al., 2015; Ballinas and Barradas, 2016) and have been shown to be a significant cause of weather-related mortality in low socioeconomic settings (Rosenzweig et al., 2009; Norton et al., 2015; McDonald et al., 2016). Higher canopy density in urban areas tend to have stronger effects through a combination in increased evapotranspiration rates and direct shade (Nowak and Dwyer, 2007), which ultimately lessens heat and its associated effects. Urban forests temper urban climate (Nowak and Dwyer, 2007), lowering temperatures by as much as 3.3 °C (Kurn et al., 1994), and appropriate tree species properly positioned can shield heat absorbing and re-emitting surfaces such as road and parking pavement (Harvey et al., 2000; Srivastava and van Rooijen, 2000). As a result, a road’s solar aspect can be used to locate relative best planting areas to plant street trees, shade road surfaces, and lessen urban heat island effects (Asaeda et al., 1996). Often communities in higher need of urban forest expansion and management are those that encompass low socioeconomic status (< $24,000 median household income) (Perkins et al., 2004; Wolch et al., 2005; Dai, 2011). A multi-city study in the United States indicated a statistically significant correlation between low household income and low urban tree canopy coverage (Schwarz et al., 2015). Additionally, those with incomes of < US$20,000 a year were less likely to value trees on their property and streetscapes due to the perceived potential for increased criminal activity and costs of tree establishment and maintenance (Lohr et al., 2004). The established links that low-income areas on average harbor less tree canopy, experience intensified health problems, and have less likelihood of planting trees in the future provides opportunities for community planting efforts and studies such as the one described here.

1.1. Theoretical approach Combining biological, physical, and socioeconomic factors provides for a well-rounded assessment strategy for urban forest planning, development, and implementation, while providing flexibility to the local community during the decision-making process. This approach not only considers areas that are ecologically important, but accounts for areas lacking canopy due to socioeconomic conditions and attempts to restore the ecology beginning with tree canopy cover. The biophysical and socioeconomic factors considered in the development of each submodel that effects urban forest planning are described below. 1.1.1. Biological and physical effects of urban forests The biophysical effects of urban trees and forests have been well documented. Urban trees in U.S. counties comprise nearly 25% of the total tree cover (Dwyer et al., 2000), provide a substantial source of biodiversity (Cornelis and Hermy, 2004; Stewart et al., 2004) and mitigate multiple types of pollution (Lavabre et al., 1993; Nowak and Dwyer, 2007; Doick et al., 2014; Norton et al., 2015). Forest fragmentation created through development decreases biodiversity through losses in habitat connectivity, decreases species abundance, and decreases the efficacy of functional urban ecosystems (Alvey, 2006; Borges-Matos et al., 2016). For example, streams with their associated floodplains and riparian forests provide connections between forest patches in the urban setting. Thus, urban forests and their associated streams and floodplains should be carefully planned, protected, and managed; especially those of significant size and age. The role of trees in riparian communities is important as they moderate water temperature (Garner et al., 2017; Dugdale et al., 2018), provide quality fish habitat (Jones III et al., 1999; Teixeira-de Mello et al., 2015), provide biodiversity sinks, and increase corridors to additional habitats (Forman and Godron, 1986). While not encompassing as broad a geographic area as floodplains, stormwater basins serve as critical control measures for stormwater, pollutants, and sediment in the urban environment (Zimmerman, 2012) and can provide ecological functions when they are naturalized and maintained (Lovell and Taylor, 2013). Planting trees around and within stormwater storage basins can serve similar roles as those in wetlands and floodplains including soil stabilization, wildlife habitat, pollutant removal (e.g. phosphorus), evapotranspiration, and runoff reduction (Milner, 2001) while transpiring and infiltrating stormwater with minimal volume loss from basal area to the basin storage. However, these biophysical values, when combined with socioeconomic values, begin to tell a more complete story of urban forests and the disparity that can be mitigated. 1.1.2. Socioeconomic value of urban forests Utilizing socioeconomic factors aids in identifying available tree planting sites in neighborhoods with inadequate tree canopy and its associated disparity. The value of established trees acting as windbreaks and providing shade can save up to 25% in energy costs (U.S. Department of Energy, 2011), conservatively increase property values by 5% (Dwyer et al., 1992; Escobedo et al., 2015; Czembrowski and Kronenberg, 2016) and provide monetary value in stormwater retention and pollution mitigation services (McPherson et al., 1997; Nowak et al., 2018). These services, when in place and maintained, conserve money over time. Neighborhoods with canopy cover tend to have lower crime rates (Kou and Sullivan, 2001; Troy et al., 2012), reduced air and water pollution (Nowak et al., 2018), increased general happiness, and overall healthy human populations (Van Herzele and de Vries, 2012; Abelt and McLafferty, 2017). Urban forest value and influence on the psyche and physical health of people can be difficult to quantify (Yoon, 2012; Nowak et al., 2014), yet lack of canopy and the resultant effects can be measured (e.g., Thompson et al., 2012; Villeneuve et al., 2012; Wolch et al., 2014; McDonald et al., 2016). Children can be especially vulnerable to a lack of vegetation because those who visually or physically experience nature daily tend to do better academically and are

1.2. Study objective The expected increase in urban populations creates the need for increased urban forest management and planning to ameliorate the effects for human health and the environment before increased development pressures exacerbate further disparity. Urban forests can provide a stopgap measure for Carbon Dioxide sequestration allowing for ecosystem services that foster healthy, productive human lives; improve social conditions; and support physical and mental health. Most urban forestry planning models use biophysical and ecological endpoints as their strategy; here we employ a combination of biophysical and socioeconomic endpoints to provide a more holistic intervention. This mitigation strategy encompasses biophysical and socioeconomic 372

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

conditions in a flexible modeling framework and is where this proposed model can influence how to address urban tree planting strategies in neighborhoods that are disparate of tree canopy. In this study we demonstrate a geospatial suitability method for identifying high priority planting areas using publicly available datasets that focus on urban forest disparity while embracing ecosystem benefits. Our approach is an alternative to other models (e.g. Locke et al., 2010; Bodnaruk et al., 2017) by focusing on socioeconomic indicators to predict available planting spaces and addressing urban canopy disparity (both in quality and quantity) (Pham et al., 2012; Kabisch and Haase, 2014; Danford et al., 2014). For example, the Locke et al. (2010) and Bodnaruk et al. (2017) models highlight areas tailored to specific ecological outcomes, such as pollution mitigation and habitat fragmentation. Here we make the case for including socioeconomic variables to such models to locate available planting areas that address both ecological and social disparity. Our objective was to test and validate a prioritized planting model for restoring local tree canopy using both socioeconomic and biophysical indicators in suitability model development. Providing an accessible planning approach that incorporates socioeconomic influences for the implementation of trees in urban and urbanizing areas promotes a more complete green infrastructure plan.

assessment of the study area. Their report utilized the iTree tools suite (iTree, 2017) to identify land cover types and estimated canopy coverage in 1994, 2004, and 2012 (Davey Resource Group, 2013). Using 2016 National Agriculture Imagery Program (NAIP) data (Kentucky Geography Network (KYGEONET, 2017), we added the 2016 tree canopy coverage and land cover types to update the results of the Davey study. These combined studies demonstrated tree canopy coverage changes and projected trends in canopy development within the study area, which became the baseline tree canopy coverage (Fig. 2). A decrease in tree canopy from 2012 to 2016 was partially due to the removal of dead ash (Fraxinus spp.) trees because of the Emerald Ash Borer, Agrilus planipennis (Poland and McCullough, 2006) infestation, and an increase in impervious surface (new construction). The city has maintained about 20% canopy coverage since 1994 (Davey Resource Group, 2013). In 2016, Lexington exhibited a slightly higher tree canopy (˜22%) with less open space (˜36%) available for planting within the study area than in 1994 (Fig. 2). The canopy is not evenly distributed across the urban area and is highest in neighborhoods having higher academic attainment and affluence. This canopy disparity helped create this study by including socioeconomic variables in ecological models and aid human health in our region. 2.3. Biophysical sub-model development

2. Methods Biophysical sub-model development began by considering the ecosystem services trees provide and how those services could be represented by available data layers. Ecosystem services are defined as the benefits that ecosystems provide society, for example; carbon storage, carbon sequestration, wildlife habitat, stormwater management, and food production (Bagstad et al., 2013). Table 1 lists characteristics used to build the biophysical model with each factors associated overall weighted influence that was determined by literature and local expertise. The model result is a weighted overlay surface that identifies the highest priority planting areas based on ecosystem services. A natural breaks statistical classification was used to determine three basic suitability levels (High, Moderate, Low). Note that scoring, weighting, and layers can be changed to fit each area’s goals and professional understanding of the planting strategy. The biophysical sub-model was developed using variables chosen by the authors and local professionals in respective fields. Forest fragmentation and floodplain proximity provided ways to create habitat centers and connections throughout the city between the habitat centers. The forest fragmentation layer (25% of overall sub-model) used was created by Davey Resource Group (2013) and included all of the Lexington Tree Canopy Area as of 2012. We scored areas adjacent to existing tree canopy as high (5) and areas with no tree canopy or connection to tree canopy as low (1) to create further canopy connections in the landscape. Areas close to floodplains also offer connections using stream corridors for wildlife from habitat patch to habitat patch. A 15.25 m buffer was created as a high suitability (5) from centerlines of streams. This buffer was followed by a 30.5 m buffer (4) and a 45.75 m buffer (3) to aid in finding areas close to the stream that could be used as corridor habitat for wildlife. Areas outside the buffer zone were scored at a (1) since they would not contribute to the overall corridor. Hill slope percent and soil hydrologic groups were used to rate potential erosion of soils across Lexington. For this section of the model, these two variables provided insight into how trees could provide soil protection while aerating and incorporating organic matter into the soil. High slopes received high suitability (5), while low slopes received low suitability (1). Soil hydrologic groups that were characterized as a Group D (high runoff potential) were scored as high (5) with Group C soils scoring a (4). Group B and Group A were scored lower at (2) and (1) respectively because they allow water to infiltrate at a higher rate than groups D and C. Stormwater detention basins and underutilized spaces were

Our conceptual approach in using socioeconomic factors was based on the lack of use in other models in choosing plantable areas. The socioeconomic factors included; low income housing (Mitchell and Popham, 2008; Thompson et al., 2012; Norton et al., 2014), densely populated urban areas, and urban heat island (Nowak and Dwyer, 2007; Doick et al., 2014) as indicators of plantable areas lacking adequate tree canopy. 2.1. Study site Lexington, Kentucky, was chosen as a case study area for several reasons. The urban county area does not meet a recommended 30% forest canopy coverage resource goal set by Davey and the LexingtonFayette Urban County Government (2013), while also exhibiting a disparity based on social context. Additionally, the expected population increase in combination with existing development strategies will place higher demands on existing canopy resources. This urban geography is constrained by the nation’s oldest urban service boundary, which was established in 1958. Geographically, Lexington (Fig. 1) is located within the Inner Bluegrass Ecoregion and is described as a level to rolling landscape, that is weakly dissected, and is expanding its urbansuburban areas (Woods et al., 2002). Culturally, 41%of Lexington’s population holds an undergraduate degree or higher, while 19% have a household income below the U.S. standard poverty level (U.S. Census Bureau, 2017). The region provides relatable size and density to many urbanizing U.S. and global cities. At the time of this study (March 2018) the population in Lexington was estimated at 318,449, an increase of 7.7% from 2010 (U.S. Census Bureau, 2017)). The population is projected to grow over the next 23-years at an estimated rate of 4.4% annually, to 419,900 by 2040 (Ruther et al., 2016). Population density within the Urban Service Area is 1276 people/km2, which is comparable to cities such as Cincinnati, Ohio (1465 people/km2), and St. Louis, Missouri (1983 people/km2) (U.S. Census Bureau, 2017)). With a projected population increase and no expansion in the Urban Service Area, Lexington will reach a density of 1677 people/km2 by 2040, increasing stresses on exiting land resources such as urban forests. 2.2. Land cover tree canopy assessment In 2012, the Davey Resource Group (2013) conducted a tree canopy 373

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

Fig. 1. Lexington, Kentucky study area (in red) is within the Bluegrass Region of Central Kentucky. Location A is in the Cardinal Valley neighborhood, which had a US$25-28,000 median household income with 21% living below the poverty level in 2010. Location B is in the Ashland neighborhood, which had a US$60-116,000 median household income with 12% living below the poverty level in 2010 (U.S. Census Bureau, 2017). 2016 NAIP Imagery used (Kentucky Geography Network (KYGEONET, 2017) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Fig. 2. Comparison of estimated land cover change between 1994 and 2016 in the Lexington’s Urban Service Area (Tree canopy coverage 1994 -19.2%: 2004 – 21%: 2012 – 24.5%: 2016 – 22.2%). Data for 1994, 2004 and 2012 was obtained from the Davey Tree Study (Davey Resource Group, 2013); we determined numbers for 2016 using the iTree Tools suite (iTree, 2017).

2.4. Socioeconomic model development

envisioned as potential habitat settings within the urban fabric that could provide unique connections or habitat centers. LFUCG provided GIS layers illustrating the locations of stormwater detention basins and underutilized spaces. Locations of each feature within the layer was scored as high (5) and a three-ring buffer was used to score land surrounding each feature. The first ring was 150 m (4), the second was 200 m (3) and the final buffer was 250 m (2). Outside the buffer zone scored low suitability (1).

Several factors were incorporated into the socioeconomic sub-model for strategically increasing urban tree canopy based on socioeconomic and anthropogenic considerations. The sub-model product is a weighted overlay surface that identifies planting suitability through socioeconomic indicators. Each overlay layer was valued by the contents within it based on a common scale (1-low suitability to 5-high 374

Underutilized spaces

Stormwater detention and retention basin proximity

Soils

Slopes

Soils that are compacted can be aided by trees and tree litter to infiltrate and control stormwater runoff. Soils that were highly compacted were chosen over soils with high infiltration rates. Trees surrounding basins can serve similar roles as those that apply to wetlands and floodplains including soil stabilization, habitat, pollution sink, and runoff reduction. Properties that have been abandoned, are without structure, or are entirely non-serviceable parking were deemed by the City as "underutilized." These areas could be planted with trees and help in mental, social (crime) and environmental health.

Fragmentation of habitat can limit biodiversity and decrease the function of urban ecosystems. Connections to habitat cores of different sizes may increase biodiversity and function. Connections to habitat can form along stream corridors. Other ecosystem services such as erosion control, evapotranspiration of stormwater and cooling of streams can be aided. Trees can mitigate a slope's erosion potential and increase the soil's ability to hold moisture.

Rationale

Lexington Tree Canopy Area (Davey Resource Group, 2013) Stream centerline data (Kentucky Geography Network (KYGEONET, 2017) Kentucky DEM: Raster to slope percentage tool (Kentucky Geography Network (KYGEONET, 2017) Davey Resource Group, 2013 Hydrologic groups Lexington-Fayette Urban County Government Stormwater Basin GIS layer (LFUCG, 2017) Lexington-Fayette Urban County Government Underutilized Properties GIS layer(LFUCG, 2017)

25%

15%

15%

5%

15%

25%

Data used

Model Weight

375

Densely populated area

Social

Infrastructure

Shared greenspace

Urban heat island

Anthropogenic

Planting trees on the south and west sides of streets provides direct shading to asphalt and lessens temperature by up to 7 °C through evapotranspiration.

Properties that have been abandoned, are without structure, or are entirely nonserviceable parking were deemed by the City as "underutilized." These areas could be planted with trees.

Underutilized spaces

Trees' ability to mitigate air temperature and pollution lies greatest within a 300 m radius. The closer the trees are too many people, the greater effect they have on a larger number. Low income neighborhoods tend to have less tree canopy, more health issues, and less likelihood of planting, making them prime targets for both planting and educational efforts. Households of 2-adults and 2-children with incomes < US$24,399 were designated as low income (U.S Census Bureau, 2017) Urban parks provide ecosystem services such as carbon storage and can increase human health. Greenways promote health and well-being by providing connections between urban destinations while experiencing nature.

Planting trees in areas with high concentrations of concrete and asphalt decreases radiation of heat and increases evapotranspiration (cooling effect).

Rationale

Road aspect

Greenways

Parks

Low income housing

Factor

Cluster

2010 Census Block Groups (U.S. Census Bureau, 2017)

20%

5%

10%

10%

Kentucky DEM: Raster Surface to Aspect tool (Kentucky Geography Network (KYGEONET, 2017) Lexington-Fayette Urban County Government Underutilized Properties GIS layer(LFUCG, 2017)

Lexington-Fayette Urban County Government Parks GIS layer (LFUCG, 2017) Lexington-Fayette Urban County Government Greenways GIS layer (LFUCG, 2017)

2010 Census Block Groups (U.S. Census Bureau, 2017)

25%

10%

Davey (2013) Lexington Potential Urban Heat Island Effect

Data used

20%

Model Weight

Table 2 Socioeconomic suitability model development variable rationale with weight percentage distribution, data source, and literature reference grounding.

Corridor connections

Erosion potential

Forest fragmentation

Habitat center and connections

Floodplain proximity

Factor

Cluster

Table 1 Biophysical suitability model variable rationale with weight percentage distribution, data source, and literature reference grounding.

Kou and Sullivan, 2001; Bell et al., 2008; Onishi et al., 2010; Troy et al., 2012

Nowak and Heisler, 2010; Jennings and Gaither, 2015; Larson et al., 2016 Furuseth and Altman, 1991; Gobster and Westphal, 2004; Gies, 2006; Wolch et al., 2014 Asaeda et al., 1996; Harvey et al., 2000; Srivastava and van Rooijen, 2000

Lohr et al., 2004; Jennings and Gaither, 2015; Schwarz et al., 2015

Kurn et al., 1994; Nowak and Dwyer, 2007; Davey Resource Group, 2013; McDonald et al., 2016 Nowak and Dwyer, 2007; McDonald et al., 2016

Literature

Kou and Sullivan, 2001; Bell et al., 2008; Onishi et al., 2010; Troy et al., 2012

Milner, 2001; Denman et al., 2016; Berland et al., 2017

Reubens et al., 2007;

Gurnell, 1997; Hubble et al., 2010; Fattet et al., 2011

Forman and Godron, 1986; Godefroid and Koedam, 2003; Cornelis and Hermy, 2004; Alvey, 2006 Naiman and Decamps, 1997; Moore et al., 2005; Hubble et al., 2010;

Literature

C.K. Sass et al.

Urban Forestry & Urban Greening 38 (2019) 371–382

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

suitability; e.g., low income < US$24,000 was given a 5-rating or highly suitable), then given a holistic influence percentage with relative importance according to the literature review (Table 2). A natural breaks statistical classification was used to determine three suitability levels (High, Moderate, Low). Potential urban heat island locations throughout the city were modeled by Davey Resource Group (2013) in their urban tree canopy study. We used this layer as an indicator of lower socioeconomic status, increased health risk in the community, and lack of vegetation (increased exposed pavement). If the potential of urban heat island was high, then the area received a high score of (5), mid potential scoring a (3) with low potential scoring (1). Densely populated neighborhoods and low-income housing areas are indicators of social status and can signify areas lacking urban tree canopy. Areas with high populations per square mile were scored as highly suitable (5) and those areas with low density were scored as low suitability (1). Low-income areas were described as earning less than US$24,000 annually per household. These neighborhoods scored a (5), or highly suitable for tree plantings. Neighborhoods with higher than US$150,000 scored as (1) low suitability, while the middle incomes scored a (3). Parks and greenways provide open space for residents and places to plant trees near densely populated areas. A 3-ring buffer was used to score surrounding areas of parks and greenways, scoring the same as stormwater detention basins described above. One issue using parks and greenways in the socioeconomic setting is that most parks and greenways are planned and built in higher socioeconomic neighborhoods. Underutilized spaces were considered here because they could be used as a surrogate park setting. Road aspect, or the direction a road faces, can directly influence heat island effect. A southern facing road in the Northern Hemisphere absorbs heat over a longer period than a northern facing slope. Thus, southern facing roads scored high suitability (5), followed by western slopes (4) due to afternoon sun exposure, and then eastern (2) and northern (1). Note, again that scoring, weighting, and layers can be changed to fit each area’s goals and professional understanding of the planting strategy. 2.5. GIS methods ESRI’s ArcGIS – Spatial Analyst was used for geospatial operations and model building. These publicly available data layers were used for biophysical and socioeconomic sub-model development. Weighting for each data source layer was informed by literature research, author interpretation of the literature, and a modified Delphi process using topical experts and local professionals (Tables 1 and 2). Layers were analyzed using a weighted overlay for each sub-model on a 1 m × 1 m grid for the biophysical and socioeconomic sub-models. The sub-models were assessed and then combined using the multiplication tool to create a final suitability model as a first-pass planting site identification and field verification. While combining the two sub-models may cause undue compounded error in the final combined model, it is important in the overall development to have sub-model flexibility to weight and assess differing points of view for planting approaches. The final combined model was created by weighting the biophysical and socioeconomic sub-models equally (50%) (Fig. 3).

Fig. 3. Illustrative model showing Biophysical factors and Socioeconomic factors with weights. *Layer Percent Influence can be changed based upon professional experience and goal of the model.

Using publicly available data has strengths and weaknesses. First, publicly available data provides municipalities with few financial resources the option of using relatively recent data to inform decision their making. Although we used proprietary commercially available software, the same capability is available in several open source geospatial software such as GRASS GIS or QGIS. A weakness to using publicly available data is it was not specifically collected for use in this model and may produce error. Regardless, as a first-pass site selection approach the data and approach provide information on the strategic use of resources to aid decision makers.

2.6. Combined model analysis The overall suitability was generated by combining the biophysical and socioeconomic sub-models to identify areas of relative suitability of tree planting locations within the city on a 1 m × 1 m cell-by-cell basis. The “Times Tool” was used to create a larger range between low suitability and high suitability than simple additive combination. A natural breaks classification was used to separate the sub-model results to four suitability levels (Low, Low-Medium, High-Medium, and High). 376

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

Fig. 4. Socioeconomic suitability locations for areas within Lexington, Kentucky, generated using the ArcGIS Spatial Analyst Weighted Overlay Tool.

377

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

3. Results

ignored for decades tend to be in low socioeconomic neighborhoods and have rarely benefited from any improvement projects. Providing ecosystem services for these areas would help improve health and wellbeing for residents and the area’s overall environment. Not all of the highly suitable planting sites identified through this study are the best tree planting sites due to multiple urban site conditions. However, some planting condition impediments can be mitigated by increasing soil organic matter (Day, 2016), planting in structural soil (Bassuk et al., 2015), and/or using structural cells (Ow and Ghosh, 2017). Incorporating socioeconomic factors into canopy development planning models allows both social and environmental disparity to be addressed. The model results can then be used by different stakeholder groups for an action initiation point. As the top-down approach rarely produces investment by all (Irvin and Stansbury, 2004), the resulting geospatial analyses depicted in maps can help all stakeholders within the municipality understand the environmental and social factors and then address the tree canopy disparity throughout an urbanized area. These results also provide a beginning point to assign stakeholder variable priority to be used in a Delphi process to assign factor importance weighting. Future steps to build upon this study consist of detailed on-theground site assessment in individual neighborhoods at identified planting locations. Characterizing and overcoming the barriers to maintenance and citizenry education in these areas will be required to develop inclusive and successful partnerships that address the overall ecological issues we all face. Stepping away from implementation of the suitability modeling results and comparing historical “red line” maps and social factors, such as education level, race, and household income, with areas that lack canopy cover may show a long history of landscape inequality and disparity that can aid further understanding as to why conditions exist as they do in the landscape. With this knowledge base, planning to mitigate issues including social and ecological, while providing biodiversity and aesthetic value in neighborhoods will be more possible and can contribute to positive outcomes in social disparity, resident health, and our collective futures. As urban environments continue to develop, it is important to plan for the combinations of multiple uses of open space that curb negative environmental and human health impacts in a limited space (Lovell and Taylor, 2013). Maintaining and even increasing the urban tree canopy is an essential part of creating livable urban environments in the 21st century. For example, urban canopy development allows children to experience contact with nature in their everyday lives by providing wildlife viewing opportunities. Managing urban environmental issues for residents such as reducing air pollution, lowering stress levels, and lowering heat re-radiation (urban heat island) will make all neighborhood residents healthier and more productive. Trees are not an instant solution for cities’ problems; rather, they provide an opportunity for future proofing. Properly planted trees will help ensure that future urban populations will benefit from the ecosystem services provided by the tree canopy for decades. Said another way:

3.1. Biophysical model result The data and values described in Table 1 were used to geospatially identify opportunity areas for tree planting. Two example sites of the biophysical suitability model are shown and were chosen as example sites because they illustrate differences in population density, existing tree canopy coverage, and vast socioeconomic differences (Fig. 4). These two sites represent the value in using a geospatial modeling approach at the site scale, differences in the model results in different socioeconomic settings, and provide a scaled comparison. The most suitable areas are shown in red and contain high values in multiple factors or heavily influential layers. Site A is located in Cardinal Valley, historically a low-income neighborhood with ample open space, few trees, and open space connections. Site B is located in the Ashland neighborhood, a designed, historically affluent, highly educated neighborhood with high tree canopy coverage, open space, and multiple, high-quality parks and greenspaces. 3.2. Socioeconomic model result The socioeconomic data and values described in Table 2 were used to geospatially identify opportunity areas for tree planting through a social lens. Again, Sites A and B are shown as examples in Fig. 5. The most suitable areas (shown in red) contained high values in multiple and/or heavily weighted layers and provided defined areas of high priority planting zones as a first pass suitability screening. Many areas identified as most suitable are located downtown and in the northern section of Lexington (Cardinal Valley’s location), which includes multiple commercial and multi-dwelling residential parcels. These neighborhoods are largely occupied by low-income families and multiple ethnic groups based on U.S. Census Bureau data (2017). 3.3. Combined model result Combining the biophysical sub-model and the socioeconomic submodel equally using the times tool identifies different opportunities for further enhancing the tree canopy coverage through ecological and social lenses within the study area. Again, Sites A and B are used as example sites in Fig. 6. Through the three models, Site B illustrates little suitability change. However, Site A illustrates how the social component changes the suitability from low in the biophysical sub-model to higher suitability in the socioeconomic and combined models indicating a need to incorporate socioeconomic indicators in planting strategies. This inclusion would help alleviate disparity in urban settings. 4. Discussion and conclusion The study described is intended as a first-pass flexible analysis approach to inform municipal decision makers seeking to increase the beneficial effects of urban forests using ecosystem services and socioeconomic viewpoints. These data driven models, informed by literature and local experts, can be used to provide a landscape conditions inventory and then identify potential planting areas for increasing urban tree canopy coverage. The approach identifies neighborhoods to engage with and build inclusive community partnerships when limited tree planting resources are available. This study demonstrates that when biophysical characteristics are combined with socioeconomic characteristics the results are multidimensional addressing both quality of human life and environmental issues. Although this approach was demonstrated with Lexington, Kentucky, data, it provides a modeling approach for other cities facing similar circumstances regarding treeplanting prioritization to build urban forests for ecological and socioeconomic benefits. Urban forests that are fragmented or are in areas that have been

“Society grows great when old men plant trees whose shade they know they shall never sit in.”- Anonymous Greek Proverb

Acknowledgements We greatly appreciate the two anonymous reviewers who helped improve this article. We would also like to thank the Department of Landscape Architecture, College of Agriculture, Food and Environment at the University of Kentucky for their support. We extend our sincerest gratitude to Ned Crankshaw, Karen Goodlet, Dr. Adina Cox, and Dr. Ellen Crocker for their reviews and suggestions on earlier drafts. This research did not receive any specific grant funding from agencies in the public, commercial, or not-for-profit sectors. 378

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

Fig. 5. Biophysical suitability maps for select sites within the Lexington, KY, study area showing the results using ArcGIS Spatial Analyst Weighted Overlay Tool and publicly available datasets. Using the typical biophysical variables in this suitability sub-model, these two areas illustrate low suitability for canopy planting.

379

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

Fig. 6. The result using the ArcGIS Spatial Analyst Weighted Overlay Tool to combine socioeconomic and biophysical suitability analyses for the entire Urban Services Area. Using the combined suitability model, Cardinal Valley (A) would benefit much more than the Ashland neighborhood (B) because of the inclusion of socioeconomic datasets.

380

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

References

Gomez-Baggethun, E., Nowak, D.J., Kronenberg, J., de Groot, R., 2015. Benefits of restoring ecosystem services in urban areas. Curr. Opin. Environ. Sustain. 14, 101–108. https://doi.org/10.1016/j.cosust.2015.05.001. Escobedo, F.J., Kroeger, T., Wagner, J.E., 2011. Urban forests and pollution mitigation: analyzing ecosystem services and disservices. Environ. Pollut. 159, 2078–2087. https://doi.org/10.1016/j.envpol.2011.01.010. Escobedo, F.J., Adams, D.C., Timilsina, N., 2015. Urban forest structure effects on property value. Ecosyst. Serv. 12, 209–217. https://doi.org/10.1016/j.ecoser.2014. 05.002. Fattet, M., Fu, Y., Ghestem, M., Ma, M., Foulonneau, M., Nespoulous, J., Le Bissonnais, Y., Stokes, A., 2011. Effects of Vegetation Type on Soil Resistance to Erosion: Relationship Between Aggregate Stability and Shear Strength. Catena 87, 60–69. https://doi.org/10.1016/j.catena.2011.05.006. Forman, R.T.T., Godron, M., 1986. Landscape Ecology. John Wiley & Sons, Inc., New York, NY. Furuseth, O.J., Altman, R.E., 1991. Who’s on the greenway: socioeconomic, demographic, and locational characteristics of greenway users. Environ. Manage. 15 (3), 329–336. https://doi.org/10.1007/BF02393879. Garner, G., Malcolm, I.A., Sadler, J.P., Hannah, D.M., 2017. The role of riparian vegetation density, channel orientation and water velocity in determining river temperature dynamics. J. Hydrol. 553, 471–485. https://doi.org/10.1016/j.jhydrol. 2017.03.024. Gies, E., 2006. The Health Benefits of Parks: How Parks Help Keep Americans and Their Communities Fit and Healthy. The Trust for Public Land, pp. 26pp. Gobster, P.H., Westphal, L.M., 2004. The human dimensions of urban greenways: planning for recreation and related experiences. Landsc. Urban Plan. 68, 147–165. https://doi.org/10.1016/s0169-2046(03)00162-2. Godefroid, S., Koedam, N., 2003. Distribution pattern of the flora in a peri-urban forest: an effect of the city–forest ecotone. Landsc. Urban Plan. 65, 169–185. https://doi. org/10.1016/S0169-2046(03)00013-6. Harvey, J., Chong, A., Roesler, J., 2000. Climate Regions for Mechanistic-empirical Pavement Design in California and Expected Effects on Performance. Draft Report for California department of Transportation. Retrieved 3 March 2017 from. University of California at Berkeley Pavement Research Center Publications.htm.. http://www. its.berkeley.edu/pavementresearch/. Hubble, T.C.T., Docker, B.B., Rutherfurd, I.D., 2010. The role of riparian trees in maintaining riverbank stability: a review of Australian experience and practice. Ecol. Eng. 36, 292–304. https://doi.org/10.1016/j.ecoleng.2009.04.006. Irvin, R.A., Stansbury, J., 2004. Citizen participation in decision making: is it worth the effort? Public Adm. Rev. 64 (1), 55–65 https://dx.doi.org/jstor.org/stable/3542626. iTree Tool Suite, 2017. i-Tree Canopy v6.1. Retrieved 25 April 2017 from. https:// canopy.itreetools.org/. Jennings, V., Gaither, C.J., 2015. Approaching Environmental health disparities and green spaces: an ecosystem services perspective. Int. J. Environ. Res. Public Health 12, 1952–1968. https://doi.org/10.3390/ijerph120201952. Jones III, E.B.D., Helfman, G.S., Harper, J.O., Bolstad, P.V., 1999. Effects of riparian forest removal on fish assemblages in Southern Appalachian streams. Conserv. Biol. 13 (6), 1454–1465. Kabisch, N., Haase, D., 2014. Green justice or just green? Provision of urban green spaces in Berlin, Germany. Landsc. Urban Plan. 122, 129–139. https://doi.org/10.1016/j. landurbplan.2013.11.016. Kentucky Geography Network (KYGEONET), 2017. 2016 NAIP Imagery. Website. http://www.kygeonet.ky.gov/. Knight, T., Price, S., Bowler, D., King, S., 2016. How effective is ‘greening’ of urban areas in reducing human exposure to ground-level ozone concentrations, UV exposure and the ‘urban heat island effect’?: a protocol to update a systematic review. Environ. Evid. 5 (3), 1–6. https://doi.org/10.1186/s13750-016-0054-y. Kou, F.E., Sullivan, W.C., 2001. Environment and crime in the inner city: does vegetation reduce crime? Environ. Behav. 33 (3), 343–367. https://doi.org/10.1177/ 0013916501333002. Kurn, D., Bretz, S., Huang, B., Akbari, H., 1994. The potential for reducing urban air temperatures and energy consumption through vegetative cooling. ACEEE Summer Study on Energy Efficiency in Buildings. Retrieved 22 March 2017 from. American Council for an Energy Efficient Economy, Pacific Grove, California. https://www. osti.gov/scitech/servlets/purl/10180633. Larson, L.R., Keith, S.J., Fernandez, M., Hallo, J.C., Shafer, C.S., Jennings, V., 2016. Ecosystem services and urban greenways: what’s the public’s perspective? Ecosyst. Serv. 22(A, 111–116. https://doi.org/10.1016/j.ecoser.2016.10.004. Lavabre, J., Torres, D.S., Cernesson, F., 1993. Changes in the hydrological response of a small Mediterranean basin a year after a wildfire. J. Hydrol. (Amst) 142, 273–299. https://doi.org/10.1016/0022-1694(93)90014-Z. Lexington-Fayette Urban County Government (LFUCG), 2017. GIS Data Portal. https:// data.lexingtonky.gov/. Li, D., Sullivan, W.C., 2016. Impact of views to school landscapes on recovery from stress and mental fatigue. Landsc. Urban Plan. 148, 149–158. Locke, D.H., Grove, J.M., Lu, J.W.T., Troy, A., O’Neil-Dunne, J.P.M., Beck, B.D., 2010. Prioritizing preferable locations for increasing urban tree canopy in New York City. Cities Environ. 3 (1), 1–18. http://escholarship.bc.edu/cate/vol3/iss1/4. Lohr, V.I., Pearson-Mims, C.H., Tarnai, J., Dillman, D.A., 2004. Urban residents rate and rank the benefits and problems associated with trees in cities. J. Arboricult. 30 (1), 28–35. Lovell, S.T., Taylor, J.R., 2013. Supplying urban ecosystem services through multifunctional green infrastructure in the United States. Landsc. Ecol. 28, 1447–1463. https://doi.org/10.1007/s10980-013-9912-y. Maas, J., Verheij, R.A., Groenewegen, P.P., de Vries, S., Spreeuwenberg, P., 2006. Green space, urbanity and health: how strong is the relation? J. Epidemiol. Commun. Health

Abelt, K., McLafferty, S., 2017. Green streets: urban green and birth outcomes. Int. J. Environ. Res. Public Health 14, 771–789. https://doi.org/10.3390/ijerph14070771. Alvey, A.A., 2006. Promoting and preserving biodiversity in the urban forest. Urban For. Urban Green. 5 (4), 195–201. https://doi.org/10.1016/j.ufug.2006.09.003. Asaeda, T., Ca, V.T., Wake, A., 1996. Heat storage of pavement and its effect on the lower atmosphere. Atmos. Environ. 30, 413–427. https://doi.org/10.1016/1352-2310(94) 00140-5. Bagstad, K.J., Semmens, D.J., Waage, S., Winthrop, R., 2013. A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosyst. Serv. 5, e27–e39. https://doi.org/10.1016/j.ecoser.2013.07.004. Ballinas, M., Barradas, V.L., 2016. The urban tree as a tool to mitigate the urban heat island in Mexico city: a simple phenomenological model. J. Environ. Qual. 45 (1), 157–166. https://doi.org/10.2134/jeq2015.01.0056. Bassuk, N., Denig, B.R., Haffner, T., Grabosky, J., Trowbridge, P., 2015. CU-Structural Soil: a Comprehensive Guide. Urban Horticulture Institute. Cornell University, pp. 57pp. Bell, J.F., Wilson, J.S., Liu, G.C., 2008. Neighborhood greenness and 2-year changes in body mass index of children and youth. Am. J. Prev. Med. 35 (6), 547–553. https:// doi.org/10.1016/j.amepre.2008.07.006. Berland, A., Shiflett, S.A., Shuster, W.D., Garmestani, A.S., Goddard, H.C., Herrmann, D.L., Hopton, M.E., 2017. The role of trees in urban stormwater management. Landsc. Urban Plan. 162, 167–177. https://doi.org/10.1016/j.landurbplan.2017.02.017. Bodnaruk, E.W., Kroll, C.N., Yang, Y., Hirabayashi, S., Nowak, D.J., Endreny, T.A., 2017. Where to plant urban trees? A spatially explicit methodology to explore ecosystem service tradeoffs. Landsc. Urban Plan. 157, 457–467. https://doi.org/10.1016/j. landurbplan.2016.08.016. Borges-Matos, C., Aragon, S., da Silva, M.N.F., Fortin, M.J., Magnusson, W.E., 2016. Importance of the matrix in determining small-mammal assemblages in an Amazonian forest-savanna mosaic. Biol. Conserv. 204, 417–425. https://doi.org/10. 1016/j.biocon.2016.10.037. Bornstein, R.D., 1968. Observations of the urban heat island effect in New York City. J. Appl. Meteorol. 7, 575–582. Brandt, L.A., Lewis, A.D., Scott, L., Darling, L., Fahey, R.T., Iverson, L., Nowak, D.J., Bodine, A.R., Bell, A., Still, S., Butler, P.R., Dierich, A., Handler, S.D., Janowiak, M.K., Matthews, S.N., Miesbauer, J.W., Peters, M., Prasad, A., Shannon, P.D., Stotz, D., Swanston, C.W., 2017. Chicago Wilderness Region Urban Forest Vulnerability Assessment and Synthesis: a Report From the Urban Forestry Climate Change Response Framework Chicago Wilderness Pilot Project (General Technical Report NRS-168). U. S. Forest Service, Newtown Square, PA. https://www.fs.fed.us/nrs/ pubs/gtr/gtr_nrs168.pdf. Braveman, P., 2006. Health disparities and health equity: concepts and measurement. Annu. Rev. Public Health 27, 167–194. https://doi.org/10.1146/annurev.publhealth. 27.021405.102103. Carmichael, C.E., McDonough, M.H., 2019. Community stories: Explaining resistance to street tree planting programs in Detroit, Michigan, USA. Soc. Nat. Resour. https:// doi.org/10.1080/08941920.2018.1550229. Conway, T.M., Shakeel, T., Atallah, J., 2011. Community groups and urban forest activity: drivers of uneven canopy cover? Landsc. Urban Plan. 101 (4), 321–329. https://doi. org/10.1016/j.landurbplan.2011.02.037. Cornelis, J., Hermy, M., 2004. Biodiversity relationships in urban and suburban parks in Flanders. Landsc. Urban Plan. 69, 385–401. https://doi.org/10.1016/j.landurbplan. 2003.10.038. Czembrowski, P., Kronenberg, J., 2016. Hedonic pricing and different urban green space types and sizes: insights into the discussion on valuing ecosystem services. Landsc. Urban Plan. 146, 11–19. https://doi.org/10.1016/j.landurbplan.2015.10.005. Dai, D., 2011. Racial/ethnic and socioeconomic disparities in urban green space accessibility: where to intervene? Landsc. Urban Plan. 102, 234–244. https://doi.org/10. 1016/j.landurbplan.2011.05.002. Danford, R.S., Cheng, C., Strohbach, M.W., Ryan, R., Nicolson, C., Warren, P.S., 2014. What does it take to achieve equitable urban tree canopy distribution? A Boston case study. Cities Environ. 7 (1), 2:1–20. Davey Resource Group, 2013. Urban Tree Canopy Assessment and Planting Plan: Urban Service Area in Fayette County. Kentucky. http://www.lexingtonky.gov/Modules/ ShowDocument.aspx?documentid=26521. Accessed 11 April 2017. . Day, S.D., 2016. Soil profile rebuilding: an alternative to soil replacement. City Trees 2016 (September/October), 30–35. Denman, E.C., May, P.B., Moore, G.M., 2016. The potential role of urban forests in removing nutrients from stormwater. J. Environ. Qual. 45, 207–214. https://doi.org/ 10.2134/jeq2015.01.0047. Doick, K.J., Peace, A., Hutchings, T.R., 2014. The role of greenspace in mitigating London’s nocturnal urban heat island. Sci. Total Environ. 493, 662–671. https://doi. org/10.1016/j.scitotenv.2014.06.048. Dugdale, S.J., Malcolm, I.A., Kantola, K., Hannah, D.M., 2018. Stream temperature under constraining riparian forest cover: understanding thermal dynamics and heat exchange processes. Sci. Total Environ. 610-611, 1375–1389. https://doi.org/10.1016/ j.scitotenv.2017.08.198. Dwyer, J.F., McPHerson, E.G., Schroeder, H.W., Rowntree, R.A., 1992. Assessing the benefits and costs of the urban forest. J. Aboricult. 18 (5), 227–234. Dwyer, J.F., Nowak, D.J., Noble, M.H., Sisinni, S.M., 2000. Connecting People with Ecosystems in the 21st Century, an Assessment of Our Nation’s Urban Forests. General Technical Report, PNW-GTR-490. USDA Forest Service, Pacific Northwest Research Station, Portland, OR. Elmqvist, T., Setala, H., Handel, S.N., van der Ploeg, S., Aronson, J., Blignaur, J.N.,

381

Urban Forestry & Urban Greening 38 (2019) 371–382

C.K. Sass et al.

State of Kentucky, Kentucky Counties, and Area Development Districts (2015-2040). Kentucky State Data Center, University of Louisville. http://www.ksdc.louisville. edu/wp-content/uploads/2016/10/projection-report-v16.pdf. Schwarz, K., Fragkias, M., Boone, C.G., Zhou, W., McHale, M., Grove, J.M., O’Neil-Dunne, J., McFadden, J.P., Buckley, G.L., Childers, D., Ogden, L., Pincetl, S., Pataki, D., Whitmer, A., Cadenasso, M.L., 2015. Trees grow on money: urban tree canopy cover and environmental justice. PLoS One 10 (4), e0122051. https://doi.org/10.1371/ journal.pone.0122051. Srivastava, A., van Rooijen, R., 2000. Bitumen Performance in Hot and Arid Climates. Prepared for the Innovative Road Rehabilitation and Recycling Technologies Pavement Seminar. Retrieved 23 February 2017 from http://docs. vircomnet.com/ mobility/urban_mobility_vc/ paper_for_jordan.htm. . Stewart, G.H., Ignatieva, M.E., Meurk, C.D., Earl, R.D., 2004. The re-emergence of indigenous forest in an urban environment, Christchurch, New Zealand. Urban For. Urban Green. 2, 149–158. https://doi.org/10.1078/1618-8667-00031. Taylor, A.F., Kuo, F.E., 2006. Is contact with nature important for healthy child development? State of the evidence. In: Spencer, C., Blades, M. (Eds.), Children and Their Environments. Cambridge University Press, Cambridge, UK, pp. 124–140. Taylor, A.F., Kuo, F.E., Sullivan, W.C., 2002. Views of nature and self-discipline: evidence from inner city children. J. Environ. Psychol. 22, 49–63. https://doi.org/10.1006/ jevp.2001.0241. Teixeira-de Mello, F., Meerhoff, M., Gonzalez-Bergonzoni, I., Kristensen, E.A., BaattrupPedersen, A., Jeppesen, E., 2015. Influence of riparian forests on fish assemblages in temperate lowland streams. Environ. Biol. Fishes 99, 133–144. https://doi.org/10. 1007/s10641-015-0462-9. Thompson, C.W., Roe, J., Aspinall, P., Mitchell, R., Clow, A., Miller, D., 2012. More green space is linked to less stress in deprived communities: evidence from salivary cortisol patterns. Landsc. Urban Plan. 105, 221–229. https://doi.org/10.1016/j.landurbplan. 2011.12.015. Troy, A., Grove, J.M., O’Neil-Dunne, J., 2012. The relationship between tree canopy and crime rates across an urban-rural gradient in the greater Baltimore region. Landsc. Urban Plan. 106 (3), 262–270. https://doi.org/10.1016/j.landurbplan.2012.03.010. United States Census Bureau. 2017. https://www.census.gov/data/tables/2016/demo/ popest/counties-total.html, Accessed November 24, 2017. United States Department of Energy, 2011. Energy Savers: Tips on Saving Money & Energy at Home. 44pp Retrieved DATE from. Office of Energy Efficiency & Renewable Energy, Washington, DC. https://energy.gov/sites/prod/files/energy_ savers.pdf. van Herzele, A., de Vries, S., 2012. Linking green space to health: a comparative study of two urban neighbourhoods in Ghent, Belgium. Popul. Environ. 34 (2), 171–193. https://doi.org/10.1007/s11111-011-0153-1. Villeneuve, P.J., Jerrett, M., Su, J.G., Burnett, R.T., Chen, H., Wheeler, A.J., Goldberg, M.S., 2012. A cohort study relating urban green space with mortality in Ontario, Canada. Environ. Res. 115, 51–58. https://doi.org/10.1016/j.envres.2012.03.003. Whitehead, M., 1991. The concepts and principles of equity and health. Health Promot. Int. 6 (3), 217–228. https://doi.org/10.1093/heapro/6.3.217. Whitehead, M., 1992. The concepts and principles of equity in health. Int. J. Health Serv. 22, 429–445. https://doi.org/10.2190/986L-LHQ6-2VTE-YRRN. Wolch, J., Wilson, J.P., Fehrenbach, J., 2005. Parks and park funding in Los Angeles: an equity-mapping analysis. Urban Geogr. 26 (1), 4–35. https://doi.org/10.2747/02723638.26.1.4. Wolch, J.R., Byrne, J., Newell, J.P., 2014. Urban green space, public health, and environmental justice: the challenge of making cities ‘just green enough’. Landsc. Urban Plan. 125, 234–244. https://doi.org/10.1016/j.landurbplan.2014.01.017. Woods, A.J., Omernik, J.M., Martin, W.H., Pond, G.J., Andrews, W.M., Call, S.M., Comstock, J.A., Taylor, D.D., 2002. Ecoregions of Kentucky (color Poster With Map, Descriptive Text, Summary Tables, and Photographs). U.S. Geological Survey, Reston, VA (map scale 1:1,000,000). Yoon, P., 2012. Urban Forest and Mental Health: the Evidence of a Positive Correlation. Unpublished Thesis. Georgetown University, Washington, DC. Zimmerman, S., 2012. Naturalized detention Basin design & maintenance recommendations. ILMA 2012 Conference Presentation. Retrieved 17 March 2017 from. https:// www.ilma-lakes.org/2012/02_Zimmerman-ILMA.pdf.

60 (7), 587–592. https://doi.org/10.1136/jech.2005.043125. McDonald, R., Kroeger, T., Boucher, T., Longzhu, W., Salem, R., 2016. Planting Healthy Air Report. Retrieved 13 April 2017 from. . https://global.nature.org/content/ healthyair. McPherson, E.G., Nowak, D., Heisler, G., Grimmond, S., Souch, C., Grant, R., Rowntree, R., 1997. Quantifying urban forest structure, function, and value: the Chicago Urban Forest Climate Project. Urban Ecosyst. 1 (1), 49–61. https://doi.org/10.1023/ A:1014350822458. Merse, C.L., Buckley, G.L., Boone, C.G., 2009. Street trees and urban renewal: a Baltimore case study. Geogr. Bull. 5065–5081. Milner, G.R., 2001. Conventional vs. Naturalized Detention Basins a cost/benefit Analysis. The Illinois Association for Floodplain and Stormwater Management. Retrieved 8 March 2017 from. pp. 1–11. http://www.illinoisfloods.org/documents/ fall_2001.pdf. Mish, F.C., 2004. Merriam-Webster Dictionary. Merriam-Webster Inc, Springfield, MA 087779-930-X. Mitchell, R., Popham, F., 2008. Effect of exposure to natural environment on health inequalities: an observational study. Lancet 372 (9650), 8–14. https://doi.org/10. 1016/S01406736(08)61689-X. Moore, R.D., Spittlehouse, D.L., Story, A., 2005. Riparian microclimate and stream temperature response to forest harvesting: a review. J. Am. Water Resour. Assoc. 41 (4), 813–834. https://doi.org/10.1111/j.1752-1688.2005.tb03772.x. Naiman, R.J., Decamps, H., 1997. The ecology of interfaces: riparian zones. Annu. Rev. Ecol. Syst. 28, 621–658. https://doi.org/10.1146/annurev.ecolsys.28.1.621. Norton, B.A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M., Williams, N.S.G., 2015. Planning for cooler cities: a framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landsc. Urban Plan. 134, 127–138. https://doi.org/10.1016/j.landurbplan.2014.10.018. Nowak, D.J., Dwyer, J.F., 2007. Understanding the benefits and costs of urban forest ecosystems. In: Kuser, J.E. (Ed.), Urban and Community Forestry in the Northeast, 2nd ed. Springer, New York, NY, pp. 25–46. Nowak, D.J., Heisler, G.M., 2010. Air Quality Effects of Urban Trees and Parks (research Series, Report). Retrieved 16 March 2017. National Recreation and Park Association, Ashburn, VA. http://www.nrpa.org/uploadedFiles/nrpa.org/ Publications_and_Research/Research/Papers/Nowak-Heisler-Summary.pdf. Nowak, D.J., Hirabayashi, S., Bodine, A., Greenfield, E., 2014. Tree and forest effects on air quality and human health in the United States. Environ. Pollut. 193, 119–129. https://doi.org/10.10116/j.envpol.2014.05.028. Nowak, D.J., Hirabayashi, S., Doyle, M., McGovern, M., Pasher, J., 2018. Air pollution removal by urban forests in Canada and its effect on air quality and human health. Urban For. Urban Green. 29, 40–48. Onishi, A., Cao, X., Ito, T., Shi, F., Imura, H., 2010. Evaluating the potential for urban heat-island mitigation by greening parking lots. Urban For. Urban Green. 9 (4), 323–332. https://doi.org/10.1016/j.ufug.2010.06.002. Ow, L.F., Ghosh, S., 2017. Growth of street trees in urban ecosystems: structural cells and structural soil. J. Urban Ecol. 3 (1), 1–7. https://doi.org/10.1093/jue/jux017. Perkins, H.A., Heynen, N., Wilson, J., 2004. Inequitable access to urban reforestation: the impact of urban political economy on housing tenure and urban forests. Cities 21 (4), 291–299. https://doi.org/10.1016/j.cities.2004.04.002. Pham, T.T.H., Apparicio, P., Seguin, A.M., Landry, S., Gagnon, M., 2012. Spatial distribution of vegetation in Montreal: An uneven distribution or environmental inequity? Landsc. Urban Plan. 107, 214–224. https://doi.org/10.1016/j.landurbplan. 2012.06.002. Poland, T.M., McCullough, D.G., 2006. Emerald Ash Borer: invasion of the urban forest and the threat to North America’s ash resource. J. For. 104 (3), 118–124. https://doi. org/10.1093/jof/104.3.118. Reubens, B., Posen, J., Danjon, F., Geudens, G., Muys, B., 2007. The role of fine and coarse roots in shallow slope stability and soil erosion control with a focus on root system architecture: a review. Trees 21, 385–402. https://doi.org/10.1007/s00468007-0132-4. Rosenzweig, C., Solecki, W.D., Parshall, L., Lynn, B., Cox, J., Goldberg, R., Hodges, S., Gaffin, S., Slosberg, R.B., Savio, P., Dunstan, F., Watson, M., 2009. Mitigating New York City’s heat island. Am. Meteorol. Soc. (BAMS) (September), 1297–1312. Ruther, M., Sawyer, T., Ehresman, S., 2016. Projections of Population and Households:

382