United States Department of Agriculture

The Urban Forest of 

New York City

Resource Bulletin 

Forest Service NRS-117 September 2018

Northern 

Research Station


Abstract

An analysis of the urban forest in New York, New York, reveals that this city has an 

estimated 7.0 million trees (encompassing all woody plants greater than one-inch 

diameter at breast height [d.b.h.]) with tree canopy that covers 21 percent of the city. 

The most common tree species across public and private land are Norway maple, 

northern white-cedar, tree-of-heaven, sassafras, and white oak, but the most dominant 

species in terms of leaf area are Norway maple, London planetree, black locust, pin oak, 

and red maple. Trees in New York City currently store about 1.2 million tons of carbon 

(4.2 million tons carbon dioxide [CO2]) valued at $153 million. In addition, these trees 

remove about 51,000 tons of carbon per year (186,000 tons CO2/year) ($6.8 million 

per year) and about 1,100 tons of air pollution per year ($78 million per year). New 

York City’s urban forest is estimated to reduce annual residential energy costs by $17.1 

million per year and reduce runoff by 69 million cubic feet/year ($4.6 million/year). The 

compensatory value of the trees is estimated at $5.7 billion. 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 

New York City. The analyses also provide a basis for monitoring changes in the urban 

forest over time.

Cover Photos

Front, View from Freshkills Park. Photo by Richard Hallett, USDA Forest Service. Back, 

cherry tree on the High Line in Manhattan. Photo by D.S. Novem Auyeung, used wtih 

permission.

Manuscript received for publication 9 March 2018

Published by

U.S. FOREST SERVICE

11 CAMPUS BLVD SUITE 200

NEWTOWN SQUARE PA 19073

September 2018

For additional copies:

U.S. Forest Service

Publications Distribution

359 Main Road

Delaware, OH 43015-8640

Email: nrspubs@fs.fed.us
The Urban Forest of New York City

The Authors

DAVID J. NOWAK is a senior scientist and i-Tree team leader with the U.S. 

Forest Service’s Northern Research Station at Syracuse, New York.

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

Institute at Syracuse, New York.

ROBERT E. HOEHN III is a forester with the U.S. Forest Service’s Northern 

Research Station at Syracuse, New York.

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

Institute at Syracuse, New York.

SATOSHI HIRABAYASHI is an environmental modeler with Davey Tree’s 

Davey Institute at Syracuse, New York.

ROBERT COVILLE is an urban forest hydrology specialist with Davey Tree’s 

Davey Institute at Syracuse, New York.

D.S. NOVEM AUYEUNG is a senior scientist with the NYC Department of 

Parks and Recreation at New York, New York.

NANCY FALXA SONTI is an ecologist with the U.S. Forest Service’s 

Northern Research Station at Baltimore, Maryland.

RICHARD A. HALLETT is a research ecologist with the U.S. Forest Service’s 

Northern Research Station at New York, New York.

MICHELLE L. JOHNSON is an interdisciplinary research scientist with the 

U.S. Forest Service’s Northern Research Station at New York, New York.

EMILY STEPHAN is a former research assistant with the SUNY College of 

Environmental Science and Forestry at Syracuse, New York.

TOM TAGGART is a former research assistant with the SUNY College of 

Environmental Science and Forestry at Syracuse, New York.

TED ENDRENY is a professor with the SUNY College of Environmental 

Science and Forestry at Syracuse, New York.

Resource Bulletin NRS-117
Street trees in Staten Island near a residential area. Photo by D.S. Novem Auyeung, used with permission.
CONTENTS

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

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

METHODS......................................................................................................................................... 5

Tree Cover Assessment........................................................................................................... 5

Urban Forest Composition, Structure, and Values........................................................ 5

RESULTS...........................................................................................................................................13

Tree Cover Assessment.........................................................................................................13

Urban Forest Structure, Composition, and Values......................................................14

MANAGEMENT IMPLICATIONS ..............................................................................................38

Current Tree Size Distribution and Potential Species Changes..............................38

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

CONCLUSION ................................................................................................................................41

ACKNOWLEDGMENTS...............................................................................................................41

APPENDIX 1: Bronx River Watershed Analysis...................................................................42

APPENDIX 2: Ecosystem Services by Community District ............................................51

APPENDIX 3: Woody Trees and Shrubs Sampled in the New York City 

Urban Forest.............................................................................................................................54

APPENDIX 4: Tree Species Distribution................................................................................60

APPENDIX 5: Relative Tree Effects..........................................................................................65

APPENDIX 6: Temperature Index Map..................................................................................67

APPENDIX 7: General Recommendations for Air Quality Improvement .................69

APPENDIX 8: Potential Insect and Disease Impacts........................................................70

LITERATURE CITED......................................................................................................................74
Trees in Forest Park, Queens. Photo by Alaine Ball, USDA Forest Service.
1

EXECUTIVE SUMMARY

The urban forest in New York City contributes to local environmental quality and 

human health. The urban forest resource, as defined in this report, is made up of all 

the trees within the city limits. Urban greening programs that are sponsored locally, 

such as MillionTreesNYC and Cool Neighborhoods NYC, are investing in tree planting 

campaigns in an effort to improve the city’s environment and provide equal access 

to green space for all people in the city. However, there are mounting threats from 

insects, diseases, invasive plant species, climate change, development, and changing 

infrastructure that are negatively affecting urban forest resources. Addressing the 

challenge of developing a sustainable and healthy urban forest is complicated by a 

diversity of tree species, their dynamic characteristics, a fragmented ownership pattern, 

and a lack of comprehensive information about the urban forest resource. To address 

these critical information needs, the USDA Forest Service assessed New York City’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 methods and tools used for this assessment 

have also been used to assess the urban forest in Baltimore, Phildelphia, and many other 

cities in the United States and abroad. Thus, this assessment is part of a larger set of 

urban forest assessments happening globally.

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

understanding of New York City’s urban forest. i-Tree Eco is a software application that 

uses data to quantify forest structure, environmental effects, and value to communities. 

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 New York City’s urban forest and establish a baseline for future monitoring, 

field data were collected during the summer of 2013 and processed and analyzed using 

the i-Tree Eco model. A total of 296 one-tenth-acre field plots were sampled throughout 

the city. This report summarizes the results of this study (Table 1), including analysis 

of the field data, model outputs, and management implications for New York City. 

i-Tree Eco results are also compared with a previous urban forest assessment from 1996 

(Nowak et al. 2007) and the results from the Natural Areas Conservancy’s Ecological 

Assessment of forested parkland (Forgione et al. 2016).
2

Table 1.—Summary of urban forest features, New York City, 2013

Feature Estimate

Number of treesa 6,977,000

Tree cover 21%b

Most dominant species by:

Number of trees Norway maple, northern white-cedar, tree-ofheaven, sassafras, white oak

Leaf area Norway maple, London planetree, black locust, 

pin oak, red maple

Trees 1-6 inches d.b.h. 69.7%

Air temperature reductionc 0.13 °F

Average UV radiation reductiond 25.1%

Pollution removal 1,100 tons/year ($77.9 million/year)

VOC emissions 804 tons/year

Avoided runoff 69 million cubic feet/year ($4.6 million/year)

Carbon storage 1.2 million tons ($153 million)

Carbon sequestration 51,000 tons/year ($6.8 million/year)

Value of reduced building energy use $17.1 million/year

Value of reduced carbon emissions $1.6 million/year

Compensatory valuee $5.7 billion

a all woody vegetation >1 inch diameter

b assessed using LiDAR in an earlier report (O’Neil-Dunne 2012)

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

day (7/23/2008)

d noon-time conditions

e 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 pounds)

Participants at an event at Joyce Kilmer Park in the Bronx celebrating the planting of 1 million trees in New 

York City in 2015. Photo by NYC Parks, used with permission.
3

BACKGROUND

This report is a product of the New York City Urban Field Station1, a partnership between 

the USDA Forest Service Northern Research Station (Forest Service), New York City 

Department of Parks and Recreation (NYC Parks), and the nonprofit Natural Areas 

Conservancy2 (NAC). The NYC Urban Field Station, in addition to field stations in 

Baltimore, Chicago, and Philadelphia, grew from a commitment within the Forest Service 

to study the forests where most Americans live and work—in urban landscapes. The NYC 

Urban Field Station’s goals are to foster collaborative science, science-delivery, and tools 

to assist partner organizations with natural resource management in the greater New York 

City region.

Urban trees are a vital component of New York City’s infrastructure, providing numerous 

benefits to human health and environmental quality. Since the 1990s, the City of New 

York has supported citywide inventories aimed at quantifying the benefits of the urban 

forest, which is defined in this report as all trees in the city including street trees, trees 

in public parklands, as well as trees on private properties. A 1996 assessment used the 

Urban Forest Effects (UFORE) computer model, a precursor to the current i-Tree Eco 

computer model developed by the Forest Service. This report found that New York City 

trees store 1.35 million tons of carbon (valued at $24.9 million) and remove 42,300 tons 

of carbon per year (valued at $779,000 per year) and 2,202 tons of air pollution per 

year (valued at $10.6 million per year) (Nowak et al. 2007). Similarly, using data from 

the NYC Parks’s 1995 street tree census, Forest Service scientists quantified the benefits 

provided by street trees using the Street Tree Resource Assessment Tool for Urban Forest 

Managers (STRATUM), a precursor to the current i-Tree Streets model. They found that 

New York City street trees produce annual benefits totaling $121.9 million based on their 

ability to store and sequester carbon, reduce air pollution, intercept stormwater, and 

improve aesthetics and property values (Peper et al. 2007). An updated analysis using 

data from NYC Parks’s 2015–2016 street tree census found that street trees currently 

produce annual benefits totaling $151.2 million (New York City Department of Parks and 

Recreation 2016). These reports played an important role in the creation of programs like 

MillionTreesNYC, which was launched in 2007 and led to the successful planting of 1 

million trees on public, private, and commercial land by 2015.

In addition to these models, the City of New York and its partners continually assess 

different aspects of the city’s urban forest as it changes over time. Variables of interest 

include tree species and size class distribution, spatial distribution of trees, tree health, 

and ecosystem services. Since 1995, the NYC Parks’s decadal street tree censuses have 

documented the location and species of all street trees. Data from the most recent 2015 

street tree census are publicly available through an online report (New York Parks and 

Recreation 2016) and interactive map known as the NYC Street Tree Map (https://

1 For more information about the New York City Urban Field Station, visit http://www.nrs.fs.fed.

us/nyc.

2 For more information about the Natural Areas Conservancy, visit http://www.naturalareasnyc.org.
4

tree-map.nycgovparks.org/), which provides detailed information on the structure, 

composition, ecosystem service value, and stewardship activity of trees along the public 

rights-of-way. Urban tree canopy cover analyses conducted by the Forest Service and the 

Spatial Analysis Laboratory of the University of Vermont in 2006, 2010, and 2017 monitor 

tree canopy using LiDAR data (Grove et al. 2006, O’Neil-Dunne 2012). New York City was 

also part of a multi-city study of tree cover change using paired aerial photographs from 

2004 and 2009 (Nowak and Greenfield 2012). The Natural Areas Conservancy’s ecological 

cover type map (O’Neil-Dunne et al. 2014) and upland forest ecological assessment in 

2013-2014 (Forgione et al. 2016) provide landscape level information on land cover types 

citywide and plot level information on forested areas in New York City parks, respectively. 

A subset of the data from the upland forest assessment is included in this report and 

provides insight into the forest structure, composition, and value specifically in areas that 

are part of NYC Parks’s “Forever Wild” program, which was created in 2001 to protect 

nearly 9,000 acres of forests, wetlands, and meadows citywide.

In response to continued interest from NYC Parks and as an update to the 1996 UFORE 

assessment, the Forest Service established and measured permanent plots in 2013 to 

analyze New York City’s urban forest using the i-Tree Eco model. This report summarizes 

these findings and lays the foundation for future data collection to monitor changes in the 

urban forest over time. This report also provides information on the spatial distribution 

of urban tree benefits and examines how these benefits vary across the city’s five boroughs 

and 71 community districts.3 The goal of this study is to provide information relevant to 

the sustainable and equitable management of New York City’s urban forest (e.g., Design 

Trust for Public Space 2010, New York City Department of Parks and Recreation 2014).

3 Large neighborhood areas defined by the New York City Department of City Planning

Field data collection in New 

York City. Photo by Richard 

Hallett, USDA Forest Service.
5

METHODS

Two analyses were conducted for New York City: 1) urban tree cover variation based on 

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

field plot data and remotely-sensed tree cover data where specified. In addition, the i-Tree 

Hydro model was used to predict effects of tree cover and impervious surface on stream 

flow in the Bronx River watershed. The methods and results of the Bronx River watershed 

analysis are discussed in appendix 1.

Tree Cover Assessment

New York City’s tree cover estimates were derived from 2010 LiDAR and high resolution 

aerial imagery processed by the University of Vermont’s Spatial Analytics Lab (O’NeilDunne 2012). Tree cover was defined as leaf area from vegetation at a height of 8 feet or 

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

the community district and neighborhood level using a geographic information system 

(GIS).

Urban Forest Composition, Structure, and Values

To help assess the urban forest, data were collected in 2013 on field plots located within 

the boundaries of New York City 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 hourly air pollution and meteorological data to quantify forest structure and its 

numerous effects, including: 

• Species composition

• Tree density

• Leaf area and biomass

• Air pollution removal

• Carbon storage

• Annual carbon 

sequestration

• Changes in building energy 

use

• Compensatory value

• Potential risk from insects 

or diseases Field data collection in New York City. Photo by Richard Hallett, 

USDA Forest Service.
6

Field Measurements

Field crews sampled 296 one-tenth-acre plots that were randomly distributed throughout 

New York City proportional to each borough’s land area (Fig. 1, Table 2). These plots fell 

randomly across the city’s various land uses (Table 3). Values estimated from sample plots 

were expanded to provide estimates for citywide totals and by borough.

Field data were collected by trained interns hired through Yale University. Data collection 

took place during the leaf-on season, from May to September of 2013. For each one-tenthacre circular plot, ground cover was assessed as a proportion of plot area by type. Trees 

were defined as woody plants with a diameter at breast height (d.b.h.; measured at 4.5 feet 

above ground level) greater than or equal to 1 inch. For each tree in the plot, the variables 

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

Figure 1.—Urban inventory plot locations by borough, New York City, 2013. Plot locations are approximate.

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

0 2 4 8 Miles

±

!(

!(

!( !(

!(
7

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

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

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

For trees with more than six stems, tree stem diameter was measured below the fork and 

the height of the diameter measurement was recorded. For multi-stemmed 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 most specific taxonomic classification possible, e.g., the species 

or genus level. Trees designated as “hardwood” include broadleaved deciduous trees that 

could not be identified to a species or genera. Ninety-five percent of the trees designated 

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

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

i-Tree Eco Model

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

standard errors by species, borough, 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 https://doi.org/10.2737/NRS-RB-117.

The ecosystem services estimated through i-Tree Eco include:

Carbon storage and sequestration. 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.
8

Table 3.—Distribution of plots by borough and land use, New York City, 2013

Borough and land usea Plots Borough and land usea Plots

number number

Queens Brooklyn (continued)

Commercial & Office Buildings 1 Open Space & Outdoor Recreation 9

Industrial & Manufacturing 5 Parking Facilities 2

Mixed Residential & Commercial Buildings 1 Public Facilities & Institutions 4

Multi-Family Elevator Buildings 3 Transportation & Utility 4

Multi-Family Walk-Up Buildings 7 Unclassifiedb 11

One- & Two-Family Buildings 36 Total 59

Open Space & Outdoor Recreation 14

Public Facilities & Institutions 1 Bronx

Transportation & Utility 7 Industrial & Manufacturing 2

Vacant Land 2 Multi-Family Elevator Buildings 2

Unclassifiedb 37 Multi-Family Walk-Up Buildings 2

Total 114 One &Two Family Buildings 4

Open Space & Outdoor Recreation 4

Staten Island Public Facilities & Institutions 2

Commercial & Office Buildings 5 Transportation & Utility 5

Industrial & Manufacturing 1 Vacant Land 1

One &Two Family Buildings 22 Unclassifiedb 15

Open Space & Outdoor Recreation 11 Total 37

Public Facilities & Institutions 4

Transportation & Utility 4 Manhattan

Vacant Land 5 Mixed Residential & Commercial Buildings 1

Total 64 Multi-Family Walk-Up Buildings 3

Open Space & Outdoor Recreation 3

Brooklyn Parking Facilities 2

Commercial & Office Buildings 2 Public Facilities & Institutions 1

Industrial & Manufacturing 3 Vacant Land 1

Multi-Family Elevator Buildings 2 Unclassifiedb 9

Multi-Family Walk-Up Buildings 5 Total 22

One &Two Family Buildings 17

a Land use categories are derived from New York City’s Department of City Planning primary land use tax lot output 

(PLUTO) dataset, http://www1.nyc.gov/site/planning/data-maps/open-data.page (June 2015)

b The land use category “Unclassified” is assigned to plots that fell outside the bounds of the PLUTO dataset. These areas 

are typically roadways, as PLUTO is based on tax lot data.

Table 2.—Distribution of plots among boroughs, New York City, 2013

Borough Plots with trees Plots without trees Proportion of NYC land area

number number percent

Queens 67 47 35.9

Staten Island 44 20 19.1

Brooklyn 33 26 23.2

Bronx 21 16 13.9

Manhattan 9 13 7.8

Citywide total 174 122 100
9

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

were multiplied by $133.08 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. Poor air quality is a common problem in many urban areas that 

can affect human health, damage materials and ecosystem processes, and reduce visibility 

(e.g., Pope et al. 2002). The urban forest can help 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 (Nowak et al. 2017). 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).

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

City tree cover map (O’Neil-Dunne 2012) in conjunction with U.S. Census and local 

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

block group 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.

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) using 2010 

hourly pollution data from all city pollution monitors and 2010 hourly weather data from 

LaGuardia airport. 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. 2013).

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 

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

removal by trees. BenMAP is a MS® 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. 2013, 2014; U.S. EPA 2012).

Pollution removal and value estimates were calculated at the community district 

and neighborhood tabulation level to explore how these benefits vary across the city. 
10

Neighborhood tabulation areas (NTAs) are aggregations of Census tracts used to 

represent New York City neighborhoods.4 Pollution removal and value estimates were 

calculated for NTAs by summing block group level results within each NTA. To estimate 

pollution removal and value at the community district level, values for each NTA within 

a community district were summed. If only a proportion of an NTA existed within a 

community district, the NTA value was reduced proportional to the percentage of the 

NTA in the community district.

Mitigated surface water runoff. Annual avoided surface water runoff (commonly referred 

to as surface runoff) is calculated based on rainfall interception by vegetation, or more 

specifically the difference between annual runoff with and without vegetation, based on 

2010 weather data. Interception by tree leaves, branches, and bark are accounted for in 

this analysis. To estimate the monetary value of avoided runoff, avoided runoff values were 

multiplied by $0.067 per cubic foot of runoff based on estimated national average water 

treatment and runoff control costs (e.g., McPherson et al. 2007). Avoided runoff by trees 

is estimated for the entire city. These results are apportioned by species based on leaf area 

proportion. Citywide results are apportioned by community district based on tree cover.

Energy use. 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 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. The estimated value of compensation for a loss of a tree was 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).

Invasive species. Insects and tree 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. Invasive species 

in the New York City urban forest are identified using an invasive species list (New York 

State Department of Environmental Conservation 2011).

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

2008) visit www.itreetools.org.

4 For more information on neighborhood tabulation areas, see NYC Department of City Planning 

website: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-nynta.page

Measuring residential trees. Photo by Richard 

Hallett, USDA Forest Service.
11

Tree Effects on Ultraviolet Radiation Exposure

Ultraviolet (UV) radiation is emitted by the sun and is classified as a human carcinogen 

(e.g., skin cancer), according to the World Health Organization (IARC 2012) and the U.S. 

Department of Health and Human Services (National Toxicology Program 2011). While 

a small amount is beneficial in the production of vitamin D, prolonged exposure to UV 

radiation can also cause adverse health effects on eyes, skin, and the immune system.

A UV index was developed by the World Health Organization to more easily report daily 

levels of UV radiation and alert people when protection from overexposure is needed 

most. Ultraviolet index values are estimated from UV radiation amounts and adjustments 

made based on local cloud cover.

Tree leaves absorb about 90 to 95 percent of UV radiation (Grant et al. 2003), reducing 

the amount of UV radiation that reaches the ground and providing people with additional 

protection from the sun’s harmful rays.

Using methods described by Na et al. (2014), i-Tree Eco model estimates this reduction in 

UV radiation for two scenarios:

• Shade: Reduction in UV exposure for a person who is always shaded by tree 

canopy in the local area.

• Overall: Reduction in UV exposure for a person who is in areas that are shaded 

and unshaded, based on the average tree cover in the local area.

For each of these two reduced-exposure classes, the effects of trees on UV radiation 

exposure are calculated for each land use and then combined to produce a weighted 

average effect for each New York City borough. The effects are as follows:

• Protection factor—a unitless value that captures the UV radiation-reducing capacity 

of trees. It is calculated as the unshaded UV index divided by the shaded or overall 

UV index, depending on exposure class. This factor is conceptually equivalent to the 

sun protection factor (SPF) used to indicate sun screen protection (Na et al. 2014). 

The protection factor of urban trees can be defined as how many times longer a 

person would have to spend in a particular environment to receive the same exposure 

as in an open location with no solar UV protection (Grant and Heisler 2006).

• Reduction in UV index —the change in UV index as the result of trees; it is 

calculated as unshaded UV index minus shaded or overall UV index.

• Percent reduction—the reduction in UV index expressed as a percent change 

and calculated as the reduction in UV index divided by unshaded (no reduced 

exposure) UV index.

Effects are estimated based on noon-time exposure conditions for every day of the year 

based on average UV data for New York City (2008–2013).
12

Tree Effects on Air Temperature

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).

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, and wind speed. The model uses GIS 

datasets to estimate hourly temperatures in each 30-meter cell using current tree cover 

conditions and a baseline scenario of zero percent tree cover based on the land cover maps 

(O’Neil-Dunne 2012). The differences between the two estimates represent the tree effects 

on air temperature.

Weather data from 2008 were examined 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: 

• Windiest day (day with the highest average wind speed): June 22, 2008

• Least windy day (day with the lowest average wind speed): July 4, 2008

• Average temperature day (day with the average temperature closest to the 

summer average temperature): July 23, 2008

• Warmest day (day with the highest average summer daytime temperature): June 

9, 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 nighttime (6 p.m. to 5 a.m.) conditions. Results were analyzed for each 

community district and NTA. Maps illustrating results by NTA are not displayed in this 

report, but are available at https://doi.org/10.2737/NRS-RB-117.
13

Figure 2.—Urban tree cover percentage by community district, New York City, 

imagery from 2010. Note: Many maps shown in this report are also available by NTA 

at https://doi.org/10.2737/NRS-RB-117.

RESULTS

Tree Cover Assessment

Existing tree cover in New York City is estimated at 21 percent (O’Neil-Dunne 2012). 

Among the boroughs, Staten Island has the highest tree cover, estimated at 30 percent, 

followed by the Bronx (23 percent tree cover), Manhattan (20 percent), Queens (18 

percent), and Brooklyn (16 percent).

Tree cover varies among community districts, from 2.3 percent to 77.2 percent (Fig. 2). 

Community districts with greater tree cover percentages correspond to some of the city’s 

largest parks, including Forest Park in Queens, Prospect Park in Brooklyn, Van Cortlandt 

Park in the Bronx, and Central Park in Manhattan. Appendix 2 provides a community 

district key and tree cover estimates for each district.

0 2.25 4.5 9 Miles

±

Tree Cover (percent)

2.3 - 10

10.1 - 20

20.1 - 30

30.1 - 40

40.1 - 50

50.1 - 60

60.1 - 70

70.1 - 77.2
14

Urban Forest Structure, Composition, and Values

Tree Characteristics of the Urban Forest

New York City’s urban forest has an estimated 6,977,000 trees (standard error of 874,000). 

The five most common species in the urban forest, in terms of number of trees, are 

Norway maple, northern white-cedar, tree-of-heaven, sassafras, and white oak (Fig. 3) 

(scientific names of all tree species are listed in appendix 3). The 10 most common species 

account for 44.4 percent of all trees. In total, 138 woody species/genera were sampled in 

New York City; these species and their relative abundance are presented in appendix 3. 

See appendix 4 for more information on species distribution by borough.

The overall tree density in New York City is 35.9 trees per acre. The highest density of 

trees occurs in Staten Island (67.9 trees/acre), followed by the Bronx (48.4 trees/acre) and 

Brooklyn (27.0 trees/acre) (Fig. 4). Staten Island makes up 19.1 percent of the city land 

area (Table 2) and contains the most trees (36.2 percent of tree population), followed by 

Queens (35.9 percent of the land area, 24.6 percent of the trees).

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

measure of the total leaf surface area (one side) of trees in an area divided by land area. 

Figure 3.—Urban forest species composition as a percentage of all trees, New York City, 2013. 

Hardwood refers to broadleaved deciduous trees that could not be identified to a species or genera.

Norway maple

6.1%

Northern whitecedar

5.7% Tree-of-heaven

5.5%

Sassafras

4.7%

White oak

4.3%

Black locust

4.1%

Black birch

3.9%

Bayberry

3.6%

Hardwood

3.4%

Red maple

3.3%

other species

55.4%
15

As each borough has a different land area, LAI standardizes the canopy depth on an equal 

area basis. Total leaf area is greatest in Staten Island (28.3 percent of total tree leaf area) and 

Queens (28.3 percent). A higher LAI indicates a greater leaf surface area per acre of land. 

Boroughs that have the highest LAI are Staten Island (1.5) and the Bronx (1.4) (Fig. 5).

Figure 5.—Total leaf area and leaf area index by borough, New York City, 2013.

Figure 4.—Number of trees and tree density by borough, New York City, 2013.

0

10

20

30

40

50

60

70

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Bronx Brooklyn Queens Staten Island

Density (trees per acre)

Number of Trees (millions)

Manhattan

Borough

Number of trees

Density

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0

10

20

30

40

50

60

Bronx Brooklyn Queens Staten Island

Leaf Area Index (LAI)

Leaf Area (thousands of acres)

Manhattan

Borough

Leaf area

Leaf area index
16

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

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 69.7 percent of the population (Fig. 6). Trees in 

this diameter class also contain 15.6 percent of the total leaf area. The 10 most abundant 

species in New York City have more than 50 percent of their population in the 1 to 6 

inch d.b.h. class (Fig. 7). Trees that have diameters greater than 18 inches account for 7.3 

percent of the tree population, but comprise 41.5 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 New York City. For more information about the 

environmental benefits by tree diameter class, see appendix 5.

Tree species composition varies 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 northern white-cedar (9.6 percent of trees in small d.b.h. 

class), sassafras (9.2 percent), bayberry (6.5 percent), tree-of-heaven (5.1 percent), 

pignut hickory (5.0 percent), Norway maple (4.7 percent), black birch (4.1 percent), 

white oak (3.9 percent), red maple (3.6 percent), and hardwood (3.5 percent). The 10 

most common species of large diameter trees are Norway maple (15.1 percent of trees in 

large diameter class), London planetree (14.9 percent), pin oak (11.7 percent), red maple 

(5.9 percent), northern red oak (5.7 percent), white oak (4.5 percent), black oak (3.8 

percent), swamp white oak (3.4 percent), black locust (2.9 percent), and eastern hemlock 

(2.9 percent). Norway maple, white oak, and red maple are among the 10 most common 

small diameter trees and the 10 most common large diameter trees (Fig. 8). Some species 

Figure 6.—Percentage of total tree population and leaf area by diameter class, New York City, 2013. 

Lower limit of the diameter 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

45

50

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

Percent

Diameter Class (inches)

Leaf area

Abundance
17

Figure 7.—Percentage of tree 

species population by diameter 

class for 10 most common 

species, New York City, 2013. 

Lower limit of the diameter 

class is greater than displayed 

(e.g., 3-6 is actually 3.01 to 6 

inches). Hardwood refers to 

broadleaved deciduous trees 

that could not be identified to 

a species or genera.

Tree-of-heaven

Sassafras

White oak

Black locust

Black birch

Bayberry

Hardwood

Red maple

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

Species

Species Population (percent)

Diameter Class

(inches)

30+

Northern white-cedar

Norway

maple

0

50

100

150

200

250

300

350

Number of Trees (thousands)

Species

< 3 inches > 18 inches

Diameter Class

Figure 8.—Most common tree species in the small (<3 inches) and large (>18 inches) diameter 

classes, New York City, 2013. Hardwood refers to broadleaved deciduous trees that could not be 

identified to a species or genera.
18

(e.g., London planetree, pin oak, eastern hemlock, black oak, and northern red oak) 

have more large trees than small trees, which may lead to reduction of this species in the 

future as there is minimal planting or regeneration of these species to sustain the species 

population. Mean and median stem diameter by species are presented in appendix 3.

New York City’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 greater 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 (Laćan and McBride 2008, Santamour 1990), but the increase in the number of 

exotic species can also pose a risk to native plants if exotic species are invasive and outcompete and displace native species. In New York City, about 46.9 percent of the trees are 

native to New York State, and 65.4 percent native to North America. Trees with a native 

origin outside of North America are mostly from Asia (28.7 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). Five of the 138 tree species 

sampled in New York City are identified on the state invasive species list (New York State 

Department of Environmental Conservation 2011). These invasive species comprise 17.5 

percent of the population with the most common species being Norway maple, tree-ofheaven, and black locust (Table 4).

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

benefit. In New York City’s urban forest, tree species with the greatest leaf area are London 

planetree, Norway maple, and black locust (Fig. 9). Common tree species (>1% of the total 

population) with relatively large individual trees (percentage of total leaf area greater than 

percent of total tree population) are London planetree, pin oak, and Norway maple. Tree 

species dominated by smaller individuals with relatively low amounts of leaf area per stem 

are bayberry, cedar spp., and northern white-cedar.

Table 4.—Inventoried species listed on the New York State 

invasive species list, New York City, 2013

Common name Population Leaf area

percent percent

Norway maple 6.1 10.7

Tree-of-heaven 5.5 1.6

Black locust 4.1 7.0

Callery pear 2.3 1.3

Hall’s honeysuckle 0.1 <0.1
19

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

value of species with the greatest importance values, New York City, 2013

Common Name Populationa Leaf areab IVc

percent percent

Norway maple 6.1 10.7 16.8

London planetree 2.4 11.2 13.6

Black locust 4.1 7.0 11.1

White oak 4.3 4.2 8.5

Red maple 3.3 4.3 7.6

Pin oak 1.8 5.4 7.2

Tree-of-heaven 5.5 1.6 7.1

Black birch 3.9 2.7 6.6

Northern white-cedar 5.7 0.7 6.4

Sassafras 4.7 0.8 5.5

a The percent of total tree population

b The percent of total leaf area

c

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

Figure 9.—Percentage of tree population and total leaf area for 10 species contributing the greatest 

amount of leaf area, New York City, 2013.

0

2

4

6

8

10

12

London

planetree

Norway

maple

Black

locust

Black birch Norway

spruce

Sweetgum

Percent

Pin oak Red maple White oak Silver

maple

Species

Leaf area

Abundance

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 Norway 

maple, London planetree, and black locust (Table 5).
20

1996 Urban Forest Assessment

An earlier assessment of New York City’s urban forest was based on field data collected 

in 1996 and used the Urban Forest Effects (UFORE) model (Nowak et al. 2007). These 

UFORE results were based on 206 field plots randomly located in different land use 

strata of New York City. These older plots were not permanently referenced, hence new 

permanent plots were established for the current study. The 1996 assessment and this 

report are independent and cannot be directly compared due to differing samples and 

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

differences.

The five most common species recorded in the 1996 UFORE study were tree-of-heaven, 

black cherry, sweetgum, northern red oak, and Norway maple; the current i-Tree 

Eco assessment finds the five most common species to be Norway maple, northern 

white-cedar, tree-of-heaven, sassafras, and white oak. In 1996, the 10 most common 

species accounted for 61.6 percent of all trees, compared to 44.4 percent in the current 

assessment. In total, 138 tree species/genera were recorded in New York City during the 

i-Tree Eco assessment, while 66 species were recorded in 1996 (Table 6).

In 1996, 42.7 percent of trees were less than 6 inch diameter compared to 69.7 percent of 

trees under 6 inch in 2013. The overall urban tree density in New York City is 35.9 trees 

per acre with an average diameter of 6.3 inches. In 1996 the overall urban tree density was 

26.4 trees per acre with an average diameter of 9.2 inches.

This apparent increase in species and number of trees is, in part, due to a difference in 

definition of trees between the studies. In 1996, field data were collected for tree species 

with a minimum diameter of 1 inch and a minimum of 4 inches for shrub species. Using 

this procedure, species was used to determine trees, not size. Conversely, the 2013 field 

study included all woody plants with a minimum d.b.h. of 1 inch. Thus, the most recent 

assessment will classify more small shrub-like species (such as northern white-cedar, 

one of the most common species observed in 2013) as trees, making these estimates 

incomparable.

Forest Parkland Assessment

Forested parkland is a critical subset of New York City’s urban forest. From 2013-2014, 

the Natural Areas Conservancy (NAC) conducted an assessment of upland forests 

designated as part of NYC Parks’ Forever Wild program. Within these 7,200 acres of 

upland forests, which represent roughly 4 percent of New York City, the NAC established 

1,124 randomized 10-m radius plots and collected data on vegetation, soils, and other 

characteristics described in Forgione et al. (2016). The woody species data from these 

plots were analyzed separately using the i-Tree Eco model to determine the structure, 

ecosystem services, and values provided by trees in Forever Wild upland forests (Table 7). 

Out of the 296 citywide i-Tree plots, only 40 were on NYC Parks properties and only 11 

of those were in forested parkland. Thus, the NAC’s upland assessment provides a more 

in-depth look at forested parkland in NYC.
21

Table 7.—Summary of forested parkland features, New York City, 2013-2014

Feature Estimate

Number of treesa 3,310,000

Tree coverb 88.0%

Most dominant species by:

Number of trees Sweetgum, black cherry, sassafras, red maple, 

spicebush

Leaf area Sweetgum, northern red oak, red maple, black 

cherry, sassafras

Trees 1-6 inches d.b.h. 79.4%

Air temperature reduction Not analyzedc

Pollution removal 314 ton/year ($55.4 million/year)

VOC emissions Not analyzedc

Avoided runoff 13.8 million feet3/year ($920,000/yr)

Carbon storage 362,000 tons ($48.2 million)

Carbon sequestration 13,700 tons/year ($1.82 million/year)

Value of reduced building energy use Not analyzedc

Value of reduced carbon emissions Not analyzedc

Compensatory valued $491 million

a All woody vegetation >1 inch diameter

b Assessed using LiDAR in an earlier report (O’Neil-Dunne 2012)

c Not analyzed due to missing required data

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 6.—Summary of 1996 and 2013 urban forest assessments, New York City

1996 2013

Methods

Number of plots 206 296

Stratification method By land use By borough

Model used Urban Forest Effects (UFORE) i-Tree Eco (f.k.a. UFORE)

Urban forest characteristicsa

Most common species recorded Tree-of-heaven, black cherry, 

sweetgum, northern red oak, 

and Norway maple

Norway maple, northern 

white-cedar, tree-of-heaven, 

sassafras, and white oak

Number of species recorded 66 138

Percentage of trees <6 inches diameter 42.7% 69.7%

Tree density 26.4 trees per acre 35.9 trees per acre

Average diameter (d.b.h.) 9.2 inches 6.3 inches

a Results were generated using different methods and are directly not comparable.
22

Dominant species, tree cover, and tree density in forested parkland differ greatly compared 

to the citywide assessment across public and private land. Within forested parkland, all 

five of the most abundant species are native—sweetgum, black cherry, sassafras, red maple, 

and spicebush – compared to only two out of five citywide—sassafras and white oak. 

Both tree cover and tree density in forested parkland are much higher than tree cover and 

density citywide, so forested parkland has a disproportionately higher number of trees and 

ecosystem service benefits relative to its geographic size.

Air Temperature Reductions

Average air temperature reductions by trees in New York City varied across the 

community districts among the four representative days and the time of day (Table 8). 

Average air temperature reduction in the community districts was greatest (0.4 °F) during 

the daytime (6 a.m. – 5 p.m.) hours on the warmest summer day of 2008. The greatest 

maximum temperature reduction in the community districts (1.4 °F) was also recorded on 

this day.

For the average-temperature summer day of 2008, daytime air temperature reductions 

varied by community district (Fig. 10). The greatest temperature reduction was estimated 

at 0.5 °F and the smallest reduction was estimated at less than 0.1 °F. Community districts 

that showed the greatest temperature reductions are areas that also have greater percentage 

of tree cover (Fig. 10) and overlap with some of the city’s larger parks. More information 

on the distribution of temperature reduction across the neighborhoods appendix 2. Maps 

illustrating results by NTA are available at https://doi.org/10.2737/NRS-RB-117.

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

temperature. Estimated air temperatures varied across New York City and are reported 

in appendix 6. 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 of people exposed to the warmest temperatures 

(appendix 6) where tree planting 

would likely be most beneficial in 

reducing temperature around people.

Clove Lakes Park in Staten Island in 2014. 

Photo by David Chang, USDA Forest Service.
23

Table 8.—Average, minimum, and maximum air temperature reductions in community 

districts, New York Citya, 2010

Representative days Time Average Minimum Maximum

°F °F °F

Windiest (6/22/08) AMb 0.2 <0.1 0.6

PMc 0.1 <0.1 0.5

Least windy (7/4/08) AM 0.2 <0.1 0.6

PM 0.2 <0.1 0.8

Average temperature (7/23/08) AM 0.1 <0.1 0.5

PM 0.1 <0.1 0.4

Warmest (6/9/08) AM 0.4 <0.1 1.4

PM 0.2 <0.1 0.7

a Derived using 2008 air pollution and weather data and tree canopy data from O’Neil-Dunne 2012.

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

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

Figure 10.—Daytime (6 a.m.–5 p.m.) air temperature reductions by trees, by community 

district, for the average temperature summer day, New York City, 2008.

0 2.25 4.5 9 Miles

± Temperature Reduction (°F)

0.01 - 0.09

0.1 - 0.19

0.2 - 0.29

0.3 - 0.39

0.4 - 0.47
24

Air Pollution Removal

Pollution removal by trees in New York City 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 2010. Pollution removal was greatest for O3 (735 tons removed 

per year), followed by NO2 (242 tons/year), SO2 (88 tons/year), and PM2.5 (41 tons/year) 

(Fig. 11). By contrast, the value associated with pollution removal was greatest for PM2.5

($59.9 million), followed by O3 ($16.9 million), NO2 ($985,000) and SO2 ($135,000). It is 

estimated that trees remove 1,100 tons of air pollution (NO2, O3, PM2.5, and SO2) per year 

with an associated value of $77.9 million.

Reduction in pollution concentration due to pollution removal by trees also have a 

positive effect on human health in New York City. The economic value of pollution 

removal is based on the number of cases per year of avoided health effects. For example, 

in 2010, reductions in NO2 concentration resulted in about 320 fewer cases of acute 

respiratory symptoms with an associated value of $10,100 (Tables 9-10).

Figure 11.—Annual air pollution removal and value by urban trees, New York City, 2010.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0

100

200

300

400

500

600

700

800

NO O PM SO

Value (million dollars / year)

Pollution Removed (tons / year)

Pollutant

Pollution removed

Value

2 3 2.5 2
25

Table 9.—Associated value ($/year) of avoided health effects from changes in pollution 

concentrations due to pollution removal by trees, New York City, 2010

Health Effect NO2 SO2 O3 PM2.5

$/year

Acute bronchitis n/aa n/a n/a 400

Acute myocardial infarction n/a n/a n/a 140,100

Acute respiratory symptoms 10,100 1,500 392,600 259,700

Asthma exacerbation 392,100 31,400 n/a 138,100

Chronic bronchitis n/a n/a n/a 613,400

Emergency room visits 8,700 4,100 4,600 5,500

Hospital admissions 574,400 98,100 258,500 n/a

Hospital admissions, cardiovascular  n/a n/a  n/a 50,600

Hospital admissions, respiratory n/a n/a n/a 26,600

Lower respiratory symptoms n/a n/a n/a 2,600

Mortality n/a n/a 16,033,700 58,580,500

School loss days  n/a n/a 162,300  n/a

Upper respiratory symptoms n/a n/a n/a 1,800

Work loss days n/a n/a n/a 69,400

Total value 985,300 135,100 16,851,600 59,888,800

a n/a indicates that the value is not estimated for that pollutant and health effect. The same health effects were 

not analyzed for each pollutant.

Table 10.—Incidences (number of cases/year) of avoided health effects from changes in 

pollution concentrations due to pollution removal by trees, New York City, 2010

Health Effect NO2 SO2 O3 PM2.5

number of cases/year

Acute bronchitis n/aa n/a n/a 4.1 

Acute myocardial infarction n/a n/a n/a 1.6

Acute respiratory symptoms 319.9 46.1 4,592.6 2,649.9

Asthma exacerbation 4,686.2 399.1 n/a 1,699.4

Chronic bronchitis n/a n/a n/a 2.2

Emergency room visits 20.8 9.8 10.9 13.2

Hospital admissions 19.4 3.2 8.6 n/a

Hospital admissions, cardiovascular n/a n/a n/a 1.3

Hospital admissions, respiratory n/a n/a n/a 0.8

Lower respiratory symptoms n/a n/a n/a 50.9

Mortality n/a n/a 2.1 7.5

School loss days n/a n/a 1,652.6 n/a

Upper respiratory symptoms n/a n/a n/a 40.4

Work loss days n/a n/a n/a 454.8

a n/a indicates that the number of cases is not estimated for that pollutant and health effect. The same health 

effects were not analyzed for each pollutant.
26

Figure 12.—Pollution removal by trees by community district, New York City, 2010.

0 2.25 4.5 9 Miles

±

Pollution Removal (tons/yr)

0.5 - 8.0

8.1 - 20.0

20.1 - 30.0

30.1 - 60.0

60.1 - 120.0

Pollution removal by trees in New York City varies across the city’s community districts 

and, as with other ecosystem services, is correlated with leaf area. Pollution removal ranged 

from 0.6 tons per year to 117 tons per year. Removal was highest in the community districts 

on Staten Island and Queens (Fig. 12), which are the boroughs with the greatest leaf area 

and LAI. Pollution removal value also varied across the city’s community districts. The 

maximum pollution removal value was estimated at $2.8 million per year and is located 

in community district 318 in Brooklyn (Fig. 13 and appendix 2). Several community 

districts, mostly located in Queens, Staten Island, and Brooklyn, had an estimated annual 

pollution removal value over $1.85 million per year (Fig. 13). The value of pollution removal 

is dependent on both the size of the community district population and the change in 

pollution concentration. The more people receiving the benefits of pollution removal in 

a given community district, the larger the value of those benefits (Nowak et al. 2014). For 

example, trees in the community district in which Central Park in Manhattan is located 

removed 10.5 tons per year of pollution removal, but has an associated value of $6,600 per 

year because it has a low human population estimated from U.S. Census data. Conversely, 

the community district that encompasses the Upper East Side and Yorkville, located east of 

Central Park, removed 7.2 tons per year of pollution with an associated value of $1.7 million 

per year. Estimates of pollution removal by community district are provided in appendix 2.
27

Estimates of total VOC emissions from anthropogenic sources in New York City are 

around 340 tons per day (U.S. EPA 2011), however, trees also emit VOCs. In 2013, trees 

in New York City emitted an estimated 717 tons of VOCs (566 tons of isoprene and 151 

tons of monoterpenes) over the entire year. Emissions vary among genera and amount 

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

sycamore genera (Fig. 14). These VOCs are precursor chemicals to ozone formation.5

General recommendations for improving air quality with trees are given in appendix 7.

5 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. A study of increasing urban tree cover in the New York City region 

showed a general reduction in ozone concentrations in cities, but a tendency to increase average ozone 

concentrations in the overall modeling domain (Nowak et al. 2000).

Figure 13.—Pollution removal value by community district, New York City, 2010.

0 2.25 4.5 9 Miles

± Pollution Removal Value ($/yr)

3,500 - 280,000

280,001 - 900,000

900,001 - 134,0000

134,001 - 1,850,000

1,850,001 - 3,000,000
28

Reduction in UV Radiation

Tree leaves absorb about 90 to 95 percent of UV radiation (e.g., Grant et al. 2003), reducing 

the amount of UV radiation that reaches the ground and providing people with additional 

protection from the sun’s harmful rays. Overall, trees are estimated to reduce UV exposure 

by about 25 percent and within tree shade by about 48 percent. This reduction equates 

to an overall protection factor of 1.35 and 2.26 with tree shade (Table 11). The greatest 

reductions are in Staten Island due to its relatively high percentage tree cover.

Figure 14.—Annual volatile organic compounds (VOCs) emitted by tree genera with highest 

total emissions, New York City, 2013.

Inwood Hill Park. Photo by 

Lakshman Kalasapudi, USDA 

Forest Service.

0 100 200 300 400 500

Fig

Bayberry

Willow

Fir

Robinia

Sweetgum

Maple

Spruce

Sycamore

Oak

VOCs Emitted (tons)

Genera

Isoprene

Monoterpene

VOCs Emitted
29

Avoided Runoff

Surface water runoff can be a cause for concern in many urban areas as it can contribute 

pollution to streams, wetlands, rivers, lakes, and oceans. During precipitation events, 

some portion of the precipitation is intercepted by vegetation while the other portion 

reaches the ground. The portion of the precipitation that reaches the ground and does not 

infiltrate into the soil or end up in depression storage becomes surface runoff (Hirabayashi 

2012). In urban areas, the large extent of impervious surfaces increases the amount of 

surface runoff.

Urban trees, however, are beneficial in reducing surface runoff. Trees intercept 

precipitation and transpire water, while their root systems promote infiltration and 

water storage in the soil. Although trees have other impacts on local hydrology, avoided 

runoff is estimated by i-Tree Eco as a function of the annual precipitation interception, 

evaporation, and transpiration by trees. The trees of New York City help to reduce runoff 

by an estimated 69 million cubic feet a year (valued at $4.6 million per year). Tree species 

with the greatest overall impact on runoff are London planetree, Norway maple, and black 

locust (Fig. 15). Avoided runoff and value are also apportioned by community district 

based on tree cover (Figs. 16 and 17).

Table 11.—Estimated effects of trees shade on UV radiation in New York City.

UV Effects in Tree Shade UV Effects Overall

Borough Protection 

factora

Reduction in 

UV Index

Percent 

reduction 

Protection 

factor

Reduction in 

UV Index

Percent 

reduction

Bronx 2.25 2.11 48.1 1.34 0.91 24.9

Brooklyn 2.16 2.05 45.6 1.28 0.78 21.2

Manhattan 1.86 1.82 37.2 1.08 0.32 7.8

Queens 2.23 2.09 47.5 1.33 0.88 24.1

Staten Island 2.70 2.33 57.3 1.65 1.36 38.7

Study Area 2.26 2.11 48.2 1.35 0.91 25.1

a Protection factor is a unitless value meant to capture the UV radiation-blocking capacity of trees. It is 

comparable to the SPF rating in sunscreen and is calculated as the unshaded UV index divided by the shaded 

or overall UV index (depending on exposure class). The protection factor of urban trees can be defined as how 

many times longer a person would have to spend in a particular environment to receive the same exposure as 

in an open location with no solar UV protection (Grant and Heisler 2006).
30

Figure 15.—Tree species with greatest overall impact on surface water runoff, New York City, 2010. 

Avoided runoff by species is proportional to leaf area as runoff reduction is estimated on a citywide basis.

0

1

2

3

4

5

6

7

8

London

planetree

Norway

maple

Black

locust

Pin oak Red

maple

White oak Silver

maple

Black

birch

Norway

spruce

Sweetgum

Avoided Runoff (million cubic feet / year)

Street trees in Queens, near a residential area (left), and street trees in the Bronx, near a ballfield in a 

residential area (right). Photos by D.S. Novem Auyeung, used with permission.
31

Figure 16.—Avoided surface water runoff by trees by community district, New York City, 2010. 

Avoided runoff by community district is proportional to tree cover as runoff reduction is 

estimated on a citywide basis..

0 2.25 4.5 9 Miles

±

Avoided Runoff (ft3/yr)

142,000 - 500,000

500,001 - 1,500,000

1,500,001 - 3,000,000

3,000,001 - 5,000,000

5,000,001 - 7,830,000
32

Figure 17.—Avoided surface water runoff value by community district, New York City, 2010.

0 2.25 4.5 9 Miles

± Avoided Runoff Value ($/yr)

3,500 - 25,000

25,001 - 50,000

50,001 - 100,000

100,001 - 200,000

200,001 - 524,000

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 its tissue and by reducing the amout of energy used to 

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

(Abdollahi et al. 2000).
33

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 

productions and emissions from fossil-fuel based power sources. Trees in New York City

store an estimated 1.2 million tons of carbon (4.2 million tons of CO2 valued at $153 

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 

New York City is about 51,000 tons of carbon per year (186,000 tons per year of CO2) with 

an associated value of $6.8 million per year. Net carbon sequestration in New York City 

is estimated at about 36,000 tons per year (132,000 tons per year of CO2) by subtracting 

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

Highbridge Park in Queens. Photo by D.S. Novem Auyeung, used with permission.
34

Figure 19.—Estimated annual carbon storage and value for urban tree species with the greatest 

storage, New York City, 2013.

0

5

10

15

20

25

0

20

40

60

80

100

120

140

160

180

Pin oak Norway

maple

London

planetree

Black oak White

oak

Oak spp Northern

red oak

Red

maple

Copper

beech

Silver

maple

Value (million dollars)

Storage (thousand tons)

Carbon storage

Value

Figure 18.—Estimated annual carbon sequestration and value for urban tree species with the 

greatest sequestration, New York City, 2013.

0

100

200

300

400

500

600

700

0

1

2

3

4

5

6

Norway

maple

Pin oak London

planetree

White

oak

Black

locust

Black

oak

Red

maple

Northern

red oak

Oak spp Swamp

white oak

Value (thousand dollars / year)

Sequestration (thousand tons / year)

Carbon sequestration

Value

Of all the species sampled, pin oak stores the most carbon (approximately 14.0 percent of 

total estimated carbon stored) and Norway maple annually sequesters the most carbon 

(10.6 percent of all sequestered carbon) (Figs. 18 and 19). Trees greater than 30 inches in 

diameter store the most carbon in the city (Figs. 20 and 21).
35

Figure 20.—Estimated total carbon storage and sequestration by diameter class, New York City, 

2013. The lower limit of each diameter class is greater than displayed (e.g., 3-6 is actually 3.01 to 

6 inches).

0

2

4

6

8

10

12

0

50

100

150

200

250

300

350

400

450

Total Sequestration (thousand tons / year)

Total Storage (thousand tons)

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

Diameter Class (inches)

Carbon storage

Carbon sequestration

Figure 21.—Estimated average carbon storage and sequestration per tree by diameter class, 

New York City, 2013. The lower limit of each diameter class is greater than displayed (e.g., 3-6 is 

actually 3.01 to 6 inches).

0

20

40

60

80

100

120

140

160

0

1

2

3

4

5

6

7

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 (thousand lbs)

Diameter Class (inches) Carbon storage

Carbon sequestration
36

Energy Consumption

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 space-conditioned 

residential buildings (McPherson and Simpson 1999).

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

2014b, 2014c), trees in New York City reduce energy costs from residential buildings by an 

estimated $17.1 million annually (Table 12). Trees also provide an additional $1.6 million 

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

sources (a reduction of 11,800 tons of carbon emissions or 43,000 tons of CO2) (Table 13).

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 tree 

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

structure of the tree). For small trees, a replacement cost can be used. For larger trees, several 

estimation procedures are used (Nowak et al. 2002a). The compensatory value of the trees in 

New York City is about $5.7 billion (Fig. 22). 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 

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

compensatory value do not necessarily discount invasives species in the species rating.

Table 12.—Annual monetary savingsa ($) in residential energy 

expenditures during heating and cooling seasons, New York City, 2012

Heating Cooling Total

MBTUb 4,833,000 n/a 4,833,000

MWHc 838,000 11,455,000 12,293,000

Carbon avoided 844,000 729,000 1,574,000

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

2012b, 2014b, 2014c)

b MBTU – Million British Thermal Units (not used for cooling)

c MWH – Megawatt-hour

Table 13.—Annual energy savings (MBTU, MWH, or tons) due to trees 

near residential buildings, New York City, 2012

Heating Cooling Total

MBTUa 305,100 n/a 305,100 

MWHb 4,700 64,900 69,700 

Carbon avoided (t)c 6,300 5,500 11,800 

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
37

Figure 22.—Figure 22—Tree species with the greatest compensatory value, New York City, 2013.

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., aesthetics, wildlife habitat). Thus the 

functional estimates 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., 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 New York City have the following structural values:

• Compensatory value: $5.7 billion

• Carbon storage: $153 million

Urban trees in New York City have the following annual functional values:

• Carbon sequestration: $6.8 million

• Pollution removal: $77.9 million

• Avoided surface water runoff: $4.6 million

• Reduced energy costs: $17.1 million

0

100

200

300

400

500

600

Norway

maple

Pin oak London

planetree

White oak Sweetgum Red

maple

Black oak Northern

red oak

Black

locust

Swamp

white oak

Compensatory Value (milions of dollars)
38

MANAGEMENT IMPLICATIONS

The urban forest of New York City 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 is 21 percent, it ranges from 2.3 to 77.2 percent 

among the community districts. 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 New York. One option for determining priority 

planting areas 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 6). 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; or c) near buildings to reduce energy use, or any 

combination of these or other options.

Current Tree Size Distribution and Potential Species Changes

Change in species composition and tree sizes 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, New York City has more small trees than large trees, which leads to 

an inverse J-shaped distribution of diameter structure (Fig. 6). This pattern indicates 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 are revealed. 

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

changes, climate change, development, and natural resource management. Several of the 

most common large diameter tree species, particularly northern red oak, black oak, pin 

oak, and London planetree, are underrepresented among the small diameter trees (Fig. 

8), 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 diameter classes and appear to be regenerating well (including tree 
39

planting by humans) are northern white-cedar and white oak. There are several factors 

that may contribute to dominance in the small diameter classes. Species, such as tree-ofheaven, that tend to be prolific seeders can easily become established in open areas and 

corridors throughout New York City. Tree-of-heaven is classified as an invasive species in 

New York State and can contribute to future changes in species composition. Additionally, 

if individual small-stature trees (e.g., bayberry) are replacing large trees in the urban 

landscape, this will likely lead to lower tree cover and altered size structure.

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, 2016). The USDA Forest Service 

Forest Inventory and Analysis program, in cooperation with New York City, started a 

long-term urban forest monitoring program in New York City in 2018. This program will 

measure urban forest data annually to assess urban forest structure, ecosystem services 

and values, and changes in structure, services and values through time (Forest Service 

2016).

Insect and Disease Impacts

Thirty-one exotic insects and tree diseases were assessed 

for their potential impact on New York City’s urban forest 

using range maps of the pests in the coterminous United 

States (USDA Forest Service 2013, 2014; Worrall 2007). 

For a complete list of the 31 exotic insects and diseases, see 

appendix 8.

Although there are numerous pests that could impact New 

York City’s urban forest, Asian longhorned beetle (ALB), 

gypsy moth (GM), oak wilt (OW), large aspen tortrix (LAT), 

and laurel wilt disease (LWD) pose the most serious threats 

based on the number of trees at risk to infestation.

Of these five insects and diseases, ALB, GM, and LAT 

were confirmed present in New York City at the time 

of this study (summer 2013). As of 2018, OW has also 

been confirmed, and the USDA Animal and Plant Health 

Inspection Service (USDA-APHIS) has declared that the 

boroughs of Manhattan and Staten Island are free from 

ALB although parts of Queens and Brooklyn are still under 

quarantine. Potential loss from ALB is 1.5 million trees 

with an associated compensatory value of $1.6 billion, GM 

is 1.4 million trees ($2.3 billion), and LAT is 372,000 ($118 

million). Potential loss of trees from OW is 733,000 ($1.5 

billion in compensatory value). LWD has been found within 

Asian longhorned beetle. Photo by 

Kenneth R. Law USDA APHIS PPQ, 

from bugwood.org, 0949056.

Large aspen tortrix. Photo by Steven 

Katovich, USDA Forest Service, from 

bugwood.org, 1398256.

Oak wilt symptom. Photo by Joseph 

O'Brien, USDA Forest Service, from 

bugwood.org, 5039074.
40

750 miles of New York City. Potential loss of trees from LWD is 335,000 ($30 million in 

compensatory value) (Fig. 23). These five insects and diseases threaten common trees such 

as willow, pine, spruce, and birch (appendix 8).

Two other pests that are of concern in New York City are the southern pine beetle (SPB) 

and emerald ash borer (EAB). Though SPB is not currently found in the city, it has been 

detected in New York State as close as Rockland and Orange Counties. Potential loss of 

trees due to an infestation of SPB is 163,000 trees ($246 million in compensatory value). 

EAB was confirmed in Brooklyn and Queens in 2017 and Staten Island in 2018. This pest 

poses a risk to 40,000 trees with an associated value of $99 million.

Figure 23.—Number of trees at risk and associated compensatory value for five most threatening 

insects/ or diseases, New York City, 2013.

0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

ALB GM LAT LWD

Compensatory Value (billions of dollars)

Number of Trees (millions)

OW

Pest/Disease

Trees at risk

Compensatory value

Emerald ash borer feeding on ash 

leaf. Photo by Leah Bauer, USDA 

Forest Service, from bugwood.org, 

5473689.

Adult southern pine beetle. Photo by 

USDA Forest Service, Region 8, from 

bugwood.org, 1510071.
41

CONCLUSION

New York City’s urban forest contributes significantly to the environment, the economy, 

and residents’ well-being. Throughout the city, nearly 7.0 million trees, representing at 

least 138 species/genera, provide a canopy cover of 21 percent. That canopy 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 runoff reduction, and many other benefits (and costs). Forested parkland, in 

particular, provides a disproportionate amount of ecosystem service benefits relative to its 

geographic size because of its greater tree density.

There are a number of change forces that will impact New York City’s forest structure, 

health, management costs, and environmental benefits provided to the city’s 8.2 million 

residents. The forces discussed in this report include insects and disease infestation, 

invasive trees, and aging and loss of larger trees. Additional change forces that could be 

considered in urban forest management plans and policies include climate change, the 

expansion of nonnative, opportunistic species, future changes due to urban development, 

and changes in the use of the forest. Managers can use 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 New York City can be found at: https://doi.org/10.2737/

NRS-RB-117

ACKNOWLEDGMENTS

We would like to thank Jennifer Greenfeld, Fiona Watt, Bram Gunther, Sarah CharlopPowers, Kristy King, and Kat Bounds for their valuable input and support; Helen 

Forgione, Clara Pregitzer, and Justin Bowers for sharing the results of the i-Tree Eco 

analysis of the Natural Area Conservancy’s upland forest assessment of parkland; Charles 

Biada, Madeline Faucher, Douglas Gutierrez, John Schroeder, and Andrew De Luna for 

their help with data collection; Mark Bradford and Emily Oldfield for administrative 

support.
42

APPENDIX 1

Bronx River Watershed Analysis

Introduction

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

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

approximately 25,000-acre Bronx River watershed (Fig. 24). The model was calibrated 

using existing stream flow data and used to compare how simulated flow and runoff 

change 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 volume of flow due to changes 

in tree or impervious cover.

Figure 24.—Figure 24—Digital elevation model of 

Bronx River watershed and U.S. Geologic Survey 

(USGS) gauge for Bronx River at New York Botanical 

Garden, Bronx, NY, 2012.
43

Table 14.—Cover estimates for Bronx River Watershed, 2016

Cover (%)a

Area (ac) Impervious

Tree/

shrubb

Grass/

herbaceous Bare soil Water

24,576 40.75 45.8 20.0 0.5 1.25

a Total is greater than 100% because 8.3% of ground cover beneath tree canopies was 

modeled as impervious.

b Tree/shrub is hereafter referred to as tree cover.

Methods

Data and Model Calibration

Hourly weather data were obtained from LaGuardia Airport weather station (i.e., station 

number: 725030-14732). Tree and impervious land cover parameters for the watershed 

were estimated by interpretation of Google Earth imagery (survey completed in April 

2016) using a sample of 400 random locations (Table 14). Leaf area index (LAI) was 

set to 5 based on various field studies (Asner et al. 2003, and unpublished data6). The 

impervious land area directly connected to the stream is a key model input. Under the 

current condition case of 40.75 percent of the land area impervious, 34 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 

percent impervious land cover increased, the percent connected also increased, based on 

effective impervious area (EIA) equations from Sutherland (2000).

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 rain 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 Bronx River 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 “Bronx River at NY Botanical Garden at Bronx NY” for the 2012 calendar year. 

The natural contributing area of this stream gauge includes an area that is now diverted 

for municipal water supplies. To perform model calibration, it was necessary to reduce 

the difference between model inputs affecting predicted streamflow and actual conditions 

affecting observed streamflow. For this purpose, the contributing area of this stream gauge 

6 Auyeung, N.; Larson, M. 2016. Shapefiles for watersheds and sub-catchments of New York City. 

New York City Parks. Unpublished information and personal communication on April 11, 2016.
44

was reduced from its natural area (51.1 square miles) to only the undiverted area (38.4 

square miles; Fig. 24) based on maps of watersheds provided by the NYC Parks.6

Water Quality Effects and Pollution Load

Event mean concentration (EMC) data are used for estimating pollutant loading into 

watersheds. EMC is a statistical parameter representing the flow-proportional average 

concentration of a given parameter during a storm event and is defined as the total 

constituent mass divided by the total runoff volume. EMC estimates are usually obtained 

from a flow-weighted composite of concentration samples taken during a storm and are 

calculated as (Charbeneau and Barretti 1998, Sansalone and Buchberger 1997):

EMC C M

V

C t Q t dt

Q t dt

Ct Qt t

Qt t     







() ()

( )

() ()

( )





 (1)

Where C(t) and Q(t) are the time-variable concentration and flow measured during the 

runoff event, and M and V are pollutant mass and runoff volume. It is clear that the EMC 

results from a flow-weighted average, not simply a time average of the concentration. 

EMC data is used for estimating pollutant loading into watersheds. EMCs are reported as 

a mass of pollutant per unit volume of water (usually millagrams per liter).

The pollution load (L) calculation from the EMC method is

L EMC Q EMC d A   r * ** (2)

Where EMC is event mean concentration (mg/l, mg/m3, ...), Q is runoff of a time period 

associated with EMC (l/h, m3 /day...), dr is runoff depth of unit area (mm/h, m/h, m/

day...), A is the land area (m2, ...) which is catchment area in i-Tree Hydro.

Thus, when the EMC is multiplied by the runoff volume, an estimate of the pollution 

loading to the receiving water is provided. The instantaneous concentration during 

a storm can be higher or lower than the EMC, but the use of the EMC as an event 

characterization replaces the actual time variation of C versus t in a storm with a pulse of 

constant concentration having equal mass and duration as the actual event. This process 

ensures that mass loadings from storms will be adequately represented. EMCs represent 

the concentration of a specific pollutant contained in stormwater runoff coming from a 

particular land use type or from the whole watershed. Under most circumstances, the 

EMC provides the most useful means for quantifying the level of pollution resulting from 

a runoff event (U.S. EPA 2002).

Figure 25 illustrates the inter-storm variation of pollutographs and EMC.

Since collecting the data necessary for calculating site-specific EMCs can be costprohibitive, researchers or regulators will often use values that are already available in the 

literature. If site-specific numbers are not available, regional or national averages can be 

used although the accuracy of using these numbers is questionable. Due to the specific 
45

climatological and physiographic characteristics of individual watersheds, agricultural 

and urban land uses can exhibit a wide range of variability in nutrient export (Beaulac and 

Reckhow 1982).

To understand and control urban runoff pollution, the U.S. Congress included the 

establishment of the Nationwide Urban Runoff Program (NURP) in the 1977 amendments 

of the Clean Water Act. The U.S. Environmental Protection Agency developed the NURP 

to expand the state knowledge of urban runoff pollution by applying research projects and 

instituting data collection in selected urban areas throughout the country.

In 1983, the U.S. Environmental Protection Agency published the results of the NURP, 

which nationally characterizes urban runoff for 10 standard water quality pollutants,

based on data from 2,300 station-storms at 81 urban sites in 28 metropolitan areas (U.S. 

EPA 1983).

Subsequently, the USGS created another urban stormwater runoff base (Driver et al. 

1985), based on data measured through mid-1980s for over 1,100 stations at 97 urban

sites located in 21 metropolitan areas. Additionally, many major cities in the United States 

collected urban runoff quality data as part of the application requirements for stormwater 

discharge permits under the National Pollutant Discharge Elimination System (NPDES). 

The NPDES data are from over 30 cities and more than 800 station-storms for over 150 

paramaters (Smullen et al. 1999).

The data from the three sources (NURP, USGS, and NPDES) were used to compute 

new estimates of EMC population means and medians for the 10 pollutants with many 

more degrees of freedom than were available to the NURP investigators (Smullen et al. 

1999). A “pooled” mean was calculated representing the mean of the total population of 

sample data. The NURP and pooled mean EMCs for the 10 constitutes are listed in Table 

15 (Smullen et al. 1999). NURP or pooled mean EMCs were selected because they are 

based on field data collected from thousands of storm events. These estimates are based 

on nationwide data, however, so they do not account for regional variation in soil types, 

climate, and other factors.

Figure 25.—Interstorm variation of pollutographs and EMCs.
46

For i-Tree-Hydro, the pooled median and mean EMC value for each pollutant (Table 15) 

were applied to the runoff regenerated from pervious and impervious surface flow, not 

the base flow values, to estimate effects on pollutant load across the entire modeling time 

frame. All rain events are treated equally using the EMC values, which means some events 

may be over-estimated and others underestimated. In addition, local management actions 

(e.g., street sweeping) can affect these values. However, across the entire season, if the 

EMC value is representative of the watershed, the estimate of cumulative effects on water 

quality should be relatively accurate. Accuracy of pollution estimates will be increased 

by using locally derived coefficients. It is not known how well the national EMC values 

represent local conditions.

Results

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

flow and the modeled stream flow for the Bronx River 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 (see https://doi.org/10.2737/NRS-RB-117 for calibration evaluation).

Table 15.—National pooled EMCs and NURP EMCs

Constitute Data Source EMCs (mg/l) Events

mean median number

Total suspended solids TSS Pooleda

NURP

78.4

17.4

54.5

113

3,047

2,000

Biochemical oxygen demand BOD5 Pooledb

NURP

14.1

10.4

11.5

8.39

1,035

474

Chemical oxygen demand COD Pooleda

NURP

52.8

66.1

44.7

55

2,639

1,538

Total phosphorus TP Pooleda

NURP

0.315

0.337

0.259

0.266

3,094

1,902

Soluble phosphorus Soluble P Pooledc

NURP

0.129

0.1

0.103

0.078

1,091

767

Total Kjeldhal nitrogen: TKN Pooleda

NURP

1.73

1.67

1.47

1.41

2,693

1,601

Nitrite and nitrate: NO2 and NO3 Pooleda

NURP

0.658

0.837

0.533

0.666

2,016

1,234

Copper: Cu Pooleda

NURP

0.0135

0.0666

0.0111

0.0548

1,657

849

Lead: Pb Pooleda

NURP

0.0675

0.175

0.0507

0.131

2,713

1,579

Zinc: Zn Pooleda

NURP

0.162

0.176

0.129

0.140

2,234

1,281

a Pooled data sources include: NURP, USGS, NPDES.

b Pooled data sources include: NURP, NPDES. No BOD5 data available in the USGS dataset.

c Pooled data sources include: NURP, USGS. No Soluble P data available in the NPDES dataset.
47

Existing Cover Effects

The LaGuardia Airport weather station used for the i-Tree Hydro simulation of Bronx 

River watershed recorded 32.6 inches of rainfall during the 2012 simulation year. It was 

assumed that this amount fell over the entire 24,576-acre watershed and contributed 1.12 

billion cubic feet of rainfall in the watershed during 2012.

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

existing cover (i.e., no cover change from measured conditions) was 453.0 million 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 

pervious and impervious areas are the biggest contributors to stream flow with 37.2 

and 33.3 percent of total flow generated from pervious runoff and impervious runoff, 

respectively. Base flow was estimated to generate 29.5 percent of the total flow.

Tree canopies were estimated to intercept about 13.5 percent of the total rainfall, but only 

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

was only 6.2 percent (69.5 million cubic feet). Areas of herbaceous cover were estimated 

to intercept about 5.4 percent of the total rainfall, but only 20 percent of the watershed 

was herbaceous cover, so precipitation interception by this vegetation was only 1.1 percent 

(12.2 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 2012 by around 26.2 tons based on median 

EMC values. Other chemical constituents were also reduced (Table 16).

Table 16.—Estimated reduction in chemical constituents due 

to existing tree cover, Bronx River Watershed, 2012

Reduction

Constituent Median Mean

tons tons

Total suspended solids 26.2 37.6

Biochemical oxygen demand 5.5 6.8

Chemical oxygen demand 21.5 25.4

Total phosphorus 0.12 0.15

Soluble phosphorus 0.05 0.06

Total Kjeldhal nitrogen 0.71 0.83

Nitrite and nitrate 0.26 0.32

Copper 0.005 0.006
48

Tree Cover Effects

Reducing the existing 45.8 percent tree cover in the Bronx River Watershed to 0 percent 

would increase total runoff by 15.4 million cubic feet (4.8 percent of total runoff under 

existing conditions) for 2012 (Fig. 26). Increasing canopy cover from 45.8 percent to 50.0 

percent would reduce total runoff by 2.2 million cubic feet (0.7 percent of total runoff under 

existing conditions) for the same period (Fig. 26). Increasing tree cover reduces runoff 

generated from impervious areas and generally reduces runoff from pervious land (Fig. 27).

Figure 26.—Percentage change in total annual runoff by percentage tree cover, 

Bronx River watershed, 2012.

Figure 27.—Changes in total flow and its components (pervious area runoff, impervious area 

runoff, and base flow) with changes in percentage tree cover in Bronx River watershed, 2012.

-12

-10

-8

-6

-4

-2

0

2

4

6

0% 10% 20% 30% 40%

50% 60% 70% 80% 90% 100%

Percent Tree Cover

Change in Total Runoff (%)

0

100

200

300

400

500

600

0 10 20 30 40 50 60 70 80 90 100

Total Flow

Pervious Area Flow

Base Flow

Flow

Impervious Area Flow

 (million ft3)

Tree Cover (percent)
49

Impervious Cover Effects

Removing all impervious cover in the Bronx River watershed (currently 41.9 percent) 

would reduce total annual runoff by 210.6 million cubic feet (66.0 percent of total runoff 

under existing conditions) for 2012. Increasing impervious cover from 41.9 percent to 

50 percent of the watershed would increase total annual runoff by 56.2 million cubic feet 

(17.6 percent of total runoff under existing conditions) for the same period (Fig. 28). 

Increasing impervious cover reduces base flow and pervious runoff while significantly 

increasing runoff from impervious surfaces (Fig. 29).

Figure 29.—Changes in total flow and its components (pervious area runoff, impervious area runoff, 

and base flow) with changes in percentage impervious cover in the Bronx River watershed, 2012.

Figure 28.—Percent change in total annual runoff by percentage impervious cover, 

Bronx River Watershed, 2012.

-100

-50

0

50

100

150

200

250

0% 10% 20% 30% 40%

50% 60% 70% 80% 90% 100%

Percent Impervious Cover

Change in Total Runoff (%)

0

200

400

600

800

1,000

1,200

0 10 20 30 40 50 60 70 80 90 100

Total Flow

Pervious Area Flow

Base Flow

Flow

Impervious Area Flow

 (million ft3)

Impervious Cover (percent)
50

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 an almost eight times greater 

impact on total flow than tree cover. Increasing impervious cover by 1 percent resulted 

in an average 1.45 percent increase in annual stream flow, while increasing tree cover by 

1 percent resulted in an average of only 0.19 percent decrease in annual stream flow. The 

interactions between changing both tree and impervious cover are illustrated for changes 

in percent flow in Figure 30.

Figure 30.—Percentage change in total flow during simulation period based on percent impervious 

and percent tree cover, Bronx River watershed, 2012. Red star indicates existing conditions. Note: 

some simulation scenarios (e.g., 100 percent tree cover and 100 percent impervious cover) are not 

realistic, but are included to illustrate the range of possibilities.
51

APPENDIX 2

Ecosystem Services by Community District

Ecosystem services are presented by community district for New York City and estimated 

using the LiDAR urban tree canopy assessment (O’Neil-Dunne 2012) (Table 17). Each 

community district is assigned a code (New York City Department of City Planning 2015) 

(Fig. 31). Detailed information on ecosystem services by neighborhood tabulation area 

(NTA) can be found at: https://doi.org/10.2737/NRS-RB-117.

Figure 31.—Community districts, New York City, 2010.

503

502

501

413

407

411

412

318

405

408

483

410

210

305

212

414

401

402

301

315

209

310

311

409

211

307

312

317

208

313

306

403

406

314

112

303 302

228

404

202 201

356

304

204

207

316

226

108

481

103

111

308

309

203

206 205

102

101

414

107

104 105

109110

106

164 480

484

227

484

595

355

111

482

595

401

228

484

414

356

484

210

356 484

101

356

108

356

356

356

356

484

484

356

484

356

484

484

0 2 4 8 Miles

±
52

continued

Table 17.—Current tree cover, temperature planting index values, and available space for 

planting by community district (CD code), New York City, 2010

CD 

Code Borough

Tree 

Cover Pollution Removal TPIa

Plantable 

Spaceb

Temperature 

Reductionc

% lbs/yr $/yr acres °F

101 Manhattan 12.1 9,100 245,000 34.4 204.77 0.1 

102 Manhattan 11.9 6,900 580,300 60.0 116.87 0.1 

103 Manhattan 21.1 15,200 1,405,600 87.2 293.76 0.1 

104 Manhattan 8.5 6,300 589,500 53.6 118.51 0.1 

105 Manhattan 3.4 3,300 235,500 31.4 40.30 <0.1 

106 Manhattan 16.6 9,100 1,049,600 90.6 172.95 0.1 

107 Manhattan 21.7 18,200 1,716,200 98.0 340.14 0.1 

108 Manhattan 16.9 14,400 1,716,500 100.0 279.02 0.1 

109 Manhattan 23.5 12,900 771,400 65.0 308.17 0.1 

110 Manhattan 17.1 7,500 785,800 75.6 213.89 0.1 

111 Manhattan 15.7 23,500 963,400 45.7 533.43 0.1 

112 Manhattan 29.2 22,900 960,700 61.3 686.69 0.2 

164 Manhattan 62.9 21,000 6,600 0.1 639.63 0.4 

201 Bronx 11.5 9,200 844,300 38.4 268.38 0.1 

202 Bronx 6.3 4,300 284,900 21.5 182.52 <0.1 

203 Bronx 16.6 9,800 560,600 44.8 302.07 0.1 

204 Bronx 13.7 8,300 726,200 66.5 264.72 0.1 

205 Bronx 13.7 6,500 748,900 84.0 185.81 0.1 

206 Bronx 13.0 7,200 568,000 48.8 214.20 0.1 

207 Bronx 17.2 11,400 1,110,100 66.6 315.82 0.1 

208 Bronx 40.1 42,400 1,632,500 27.2 1,099.33 0.2 

209 Bronx 17.3 25,500 1,850,600 38.3 853.91 0.1 

210 Bronx 15.5 36,100 1,234,200 16.7 1,717.61 0.1 

211 Bronx 17.9 22,000 1,211,000 28.7 725.89 0.1 

212 Bronx 22.5 43,600 1,589,000 24.9 1,429.45 0.1 

226 Bronx 64.0 29,500 14,400 0.2 1,013.53 0.4 

227 Bronx 60.5 18,200 9,300 1.2 558.20 0.4 

228 Bronx 45.6 53,600 31,700 0.2 1,796.19 0.3 

301 Brooklyn 8.7 19,000 1,153,900 33.1 420.57 0.1 

302 Brooklyn 18.1 24,500 1,212,800 32.0 430.18 0.1 

303 Brooklyn 18.2 23,700 1,518,500 48.6 477.35 0.1 

304 Brooklyn 14.2 11,900 716,300 50.4 323.80 0.1 

305 Brooklyn 13.8 25,200 1,758,800 29.6 1,079.58 0.1 

306 Brooklyn 15.5 20,400 1,173,300 30.9 403.80 0.1 

307 Brooklyn 15.7 18,900 820,000 30.3 697.83 0.1 

308 Brooklyn 17.8 13,500 842,900 52.7 255.91 0.1 
53

CD 

Code Borough

Tree 

Cover Pollution Removal TPIa

Plantable 

Spaceb

Temperature 

Reductionc

309 Brooklyn 15.1 10,900 693,700 55.1 210.94 0.1 

310 Brooklyn 17.8 22,000 1,057,000 28.1 790.02 0.1 

311 Brooklyn 9.8 12,400 894,100 44.5 322.60 0.1 

312 Brooklyn 15.5 23,500 1,265,200 48.9 503.76 0.1 

313 Brooklyn 13.0 15,000 1,143,800 29.9 691.53 0.1 

314 Brooklyn 21.5 29,200 1,554,000 49.1 529.66 0.1 

315 Brooklyn 17.1 40,400 2,097,300 30.1 769.41 0.1 

316 Brooklyn 14.3 12,000 842,500 42.4 280.23 0.1 

317 Brooklyn 14.3 23,000 1,274,000 41.7 495.68 0.1 

318 Brooklyn 18.3 69,400 2,808,600 20.1 2,205.67 0.1 

355 Brooklyn 60.2 8,300 12,600 1.2 452.69 0.4 

356 Brooklyn 13.1 38,700 277,200 0.3 2,214.15 0.1 

401 Queens 11.8 29,400 1,483,600 27.9 931.34 0.1 

402 Queens 8.5 22,100 862,200 20.7 817.32 0.1 

403 Queens 16.5 17,200 1,435,400 52.0 463.86 0.1 

404 Queens 12.8 11,000 1,168,600 65.8 284.60 0.1 

405 Queens 17.6 50,100 1,444,300 20.3 1,891.00 0.1 

406 Queens 25.1 25,400 1,466,200 34.7 685.36 0.2 

407 Queens 19.6 79,500 2,572,800 19.0 2,938.37 0.1 

408 Queens 31.2 75,000 2,770,000 18.4 2,381.87 0.2 

409 Queens 17.7 25,600 1,799,100 33.6 675.41 0.1 

410 Queens 14.2 31,600 1,341,800 17.7 1,344.35 0.1 

411 Queens 31.0 94,700 2,171,600 11.2 3,063.81 0.2 

412 Queens 17.3 60,800 2,709,100 21.4 1,985.89 0.1 

413 Queens 19.6 90,100 2,620,700 13.5 3,340.77 0.1 

414 Queens 10.5 26,500 841,400 14.1 1,988.92 0.1 

480 Queens 4.5 1,100 10,700 0.8 143.99 <0.1 

481 Queens 20.3 17,900 3,600 0.1 490.98 0.1 

482 Queens 77.2 9,200 38,900 1.5 502.41 0.5 

483 Queens 2.3 5,800 9,500 0.0 1,411.24 <0.1 

484 Queens 15.8 39,400 48,000 0.2 2,003.97 0.1 

501 Staten Island 26.4 111,000 2,309,000 11.5 3,905.66 0.2

502 Staten Island 29.8 185,600 1,560,500 5.4 8,644.42 0.2 

503 Staten Island 33.3 233,400 2,500,100 6.6 8,210.10 0.2 

595 Staten Island 17.3 11,700 135,600 2.3 1,008.10 0.1 

a TPI = temperature planting index; see appendix 6 for explanation

b Plantable space is the area of grass/shrub and bare soil as estimated using the LiDAR urban tree canopy 

assessment (O’Neil-Dunne 2012)

c Temperature reduction was based on daytime hours (6 a.m. – 5 p.m.) for average temperature summer day 

(7/23/08)

Table 17.—continued
54APPENDIX 3

Woody Trees and Shrubs Sampled in the New York City Urban ForestGenus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Abies fraseri Fraser fir 6,000 0.1 0.0 0.1 2.5 2.5 300 0.6 0Abies grandis Grand fir 6,000 0.1 0.9 1.0 23.5 23.5 19,240 39.3 2Acer buergerianum Trident maple 19,000 0.3 0.1 0.4 3.5 3.5 1,740 4.6 4Acer campestre Hedge maple 6,000 0.1 0.0 0.1 4.5 4.5 800 3.0 4Acer negundo Boxelder 6,000 0.1 0.0 0.1 4.5 4.5 840 2.9 4Acer palmatum Japanese maple 38,000 0.5 0.6 1.1 4.7 7.5 22,060 40.8 4Acer platanoides Norway maple 425,000 6.1 10.7 16.8 5.1 9.7 451,580 525.2 4Acer pseudoplatanus Sycamore maple 6,000 0.1 0.1 0.2 10.5 10.5 4,040 5.9 4Acer rubrum Red maple 230,000 3.3 4.3 7.6 3.2 6.7 119,280 212.9 4Acer saccharinum Silver maple 64,000 0.9 2.9 3.8 8.5 12.7 130,000 99.7 4Acer saccharum Sugar maple 6,000 0.1 0.0 0.1 1.5 1.5 130 0.5 4Acer species Maple spp 22,000 0.3 0.0 0.3 1.7 1.8 690 1.6 4Aesculus hippocastanum Horse chestnut 6,000 0.1 0.0 0.1 2.5 2.5 300 0.8 4Ailanthus altissima Tree-of-heaven 387,000 5.5 1.6 7.1 1.6 2.6 37,960 43.9 0Albizia species Albizia spp 14,000 0.2 0.1 0.3 5.1 5.1 2,360 7.2 0Amelanchier species Serviceberry spp 33,000 0.5 0.2 0.7 2.4 6.6 17,040 21.7 0Aucuba japonica Japanese aucuba 18,000 0.3 0.0 0.3 2.7 2.8 1,140 2.4 0Betula lenta Black birch 274,000 3.9 2.7 6.6 3.1 4.3 53,700 83.7 8Betula nigra River birch 22,000 0.3 0.1 0.4 2.7 2.8 1,360 4.0 12Betula occidentalis Water birch 14,000 0.2 0.2 0.4 3.0 5.0 2,740 6.2 8Betula papyrifera Paper birch 12,000 0.2 0.1 0.3 4.5 4.5 1,600 4.9 12

continued

Table 18.—Species sampled in the urban forest, New York City, 2013 
55

Genus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Betula populifolia Gray birch 12,000 0.2 0.0 0.2 4.5 4.5 1,670 6.4 12Betula species Birch spp 6,000 0.1 0.0 0.1 3.5 3.5 530 0.0 8Buddleja species Butterflybush spp 6,000 0.1 0.0 0.1 3.5 3.5 510 1.2 0Carpinus caroliniana American hornbeam 8,000 0.1 0.0 0.1 3.5 3.5 670 2.1 0Carya alba Mockernut hickory 18,000 0.3 0.1 0.4 4.5 5.2 3,690 12.3 0Carya glabra Pignut hickory 159,000 2.3 0.3 2.6 1.5 1.6 3,750 9.3 0Carya species Hickory spp 6,000 0.1 0.0 0.1 2.5 2.5 290 0.6 0Carya x ludoviciana Hickory 15,000 0.2 0.1 0.3 3.0 3.0 1,010 2.4 0Castanea dentata American chestnut 6,000 0.1 0.0 0.1 2.5 2.5 300 0.7 4Cedrus atlantica glauca Blue atlas cedar 12,000 0.2 0.0 0.2 4.0 3.0 960 3.3 0Cedrus species Cedar spp 165,000 2.4 0.2 2.6 2.8 3.9 24,300 63.2 0Celtis occidentalis Northern hackberry 121,000 1.7 1.6 3.3 3.2 4.7 31,110 50.4 0Celtis species Hackberry spp 31,000 0.5 1.0 1.5 10.7 10.3 24,700 36.6 0Cephalotaxus fortunei Chinese plum yew 8,000 0.1 0.0 0.1 10.5 10.5 5,090 5.2 0Cercis reniformis Southwestern redbud 12,000 0.2 0.0 0.2 2.5 2.5 600 1.3 0Chamaecyparis thyoides Atlantic white cedar 14,000 0.2 0.1 0.3 13.1 9.0 8,440 16.0 0Cornus florida Flowering dogwood 73,000 1.0 0.5 1.5 2.4 3.7 9,480 20.2 4Cornus mas Cornelian cherry 6,000 0.1 0.0 0.1 2.5 2.5 290 0.5 4Cornus species Dogwood spp 45,000 0.7 0.3 1.0 4.3 4.7 8,850 17.7 4Crataegus mollis Downy hawthorn 7,000 0.1 0.0 0.1 1.5 1.5 150 0.3 4Crataegus phaenopyrum Washington hawthorn 34,000 0.5 1.1 1.6 13.3 17.7 73,990 94.1 4Crataegus species Hawthorn spp 46,000 0.7 0.1 0.8 3.3 3.3 4,080 9.4 4Cryptomeria species Japanese cedar spp 6,000 0.1 0.0 0.1 2.5 2.5 300 0.7 0Diospyros virginiana Common persimmon 12,000 0.2 0.0 0.2 2.0 3.0 930 3.3 0Table 18.—continued

continued
56

Genus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Fagus species Beech spp 12,000 0.2 0.9 1.1 11.0 12.5 11,090 23.0 4Fagus sylvatica ‘purpurea’ Copper beech 6,000 0.1 1.9 2.0 43.5 43.5 64,660 93.5 4Ficus carica Common fig 74,000 1.1 0.3 1.4 3.1 3.2 6,540 19.5 0Ficus species Fig spp 76,000 1.1 0.4 1.5 3.6 4.2 11,170 25.9 0Fraxinus americana White ash 6,000 0.1 0.4 0.5 32.5 32.5 36,370 51.2 3Fraxinus nigra Black ash 8,000 0.1 0.3 0.4 8.5 8.5 3,410 5.4 3Fraxinus pennsylvanica Green ash 26,000 0.4 1.6 2.0 14.1 12.9 26,240 42.0 7Ginkgo biloba Ginkgo 37,000 0.5 0.8 1.3 9.5 8.4 21,130 31.0 0Gleditsia triacanthos Honeylocust 64,000 0.9 1.4 2.3 13.4 11.4 56,600 106.0 0Gymnocladus dioicus Kentucky coffeetree 6,000 0.1 0.3 0.4 13.5 13.5 6,280 11.1 0Hibiscus syriacus Rose-of-sharon 58,000 0.8 0.1 0.9 2.9 3.2 5,210 13.5 0Hydrangea species Hydrangea spp 8,000 0.1 0.0 0.1 2.5 2.5 380 1.1 0Ilex aquifolium English holly 35,000 0.5 0.2 0.7 2.6 4.1 6,860 11.0 0Ilex species Holly spp 12,000 0.2 0.0 0.2 2.0 2.0 430 1.2 0Juglans species Walnut spp 6,000 0.1 0.4 0.5 19.5 19.5 13,360 20.5 0Juniperus species Juniper spp 28,000 0.4 0.0 0.4 3.0 2.8 1,720 3.8 0Juniperus virginiana Eastern red cedar 44,000 0.6 0.3 0.9 3.2 4.4 10,840 19.4 0Juniperus virginiana silicicol Southern redcedar 12,000 0.2 0.1 0.3 8.0 8.0 4,650 9.7 0Koelreuteria paniculata Goldenrain tree 20,000 0.3 0.3 0.6 6.6 7.0 6,390 14.1 0Lagerstroemia indica Common crapemyrtle 18,000 0.3 0.1 0.4 5.5 6.2 5,060 10.3 0Ligustrum species Privet spp 28,000 0.4 0.0 0.4 1.7 1.8 820 1.7 0Lindera benzoin Spicebush 6,000 0.1 0.0 0.1 1.5 1.5 130 0.4 2Liquidambar styraciflua Sweetgum 110,000 1.6 2.4 4.0 5.0 10.5 129,610 254.3 4Liriodendron tulipifera Tulip tree 92,000 1.3 0.4 1.7 1.5 1.9 3,520 11.9 0Table 18.—continued

continued
57

Genus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Lonicera japonica Hall’s honeysuckle 6,000 0.1 0.0 0.1 3.5 3.5 530 1.4 0Maclura pomifera Osage orange 7,000 0.1 0.3 0.4 26.5 26.5 29,410 30.8 0Magnolia species Magnolia spp 19,000 0.3 0.2 0.5 1.8 4.7 4,470 8.9 0Malus species Apple spp 43,000 0.6 0.1 0.7 3.0 4.3 7,570 13.1 4Malus spectabilis Asiatic apple 7,000 0.1 0.3 0.4 17.5 17.5 13,070 17.1 0Malus tschonoskii Crabapple 12,000 0.2 0.2 0.4 3.0 6.0 3,640 6.6 4Morella pensylvanica Bayberry 255,000 3.6 0.3 3.9 2.2 2.2 11,200 21.2 0Morus species Mulberry spp 57,000 0.8 0.4 1.2 3.8 7.1 25,580 37.3 0Nyssa sylvatica Black tupelo 12,000 0.2 0.2 0.4 5.0 5.5 2,370 7.1 0Phellodendron amurense Amur corktree 6,000 0.1 0.5 0.6 29.5 29.5 30,060 35.1 0Photinia species Chokeberry spp 6,000 0.1 0.1 0.2 5.5 5.5 1,150 3.0 0Picea abies Norway spruce 51,000 0.7 2.4 3.1 2.6 9.6 61,040 123.6 13Picea pungens Blue spruce 19,000 0.3 0.1 0.4 4.6 5.0 3,710 10.0 8Picea species Spruce spp 6,000 0.1 0.0 0.1 2.5 2.5 300 0.8 7Pinus species Pine spp 18,000 0.3 0.2 0.5 2.5 6.4 8,640 11.0 10Pinus strobus Eastern white pine 12,000 0.2 0.0 0.2 2.0 3.0 930 3.3 14Platanus hybrida London planetree 166,000 2.4 11.2 13.6 17.1 14.8 307,870 415.3 4Prunus cerasus Sour cherry 6,000 0.1 0.1 0.2 5.5 5.5 1,200 3.5 0Prunus mume Japanese apricot 8,000 0.1 0.0 0.1 4.5 4.5 1,050 3.0 0Prunus persica Peach 32,000 0.5 0.1 0.6 3.3 3.5 3,260 8.0 0Prunus pissardii Purpleleaf plum 29,000 0.4 0.2 0.6 4.5 4.8 5,450 13.0 0Prunus serotina Black cherry 25,000 0.4 0.4 0.8 8.1 6.9 8,490 14.3 0Prunus serrulata Kwanzan cherry 30,000 0.4 0.1 0.5 4.0 4.3 4,080 10.4 0Prunus species Plum spp 86,000 1.2 0.4 1.6 3.5 3.4 8,090 17.7 0Table 18.—continued

continued
58

Genus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Prunus subhirtella Higan cherry 6,000 0.1 0.1 0.2 5.5 5.5 1,200 3.4 0Prunus x orthosepala Prunus 6,000 0.1 0.0 0.1 6.5 6.5 1,570 3.6 0Prunus yedoensis Yoshino flowering cherry 6,000 0.1 0.0 0.1 2.5 2.5 290 0.5 0Pyrus calleryana Callery pear 160,000 2.3 1.3 3.6 3.3 5.7 54,820 95.8 4Pyrus species Pear spp 14,000 0.2 0.1 0.3 9.1 6.0 4,340 8.7 4Quercus alba White oak 302,000 4.3 4.2 8.5 4.2 7.5 178,960 322.4 7Quercus bicolor Swamp white oak 89,000 1.3 1.0 2.3 10.7 11.3 86,810 156.6 7Quercus palustris Pin oak 123,000 1.8 5.4 7.2 17.9 19.2 352,640 506.9 8Quercus fastigiata Fastigiate pin oak 7,000 0.1 0.0 0.1 2.5 2.5 360 1.2 7Quercus phellos Willow oak 12,000 0.2 0.7 0.9 16.0 16.5 19,370 31.5 7Quercus prinus Chestnut oak 6,000 0.1 0.0 0.1 7.5 7.5 2,050 4.9 7Quercus rubra Northern red oak 98,000 1.4 2.1 3.5 9.7 12.3 112,590 188.7 8Quercus species Oak spp 36,000 0.5 0.8 1.3 13.0 19.1 97,820 135.8 7Quercus velutina Black oak 61,000 0.9 2.3 3.2 16.2 17.0 134,720 190.5 7Rhododendron arborescens Smooth azalea 14,000 0.2 0.0 0.2 2.9 3.0 900 1.9 1Rhododendron azalea Azalea 31,000 0.4 0.1 0.5 4.3 4.9 5,850 13.4 1Rhus hirta Staghorn sumac 15,000 0.2 0.0 0.2 0.0 1.1 200 0.8 4Rhus species Sumac spp 52,000 0.7 0.1 0.8 1.9 2.2 2,360 5.6 0Robinia pseudoacacia Black locust 285,000 4.1 7.0 11.1 5.3 6.8 117,550 186.3 0Salix fragilis Crack willow 8,000 0.1 0.1 0.2 14.5 14.5 9,470 6.4 14Salix species Willow spp 18,000 0.3 0.2 0.5 7.5 7.5 6,680 5.9 14Salix x sepulcralis simonk Weeping willow 6,000 0.1 0.3 0.4 3.5 3.5 510 0.5 14Sassafras albidum Sassafras 329,000 4.7 0.8 5.5 1.8 2.3 23,560 29.9 2Schizomeria ovata Cherry birch 6,000 0.1 0.0 0.1 1.5 1.5 130 0.3 0Table 18.—continued

continued
59

Genus Speciesa Common Name Trees

Leaf 

Area IVb

Median 

d.b.h.

Average 

d.b.h.

Basal 

Areac

Compensatory 

Value

Pest 

Scored

number % % inches inches ft2 $ millions

Sophora japonica Japanese pagoda tree 32,000 0.5 1.2 1.7 17.3 16.7 52,350 70.0 0Syringa reticulata Japanese tree lilac 7,000 0.1 0.0 0.1 2.5 2.5 340 0.6 0Syringa species Lilac spp 6,000 0.1 0.0 0.1 0.0 0.8 30 0.3 0Tabebuia heterophylla White cedar 6,000 0.1 0.0 0.1 3.5 3.5 530 1.7 0Taxus canadensis Canada yew 14,000 0.2 0.0 0.2 6.1 5.2 2,600 6.4 0Taxus species Yew spp 110,000 1.6 0.4 2.0 3.1 3.9 15,760 36.8 0Thuja occidentalis Northern white-cedar 395,000 5.7 0.7 6.4 1.9 2.6 38,810 78.7 0Thuja species Red cedar spp 46,000 0.7 0.1 0.8 2.4 2.3 2,060 6.6 0Tilia americana American basswood 20,000 0.3 0.3 0.6 3.4 7.5 11,760 22.2 4Tilia cordata Littleleaf linden 27,000 0.4 1.4 1.8 8.0 13.2 54,900 92.6 4Tilia tomentosa Silver linden 14,000 0.2 1.5 1.7 9.9 21.7 49,760 101.8 4Tilia x vulgaris Common linden 6,000 0.1 0.2 0.3 9.5 9.5 3,340 7.1 4Tsuga canadensis Eastern hemlock 51,000 0.7 2.1 2.8 10.3 11.4 58,220 96.3 7Tsuga species Hemlock spp 6,000 0.1 0.0 0.1 3.5 3.5 510 1.5 3Ulmus americana American elm 21,000 0.3 1.7 2.0 21.4 17.0 47,200 45.7 8Ulmus parvifolia Chinese elm 48,000 0.7 2.1 2.8 9.5 9.9 31,710 33.9 8Ulmus species Elm spp 14,000 0.2 0.1 0.3 3.0 3.0 960 1.6 8Unknown species Hardwood 237,000 3.4 0.1 3.5 3.2 4.7 52,720 3.7 0Viburnum species Viburnum spp 6,000 0.1 0.0 0.1 2.5 2.5 290 0.5 0Vitis species Grape spp 22,000 0.3 0.1 0.4 1.8 1.9 680 1.6 0Zelkova serrata Japanese zelkova 50,000 0.7 0.9 1.6 9.7 8.2 26,300 63.3 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 Table 18.—continuedpoints if the pest is located in the city, 3 points if within 250 miles, 2 points if within 750 miles, and 1 point if greater than 750 miles away). See Table 21 in appendix 8.* Invasive species
60

APPENDIX 4

Tree Species Distribution

The tree species distributions for New York City and for each borough for the 20 most 

common species, or all species if there are less than 20 species in the borough, are 

presented in Figs. 32 through 39.

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 designation “hardwood” 

includes the sampled hardwood trees that could not be identified to a species or genera 

classification.

Figure 32.—The 20 most common tree species as a percentage of the total urban tree population, 

New York City, 2013.

0 1 2 3 4 5 6 7

Norway maple

Northern-white cedar

Tree-of-heaven

Sassafras

White oak

Black locust

Black birch

Bayberry

Hardwood

Red maple

London planetree

Cedar spp

Callery pear

Pignut hickory

Pin oak

Northern hackberry

Yew spp

Sweetgum

Northern red oak

Tulip tree

Total Population (percent)

Species
61

Figure 33.—The percentage of borough tree population occupied by the 10 most common tree species, 

New York City, 2013. For example, bayberry comprises 21 percent of Brooklyn’s tree population.

Figure 34.—The percentage of species population in each borough, New York City, 2013. For example, 

100 percent of bayberry was found in Brooklyn.

0

5

10

15

20

25

Norway

maple

Northern

white- cedar

Tree-ofheavenSassafras White oak Black

locust

Black

birch

Bayberry Hardwood Red maple

Borough Population (percent)

Species

Brooklyn

Queens

Bronx

Manhattan

Staten Island

Borough Category

0

10

20

30

40

50

60

70

80

90

100

Norway

maple

Northern

white- cedar

Tree-ofheavenSassafras White oak Black

locust

Black

birch

Bayberry Hardwood Red maple

Species Population (percent)

Species

Brooklyn

Queens

Bronx

Manhattan

Staten Island

Borough Category
62

Figure 35.—Percentage of trees by species in the Bronx, New York City, 2013

Figure 36.—Percentage of trees by species in Brooklyn, New York City, 2013

0 5 15 20

Black locust

Tree-of-heaven

Black birch

Norway maple

Northern hackberry

Hardwood

London planetree

Northern white-cedar

Tulip tree

Ginkgo

Apple spp

Pin oak

Maple spp

Privet spp

Kwanzan cherry

Hickory

Dogwood spp

Fig spp

Honeylocust

Norway spruce

10

Percent of Trees

Species

0 5 10 15 20 25

Bayberry

Northern white-cedar

Norway maple

Common fig

Hawthorn spp

Sumac spp

Black locust

Red cedar spp

London planetree

Silver maple

Japanese zelkova

Black birch

English holly

Hardwood

River birch

Washington hawthorn

Rose-of-sharon

Juniper spp

Pin oak

Yew spp

Percent of Trees

Species
63

Figure 37.—Percentage of trees by species in Manhattan, New York City, 2013.

Figure 38.—Percentage of trees by species in Queens, New York City, 2013.

0 5 10 15 20 25

Elm spp

Japanese tree lilac

Callery pear

London planetree

Mulberry spp

Washington hawthorn

Downy hawthorn

Northern hackberry

Black birch

Red maple

Littleleaf linden

American basswood

Northern red oak

Purpleleaf plum

Goldenrain tree

River birch

Chinese elm

Percent of Trees

Species

0 5 10 15

Tree-of-heaven

Norway maple

Cedar spp

Northern white-cedar

Yew spp

Hardwood

Plum spp

Red maple

London planetree

Honeylocust

Pin oak

Rose-of-sharon

Dogwood spp

Mulberry spp

Azalea

Japanese maple

Black birch

Black locust

Japanese pagoda tree

Northern hackberry

Percent of Trees

Species
64

Figure 39—Percentage of trees by species in Staten Island, New York City, 2013.

0 5 10 15

Sassafras

White oak

Red maple

Pignut hickory

Northern white-cedar

Norway maple

Callery pear

Hardwood

Black birch

Sweetgum

Swamp white oak

Northern red oak

Black oak

Cedar spp

Flowering dogwood

Tulip tree

Fig spp

Pin oak

Oak spp

Norway spruce

Percent of Trees

Species
65

APPENDIX 5

Relative Tree Effects

The urban forest in New York City provides benefits that include carbon storage and 

sequestration, and air pollutant removal. These benefits vary across d.b.h classes (Table 

19). 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), SO2 for 2010 

(Bureau of Transportation Statistics 2010; Heirigs et al. 2004), and CO2 for 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 one-third 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); Georgia Forestry 

Commission (2009).

General tree information:

Average tree diameter = 6.3 inches

Median tree diameter = 3.3 inches

Number of trees sampled = 1,015

Number of species/genera sampled = 138
66

The trees in New York City provide:

Carbon storage (C) equivalent to:

Amount of C emitted in region in 10 days or

Annual C emissions from 814,000 automobiles or 

Annual C emissions from 333,800 single family houses 

Nitrogen dioxide (NO2) removal equivalent to:

Annual NO2 emissions from 34,700 automobiles or

Annual NO2 emissions from 15,600 single family houses 

Sulfur dioxide (SO2) removal equivalent to:

Annual SO2 emissions from 951,600 automobiles or

Annual SO2 emissions from 2,500 single family houses 

Annual C sequestration equivalent to:

Amount of C emitted in region in 0.5 days or

Annual C emissions from 38,300 automobiles or

Annual C emissions from 15,700 single family home

Table 19.—Average tree effects by tree diameter class (d.b.h.), New York City, 2013

d.b.h.a Carbon storage Carbon sequestration Pollution removal

inch lbs $ miles b lbs/yr $/yr miles b lbs/yr $/yr

1-3 6 0.38 10 1.6 0.11 2 0.04 1.30

3-6 35 2.34 40 6.1 0.40 7 0.14 4.97

6-9 130 8.65 140 13.2 0.88 14 0.35 12.25

9-12 302 20.08 330 21.3 1.42 23 0.55 19.20

12-15 579 38.53 630 32.7 2.17 36 0.66 23.05

15-18 924 61.48 1,010 47.8 3.18 52 0.97 34.10

18-21 1,419 94.40 1,550 66.4 4.42 73 1.17 41.26

21-24 1,884 125.38 2,060 76.6 5.10 84 1.16 40.92

24-27 2,465 164.05 2,690 99.4 6.61 109 2.17 76.23

27-30 3,206 213.36 3,500 80.9 5.38 88 1.92 67.36

30+ 6,027 401.08 6,580 157.6 10.49 172 2.68 94.28

a lower limit of the diameter 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
67

APPENDIX 6

Temperature Index Map

Air temperature is an important climatic variable in urban areas. The air temperature 

model (Heisler et al. 2006, 2007, 2015) was used to determine the average temperature for 

four different days between June 1, 2008, and August 31, 2008. The representative days 

were as follows: 

• Windiest day (day with the highest average wind speed): June 22, 2008

• Least Windy day (day with the lowest average wind speed): July 4, 2008

• Average day (day with the average temperature closest to the summer average 

temperature): July 23, 2008 (Fig. 40)

• Warmest temperature day (day with the highest average summer daytime 

temperature): June 9, 2008

Figure 40.—Average temperature distribution (6 a.m. to 5 p.m.) for the 

average summer day, New York City, 2010.

± 0 2 4 8 Miles

Average Temperature (°F)

76.0 78.5 81.0
68

Tree cover can reduce air temperatures in urban areas. Based on modeled temperature 

estimates, 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 and a standardized air temperature 

planting index (TPI) was calculated for each of the representative days.

TPI = (n – m) / r

Where TPI is the air temperature planting index (0-1), 

n is the daily average temperature x 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 produced similar TPI index maps as the relative temperature difference 

among block groups were similar, though the actual temperatures would differ. The results 

of the block groups were averaged within each community district (weighted average 

proportional to block group area within neighborhood) and standardized a second time 

on a scale of 0 to 100 to produce community district temperature index maps. 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. 41).

Figure 41.—Temperature planting 

index (TPI) by community district for 

average summer day, New York City, 

2010. Higher index scores indicate 

higher priority areas for planting 

trees to reduce air temperature.

± 0 2 4 8 Miles

TPI

0.0 - 10.0

10.1 - 20.0

20.1 - 30.0

30.1 - 40.0

40.1 - 50.0

50.1 - 60.0

60.1 - 70.0

70.1 - 80.0

80.1 - 90.0

90.1 - 100.0
69

APPENDIX 7

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
70

APPENDIX 8

Potential Insect and Disease Impacts

Thirty-one insects and tree 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 New York City will vary by pest. The number of trees at risk reflects only 

the known host species that are likely to experience mortality (Table 20). The species host 

lists used for these insects and diseases can be found at https://www.itreetools.org/eco/

resources/iTreeEco_pest_host_list_06292016.xlsx.

Table 20.—Potential risk to trees by insect or disease, New York City, 2013

Code Scientific name Common name Trees at risk

Compensatory 

value

number $ millions

AL Phyllocnistis populiella Aspen Leafminer 32,000 13 

ALB Anoplophora glabripennis Asian Longhorned Beetle 1,476,000 1,555 

BBD Cryptococcus fagisuga Beech Bark Disease 18,000 116 

BC Sirococcus clavigignentijuglandacearumButternut Canker 0 0

CB Cryphonectria parasitica Chestnut Blight 6,000 0.7 

DA Discula destructive Dogwood Anthracnose 79,000 21 

DED Ophiostoma novo-ulmi Dutch Elm Disease 36,000 47 

DFB Dendroctonus pseudotsugae Douglas-Fir Beetle 0 0

EAB Agrilus planipennis Emerald Ash Borer 40,000 99 

FE Scotylus ventralis Fir Engraver 6,000 39 

FR Cronartium fusiforme Fusiform Rust 0 0

GSOB Agrilus auroguttatus Goldspotted Oak Borer 0 0

GM Lymantria dispar Gypsy Moth 1,367,000 2,307 

HWA Adelges tsugae Hemlock Woolly Adelgid 51,000 96 

JPB Dendroctonus jeffreyi Jeffrey Pine Beetle 0 0

LAT Choristoneura conflictana Large Aspen Tortrix 372,000 118 

LWD Raffaelea lauricola Laurel Wilt 335,000 30 

MPB Dendroctonus ponderosae Mountain Pine Beetle 51,000 124 

NSE Ips perturbatus Northern Spruce Engraver 0 0

OW Ceratocystis fagacearum Oak Wilt 733,000 1,538 

POCRD Phytophthora lateralis Port-Orford-Cedar Root Disease 0 0

PSB Tomicus piniperda Pine Shoot Beetle 80,000 138 

SB Dendroctonus rufipennis Spruce Beetle 76,000 134 

SBW Choristoneura fumiferana Spruce Budworm 0 0

SOD Phytophthora ramorum Sudden Oak Death 265,000 711 

SPB Dendroctonus frontalis Southern Pine Beetle 163,000 246 

SW Sirex noctilio Sirex Woodwasp 30,000 14 

TCD Pityophthorus juglandis & 

Geosmithia spp.

Thousand Canker Disease 0 0

WPB Dendroctonus brevicomis Western Pine Beetle 0 0

WPBR Cronartium ribicola White Pine Blister Rust 12,000 3 

WSB Choristoneura occidentalis Western Spruce Budworm 76,000 173 
71

Figure 42.—Number of trees at risk and associated compensatory value of insect/disease 

effects, New York City, 2013 tree population, 2018 pest information. 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 20.

The range of these pests was based on known occurrence and the tree-host range, 

respectively (U.S. Forest Service 2013, 2014; Worrall 2007). We used pest range maps from 

the Forest Health Technology Enterprise Team (FHTET) (U.S. Forest Service 2013, 2014; 

Worrall 2007)to determine the proximity of each pest to the five counties (i.e., Bronx, 

Kings, New York, Queens, and Richmond) that make up New York City. Pest proximity 

was classified into the following categories: within New York City, within 250 miles, 

within 750 miles, greater than 750 miles; these are illustrated in Figure 42. FHTET did not 

have pest range maps for Dutch elm disease and chestnut blight. 

Based on the host tree species for each pest and the current range of the pest, it is possible 

to determine the potential risk from insects and disease for each tree species sampled in 

New York City. In Table 21, tree species risk is designated as one of the following: 

• Red - tree species is at risk to at least one pest within city

• Orange - tree species has no pest risk within city, but has a risk to at least one 

pest within 250 miles from the city

• Yellow - tree species has no risk to pests within 250 miles of city, but has a risk to 

at least one pest that is 250 to 750 miles from the city

• Green - tree species has no risk to pests within 750 miles of city, but has a risk to 

at least one pest that is greater than 750 miles from the city

0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

ALB

GM

OW

LAT

PSB

DA

SB

HWA

EAB

DED

BBD

WPBR

CB

SPB

SW

LWD

AL

SOD

WSB

MPB

FE

Compensatory Value 

(billions of dollars)

Number of Trees (millions) Trees at risk Pest/Disease

Compensatory value

within boroughs

within 250 miles of boroughs

within 750 miles of boroughs

outside of these ranges
72

Table 21.—Potential insect and disease risk for tree species, New York City, 2013 tree population, 2018 

pest information Spp. riska Pest scoreb

Common nameALBGMOWLATDAPSBSBHWAEABDEDBBDWPBRCBSPBSWLWDALSODWSBMPBFE

14 Crack willow 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 3 6 6 6 6

14 Eastern white pine 6 6 6 6 6 1 6 6 6 6 6 1 6 2 2 6 6 6 6 6 6

14 Weeping willow 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 3 6 6 6 6

14 Willow spp 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 3 6 6 6 6

13 Norway spruce 6 6 6 6 6 1 1 6 6 6 6 6 6 2 6 6 6 6 4 4 6

12 Gray birch 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

12 Paper birch 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

12 River birch 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

10 Pine spp 6 6 6 6 6 1 6 6 6 6 6 6 6 2 2 6 6 6 6 6 6

9 Northern red oak 6 1 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 6 6

9 Pin oak 6 1 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 6 6

8 American elm 1 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6

8 Birch spp 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

8 Black birch 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

8 Black oak 6 1 6 6 6 6 6 6

8 Blue spruce 6 6 6 6 6 6 1 6 6 6 6 6 6 2 6 6 6 6 4 6 6

8 Chestnut oak 6 1 6 6 6 6 6 6 6 6 6 6 6

8 Chinese elm 1 1 6 6 6 6 6 6 6 6

8 Elm spp 1 6 6 6 6 6 6 6 6 1 6 6 6

8 Fastigiate pin oak 6 1 2 6 6 6 6

8 Green ash 1 6 6 6 6 6 6 6 2 6 6 6

8 Oak spp 6 1 2 6 6 6 6

8 Swamp white oak 6 1 2 6 6

8 Water birch 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6

8 White oak 6 1 2 6

8 Willow oak 6 1 2 6 6

7 Eastern hemlock 6 6 6 6 6 6 6 1 6 6 6 6 6 2 6 6 6 6 6 6 6

7 Spruce spp 6 6 6 6 6 6 1 6 6 6 6 6 6 2 6 6 6 6 6 6 6

1 4 Albizia spp 1 6

4 American basswood 6 1 6

4 American chestnut 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6

4 Apple spp 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

4 Beech spp 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6

4 Black ash 6 6 6 6 6 6 6 6 2 6 6 6 6 6 6 6

4 Boxelder 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

4 Callery pear 6 1 6 6 6 6 6 6 6 6 6 6 6

4 Common linden 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

Species that were sampled in New York City, but that are not listed in this matrix, are not 

known to be hosts to any of the 31 insects and diseases analyzed. Tree species at the greatest 

risk to existing pest infestations in New York City are willow species and eastern white pine.
73

Spp. riskaPest scorebCommon nameALBGMOWLATDAPSBSBHWAEABDEDBBDWPBRCBSPBSWLWDALSODWSBMPBFE

1 4 Copper beech 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6

1 4 Cornelian cherry 6 6 6 6 1 6 6 6 6 6 6

4 Crabapple 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

4 Dogwood spp 6 6 6 6 1 6 6

4 Downy hawthorn 6 1 6 6 6 6

4 Flowering dogwood 6 6 6 6 1 6 6

4 Hawthorn spp 6 1 6 6

4 Hedge maple 1 6

4 Horse chestnut 1 6

4 Japanese maple 1 6 6 6 6

4 Littleleaf linden 6 1 6 6

4 London planetree 1 6

4 Maple spp 1 6

4 Norway maple 1 6

4 Pear spp 6 1 6 6

4 Red maple 1 6 6

4 Silver linden 6 1 6

4 Silver maple 1 6

4 Staghorn sumac 6 1 6

4 Sugar maple 1 6 6 6

4 Sweetgum 6 1 6

4 Sycamore maple 1 6 6

4 Trident maple 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

4 Washington hawthorn 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

4 White ash 6 6 6 6 6 6 6 6 2 6 6 6 6 6 6 6 6 6 6 6 6

3 Hemlock spp 6 6 6 6 6 6 6 6 6 6 6 6 6 2 6 6 6 6 6 6 6

2 Sassafras 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 3 6 6 6 6 6

3 2 Spicebush 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 3 6 6 6 6 6

4 2 Grand fir 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 4

4 1 Azalea 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 6 6

4 1 Smooth azalea 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 6 6 a Species risk

Red indicates that the tree species is at risk to at least one pest within New York City.

Orange indicates that the tree species has no risk to pests within New York City, but has a risk to at least one pest within 

250 miles of the city.

Yellow indicates that the tree species has no risk to pests within 250 miles of New York City, but has a risk to at least one 

pest that is 250 to 750 miles from the city.

Green indicates that the tree species has no risk to pests within 750 miles of New York City, but has a risk to at least one 

pest that is greater than 750 miles from the city.

b Risk weight

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 New York City.

Orange indicates pest is within 250 miles of New York City.

Yellow indicates pest is within 750 miles of New York City.

Green indicates pest is outside of these ranges.
74

LITERATURE CITED

Abdollahi, K.K.; Ning, Z.H.; Appeaning, A., eds. 2000. Global climate change and the 

urban forest. Baton Rouge, LA: GCRCC and Franklin Press. 77 p.

Asner, G. P.; Scurlock, J.M.O.; Hicke, J.A. 2003. Global synthesis of leaf area 

index observations: implications for ecological and remote sensing studies.

Global Ecology and Biogeography. 12: 191–205. https://doi.org/10.1046/j.1466-

822X.2003.00026.x

Baldocchi, D. 1988. A multi-layer model for estimating sulfur dioxide deposition to 

a deciduous oak forest canopy. Atmospheric Environment. 22: 869-884. https://doi.

org/10.1016/0004-6981(88)90264-8.

Baldocchi, D.D.; Hicks, B.B.; Camara, P. 1987. A canopy stomatal resistance model for 

gaseous deposition to vegetated surfaces. Atmospheric Environment. 21: 91-101.

https://doi.org/10.1016/0004-6981(87)90274-5.

Beaulac, M.N.; Reckhow, K.H. 1982. An examination of land use-nutrient 

export relationships. Water Resources Bulletin. 18(6): 1013-1024. https://doi.

org/10.1111/j.1752-1688.1982.tb00109.x.

British Columbia Ministry of Water, Land, and Air Protection. 2005. Residential wood 

burning emissions in British Columbia. Victoria, BC: British Columbia, Ministry of 

Water, Land, and Air Protection. 45 p. https://www2.gov.bc.ca/assets/gov/environment/

air-land-water/air/reports-pub/wood_emissions.pdf (accessed December 30, 2015).

Bureau of Transportation Statistics. 2010. Estimated national average vehicle emissions 

rates per vehicle by vehicle type using gasoline and diesel. Table 4-43. Washington, 

DC: U.S. Department of Transportation, Bureau of Transportation Statistics. http://

www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_

statistics/html/table_04_43.html (accessed August 11, 2015).

Cardelino, C.A.; Chameides, W.L. 1990. Natural hydrocarbons, urbanization, and 

urban ozone. Journal of Geophysical Research. 95(D9): 13,971-13,979.https://dx.doi.

org/10.1029/JD095iD09p13971.

Charbeneau, R.J.; Barretti, M. 1998. Evaluation of methods for estimating stormwater 

pollutant loads. Water Environment Research. 70: 1295-1302.

Chow, P.; Rolfe, G.L. 1989. Carbon and hydrogen content of short-rotation biomass of 

five hardwood species. Wood and Fiber Science. 21(10): 30-36.

Council of Tree and Landscape Appraisers. 2000. Guide for plant appraisal. Ninth 

edition. Champaign, IL: International Society of Arboriculture. 143 p.
75

Design Trust for Public Space; New York City Department of Parks & Recreation. 2010. 

High performance landscape guidelines: 21st Century Parks for NYC. New York, NY: 

Design Trust for Public Space and New York City Department of Parks & Recreation. 

273 p. http://www.nycgovparks.org/sub_about/go_greener/design_guidelines.pdf

(accessed June 19, 2018).

Driver, N.E.; Mustard, M.H.; Rhinesmith, R.B.; Middelburg, R.F. 1985. U.S. Geological 

Survey urban-stormwater data base for 22 metropolitan areas throughout the 

United States. Open-File Report 85-337. Lakewood, CO: U.S. Department of Interior, 

Geological Survey. 219 p. https://pubs.usgs.gov/of/1985/0337/report.pdf (accessed 

April 10, 2018).

Energy Information Administration. 1994. Energy use and carbon emissions: nonOECD countries. OE/EIA-0579(94). Washington, DC: Department of Energy, Energy 

Information Administration. http://webapp1.dlib.indiana.edu/virtual_disk_library/

index.cgi/4265704/FID1578/pdf/internat/0587.pdf (accessed August 11, 2015).

Energy Information Administration. 2012a. Average retail price of electricity to ultimate 

customers by end-use sector. Table 5.6.A. Washington, DC: U.S. Department of 

Energy, Energy Information Administration. http://www.eia.gov/electricity/monthly/

epm_table_grapher.cfm?t=epmt_5_6_a (accessed October 15, 2014).

Energy Information Administration. 2012b. Residential sector energy price estimates, 

2012. Table E3. Washington, DC: U.S. Department of Energy, Energy Information 

Administration.

Energy Information Administration. 2013. Fuel consumption totals and averages, U.S. 

homes. Table CE2.1. Washington, DC: U.S. Department of Energy, Energy Information 

Administration. 

Energy Information Administration. 2014a. Household wood consumption. 

Table CE5.2. Washington, DC: U.S. Department of Energy, Energy Information 

Administration. https://www.eia.gov/consumption/residential/data/2009/index. 

cfm?view=consumption (accessed October 15, 2014).

Energy Information Administration. 2014b. Natural gas prices. Washington, DC: U.S. 

Department of Energy, Energy Information Administration. http://www.eia.gov/dnav/

ng/ng_pri_sum_a_EPG0_PRS_DMcf_a.htm (accessed October 15, 2014).

Energy Information Administration. 2014c. Residential heating oil weekly oil and 

propane prices (October – March). Washington, DC: U.S. Department of Energy, 

Energy Information Administration.

Federal Highway Administration. 2013. Highway statistics 2011. Table VM1. Washington, DC: U.S. Department of Transportation, Federal Highway 
76

Administration. https://www.fhwa.dot.gov/policyinformation/statistics/2011/vm1.cfm 

(accessed August 11, 2015).

Forgione, H M.; Pregitzer, C.C.; Charlop-Powers, S.; Gunther, B. 2016. Advancing urban 

ecosystem governance in New York City: Shifting towards a unified perspective for 

conservation management. Environmental Science & Policy. 62: 127-132. https://doi.

org/10.1016/j.envsci.2016.02.012.

Georgia Forestry Commission. 2009. Biomass energy. Dry Branch, GA: Georgia 

Forestry Commission. http://www.gfc.state.ga.us/utilization/forest-biomass/research/

BiomassEnergyConversions.pdf. 1 p. (accessed August 11, 2015).

Grant, R.; Heisler, G.M. 2006. Effect of cloud cover on UVB exposure under tree 

canopies: will climate change affect UVB exposure? Photochemistry and 

Photobiology. 82(2): 487–494. https://doi.org/10.1562/2005-07-07-RA-604.

Grant, R.; Heisler, G.M.; Gao, W.; Jenks, M. 2003. Ultraviolet leaf reflectance of common 

urban trees and the prediction of reflectance from leaf surface characteristics. 

Agricultural and Forest Meteorology. 120(1-4): 127–139. https://doi.org/10.1016/j.

agrformet.2003.08.025.

Grove, J.M.; O’Neil-Dunne, J.; Pelletier, K.; Nowak, D.; Walton, J. 2006. A report on New 

York City’s present and possible urban tree canopy. South Burlington, VT: U.S. 

Department of Agriculture, Forest Service, Northeastern Research Station. 25 p. http://

www.nrs.fs.fed.us/nyc/local-resources/downloads/Grove_UTC_NYC_FINAL.pdf

(accessed April 10, 2018).

Heirigs, P.L.; Delaney, S.S.; Dulla, R.G. 2004. Evaluation of MOBILE models: 

MOBILE6.1 (PM), MOBILE6.2 (Toxics), and MOBILE6/CNG. Sacramento, CA: 

National Cooperative Highway Research Program, Transportation Research Board. 123 

p. http://onlinepubs.trb.org/onlinepubs/archive/NotesDocs/25-25(7)_FR.pdf (accessed 

August 11, 2015).

Heisler, G.M.; Ellis, A.; Nowak, D.J.; Yesilonis, I. 2015. Modeling and imaging land-cover 

influences on air temperature in and near Baltimore, MD. Theoretical and Applied 

Climatology. 24: 497–515. http://dx.doi.org/10.1007/s00704-015-1416-z.

Heisler, G.; Tao, B.; Walton, J.; Grant, R.; Pouyat, R. [et al.]. 2006. Land cover influences 

on below-canopy temperatures in and near Baltimore, MD. In: Preprints of sixth 

international conference on urban climate; 2006 June 12-16; Göteborg, Sweden. 

Göteborg, Sweden: Göteborg University, Department of Geosciences: 392-395. http://

urban-climate.com/wp3/wp-content/uploads/2011/04/ICUC6_Preprints.pdf (accessed 

February 3, 2016).

Heisler, G.; Walton, J.; Yesilonis, I.; Nowak, D.; Pouyat, R. [et al.]. 2007. Empirical 

modeling and mapping of below-canopy air temperatures in Baltimore, MD 
77

and vicinity. In: Proceedings of the seventh urban environment symposium; 2007 

September 10-13; San Diego, CA. Boston, MA: American Meteorological Society. 7 p.

Heisler, G.M.; Wang, Y. 2002. Applications of a human thermal comfort model. In: 

Fourth symposium on the urban environment; 2002 May 20-24; Norfolk, VA. Boston, 

MA: American Meteorological Society: 70-71.

Hirabayashi, S. 2012. i-Tree Eco precipitation interception model descriptions. 

Washington, DC: U.S. Department of Agriculture, Forest Service and Kent, OH: Davey 

Tree Expert Co. http://www.itreetools.org/eco/resources/iTree_Eco_Precipitation_

Interception_Model_Descriptions_V1_2.pdf (accessed March 2015).

i-Tree. 2009. i-Tree eco manual. Version 4.1.0. Washington, DC: U.S. Department 

of Agriculture, Forest Service and Kent, OH: Davey Tree Expert Co., and other 

cooperators. 113 p. http://www.itreetools.org/resources/manuals/i-Tree%20Eco%20

Users%20Manual.pdf (accessed August 2013).

International Agency for Research on Cancer [IARC]. 2012. Review of human 

carcinogens: radiation. In: IARC monographs on the evaluation of carcinogenic risks 

to humans, volume 100D. Geneva, Switzerland: World Health Organization. 599 p.

Interagency Working Group on Social Cost of Carbon. 2013. Technical support 

document: technical update of the social cost of carbon for regulatory impact 

analysis under Executive Order 12866. Washington, DC: Office of the President.

https://obamawhitehouse.archives.gov/sites/default/files/omb/inforeg/scc-tsd-finaljuly-2015.pdf (accessed April 10, 2018).

Intergovernmental Panel on Climate Change. 2014. Climate change 2014 synthesis 

report. Geneva, Switzerland: World Meteorological Organization and United Nations 

Environment Programme. 138 p. http://www.ipcc.ch/pdf/assessment-report/ar5/syr/

SYR_AR5_FINAL_full_wcover.pdf (accessed May 8, 2018).

Laćan, I.; McBride, J.R. 2008. Pest vulnerability matrix (PVM): A graphic model for 

assessing the interaction between tree species diversity and susceptibility to insects 

and diseases. Urban Forestry & Urban Greening 7: 291-300. https://doi.org/10.1016/j.

ufug.2008.06.002.

Leonardo Academy. 2011. Leonardo Academy’s guide to calculating emissions 

including emission factors and energy prices. Madison, WI: Leonardo Academy Inc.

Martens, W.J.M. 1998. Climate change, thermal stress and mortality changes. Social 

Science and Medicine. 46(3): 331-344. https://doi.org/10.1016/S0277-9536(97)00162-7.

McPherson, E.G.; Simpson, J.R. 1999. Carbon dioxide reduction through urban 

forestry: guidelines for professional and volunteer tree planters. Gen. Tech. 
78

Rep. PSW-171. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 237 p. https://doi.org/10.2737/PSW-GTR-171.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Xiao, Q. 2007. 

Northeast community tree guide: benefits, costs, and strategic planning. Gen. Tech. 

Rep. PSW-GTR-202. Albany, CA: U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station. 106 p. https://doi.org/10.2737/PSW-GTR-202.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, S.E.; Xiao, Q. 2005. Municipal forest 

benefits and costs in five US cities. Journal of Forestry. 103: 411-416.

Na, H.R.; Heisler, G.M.; Nowak, D.J.; Grant, R.H. 2014. Modeling of urban trees’ effects 

on reducing human exposure to UV radiation in Seoul, Korea. Urban Forestry & 

Urban Greening 13: 785–792. https://doi.org/10.1016/j.ufug.2014.05.009.

National Agriculture Library. 2011. Plants. Beltsville, MD: U.S. Department of 

Agriculture, National Agriculture Library, Invasive Species Information Center. http://

www.invasivespeciesinfo.gov/plants/main.shtml (accessed July 2011).

National Toxicology Program. 2011. Report on carcinogens, 12th ed. Research Triangle 

Park, NC: U.S. Department of Health and Human Services, Public Health Service. 499 p.

New York City Department of City Planning. 2015. New York City community districts. 

New York, NY: City of New York, Department of City Planning. http://www1.nyc.gov/

site/planning/data-maps/open-data/districts-download-metadata.page (accessed May 

8, 2018).

New York City Department of Parks and Recreation. 2014. NYC Parks: Framework 

for an equitable future. New York, NY: New York City Department of Parks and 

Recreation. 23 p. http://www.nycgovparks.org/downloads/nyc-parks-framework.pdf

(accessed April 10, 2018).

New York City Department of Parks and Recreation. 2016. 2015 street tree census report.

New York, NY: City of New York, NYC Parks. http://media.nycgovparks.org/images/

web/TreesCount/Index.html (accessed April 10, 2018).

New York State Department of Environmental Conservation. 2011. Interim invasive 

species plant list. Albany, NY: New York Department of Environmental Conservation. 

https://www.nrcs.usda.gov/wps/portal/nrcs/main/ny/technical/ecoscience/invasive/ 

(accessed October 14, 2011).

Nowak, D.J. 1994. Atmospheric carbon dioxide reduction by Chicago’s urban forest. In: 

McPherson, E.G.; Nowak, D.J.; Rowntree, R.A., eds. Chicago’s urban forest ecosystem: 

results of the Chicago Urban Forest Climate Project. Gen. Tech. Rep. NE-186. Radnor, 

PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment 

Station: 83-94.
79

Nowak, D.J. 2010. Urban biodiversity and climate change. In: Muller, N.; Werner, P.; 

Kelcey, J.G., eds. Urban biodiversity and design. Hoboken, NJ: Wiley-Blackwell: 101-

117. https://doi.org/10.1002/9781444318654.ch5.

Nowak, D.J.; Appleton, N.; Ellis, A.; Greenfield, E. 2017. Residential building energy 

conservation and avoided power plant emissions by urban and community trees 

in the United States. Urban Forestry & Urban Greening. 21: 158–165. https://doi.

org/10.1016/j.ufug.2016.12.004.

Nowak, D.J.; Civerolo, K.L.; Rao, S.T.; Sistla, S.; Luley, C.J.; Crane, D.E. 2000. A modeling 

study of the impact of urban trees on ozone. Atmospheric Environment. 34:1601-

1613. https://doi.org/10.1016/s1352-2310(99)00394-5.

Nowak, D.J.; Crane, D.E. 2000. The Urban Forest Effects (UFORE) model: quantifying 

urban forest structure and functions. In: Hansen, M.; Burk, T., eds. Integrated 

tools for natural resources inventories in the 21st century. Proceedings of IUFRO 

conference; 1998 August 16-20; Boise, ID. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. 

Department of Agriculture, Forest Service, North Central Research Station: 714-720.

Nowak, D.J.; Crane, D.E. 2002. Carbon storage and sequestration by urban trees in 

the USA. Environmental Pollution. 116(3): 381-389. https://doi.org/10.1016/S0269-

7491(01)00214-7.

Nowak, D.J.; Crane, D.E.; Dwyer, J.F. 2002a. Compensatory value of urban trees in the 

United States. Journal of Arboriculture. 28(4): 194-199.

Nowak, D.J.; Crane, D.E.; Stevens, J.C.; Ibarra, M. 2002b. Brooklyn’s urban forest. Gen. 

Tech. Rep. NE-290. Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northeastern Research Station. 107 p. https://doi.org/10.2737/ne-gtr-290.

Nowak, D.J.; Greenfield, E.G. 2012. Tree and impervious cover change in U.S. 

cities. Urban Forestry & Urban Greening. 11: 21-30. https://doi.org/10.1016/j.

ufug.2011.11.005.

Nowak, D.J.; Hirabayshi, S.; Bodine, A.; Hoehn, R. 2013. Modeled PM2.5 removal by 

trees in ten U.S. cities and associated health effects. Environmental Pollution. 178: 

395-402. https://doi.org/10.1016/j.envpol.2013.03.050.

Nowak, D.J.; Hirabayashi, S.; Ellis, E.; Greenfield, E.J. 2014. Tree and forest effects on air 

quality and human health in the United States. Environmental Pollution. 193:119-

129. https://doi.org/10.1016/j.envpol.2014.05.028.

Nowak, D.J.; Hoehn, R.E.; Bodine, A.R.; Greenfield, E.J.; O’Neil-Dunne, J. 2016. Urban 

forest structure, ecosystem services and change in Syracuse, NY. Urban Ecosystems. 

https://doi.org/10.1007/s11252-013-0326-z.
80

Nowak, D.J.; Hoehn, R.E., III; Crane, D.E.; Stevens, J.C.; Walton, J.T. 2007. Assessing 

urban forest effects and values: New York City’s urban forest. Resour. Bull. NRS9. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 22 p. https://doi.org/10.2737/NRS-RB-9.

Nowak, D.J.; Hoehn, R.E.; Crane, D.E.; Stevens, J.C.; Walton, J.T; Bond, J. 2008. A 

ground-based method of assessing urban forest structure and ecosystem services. 

Arboriculture and Urban Forestry. 34(6): 347-358.

Nowak, D.J.; Kuroda, M.; Crane, D.E. 2004. Tree mortality rates and tree population 

projections in Baltimore, Maryland, USA. Urban Forestry & Urban Greening 2: 139-

147. https://doi.org/10.1078/1618-8667-00030.

Nowak, D.J.; Stevens, J.C.; Sisinni, S.M.; Luley, C.J. 2002c. Effects of urban tree 

management and species selection on atmospheric carbon dioxide. Journal of 

Arboriculture. 28(3): 113-122.

O’Neil-Dunne, J. 2012. A report on the City of New York’s existing and possible tree 

canopy. Burlington, VT: University of Vermont, Spatial Analysis Laboratory. http://

www.nrs.fs.fed.us/urban/utc/local-resources/downloads/TreeCanopy_Report_

NYC2010.pdf (accessed July 2015).

O’Neil-Dunne, J.P.M.; MacFaden, S.W.; Forgione, H.M.; Lu, J.W.T. 2014. Urban 

ecological land-cover mapping for New York City. Final report to the Natural Areas 

Conservancy. Pleasanton, CA: Spatial Informatics Group; Burlington, VT: University 

of Vermont [et al.]. 21 p. http://naturalareasnyc.org/content/3-in-print/2-research/

urbanecologicalmap_newyorkcity_report_2014.pdf (accessed April 12, 2018).

Peper, P.J.; McPherson, E.G.; Simpson, J.R.; Gardner, S.L.; Vargas, K.E.; Xiao, Q. 2007. New 

York City, New York: municipal forest resource analysis. Davis, CA: U.S. Department 

of Agriculture, Forest Service, Pacific Southwest Research Station. 72 p.

Pope, C.A.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D. [et al.]. 2002. Lung cancer, 

cardiopulmonary mortality, and long-term exposure to fine particulate air 

pollution. Journal of the American Medical Association. 287(9): 1132-1141. https://

doi.org/10.1001/jama.287.9.1132.

Sansalone, J.J.; Buchberger, S.G. 1997. Partitioning and first flush of metals in urban 

roadway storm water. Journal of Environmental Engineering. 123: 134–143. https://

doi.org/10.1061/(asce)0733-9372(1997)123:2(134).

Santamour, F.S. 1990. Trees for urban planting: diversity, uniformity, and common 

sense. In: Proceedings of the seventh conference of the Metropolitan Tree 

Improvement Alliance (METRIA 7); 1990 June 11-12; Lisle, IL. Mills River, NC: 

Mountain Horticultural Crops Research and Extension Center: 57-65. https://pdfs.
81

semanticscholar.org/26a2/4c5361ce6d6e618a9fa307c4a34a3169e309.pdf (accessed May 

8, 2018).

Smullen, J.T.; Shallcross, A.L.; Cave, K.A. 1999. Updating the U.S. nationwide urban 

runoff quality database. Water Science Technology. 39(12): 9-16. https://doi.

org/10.1016/s0273-1223(99)00312-1.

Sutherland, R.C. 2000. Methods for estimating the effective impervious area of urban 

watersheds. In: The practice of watershed protection. Elliott City, MD: Center for 

Watershed Protection. Article 32: 193-195.

Taha, H. 1996. Modeling impacts of increased urban vegetation on ozone air quality in 

the South Coast air basin. Atmospheric Environment. 30(20): 3423-3430. https://doi.

org/10.1016/1352-2310(96)00035-0.

U.S. Environmental Protection Agency. 1983. Results of the nationwide urban runoff 

program: volume I – final report. PB84-185552. Washington, DC: U.S. Environmental 

Protection Agency. 198 p. https://www3.epa.gov/npdes/pubs/sw_nurp_vol_1_

finalreport.pdf (accessed April 12, 2018).

U.S. Environmental Protection Agency. 2002. Urban stormwater BMP performance 

monitoring, a guidance manual for meeting the national stormwater BMP database 

requirements. EPA-821-B-02-001. Washington, DC: U.S. Environmental Protection 

Agency. 236 p. https://www3.epa.gov/npdes/pubs/montcomplete.pdf (accessed April 

12, 2018).

U.S. Environmental Protection Agency. 2010. Light-duty vehicle greenhouse gas 

emission standards and corporate average fuel economy standards. EPA-420-R-10-

012a. Washington, DC: U.S. Environmental Protection Agency. 841 p.

U.S. Environmental Protection Agency. 2011. National emissions inventory. https://www.

epa.gov/air-emissions-inventories/national-emissions-inventory-nei (accessed May 8, 

2018).

U.S. Environmental Protection Agency. 2012. Environmental benefits mapping and 

analysis program (BenMAP), version 4.0. Washington, DC: U.S. Environmental 

Protection Agency. https://www.epa.gov/benmap (accessed May 8, 2018).

U.S. Environmental Protection Agency. 2015a. The social cost of carbon. Washington, 

DC: U.S. Environmental Protection Agency. https://19january2017snapshot.epa.gov/

climatechange/social-cost-carbon_.html (accessed April 12, 2018).

U.S. Environmental Protection Agency. 2015b. Downscaler model for predicting daily 

air pollution. Washington, DC: U.S. Environmental Protection Agency. http://www.

epa.gov/air-research/fused-air-quality-surfaces-using-downscaling-tool-predictingdaily-air-pollution (accessed February 3, 2016).
82

USDA Forest Service. 2013. Dutch elm disease. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Forest Health Protection. http://www.fs.fed.us/nrs/tools/

afpe/maps/pdf/DED.pdf (accessed December 17, 2014).

USDA Forest Service. 2014. 2012 National insect & disease risk maps. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Forest Health Technology Team. http://

www.fs.fed.us/foresthealth/technology/nidrm2012.shtml

USDA Forest Service. 2016. Urban Forest Inventory and Analysis (FIA). http://www.

fs.fed.us/research/urban/fia.php. (accessed December 15, 2016).

Wang, J.; Endreny, T.A.; Nowak, D.J. 2008. Mechanistic simulation of urban tree effects 

in an urban water balance model. Journal of American Water Resource Association. 

44(1): 75-85. https://doi.org/10.1111/j.1752-1688.2007.00139.x.

World Bank. 2010. CO2 emissions (metric tons per capita). Washington, DC: The 

World Bank, Carbon Dioxide Information Analysis Center. http://data.worldbank.org/

indicator/EN.ATM.CO2E.PC (accessed August 14, 2015).

Worrall, J.J. 2007. Chestnut blight. Forest and shade tree pathology. http://www.

forestpathology.org/dis_chestnut.html (accessed August 11, 2015).
Nowak, David J.; Bodine, Allison R.; Hoehn, Robert E., III; Ellis, Alexis; Hirabayashi, Satoshi; 

Coville, Robert; Auyeung, D.S. Novem; Sonti, Nancy Falxa; Hallett, Richard A.; Johnson, 

Michelle L.; Stephan, Emily; Taggart, Tom; Endreny, Ted. 2018. The urban forest of 

New York City. Resource Bulletin NRS-117. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 82 p. https://doi.org/10.2737/NRSRB-117.

An analysis of the urban forest in New York, New York, reveals that this city has an estimated 

7.0 million trees (encompassing all woody plants greater than one-inch diameter at breast 

height [d.b.h.]) with tree canopy that covers 21 percent of the city. The most common tree 

species across public and private land are Norway maple, northern white-cedar, tree-ofheaven, sassafras, and white oak, but the most dominant species in terms of leaf area are 

Norway maple, London planetree, black locust, pin oak, and red maple. Trees in New York 

City currently store about 1.2 million tons of carbon (4.2 million tons carbon dioxide [CO2]) 

valued at $153 million. In addition, these trees remove about 51,000 tons of carbon per year 

(186,000 tons CO2/year) ($6.8 million per year) and about 1,100 tons of air pollution per year 

($78 million per year). New York City’s urban forest is estimated to reduce annual residential 

energy costs by $17.1 million per year and reduce runoff by 69 million cubic feet/year ($4.6 

million/year). The compensatory value of the trees is estimated at $5.7 billion. 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 New York 

City. The analyses also provide a basis for monitoring changes in the urban forest over time.

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