Resource Bulletin 

NRS-106

The Urban Forest of 

Philadelphia

United States Department of Agriculture

Forest Service November 2016

Northern 

Research Station


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November 2016

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Manuscript received for publication 19 October 2015

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for 

Research and unknown to the author. This review policy promotes the Forest Service 

guiding principles of using the best scientific knowledge, striving for quality and 

excellence, maintaining high ethical and professional standards, and being responsible 

and accountable for what we do.

Abstract

An analysis of the urban forest in Philadelphia, Pennsylvania, reveals that this city has an 

estimated 2.9 million trees (encompassing all woody plants greater than 1 inch diameter 

at breast height [d.b.h]) with tree canopy that covers 20 percent of the city. The most 

common tree species are spicebush, black cherry, ash, tree-of-heaven, and boxelder, but 

the most dominant species in terms of leaf area are sycamore spp. (including London 

planetree), northern red oak, black walnut, red maple, and Norway maple. Trees in 

Philadelphia currently store about 702,000 tons of carbon (2.6 million tons of carbon 

dioxide [CO2]) valued at $93.4 million. In addition, these trees remove about 27,000 tons 

of carbon per year (99,000 tons CO2/year) ($3.6 million per year) and about 513 tons of 

air pollution per year ($19.0 million per year). Philadelphia’s urban forest is estimated 

to reduce annual residential energy costs by $6.9 million per year. The compensatory 

value of the trees is estimated at $1.7 billion. The city’s parklands constitute 9.3 percent 

of the total land area, have an estimated 1.1 million trees, 64 percent canopy cover, 

and account for 38.8 percent of carbon storage and 34.8 percent of air pollution 

removal performed by the city’s urban forest. The information presented in this report 

can be used by local organizations to advance urban forest policies, planning, and 

management to improve environmental quality and human health in Philadelphia.

Cover Photo

Transportation corridor planting between railway and John F. Kennedy Boulevard in 

Philadelphia, PA. Photo used with permission from Philadelphia Horticultural Society.
The Urban Forest of Philadelphia

The Authors

DAVID J. NOWAK is a research forester and project leader with the U.S. 

Forest Service’s Northern Research Station at Syracuse, NY.

ALLISON R. BODINE is a research forester with Davey Tree’s 

Davey Institute at Syracuse, NY.

ROBERT E. HOEHN III is a forester with the U.S. Forest 

Service’s Northern Research Station at Syracuse, NY. 

ALEXIS ELLIS is a research urban forester with Davey Tree’s 

Davey Institute at Syracuse, NY.

SARAH C. LOW is a field station coordinator and biological scientist with 

the U.S. Forest Service’s Northern Research Station at Philadelphia, PA.

LARA A. ROMAN is a research ecologist with the U.S. Forest Service’s 

Northern Research Station at Philadelphia, PA.

JASON G. HENNING is a research urban forester with Davey 

Tree’s Davey Institute at Philadelphia, PA.

EMILY STEPHAN is a research assistant with the SUNY College of 

Environmental Science and Forestry at Syracuse, NY.

TOM TAGGERT is a research assistant with the SUNY College of 

Environmental Science and Forestry at Syracuse, NY.

TED ENDRENY is a professor with the SUNY College of 

Environmental Science and Forestry at Syracuse, NY.
Street trees in Philadelphia, PA. Photo by Sarah Tse, used with permission.
CONTENTS

EXECUTIVE SUMMARY.............................................................................................................................1

BACKGROUND............................................................................................................................................3

METHODS.....................................................................................................................................................3

Tree Cover Assessment.......................................................................................................................3

Urban Forest Composition, Structure, and Values....................................................................3

Tree Cover Change Analysis..............................................................................................................9

Cobbs Creek Watershed Analysis....................................................................................................9

Species Score .......................................................................................................................................11

RESULTS.......................................................................................................................................................12

Tree Cover Assessment.....................................................................................................................12

Urban Forest Structure, Composition, and Values..................................................................12

Land Cover Change Analysis..........................................................................................................33

Cobbs Creek Watershed Analysis..................................................................................................34

MANAGEMENT IMPLICATIONS ..........................................................................................................37

Current Tree Size Distribution and Potential Species Changes..........................................37

Invasive Species..................................................................................................................................39

Insect and Disease Impacts.............................................................................................................39

Species Score .......................................................................................................................................41

CONCLUSION ............................................................................................................................................43

ACKNOWLEDGMENTS...........................................................................................................................43

APPENDIX 1: Land Use Category Descriptions.............................................................................44

APPENDIX 2: Invasive Plant Species Assessed in Philadelphia’s Parkland Areas...............46

APPENDIX 3: Ecosystem Services by Neighborhood..................................................................47

APPENDIX 4: Potential Insect and Disease Impacts....................................................................52

APPENDIX 5: Species Sampled in the Philadelphia Urban Forest..........................................56

APPENDIX 6: Tree Species Distribution............................................................................................59

APPENDIX 7: Relative Tree Effects......................................................................................................63

APPENDIX 8: Temperature Index Map..............................................................................................65

APPENDIX 9: General Recommendations for Air Quality Improvement .............................68

APPENDIX 10: Species Sampled in the Philadelphia Parkland Areas....................................69

APPENDIX 11: Tree Planting Index Map...........................................................................................72

LITERATURE CITED..................................................................................................................................74
Street trees in Philadelphia, PA. Photo used with permission from Philadelphia Horticultural Society.
1

EXECUTIVE SUMMARY

The urban forest in Philadelphia (i.e., all trees1 within the city) affects environmental 

quality and human health. In Philadelphia, local organizations are investing in tree 

planting campaigns to encourage the growth of a healthy urban forest. At the same 

time, insects, diseases, invasive species, climate change and development continually 

alter the urban forest. Addressing the challenge of developing a sustainable and healthy 

urban forest is complicated by a diversity of tree species, their dynamic character, a 

fragmented ownership pattern, and a lack of comprehensive information about the 

urban forest. To address these critical information needs, the U.S. Forest Service 

assessed Philadelphia’s trees to quantify its urban forest structure, and the associated 

services and values provided to society. This assessment consisted of field data 

collection and model analyses to inform and improve urban forest management.

The i-Tree Eco model (www.itreetools.org) was one of the tools used to advance the 

understanding of Philadelphia’s urban forest. This computer model quantifies forest 

structure and associated ecosystem services and monetary values based on local data. 

Structure is a measure of physical attributes of the forest (e.g., species composition, 

number of trees, tree health, leaf area, species diversity). Ecosystem services are 

determined by forest structure and include such attributes as air pollution removal 

and reductions in air temperatures. Monetary values then are estimated for various 

ecosystem services.

To assess Philadelphia’s urban forest and establish a baseline for future monitoring, 

field data were collected during the summer of 2012 and processed and analyzed 

using the i-Tree Eco model. A total of 133 one-tenth-acre field plots were sampled 

throughout the city, with supplemental plots added in parklands. The 72 additional 

parkland plots include forested natural areas as well as neighborhood parks. There 

are an estimated 2.9 million trees in Philadelphia that cover 20 percent of the city 

area and provide millions of dollars of benefits annually (Table 1). This report details 

these findings, provides analyses (tree effects on air temperatures and stream flow), 

and discusses various management issues to help guide urban forest management and 

policy in Philadelphia.

1 Trees in this report include all woody plants with a stem diameter at breast height (d.b.h.; 

measured at 4.5 feet above the ground) greater than or equal to 1 inch. Thus, some species 

commonly thought of as shrubs are considered trees as these species are often defined in the 

literature as large shrub/small tree (i.e., there is no strict definition differentiating a tree vs. 

shrub).
2

Feature Estimate

Number of treesa 2,918,000

Tree cover 20%b

Most dominant species by:

Number of trees spicebush, black cherry, ash species, tree-of-heaven, 

boxelder

Leaf area sycamore species, northern red oak, black walnut, 

red maple, Norway maple

Trees 1 to 6 inches d.b.h. 62.2%

Air temperature reductionc 0.3 °F

Pollution removal 513 tons/year ($19.0 million/year)

VOC emissions 228 tons/year

Carbon storage 702,000 tons ($50.0 million)

Carbon sequestration 27,000 tons/year ($1.9 million/year)

Value of reduced building energy use $6.9 million/year

Value of reduced carbon emissions $764,000/year

Compensatory valued $1.7 billion

Rainfall interception 81.0 million cubic feet a all woody vegetation >1 inch d.b.h. 

b assessed using LiDAR in an earlier report (O’Neil-Dunne 2011)

c Average daytime (6 a.m.-5 p.m.) air temperature reduction on the average temperature summer day

d Estimated value of compensation for the loss of the urban forest structure (a value of the forest’s physical 

structure)

Note: ton = short ton (U.S.) (2,000 lbs)

Table 1.—Summary of city-wide urban forest features, Philadelphia, 2012
3

BACKGROUND

Understanding the magnitude, composition, and value of trees and forests in a city is the 

first step toward developing management plans that can help sustain healthy and vibrant 

forests throughout a city. Good management requires good information on the resource 

being managed. As urban trees and forests provide numerous essential services to society 

(e.g., cooler air temperatures, reduced energy use, cleaner air and water), it is important 

to understand and quantify these services so that they can be optimized for future 

generations. However, many urban forest services cannot be quantified or valued at this 

time (e.g., aesthetics, sense of place, historical value). To help understand the urban forest 

of Philadelphia and its associated services and values, the Philadelphia Field Station 

of U.S. Forest Service, Northern Research Station (U.S. Forest Service 2011), assessed 

the city’s urban forest. The Philadelphia Field Station, established in 2011, is part of a 

network of urban field stations that foster collaborative science, science-delivery, and tools 

to assist partner organizations with natural resource management. This report summarizes 

findings of this assessment and lays the foundation for future data collection to monitor 

changes in the urban forest. The goal of this assessment and subsequent analysis is to 

provide local organizations with information relevant to the sustainable management of 

Philadelphia’s urban forest. For more information about the Philadelphia Field Station, 

visit http://www.nrs.fs.fed.us/philadelphia.

METHODS

Four types of analyses were conducted for Philadelphia: 1) urban tree cover variation 

based on remote sensing; 2) urban forest structure, ecosystem services, and values based 

on field plot data and the i-Tree Eco model; 3) change in urban tree and shrub cover from 

2008 to 2012 based on photo interpretation of aerial imagery; and 4) modeled effects of 

tree cover and impervious surface on stream flow in the Cobbs Creek watershed using the 

i-Tree Hydro model.

Tree Cover Assessment

Philadelphia’s tree cover estimates were derived from 2008 LiDAR and high resolution 

aerial imagery processed by the University of Vermont’s Spatial Analytics Lab (O’NeilDunne 2011). Tree cover was defined as leaf area with a height of 8 feet or greater. A 

tree cover map was created from the imagery and used to estimate tree cover at the 

neighborhood and block group level using a geographic information system (GIS).

Urban Forest Composition, Structure, and Values

To help assess the urban forest, data were collected on field plots located within the 

boundaries of Philadelphia and analyzed using the i-Tree Eco model (Nowak and Crane 

2000, Nowak et al. 2008). The i-Tree Eco model uses standardized field data and local 
4

hourly air pollution and meteorological data to quantify forest structure and its numerous 

effects, including: 

• Species composition

• Tree density

• Leaf area and biomass

• Air temperature reduction

• Air pollution removal

• Carbon storage

• Annual carbon sequestration

• Changes in building energy use

• Compensatory value

• Potential risk from insects or diseases

Field Measurements

Field crews sampled 133 one-tenth-acre plots that were distributed throughout 

Philadelphia (Table 2). Plot locations were randomly placed within the boundaries of 

the entire city. The distribution of samples included 50 plots on residential land, 29 

plots on commercial/industrial/utility land, 21 on institutional land, 14 in wooded areas, 

8 in transportation (e.g., major highways, airports), 8 in other lands, and 3 in water. 

Land use locations and definitions are based on the Delaware Valley Regional Planning 

Commission’s 2010 land use map (Delaware Valley Regional Planning Commission 

2010) (appendix 1).

Table 2.—Distribution of plots among land use categories, Philadelphia, 2012. 

Total city land area is 90,990 acres.

Land use Plots City land area

number percent

Citywide plots

Residential 50 40.0

Commercial/Industrial/Utility 29 19.8

Institutional 21 13.0

Wooded 14 8.4

Transportation 8 6.2

Other 8 6.9

Water 3 5.7

Subtotal 133a 100.0

Parkland 73 9.3

Grand total 192 n/a

a The 133 citywide plots includes 14 plots also used in the parkland assessment
5

To help assess the structure and function of parkland in the city, Philadelphia Parks and 

Recreation provided a map of parkland areas (Fig. 1). As only 14 of the original 133 plots 

fell within parkland, an additional 59 randomly located plots were sampled within the 

parkland area. These parklands included forested natural areas as well as neighborhood 

parks managed by the Fairmount Park Commission prior to its merger with the 

Philadelphia Department of Recreation (now the Philadelphia Department of Parks and 

Recreation).

Thus, two analyses were conducted: one for the entire city (133 plots) and one as a 

separate analysis of parklands (14+59=73 plots). Although there is some overlap between 

14 plots used for the parkland and citywide analyses, they were treated as separate samples 

for estimation purposes.

Field data were collected by trained interns from the Philadelphia Horticultural Society 

and University of Pennsylvania. Data collection took place during the leaf-on season, 

from May to August of 2012. Within each one-tenth acre circular plot, ground cover 

was assessed and characteristics of all trees were recorded. Trees were defined as woody 

plants with a diameter at breast height (4.5 feet; d.b.h.) greater than or equal to 1 inch, 

n 

, • 

• 

• 

, 

., 

.. I 

Ji.,, - • �1111

Parkland

Figure 1.—Philadelphia city boundaries and designated parkland areas, 2012.
6

and therefore include some species commonly considered to be shrubs. For each tree, data 

collected included species, d.b.h., tree height, height to base of live crown, crown width, 

percentage crown canopy missing and dieback, crown light exposure, and distance and 

direction to residential buildings (i-Tree 2009). Measurements of crown dimensions and 

percentage crown canopy missing were used to assess tree leaf area.

For trees with more than six stems at breast height, tree stem diameter was measured 

below the fork and the height of the diameter measurement was recorded. For multistemmed trees with two to six stems at breast height, each stem d.b.h. was measured and 

a quadratic mean d.b.h. was calculated for the tree based on the basal area of each stem.

Trees were identified to the species or genus level. Certain species (e.g., green ash 

[Fraxinus pennsylvanicus]) were only classified to the genus level (e.g., ash [Fraxinus]) to 

ensure consistent identification across field data collection teams. Some species, such as 

apple (Malus spp.), were identified at the genus level due to many hybrids and varieties. 

Trees designated as “other hardwood” include broadleaved deciduous trees that could not 

be identified to a specific species or genera. Eighty-four percent of the trees designated as 

“other hardwood” were standing dead. In this report, tree species, genera, or species groups 

(e.g., other hardwood) are hereafter referred to as tree species.

On parkland plots, field data were also collected on the presence of native and nonnative 

invasive plant species. Data were collected on 72 one-tenth-acre plots (one plot had 

missing data) and included 42 invasive tree, woody shrub, herbaceous plants, vines, and 

grass genera or species (based on a list developed from Huebner et al. [2007] and through 

a 2012 consultation2). The invasive plant list is found in appendix 2. Although some 

native species can exhibit similar invasive characteristics, they were not included in this 

analysis. The relative abundance of each of these invasive species found on a plot was 

recorded based on the percentage of plot area occupied by the species and categorized as 

follows: a) <5 percent; b) 5 to 25 percent; c) 26 to 50 percent; d) 51 to 75 percent; or e) 76 

to 100 percent.

i-Tree Eco Model

The i-Tree Eco model was used to calculate totals, averages, and standard 

errors by species, land use, and city totals for forest structure and 

associated ecosystem services and values. The standard errors for derived 

estimates (i.e., leaf area, leaf biomass, carbon) report sampling error rather 

than error of estimation. The reported sampling errors underestimate 

the actual standard errors. Lack of information regarding errors in the 

allometric equations and adjustment factors make it impossible to fully account for 

estimation errors. The tabular results, including standard error estimates, of the i-Tree Eco 

analysis are available at http://nrs.fs.fed.us/data/urban.

2 Personal communication, May 25, 2012, Tom Witmer, Director of Natural Resources, 

Philadelphia Department of Conservation and Natural Resources; and Jason Lubar, Associate 

Director of Urban Forestry, Morris Arboretum, Univesrity of Pennsylvania.
7

Whole tree carbon storage was calculated for each tree using forest-derived biomass 

equations and field measured tree data (Nowak 1994, Nowak and Crane 2002, Nowak et 

al. 2002b). As deciduous trees drop their leaves annually, leaf biomass was not included 

in whole tree carbon storage for deciduous trees. Open-grown, maintained urban trees 

(e.g., street trees) tend to have less biomass than predicted by forest biomass equations. To 

adjust for this difference, biomass results for open-grown urban trees were multiplied by 

0.8 (Nowak 1994). No adjustment was made for trees found in natural stand conditions. 

Tree dry-weight biomass was converted to stored carbon by multiplying by 0.5 (e.g., 

Chow and Rolfe 1989).

Carbon sequestration is the amount of carbon annually removed from the atmosphere 

and stored in the tree’s biomass. To estimate annual carbon sequestration, average annual 

diameter growth from appropriate genera, diameter class, and tree condition was added 

to the existing tree diameter (year x) to estimate tree diameter and carbon storage in year 

x+1. Projected carbon estimates from year x+1 were subtracted from carbon estimates in 

year x to determine gross carbon sequestration.

To estimate the monetary value of carbon storage and sequestration, tree carbon values 

were multiplied by $133.10 per ton of carbon based on the estimated social costs of 

carbon for 2015 using a 3 percent discount rate (Interagency Working Group 2013, U.S. 

EPA 2015a). The social cost of carbon is a monetary value that encompasses the economic 

impact of increased carbon emissions on factors such as agricultural productivity, human 

health, and property damages (Interagency Working Group 2013).

Air pollution removal estimates were calculated for ozone (O3), sulfur dioxide (SO2), 

nitrogen dioxide (NO2), and particulate matter less than 2.5 microns (PM2.5). Estimates 

are derived from calculated hourly tree-canopy resistances for O3, SO2, and NO2 based on 

a hybrid of big-leaf and multi-layer canopy deposition models (Baldocchi 1988, Baldocchi 

et al. 1987). Removal and resuspension rates for PM2.5 varied with wind speed and leaf 

area (Nowak et al. 2013a).

Pollution removal value is estimated as the economic value associated with avoided 

human health impacts (i.e., cost of illness, willingness to pay, loss of wages, and the value 

of statistical life). The U.S. Environmental Protection Agency’s (EPA) Environmental 

Benefits Mapping and Analysis Program (BenMAP) was used to estimate the monetary 

values that result from changes in NO2, O3, PM2.5 and SO2 concentrations due to 

pollution removal by trees. BenMAP is a Windows-based computer program that uses 

local pollution and population data to estimate the health impacts of human exposure 

to changes in air quality and calculates the associated economic value of those changes 

(Nowak et al. 2013a, 2014; U.S. EPA 2012).

Tree effects on residential building energy use was calculated using distance and direction 

of trees from residential structures, tree height, and tree condition data (McPherson and 

Simpson 1999). Savings in residential energy costs were calculated based on state average 
8

2012 costs for natural gas (Energy Information Administration 2014b), 2012/2013 

heating season fuel oil costs (Energy Information Administration 2014c), 2012 residential 

electricity costs (Energy Information Administration 2012a), and 2012 costs of wood 

(Energy Information Administration 2012b).

Compensatory values (i.e., estimated value of compensation for a loss of a tree) were 

based on valuation procedures of the Council of Tree and Landscape Appraisers (2000), 

which uses tree species, diameter, condition, and location information (Nowak et al. 

2002a).

To learn more about i-Tree Eco methods (Nowak and Crane 2000; Nowak et al. 2002b, 

2008) visit: www.itreetools.org.

Local Scale Estimates of Air Pollution Removal by Trees

The local effects of urban forest cover on air pollution removal were estimated using the 

Philadelphia tree cover map (O’Neil-Dunne 2011) in conjunction with U.S. Census and 

local pollutant concentrations. Tree cover in each U.S. Census block group was combined 

with block population data and hourly pollutant concentrations from the closest air 

quality monitor to estimate pollution removal and value at each block group. For PM2.5, 

daily concentration estimates were for each Census tract based on EPA’s fused air quality 

surfaces data (U.S. EPA 2015b). If a block group’s tract was not included in the EPA’s 

fused air quality surfaces, data for the nearest tract was used.

To estimate pollution removal and value at the neighborhood level (appendix 3), values 

for each block group within a neighborhood were summed. If only a proportion of block 

group existed within a neighborhood, the block group value was reduced proportional to 

the percent of the block group in the neighborhood.

Tree Effects on Local Air Temperature

To estimate the effects of trees on air temperatures, an urban-forest regression-based air 

temperature model was used (Heisler et al. 2006, 2007, 2015). This model was developed 

in Baltimore, MD, and estimates changes in hourly air temperatures using tree and 

impervious cover at the site and within the upwind direction up to 3.1 miles (5 km). 

Changes in hourly air temperature were based on elevation difference from the weather 

station, cold air drainage from the site, Turner class (atmospheric stability), rain within 

the last hour, vapor pressure deficit, wind direction, upwind tree and impervious cover 

at varying distances, and wind speed. The model uses GIS data sets to estimate hourly 

temperatures in each 30 meter pixel cell using current tree cover conditions and a baseline 

scenario of zero percent tree cover based on the land cover maps (O’Neil-Dunne 2011). 

The differences between the two estimates represent the tree effects on air temperature.
9

Weather data from 2008 were explored to determine four representative days between 

June 1 and August 31 that could be modeled for tree effects on air temperatures. The air 

temperature model was run to estimate the average air temperature reduction due to trees 

for the following days: 

a. Windiest day (day with the highest average wind speed): June 1, 2008

b. Least windy day (day with the lowest average wind speed): August 21, 2008

c. Average temperature day (day with the average temperature closest to the 

summer average temperature): August 4, 2008

d. Warmest day (day with the highest average summer daytime temperature): 

June 10, 2008

The days were selected to illustrate a range of temperature effects under different 

meteorological conditions. Days were divided into 12 hour blocks to compare daytime 

(6 a.m. to 5 p.m.) and night-time (6 p.m. to 5 a.m.) conditions. Results were analyzed 

for each block group and neighborhood. Maps illustrating results by block group are not 

displayed in this report but are available at http://nrs.fs.fed.us/data/urban.

Tree Cover Change Analysis

In addition to the tree cover assessment based on remote sensing and i-Tree Eco analysis, 

a tree and shrub cover change analysis was conducted based on photo interpretation of a 

sampling of 1,000 randomly paired points on aerial photographs of Philadelphia in 2008 

and 2012. This analysis is based on methods detailed in Nowak and Greenfield (2012) 

and uses the McNemar test (Sokal and Rohlf 1994) to determine if tree cover change 

between 2008 and 2012 is statistically significant at alpha level = 0.05.

Cobbs Creek Watershed Analysis

To better understand the impact of tree and impervious cover on stream flow and water 

quality in Philadelphia, the i-Tree Hydro model (Wang et al. 2008) was applied to the 

13,000-acre Cobbs Creek watershed (Fig. 2). The model was calibrated using existing 

flow data and used to compare how simulated flow and runoff changed when tree or 

impervious surface cover is changed in the watershed. Due to limitations in calibration 

and determining the exact hourly flow rates, the i-Tree Hydro model is used to evaluate 

relative changes in stream flow under different scenarios, as opposed to determining 

exact increases or decreases in the cubic-foot volume of flow due to changes in tree or 

impervious cover.
10

Data and Model Calibration

Hourly weather data were obtained from the Philadelphia International Airport weather 

station (National Weather Service station number 724080). Tree and impervious land cover 

parameters for the watershed were estimated by interpretation of Google Earth imagery 

(image date circa 2013) using a sample of 300 random locations (Table 3). A default tree 

canopy leaf area index (LAI = 5) was used. The amount of impervious area directly connected 

to the stream is a key model input. Under the current condition case of 54 percent impervious, 

48 percent of the impervious cover was assumed directly connected to the stream. The 

percentage of impervious cover connected to the stream varied with percentage impervious so 

that as percentage impervious cover increased, the percent connected also increased.

Figure 2.—Digital elevation model of Cobbs Creek watershed and U.S. 

Geological Survey (USGS) gauge at Mt. Moriah Cemetery, 2011.

Cover

Area Impervious Tree/shrub Grass/herbaceous Bare Soil

acres ----------------------------------------percent-----------------------------------------

12,676 54.0a 46.8 10.5 0.0

a Total is greater than 100 percent because 24 percent of ground cover beneath tree canopies 

was modeled as impervious.

Table 3.—Cover estimates for Cobbs Creek watershed, 2011
11

Model calibration is necessary to adjust several model parameters, mostly related to soils, 

to find the best fit between the observed and modeled flows on an hourly basis. There 

can be mismatches between the precipitation data, which were collected outside of the 

watershed, and the actual precipitation that occurred in the watershed. Since the model 

assumes the same amount of rainfall fell everywhere in the watershed, local variations in 

precipitation intensity can lead to differences between the actual precipitation reaching 

the watershed and precipitation observed at the weather station. These differences 

in precipitation can lead to a lack of agreement between the observed and modeled 

estimates of flow as precipitation is a main driver of the stream flow. For the Cobbs Creek 

watershed, i-Tree Hydro model parameters were estimated by calibrating the model for 

the best match between predicted stream flow and observed hourly stream flow data. 

Observed hourly stream flow data was collected at the U.S. Geological Survey (USGS) 

gauging station “Cobbs Creek at Mt. Moriah Cemetery” for the 2011 calendar year.

A full report of the Cobbs Creek analysis is available at http://nrs.fs.fed.us/data/urban.

Species Score

The tree species sampled in Philadelphia were ranked to guide species selections for 

the urban forest. Tree species received a service score based on their ability, at maturity, 

to provide ecosystem services, as well as a pest score based on their risk to potentially 

harmful insects and diseases.

First, the i-Tree Species model was used to rank the trees based on their ability to provide 

nine potential ecosystem services including:

• Air pollution removal

• Low emissions of volatile organic compounds (VOCs)

• Air temperature reduction

• Streamflow reduction

• Carbon storage

• Ultraviolet (UV) radiation reduction

• Low allergenicity

• Wind reduction

• Building energy use reduction

The nine ecosystem services were considered equally important and were thus weighted 

equally. i-Tree Species evaluated how well each tree species provided the nine ecosystem 

services relative to the other species analyzed. Based on this evaluation, tree species were 

ranked and divided into 10-percent classes (e.g., top 10%, 10-20%, 20-30%, and so on). 

The percentage classes determined by i-Tree Species were used to assign each tree species 

a service score. All tree species in the top 10 percent received a service score of 1, species 
12

in the 10 to 20 percent range received a service score of 2, and so on, with a maximum 

species service score of 10 (species in the 90-100%; bottom 10%).

Each tree species was also scored based on risk to potential insects and diseases (appendix 

4) and as to whether it was an invasive species (appendix 2). Pest scores were designated 

using a numerical point system where each pest that could attack a tree species was 

assigned 4 points if the pest was within Philadelphia County, 3 points if it was within 250 

miles of the county, 2 points if it was within 750 miles, and 1 point if it was outside of 

these ranges. The sum of all points for each tree species was used as the pest score.

To compile a final recommendation list, all tree species classified as invasive were removed 

from the list as well as species not analyzed by i-Tree Species (including Japanese 

angelica tree, barberry spp., butterflybush spp. boxwood spp., rose-of-sharon, honeysuckle 

spp., spicebush, Japanese maple, red-osier dogwood, witch hazel, and bigtooth aspen). 

In addition, all tree species with a potential insect or disease pest within Philadelphia 

County were removed from the list. For the remaining tree species, the pest risk score 

was added to the ecosystem services score to produce a combined score. Tree species with 

the lowest combined score were considered the most highly recommended as they have 

the fewest number of pests or pests at greater distances away from Philadelphia, and the 

greatest ability to provide ecosystem services.

RESULTS

Tree Cover Assessment

Existing tree cover (O’Neil-Dunne 2011) in Philadelphia is estimated at 20 percent. 

Tree cover varied by neighborhood with the highest percent tree cover exhibited in 

the northern and western neighborhoods. Percentage tree cover varies tremendously 

throughout the city with Pennypack Park and Wissahickon Park (shown in Fig. 3 in dark 

green) having the greatest percentage tree cover (81 and 84 percent, respectively) and the 

southeastern part of the city (i.e., Newbold [2.4 percent], Callowhill [2.5 percent] and 

Chinatown [2.6 percent]) have the lowest percentage tree cover. Estimates of tree cover 

by neighborhood are given in appendix 3. Note: Many maps shown in this report are also 

available by U.S. Census block group at http://nrs.fs.fed.us/data/urban.

Urban Forest Structure, Composition, and Values

Tree Characteristics of the Urban Forest

Philadelphia’s urban forest has an estimated 2,918,000 trees (standard error of 456,000). 

The five most common species (or species group) in the urban forest are spicebush, black 

cherry, ash species, tree-of-heaven, and boxelder (Fig. 4). The 10 most common species 

account for 55.4 percent of all trees. In total, 69 tree species were sampled in Philadelphia; 

these species and their relative abundance are presented in appendix 5. See appendix 6 for 

more information on species distribution by land use.
13

Figure 3.—Urban tree cover percentage by neighborhood, Philadelphia, 2008.

Figure 4.—Urban forest species composition as a percentage of all trees, Philadelphia, 2012.

Spicebush

8.7%

Black cherry

8.3%

Ash spp

7.1%

Tree-of-heaven

6.3%

Boxelder

6.0%

Northern white 

cedar

4.9% Yew spp

3.8%

Northern red oak

3.6% Red maple

3.5%

White mulberry

3.2%

other species

44.6%
14

The overall tree density in Philadelphia is 32.1 trees per acre. The highest density of trees 

occurs in wooded areas (191.2 trees/ac), followed by other (34.6 trees/ac) and residential 

land (22.4 trees/ac) (Fig. 5). Although the wooded land use only covers 8.4 percent of the 

city, it contains the most trees (49.8 percent of tree population), followed by residential 

areas (40 percent of the land area, 27.9 percent of the trees) (Fig. 5).

Leaf area is a measure of leaf surface area (one side). Leaf area index (LAI) is a measure 

of the total leaf surface area (one side) divided by land area. As each land use has a 

different land area, LAI standardizes the canopy depth on an equal area basis. Total 

leaf area is greatest in wooded (44.4 percent of total tree leaf area) and residential (39.5 

percent) land uses (Fig. 6). Higher LAIs indicate a greater leaf surface area per acre of 

land. Land uses that have the highest LAI are wooded (5.4) and residential (1.0) (Fig. 6).

Figure 5.—Number of trees and tree density by land use, Philadelphia, 2012.

Figure 6.—Total leaf area and leaf area index by land use, Philadelphia, 2012.

0

20

40

60

80

100

120

140

160

180

200

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Wooded Residential Other Institutional Comm/Ind/Util Transportation Water

Density (trees per acre)

Number of Trees (millions)

Land Use

Number of trees

Density

0

1

2

3

4

5

6

0

5

10

15

20

25

30

35

40

45

Wooded Residential Institutional Comm/Ind/Util Other Transportation Water

Leaf Area Index (LAI)

Leaf Area (thousands of acres)

Land Use

Leaf area

Leaf area index
15

Tree size is an important characteristic of the urban forest structure. Large diameter 

trees contribute significantly to the ecosystem services provided by the urban forest 

primarily because leaf area has a strong correlation with environmental benefits. Trees 

with diameters 1 to 6 inches account for 62.2 percent of the population (Fig. 7). Trees 

in this d.b.h. class also contain 13.4 percent of the total leaf area. Nearly all of the 10 

most abundant trees in Philadelphia, with the exception of boxelder, northern red oak, 

and red maple, have more than 50 percent of their population in the 1 to 6 inch d.b.h. 

class (Fig. 8). Trees that have diameters greater than 18 inches account for 9.5 percent of 

the tree population, but comprise 51.5 percent of the total leaf area. Though these large 

diameter trees are a small percentage of the tree population, they are an important part of 

the urban forest in Philadelphia. For more information about environmental benefits by 

d.b.h. class, see appendix 7.

Tree species composition varied between the small diameter (less than 3 inches diameter) 

and large diameter trees (greater than 18 inches diameter). The 10 most common species 

of small diameter trees are spicebush (22.2 percent of trees in ≤3 inch d.b.h. class), ash 

species (10.9 percent), staghorn sumac (8.6 percent), tree-of-heaven (6.6 percent), black 

cherry (6.1 percent), honeysuckle species (4.9 percent), boxelder (4.4 percent), northern 

white cedar (2.8 percent), apple species (2.7 percent), and red maple (2.4 percent). The 10 

most common species of large diameter trees are northern red oak (14.4 percent of trees 

in d.b.h. classes > 18 inches), boxelder (9.5 percent), red maple (7.0 percent), black walnut 

(6.6 percent), white mulberry (5.2 percent), white oak (4.6 percent), American beech 

(4.0 percent), sycamore maple (4.0 percent), sycamore spp. (4.0 percent), and pin oak (4.0 

percent). Boxelder and red maple are among the 10 most common small diameter trees 

and the 10 most common large diameter trees (Fig. 9).

Figure 7.—Percentage of total population and leaf area by diameter class, Philadelphia, 2012. 

Lower limit of each diameter (d.b.h.) class is greater than displayed (e.g., 3-6 is actually 3.01 to 

6 inches).

0

5

10

15

20

25

30

35

40

1-3 3-6 6-9 9-12 12-15 15-18 18-21 21-24 24-27 27-30 30+

Percent

Diameter Class (d.b.h. in inches)

Leaf area

Abundance
16

Figure 9.—Number of trees in diameter class (small trees, <3 inches and large trees, >18 

inches) made up by the most common tree species in those classes, Philadelphia, 2012.

0

50

100

150

200

250

Number of Trees (thousands)

Species

<3 inches

>18 inches

Diameter Class (d.b.h.)

Spicebush

Black cherry

Ash spp

Tree-of-heaven

Boxelder

Northern white cedar

Yew spp

Northern red oak

Red maple

White mulberry

0

10

20

30

40

50

60

70

80

90

1-3

3-6

6-9

9-12

12-15

15-18

18-21

21-24

24-27

27-30

30+

Species

Species Population (percent)

Diameter Class

(d.b.h. in inches)

Figure 8.—Percentage of species population by diameter class for 10 most 

common species, Philadelphia, 2012. Lower limit of each diameter (d.b.h.) class 

is greater than displayed (e.g., 3-6 is actually 3.01 to 6 inches).
17

Tree-of-heaven, one of the 10 most common small diameter trees, is classified as invasive. 

Sycamore maple is one of the 10 most common large diameter trees and is also classified 

as invasive. Mean and median stem d.b.h. by species are presented in appendix 5.

Philadelphia’s urban forest is a mix of native tree species and exotic species that were 

introduced by residents or other means. Urban forests often have higher tree species 

diversity than the surrounding native landscapes because of the large impact of species 

imported from outside the region and the country (Nowak 2010). Increased tree species 

diversity can minimize the overall impact or destruction by a species-specific insect or 

disease (Lacan and McBride 2008, Santamour 1990), but the increase in the number 

of exotic plants can also pose a risk to native plants if exotic species are invasive and 

out-compete and displace native species. In Philadelphia, about 54 percent of the trees 

are native to Pennsylvania, and 78 percent native to North America. Trees with a native 

origin outside of North America are mostly from Asia (12 percent of the trees).

Invasive plant species are often characterized by their vigor, ability to adapt, reproductive 

capacity, and lack of natural enemies. These factors enable them to displace native plants 

and threaten natural areas (National Agriculture Library 2011). Eleven of the 69 species 

sampled in Philadelphia are identified on the state invasive species list (Pennsylvania 

Department of Conservation and Natural Resources, n.d.). These invasive species 

comprise 17.8 percent of the population with the most common being tree-of-heaven, 

white mulberry, and Norway maple (Table 4).

Table 4.—Inventoried species listed on the Pennsylvania invasive 

species list and Philadelphia Parks and Recreation invasive 

species list, Philadelphia, 2012

Common name Population Leaf area

percent percent

Tree-of-heavena,b 6.3 4.2

White mulberrya 3.2 2.5

Norway maplea,b 2.5 5.4

Honeysuckle spp.a 2.1 0.3

Corktree spp.a 1.1 0.5

Sycamore mapleb 1.0 1.9

Princesstree spp.a 0.8 1.3

Siberian elma,b 0.2 0.9

Japanese angelica treea,b 0.2 <0.1

Barberry spp.a 0.2 <0.1

Callery peara,b 0.2 0.2

a Species found on parkland invasive species list (Huebner et al. 2007; Tom 

Witmer and Jason Lubar, personal communication, 2012)

b Species found on state invasive species list (Pennsylvania Department of 

Conservation and Natural Resources, n.d.)
18

Many tree benefits are linked to the leaf area of the plant, i.e., the greater the leaf area, 

the greater the benefit. In Philadelphia’s urban forest, tree species with the greatest leaf 

area are sycamore spp., northern red oak, and black walnut (Fig. 10). Tree species that 

represent a much greater percentage of Philadelphia’s leaf area than percentage of total 

population are sycamore species (including London planetree), tulip tree, and black 

walnut. Tree species dominated by smaller individuals with relatively low amounts of leaf 

area per stem are staghorn sumac and honeysuckle species.

Importance values (IV) are calculated using a formula that combines the relative leaf 

area and relative abundance. High importance values do not mean that these trees should 

necessarily be encouraged in the future; rather these species currently dominate the urban 

forest structure. The three species in the urban forest with the greatest IVs are ash species, 

black cherry, and northern red oak (Table 5).

Air Temperature Reductions

Air temperature reductions provided by trees are a critical ecosystem service as air 

temperatures affect many aspects of the environment and human health. Changes in air 

temperatures alter tree transpiration and volatile organic compound (VOC) emissions 

and thereby affect the hydrologic cycle as well as tree effects on air pollution. In addition, 

air temperatures affect building energy usage and consequent emissions from power 

plants and other pollutant sources. Changes in air temperature also affect human comfort 

and thermal stress related illnesses (Heisler and Wang 2002, Martens 1998).

Figure 10.—Percentage of total tree population and total leaf area for 10 species contributing the greatest 

amount of leaf area, Philadelphia, 2012.

0

1

2

3

4

5

6

7

8

9

Sycamore

spp

Northern

red oak

Black

walnut

Red maple Norway

maple

Tulip tree Ash spp Boxelder European

beech

Tree-ofheaven

Percent

Species

Leaf area

Abundance
19

Average air temperature reductions by trees citywide in Philadelphia varied among 

the four representative days and the time of day (Table 6). The greatest average air 

temperature reduction for the city was estimated as 0.9 °F during the nighttime hours (6 

p.m. to 5 a.m.) on the warmest summer day of 2008. The lowest average air temperature 

reduction for the city was estimated as 0.3 °F during the daytime hours (6 a.m. to 5 p.m.) 

on the average temperature summer day of 2008.

Air temperature reductions by trees in Philadelphia also varied among the city’s 

neighborhoods. On all four of the representative days, the area with the greatest reduction 

in daytime or nighttime air temperature was Wissahickon Park. The neighborhood with 

the smallest daytime or nighttime temperature reduction was Riverfront. The maximum 

temperature reduction occurred during the nighttime hours with the maximum reduction 

during this period being 3.6 °F in Wissahickon Park on the warmest day. Figure 11 

shows the variability of daytime air temperature reductions by neighborhood for the 

average temperature summer day of 2008. On this average temperature day, the greatest 

temperature reduction was estimated at 1.5 °F and the smallest reduction was estimated 

at less than 0.1 °F. For a neighborhood key and more information on the distribution of 

temperature reduction across the neighborhoods, see appendix 8.

An important factor in the estimation of temperature reductions by trees is the local air 

temperature. Estimated air temperatures varied across Philadelphia and are reported 

in appendix 8. Local air temperature can also be used to identify priority areas for tree 

planting. One method of doing this is to estimate potential heat exposure to the city 

population by mapping air temperature combined with city population data. This method 

determines areas with the greatest number people exposed to the warmest temperatures 

(appendix 8) where tree planting would be most beneficial.

Table 5.—Percentage of total population and leaf area, and 

importance value of species with the greatest importance values, 

Philadelphia, 2012

Common name Populationa Leaf areab IVc

percent percent

Ash spp. 7.1 5.2 12.3

Black cherry 8.3 3.6 11.9

Northern red oak 3.6 7.6 11.2

Boxelder 6.0 5.0 11.0

Tree-of-heaven 6.3 4.2 10.5

Spicebush 8.7 1.5 10.2

Sycamore spp. 1.5 8.7 10.2

Red maple 3.5 5.6 9.1

Black walnut 2.0 6.3 8.3

Norway maple 2.5 5.4 7.9

a The percent of total tree population

b The percent of total leaf area

c IV = Population (%) + Leaf area (%)
20

Table 6.—Average, minimum, and maximum air temperature reductions, Philadelphia, 2008

Representative days Time

Average

Philadelphia 

°F

Minimum Maximum

neighborhood °F neighborhood °F

Windiest (6/1/08) AMa 0.5 Riverfront <0.1 Wissahickon Park 1.3

PMb 0.5 Riverfront 0.1 Wissahickon Park 2.0

Least windy (8/21/08) AM 0.4 Riverfront <0.1 Wissahickon Park 1.3

PM 0.7 Riverfront 0.1 Wissahickon Park 3.1

Average temperature (8/4/08) AM 0.3 Riverfront <0.1 Wissahickon Park 1.5

PM 0.6 Riverfront 0.1 Wissahickon Park 2.7

Warmest (6/10/08) AM 0.5 Riverfront <0.1 Wissahickon Park 2.0

PM 0.9 Riverfront 0.1 Wissahickon Park 3.6

a The average air temperature reduction for the daytime hours (6 a.m. to 5 p.m.) of the representative day

b The average air temperature reduction for the nighttime hours (6 p.m. to 5 a.m.) of the representative day

Figure 11.—Daytime (6 a.m. to 5 p.m.) air temperature reductions by trees, by 

neighborhood for the average temperature summer day, Philadelphia, 2008.
21

Air Pollution Removal

Poor air quality is a common problem in many urban areas. It can damage landscape 

materials and ecosystem processes, and reduce visibility. Air pollution is also associated 

with significant human health effects that impact pulmonary, cardiac, vascular, and 

neurological systems (e.g., Pope et al. 2002). The urban forest can improve air quality by 

directly removing air pollutants and reducing energy consumption in buildings, which 

consequently reduces air pollutant emissions from power plants and other sources. Trees 

also emit VOCs that can contribute to ozone formation. However, integrative studies 

have revealed that an increase in tree cover leads to reduced ozone formation (e.g., 

Cardelino and Chameides 1990, Nowak et al. 2000, Taha 1996).

Pollution removal by trees in Philadelphia was estimated using the i-Tree Eco model in 

conjunction with field data, the high resolution land cover map, and hourly pollution and 

weather data for the year 2008. Pollution removal was greatest for ozone (O3, 332 tons 

removed per year), followed by nitrogen dioxide (NO2, 109 tons/year), sulfur dioxide 

(SO2, 48 tons/year), and particulate matter less than 2.5 microns in size (PM2.5, 24 tons/

year) (Fig. 12). By contrast, the value associated with pollution removal was greatest 

for PM2.5 ($14.9 million), followed by O3 ($3.9 million), NO2 ($174,000), and SO2

($28,000). It is estimated that trees alone remove 513 tons of air pollution (NO2, O3, 

PM2.5, and SO2) per year with an associated value of $19.0 million.

Figure 12.—Annual air pollution removal and value by urban trees, Philadelphia, 2012.

0

2

4

6

8

10

12

14

16

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

NO O PM SO

Value (

million dollars / year)

Pollution Removed (100 tons/year)

Pollutant

Pollution removed

Value

2 3 2.5 2
22

Pollution removal by trees in Philadelphia varies among city neighborhoods and is 

associated with total leaf area (Fig. 13). The area with the greatest pollution removal is 

Wissahickon Park (41 tons per year), followed by Pennypack Park (35 tons per year) and 

Chestnut Hill (27 tons per year). 

Figure 13.—Pollution removal by trees by neighborhood, Philadelphia, 2012.
23

The value of pollution removal also varied by neighborhood (Fig. 14). The maximum 

pollution removal value was $627,000 per year in the Somerton neighborhood. The 

Bustleton and Overbrook neighborhoods also had annual pollution removal values over 

one-half million dollars. The values vary by neighborhood depending upon pollution 

removal and population totals. Neighborhoods with greater pollution removal and/

or more people, tend to have high values as more people receive the health benefits 

associated with pollution reduction (Nowak et al. 2014). Estimates of pollution removal 

by neighborhood are given in appendix 3.

Figure 14.—Pollution removal value by neighborhood, Philadelphia, 2012.
24

In 2012, trees in Philadelphia emitted an estimated 228 tons of VOCs (161.3 tons of 

isoprene and 66.3 tons of monoterpenes). Emissions vary among genera and amount of 

leaf biomass. Sixty-nine percent of the urban forest’s VOC emissions were from the oak 

and maple genera (Fig. 15). These VOCs are precursor chemicals to ozone formation.3

General recommendations for improving air quality with trees are given in appendix 9.

Carbon Storage and Sequestration

Climate change is an issue of global concern that threatens to impact species extinctions, 

vulnerable ecosystems such as coral reefs and polar or coastal areas, food production, 

water resources, and human health (Intergovernmental Panel on Climate Change 2014). 

The city’s trees can help mitigate climate change by sequestering atmospheric carbon 

(from carbon dioxide [CO2]) in tissue and by reducing the amount of energy used to 

heat or cool buildings, thus reducing CO2 emissions from fossil-fuel based power sources 

(Abdollahi et al. 2000).

3 Some economic studies have estimated VOC emission costs. These costs are not included here 

as there is a tendency to add positive dollar estimates of ozone removal effects with negative 

dollar values of VOC emission effects to determine whether tree effects are positive or negative 

in relation to ozone. This combining of dollar values to determine tree effects should not be 

done, rather estimates of VOC effects on ozone formation (e.g., via photochemical models) 

should be conducted and directly contrasted with ozone removal by trees (i.e., ozone effects 

should be directly compared, not dollar estimates). In addition, air temperature reductions by 

trees have been shown to significantly reduce ozone concentrations (Cardelino and Chameides 

1990, Nowak et al. 2000), but are not considered in this analysis. Photochemical modeling that 

integrates tree effects on air temperature, pollution removal, VOC emissions, and emissions from 

power plants can be used to determine the overall effect of trees on ozone concentrations.

0 20 40 60 80 100 120 140

Pine

Yew

Beech

Cedar

Willow

Spruce

Walnut

Sycamore

Maple

Oak

VOCs Emitted (tons)

Genera

Isoprene

Monoterpene

VOCs Emitted

Figure 15.—Annual volatile organic compounds (VOCs) emitted by tree genera with highest total 

emissions, Philadelphia, 2012.
25

Carbon storage is one way trees can influence global climate change. As a tree grows, 

it stores carbon in its accumulated tissue. As a tree dies and decays, it releases much of 

the stored carbon back into the atmosphere. Thus, carbon storage is an indication of 

the amount of carbon that can be released if trees are allowed to die and decompose. 

Maintaining healthy trees will keep the carbon stored in trees, but tree maintenance 

can contribute to carbon emissions (Nowak et al. 2002c). Using the wood contained in 

dead trees for wood products is one way to help forestall carbon emissions due to wood 

decomposition. Wood from dead trees can also be used to produce energy (e.g., heat 

buildings). This energy use will release stored carbon, but can reduce energy production 

and emissions from fossil-fuel based power sources. Trees in Philadelphia store an 

estimated 702,000 tons of carbon (2.6 million tons of CO2 valued at $93.4 million).

In addition to carbon storage, trees reduce the amount of carbon in the atmosphere by 

sequestering carbon in new tissue growth. The amount of carbon annually sequestered is 

increased with healthier and larger diameter trees. Gross sequestration by urban trees in 

Philadelphia is about 27,000 tons of carbon per year (99,000 tons per year of CO2) with 

an associated value of $3.6 million per year. Net carbon sequestration in Philadelphia 

is estimated at about 23,000 tons per year (86,000 tons per year of CO2) by subtracting 

estimated carbon loss due to tree mortality and decomposition from gross sequestration.

Of all the species sampled, northern red oak stores the most carbon (approximately 8.4 

percent of total estimated carbon stored) and annually sequesters the most carbon (9.0 

percent of all sequestered carbon) (Figs. 16-17). Trees greater than 30 inches in diameter 

store the most carbon in the city (Figs. 18-19).

Figure 16.—Estimated annual carbon sequestration and value for urban tree species with the greatest 

sequestration, Philadelphia, 2012.

0

20

40

60

80

100

120

140

160

180

0.0

0.5

1.0

1.5

2.0

2.5

Northern

red oak

Tree-ofheavenBoxelder Red

maple

White oak White

mulberry

Black

cherry

Sycamore

spp

Black

walnut

Norway

maple

Value (1000 dollars/year)

Sequestration (1000 tons/year)

Carbon sequestration

Value
26

Figure 18.—Estimated total carbon storage and sequestration by diameter class, Philadelphia, 2012. 

Lower limit of each diameter class is greater than displayed (e.g., 3-6 is actually 3.01-6 inches).

Figure 17.—Estimated annual carbon storage and value for urban tree species with the 

greatest storage, Philadelphia, 2012.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0

10

20

30

40

50

60

70

Northern

red oak

Sycamore

spp

White oak Black

walnut

Red maple Boxelder Ash spp Pin oak European

beech

Norway

maple

Value (million dollars)

Storage (1000 tons)

Carbon storage

Value

0

1

2

3

4

5

6

7

0

50

100

150

200

250

1 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18 18 - 21 21 - 24 24 - 27 27 - 30 30+

Total Sequestration (1000 tons/year)

Total Storage (1000 tons)

Diameter Class (d.b.h. in inches)

Carbon storage

Carbon sequestration
27

Building Energy Use

Trees affect building energy use 

by shading buildings, providing 

evaporative cooling, and blocking 

winter winds. Trees tend to reduce 

building energy consumption in the 

summer months and can either increase 

or decrease building energy use in 

the winter months, depending on the 

location of trees around the building. 

Estimates of tree effects on energy use 

are based on field measurements of 

tree distance and direction to spaceconditioned residential buildings 

(McPherson and Simpson 1999).

Based on average energy costs in 2012 (Energy Information Administration 2012a, 

2012b, 2014b, 2014c), trees in Philadelphia reduce energy costs from residential buildings 

by an estimated $6.9 million annually (Table 7). Trees also provide an additional $1.4 

million in value per year by reducing the amount of carbon released by fossil-fuel based 

power sources (a reduction of 11,000 tons of carbon emissions or 39,000 tons of CO2) 

(Table 8).

Figure 19.—Estimated average carbon storage and sequestration per tree by diameter class, 

Philadelphia, 2012. Lower limit of each diameter (d.b.h.) class is greater than displayed (e.g., 3-6 is 

actually 3.01-6 inches).

0

20

40

60

80

100

120

140

160

180

200

0

1

2

3

4

5

6

7

8

1 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18 18 - 21 21 - 24 24 - 27 27 - 30 30+

Average Sequestration (lbs/year)

Average Storage (1000 lbs)

Diameter Class (d.b.h. in inches)

Carbon storage

Carbon sequestration

London planetrees at the Rodin Museum in Philadelphia, PA. Photo by Lara A. 

Roman, U.S. Forest Service.
28

Structural and Functional Values

The city’s forest has a structural value based on the tree itself that includes compensatory 

value and carbon storage value. The compensatory value is an estimate of the value of 

the forest as a structural asset (e.g., how much should one be compensated for the loss of 

the physical structure of the tree). The compensatory value (Nowak et al. 2002a) of the 

trees in Philadelphia is about $1.7 billion (Fig. 20). For small trees, a replacement cost 

can be used. For larger trees, several estimation procedures are used based on species, size, 

condition and location (Nowak et al. 2002a). The structural value of the forest resource 

tends to increase with an increase in the number and size of healthy trees. Note that some 

invasive species are listed with a high compensatory value because the methods used to 

estimate compensatory value do not necessary discount invasive species in the species 

rating.

Forests also have functional values (either positive or negative) based on the functions 

the trees perform, including sequestering carbon, removing air pollutants, and reducing 

the amount of energy used to heat or cool buildings. Annual functional values also 

tend to increase with increased number and size of healthy trees and are usually on 

the order of several million dollars per year. There are many other functional values of 

the forest, though they are not quantified here (e.g., reduction in ultra-violet radiation, 

aesthetics, and wildlife habitat). Thus the functional values provided in this report only 

represent a portion of the total forest functional values. Through proper management, 

urban forest values can be increased. However, the values and benefits also can decrease 

as the amount of healthy tree cover declines or if improper forest designs are used (e.g., 

Table 7.—Annual monetary savingsa ($) in residential energy expenditures 

during heating and cooling seasons, Philadelphia, 2012

Heating Cooling Total

MBTUb ($) 2,676,000 n/a 2,676,000

MWHc ($) 369,000 3,899,000 4,268,000

Carbon avoided ($) 479,000 950,000 1,429,000

a Based on 2012 statewide energy costs (Energy Information Administration 2012a, 2012b, 

2014b, 2014c) and 2015 social cost of carbon (Interagency Working Group 2013)

b MBTU – Million British thermal units (not used for cooling)

c MWH – Megawatt-hour

Table 8—Annual energy savings (MBTU, MWH, or tons) due to trees near 

residential buildings, Philadelphia, 2012

Heating Cooling Total

MBTUa 184,000 n/a 184,000 

MWHb 3,000 30,000 33,000 

Carbon avoided (tons)c 4,000 7,000 11,000 

a MBTU – Million British thermal units (not used for cooling)

b MWH – Megawatt-hour

c To convert carbon estimates to CO2, multiply carbon value by 3.667
29

increasing energy use). There are also various monetary costs associated with urban 

forest management, such as tree pruning, inspection, removal and disposal, which are not 

accounted for in this assessment (McPherson et al. 2005).

Urban trees in Philadelphia have the following structural values:

• Compensatory value: $1.7 billion

• Carbon storage: $93.4 million

Urban trees in Philadelphia have the following annual functional values:

• Carbon sequestration: $3.6 million

• Pollution removal: $19.0 million

• Reduced energy costs: $6.9 million

Parkland

Parkland areas are an important subset of Philadelphia’s urban forest and include forested 

natural areas as well as neighborhood parks. Parkland comprises 9.3 percent of the city’s 

total land area (Table 2). These publicly managed lands contribute substantially to the 

environmental benefits provided by the city’s urban forest (Table 9). Based on 73 plots, 

parklands have an estimated 1.1 million trees and store 273,000 tons of carbon, among 

other environmental benefits. The compensatory value of Philadelphia’s parkland is 

estimated at $350 million. Tree cover in parkland is 64 percent. In total, 72 tree species 

were sampled in Philadelphia’s parkland; these species and their relative abundance are 

presented in appendix 10.

Figure 20.—Tree species with the greatest compensatory value, Philadelphia, 2012.

0

20

40

60

80

100

120

140

160

180

Northern

red oak

White oak Ash spp White

mulberry

Red maple European

beech

Norway

maple

Tree-ofheavenSycamore

spp

Northern

white cedar

Compensatory

Value (milions of dollars)
30

The most abundant species in parklands are ash species, boxelder, spicebush, black 

cherry, and American beech (Figs. 21, 22). The two most common invasive tree species 

in parkland are Norway maple and Japanese angelica tree (both nonnative). Another 

nonnative invasive, sycamore maple, is also present but comprises less than 1 percent of 

the population.

The specific assessment of invasive trees, woody shrubs, herbaceous plants, vines, and 

grasses in parkland revealed that at least one invasive species was found on 79 percent of 

assessed plots, with 46 percent of plots having greater than five different invasive species. 

The most common invasive plant species in parklands were garlic mustard (nonnative 

species present on 53 percent of plots), followed by poison ivy (native, 49 percent), 

Virginia creeper (native, 37 percent), oriental bittersweet (nonnative, 33 percent), wine 

raspberry (nonnative, 32 percent), wild grape (native and nonnative species, 32 percent) 

and multiflora rose (nonnative, 31 percent) (Fig. 23, 24). Invasive species have the 

potential to outcompete other species, altering both the character of the forest and the 

habitat of animal and plant species that live there. The capacity of invasive species to 

dominate an area to the exclusion of other plants is of special concern in parkland areas 

as it could reduce species diversity and alter many of the functional benefits derived from 

urban forests.

Table 9.—Summary of urban forest features, Philadelphia parklands, 2012

Feature Estimate

Number of treesa 1,100,000

Tree cover 64%b

Most abundant species by:

Number of trees ash species, boxelder, spicebush, black cherry, American beech

Leaf surface area American beech, tulip tree, sycamore species, ash species, 

black cherry

Trees 1-6 inches d.b.h. 66.7%

Pollution removal 179 tons/year ($6.6 million/year)

VOC emissions 54 tons/year

Carbon storage 273,000 tons ($19.4 million)

Carbon sequestration 6,900 tons/year ($489,000/year)

Value of reduced building energy use $21,400/year

Value of reduced carbon emissions $2,400/year

Compensatory valuec $350 million

a

 all woody vegetation > 1 inch d.b.h. 

b assessed using LiDAR in an earlier report (O’Neil-Dunne 2011)

c

 Estimated value of compensation for the loss of the urban forest structure (a value of the forest’s physical structure)

Note: ton = short ton (U.S.) (2,000 lbs)
31

Ash spp

13.0%

Boxelder

12.2%

Spicebush

8.4%

Black cherry

7.4%

American beech

6.9%

Japanese angelica 

tree

5.4%

Norway maple

5.1%

Red maple

3.5%

Bitternut hickory

3.3%

Tulip tree

2.6%

other species

32.2%

Figure 21.—Species composition as a percentage of all Philadelphia parkland trees, 2012.

Figure 22.—Percentage of species population by diameter class for five most common 

species, Philadelphia parkland, 2012. Lower limit of each diameter (d.b.h.) class is 

greater than displayed (e.g., 3-6 is actually 3.01-6 inches).

Ash spp

Boxelder

Spicebush

Black cherry

American beech

0

10

20

30

40

50

60

70

80

90

1-3

3-6

6-9

9-12

12-15

15-18

18-21

21-24

24-27

27-30

30+

Species

Species Population (percent)

Diameter Class

(d.b.h. in inches)
32

Figure 23.—Percentage of total plots occupied by invasive species (excluding vines; see Fig. 24) 

subdivided by proportion of plot invaded, Philadelphia parklands, 2012. This figure does not 

include the invasive species (appendix 2) that were not found on plots.

0 5 10 15 20 25 30 35 40 45 50 55

Purple loosestrife

Canada thistle

Japanese knotweed

Garlic mustard

Reed canarygrass

Chinese silvergrass

Common reed

Japanese stiltgrass

Jet Bead

Viburnum species

Barberry species

Honeysuckle species

Burning bush

Privet species

Multiflora rose

Wineberry

Callery pear

Princesstree

Corktree species

White mulberry

Tree-of-heaven

Japanese angelica tree

Norway maple

Herbaceous Grasses Woody Shrubs Trees

Percent of Total Plots

<5%

5-25%

26-50%

51-75%

76-100%

Percent of plot cover

Figure 24.—Percentage of total plots occupied by invasive vines, Philadelphia parklands, 

2012. This figure does not include the invasive vine species that were not found (appendix 2).

0 5 10 15 20 25 30 35 40 45 50 55

Wisteria species

Winter creeper

Roundleaf greenbrier

Porcelain berry

English ivy

Mile-a-minute weed

Japanese honeysuckle

Wild grape

Oriental bittersweet

Virginia creeper

Poison ivy

Percent of Plots Present
33

Land Cover Change Analysis

Photo interpretation of aerial imagery from different dates provides a method for 

estimating land cover within a study area (e.g., Nowak and Greenfield 2012). Statistical 

information on land cover change was derived from a paired point analysis of 2008 and 

2012 aerial imagery for the city of Philadelphia. Photo interpretation was conducted 

on a random sample of 1,000 paired points. From this analysis, tree and shrub cover is 

estimated at 21.2 percent (standard error (SE) = 1.3 percent) in 2008 and 21.0 percent 

(SE = 1.3 percent) in 2012 (Table 10).

The 2008 cover estimates provided by the high resolution cover map and photo 

interpretation cannot be compared as they use different definitions of cover. The cover 

map defines tree cover based on leaf area at a height of at least 8 feet, while the photointerpretation of tree cover includes both trees and shrubs (all leaf area not at ground 

surface). Both photo interpretation and high resolution cover maps have advantages and 

limitations when estimating cover.

Based on the photo interpretation estimates, tree and shrub cover dropped slightly from 

21.2 percent in 2008 to 21.0 percent in 2012. Some tree and shrub cover in 2008 (0.4 

percent) was replaced by other impervious cover, while some tree cover (0.2 percent) 

was gained from the grass/herb class (Table 11). Thus tree and shrub cover changed in 

Philadelphia between the years, with some tree cover gains (+0.2 percent) and some tree 

cover losses (-0.4 percent), with the net change (-0.2 percent) not statistically different 

from zero. However, there was a statistically significant increase in impervious cover with 

impervious cover increasing from 58.0 percent in 2008 to 59.2 percent in 2012.

Table 10.—Percentage land cover in Philadelphia in 2008 and 2012

Cover type 2008 2012

Percent SE Percent SE

tree/shrub 21.2 1.3 21.0 1.3

grass/herb 18.5 1.2 17.9 1.2

impervious - bldg 22.7 1.3 22.8 1.3

impervious - road 14.1 1.1 13.8 1.1

impervious - other 21.2 1.3 22.6 1.3

water 0.2 0.1 0.2 0.1

soil/bare ground 2.0 0.4 1.6 0.4

SE = standard error
34

Cobbs Creek Watershed Analysis

The i-Tree Hydro model was calibrated to find the “best fit” between the observed stream 

flow and the modeled stream flow for the Cobbs Creek watershed. Following calibration, 

a number of scenarios were modeled by increasing or decreasing existing tree canopy 

and impervious cover parameters to evaluate the effects of land cover change within the 

watershed. (The full report is available at http://nrs.fs.fed.us/data/urban for calibration 

evaluation and description of scenarios).

Existing Cover Effects

The Philadelphia International Airport weather station used for the i-Tree Hydro 

simulation of Cobbs Creek watershed recorded 53.8 inches of rainfall during 2011, the year 

selected for the simulation. It was assumed that this amount fell over the entire 12,676-acre 

watershed and contributed a total of 2.48 billion cubic feet of rainfall during 2011.

The modeled stream flow in the watershed throughout the simulation period for the 

existing cover (i.e., no cover change from measured conditions) was 1.07 billion cubic feet. 

The total stream flow is made up of surface runoff (from pervious and impervious areas) 

and baseflow (i.e., water that travels underground to the stream). Runoff from impervious 

areas and baseflow are the biggest contributors to stream flow with 55.3 and 33.8 percent 

of total flow generated from impervious runoff and baseflow, respectively. Runoff from 

pervious areas was estimated to generate 10.9 percent of the total flow.

Table 11.—Change in percentage of city land area occupied by various land cover class, 

Philadelphia, 2008-2012

2012 2012 2012 2012 2012 2012 2012 2008

Year Cover type

Grass/

herb

Tree/

shrub

Imp. 

Bldg

Imp. 

Road

Imp. 

Other Water Soil Total SE

2008 grass/herb 17.4 0.2 0.2 0.0 0.5 0.0 0.2 18.5 1.2

2008 tree/shrub 0.0 20.8 0.0 0.0 0.4 0.0 0.0 21.2 1.3

2008 imp. bldg 0.0 0.0 22.5 0.0 0.2 0.0 0.0 22.7 1.3

2008 imp. road 0.0 0.0 0.0 13.8 0.3 0.0 0.0 14.1 1.1

2008 imp. other 0.0 0.0 0.1 0.0 21.1 0.0 0.0 21.2 1.3

2008 water 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.1

2008 soil 0.5 0.0 0.0 0.0 0.1 0.0 1.4 2.0 0.4

2012 total 17.9 21.0 22.8 13.8 22.6 0.2 1.6

SE 1.2 1.3 1.3 1.1 1.3 0.1 0.4

Net Change (2008-12) -0.6 -0.2 0.1 -0.3 1.4 0.0 -0.4

Bold values in diagonal indicate amount of class that did not change between 2008 and 2012.

Nonbold values in a row are losses in cover to that row class between the years.

Nonbold values in the columns are gains to the column class between the years. 

For example, tree/shrub cover in 2008 = 21.2 percent, 20.8 percent had no change; 0.4 was lost to the 

impervious (imp.) Other class; and 0.2 was gained from grass/herb class, such that the 2012 tree cover dropped 

by 0.2 percent to 21.0 percent.
35

Tree canopies were estimated to intercept about 7.0 percent of the total rainfall, but 

only 46.8 percent of the watershed was covered by trees, so precipitation interception by 

trees was only 3.3 percent (81.0 million cubic feet). Areas of non-tree vegetation (i.e., 

grasses and shrubs) were estimated to intercept about 3.0 percent of the total rainfall, 

but only 10.5 percent of the watershed was covered by grass and shrubs, so precipitation 

interception by this vegetation was only 0.3 percent (7.6 million cubic feet).

Estimated reduction in chemical constituents is based on the simulated changes in runoff 

rates and national pooled event mean concentration (EMC) values. EMC represents 

the average concentration of a given constituent during a storm event and is defined as 

the total constituent mass divided by the total runoff volume. The current tree cover is 

estimated to reduce total suspended solids in 2011 by around 23.4 tons based on median 

EMC values. Other chemical constituents were also reduced (Table 12).

Tree Cover Effects

Reducing the existing 46.8 percent tree cover in the Cobbs Creek watershed to 0 percent 

would increase total stream flow by 44.4 million cubic feet (4.2 percent of total flow 

under existing conditions) for 2011. Increasing canopy cover from 46.8 percent to 50.0 

percent would reduce overall flow by 1.2 million cubic feet (0.1 percent of total flow 

under existing conditions) for the same period (Fig. 25).

Table 12—Estimated reduction in chemical constituents due to existing 

tree cover, Cobbs Creek watershed, 2011

Reduction

Constituent Median Mean

tons tons

Total suspended solids 23.4 33.7

Biochemical oxygen demand 4.9 6.1

Chemical oxygen demand 19.2 22.7

Total phosphorus 0.1 0.1

Soluble phosphorus 0.04 0.06

Total Kjeldhal nitrogen 0.6 0.7

Nitrite and nitrate 0.2 0.3

Copper 0.005 0.006

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

0 10 20 30 40 50 60 70 80 90 100

Tree Cover (%)

Change in Total Flow (%)

Figure 25.—Percentage change in total annual stream flow by percentage tree 

cover, Cobbs Creek watershed, 2011.
36

Impervious Cover Effects

Removing all impervious cover in the Cobbs Creek watershed (currently 54 percent) 

would reduce total annual flow by 272 million cubic feet (25.6 percent of total flow under 

existing conditions) for 2011. Increasing impervious cover from 54 percent to 60 percent 

of the watershed would increase total annual flow by 86.6 million cubic feet (8.1 percent 

of total flow under existing conditions) for the same period (Fig. 26).

The i-Tree Hydro model projects that increasing tree cover will reduce annual stream 

flow, but the dominant cover type influencing stream flow is impervious surfaces. Relative 

to current cover conditions, increasing impervious cover had a 12 times greater impact 

on flow than tree cover. Increasing impervious cover by 1 percent averaged a 1.1 percent 

increase in annual stream flow, while increasing tree cover by 1 percent averaged only a 

0.09 percent decrease in stream flow. The interactions between changing both tree and 

impervious cover are illustrated for changes in percentage flow in Figure 27.

Figure 26.—Percentage change in total annual stream flow by percent impervious 

cover, Cobbs Creek watershed, 2011.

Figure 27.—Percentage change in total flow during simulation period based on percentage 

impervious and percentage tree cover, Cobbs Creek watershed, 2011. Red star indicates 

existing conditions. Note: some simulation scenarios (e.g., 100 percent tree cover and 100 

percent impervious cover) are not realistic, but included to illustrate the range of possibilities.

-40.0

-20.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 10 20 30 40 50 60 70 80 90 100

Impervious Cover (%)

Change in Total Flow (%) 

%
37

MANAGEMENT IMPLICATIONS

The urban forest of Philadelphia and its associated benefits vary across the city and 

inevitably will change through time. An important aspect of managing the urban forest 

for current and future residents is to understand how to sustain the benefits for all 

city residents. This report provides a means to communicate urban forest benefits and 

provides a baseline by which to start making decisions about planting and management. 

Future measurements will ascertain how the forest is changing due to human and natural 

forces.

While current tree cover for the entire city of Philadelphia is 20 percent, it ranges from 

2.4 percent in the Newbold neighborhood to 83.7 percent in the Wissahickon Park area. 

There are numerous options in determining areas to target tree cover enhancements, 

and these priorities should be determined locally based on issues that are important 

to the City of Philadelphia. One option for determining priority planting areas is to 

map tree cover with population density (appendix 11) to identify areas that have low 

tree cover relative to their population. Using this method, the neighborhoods with the 

highest planting priority (calculated from population density, tree stocking levels and 

tree cover per capita) are Dickinson Narrows, McGuire, and Greenwich. Although 

these neighborhoods have limited plantable space (appendix 3), targeting these areas 

may enhance benefits to the greatest number of people on a per tree basis. This type of 

targeted enhancement is just one of many ways to determine priority areas to enhance 

tree cover. Other methods to target enhancing tree cover could be based on enhancing 

desired ecosystem services. For example, tree planting could be targeted in the warmest 

areas of the city to help cool air temperatures (appendix 8). Based on air temperature 

data and population, the greatest human impact to reducing air temperatures through 

tree planting would occur in the Chinatown, Mechanicsville, and Woodland Terrace 

neighborhoods (appendix 8). Other options for enhancing ecosystem services from trees 

could be to target planting in a) riparian zones to enhance water quality; b) the most 

polluted areas to help enhance pollution removal; c) near buildings to reduce energy use, 

or any combination of these options.

Current Tree Size Distribution and Potential Species Changes

Change in species composition and tree size structure of Philadelphia’s urban forest 

may have a significant influence on the benefits provided by the urban forest for the 

next several decades. These changes are likely to require a different approach in forest 

management strategies that affect species composition, including pest management, 

regeneration, and restoration efforts.

The future forest will be determined, in part, by the structure and composition of today’s 

urban forest. Younger trees will grow to larger sizes and older trees will eventually 

decline and die. Overall, Philadelphia has more small trees than large trees (which leads 

to an inverse J-shaped distribution of diameter structure, Fig. 7). This pattern indicates 
38

a potential for long-term sustainability of tree cover. The shape of the diameter curve is 

dependent on many factors such as mortality rates, growth rates, and influx rates (i.e., the 

number of trees being planted or naturally regenerating each year).

By comparing the species composition of small trees with that of the large trees, potential 

changes in the species composition and size structure of the forest over time is revealed. 

Other factors that will influence future forest structure include insects, disease, land 

use changes, climate change, development, and natural resource management. Several 

of the most common large diameter tree species, particularly pin oak, white oak, and 

black walnut, are underrepresented among the small diameter trees (Fig. 9), which is an 

indication that there may not be enough regeneration and planting of these species to 

sustain the current species population totals into the future. Species that dominate the 

small d.b.h. class and appear to be regenerating well (including tree planting by humans) 

are spicebush, ash species, staghorn sumac, tree-of-heaven, black cherry, honeysuckle 

species, boxelder, northern white cedar, and apple species. Many of these species tend to 

be prolific seeders that have become established in open areas and corridors throughout 

Philadelphia. Some of these species do not attain a large stature at maturity (e.g., 

spicebush, honeysuckle, and apple). If individual small trees are replacing large trees in 

the urban landscape, this will likely lead to lower canopy levels and altered size structure. 

These data suggest that Philadelphia’s urban forest maybe shift toward more smaller and 

often invasive species, which will have impacts on future urban forest structure and value. 

Long-term monitoring will help better determine how species composition is changing. 

Additionally, the small ash trees may not reach mature size due to future emerald ash 

borer infestation.

Changes in urban forest structure and diversity can be assessed over time. Urban forest 

monitoring is important as long-term urban forest plot data can be used to assess changes 

in species composition, size class distribution, and environmental benefits, in addition 

to assessing tree growth and mortality (Nowak et al. 2004, 2013b). This study provides a 

baseline for future monitoring.

An earlier assessment of Philadelphia’s urban forest was conducted based on field data 

collected in 1996 (Nowak et al. 2007). That assessment and this report are independent 

and cannot be directly compared due to differing samples and methods. These older 

plots were not permanently referenced, hence new permanent plots were laid for the 

current study. However, results from 1996 are mentioned here to help explain potential 

differences. The 1996 assessment reported 2.1 million trees with tree cover 15.7 percent. 

Tree cover in 1996 was estimated based on 210 field plots and was estimated within 5 

percent categories on each plot. Given this relatively low precision measurement and 

the fact that the 1996 cover estimate was based on a sample, there is little evidence to 

show that this cover estimate is different from the more precise 2008 LiDAR tree cover 

estimate of 20 percent (O’Neil-Dunne 2011). Additionally, “tree” is defined differently in 

the field plots and LiDAR methods: the 1996 field study had a minimum d.b.h. of 1 inch 

for tree species and 4 inch for shrub species, the 2012 field study had a minimum d.b.h. 
39

of 1 inch for all woody plants, and the 2008 LiDAR tree cover estimate had a minimum 

tree height of 8 feet. Therefore, the 1996 sample did not include many species considered 

shrubs (e.g., spicebush, yew) unless they reached a d.b.h. of 4 inches. Thus, the apparent 

increase in the number of trees based on 1996 and 2012 field data, particularly shrub 

species, is likely due to this difference in methods. The 10 most common species for both 

the 1996 and 2012 plots were similar: eight species were on both lists, while spicebush 

and yew were in the top 10 for 2012, and apple species and tulip tree were in the top 10 

in 1996.

Invasive Species

Invasive species are another concern in Philadelphia and account for 519,000 trees with a 

leaf area of 697 million square feet. Within parklands, invasive species were found on 79 

percent of the sampled plots. The invasive species observed in Philadelphia can alter the 

urban forest composition through time as they can spread into the surrounding landscape, 

displacing native species and altering local ecosystems (Pimentel et al. 2000).

Insect and Disease Impacts

Insects and diseases can infest urban forests, potentially killing trees and reducing the 

health, value, and sustainability of the urban forest. Various pests have different tree 

hosts, so the potential damage or risk of each pest will differ. Thirty-one exotic insects/

diseases were considered for their potential impact using range maps of the pests in 

the coterminous United States (U.S. Forest Service 2013, 2014; Worrall 2007). For a 

complete list of the 31 exotic insects/diseases, see appendix 4.

Although there are numerous pests that could impact Philadelphia’s urban forest, Asian 

longhorned beetle (ALB), gypsy moth (GM), emerald ash borer (EAB), and oak wilt 

(OW) pose the most serious threats based on their range and 

the number of trees at risk to infestation.

Of these four insects and diseases, GM and EAB were 

confirmed present in Philadelphia. Potential loss from GM is 

455,000 trees with an associated compensatory value of $394 

million; EAB is 207,000 ($90 million). ALB and OW have 

been found within 250 miles of Philadelphia. Potential loss 

of trees from ALB is 584,000 ($439 million in compensatory 

value) and from OW is 193,000 ($321 million) (Fig. 28).

These four insects and diseases threaten common trees such as willow, ash, birch, maple, 

oak, and elm (appendix 4). The two most significant threats to the forest are likely EAB 

and ALB due to either rapid rate of spread or close proximity to the city. The ALB is 

currently located in New York City and although not spreading rapidly, this pest can have 

devastating impacts due to its large host list.

Asian longhorned beetle. Photo by 

Kenneth R. Law USDA APHIS PPQ, 

from bugwood.org, 0949056.
40

EAB is the pest of most concern and has been recently detected in Philadelphia as of 

June 2016 (Kannan 2016). Ash is a significant tree in Philadelphia (Table 13) and is 

found in three of the seven land use categories (Table 14). Citywide, ash ranks second 

in the number of trees with a d.b.h. between 1 and 3 inches and third in total number 

of trees and leaf biomass. Ash species also comprise 13 percent of the population in 

parkland (Fig. 21) and one-quarter of the total tree population in wooded areas.

Figure 28.—Number of trees at risk and associated compensatory value for four most 

threatening insects or diseases, Philadelphia, 2012.

0

50

100

150

200

250

300

350

400

450

0

100

200

300

400

500

600

ALB GM EAB OW

Compensatory

Value (millions of dollars)

Number of Trees (thousands)

Pest/Disease

Trees at risk

Compensatory value

Table 13.—Ash estimates, Philadelphia, 2012

Parameter Units Estimate Total city Rank

percent

Population number 206,996 7.1 3

Density trees/acre 2.3 n/a 3

Carbon stored tons 35,742 5.1 7

Carbon sequestered tons/year 1,025 3.8 11

Net carbon sequestered tons/year 935 4.0 10

Leaf area acres 4,818 5.2 7

Leaf biomass tons 1,936 6.3 3

Trees, d.b.h. 1-3 in. number 111,777 54.1a 2

Trees, d.b.h. >18 in. number 10,557 5.1a 12

a

 Percentage of all ash trees
41

The expected loss of ash species to EAB will have a significant 

impact on the forest of Philadelphia. Tree removal costs will 

be substantial for government agencies and other land owners. 

It will be necessary to identify other species that can fill the 

important role that ash has played in Philadelphia’s urban 

forest, including not only the ecosystem services discussed in 

this report, but also wildlife habitat.

Species Score

Data from this report can be used to help inform species selections for Philadelphia’s 

urban forest. The species scores presented in Table 15 illustrate one way of evaluating 

existing tree species relative to producing the greatest ecosystem services with minimal 

pest risks, based on existing composition in Philadelphia. Based on the procedures 

detailed in the methods, the species with the lowest combined score (low pest risk and 

high ecosystem service value) are black cherry, northern hackberry, tulip tree, and zelkova. 

This list does not consider whether the species are native, and there may be other species 

missing from this list that are suitable for Philadelphia. Species selections should be made 

by local experts based on knowledge of tree performance, pest risk, and site conditions.

Table 14.—Ash trees by land use, Philadelphia, 2012

Land usea Trees Density

All trees in 

land use

Ash trees in 

land use with 

d.b.h. > 18 in

Ash trees in 

land use with 

d.b.h. 1-3 in

number trees/ac percent percent percent

Wooded 190,158 13.7 25.0 2.9 58.8

Institutional 10,130 4.7 0.9 50.0 0.0

Comm/Ind/Util 6,708 0.4 0.4 0.0 0.0

Philadelphia 206,996 2.3 7.1 n/a n/a

a No ash trees were found on other, residential, water, or transportation land uses

Emerald ash borer feeding on ash leaf. 

Photo by Leah Bauer, US Forest Service, 

from bugwood.org, 5473689.

Emerald ash borer infested trees. 

Photo by Steven Katovich, 

US Forest Service, from 

bugwood.org, 1457016.
42

Table 15.—Species scores based on the current species composition, Philadelphia, 2012. 

Low combined scores indicate low pest risk and high ecosystem service value.

Common namea Pest scoreb Service scorec Combined scored

Black cherry 0 1 1

Northern hackberry 0 1 1

Tulip tree 0 1 1

Zelkova spp. 0 1 1

Common persimmon 0 2 2

Red mulberry 0 2 2

Black locust 0 3 3

Black tupelo 0 3 3

Red maple 3 1 4

Sassafras 2 2 4

Silver maple 3 1 4

Sycamore spp 3 1 4

Yew spp 0 4 4

Black birch 4 1 5

Boxelder 3 2 5

American holly 0 7 7

Atlantic white cedar 0 7 7

Northern catalpa 0 7 7

Honeylocust 0 8 8

Northern white cedar 0 9 9

a

 Invasive species, species not analyzed by i-Tree Species and tree species with pests within Philadelphia 

were not included.

b Pest score (0-4) is a numerical scoring system based on the sum of points assigned to pest risks for 

species. Each pest that could attack the tree species is assigned points based on pest range (i.e., 4 points 

if the pest is located in the county, 3 points if within 250 miles, 2 points if within 750 miles, and 1 point if 

greater than 750 miles away). See Table 19 in appendix 4.

c Service score (1-10) is a numerical scoring system based on a relative ranking of species’ ability to 

provide ecosystem services. Lower scores indicate that ecosystem services are better provided.

d Combined score is the sum of the pest score and service score. Lower scores indicate more highly 

recommended species based on their ability to provide ecosystem services and their status as a host to 

pests.
43

CONCLUSION

The Philadelphia urban forest contributes significantly to the environment, the economy, 

and residents’ well-being. Throughout the city, an estimated 2.9 million trees, representing 

more than 69 species, provide a canopy cover of 20 percent. That canopy, particularly leaf 

surface area, provides a wide range of important environmental benefits including air 

pollution removal, reduced carbon emissions, carbon storage and sequestration, reduced 

energy use for buildings, storm water capture, and many others benefits (and costs).

Publicly managed urban forests play a substantial role in providing ecosystem services. 

Philadelphia’s parklands make up 9.3 percent of the city’s total land area. The estimated 1.1 

million trees in parklands provide a canopy cover of 64 percent and constitute 37.7 percent 

of the total city tree population. Parkland trees are also responsible for 38.8 percent of 

carbon storage and 34.8 percent of air pollution removal performed by the city’s urban forest.

There are a number of forces for change that will impact Philadelphia’s forest structure, 

health, management costs, and environmental benefits provided to the city’s 1.5 million 

residents. The forces discussed in this report include insects and disease infestation, 

invasive plants, and aging and loss of larger trees. Additional forces for change that should 

be considered in urban forest management plans and policies include the overabundance 

of deer (Rawinski 2014), climate change impacts, the expansion of native, opportunistic 

species, and future changes due to urban development and changes in the use of the 

forest. There are numerous forces that have and will alter urban forests. Management 

needs to determine what type of future forest is desired and then develop and implement 

management plans that consider these dominant forces for change to help guide the 

urban forest to sustainable and desirable future state. Managers can use these data in this 

report to inform long-term management plans and policies to sustain a healthy urban tree 

population and ecosystem services for future generations.

More information on trees in Philadelphia can be found at: http://nrs.fs.fed.us/data/urban 

or http://www.nrs.fs.fed.us/philadelphia/

ACKNOWLEDGMENTS

We thank: Philadelphia Parks & Recreation (PPR), the Pennsylvania Horticultural 

Society (PHS), the University of Pennsylvania, and the Davey Institute for their support 

of this field study; Joan Blaustein and Thomas Witmer from PPR and Jason Lubar from 

the University of Pennsylvania’s Morris Arboretum for assistance with field crew training; 

Elizabeth Anderson and Heidi Hiteshue from PHS for assistance with outreach materials; 

Al Zelaya from Davey for support of i-Tree tools; Phillip Rodbell from the U.S. Forest 

Service for helping to launch the Forest Service Philadelphia Field Station; interns David 

Bruhn, Julian Golec, Kathleen Kranich, and Rebecca Lotka for field data collection; and 

Dana Dentice, Glen Abrams, and Mindy Maslin from PHS and Tim Witmer and Curtis 

Helm from PPR for report review.
44

APPENDIX 1

Land Use Category Descriptions

The land use categories used in this report were adapted from definitions used by the 

Delaware Valley Regional Planning Commission (DVRPC) (Delaware Valley Regional 

Planning Commission 2010), and are copied here in whole or in part for informational 

purposes. Some land use categories from DVRPC were combined (e.g., several types of 

residential were grouped together, along with their associated parking areas).

Residential: Includes single family detached, multi-family, 

row home and mobile home areas as well as any associated 

parking of 10 spaces or more. Single-family detached units are 

identified including their lots where lot boundaries are evident. 

In cases where no lot boundaries are evident, their boundaries 

are estimated. Multi-family dwellings are any multiple residential 

units, with the exception of row homes and mobile homes. 

Examples of multi-family units include duplexes, apartments, 

condominiums, quads, etc. Row home areas are defined as a series 

of connected single-family houses forming a continuous group 

usually located in an urban area. Condominiums are identified 

as multi-family, not row homes. Mobile home areas are those 

containing a large group of transportable single-family dwellings.

Commercial/Industrial/Utility: Includes commercial, heavy 

industrial, light industrial, and utility areas as well as any associated parking of 10 spaces 

or more. Commercial areas contain structures predominantly used for the sale of products 

and services. Examples include central business districts, malls, strip malls, shopping 

centers, hotels and motels, and warehousing and distribution centers. All landscaped areas 

associated with a commercial area are also included in this category. Heavy industrial 

uses include oil refineries, chemical plants, steel and metal fabrication, shipbuilding, 

grain elevators, port terminals, manufacturing, and assembly facilities (i.e. automobile 

production, pharmaceutical). Light industrial uses include industrial parks and smallscale manufacturing and assembly. Light industrial sites are often “cleaner” looking than 

heavy industrial sites, with uses that create less smoke, dust, and noise. Utility includes 

power generation and substations, major transmission lines and towers, water filtration 

and storage tanks, wastewater treatment, landfills, and recycling centers. Reservoirs are 

identified as water, not utility. Transmission lines take priority only over certain coexisting 

land uses (agriculture, vacant, and wooded) if present.

Institutional: Includes community services, recreation, and military areas as well as any 

associated parking of 10 spaces or more. Community services include structures that 

provide noncommercial services. Examples include educational facilities (schools, but 

not the recreational fields associated with them), places of worship, cemeteries, hospitals 

and medical centers, museums, government centers (other than military), correctional 

Littleleaf linden street trees in the 

University City neighborhood of 

Philadelphia, PA. Photo by Lara A. Roman, 

U.S. Forest Service.
45

facilities, and social clubs. All landscaped areas associated with a community services area 

are also included in this category. Recreation areas are those developed for recreational 

activities. This includes recreational parks and playgrounds (including those associated 

with schools), golf courses, picnic areas, camps, fairgrounds, recreational boat launches, 

swimming pools, theatres, stadiums and arenas, zoos, amusement parks, and nonmilitary 

firing ranges. Military includes all military installations, such as bases and camps, 

armories, air bases, naval bases and air stations, and U.S. Coast Guard bases.

Wooded: Wooded (forested) areas are areas of continuous canopy or solid tree cover, 

woodlands, and natural lands. Hedgerows (windrows) and wooded areas associated with 

residences are not interpreted as wooded. If large transmission lines (utility) clearly 

continue through a wooded area, the utility land use takes priority.

Transportation: Includes transportation areas and any associated parking of 10 

spaces or more. Transportation includes areas devoted to rail, air, marine, and highway 

transportation. Examples include limited-access highways (highways that are at least 

double lane divided) and their ramps, railroad facilities (stations, roundhouses, and 

switching yards), airports, and truck and bus terminals. Two lane roads and residential 

streets are not identified as transportation. The transportation category takes priority over 

any other coexisting land use that may be present (i.e., highways over rivers or utility 

rights-of-way).

Other: Includes vacant and agriculture areas. Vacant lands are areas that are not clearly 

wooded, not agricultural, not developed, not landscaped, or are cleared or unused but 

not tied to other uses. If large transmission lines (utility) clearly continue through a 

vacant area, the utility land use takes priority. Agriculture includes land devoted to crops, 

pastures, orchards, tree farms, or other agricultural uses. Also included are nurseries, 

greenhouses, sod farms, horse farms, and cattle, pig, poultry, and dairy farms. The 

farmstead and associated buildings are included as well. If large transmission lines (utility) 

clearly continue through an agricultural area, the utility land use takes priority.

Water: Water areas are rivers, canals, streams, lakes, reservoirs, and ponds that have two 

definable boundaries. Single line hydrology is not defined. When coexisting with another 

land use, other than transportation, the 

water land use takes priority.

Planted trees along Schuylkill River Trail in 

Philadelphia. Photo by Sarah Low, U.S. Forest Service.
46

APPENDIX 2

Invasive Plant Species Assessed in Philadelphia’s Parkland Areas

Table 16.—Invasive speciesa assessed in urban forest, Philadelphia parkland, 2012. Species are 

nonnative unless otherwise indicated.

Functional Type Common Name Genus Species

Trees Norway maple Acer platanoides

tree-of-heaven Ailanthus altissima

Japanese angelica tree Aralia elata

white mulberry Morus alba

princesstree Paulownia tomentosa

corktree species Phellodendron species

callery pear Pyrus calleryana

Siberian elm Ulmus pumila

Woody shrubs barberry species** Berberis species

Elaeagnus species** Elaeagnus species

burning bush Euonymus alatus

privet species Ligustrum species

honeysuckle species** Lonicera species

buckthorn species** Rhamnus species

jet bead Rhodotypos scandens

multiflora rose Rosa multiflora

wine raspberry Rubus phoenicolasius

Viburnum species** Viburnum species

Grasses Japanese stiltgrass Microstegium vimineum

Chinese silvergrass Miscanthus sinensis

reed canarygrass* Phalaris arundinacea

common reed** Phragmites australis

Herbaceous garlic mustard Alliaria petiolata

Canada thistle Cirsium arvense

Japanese hop Humulus japonicus

purple loosestrife Lythrum salicaria

Allegheny-spurge* Pachysandra procumbens

Japanese knotweed Polygonum cuspidatum

common periwinkle Vinca minor

Vines chocolate vine Akebia quinata

porcelain berry Ampelopsis brevipedunculata

oriental bittersweet Celastrus orbiculatus

winter creeper Euonymus fortunei

English ivy Hedera helix

Japanese honeysuckle Lonicera japonica

Virginia creeper* Parthenocissus quinquefolia

mile-a-minute weed Persicaria perfoliata

kudzu Pueraria montana

roundleaf greenbrier* Smilax rotundifolia

poison ivy* Toxicodendron radicans

wild grape species** Vitis species

Wisteria species** Wisteria species a Invasive species list derived from Huebner et al. 2007 and Tom Witmer and Jason Lubar, personal communication, 2012

* native species

** both native and nonnative species
47

APPENDIX 3

Ecosystem Services by Neighborhood

Ecosystem services are presented by neighborhood for the City of Philadelphia and 

estimated using the LiDAR urban tree canopy assessment (O’Neil-Dunne 2011) (Table 

17). Neighborhoods have been assigned a key identification number so they may be 

located on the neighborhood key (Branigan 2013) (Fig. 29). Detailed information on 

ecosystem services by block group can be found at: http://nrs.fs.fed.us/data/urban.

Figure 29.—Neighborhood key, Philadelphia, 2012.
48

Table 17.—Current tree cover, planting index values, and available space for planting by neighborhood, 

Philadelphia, 2012

Key ID Neighborhood

Tree 

cover

Pollution 

removal Avg.a PPIb TPIc

Plantable 

spaced

Temp. 

reductione

percent tons/yr $/yr acres °F

1 Academy Gardens 15.9 1.79 54,500 40.3 70.5 10.2 149.98 0.3

2 Airport 4.2 2.65 100 17.5 34.9 0.0 688.73 0.1

3 Allegheny West 14.6 2.92 168,300 34.4 65.5 3.3 187.09 0.3

4 Andorra 54.4 9.76 143,400 25.5 45.5 5.5 136.89 1.0

5 Aston-Woodbridge 29.1 3.07 134,500 35.3 58.2 12.3 93.99 0.5

6 Bartram Village 20.3 2.02 62,800 30.0 55.0 4.9 97.24 0.3

7 Bella Vista 6.9 0.22 42,700 47.7 66.6 28.7 6.18 0.1

8 Belmont 11.7 0.42 44,800 43.8 69.6 17.9 25.71 0.2

9 Brewerytown 10.8 0.78 99,100 40.0 70.3 9.7 41.30 0.2

10 Bridesburg 7.5 1.46 34,000 37.5 72.1 2.9 214.67 0.1

11 Burholme 27.1 1.66 56,500 35.4 60.0 10.8 68.27 0.5

12 Bustleton 22.7 19.34 595,000 32.2 62.6 1.8 836.05 0.4

13 Byberry 23.4 7.43 17,900 31.7 61.8 1.6 513.79 0.4

14 Callowhill 2.5 0.11 6,400 46.5 66.2 26.9 6.28 <0.1

15 Carroll Park 10.7 0.90 125,500 39.2 72.0 6.4 55.52 0.2

16 Cedar Park 19.1 1.29 193,300 37.8 64.9 10.6 38.47 0.3

17 Cedarbrook 14.2 2.07 118,500 37.6 70.5 4.6 181.73 0.3

18 Center City East 4.2 0.13 12,300 59.1 53.4 64.7 3.18 0.1

19 Chestnut Hill 48.5 26.83 363,100 24.8 47.9 1.7 507.76 0.9

20 Chinatownc 2.6 0.03 3,100 140.8 66.5 215.2 1.07 0.1

21 Clearview 21.8 1.26 78,900 44.9 63.6 26.2 49.65 0.4

22 Cobbs Creek 12.9 3.56 393,700 36.0 69.2 2.8 131.70 0.2

23 Crescentville 5.1 0.48 7,700 36.9 68.1 5.7 85.74 0.1

24 Crestmont Farms 46.3 1.07 36,800 63.8 59.1 68.6 26.52 0.8

25 Dearnley Park 41.3 4.64 52,400 28.7 50.7 6.7 116.28 0.7

26 Dickinson Narrows 4.1 0.15 36,500 53.9 81.0 26.7 11.38 0.1

27 Dunlap 13.1 0.23 31,900 51.3 69.8 32.8 9.12 0.2

28 East Falls 32.8 8.10 312,700 29.5 55.1 3.8 242.97 0.6

29 East Kensington 6.0 0.28 28,300 48.6 75.2 22.0 28.86 0.1

30 East Oak Lane 28.3 3.91 209,000 31.7 58.5 5.0 134.44 0.5

31 East Park 42.1 6.72 4,500 22.5 43.2 1.9 201.52 0.6

32 East Parkside 10.5 0.29 35,700 46.0 70.2 21.9 21.03 0.2

33 East Passyunk 3.0 0.14 33,600 41.6 64.4 18.8 2.72 0.1

34 East Poplar 13.2 0.40 27,600 45.6 67.9 23.3 17.18 0.2

35 Eastwick 18.9 10.68 173,500 25.5 49.9 1.1 601.61 0.3

36 Elmwood 9.4 1.68 153,000 39.4 72.5 6.2 102.63 0.2

37 Fairhill 6.7 0.29 23,900 44.5 74.2 14.9 32.76 0.1

38 Fairmount 9.7 0.49 105,400 42.0 71.1 13.0 23.29 0.2

39 Feltonville 11.0 1.80 106,600 37.6 69.3 5.9 138.83 0.2

40 Fern Rock 14.3 0.70 46,400 37.9 61.7 14.1 31.16 0.3

41 Fishtown - Lower Kensington 5.8 0.98 108,300 37.5 71.8 3.3 97.36 0.1

42 Fitler Square 18.8 0.35 50,100 49.7 59.2 40.2 6.80 0.3

continued
49

Key ID Neighborhood

Tree 

cover

Pollution 

removal Avg.a PPIb TPIc

Plantable 

spaced

Temp. 

reductione

percent tons/yr $/yr acres °F

43 Fox Chase 20.3 7.67 313,100 33.5 64.7 2.3 403.42 0.4

44 Francisville 10.4 0.39 44,500 46.3 71.0 21.6 29.10 0.2

45 Frankford 9.5 2.28 165,300 36.3 69.5 3.2 163.88 0.2

46 Franklin Mills 14.7 1.45 16,200 36.7 60.0 13.4 41.33 0.2

47 Franklinville 7.4 0.62 44,500 37.3 66.1 8.4 41.33 0.1

48 Garden Court 21.6 0.51 80,200 50.3 66.0 34.6 11.95 0.4

49 Germantown, East 25.7 3.86 159,600 32.2 60.5 3.9 155.62 0.5

50 Germantown, Morton 16.2 0.96 72,900 37.1 64.4 9.9 49.59 0.3

51 Germantown, Penn Knox 25.4 0.76 50,400 39.2 55.8 22.6 21.69 0.4

52 Germantown, Southwest 24.8 2.17 181,200 34.7 61.1 8.4 71.10 0.4

53 Germantown, West Central 36.6 3.82 295,300 30.0 54.3 5.7 86.20 0.7

54 Germantown, Westside 22.4 0.92 96,700 38.0 61.7 14.2 33.67 0.4

55 Germany Hill 31.5 1.54 66,100 35.3 57.0 13.5 39.98 0.5

56 Girard Estates 7.5 0.99 118,600 35.9 65.1 6.7 41.78 0.1

57 Glenwood 5.9 0.36 35,200 44.6 70.1 19.1 31.28 0.1

58 Graduate Hospital 9.1 0.92 135,600 39.0 68.0 10.1 40.02 0.2

59 Grays Ferry 7.5 1.71 107,700 34.8 65.9 3.8 131.89 0.1

60 Greenwich 5.9 0.09 16,900 78.8 77.9 79.8 6.52 0.1

61 Haddington 8.7 1.33 186,800 38.9 73.2 4.6 87.37 0.2

62 Harrowgate 5.9 0.88 49,000 36.2 67.9 4.5 55.26 0.1

63 Hartranft 11.4 1.85 187,400 37.9 69.5 6.3 109.13 0.2

64 Haverford North 12.2 0.26 20,900 47.7 67.9 27.4 13.65 0.2

65 Hawthorne 6.5 0.20 29,900 53.0 67.8 38.2 7.70 0.1

66 Holmesburg 12.0 6.10 234,800 38.0 71.7 4.2 504.01 0.2

67 Hunting Park 9.0 2.03 88,600 34.1 64.0 4.3 171.19 0.2

68 Industrial 7.4 6.32 28,400 19.5 38.9 0.1 474.94 0.1

69 Juniata Park 12.8 2.40 111,000 35.7 66.9 4.5 117.01 0.2

70 Kingsessing 17.1 3.67 267,400 35.6 68.1 3.1 163.47 0.3

71 Lawndale 16.9 3.75 247,200 33.9 64.7 3.1 173.58 0.3

72 Lexington Park 12.5 1.06 43,100 41.9 71.1 12.8 82.11 0.2

73 Logan 17.3 3.94 247,000 32.6 61.6 3.6 179.30 0.3

74 Logan Square 12.4 1.64 166,200 35.7 62.3 9.0 54.74 0.2

75 Lower Moyamensing 2.8 0.22 51,400 41.1 67.0 15.2 6.27 0.1

76 Ludlow 7.9 0.18 14,000 55.2 75.9 34.4 22.98 0.1

77 Manayunk 18.1 1.87 150,700 35.0 63.2 6.8 64.37 0.3

78 Mantua 18.3 2.04 95,800 30.5 57.1 3.9 70.79 0.3

79 Mayfair 9.4 3.23 247,400 38.2 73.9 2.5 308.14 0.2

80 McGuire 4.1 0.09 10,000 62.5 78.9 46.1 10.98 0.1

81 Mechanicsvillec 34.2 0.37 3,800 96.7 61.8 131.6 17.06 0.6

82 Melrose Park Gardens 18.0 0.76 58,000 44.8 67.0 22.6 47.18 0.3

83 Mill Creek 13.5 1.43 121,000 38.4 68.9 7.8 99.25 0.2

84 Millbrook 20.7 1.72 70,500 39.2 63.5 14.9 97.87 0.3

85 Modena 17.9 2.89 152,200 37.2 68.5 5.8 183.26 0.3

Table 17.—continued

continued
50

Key ID Neighborhood

Tree 

cover

Pollution 

removal Avg.a PPIb TPIc

Plantable 

spaced

Temp. 

reductione

percent tons/yr $/yr acres °F

86 Morrell Park 23.7 4.23 151,100 35.8 63.2 8.5 194.26 0.4

87 Mount Airy, East 28.8 7.26 386,300 30.8 59.3 2.3 266.59 0.5

88 Mount Airy, West 47.1 16.01 493,600 25.5 49.0 2.0 301.42 0.9

89 Navy Yard 6.3 2.11 2,200 28.3 56.5 0.0 334.35 0.1

90 Newbold 2.4 0.10 24,600 43.3 64.4 22.2 1.51 <0.1

91 Nicetown 11.5 0.46 23,500 43.1 66.0 20.2 35.32 0.2

92 Normandy Village 27.1 1.49 41,600 38.1 61.9 14.3 60.46 0.5

93 North Central 10.1 1.29 121,700 40.6 73.8 7.3 107.72 0.2

94 Northeast Philadelphia Airport 15.1 9.23 1,000 8.6 17.1 0.0 822.17 0.3

95 Northern Liberties 5.8 0.73 51,500 40.3 70.7 9.9 68.68 0.1

96 Northwood 33.3 5.39 162,400 30.6 56.7 4.6 183.05 0.6

97 Ogontz 13.3 1.40 121,100 36.2 66.1 6.4 71.09 0.2

98 Old City 11.1 1.03 115,300 34.7 54.2 15.2 20.59 0.2

99 Old Kensington 6.6 0.31 25,900 44.2 72.6 15.8 31.20 0.1

100 Olney 15.7 4.57 357,500 35.0 67.2 2.9 216.55 0.3

101 Overbrook 31.1 18.82 569,100 24.1 46.9 1.4 485.67 0.6

102 Oxford Circle 9.2 3.22 275,700 39.4 76.5 2.3 333.73 0.2

103 Packer Park 17.0 3.41 62,400 34.1 63.2 5.0 279.11 0.3

104 Parkwood Manor 23.3 7.57 246,800 33.8 63.5 4.1 412.20 0.4

105 Paschall 14.7 2.34 166,300 37.0 66.8 7.1 89.15 0.3

106 Passyunk Square 4.4 0.28 53,900 43.4 69.4 17.5 12.68 0.1

107 Pennsport 8.1 0.39 60,500 41.3 66.6 16.0 10.04 0.1

108 Pennypack 17.2 4.48 117,200 34.5 62.5 6.5 217.66 0.3

109 Pennypack Park 81.0 34.96 128,000 16.2 31.4 1.0 194.87 1.4

110 Pennypack Woods 21.7 2.20 79,300 37.9 64.5 11.2 107.59 0.4

111 Penrose 10.5 0.71 51,800 48.7 73.6 23.9 64.81 0.2

112 Point Breeze 7.8 0.98 140,700 39.2 73.0 5.5 51.98 0.1

113 Port Richmond 16.2 0.86 2,900 35.9 67.8 4.1 78.03 0.2

114 Powelton 22.3 0.55 45,000 53.5 66.5 40.4 14.58 0.4

115 Queen Village 10.6 0.52 97,800 42.1 66.2 18.1 20.72 0.2

116 Rhawnhurst 10.5 4.45 232,300 37.3 71.8 2.7 384.78 0.2

117 Richmond 5.2 2.02 98,500 35.4 69.7 1.1 237.16 0.1

118 Rittenhouse 9.6 1.02 273,200 38.0 66.8 9.2 19.37 0.2

119 Riverfront 3.2 1.26 18,100 29.6 58.6 0.5 180.28 <0.1

120 Roxborough 17.7 2.71 214,000 35.3 66.1 4.5 150.96 0.3

121 Roxborough Park 33.1 2.29 51,400 31.5 53.2 9.7 56.25 0.6

122 Sharswood 13.0 0.45 44,300 46.2 70.1 22.4 29.40 0.2

123 Society Hill 21.9 1.11 181,100 36.2 53.2 19.2 14.34 0.4

124 Somerton 22.2 20.61 627,000 32.6 63.4 1.9 985.06 0.4

125 Southwest Schuylkill 9.9 0.99 82,100 38.2 69.9 6.5 55.71 0.2

126 Spring Garden 12.1 0.57 122,800 44.1 68.7 19.6 19.43 0.2

127 Spruce Hill 22.5 1.53 251,200 40.3 64.5 16.1 30.28 0.4

128 Stadium District 2.9 1.01 39,400 27.0 53.4 0.7 186.07 0.1

Table 17.—continued

continued
51

Key ID Neighborhood

Tree 

cover

Pollution 

removal Avg.a PPIb TPIc

Plantable 

spaced

Temp. 

reductione

percent tons/yr $/yr acres °F

129 Stanton 10.4 0.84 104,400 39.9 70.9 8.9 48.17 0.2

130 Strawberry Mansion 10.7 1.76 175,600 37.1 69.7 4.5 123.72 0.2

131 Summerdale 10.8 0.46 31,400 44.5 74.4 14.7 31.21 0.2

132 Tacony 8.1 2.09 132,200 37.4 73.2 1.6 255.31 0.1

133 Tioga 10.1 1.97 167,200 35.1 66.3 3.8 134.64 0.2

134 Torresdale 18.0 6.68 211,900 35.9 66.8 5.0 450.16 0.3

135 University City 12.2 3.18 116,300 32.2 59.7 4.7 124.99 0.2

136 Upper Kensington 6.8 0.91 103,000 40.0 73.8 6.2 65.44 0.1

137 Upper Roxborough 42.3 20.77 449,300 28.2 54.1 2.4 554.23 0.7

138 Walnut Hill 8.6 0.43 62,800 43.0 70.9 15.1 23.55 0.1

139 Washington Square West 9.5 0.65 171,400 38.0 59.3 16.6 8.95 0.2

140 Kensington, West 6.4 0.78 75,800 39.1 73.5 4.7 81.34 0.1

141 West Oak Lane 12.1 3.71 319,400 36.5 71.1 1.9 298.34 0.2

142 West Park 52.5 19.37 43,600 22.1 43.3 1.0 388.96 0.9

143 West Parkside 5.6 0.27 4,100 39.3 71.3 7.3 39.39 0.1

144 West Passyunk 5.2 0.25 55,600 43.2 64.6 21.8 4.56 0.1

145 West Poplar 4.6 0.24 23,900 46.0 73.7 18.4 41.04 0.1

146 West Powelton 12.6 0.64 55,900 45.5 69.5 21.6 40.68 0.2

147 West Torresdale 35.0 1.51 34,200 51.7 60.7 42.6 68.01 0.6

148 Whitman 5.3 0.29 56,400 48.4 75.9 21.0 15.80 0.1

149 Winchester Park 19.0 0.94 30,700 40.5 64.8 16.3 57.78 0.4

150 Wissahickon 18.4 1.00 88,200 36.0 61.7 10.3 44.44 0.3

151 Wissahickon Hills 28.0 0.65 30,600 45.8 59.8 31.8 22.32 0.5

152 Wissahickon Park 83.7 40.76 46,400 1.6 3.0 0.2 183.59 1.5

153 Wissinoming 10.6 2.44 154,200 37.2 72.2 2.3 227.38 0.2

154 Wister 34.8 2.13 93,500 33.7 55.3 12.1 47.08 0.6

155 Woodland Terrace 25.7 0.24 20,900 78.9 57.9 100.0 5.20 0.5

156 Wynnefield 19.4 4.04 267,200 34.2 64.8 3.5 197.26 0.3

157 Wynnefield Heights 19.2 1.94 201,700 36.6 63.3 9.8 98.27 0.3

158 Yorktown 9.8 0.49 41,200 48.8 72.9 24.7 36.01 0.2

a

 Average PPI and TPI value

b PPI = priority planting index (see appendix 6)

c

 TPI = temperature planting index (see appendix 8)

d Plantable space is the area of grass/shrub and bare soil as estimated using the LiDAR urban tree canopy assessment 

(O’Neil-Dunne 2011)

e

 Temperature reduction was based on daytime hours (6 a.m. to 5 p.m.) for average temperature summer day (8/4/08)

Table 17.—continued
52

APPENDIX 4

Potential Insect and Disease Impacts

Thirty-one insects and diseases were analyzed to quantify their potential impact on the 

urban forest. As each insect/disease is likely to attack different host tree species, the 

implications for Philadelphia will vary by pest. The number of trees at risk (Table 18) 

reflects only the known host species that are likely to experience mortality. The species 

host lists used for these insects/diseases can be found at http://nrs.fs.fed.us/data/urban.

Table 18.—Potential risk to trees by insect or disease, Philadelphia, 2012

Code Scientific name Common name Trees at risk

Compensatory 

value

number $ millions

AL Phyllocnistis populiella Aspen leafminer 19,000 8

ALB Anoplophora glabripennis Asian longhorned beetle 584,000 439

BBD Cryptococcus fagisuga Beech Bark disease 69,000 114

BC Sirococcus clavigignentijuglandacearumButternut canker 0 0

CB Cryphonectria parasitica Chestnut blight 0 0

DA Discula destructive Dogwood anthracnose 29,000 4

DED Ophiostoma novo-ulmi Dutch elm disease 58,000 15

DFB Dendroctonus pseudotsugae Douglas-fir beetle 0 0

EAB Agrilus planipennis Emerald ash borer 207,000 90

FE Scotylus ventralis Fir engraver 0 0

FR Cronartium fusiforme Fusiform rust 0 0

GSOB Agrilus auroguttatus Goldspotted oak borer 0 0

GM Lymantria dispar Gypsy moth 455,000 394

HWA Adelges tsugae Hemlock woolly adelgid 22,000 11

JPB Dendroctonus jeffreyi Jeffrey pine beetle 0 0

LAT Choristoneura conflictana Large aspen tortrix 50,000 30

LWD Raffaelea lauricola Laurel wilt 265,000 8

MPB Dendroctonus ponderosae Mountain pine beetle 0 0

NSE Ips perturbatus Northern spruce engraver 7,000 8

OW Ceratocystis fagacearum Oak wilt 193,000 321

POCRD Phytophthora lateralis Port-Orford-Cedar root disease 0 0

PSB Tomicus piniperda Pine shoot beetle 29,000 21

SB Dendroctonus rufipennis Spruce beetle 7,000 8

SBW Choristoneura fumiferana Spruce budworm 0 0

SOD Phytophthora ramorum Sudden oak death 123,000 198

SPB Dendroctonus frontalis Southern pine beetle 58,000 40

SW Sirex noctilio Sirex woodwasp 29,000 21

TCD Pityophthorus juglandis & 

Geosmithia spp.

Thousand canker disease 58,000 58

WPB Dendroctonus brevicomis Western pine beetle 0 0

WPBR Cronartium ribicola White pine blister rust 14,000 11

WSB Choristoneura occidentalis Western spruce budworm 7,000 8
53

Pest range maps from the Forest Health Technology Enterprise Team (FHTET) (U.S. 

Forest Service 2013, 2014; Worrall 2007) were used to determine the proximity of each 

pest to the county. For Philadelphia, proximity was classified for insects/diseases within 

Philadelphia County, within 250 miles, between 250 and 750 miles, or greater than 750 

miles. FHTET did not have pest range maps for Dutch elm disease and chestnut blight. 

The range of these pests was based on known occurrence and the host range, respectively 

(U.S. Forest Service 2013, 2014; Worrall 2007).

In Figure 30, the bars representing each pest are color coded according to Philadelphia 

County’s proximity to the pest occurrence in the United States (U.S. Forest Service 2013, 

2014; Worrall 2007).

Based on the host tree species for each pest and the current range of the pest, it is possible 

to determine the potential risk to insects and diseases for each tree species sampled in 

Philadelphia. In Table 19, species risk is designated as one of the following: 

• Red: tree species is at risk to at least one pest within county

• Orange: tree species has no risk to pests within county, but has a risk to at least 

one pest within 250 miles from the county

• Yellow: tree species has no risk to pests within 250 miles of county, but has a risk 

to at least one pest that is 250 to 750 miles from the county

• Green: tree species has no risk to pests within 750 miles of county, but has a risk 

to at least one pest that is greater than 750 miles from the county

Figure 30.—Number of trees at risk and associated compensatory value of insect/disease 

effects, Philadelphia, 2012. This figure does not include the pests and diseases that were 

not a threat to any of the species sampled in the city. For a complete list of the pests and 

diseases assessed, see Table 18.

0

50

100

150

200

250

300

350

400

450

500

0

100

200

300

400

500

600

GM

EAB

BBD

SPB

DED

TCD

PSB

DACB

ALB

OW

SW

SB

LWD

AL

SOD

LAT

WPBR

NSE

WSB

Compensatory

Value (millions of dollars)

Number of Trees (thousands) Trees at risk Pest/Disease

Compensatory value

within county

within 250 miles of county

within 750 miles of county

outside of these ranges
54

Species that were sampled in Philadelphia, but that are not listed in this matrix, are not 

known to be hosts to any of the 31 insects/diseases analyzed. Tree species at the greatest 

risk to existing pest infestations in Philadelphia are eastern white pine and Austrian pine.

Table 19.—Potential insect and disease risk for tree species, Philadelphia, 2012

continued

Pestsc

Spp. riskaPest scoreb

Common 

nameGMEABBBDSPBDEDTCDPSBDAHWABCCBALBOWSWSBFRSBWLWDALSODLATWPBRNSEWSBDFBFEGSOBJPBMPBPOCRDWPB

12

eastern white 

pine

1 11 Austrian pine

1 10 black willow

1 9 white spruce

1 8

eastern 

hemlock

1 8 gray birch

1 8

northern red 

oak

1 8 pin oak

1 8 river birch

1 7 black oak

1 7 blackjack oak

1 7 chestnut oak

1 7 Chinese elm

1 7 English elm

1 7 scarlet oak

1 7 Siberian elmd

1 7 slippery elm

1 7 white oak

1 5 bigtooth aspen

4 ash species

1 4

American 

basswood

1 4

American 

beech

1 4 apple spp

1 4 beech spp

1 4 black walnut

1 4 callery pear

1 4

European 

beech

1 4

flowering 

dogwood
55

Table 19.—continued

a Species risk

Red indicates that the tree species is at risk to at least one pest within Philadelphia County

Orange indicates that the tree species has no risk to pests within Philadelphia County, but has a risk to at least 

one pest within 250 miles of the county

Yellow indicates that the tree species has no risk to pests within 250 miles of Philadelphia County, but has a risk 

to at least one pest that is 250 to 750 miles from the county

Green indicates that the tree species has no risk to pests within 750 miles of Philadelphia County, but has a risk to 

at least one pest that is greater than 750 miles from the county

b Pest score: Numerical scoring system based on sum of points assigned to pest risks for species. Each pest that 

could attack tree species is scored as 4 points if red, 3 points if orange, 2 points if yellow and 1 point if green.

c Pest color codes

Red indicates pest is within Philadelphia County

Orange indicates pest is within 250 miles of Philadelphia County

Yellow indicates pest is within 750 miles of Philadelphia County

Green indicates pest is outside of these ranges

d Species in bold text indicate that species is on the state or park invasive species list

Pestsc

Spp. riskaPest scoreb

Common 

nameGMEABBBDSPBDEDTCDPSBDAHWABCCBALBOWSWSBFRSBWLWDALSODLATWPBRNSEWSBDFBFEGSOBJPBMPBPOCRDWPB

1 4 hawthorn spp

1 4

red osier 

dogwood

1 4

staghorn 

sumac

1 4 witch hazel

2 4 black birch

2 3 boxelder

2 3 horsechestnut

2 3

Japanese 

maple

2 3 Norway maple

2 3 red maple

2 3 silver maple

2 3

sycamore 

maple

2 3 sycamore spp

3 2 sassafras

3 2 spicebush
56 APPENDIX 5

Species Sampled in the Philadelphia Urban ForestTable 20.—Species sampled in the urban forest, Philadelphia, 2012

Stem d.b.h.

Genus Speciesa Common name Trees Population Leaf area IVb median avg. Basal areac Compensatory value Pest scored

number percent percent inches inches ft2 $ millions

Acer negundo boxelder 176,400 6.0 5.0 11.0 6.0 8.3 112,107 56.6 3Acer palmatum Japanese maple 21,610 0.7 0.6 1.3 3.5 5.2 4,910 7.9 3Acer platanoides Norway maple* 73,580 2.5 5.4 7.9 4.7 8.4 77,076 76.3 3Acer pseudoplatanus Sycamore maple* 29,570 1.0 1.9 2.9 7.4 13.0 48,630 44.0 3Acer rubrum red maple 101,220 3.5 5.6 9.1 7.7 10.6 100,503 86.1 3Acer saccharinum silver maple 5,590 0.2 0.0 0.2 20.5 20.5 13,453 0.0 3Aesculus hippocastanum horsechestnut 17,860 0.6 1.8 2.4 6.5 14.9 44,706 48.5 3Ailanthus altissima tree-of-heaven* 183,180 6.3 4.2 10.5 4.8 7.6 97,393 73.2 0Aralia elata Japanese angelica tree* 5,070 0.2 0.0 0.2 2.5 2.5 249 0.2 0Berberis species barberry spp* 5,070 0.2 0.0 0.2 2.5 2.5 249 0.6 0Betula lenta black birch 5,590 0.2 0.0 0.2 4.5 4.5 763 2.3 4Betula nigra river birch 7,200 0.2 1.0 1.2 15.5 15.5 10,056 13.8 8Betula populifolia gray birch 12,800 0.4 0.3 0.7 9.1 6.0 4,050 4.3 8Buddleja species butterflybush spp 14,400 0.5 0.1 0.6 3.0 3.5 1,336 3.4 0Buxus species boxwood spp 16,780 0.6 0.1 0.7 3.7 3.8 1,739 4.2 0Catalpa speciosa northern catalpa 19,010 0.7 0.3 1.0 13.4 15.4 28,576 16.5 0Celtis occidentalis northern hackberry 7,200 0.2 1.8 2.0 29.0 33.5 45,409 46.5 0Chamaecyparis thyoides Atlantic white cedar 21,610 0.7 1.4 2.1 9.5 9.8 14,180 20.8 0Cornus florida flowering dogwood 7,200 0.2 0.0 0.2 2.5 2.5 354 0.7 4Cornus sericea red osier dogwood 21,610 0.7 0.2 0.9 3.3 2.8 1,415 3.1 4Crataegus species hawthorn spp 5,590 0.2 0.0 0.2 1.5 1.5 122 0.1 4Diospyros virginiana common persimmon 5,070 0.2 0.0 0.2 2.5 2.5 249 0.7 0

continued
57

Stem d.b.h.

Genus Speciesa Common name Trees Population Leaf area IVb median avg. Basal areac Compensatory value Pest scoredFagus grandifolia American beech 55,930 1.9 3.2 5.1 5.0 10.1 55,396 31.2 4Fagus species beech spp 5,590 0.2 0.3 0.5 5.5 5.5 1,098 0.7 4Fagus sylvatica European beech 7,200 0.2 4.7 4.9 29.0 38.5 59,746 82.5 4Fraxinus species ash spp 207,000 7.1 5.2 12.3 2.9 5.8 108,172 89.6 3Gleditsia triacanthos honeylocust 68,160 2.3 0.9 3.2 4.7 7.4 43,279 39.0 0Hamamelis virginiana witch hazel 16,780 0.6 0.2 0.8 4.5 4.5 2,349 1.8 4Hardwood species hardwood 45,590 1.6 0.0 1.6 4.8 3.7 5,288 0.4 n/aHibiscus syriacus rose-of-sharon 7,200 0.2 0.0 0.2 4.5 4.5 982 2.8 0Ilex opaca American holly 16,780 0.6 0.1 0.7 1.5 1.6 427 0.4 0Juglans nigra black walnut 57,540 2.0 6.3 8.3 12.1 15.0 98,850 58.0 4Lindera benzoin spicebush 253,360 8.7 1.5 10.2 2.0 2.1 9,954 6.2 2Liriodendron tulipifera tulip tree or yellowpoplar 27,960 1.0 5.2 6.2 9.5 13.1 40,265 17.5 0Lonicera species honeysuckle spp* 61,520 2.1 0.3 2.4 1.8 2.0 2,380 1.9 0Malus species apple spp 67,120 2.3 0.5 2.8 3.2 3.1 5,154 3.5 4Morus alba white mulberry* 92,410 3.2 2.5 5.7 5.8 8.9 72,986 87.6 0Morus rubra red mulberry 5,590 0.2 0.0 0.2 1.5 1.5 122 0.1 0Nyssa sylvatica black tupelo 50,340 1.7 0.7 2.4 7.3 7.8 22,787 10.9 0Paulownia species princesstree spp* 22,370 0.8 1.3 2.1 8.0 16.0 59,729 49.6 0Phellodendron species corktree spp* 33,560 1.1 0.5 1.6 4.0 4.0 4,240 3.1 0Picea glauca white spruce 7,200 0.2 0.5 0.7 11.5 11.5 5,656 8.1 9Pinus nigra Austrian pine 15,200 0.5 0.3 0.8 13.5 13.5 16,299 9.5 11Pinus strobus eastern white pine 13,910 0.5 0.4 0.9 7.0 11.3 12,498 11.1 12Platanus species sycamore spp 42,990 1.5 8.7 10.2 15.5 17.7 103,682 72.8 3Populus grandidentata bigtooth aspen 5,590 0.2 0.1 0.3 9.5 9.5 3,051 1.1 5Prunus serotina black cherry 241,290 8.3 3.6 11.9 4.4 5.9 75,712 40.2 0Prunus species plum spp 25,330 0.9 0.5 1.4 2.8 2.9 1,740 1.1 0Table 20.—continued

continued
58

Stem d.b.h.

Genus Speciesa Common name Trees Population Leaf area IVb median avg. Basal areac Compensatory value Pest scoredPyrus calleryana callery pear* 5,070 0.2 0.2 0.4 11.5 11.5 3,978 6.0 4Quercus alba white oak 20,000 0.7 3.7 4.4 29.0 24.1 90,022 98.5 7Quercus coccinea scarlet oak 27,960 1.0 1.0 2.0 15.5 11.7 33,158 18.7 7Quercus marilandica blackjack oak 11,190 0.4 0.1 0.5 4.0 4.5 1,586 0.7 7Quercus palustris pin oak 16,780 0.6 1.2 1.8 25.5 23.2 57,014 19.1 8Quercus prinus chestnut oak 5,070 0.2 0.0 0.2 2.5 2.5 249 0.4 7Quercus rubra northern red oak 106,340 3.6 7.6 11.2 8.2 12.2 149,099 179.1 8Quercus velutina black oak 5,590 0.2 0.6 0.8 17.5 17.5 9,884 5.1 7Rhus hirta staghorn sumac 87,920 3.0 0.3 3.3 1.6 1.7 2,447 2.0 4Robinia pseudoacacia black locust 11,190 0.4 0.0 0.4 0.0 1.2 153 0.4 0Salix nigra black willow 19,010 0.7 0.5 1.2 10.6 13.8 31,371 8.3 10Sassafras albidum sassafras 11,190 0.4 0.3 0.7 7.0 11.5 10,311 1.5 2Taxus species yew spp 110,960 3.8 0.8 4.6 5.7 6.5 34,672 64.5 0Thuja occidentalis northern white-cedar 142,060 4.9 1.9 6.8 4.0 5.5 40,424 71.0 0Tilia americana American basswood 21,610 0.7 1.6 2.3 5.7 10.2 18,526 28.6 4Tsuga canadensis eastern hemlock 21,610 0.7 0.4 1.1 3.5 5.8 7,424 11.4 8Ulmus parvifolia Chinese elm 13,420 0.5 0.3 0.8 3.0 3.0 915 3.2 7Ulmus procera English elm 22,370 0.8 0.1 0.9 2.0 3.1 2,013 4.6 7Ulmus pumila Siberian elm* 7,200 0.2 0.9 1.1 16.5 16.5 11,352 8.3 7Ulmus rubra slippery elm 27,960 1.0 0.4 1.4 3.8 4.7 4,698 1.7 7Zelkova species Zelkova spp 7,200 0.2 0.3 0.5 6.5 6.5 1,925 4.3 0a Species refers to tree species, genera, or species groups that were classified during field data collectionb IV = importance value (% population + % leaf area)c Basal area is the cross sectional area of the tree stems measured at d.b.h.d Numerical scoring system based on sum of points assigned to pest risks for species. Each pest that could attack the tree species is assigned points based on pest range (i.e., 4 points if the pest is Table 20.—continuedlocated in the county, 3 points if within 250 miles, 2 points if within 750 miles, and 1 point if greater than 750 miles away). See Table 19 in appendix 4.* Invasive species
59

APPENDIX 6

Tree Species Distribution

This appendix illustrates various species distributions in Philadelphia’s urban forest. 

During field data collection, trees are identified to the most specific classification possible. 

Some trees have been identified to the species or genus level. The designations of “other 

hardwood” include the sampled hardwood trees that could not be identified as a more 

specific species or genera classification.

The species distributions for each land use are illustrated for the 20 most common species 

or all species if there are less than 20 species in the land use category (Figs. 31-38).

0 2 4 6 8 10

Sycamore spp

Hardwood

Black tupelo

American beech

Black walnut

Honeysuckle spp

Apple spp

Honeylocust

Norway maple

Staghorn sumac

White mulberry

Red maple

Northern red oak

Yew spp

Northern white cedar

Boxelder

Tree-of-heaven

Ash spp

Black cherry

Spicebush

Total Population (percent)

Species

Figure 31.—The 20 most common tree species as a percent of the total urban tree population, 

Philadelphia, 2012.
60

0

5

10

15

20

25

30

35

40

45

Spicebush Black

cherry

Ash spp Tree-ofheavenBoxelder Northern

white cedar

Yew spp Northern

red oak

Red maple White

mulberry

Land Use Population (percent)

Species

Comm/Ind/Util Institutional

Other Residential

Wooded

Land Use Category

0

10

20

30

40

50

60

70

80

90

100

Spicebush Black cherry Ash spp Tree-ofheavenBoxelder Northern

white cedar

Yew spp Northern red

oak

Red maple White

mulberry

Species Population (percent)

Species

Comm/Ind/Util Institutional

Other Residential

Wooded

Land Use Category

Figure 32.—The percent land-use population occupied by the 10 most common tree species, Philadelphia, 2012. 

For example, spicebush comprises 42 percent of the Institutional tree population.

Figure 33.—The percentage of species population in each land use category, Philadelphia, 2012. For example, 64 

percent of spicebush is found within Wooded land use.
61

0 5 10 15 20

Black cherry

Eastern white pine

Ash spp

Chinese elm

Black willow

Sycamore spp

Honeylocust

Northern catalpa

White mulberry

Northern white cedar

Tree-of-heaven

Boxelder

Percent of Trees

Species

0 10 20 30 40 50

Chestnut oak

Callery pear

Black cherry

Honeylocust

Common persimmon

Barberry spp

Japanese angelica tree

Horsechestnut

Red maple

Boxelder

Yew spp

Ash spp

Austrian pine

Norway maple

Plum spp

Spicebush

Percent of Trees

Species

0 5 10 15 20 25 30 35 40

Honeylocust

Black cherry

Red maple

White mulberry

Tree-of-heaven

Staghorn sumac

Percent of Trees

Species

Figure 34.—Percentage of trees in Commercial/Industrial/Utility category of land use, Philadelphia, 2012.

Figure 35.—Percentage of trees in Institutional category of land use, Philadelphia, 2012.

Figure 36.—Percentage of trees in Other category of land use, Philadelphia, 2012.
62

0 5 10 15

Horsechestnut

Sycamore maple

White oak

Butterflybush spp

Eastern hemlock

American basswood

Red osier dogwood

Atlantic white cedar

Red maple

Japanese maple

Black cherry

White mulberry

Hardwood

Honeylocust

Norway maple

Boxelder

Northern red oak

Tree-of-heaven

Yew spp

Northern white cedar

Percent of Trees

Species

0 2 4 6 8 10 12 14

English elm

Sycamore spp

Paulownia spp

Sycamore maple

Norway maple

Slippery elm

Scarlet oak

Tulip tree

Corktree spp

Black tupelo

Black walnut

Red maple

Northern red oak

American beech

Honeysuckle spp

Apple spp

Boxelder

Spicebush

Black cherry

Ash spp

Percent of Trees

Species

Figure 37.—Percentage of trees in Residential category of land use, Philadelphia, 2012.

Figure 38.—Percentage of trees in Wooded category of land use, Philadelphia, 2012.
63

APPENDIX 7

Relative Tree Effects

The urban forest in Philadelphia provides benefits that include carbon storage and 

sequestration and air pollutant removal. These benefits vary across d.b.h classes (Table 21). 

Total annual pollution removal per pollutant was contrasted with annual emissions per 

city, vehicle, and household to determine offset equivalents of urban forests versus city, 

vehicle, and household emissions.

Municipal carbon emissions are based on 2010 U.S. per capita carbon emissions (World 

Bank 2010). Per capita emissions were multiplied by city population to estimate total city 

carbon emissions.

Light duty vehicle emission rates (grams/mile) for carbon monoxide (CO), nitrogen 

oxides (NOx), VOCs, particulate matter less than 10 microns (PM10), and SO2 in 2010 

(Bureau of Transportation Statistics 2010, Heirigs et al. 2004), and CO2 in 2011 (U.S. 

EPA 2010) were multiplied by average miles driven per vehicle in 2011 (Federal Highway 

Administration 2013) to determine average emissions per vehicle.

Household emissions are based on average electricity kWh usage, natural gas Btu 

usage, fuel oil Btu usage, kerosene Btu usage, LPG Btu usage, and wood Btu usage per 

household in 2009 (Energy Information Administration 2013, 2014a).

CO2, SO2, and NOx power plant emission per KWh are from Leonardo Academy 

(2011). CO emission per kWh assumes 1/3 of 1 percent of C emissions is CO based on 

Energy Information Administration (1994).

CO2, NOx, SO2, and CO emission per Btu for natural gas, propane and butane (average 

used to represent LPG), Fuel #4 and #6 (average used to represent fuel oil and kerosene) 

from Leonardo Academy (2011).

CO2 emissions per Btu of wood from Energy Information Administration (2014a).

CO, NOx and sulfur oxides (SOx) emission per Btu based on total emissions and 

wood burning (tons) from British Columbia Ministry (2005) and Georgia Forestry 

Commission (2009).

General tree information:

Average tree diameter = 7.4 inches

Median tree diameter = 4.3 inches

Number of trees sampled = 451

Number of species sampled = 69
64

The trees in Philadelphia provide:

Carbon (C) storage equivalent to:

Amount of C emitted in region in 32 days or

Annual C emissions from 497,000 automobiles or 

Annual C emissions from 203,600 single family houses

Nitrogen dioxide (NO2) removal equivalent to:

Annual NO2 emissions from 7,800 automobiles or

Annual NO2 emissions from 3,500 single family houses 

Sulfur dioxide (SO2) removal equivalent to:

Annual SO2 emissions from 7,300 automobiles or

Annual SO2 emissions from 0 single family houses 

Annual C sequestration equivalent to:

Amount of C emitted in region in 1 day or

Annual C emissions from 19,200 automobiles or

Annual C emissions from 7,900 single family home

Table 21.—Average tree effects by tree diameter class (d.b.h.), Philadelphia, 2012

d.b.h.a Carbon storage Carbon sequestration Pollution removal

inches lbs $ miles b lbs/yr $/yr miles b lbs/yr $/yr

1-3 8 0.28 10 2.2 0.08 2 0.05 0.94

3-6 41 1.48 50 6.8 0.24 7 0.11 2.01

6-9 146 5.19 160 11.9 0.42 13 0.22 4.16

9-12 328 11.67 360 23.5 0.84 26 0.40 7.40

12-15 584 20.81 640 31.3 1.11 34 0.59 10.89

15-18 1,060 37.74 1,160 42.0 1.50 46 0.83 15.36

18-21 1,454 51.78 1,590 46.3 1.65 51 0.86 15.84

21-24 2,295 81.75 2,510 77.9 2.77 85 1.38 25.52

24-27 3,415 121.62 3,730 85.5 3.04 93 1.30 24.12

27-30 4,196 149.44 4,580 93.4 3.33 102 2.13 39.50

30+ 7,039 250.69 7,690 183.2 6.52 200 3.73 69.07

a lower limit of each diameter (d.b.h.) class is greater than displayed (e.g. 3-6 is actually 3.01 to 6 inches)

b miles = number of automobile miles driven that produces emissions equivalent to tree effect
65

APPENDIX 8

Temperature Index Map

Air temperature is an important climatic variable in urban areas. The air temperature 

model (Heisler et al. 2006, 2007, 2015) was run to determine the average temperature 

between noon and 5 p.m. for four different days between June 1, 2008, and August 

31, 2008. Average temperature was also modeled for the hour with the warmest air 

temperature. The representative days and hour were as follows:

• Windiest day (day with the highest average wind speed): June 1, 2008

• Least windy day (day with the lowest average wind speed): August 21, 2008

• Average day (day with the average temperature closest to the summer average 

temperature): August 4, 2008 (Fig. 39)

• Warmest temperature day (day with the highest average summer daytime 

temperature): June 10, 2008

• Warmest temperature hour (hour with the warmest air temperature): 4 p.m. on 

June 9, 2008.

0 1.75 3.5 7 Miles ±

Average Temperature (°F)

78.0 79.5 81.0

Figure 39.—Average temperature distribution (noon to 5 p.m.) for the average 

summer day, Philadelphia, 2012.
66

Tree cover can help reduce air temperatures in urban areas. The air temperature estimates 

modeled here present one option that can be used in urban areas to determine areas to 

target tree cover enhancements. That is, tree planting could be targeted in the warmest 

areas of the city to help cool air temperatures.

To determine the areas of the city with the greatest potential to reduce heat stress, 

average air temperatures were calculated for U.S. Census block groups and multiplied 

by population density in each block group. This process provides an air temperature 

planting index (TPI) for each of the representative days. Standardized value the TPI was 

calculated as: 

TPI = (n – m) / r

Where TPI is the value (0-1), n is the value (temperature × population / km2) for the 

Census block, m is the minimum value for all Census blocks, and r is the range of values 

among all Census blocks (maximum value‒minimum value).

All four days produce the same TPI index map as the relative temperature difference 

among block groups was the same, though the actual temperatures would differ. The 

results of the block groups were averaged within each neighborhood (weighted average 

proportional to block area within neighborhood) and standardized a second time on 

a scale of 0 to 100 to produce neighborhood temperature index maps. Because two 

neighborhoods (Mechanicsville and Chinatown) had high population densities and 

temperatures (high index values relative to others), the index values dropped to very 

small values for many other neighborhoods. To avoid this skew in the index, these two 

neighborhoods were removed from the standardization. All other neighborhoods were 

standardized between 0 and 100, and these two neighborhoods had their index values 

calculated subsequently such that their index values were greater than 100. This adjusted 

index was only done for the neighborhood index only (not block groups) due to these 

outliers causing most neighborhoods toward index values close to zero. This “heat stress” 

index illustrates one method that can be used to prioritize tree planting to reduce air 

temperatures in the warmest parts of the city with the most people (Fig. 40).
67

02468 1

Miles

.

TPI

0.0 - 3.0

3.1 - 6.0

6.1 - 9.0

9.1 - 12.0

12.1 - 15.0

15.1 - 20.0

20.1 - 25.0

25.1 - 50.0

50.1 - 100.0

100.1 +

Figure 40.—Temperature planting index (TPI) by neighborhood for average summer day, 

Philadelphia, 2012. Higher index scores indicate higher priority areas for planting. Note: two 

neighborhoods (Mechanicsville and Chinatown) have values greater than 100 (see text; these 

outlier neighborhoods were excluded from standardized index to avoid most neighborhoods 

having very small index values).
68

APPENDIX 9

General Recommendations for Air Quality Improvement

Urban vegetation can directly and indirectly affect local and regional air quality by altering 

the urban atmospheric environment. Four main ways that urban trees affect air quality are:

Temperature reduction and other microclimatic effects

Removal of air pollutants

Emission of volatile organic compounds (VOC) and tree maintenance emissions

Energy conservation on buildings and consequent power plant emissions

The cumulative and interactive effects of trees on climate, pollution removal, and VOC 

and power plant emissions determine the overall impact of trees on air pollution. 

Cumulative studies involving urban tree impacts on ozone have revealed that increased 

urban canopy cover, particularly with low VOC emitting species, leads to reduced ozone 

concentrations in cities. Local urban forest management decisions also can help improve 

air quality.

Urban forest management strategies to help improve air quality include:

Strategy Reason

Increase the number of healthy trees Increases pollution removal

Sustain existing tree cover Maintains pollution removal levels

Maximize use of low VOC-emitting trees Reduces ozone and carbon monoxide 

formation

Sustain large, healthy trees Large trees have greatest per-tree effects

Use long-lived trees Reduces long-term pollutant emissions 

from planting and removal

Use low maintenance trees Reduces pollutant emissions from 

maintenance activities

Reduce fossil fuel use in maintaining 

vegetation

Reduces pollutant emissions

Plant trees in energy conserving locations Reduces pollutant emissions from power 

plants

Plant trees to shade parked cars Reduces vehicular VOC emissions

Supply ample water to vegetation Enhances pollution removal and 

temperature reduction

Plant trees in polluted or heavily populated 

areas

Maximizes tree air quality benefits

Avoid pollutant-sensitive species Improves tree health

Utilize evergreen trees for particulate matter Provides year-round removal of particles
69

APPENDIX 10

Species Sampled in the Philadelphia Parkland AreasTable 22.—Speciesa sampled in the urban forest, Philadelphia parklands, 2012Genus Species Common name Trees Population Leaf area IVb

Stem d.b.h. Basal 

areac

Compensatory 

value median avg.

number percent percent inches inches ft2 $ millions

Acer negundo boxelder 131,400 12.2 6.4 18.6 2.7 4.4 31,103 8.0Acer palmatum Japanese maple 3,490 0.3 0.3 0.6 4.5 4.8 571 0.4Acer platanoides Norway maple* 54,660 5.1 5.6 10.7 3.7 5.0 16,121 8.9Acer pseudoplatanus sycamore maple* 4,650 0.4 0.2 0.6 5.0 5.3 932 0.5Acer rubrum red maple 38,380 3.5 5.6 9.1 6.8 9.4 31,975 18.1Acer saccharinum silver maple 13,950 1.3 4.1 5.4 16.0 19.6 39,068 15.7Acer saccharum sugar maple 5,810 0.5 0.8 1.3 6.5 6.9 2,112 3.1Aesculus hippocastanum horsechestnut* 1,160 0.1 0.0 0.1 4.5 4.5 159 0.1Aesculus species buckeye spp 1,160 0.1 0.1 0.2 15.5 15.5 1,624 0.4Ailanthus altissima tree-of-heaven* 22,090 2.0 0.7 2.7 5.3 5.0 4,325 2.8Alnus species alder spp 2,330 0.2 0.1 0.3 2.0 2.0 82 0.0Aralia elata Japanese angelica 

tree

58,140 5.4 0.7 6.1 1.9 2.1 2,391 1.7

Betula lenta black birch 6,980 0.6 0.8 1.4 5.0 5.5 1,610 1.0Betula populifolia gray birch 1,160 0.1 0.1 0.2 1.5 1.5 25 0.0Carpinus caroliniana American hornbeam 1,160 0.1 0.1 0.2 3.5 3.5 101 0.1Carya alba mockernut hickory 3,490 0.3 0.3 0.6 14.5 18.8 10,339 4.4Carya cordiformis bitternut hickory 36,050 3.3 1.2 4.5 2.1 3.7 6,796 4.1Catalpa speciosa northern catalpa 2,330 0.2 0.2 0.4 12.0 17.0 4,269 0.9Celtis occidentalis northern hackberry 1,160 0.1 0.0 0.1 0.0 0.8 6 0.0Cornus florida flowering dogwood 9,300 0.9 0.4 1.3 4.0 3.9 1,097 0.8Crataegus species hawthorn spp 1,160 0.1 0.0 0.1 1.5 1.5 25 0.0Diospyros virginiana common persimmon 2,330 0.2 0.2 0.4 3.0 9.5 1,890 2.4

continued
70

Genus Species Common name Trees Population Leaf area IVb

Stem d.b.h. Basal 

areac

Compensatory 

value median avg.

number percent percent inches inches ft2 $ millions

Fagus grandifolia American beech 74,420 6.9 14.3 21.2 3.9 8.4 57,952 32.3Fraxinus species ash spp 140,710 13.0 8.7 21.7 2.7 5.8 62,168 36.3Ginkgo biloba ginkgo 3,490 0.3 0.3 0.6 21.3 20.2 8,196 13.6Gleditsia triacanthos honeylocust 6,980 0.6 0.5 1.1 8.0 12.7 9,379 6.6Hardwood species hardwood 19,770 1.8 0.0 1.8 5.5 7.7 12,024 0.0Ilex opaca American holly 3,490 0.3 0.0 0.3 1.5 1.6 89 0.1Juglans nigra black walnut 6,980 0.6 0.3 0.9 5.0 5.5 1,587 0.9Juglans species walnut spp 1,160 0.1 0.4 0.5 14.5 14.5 1,427 0.4Ligustrum species privet spp 1,160 0.1 0.0 0.1 3.5 3.5 101 0.1Lindera benzoin spicebush 90,700 8.4 1.5 9.9 1.7 2.0 4,017 2.2Liquidambar styraciflua sweetgum 1,160 0.1 0.3 0.4 14.5 14.5 1,427 1.9Liriodendron tulipifera tulip tree or yellowpoplar 27,910 2.6 12.7 15.3 23.0 23.6 104,393 40.3Lonicera species honeysuckle spp 16,280 1.5 0.3 1.8 1.8 2.1 647 0.5Malus species apple spp 11,630 1.1 0.8 1.9 3.4 4.9 3,882 5.3Morus alba white mulberry* 10,470 1.0 2.0 3.0 8.8 9.3 6,495 8.5Morus rubra red mulberry 4,650 0.4 0.2 0.6 10.3 8.3 2,327 0.6Nyssa sylvatica black tupelo 9,300 0.9 0.7 1.6 5.5 6.9 4,294 2.6Ostrya virginiana eastern 

hophornbeam

5,810 0.5 0.1 0.6 1.8 2.3 292 0.3

Paulownia species princesstree spp* 3,490 0.3 0.4 0.7 9.5 12.2 3,578 1.3Phellodendron species corktree spp* 23,260 2.2 0.9 3.1 3.3 3.9 3,254 1.9Pinus strobus eastern white pine 1,160 0.1 0.0 0.1 6.5 6.5 311 0.0Pinus sylvestris scotch pine 3,490 0.3 0.1 0.4 7.3 6.8 1,040 1.9Platanus species sycamore spp 15,120 1.4 8.9 10.3 27.5 24.7 68,345 42.9Populus grandidentata bigtooth aspen 1,160 0.1 0.1 0.2 9.5 9.5 634 0.2Populus tremuloides quaking aspen 3,490 0.3 0.0 0.3 2.5 2.8 241 0.1Prunus serotina black cherry 80,240 7.4 7.9 15.3 6.5 8.8 58,220 27.6Table 22.—continued

continued
71

Genus Species Common name Trees Population Leaf area IVb

Stem d.b.h. Basal 

areac

Compensatory 

value median avg.

number percent percent inches inches ft2 $ millions

Prunus species plum spp 16,280 1.5 0.6 2.1 3.3 4.1 2,594 0.9Quercus alba white oak 2,330 0.2 1.1 1.3 20.0 26.5 9,870 4.7Quercus coccinea scarlet oak 5,810 0.5 0.6 1.1 15.5 11.7 6,894 3.9Quercus imbricaria shingle oak 1,160 0.1 0.2 0.3 8.5 8.5 514 0.7Quercus marilandica blackjack oak 2,330 0.2 0.1 0.3 4.0 4.5 330 0.1Quercus palustris pin oak 4,650 0.4 1.5 1.9 26.0 26.5 20,535 13.3Quercus phellos willow oak 2,330 0.2 0.1 0.3 2.0 3.5 254 0.4Quercus prinus chestnut oak 1,160 0.1 0.0 0.1 2.5 2.5 57 0.1Quercus rubra northern red oak 17,440 1.6 3.0 4.6 5.5 11.6 25,155 8.9Quercus velutina black oak 2,330 0.2 0.6 0.8 18.0 27.5 11,215 4.6Rhus hirta staghorn sumac 1,160 0.1 0.0 0.1 1.5 1.5 25 0.1Robinia pseudoacacia black locust 2,330 0.2 0.3 0.5 17.0 18.0 4,370 1.1Salix nigra black willow 1,160 0.1 0.0 0.1 4.5 4.5 159 0.1Sassafras albidum sassafras 23,260 2.2 1.4 3.6 8.0 9.0 14,175 5.3Tilia americana American basswood 2,330 0.2 0.4 0.6 8.0 16.5 4,694 1.8Tsuga canadensis eastern hemlock 3,490 0.3 0.1 0.4 5.5 8.5 2,011 0.1Ulmus alata winged elm 3,490 0.3 0.0 0.3 1.5 1.5 76 0.0Ulmus americana American elm 1,160 0.1 0.1 0.2 7.5 7.5 406 0.1Ulmus rubra slippery elm 9,300 0.9 0.4 1.3 3.5 4.4 1,744 1.5Ulmus species elm spp 1,160 0.1 0.0 0.1 2.5 2.5 57 0.0Viburnum dilatatum linden arrowwood 1,160 0.1 0.0 0.1 1.5 1.5 25 0.1Viburnum prunifolium black haw 2,330 0.2 0.0 0.2 2.0 2.0 82 0.0Viburnum sieboldii Siebold’s arrowwood 1,160 0.1 0.0 0.1 2.5 2.5 57 0.0Viburnum species viburnum spp 2,330 0.2 0.1 0.3 2.0 2.0 82 0.0a Species refers to tree species, genera, or species groups that were classified during field data collectionb IV = importance value (% population + % leaf area)c Basal area is the cross sectional area of the tree stems measured at d.b.h.Table 22.—continued* Nonnative invasive species
72

APPENDIX 11

Tree Planting Index Map

To determine the best locations to plant trees, tree canopy and impervious cover 

identified from high resolution land cover maps (O’Neil-Dunne 2011) were used in 

conjunction with 2010 U.S. Census data to produce an index of priority planting areas 

for Philadelphia. Index values were produced for each Census block group; the higher 

the index value, the higher the priority of the area for tree planting. This index is a type of 

“environmental equity” index with areas with higher human population density and lower 

tree cover tending to get the higher index value. The criteria used to make the index were:

• Population density: the greater the population density, the greater the priority for 

tree planting

• Tree stocking levels: the lower the tree stocking level (i.e., the percent of 

available greenspace or tree, grass, and soil cover areas that is occupied by tree 

canopies), the greater the priority for tree planting

• Tree cover per capita: the lower the amount of tree canopy cover per capita (m2/

capita), the greater the priority for tree planting

Each criteria was standardized on a scale of 0 to 1 with 1 representing the Census block 

group with the highest value in relation to priority of tree planting (i.e., the Census block 

group with highest population density, lowest stocking density or lowest tree cover per 

capita were standardized to a rating of 1).

Standardized value for population density (PD) was calculated as:

PD = (n – m) / r

Where PD is the value (0-1), n is the value for the Census block (population/km2), m is 

the minimum value for all census blocks, and r is the range of values among all Census 

blocks (maximum value – minimum value).

Standardized value for tree stocking (TS) was calculated as:

TS = [1 – (t/(t+g)]

Where TS is the value (0-1), t is percent tree cover, and g is percent grass cover.

Standardized value for tree cover per capita (TPC) was calculated as:

TPC = 1 – [(n – m) / r]

Where TPC is the value (0-1), n is the value for the Census block (m2/capita), m is the 

minimum value for all Census blocks, and r is the range of values among all Census 

blocks (maximum value – minimum value).
73

Individual scores were combined and standardized based on the following formula to 

produce an overall priority planting index (PPI) value between 0 and 100:

PPI = (PD * 40) + (TS * 30) + (TPC * 30)

Where PPI = index value, PD is standardized population density, TS is standardized tree 

stocking, and TPC is standardized tree cover per capita. 

In the formula, criteria (PD, TS, and TPC) were each weighted with a slight increased 

weighting to population density to produce a type of “environmental equity” index. The 

Tree Planting Index gives the highest priority to tree planting in the City of Philadelphia 

where population density tends to be highest and tree cover the lowest. Priority planting 

index values were applied to the neighborhoods of Philadelphia by calculating an area 

weighted average for each neighborhood (Fig. 41).

Figure 41.—Priority planting index (PPI) by neighborhood, Philadelphia, 2012. Higher 

index scores indicate higher priority areas for planting.
74

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energy costs by $6.9 million per year. The compensatory value of the trees is estimated 

at $1.7 billion. The city’s parklands constitute 9.3 percent of the total land area, have an 

estimated 1.1 million trees, 64 percent canopy cover, and account for 38.8 percent of carbon 

storage and 34.8 percent of air pollution removal performed by the city’s urban forest. The 

information presented in this report can be used by local organizations to advance urban 

forest policies, planning and management to improve environmental quality and human 

health in Philadelphia.

KEY WORDS: urban forestry, i-Tree, ecosystem services, insects and diseases, invasive species, 

air temperature, water quality, air quality, carbon, energy savings

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations 

and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering 

USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, gender 

identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, 

income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights 

activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies 

and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., Braille, 

large print, audiotape, American Sign Language, etc.) should contact the responsible Agency or USDA’s TARGET 

Center at (202) 720-2600 (voice and TTY) or contact USDA through the Federal Relay Service at (800) 877-8339. 

Additionally, program information may be made available in languages other than English. 

To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write 

a letter addressed to USDA and provide in the letter all of the information requested in the form. To request 

a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: 

U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, 

SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov.
Northern Research Station

www.nrs.fs.fed.us