Carbon Dynamics in Suburban Philadelphia
by
Sweetie Bharat Patel
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTER OF SCIENCE IN BIOLOGY
in the Department of Biological and Allied Health Sciences

May 2021
Bloomsburg University of Pennsylvania

Sweetie Bharat Patel, 2021

i

ABSTRACT
Urban areas are one of the greatest contributors of air pollution. Many efforts are being
made to mitigate air pollution and greenhouse gas emissions to, in turn, mitigate the
damaging effects of climate change. One such effort is to understand, leverage, and
manage ecosystems for the removal of atmospheric carbon dioxide. Carbon sequestration,
or removal of carbon dioxide, by urban forests are globally researched. In the context of
carbon sequestration, however, forests that are found beyond urban settings or contiguous
preserved lands are not as popularly studied. This study assessed the environmental
impacts of Greater Philadelphia’s suburban trees on atmospheric carbon dioxide using a
top-down approach. This research set out to determine the difference in carbon
sequestration and storage of an urban forest and suburban forest. Cover class between the
study sites were significantly different. Annual carbon sequestration by trees was higher
in the suburban site (including Bucks, Delaware, and Montgomery counties) than in the
urban site (Philadelphia county). Expected values (in USD) were higher in the suburban
site than in the urban site. Total tree carbon storage estimates were higher in the suburban
site than in the urban site. While i-Tree Canopy is a useful tool to make estimations of
carbon sequestration, an i-Tree Eco analysis of the same area may produce more accurate
and detailed results, such as revealing trends in tree species and particular areas that
contribute more to carbon sequestration and storage. Due to possible limitations
discussed in conclusion, future research of this nature should continue not only in
different regions but also in further detail using i-Tree Eco, another software within the iTree Software Suite. i-Tree Eco leverages a bottom-up approach to assessing urban
forests in which field data such as diameter at breast height, tree species, percent plot
cover, and other characteristics are to generate detailed reports on the study area of one’s
choice. Assessing suburban forests and their role of carbon sequestration in the context of
climate change provides information that can be used in forest management. Implications
of this study support protection of forests, reforestation, and suburban and urban
greening.
keywords: urban forestry, suburban forestry, carbon storage, land cover

ii

Acknowledgements
I would like to thank my father for always encouraging me to ask questions, make
observations, and evaluate my surroundings since the time I began to learn to now. I
would like to thank my mother for being my utmost confidante. Her listening ear
provided me the outlet that I needed to keep myself from becoming distracted. I would
like to thank my siblings, cousins, and friends for also being my listeners; their cheering
and morale have kept me accountable and focused. And to my partner in his
encouragement of my research, in times that I was close to giving up, he gave me
unadulterated strength to continue.
This research would not have been possible without the unwavering support, advice, and
critique of my Thesis Committee, including Dr. Kevin Williams, Dr. Thomas Klinger,
and Dr. Steven Rier, the 3+2 Biology Graduate Program through which I learned the
necessary skills and was granted the opportunity to conduct my own research, and
Bloomsburg University’s constant support of student research.

iv

TABLE of CONTENTS
Chapter I – Introduction
Introduction
Understanding the i-Tree Software Suite
Research Goals and Objectives
Research Framework
Research Methods
Thesis Organization
Chapter II – Literature Review
History of Atmospheric Carbon Dioxide
Carbon Sequestration
Carbon Sequestration in Forests
Chapter III – Methodology
Case Study: Suburban Philadelphia
i-Tree County
i-Tree Canopy
Data Collection
Chapter IV – Results
i-Tree County Assessment
i-Tree Canopy Assessment
Chapter V – Conclusion and Discussion
Suburban Forest Potential Contribution to Climate Change Mitigation
Research Limitations and Future Studies
Research Implications
Literature Cited
Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5
Appendix 6

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38

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List of Tables
Table 1. Summary of features of four types of urban forest analyses (USDA Forest
Service 2019)

3

Table 2. A breakdown of the Urban Forest Effects (UFORE) Model and module
requirements (Nowak and Crane 2000)

6

Table 3. i-Tree Software Suite tools and their descriptions (USDA Forest Service 2020,
Nowak and Crane 2002)
7
Table 4. Number of points taken respective to proportion of sub-sample area within the
site area
15
Table 5. Percent tree/shrub cover in Southeastern Pennsylvania counties, respective
stored carbon in forest stands, and valuation

18

Table 6. Percent tree/shrub cover in Southeastern Pennsylvania counties, respective
carbon sequestered by forest stands, and valuation

19

Table 7. Proportions test results comparing % tree cover of urban and suburban sites

19

Table 8. Cover class distribution and land area of each cover class in Philadelphia
County.

29

Table 9. Tree Benefit Estimates: Carbon (English units)

30

Table 10. Tree Benefit Estimates: Air Pollution (English units)

30

Table 11. Tree Benefit Estimates: Hydrological (English units)

31

Table 12. Cover class distribution and land area of each cover class in Bucks County 32
Table 13. Tree Benefit Estimates: Carbon (English units)

33

Table 14. Tree Benefit Estimates: Air Pollution (English units)

33

Table 15. Tree Benefit Estimates: Hydrological (English units)

33

Table 16. Cover class distribution and land area of each cover class in Delaware County
35
Table 17. Tree Benefit Estimates: Carbon (English units)

36

Table 18. Tree Benefit Estimates: Air Pollution (English units)

36

Table 19. Tree Benefit Estimates: Hydrological (English units)

36

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Table 20. Cover class distribution and land area of each cover class in Montgomery
County
Table 21. Tree Benefit Estimates: Carbon (English units)

38
39

Table 22. Tree Benefit Estimates: Air Pollution (English units)

30

Table 23. Tree Benefit Estimates: Hydrological (English units)

40

Table 24. Tree variables required for use of i-Tree Eco tool

41

Table 25. Proportion of cover class of Philadelphia County, PA (G = grass/herbaceous,
IB = impervious building, IO = impervious other, IR = impervious road, S = sand/bare
ground, T = tree/shrub, W = water)
42
Table 26. Proportion of cover class of Bucks County, PA (G = grass/herbaceous, IB =
impervious building, IO = impervious other, IR = impervious road, S = sand/bare ground,
T = tree/shrub, W = water)
42
Table 27. Proportion of cover class of Delaware County, PA (G = grass/herbaceous, IB =
impervious building, IO = impervious other, IR = impervious road, S = sand/bare ground,
T = tree/shrub, W = water)
42
Table 28. Proportion of cover class of Montgomery County, PA (G = grass/herbaceous,
IB = impervious building, IO = impervious other, IR = impervious road, S = sand/bare
ground, T = tree/shrub, W = water)
43
List of Figures
Figure 1. Global atmospheric carbon dioxide concentrations (CO2) in parts per million
(ppm) for the past 800,000 years. The peaks and valleys track ice ages (low CO2) and
warmer interglacial (higher CO2) periods. During these cycles, CO2was never higher than
300 ppm. On the geologic time scale, the increase (orange dashed line) looks virtually
instantaneous. Graph by NOAA Climate.gov based on data from Lüthi, et al., 2008, via
NOAA NCEI Paleoclimatology Program (Lindsey 2020)
11
Figure 2. 2011 forest cover map, based on MODIS satellite data at 500-m resolution and
on IGBP-DIS (The International Geosphere-Biosphere Programme Data and Information
System) land-cover classification (Pan et al. 2013
12
Figure 3. A map of the Greater Philadelphia area, Pennsylvania. Note Philadelphia
County, Delaware County, Montgomery County, and Bucks County. Chester County was
not evaluated in this study (Mennis et al. 2013)
14
Figure 4. Total carbon stored depicted through shading by i-Tree County for four
Southeastern Pennsylvania counties, including Bucks, Delaware, Montgomery, and
Philadelphia Counties

17

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Figure 5. Carbon dioxide equivalent stored depicted through shading by i-Tree County
for four Southeastern Pennsylvania counties, including Bucks, Delaware, Montgomery,
and Philadelphia Counties
17
Figure 6. Total carbon sequestered depicted through shading by i-Tree County for four
Southeastern Pennsylvania counties, including Bucks, Delaware, Montgomery, and
Philadelphia Counties
18
Figure 7. Carbon dioxide equivalent sequestered depicted through shading by i-Tree
County for four Southeastern Pennsylvania counties, including Bucks, Delaware,
Montgomery, and Philadelphia Counties

18

Figure 8. i-Tree County cohort legend to determine estimated measurements of each
listed characteristic: a) carbon storage measured in tons. Carbon storage refers to the
amount of carbon currently contained within a plant’s woody tissue (above and below
ground, including the amount of carbon within leaves for evergreen species, b) carbon
dioxide equivalent storage, i.e. carbon dioxide storage measured in tons, c) annual carbon
sequestration. Carbon sequestration refers to the amount of atmospheric carbon removed
by trees annually. d) carbon dioxide equivalent sequestration, i.e. carbon dioxide
sequestration measured in tons.
19
Figure 9. Distribution of points and their associated cover class in Philadelphia County 29
Figure 10. Distribution of points and their associated cover class in Bucks County

32

Figure 11. Distribution of points and their associated cover class in Delaware County
Figure 12. Distribution of points and their associated cover class in Montgomery County
Figure 13. Distribution of points and their associated cover class in Bucks County
List of Appendices
Appendix 1. Raw Data Tables of Four Southeastern Pennsylvania Counties ................. 26
Appendix 2. Philadelphia County Cover Assessment and Tree Benefits Report ............ 26
Appendix 3. Bucks County Cover Assessment and Tree Benefits Report ...................... 29
Appendix 4. Delaware County Cover Assessment and Tree Benefits Report ................. 32
Appendix 5. Montgomery County Cover Assessment and Tree Benefits Report ........... 35
Appendix 6. Core Tree Variables used in i-Tree Eco (Nowak 2020) .............................. 38
Appendix 7. Cover class data of each county studied ..................................................... 39

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INTRODUCTION
Forest management applications depend on how forests are defined. Forests are classified
either by tree cover and land use, or solely tree cover (FAO 2020a) which includes land
with less tree cover. The United States Forest Service defines forestland as land that is at
least 10 percent covered by forest trees of any size (Chiras and Reganold 2013). “Other
land with tree cover” is land that spans more than 0.5 ha with a canopy of more than 10
percent comprising trees able to reach a height of 5 m at maturity (FAO 2020b). The
world has at least 162 million hectares of land with tree cover that is not classified as
forest (FAO 2020a) using the aforementioned definition. Furthermore, while trees and
shrubs are defined by their height at maturity and crown distinction (FAO 2020b), this
study does not differentiate between tree and shrub cover as they are both woody plants.
Further explanation is provided in Methodology. The lack of information on other land
with tree cover creates a gap in the information available on forests and forest
management. Misclassification creates a discrepancy between research and management
when a pool of ecosystems that can provide valuable insight into structure and function
are overlooked. This research will be referring to all land with tree cover as forestland.
Forest integrity is essential to the health of the planet due to its role in climate change.
Each ecosystem plays a balanced role in recurrent succession (National Geographic
2011). Forests contribute to 80% of the world’s primary productivity (Lorenz and Lal
2010) by cycling sunlight, which is the ultimate source of energy, into the biosphere.
Integrity of forestland has been damaged and lost due to human activities. Changes in
land use for agriculture and development resulting in deforestation (Ritchie 2020),
fragmentation, and more devastating effects at lower levels within ecosystems have
decimated the overall quality of the world’s forests. In response, their ability to provide
ecosystem services is also negatively affected. Forestland integrity, however, can be
restored and managed appropriately for ecosystems to recover from the damage humans
have caused (Steenberg et al. 2019).
As of 2020, forests cover about 31% of the earth’s terrestrial surface and stores 45% of
land’s carbon in biomass (FAO 2020a). Temperate forests are the most common biome in
eastern North America, Western Europe, Eastern Asia, Chile, and New Zealand, which is
25% of the land’s forest cover (Yale 2019). Forest distribution varies depending on the
scale through which it is viewed. These forest ecosystems provide valuable services,
namely that they have above- and below-ground carbon sequestration and storage which
is critical as greenhouse gas emissions increase.
Atmospheric carbon dioxide has risen to 416.4 ppm in February 2021 (NOAA 2021)
from 277 ppm in 1750 (Joos and Spahni 2008). Elevated atmospheric carbon dioxide has
various effects on the earth and have been evaluated at local, regional, and global levels.
Climate change has resulted in temperature and precipitation changes that have shown to
drastically change the biosphere. Habitat and species loss, changes in phenological
relationships, changes in ecosystem structure and function along with many other effects
have occurred in response to climate change. This rise in carbon concentration
necessitates mitigation efforts, whether they be through utilizing biotechnology or
ecosystem management.

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From 1970 to 2010, there had been a global expansion rate of urban areas of 20%. More
than 95% of the net increase in global population will have occurred in cities (Grimm et
al. 2008). Cities themselves represent microcosms (Grimm et al. 2008), a place
encapsulating in miniature the characteristic qualities of the larger urban environment
distributed globally. This increase in urban area implies the increase in populations and
migration to cities. However, only around 1% of global land (Ritchie and Roser 2013;
Ritchie & Roser 2018) is defined as built-up area. Human infrastructure in urban settings
grows vertically, but built-up areas that are non-urban include horizontal development in
towns and villages, which in this context are considered suburban areas.
Urban ecology combines theory and methodology of social and natural sciences to
understand the processes within urban ecosystems (Grimm et al. 2008). Urban forestry is
a relatively new age discipline. Studies capturing how much carbon is stored and
sequestered by street trees in New Jersey, New York, and California (Fleming 1988,
Nowak 1993, Nowak 1994, Nowak et al. 2002) were conducted in the 1980s to
understand the nature of these trees in comparison to those in natural forest stands.
Researchers found that these urban trees were slightly different in composition than trees
in forest stands (Nowak 1993). Urban forests provide benefits such as shading and
cooling of streets and building which minimize energy costs, mitigation pollution of
various kinds, and enhancing human health. Urban forests and their cover, i.e. urban tree
cover, have to be managed in order to maintain these benefits. Urban tree cover is the
area on the ground that is covered by tree foliage in a given area and is properly viewed
from above. Though it has been newly studied, urban forestry and planning is thoroughly
researched, developed, and implemented globally (Augustin 2011; Sumangala 2013). It
has been used not only as an environmental measure to ensure human impact can be
reversed, but also has been leveraged in the context of politics and legislation via
environmental regulation to change human behaviors that may be detrimental to the
health of the planet (FAO 2020a).
Technology and resource assessments developed for urban forestry use has been in the
works for decades (Rowntree & Nowak 1991, Nowak 1993, Nowak et al. 1996, Nowak
and Crane 2000). The methodology started off as a guideline and potential model to
assessing urban forests, to which results of city-wide forest assessments were compared.
As these city-wide assessments continued, the legitimacy of the model bolstered. The
database and strength of the model continue to grow. The model was then peer reviewed
by the USDA Forest Service, Davey Tree Expert Company, the Arbor Day Foundation,
Society of Municipal Arborists, International Society of Arboriculture, Casey Trees, and
SUNY College of Environmental Science and Forestry, and is now used at the regional
level nationally and internationally (Maco 2019). The model employs both bottom-up and
top-down approaches to understanding mechanisms and benefits of urban forestry, and
both can be used simultaneously. Bottom-up resource assessments are field-based and use
measurements of a forest’s physical structure. This approach is typically used in resource
management or forest health advocacy. The top-down approach to assess forest resources
consists of assessing canopy cover using aerial or satellite images. Those images are used
to determine the amount and distribution of tree cover, potential planting space, and
miscellaneous cover types (USDA Forest Service 2019). Furthermore, while aerial

2

imagery has been used to evaluate tree cover, advances in remote sensing have enhanced
magnification ability, resolution, accuracy, time, and costs of various projects. Each
approach provides different types of information that may be used to determine many
forest benefits (Table 1, USDA Forest Service 2019). This research found the most
appropriate tactic to be the top-down method of assessing (sub)urban forests.
Table 1. Simplified summary of features of four types of urban forest analyses (USDA
Forest Service 2019)
Urban Forest Attribute
i-Tree Eco i-Tree
i-Tree Canopy Cover Map
Landscape
(UTC)
Cover/Management Considerations
Amount or percent tree cover
√
√
√
√
Specific locations and
√
√
distribution of tree cover
Amount or percent potential
√
√
√
√
planting space
Specific locations and
√
√
distribution of planting space
Maps of tree cover and
√
√
√
plantable space
Human population
√
distribution and demographics
Human health risks
√
Forest risks
√
Future climates
√
Planting and tree preservation
√
prioritization
Urban Forest Composition and Management
Total number of trees / tree
√
density
Species composition
√
Diameter / size distribution
√
Species diversity
√
Species importance values
√
Leaf area and biomass
√
Tree health
√
Native vs. exotic composition
√
Invasive trees
√
Risk to insects and diseases
√
√
Ground cover attributes
√
√
√
√
Ecosystem services and values
Air pollution removal /
√
√
√
√
human health
Carbon storage and annual
√
√
√
√
sequestration

3

Effects on building energy
use
Rainfall interception, avoided
runoff
Ultraviolet radiation (UV)
reduction
Structural value
Mapping of ecosystem
services
Monitoring
Change in tree cover
Locations of tree cover
change
Change in species
composition, services and
values

√
√

√

√
√

√

√

√

√

√
√

√

A significant body of literature emphasizes the importance and persistence of urban
forests in removing air pollution, regulating climate change, and reducing energy-related
emissions as well (Rowntree and Nowak 1991; Nowak 1993; Nowak 1994; Nowak and
Crane 2000; Nowak and Crane 2002; Nowak et al. 2002; Nowak et al. 2007; Bonan
2008; Jim and Chen 2008; Lorenz and Lal 2010; Augustin 2011; Pan et al. 2011; Nowak
et al. 2013; Hou et al. 2019). While urban environments are point sources of air pollution,
suburban environments are not typically considered in conversation despite being in such
close proximity.
As urbanization settles to a constant, suburbanization increases due to cultural movement.
It was found that an unintended consequence of the rise of downtown consumer cities,
like Philadelphia, is a lower carbon metropolitan area (Holian and Kahn 2014) due to
increased foot traffic and use of public transportation over individual vehicles. Due to the
extensive research conducted on urban environments, it is publicly known that a change
in vegetation management is necessary in urban environments. A lack of information on
fragmented forestland surrounding cities opens a level of uncertainty that may be brushed
over when discussing serious best practices for management of these lands.
Furthermore, suburban environments cover more land area than urban environments in
the United States. Given that there is a larger area of land and equivalent or less people
than in cities, more land is less developed. Suburban forests are important to include in
regional forest resource assessments as they can provide equivalent benefits to their
surroundings. Studying surrounding suburban areas as microcosms might reveal a link
between peripheral pollution that is sourced from the city itself, whether it be from
automotive carbon dioxide emissions to industrial emissions, to possible climate change
mitigation.

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Urban forestry is a booming discipline and is avidly used in land management. However,
suburban forests are currently under-evaluated. This could be due to the recent urgency to
model urban environments to be “greener”, since they are point sources of pollution. It
cannot be ignored that suburban environments may also be sources of greenhouse gas
emissions, however spread out over the land that the emissions may be. It may also be
because urban forests have not only decrease air and water pollution as mandated by
legislation, but also improve human physical and mental health which is essential in areas
of high population densities. While cities across the United States are thoroughly
assessed and documented, studies of forest ecosystem services at the regional level are
not complete. Suburban forests are underreported unless they are included within a
protected area or are considered “residential” but still a part of the urban environment
(Zirkle 2012). Furthermore, private landowners may not commit to afforestation unless
the program has a positive economic consequence (Hou 2019). This study sought to
contribute information to the literature on carbon sequestration by forests in a suburban
environment with the use of modeling software.
Understanding the i-Tree Software Suite
The beginnings of developing a software that quantifies the role of urban forests in
removing atmospheric carbon dioxide started in 1991 (Rowntree and Nowak 1991). They
aimed to gain an understanding of forest structure and function, specifically of how much
carbon is sequestered and stored by urban forests. Urban land spanned 69 million acres
20 years ago. Carbon sequestration and storage was measured by estimating the average
number of trees per acre for an area with 28% tree cover, estimating the relationship
between crown cover and stored carbon, and calculating total fresh-weight biomass per
acre. Fresh-weight biomass is in the form of above-ground biomass, including all
biomass of living woody vegetation above the soil including stems, stumps, branches,
bark, seeds, and foliage, and below-ground biomass which is all biomass of live roots
(FAO 2020b).
As outlined in Rowntree and Nowak (1991), fresh-weight biomass was calculated using
equations found in Wenger (1984). Dry weight was then calculated as a percentage of
fresh weight depending on the tree species. Dry weight was used to finally calculate
carbon storage estimates of each tree. Annual sequestration by urban forests required an
estimation of annual growth, mortality, and leaf loss of individual trees, as net carbon
sequestration and change in carbon could be achieved by looking at growth minus carbon
release due to mortality and leaf loss. Urban tree growth was determined using age and
diameter at breast height (dbh) relations described in Fleming (1988) in which growth
estimates of street trees in New Jersey were calculated. Annual growth rates were then
taken to determine the amount of biomass accrued. Annual mortality rates were derived
from a study that estimated street tree mortality rate in Syracuse, NY (Nowak 1986).
Average crown area for hardwoods and conifers were converted by a formula from Winer
et al. (1983) to average dry-weight leaf biomass. Leaf drop was also calculated. The net
amount of carbon sequestered by urban forests was calculated by lastly subtracting the
amount of carbon lost due to mortality and leaf drop from the amount sequestered due to
growth. It is imperative to note that every estimate made in this process was conservative.
Rowntree and Nowak (1991) state that crown width estimates were taken from street tree

5

populations, most urban trees will be smaller, which necessitates a greater number of
urban trees to be used to make estimations. Furthermore, understory trees were not
included, and replanting efforts were not considered.
This study was used as a reference point for bottom-up studies conducted in various cities
since 1991, including Oakland, CA (Nowak 1993); Chicago, IL (Nowak 1994);
Brooklyn, NY (Nowak et al. 2002); and even Philadelphia, PA (Nowak et al. 2007) and
compared to the original study to ensure the most accurate representation of carbon
sequestration and storage in urban forests in US cities. In the first study conducted in
Oakland, CA, Nowak (1993) found that extrapolating Oakland’s carbon storage estimate
to the national US urban forest resulted in an estimation of 400 million tons of carbon to
be stored in the national urban forest. Extrapolating Oakland’s diameter distribution in
the carbon model that was developed by Rowntree and Nowak (1991) estimated urban
forest carbon storage at 328 million t of carbon. Rowntree and Nowak’s carbon model
underestimated carbon storage by 18% in the Oakland study. As previously mentioned,
the methodology incorporated a level of underestimation.
The results of the methodology used in these urban forest evaluations were then used as a
foundation of quantifying the role of urban forests and to develop a model. Online, peerreviewed tools, originally called the Urban Forest Effects (UFORE) Model was
developed to help managers and researchers understand urban forest structure and
functions by quantifying species composition and diversity, diameter distribution, tree
density and health, leaf area, and leaf biomass (Nowak and Crane 2000).
The Urban Forest Effects (UFORE) Model
The UFORE computer model, as outlined by Nowak and Crane (2000) was developed to
help land managers and researchers to quantify urban forest structure and function. The
UFORE model incorporates urban vegetation data, local meteorological data, and
pollution data to make localized, city-specific assumptions about forest structure and
function. This model reported annual volatile organic compound emissions, total carbon
stores and net carbon sequestered annually, and annual pollution removal and percent
improvement in air quality by trees (Nowak and Crane 2000). The model originally had
four modules: UFORE-A quantified forest structure of an entire urban area using field
data; UFORE-B estimated the hourly emission of volatile organic compounds emitted by
trees (e.g. isoprene, monoterpenes); UFORE-C analyzed carbon storage and
sequestration; UFORE-D calculated hourly dry deposition of pollutants (e.g. ozone,
sulfur dioxide, nitrogen dioxide, and carbon monoxide). Each module required a range of
variables (Table 2).
Table 2. A breakdown of the Urban Forest Effects (UFORE) Model and module
requirements (Nowak and Crane 2000)
Requirements
UFORE-A
UFORE-B
UFORE-C
UFORE-D
Number of
√
trees
Species
√
√
√
composition

6

Tree density
DBH
Leaf area
Leaf biomass
Air temperature

√
√
√
√

√
√

√

√

√

Tree biomass

√

Average height
growth
Deposition
velocity
Pollutant
concentration
Photosynthetic
active radiation
Windspeed
Carbon dioxide
concentration
Absolute
humidity

√
√
√
√
√
√
√

i-Tree Software Suite
The i-Tree Software Suite began as i-Tree Tools in August 2006 at its initial release. This
model was refined over time by data contributors from thousands of communities around
the world to become what is now the suite containing online, peer-reviewed tools. The
USDA Forest Service continues to improve the accuracy of results and usability of tools
such as i-Tree Eco, County, Landscape, and Canopy by quantifying tree carbon storage
and annual sequestration data through bottom-up approaches (Nowak and Greenfield
2020, Baró et al. 2014, Nowak et al. 2013, Nowak et al. 2002, Nowak and Crane 2000).
As more data is compiled, the accuracy of the modeling software increases. Researchers
have evaluated urban forests internationally using the methods outlined by USDA Forest
Service scientists for education and in implementing legislation (Jim and Chen 2008;
Baró et al. 2014; Mills et al. 2015). i-Tree Software Suite provides five main tools as
desktop and website applications that perform several tasks and provide information
(Table 3), including analysis of urban and rural forestry, forest management, quantifying
forest structure and ecosystem services, and information on pollution mitigation and
stormwater run-off.
Table 3. i-Tree Software Suite tools and their descriptions (USDA Forest Service 2020,
Nowak and Crane 2002).
Tool
Description

7

i-Tree Eco
i-Tree Projects
i-Tree Landscape
i-Tree County
i-Tree Canopy

Flagship tool that quantifies the structure of, threats to, benefits,
and values provided by forest populations globally
An online platform for sharing results and data from i-Tree Eco
assessments. Currently in beta - additional projects coming soon!
Rapidly assess human and forest population information; threats to
help prioritize areas for tree planting; protection
Quickly learn the numerous benefits that trees provide within your
county
Easily estimate tree canopy and benefits using aerial photographs.

Research Goals and Objectives
The objectives for this research were to determine the difference in carbon sequestration
and storage of an urban forest and suburban forest. This was done with the help of the iTree Software, i-Tree Cover, that quantifies air pollution, carbon, and hydrological data
according to various parameters which are regularly updated and refined as the database
grows and research continues. The following objectives were defined for the research:
1. Determine suburban and urban study areas of interest. This was done by
establishing high-level differences between the designated urban and suburban
sites using i-Tree County. A general idea of the differences between the two will
help determine on which suburban areas to focus and provide further evidence
either supporting or rejecting the data collected in this study.
2. Determine annual carbon sequestration rates and carbon storage in each site. The
i-Tree Canopy software was used to determine these values based on land cover
data input from Google aerial photos.
Research Framework
This study focuses on the Greater Philadelphia area as a proxy that provides probable
assumptions about suburban environments and their capacities to store and sequester
carbon, as there is sparse information addressing suburban forests, including tree canopy
cover in any arrangement, in the literature. Furthermore, only the surrounding suburban
counties on the west of Philadelphia county are considered as the Delaware River is
situated at Philadelphia’s eastern boundary.
Research Methods
There are two basic ways of assessing urban forests as outlined in by the United States
Forest Service (2019). The top-down approach assessing tree canopy cover using aerial
or satellite images. The bottom-up approach measures the physical structure of the forest
using characteristics such as species composition and number of trees. This research
employs photo interpretation, a top-down method, to quantify and evaluate carbon
storage and annual carbon sequestration in woody plants as the differentiation of trees
versus shrubs is difficult through photointerpretation. First, a preliminary evaluation is

8

conducted using i-Tree County to corroborate initial assumptions made about the urban
environment and how they compare to the suburban environment. Then, a thorough
assessment of both the urban and suburban environments is conducted using i-Tree
Canopy to verify the preliminary results.
Thesis Organization
This thesis is organized into five chapters. Chapter II addresses the literature and the
compiles the information available that is required to understand the research. Chapter III
explains the study area, sample design, methodology, and how the software suite was
used to perform the assessments. Chapter IV provides the results of the assessment and
discusses what the results imply about the study area. Chapter V concludes with a
summary of the study, addresses any possible limitations of the research, and how the
research fits into the current context of global and regional change, forest ecology, and
ecosystem management.

9

LTIERATURE REVIEW
History of Atmospheric Carbon Dioxide
Greenhouse gases are those that trap heat in the atmosphere, and they include carbon
dioxide, methane, nitrous oxide, and fluorinated gases (EPA 2021a). Carbon dioxide is a
major anthropogenic greenhouse gas accounting for 76% of the total greenhouse gas
emissions as of 2010 (Blanco et al. 2014, EPA 2021b). Atmospheric carbon dioxide
exists at approximately 416.4 ppm as of February 2021 (NOAA 2021). Sedjo (1989)
estimated an annual increase of atmospheric carbon to be 2600 million metric tons in
1989. In a span of less than three centuries, quantities have increased one and a half times
from 277 ppm in 1750 (Joos and Spahni 2008). Atmospheric carbon concentration
fluctuated within a constant range, but greenhouse gas emissions are now increasing on a
linear trend (Fig. 1). Carbon dioxide release via human activities have made it the
primary greenhouse gas in the atmosphere. There was an extent to which carbon dioxide
existed naturally due to the carbon cycle. However, human activities released carbon
dioxide that was once trapped in fossil fuels by combustion, increasing carbon dioxide at
an alarming rate. The specific drivers of greenhouse gas emissions are many and interact
with each other directly and indirectly.
As human activity exponentially developed and increased, as did the byproducts of this
activity. Through empirical evidence and experimentation, humans revealed the
repercussions of said innovation. The literature states main drivers of greenhouse gas
emissions to be consumption, international trade, population growth, economic growth,
structural change to a service economy, and energy consumption. (Blanco et al. 2014)
Carbon dioxide emissions from fossil fuel combustion and industrial process make up the
largest percentage at 78% of the total emission increase from 1970 to 2010. Agriculture
deforestation and other land use changes emissions were comparable or greater than
fossil emissions for much of the last two centuries (Blanco et al. 2014).
Another major driver for a worldwide increase in greenhouse gas emissions is per capita
production and consumption growth. Increased income resulted in increased greenhouse
gas intensity, which led to increased total emissions that grew at the rate of populations.
The global population has increased by 87% since 1970; between 1970 and 2010, world
population has increased by 87% (Wang et al. 2012). Each person added to the global
population increases greenhouse gas emissions, but contribution varies widely. While the
population has increased mainly in Asia, Latin America, and Africa, emissions increase
for an additional person varies widely depending on geographical location, income,
lifestyle, the available energy resources, and technologies.
As population size increases, there is an influx of urbanization as a response. The global
rate of urbanization has increased from 13% in 1900 to 36% in 1970 252% in 2011
(Blanco et al. 2014). The urban population occupies less than 3% of the global terrestrial
surface but 78% of C emissions from fossil fuel burning and cement manufacturing, and
76% of wood used for industrial purposes is attributed to urban areas (Brown 2002 as
cited by Lorenz and Lal 2010).

10

Furthermore, global agricultural land increased by 7% between 1970 and 2010.
According to Blanco et al. (2014), emissions of greenhouse gases in the agriculture,
forest, and other land use sector increased by 20% in 2010 contributing about 20 to 25%
of global emissions. Emissions within this sector are driven by increased life stock
numbers linked to increased demand for animal products, area under agriculture,
deforestation, use of fertilizer, area under irrigation, per capita food availability,
consumption of animal products, and increased human and animal populations.

Fig. 1. Global atmospheric carbon dioxide concentrations (CO2) in parts per million
(ppm) for the past 800,000 years. The peaks and valleys track ice ages (low CO2) and
warmer interglacial (higher CO2) periods. During these cycles, CO2was never higher than
300 ppm. On the geologic time scale, the increase (orange dashed line) looks virtually
instantaneous. Graph by NOAA Climate.gov based on data via NOAA NCEI
Paleoclimatology Program (Lindsey 2020).
An increase in carbon dioxide and other greenhouse gasses has resulting in climate
change. Climate change is expressed in temperature and precipitation changes that have
shown to drastically change the biosphere. Habitat fragmentation and species loss,
changes in phenological relationships, changes in ecosystem structure and function along
with many other effects have occurred in response to climate change and have been
evaluated at local, regional, and global levels. Climate models that include the terrestrial
and oceanic carbon cycle simulate a positive feedback between the carbon cycle and
climate warming that increases the airborne proportion of anthropogenic CO2 emission
and amplifies warming (Bonan 2008). The rise in atmospheric carbon dioxide implores
the necessity for mitigation efforts, whether they be through leveraging biotechnology or
ecosystem management.

11

Carbon Sequestration and Storage
Carbon sequestration is the processes of removing atmospheric carbon. While carbon can
be sequestered in ways other than photosynthesis, plants and other certain organisms that
fix carbon dioxide and deposit carbon as an energy store through photosynthesis are
highlighted in this research. Photosynthetic organisms are inherently valuable as the
existence of them reverses greenhouse gas emissions. This process combats climate
change by balancing the earth’s carbon budget.
Carbon Sequestration and Storage by Forests
Forests cover about 30% of the earth’s terrestrial surface and stores 45% of land’s carbon
in biomass. (NASA 2012). A study estimated a total forest sink globally of 2.4
pentagrams of carbon per year for 1990 to 2007 (Pan et al. 2011). Temperate forests are
the most common biome in eastern North America, Western Europe, Eastern Asia, Chile,
and New Zealand, covering 25% of the land’s forest cover (Yale 2019, Figure 2).

Figure 2. 2011 forest cover map, based on MODIS satellite data at 500-m resolution and
on IGBP-DIS (The International Geosphere-Biosphere Programme Data and Information
System) land-cover classification (Pan et al. 2013)
Carbon is not only removed by woody plants (hereafter, trees) by photosynthesis, but is
also stored throughout the lifetime of the plant. Most tree species have a high capacity to
store carbon, due to their high above- and below-ground biomass. Furthermore, soil
organic carbon (SOC), which is carbon deposited into the soil in the form of detrital
residue of forest biomass, plays an important role in the carbon cycle as well, by storing
and containing the carbon (Borken et al. 1999; De Vos et al. 2007; Lorenz and Lal 2010).
Throughout the literature, temperate forest SOC is continually being studied (Arrouays et
al. 1995, Borken et al. 1999, De Vos et al. 2007, Wang and Huang 2020). Thus far, SOC

12

in the temperate forests of most of Western Europe, Eastern Asia, Northern America, and
South Asia has been assessed and quantified. While forest soil carbon and climate change
relationships have also been thoroughly examined, the literature lacks information on
above ground carbon storage in temperate forests in residential (suburban) Southeastern
Pennsylvania. Additionally, urban forests are thoroughly documented as well, while
suburban forestland have not. However, both have the capacity to remove carbon dioxide
from the atmosphere. This ability is proving more important than before in age of
anthropogenic climate change and necessitates documentation which can be used in
future forest management.

13

METHODOLOGY
Case Study: Suburban Philadelphia
The study area of interest was the Greater Philadelphia area in Pennsylvania (Fig. 3).
Within the study area, there are two study sites: urban and suburban. The urban site is
Philadelphia County, which boundaries overlap with the city of Philadelphia. The city of
Philadelphia is 134.28 sq. miles on land, and 8.42 sq. miles over water. In 2007,
Philadelphia had an estimated tree cover of 15.7% (Nowak et al. 2007). The suburban site
consists of 3 sub-sample groups: Bucks County (622 sq. mi.), Delaware County (191 sq.
mi.), and Montgomery County (487 sq. mi.). When this study was conducted, tree cover
and carbon related information for each county was unknown.

Figure 3. A map of the Greater Philadelphia area, Pennsylvania. Note Philadelphia
County, Delaware County, Montgomery County, and Bucks County. Chester County was
not evaluated in this study (Mennis et al. 2013).

14

These counties are being used as a proxy for other suburban ecosystems in the temperate
deciduous United States. All three counties were under assessment as they surround the
entirety of the west boundary of Philadelphia. The eastern boundary of Philadelphia is
surrounded by the Delaware River. In their evaluation of two urban tree canopy
assessment, Hwang and Wiseman (2020) state that i-Tree Canopy requires greater than
500 points to reach a tolerable standard error of less than 1.7 percent. This study took
5,000 points per study site, for a total of 10,000 points taken. Point distribution among
the sub-sample groups of the suburban site were divided proportionate to the land area
(Table 4).
Table 4. Number of points taken respective to proportion of sub-sample area within the
site area.
County
Land area (sq. mi) Proportion of site area
No. of points taken
Urban
Philadelphia
134.1
1.0
5000
Suburban
Bucks
604.31
0.475
2377
Delaware
183.84
0.145
723
Montgomery
483.04
0.380
1900
i-Tree County
A preliminary assessment of each county was used to determine broad generalizations
and assumptions. Total carbon stored, carbon dioxide equivalent stored, total carbon
sequestered, and carbon dioxide equivalent sequestered were observed with i-Tree
County (Fig. 4, 5, 6, 7). The darker the shaded area within the boundaries of the county,
the greater the concentration of the corresponding variable (Fig. 8).
i-Tree Canopy
i-Tree Canopy, along with other i-Tree Tools, derives data including species, diameter at
breast height, total height, crown base height, crown width, crown light exposure, percent
crown missing, crown health, and field land use from the present i-Tree Eco database.
The data (Appendix 6) were collected and applied as outlined in Nowak 2020:
i-Tree Canopy uses Google aerial images of the study area to produce statistical
estimates of tree and other land cover types, such as grass, structures, and
impervious surfaces. Random points are placed in the defined area of interest and
the user identifies the land cover class at the point center. Cover classes are
defined by the user. Statistical estimates of area in each cover class (as a percent)
are calculated as:
%=n/N

Where
n = number of point[s] hitting the cover class, and

15

N = total number of points analyzed among all cover classes.
The standard error (SE) of the estimate is calculated as:
SE = √ (pq / N)
Where
p = n/N, and
q = 1 – p (Lindgren and McElrath 1969).
Percentage tree cover is multiplied by the area analyzed to determine the total tree cover
area.
Data Collection
A total of 10,000 points were taken (5,000 points per study site). Point distribution
among the sub-sample groups of the suburban site were divided proportionate to the land
area (Table 4). Random points were placed in the defined area of interest on Google
aerial images and the land cover class at the point center were identified and classified.

16

RESULTS
i-Tree County Assessment
Amount Carbon Stored
Philadelphia County was visually significantly shaded lighter than Bucks, Montgomery,
and Delaware counties (Fig. 4, 5). In both Fig. 4 and 5, Delaware County was the least
shaded, Montgomery County was shaded intermediately, and Bucks county was the
darkest in shade. The shading is correspondent to the amount of carbon stored in tons.
From this preliminary assessment, Philadelphia County trees store 160,000 to 790,000
tons of carbon above and below ground. Delaware County trees store from 790,000 to
1,700,000 tons of carbon, Montgomery County trees store 2,800,000 to 4,400,000 tons of

Figure 4. Total carbon stored depicted through
shading by i-Tree County for four
Southeastern Pennsylvania counties, including
Bucks, Delaware, Montgomery, and
Philadelphia Counties.

Figure 5. Carbon dioxide equivalent
stored depicted through shading by i-Tree
County for four Southeastern
Pennsylvania counties, including Bucks,
Delaware, Montgomery, and Philadelphia
Counties.

carbon in their biomass, and Bucks County trees store from 4,400,000 to 5,900,000 tons
of carbon (Fig. 4, Fig. 8a). This translates to proportionate amounts of carbon dioxide
stored in each counties’ trees (Fig. 8b). Philadelphia County’s urban forest stores less
carbon, and thus less carbon dioxide, than each of the suburban forests. These results
provide a basis for assessing the urban and suburban sites with i-Tree Canopy to estimate
the amount carbon stored in trees in each site.

17

Amount Carbon Sequestered

Figure 6. Total carbon sequestered
depicted through shading by i-Tree
County for four Southeastern
Pennsylvania counties, including
Bucks, Delaware, Montgomery, and
Philadelphia Counties.

Figure 7. Carbon dioxide equivalent
sequestered depicted through shading
by i-Tree County for four
Southeastern Pennsylvania counties,
including Bucks, Delaware,
Montgomery, and Philadelphia
Counties.

Philadelphia County was visually significantly shaded lighter than Bucks, Montgomery,
and Delaware counties (Fig. 6, 7). Lighter shading suggested a lesser amount of carbon
was sequestered by trees in those counties. In Fig. 6 and 7 among the suburban counties,
Delaware County was the least shaded, and is expected to sequester 15,000 to 32,000
tons of carbon (and expected 55,000 to 120,000 tons of carbon dioxide). Montgomery
County was shaded intermediately in both figures as well, sequestering 54,000 to 88,000
tons of carbon, an expected 200,000 to 320,000 tons of carbon dioxide. However, Bucks
and Montgomery counties seemed to have sequestered carbon dioxide within the same
cohort at 54,000 to 88,000 tons of carbon (Fig. 7), while Bucks County was estimated to
have sequestered more total carbon than Montgomery County (Fig. 6).

18

a)

b)

c)

d)

Figure 8. i-Tree County cohort legend to determine estimated measurements of each
listed characteristic: a) carbon storage measured in tons. Carbon storage refers to the
amount of carbon currently contained within a plant’s woody tissue (above and below
ground, including the amount of carbon within leaves for evergreen species, b) carbon
dioxide equivalent storage, i.e. carbon dioxide storage measured in tons, c) annual carbon
sequestration. Carbon sequestration refers to the amount of atmospheric carbon removed
by trees annually. d) carbon dioxide equivalent sequestration, i.e. carbon dioxide
sequestration measured in tons.
The i-Tree County assessment established overarching assumptions made about urban
and suburban environments. Generally, the urban site was found to store and sequester
less carbon, and thus less carbon dioxide, than each of the suburban sub-sites. To
reinforce through more detailed methodology, an i-Tree Canopy assessment was also
conducted.
i-Tree Canopy Assessment
The i-Tree Canopy assessment includes two estimates: amount carbon stored, and amount
carbon sequestered (Table 5, 6). Amount carbon stored is the carbon currently contained
within a plant’s woody tissue above and below ground, including the amount of carbon
within leaves for evergreen species. Amount carbon sequestered is the amount of
atmospheric carbon removed by the trees annually. The following is an analysis of both
estimates of the urban and suburban sites in their forest capacities to store and sequester
carbon in the form of carbon dioxide.

19

Amount Carbon Stored
The i-Tree Canopy assessment found that total tree carbon storage estimates were higher
in the suburban site (including Bucks, Delaware, and Montgomery counties) than in the
urban site (Philadelphia county) (Table 5). Trees in Bucks County store almost ten times
more carbon dioxide than those trees in Philadelphia County. Delaware County trees
store almost three times as much carbon dioxide as Philadelphia County trees.
Montgomery County trees store nearly seven times the carbon dioxide as trees in
Philadelphia County. Expected values (in USD) were higher in the suburban site than in
the urban site. The total value of suburban trees current state and their role in storing
carbon dioxide is almost twenty times greater than the value contributed by urban trees
(Table 5). Bucks County trees are valued at nine times higher than Philadelphia County
trees. Delaware County contributes almost three times more value than Philadelphia
County. Montgomery County trees are valued almost seven times higher than
Philadelphia County trees.
Table 5. Percent tree/shrub cover in Southeastern Pennsylvania counties, respective
stored carbon in forest stands, and valuation.
County
% Tree/Shrub Carbon (kT) Carbon dioxide
Value (USD)
Cover
Equiv. (kT)
Urban
Philadelphia
23.2
722.87
2650.52
$123,286,009
Suburban
Bucks
49.7
6778.49
24854.45
$1,156,075,992
Delaware
50.8
2133.99
7824.23
$363,934,966
Montgomery
46.0
4948.71
18145.29
$844,006,888
Total
50823.97
$2,364,017,846

Amount Carbon Sequestered
Annual carbon sequestration by trees was higher in the suburban site (including Bucks,
Delaware, and Montgomery counties) than in the urban site (Philadelphia county) (Table
6). Trees in Bucks County sequester almost eleven times more carbon dioxide than those
trees in Philadelphia County. Delaware County trees sequester more than three times as
much carbon dioxide as Philadelphia County trees. Montgomery County trees sequester
more than eight times the carbon dioxide as trees in Philadelphia County. Expected
values (in USD) were higher in the suburban site than in the urban site. The total value of
suburban trees current state and their role in sequestering carbon dioxide is almost
nineteen times greater than the value contributed by urban trees (Table 6). Bucks County
trees are valued at more than nine times higher than Philadelphia County trees. Delaware
County contributes almost three times more value than Philadelphia County.
Montgomery County trees are valued almost seven times higher than Philadelphia
County trees.

20

Table 6. Percent tree/shrub cover in Southeastern Pennsylvania counties, respective
carbon sequestered by forest and valuation.
County
% Tree/Shrub
Carbon
Carbon dioxide
Value (USD)
Cover
(kT)
Equiv. (kT)
Urban
Philadelphia 23.2
28.78
105.54
$4,909,103
Suburban
Bucks
49.7
269.91
989.68
$46,033,583
Delaware
50.8
84.97
311.55
$14,491,461
Montgomery 46.0
197.05
722.52
$33,607,359
Total
2023.75
$94,132,403

Tree cover percentages between the study sites, including suburban and urban sites, were
significantly different (Table 7, p < 0.05).
Table 7. Proportions test results comparing % tree cover of urban and suburban sites.
Proportions test
X-squared
691.68
Degrees of freedom
1
p-value
<2.2e-16

21

DISCUSSION and CONCLUSIONS
The software i-Tree County was preliminarily employed to determine an overall
difference between the suburban site and urban site. Once established, i-Tree Canopy was
used to determine the difference in annual carbon sequestration and carbon storage of
forests in an urban and suburban setting. From the analysis of the results, the difference
between the capacities of each site were clear for the evaluation of environmental
benefits. i-Tree Canopy provided a more detailed look at what was provided by i-Tree
County and was used to establish how much value each forest provided to indirectly
offset fossil fuel-based energy use.
Research Implications
Assessing suburban forests and their role of carbon sequestration in the context of climate
change provides information that can be used in forest management. Implications of this
study support protection of forests, forestation, and suburban and urban greening. This
research sought to bring to light a major gap in scientific knowledge that can be used to
understand carbon dynamics at regional and even global levels in detail. A lack of
information on suburban forests results in regional and global carbon dynamics models to
possibly over- or underestimate fluxes in atmospheric carbon. Acquisition of such
information through research can refine estimations made through these models.
Understanding the links and applying the biological support behind carbon sequestration
develops a deeper understanding of how bio can be leveraged to meet climate change
mitigation policies.

Suburban Forest Potential Contribution to Climate Change Mitigation
Suburban forests are undocumented contributors to climate change mitigation by
sequestering and storing carbon. The suburban site forest in this study stored almost
twenty times the carbon in the urban forest and sequesters almost nineteen times the
carbon annually. Suburban forests, as they are more abundant and widespread, have
higher capacities than urban forests to remove greenhouse gases from the atmosphere.
While different suburban areas may differ in their capacities to sequester carbon due to
their structure, their extent, and the particulars by which they are managed, they sequester
more carbon than in a neighboring city. The results support that suburban forests provide
equal, if not more, environmental services by sequestering and storing carbon as urban
forests. Suburban forests should be considered in quantification studies, as there is a gap
in the literature about how much atmospheric carbon dioxide is offset.
Research Limitations and Future Studies

22

This study assessed the environmental impacts of Greater Philadelphia’s suburban trees
on atmospheric carbon dioxide. While i-Tree Canopy is a useful tool to make estimations
of carbon sequestration, an i-Tree Eco analysis may produce more accurate and detailed
results, such as revealing trends in tree species and particular areas that contribute more
to carbon sequestration and storage due to possible higher tree densities. i-Tree Eco also
can distinguish the difference between shrubs and trees, the two of which were otherwise
combined the i-Tree Canopy assessment. Common trees naturally found in Southeastern
Pennsylvania are the red maple (Acer rubrum), black cherry (Prunus serotina), and the
northern red oak (Quercus rubra) (Smith 2009). However, the most popular tree species
in Philadelphia are black cherry, crab apple (Malus coronaria), and tree of heaven
(Ailanthus altissima) (Nowak 2007). The most commonly noted suburban trees overlap
with the most popular trees, but there is a lack of formally documented information in
this area. Revealing the identity of trees that have higher capacities to sequester and store
carbon can prove beneficial when considering forest management. Conservation of these
specific trees, as they could play the role of an umbrella species, might lead to the
coincidental conservation of other species.
Furthermore, because forest carbon storage and sequestration per unit of tree canopy
cover are not directly attributable to urban forests (Nowak and Crane 2002), there are
differences in carbon storage and sequestration rates between urban forests and natural
forests. “Natural” forests in suburbs are not entirely untouched by human activity,
especially in the form of management, and thus closes the gap between how differently
urban forests and suburban “natural” forests should be treated in research.
Additionally, the suburban site was 90% larger in area than the urban site. While it may
be justified by amount carbon waste emitted/produced per capita, this valuation does not
account for migration between the two sites or emigration to different areas. Therefore,
there is the assumption that an equal number of individuals reside in each site throughout
the lifespans of the suburban and urban forests, and specifically during 2020 to 2021
during which amount carbon sequestration was measured. A multivariate analysis of this
research may include a factor of migration in either direction (to the urban site versus
from the urban site) and how it might relate to carbon emissions, sequestration, and
storage.

23

LITERATURE CITED
Arrouays D, Balesdent J, Mariotti A, Girardin C. 1995. Modeling organic carbon
turnover in cleared temperate forest soils converted to maize cropping by using 13C
natural abundance measurements. Plant and Soil. 173:191-196.
Augustin B. 2011. Carbon Sequestration in Urban Ecosystems. Springer, Netherlands,
388p.
Baró F, Chaparro L, Gómez- Baggethun E, Langemeyer J, Nowak DJ, Terradas J. 2014.
Contribution of ecosystem services to air quality and climate change mitigation policies:
the case of urban forests in Barcelona, Spain. Ambio. 43:466-479.
Blanco G, Gerlagh R, Suh S, Barrett J, de Coninck HC, Diaz Morejon CF, Mathur R,
Nakicenovic N, Ahenkora AO, Pan J, et al. 2014. Climate change 2014: mitigation of
climate change. Contribution of Working Group III to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge University Press. 351411.
Bonan GB. 2008. Forests and climate change: forcings, feedbacks, and the climate
benefits of forests. Science. 320:1444-1449
Borken W, Xu YJ, Brumme R, Lamersdorf N. 1999. A climate change scenario for
carbon dioxide and dissolved organic carbon fluxes from a temperature forest soil
drought and rewetting effects. Soil science society of America journal. doi:
https://doi.org/10.2136/sssaj1999.6361848x.
Chiras DD, Reganold JP. 2013. Natural resource conservation: management for a
sustainable future. Pearson.
De Vos B, Lettens S, Muys B, Deckers JA. 2007. Walkey-Black analysis forest soil
organic carbon: recovery, limitations and uncertainty. Soil Use and Management. doi:
https://doi.org/10.1111/j.1475-2743.2007.00084.x
[EPA] United States Environmental Protection Agency. 2021a. Overview of greenhouse
gases. Available from: https://www.epa.gov/ghgemissions/overview-greenhouse-gases.
[EPA] United States Environmental Protection Agency. 2021b. Carbon dioxide
emissions. Available from: https://www.epa.gov/ghgemissions/overview-greenhousegases.
Fleming LE. 1988. Growth estimates of street trees in Central New Jersey. MS Thesis,
Rutgers, the University of New Jersey. 1-143.
[FAO] Food and Agriculture Organization of the United Nations. 2020a. The state of the
world’s forests: forests, biodiversity and people. 1-188.

24

[FAO] Food and Agriculture Organization of the United Nations. 2020b. Global forest
resources assessment 2020: terms and definitions. 1-26.
Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM. 2008.
Global change and the ecology of cities. Science. 319:756-760.
Holian MJ, Kahn ME. 2014. Household carbon emissions from driving and center city
quality of life. Marron Institute of Urban Management. 1-23.
Hou G, Deland CO, Lu X, Olschewski R. 2019. Valuing carbon sequestration to finance
afforestation projects in China. Forests. 10:1-20.
Hwang WH, Wiseman PE. 2020. Geospatial methods for tree canopy assessment: a case
study of an urbanized college campus. Arboriculture and urban forestry. 46:51-65.
Jim CY, Chen WY. 2008. Assessing the ecosystem service of air pollutant removal by
urban trees in Guangzhou (China). Journal of environmental management. 88:665-676.
Joos F, Spahni RE. 2008. Rates of change in natural and anthropogenic radiative forcing
over the past 20,000 years. Proceedings of the National Academy of Sciences (PNAS).
105(5):1425–1430.
Lindgren BW, McElrath GW. 1969. Introduction to probability and statistics. Macmillan.
Lindsey R. 2020. Climate change: atmospheric carbon dioxide [Internet]. Available from:
https://www.climate.gov/news-features/understanding-climate/climate-changeatmospheric-carbon-dioxide.
Lorenz K, Lal R. 2010. Carbon sequestration in forest ecosystems. Springer,
Netherlands, 289p.
Maco S. 2019. i-Tree [Internet]. Available from https://www.nrs.fs.fed.us/partners/itree/.
Mennis J, Dayanim SL, Grunwald H. 2013. Neighborhood collective efficacy and
dimensions of diversity: a multilevel analysis. Environment and planning. 45.
https://doi.org/10.1068/a45428
Mills G, Anjos M, Brennan M, Williams J, McAleavey C, Ningal T. 2015. The green
‘signature’ of Irish cities: an examination of the ecosystem services provided by trees
using i-Tree Canopy software. Iris Geography. 48:62-77. doi: 10.2014/igj.v48i2.625
National Aeronautics and Space Administration. 2012 Jan 9. Seeing forests for the trees
and carbon: mapping the world’s forests in three dimensions. Available from:
https://earthobservatory.nasa.gov/features/ForestCarbon.
National Geographic. https://www.nationalgeographic.org/encyclopedia/biosphere/

25

National Oceanic and Atmospheric Administration. 2021 Apr 7. Trends in atmospheric
carbon dioxide. Available from: https://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html.
Nowak DJ. 1986. Silvics of an urban tree species: Norway maple (Acer platanoides L.).
State University of New York, College of Environmental Science and Forestry.
Unpublished MS thesis. Syracuse, NY.
Nowak DJ. 1993. Atmospheric carbon reduction by urban trees. Journal of environmental
management. 37:207-217.
Nowak DJ. 1994. Atmospheric carbon dioxide reduction by Chicago’s urban forest.
General Technical Report NE-186. U.S. Department of Agriculture, Forest Service,
Northern Research Station. 83-94.
Nowak DJ. 2020. Understanding i-Tree: summary of programs and methods. General
Technical Report NRS-200. Department of Agriculture, Forest Service, Northern
Research Station. 1-105.
Nowak DJ, Crane DE. 2000. The Urban Forest Effects (UFORE) model: quantifying
urban forest structure and functions. Integrated tools proceedings. 1:714-720.
Nowak DJ, Crane DE. 2002. Carbon storage and sequestration by urban trees in the USA.
Environmental pollution. 116:381-389.
Nowak DJ, Crane DE, Stevens JC, Ibarra M. 2002. Assessing urban forest effects and
values, Brooklyn’s urban forest. General Technical Report NE-290. U.S. Department of
Agriculture, Forest Service, Northern Research Station.
Nowak DJ, Greenfield EJ, Hoehn RE, Lapoint E. 2013. Carbon storage and sequestration
by trees in urban and community areas of the United States. Environmental pollution.
178:229-236.
Nowak DJ, Greenfield EJ. 2020. The increase of impervious cover and decrease of tree
cover within urban areas globally. Urban forestry & urban greening. 1-7.
Nowak DJ, Hoehn III RE, Crane DE, Stevens JC, Walton JT. 2007. Assessing urban
forest effects and values, Philadelphia’s urban forest. Resource Bulletin NRS-7. U.S.
Department of Agriculture, Forest Service, Northern Research Station. 1-22.
Nowak DJ, Rowntree RA, McPherson EG, Sisinni SM, Kerkmann ER, Stevens JC. 1996.
Measuring and analyzing urban tree cover. Landscape and urban planning. 36:49-57.
Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shivdenko
A, Lewis SL, Canadell JG et al. 2011. A large and persistent carbon sink in the world’s
forests. Science. 333:988-993 doi: 10.1126/science.1201609.

26

Pan Y, Birdsey RA, Phillips OL, Jackson RB. 2013. The structure, distribution, and
biomass of the world’s forests. Annual review of ecology, evolution, and systematics.
44:593-622.
Hannah Ritchie and Max Roser (2013) - "Crop Yields". Published online at
OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/crop-yields' [Online
Resource]
Ritchie H, Roser M. 2018. Urbanization. Available from:
https://ourworldindata.org/urbanization.
Ritchie H. 2020. Deforestation and forest loss [Internet]. Available from
https://ourworldindata.org/deforestation.
Rowntree RA, Nowak DJ. 1991. Quantifying the role of urban forests in removing
atmospheric carbon dioxide. Journal of Arboriculture. 17:269-275.
Sedjo RA. 1989. Forests to offset the greenhouse gas effects. Journal of forestry.
87(7):12-15.
Smith SS. 2009. From the woods: ten important hardwoods [Internet]. Available from:
https://extension.psu.edu/from-the-woods-ten-important-hardwoods.
Steenberg J WN, Duinker PN, Nitolawski SA. 2019. Ecosystem-based management
revisited: updating the concepts for urban forests. Landscape and Urban Planning.
186:24-35.
Sumangala HP. 2013. Urban landscapes for carbon sequestration in climate changing
scenario. Springer. 245-253. https://doi.org/10.1007/978-81-322-0974-4_22
[USDA Forest Service] United States Department of Agriculture Forest Service. 2019. A
guide to assessing urban forests.
[USDA Forest Service] United States Department of Agriculture Forest Service. 2020.
What is i-Tree? [Internet]. Available from: https://www.itreetools.org/about.
[USDA Forest Service] United States Department of Agriculture Forest Service. 2021.
Tools [Internet]. Available from: https://www.itreetools.org/tools.
Wang H, Zhou P, Zhou DQ. 2012. An empirical study of direct rebound effect for
passenger transport in urban China. Energy Economics. 34:452-460. doi:
10.1016/j.eneco.2011.09.010, ISSN: 0140-9883.

27

Wang S, Huang Y. 2020. Determinants of soil organic carbon sequestration and its
contribution to ecosystem carbon sinks of planted forests. Global Change Biology.
26:3163-73.
Wenger KF. 1984. Forestry handbook. Wiley. 1-1335.
Winer AM, Fitz DR, Miller PR. 1983. Investigation of the role of natural hydrocarbons
in photochemical smog formation in California. Final Report, California Air Resources
Board. Statewide Air Pollution Research Center, University of California, Riverside CA,
326 p.
[Yale] Yale School of Forestry and Environmental Studies. 2019. Forest regions
temperate zone. Available from: https://globalforestatlas.yale.edu/temperate-zone.
Zirkle G, Lal R, Augustin B, Follett R. 2012. Modeling carbon sequestration in the U.S.
residential landscape. 265-276. https://doi.org/10.1007/978-94-007-2366-5_14

28

Appendix 1 Raw Data (in separate 219-page-long document)
Appendix 1 contains four data tables, one for each county assessed (Philadelphia county,
Bucks county, Delaware county, and Montgomery county). Each table has the designated
number of random coordinates and each of the points cover classes.
Appendix 2 Philadelphia County Cover Assessment and Tree Benefits Report
Appendix 2 is the Benefits Report generated by the i-Tree Canopy Model for
Philadelphia County, based upon the user’s input and classification of random points
generated. The Appendix also includes a table showing the distribution of cover class,
how many points fall within the class, what percentage cover, and how much land area is
occupied by that cover class. Carbon storage and sequestration estimates are also listed in
tables with their carbon dioxide equivalent values. The USD value amount is also noted.

Figure 9. Distribution of points and their associated cover class in Philadelphia County.
Table 8. Cover class distribution and land area of each cover class in Philadelphia
County.
Abbr. Cover class
Points
% cover ± SE
Area (mi²) ± SE
H
Grass/herbaceous
856
17.12
± 0.53 24.31
± 0.76
IB
Impervious buildings 1016
20.32
± 0.57 28.86
± 0.81
IO
Impervious other
995
19.90
± 0.56 28.26
± 0.80
IR
Impervious road
648
12.96
± 0.47 18.41
± 0.67
S
Soil/bare ground
84
1.68
± 0.18 2.39
± 0.26
T
Tree/shrub
1160
23.20
± 0.60 32.95
± 0.85

29

W
Total

Water

241
5000

4.82
100.00

± 0.30

6.85
142.02

± 0.43

Table 9. Tree Benefit Estimates: Carbon (English units)
Description Carbon
±SE
±SE
Value (USD) ±SE
CO₂
(kT)
Equiv.
(kT)
Sequestered 28.78
±0.74
105.54
±2.72
$4,909,103
±126,315
annually in
trees
Stored in
722.87
±18.60 2,650.52 ±68.20 $123,286,009 ±3,172,235
trees
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Amount sequestered is
based on 0.874 kT of Carbon, or 3.203 kT of CO₂, per mi²/yr and rounded. Amount
stored is based on 21.940 kT of Carbon, or 80.446 kT of CO₂, per mi² and rounded.
Value (USD) is based on $170,550.73/kT of Carbon, or $46,513.84/kT of CO₂ and
rounded. (English units: kT = kilotons (1,000 tons), mi² = square miles)
Table 10. Tree Benefit Estimates: Air Pollution (English units)
Abbr. Description

Amount
(T)

±SE Value
(USD)

±SE

CO

Carbon Monoxide removed annually

11.91

±0.31

$15,884

±409

NO2

Nitrogen Dioxide removed annually

65.80

±1.69

$28,752

±740

O3

Ozone removed annually

SO2

Sulfur Dioxide removed annually

PM2.5 Particulate Matter less than 2.5 microns
removed annually

508.31 ±13.08 $1,320,500

±33,977

32.35

±0.83

$4,330

±111

25.97

±0.67 $2,764,478

±71,132

±3.71

PM10* Particulate Matter greater than 2.5
microns and less than 10 microns
removed annually

144.27

$904,335

±23,269

Total

788.60 ±20.29 $5,038,279

±129,638

Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Air Pollution Estimates are
based on these values in T/mi²/yr @ $/T/yr and rounded: CO 0.362 @ $1,333.50 |
NO2 1.997 @ $436.94 | O3 15.428 @ $2,597.84 | SO2 0.982 @ $133.85 | PM2.5
0.788 @ $106,459.48 | PM10* 4.379 @ $6,268.44 (English units: T = tons (2,000
pounds), mi² = square miles)

30

Table 11. Tree Benefit Estimates: Hydrological (English units)
Abbr. Benefit
AVRO Avoided Runoff

Amount
(Kgal)
174.67

±SE Value
(USD)
±4.49

±SE

$1,561 ±40

E

Evaporation

3,922.38 ±100.93

N/A N/A

I

Interception

3,947.90 ±101.58

N/A N/A

T

Transpiration

3,714.16 ±95.57

N/A N/A

PE

Potential Evaporation

25,225.55 ±649.07

N/A N/A

PET

Potential Evapotranspiration

20,804.55 ±535.32

N/A N/A

Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Hydrological Estimates are
based on these values in Kgal/mi²/yr @ $/Kgal/yr and rounded: AVRO 5.301 @
$8.94 | E 119.049 @ N/A | I 119.823 @ N/A | T 112.729 @ N/A | PE 765.624 @ N/A
| PET 631.442 @ N/A (English units: Kgal = thousands of gallons, mi² = square
miles)

31

Appendix 3 Bucks County Cover Assessment and Tree Benefits Report
Appendix 3 is the Benefits Report generated by the i-Tree Canopy Model for Bucks
County, based upon the user’s input and classification of random points generated. The
Appendix also includes a table showing the distribution of cover class, how many points
fall within the class, what percentage cover, and how much land area is occupied by that
cover class. Carbon storage and sequestration estimates are also listed in tables with their
carbon dioxide equivalent values. The USD value amount is also noted.

Figure 10. Distribution of points and their associated cover class in Bucks County.
Table 12. Cover class distribution and land area of each cover class in Bucks County.
Abbr. Cover class
Points
% cover ± SE
Area (mi²) ± SE
H
Grass/herbaceous
744
31.30
± 0.95 194.63
± 5.91
IB
Impervious buildings 97
4.08
± 0.41 25.38
± 2.52
IO
Impervious other
96
4.04
± 0.40 25.11
± 2.51
IR
Impervious road
84
3.53
± 0.38 21.97
± 2.35
S
Soil/bare ground
102
4.29
± 0.42 26.68
± 2.58
T
Tree/shrub
1181
49.68
± 1.03 308.96
± 6.38
W
Water
73
3.07
± 0.35 19.10
± 2.20
Total
2377
100.00
191.61

32

Table 13. Tree Benefit Estimates: Carbon (English units)
Description Carbon ±SE
±SE
Value (USD)
±SE
CO₂
(kT)
Equiv.
(kT)
Sequestered 269.91
±5.57
989.68
±20.43 $46,033,583
±530,805
annually in
trees
Stored in
6,778.49 ±139.91 24,854.45 ±513.02 $1,156,075,992 ±23,862,307
trees
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Amount sequestered is
based on 0.874 kT of Carbon, or 3.203 kT of CO₂, per mi²/yr and rounded. Amount
stored is based on 21.940 kT of Carbon, or 80.446 kT of CO₂, per mi² and rounded.
Value (USD) is based on $170,550.73/kT of Carbon, or $46,513.84/kT of CO₂ and
rounded. (English units: kT = kilotons (1,000 tons), mi² = square miles)
Table 14. Tree Benefit Estimates: Air Pollution (English units)
Abbr. Description
Amount ±SE
Value (USD) ±SE
(T)
CO
Carbon Monoxide removed annually 89.15 ±1.84 $2,388
±87
NO2
Nitrogen Dioxide removed annually 486.10 ±10.03 $4,111
±151
O3
Ozone removed annually
4,841.31±99.93 $214,077
±7,841
SO2
Sulfur Dioxide removed annually
306.33 ±6.32 $718
±26
PM2.5 Particulate Matter less than 2.5
235.25 ±4.86 $442,536
±16,210
microns removed annually
PM10 Particulate Matter greater than 2.5 1,621.66±33.47 $155,414
±5,693
microns and less than 10 microns
removed annually
Total
7,579.79±156.45 $2,602,413
±53,716
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Air Pollution Estimates are
based on these values in T/mi²/yr @ $/T/yr and rounded:
CO 0.289 @ $85.08 | NO2 1.573 @ $26.86 | O3 15.670 @ $140.47 | SO2 0.991 @
$7.45 | PM2.5 0.761 @ $5,975.67 | PM10* 5.249 @ $304.43 (English units: T = tons
(2,000 pounds), mi² = square miles)
Table 15. Tree Benefit Estimates: Hydrological (English units)
Abbr. Benefit
Amount (Kgal) ±SE
AVRO Avoided runoff
102.25
±2.11
E
Evaporation
8,441.93
±174.25
I
Interception
8,489.17
±175.22
T
Transpiration
11,423.23
±235.78
PE
Potential evaporation
63,968.18
±1,320.35
PET
Potential evapotranspiration 52,192.69
±1,077.30

Value (USD)
$914
N/A
N/A
N/A
N/A
N/A

±SE
±19
N/A
N/A
N/A
N/A
N/A

33

Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Hydrological Estimates are
based on these values in Kgal/mi²/yr @ $/Kgal/yr and rounded:
AVRO 0.331 @ $8.94 | E 27.324 @ N/A | I 27.477 @ N/A | T 36.974 @ N/A | PE
207.046 @ N/A | PET 168.932 @ N/A (English units: Kgal = thousands of gallons,
mi² = square miles)

34

Appendix 4 Delaware County Cover Assessment and Tree Benefits Report
Appendix 4 is the Benefits Report generated by the i-Tree Canopy Model for Delaware
County, based upon the user’s input and classification of random points generated. The
Appendix also includes a table showing the distribution of cover class, how many points
fall within the class, what percentage cover, and how much land area is occupied by that
cover class. Carbon storage and sequestration estimates are also listed in tables with their
carbon dioxide equivalent values. The USD value amount is also noted.

Figure 11. Distribution of points and their associated cover class in Delaware County.
Table 16. Cover class distribution and land area of each cover class in Delaware County.
Abbr. Cover class
Points
% cover ± SE
Area (mi²) ± SE
H
Grass/herbaceous
167
23.10
± 1.57 44.26
± 3.00
IB
Impervious buildings 42
5.81
± 0.87 11.13
± 1.67
IO
Impervious other
51
7.05
± 0.95 13.52
± 1.82
IR
Impervious road
49
6.78
± 0.93 12.99
± 1.79
S
Soil/bare ground
22
3.04
± 0.64 5.83
± 1.22
T
Tree/shrub
367
50.76
± 1.86 97.26
± 3.56
W
Water
25
3.46
± 0.68 6.63
± 1.30
Total
723
100.00
191.61

35

Table 17. Tree Benefit Estimates: Carbon (English units)
Description Carbon
±SE
±SE
Value (USD) ±SE
CO₂
(kT)
Equiv.
(kT)
Sequestered 84.97
±3.11
311.55
±11.41 $14,491,461 ±530,805
annually in
trees
Stored in
2,133.88 ±78.16 7,824.23 ±286.59 $363,934,966 ±13,330,505
trees
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Amount sequestered is
based on 0.874 kT of Carbon, or 3.203 kT of CO₂, per mi²/yr and rounded. Amount
stored is based on 21.940 kT of Carbon, or 80.446 kT of CO₂, per mi² and rounded.
Value (USD) is based on $170,550.73/kT of Carbon, or $46,513.84/kT of CO₂ and
rounded. (English units: kT = kilotons (1,000 tons), mi² = square miles)
Table 18. Tree Benefit Estimates: Air Pollution (English units)
Abbr. Description
Amount ±SE
Value
±SE
(T)
(USD)
CO
Carbon Monoxide removed annually 28.06
±1.03 $2,388
±87
NO2
Nitrogen Dioxide removed annually 153.02
±5.61 $4,111
±151
O3
Ozone removed annually
1,524.05 ±55.82 $214,077 ±7,841
SO2
Sulfur Dioxide removed annually
96.43
±3.53 $718
±26
PM2.5 Particulate Matter less than 2.5
74.06
±2.71 $442,536 ±16,210
microns removed annually
PM10 Particulate Matter greater than 2.5
510.50
±18.70 $155,414 ±5,693
microns and less than 10 microns
removed annually
Total
2,386.13 ±87.40 $819,245 ±30,008
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Air Pollution Estimates are
based on these values in T/mi²/yr @ $/T/yr and rounded: CO 0.289 @ $85.08 | NO2
1.573 @ $26.86 | O3 15.670 @ $140.47 | SO2 0.991 @ $7.45 | PM2.5 0.761 @
$5,975.67 | PM10* 5.249 @ $304.43 (English units: T = tons (2,000 pounds), mi² =
square miles)
Table 19. Tree Benefit Estimates: Hydrological (English units)
Abbr. Benefit
Amount (Kgal) ±SE
AVRO Avoided runoff
32.19
±1.18
E
Evaporation
2,657.53
±97.34
I
Interception
2,672.41
±97.89
T
Transpiration
3,596.05
±131.72
PE
Potential evaporation
20,137.31
±737.61
PET
Potential
16,430.36
±601.82
evapotranspiration

Value (USD)
$288
N/A
N/A
N/A
N/A
N/A

±SE
±11
N/A
N/A
N/A
N/A
N/A

36

Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Hydrological Estimates are
based on these values in Kgal/mi²/yr @ $/Kgal/yr and rounded:
AVRO 0.331 @ $8.94 | E 27.324 @ N/A | I 27.477 @ N/A | T 36.974 @ N/A | PE
207.046 @ N/A | PET 168.932 @ N/A (English units: Kgal = thousands of gallons,
mi² = square miles)

37

Appendix 5 Montgomery County Cover Assessment and Tree Benefits Report
Appendix 5 is the Benefits Report generated by the i-Tree Canopy Model for
Montgomery County, based upon the user’s input and classification of random points
generated. The Appendix also includes a table showing the distribution of cover class,
how many points fall within the class, what percentage cover, and how much land area is
occupied by that cover class. Carbon storage and sequestration estimates are also listed in
tables with their carbon dioxide equivalent values. The USD value amount is also noted.

Figure 12. Distribution of points and their associated cover class in Montgomery County
Table 20. Cover class distribution and land area of each cover class in Montgomery
County.
Abbr. Cover class
Points
% cover ± SE
Area (mi²) ± SE
H
Grass/herbaceous
629
33.12
± 1.08 162.33
± 5.29
IB
Impervious buildings 133
7.00
± 0.59 34.32
± 2.87
IO
Impervious other
126
6.64
± 0.57 32.52
± 2.80
IR
Impervious road
87
4.58
± 0.48 22.45
± 2.35
S
Soil/bare ground
26
1.37
± 0.27 6.71
± 1.31
T
Tree/shrub
875
46.02
± 1.14 225.56
± 5.61
W
Water
24
1.26
± 0.26 6.19
± 1.26
Total
1900
100.00
490.08

38

Table 21. Tree Benefit Estimates: Carbon (English units)
Description Carbon
±SE
±SE
Value (USD) ±SE
CO₂
(kT)
Equiv.
(kT)
Sequestered 197.05
±4.90
722.52
±17.96 $33,607,359 ±835,176
annually in
trees
Stored in
4,948.71 ±122.98 18,145.29 ±450.93 $844,006,888 ±20,974,409
trees
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Amount sequestered is based
on 0.874 kT of Carbon, or 3.203 kT of CO₂, per mi²/yr and rounded. Amount stored is
based on 21.940 kT of Carbon, or 80.446 kT of CO₂, per mi² and rounded. Value
(USD) is based on $170,550.73/kT of Carbon, or $46,513.84/kT of CO₂ and rounded.
(English units: kT = kilotons (1,000 tons), mi² = square miles)
Table 22. Tree Benefit Estimates: Air Pollution (English units)
Abbr. Description
Amount (T) ±SE
Value
±SE
(USD)
CO
Carbon Monoxide 65.08
±1.62
$5,537
±138
removed annually
NO2
Nitrogen Dioxide
354.88
±8.82
$9,533
±237
removed annually
O3
Ozone removed
3,534.46
±87.83
$496,469
±12,338
annually
SO2
Sulfur Dioxide
223.64
±5.56
$1,666
±41
removed annually
PM2.5 Particulate Matter 171.75
±4.27
$1,026,293 ±25,504
less than 2.5
microns removed
annually
PM10 Particulate Matter 1,183.91
±29.42
$360,424
±8,957
greater than 2.5
microns and less
than 10 microns
removed annually
Total
5,533.71
±137.52
$1,899,922 ±47,215
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Air Pollution Estimates are
based on these values in T/mi²/yr @ $/T/yr and rounded: CO 0.289 @ $85.08 | NO2
1.573 @ $26.86 | O3 15.670 @ $140.47 | SO2 0.991 @ $7.45 | PM2.5 0.761 @
$5,975.67 | PM10* 5.249 @ $304.43 (English units: T = tons (2,000 pounds), mi² =
square miles)

39

Table 23. Tree Benefit Estimates: Hydrological (English units)
Abbr. Benefit
Amount
±SE
Value
±SE
(Kgal)
(USD)
AVRO Avoided runoff
74.65
±1.86
$667
±17
E
Evaporation
6,163.13
±153.16
N/A
N/A
I
Interception
6,197.62
±154.02
N/A
N/A
T
Transpiration
8,339.66
±207.25
N/A
N/A
PE
Potential evaporation
46,700.72
±1,160.56 N/A
N/A
PET
Potential
38,103.88
±946.92
N/A
N/A
evapotranspiration
Currency is in USD and rounded. Standard errors of removal and benefit amounts are
based on standard errors of sampled and classified points. Hydrological Estimates are
based on these values in Kgal/mi²/yr @ $/Kgal/yr and rounded: AVRO 0.331 @ $8.94 |
E 27.324 @ N/A | I 27.477 @ N/A | T 36.974 @ N/A | PE 207.046 @ N/A | PET
168.932 @ N/A (English units: Kgal = thousands of gallons, mi² = square miles)

Statistical tables, figures, and/or illustrations not included in the journal manuscript
(optional) (Tables, figures, and illustrations are each given their own, numbered and titled
appendix.)

40

Appendix 6. Core Tree Variables used in i-Tree Eco (Nowak 2020)
Appendix 6 contains one table that describes the variables that are required for use of iTree Eco modeling. The tree variables include species, diameter at breast height (dbh),
total tree height, crown size (which has four variables: height to live top, height to crown
base, crown width, and percent crown missing), crown dieback, crown light exposure,
and energy (includes two variables: direction, distance).
Table 24. Tree variables required for use of i-Tree Eco tool.
Tree Variables
Description
Species
Identify and record the species and genus names of
each tree
Diameter at breast height
Exact measurement or categories of the tree stem
diameter at breast height (1.37 m) for each tree
Total tree height
Height from the ground to the top (alive or dead) of
the tree
Height to live top
Height from the ground to the live top of the tree
Height to crown
Height from the ground to the base of the live crown
base
Crown
Crown width
The width of the crown in two directions: north-south
size
and east-west
Percent crown
Percent of the crown volume that is not occupied by
missing
branches and leaves
Crown dieback
Estimate of the percent of the crown volume that is
composed of dead branch
Crown light exposure
Number of sides of the tree receiving sunlight from
above (maximum of 5)
Energy Direction
Direction from tree to the closest part of the building
Distance
Shortest distance from tree to the closest part of the
building

41

Appendix 7. Cover class data of each county studied.
Appendix 7 contains four tables. Each of the four tables is designated to one county
assessed in this research. Each table lists the number of plots, proportion of land area, and
percentage of land area of each cover class (i.e. grass/herbaceous, impervious building,
impervious other, impervious road, sand/bare ground, tree/shrub, and water).
Table 25. Proportion of cover class of Philadelphia County, PA (G = grass/herbaceous,
IB = impervious building, IO = impervious other, IR = impervious road, S = sand/bare
ground, T = tree/shrub, W = water).
Cover class
n (Number of plots) Proportion
Percentage
G
856
0.171
17.1
IB
1016
0.203
20.3
IO
995
0.199
19.9
IR
648
0.130
13.0
S
84
0.017
1.68
T
1160
0.232
23.2
W
241
0.048
4.82
Total
5000
1.00
100.
Table 26. Proportion of cover class of Bucks County, PA (G = grass/herbaceous, IB =
impervious building, IO = impervious other, IR = impervious road, S = sand/bare ground,
T = tree/shrub, W = water).
Cover class
n (Number of plots) Proportion
Percentage
G
744
0.313
31.3
IB
97
0.041
4.08
IO
96
0.040
4.04
IR
84
0.035
3.53
S
102
0.043
4.29
T
1181
0.497
49.7
W
73
0.031
3.07
Total
2377
1.00
100.
Table 27. Proportion of cover class of Delaware County, PA (G = grass/herbaceous, IB =
impervious building, IO = impervious other, IR = impervious road, S = sand/bare ground,
T = tree/shrub, W = water).
Cover class
n (Number of plots) Proportion
Percentage
G
167
0.231
23.1
IB
42
0.058
5.81
IO
51
0.071
7.05
IR
49
0.068
6.78
S
22
0.030
3.04
T
367
0.508
50.8
W
25
0.035
3.46
Total
723
1.00
100.

42

Table 28. Proportion of cover class of Montgomery County, PA (G = grass/herbaceous,
IB = impervious building, IO = impervious other, IR = impervious road, S = sand/bare
ground, T = tree/shrub, W = water).
Cover class
n (Number of plots) Proportion
Percentage
G
629
0.331
33.1
IB
133
0.070
7.0
IO
126
0.066
6.64
IR
87
0.046
4.58
S
26
0.014
1.37
T
875
0.460
46.0
W
24
0.013
1.26
Total
1900
1.00
100.

43