Arboriculture & Urban Forestry 40(2): March 2014 median household income, and lower propor- tions of owner-occupied housing. More typically, researchers have utilized street tree density (i.e., the number of street trees per unit street length or per unit area) as a metric in their studies. For example, in considering street trees as a pedes- trian amenity affecting pedestrian behavior, the GIS (Geographic Information Systems) protocols of the Twin Cities Walking Study (2007) calculated a street tree density measure based on the number of street trees per summed street length contained within an area; in exploring possible relationships between street trees and childhood asthma in New York City, New York, U.S., Lovasi et al. (2008) cal- culated a street tree density measure based on the total number of street trees contained within hospi- tal catchments divided by the areas of those catch- ments; and in associating street tree prevalence with lower body mass index, Lovasi et al. (2012) evaluated the number of street trees within 1 km network buffers. Additional metrics based on a street tree count have included street trees per cap- ita, or the number of street trees in an area divided by the area’s population (McPherson and Rowntree 1989), and stocking level, or the number of street trees planted as a percentage of all available plant- ing sites, whether those sites contain trees or not. Metrics based on a street tree count require that street trees have been geo-referenced (i.e., assigned longitude and latitude coordinates), using either GPS (Global Positioning System) equipment or a street address locator (geocoding). Such metrics make the assumption that all trees function equally regardless of species type and size. This assumption may be significantly flawed. For example, Donovan and Prestemon (2012) found that larger street trees with higher crowns were associated with decreased crime occurrence in Portland, Oregon, U.S. Simi- larly, since most ecosystem services provided by trees are proportional to the amount of leaf surface area, larger statured tree species typically provide many more of these services than smaller statured street tree species (Nowak et al. 2002; Sydnor and Subburayalu 2011). Therefore, accurately analyz- ing the spatial distribution of street trees and the benefits they provide may necessitate moving beyond metrics based on a street tree count, such as street tree density, and toward metrics that fac- tor tree species and tree size into their calculation. 113 erty value increase (USDA Forest Service 2011). For example, stormwater benefits are determined by the annual precipitation (measured in gal.) intercepted by trees multiplied by the price (per gal.) required to treat and control runoff to meet minimum standards (McPherson et al. 2007). For Streets to quantify ben- efits, tree species and trunk diameter data must be collected for each tree surveyed. Benefits can be cal- culated from a complete inventory where data are collected from all street trees in a municipality or neighborhood or from a sample inventory where data are collected according to a sampling meth- odology (i.e., stratified by land use, 2,000 to 2,200 tree minimum) devised by Jaensen et al. (1992). Limitations inherent in the methods and models i-Tree The United States Forest Service developed the i-Tree suite of computer soſtware programs to quan- tify benefits provided by urban trees. These programs include Eco (previously named UFORE) and Streets (previously, STRATUM). i-Tree Streets was created expressly for street trees and quantifies the annual benefits provided by street trees in five categories: energy conservation, air quality improvement, CO2 reduction, stormwater mitigation, and prop- underlying benefit calculations must be acknowl- edged. For example, leaf area for each tree species is estimated from computer processing of tree-crown imagery, a technique whose accuracy has been found to be ±20% of actual leaf area (Peper and McPher- son 2003); leaf area for each tree species as predicted by trunk diameter data is based on best-fit statisti- cal modeling (Peper et al. 2001), which depends in turn on data collected from a representative set of street trees stratified by size (small, medium, large) and type (deciduous, evergreen) and is randomly sampled in a reference city within an i-Tree climate zone. Reliance on a reference city within an i-Tree climate zone is a particular concern (McPherson 2010). For example, the reference city for i-Tree’s northeast climate zone is the borough of Queens in New York City, and the reference city for i-Tree’s Midwest climate zone is Minneapolis, Minnesota; the modeling results from each reference city are extrapolated to other municipalities within the same climate zone (Peper et al 2001). Although benefit estimate inputs, such as the cost per gallon required to treat and control runoff, can be customized in Streets, relying on statistical modeling based on ref- ©2014 International Society of Arboriculture
March 2014
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