United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

Resource Bulletin 

NRS-75

Assessing Urban Forest 

Effects and Values: 

the Greater Kansas City Region

David J. Nowak

Allison R. Bodine

Robert E. Hoehn III

Daniel E. Crane

Alexis Ellis

Theodore A. Endreny

Yang Yang

Tom Jacobs

Kassie Shelton
Visit our homepage at: http://www.nrs.fs.fed.us/

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NEWTOWN SQUARE PA 19073 359 Main Road

 Delaware, OH 43015-8640

 Fax: (740)368-0152

February 2013 Email: nrspubs@fs.fed.us

Abstract

An analysis of trees in the greater Kansas City region of Missouri and Kansas 

reveals that this area has about 249,450,000 trees with tree and shrub canopy that 

covers 28.3 percent of the region. The most common tree species are American 

elm, northern hackberry, Osage-orange, honeylocust, and eastern redcedar. Trees 

in the greater Kansas City region currently store about 19.9 million tons of carbon 

(72.8 million tons CO2) valued at $411 million. In addition, these trees remove 

about 1.0 million tons of carbon per year (3.7 million tons CO2 per year valued 

at $20.7 million per year) and about 26,000 tons of air pollution per year ($198.3 

million per year). The greater Kansas City region’s trees are estimated to reduce 

annual residential energy costs by $14.0 million per year. The compensatory value 

of the trees is estimated at $93.4 billion. Loss of the current tree cover in the Blue 

River watershed of the greater Kansas City region would increase total fl ow over 

a 6.5-month period by an average of 2.3 percent (63.4 million ft3

). Information on 

the structure and functions of the urban forest can be used to inform urban forest 

management programs and to integrate urban forests within plans to improve 

environmental quality in the greater Kansas City region.

Cover Photo

Sister Cities Bridge across Brush Creek in the Plaza, Kansas City, MO.

Photo by Charvex, Wikimedia Commons.

Manuscript received for publication 2 February 2012
Assessing Urban Forest Effects and Values:

Greater Kansas City Region

David J. Nowak

Allison R. Bodine

Robert E. Hoehn III

Daniel E. Crane

Alexis Ellis

Theodore A. Endreny

Yang Yang

Tom Jacobs

Kassie Shelton

The Authors

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

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

ALLISON R. BODINE is a research urban forester with the 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.

DANIEL E. CRANE is an information technology specialist with the U.S. Forest 

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

ALEXIS ELLIS is a research urban forester with the Davey Institute at 

Syracuse, NY.

THEODORE A. ENDRENY is a professor with the SUNY College of 

Environmental Science and Forestry at Syracuse, NY.

YANG YANG is a research assistant with the SUNY College of Environmental 

Science and Forestry at Syracuse, NY.

TOM JACOBS is Director of Environmental Programs at Mid-America Regional 

Council (MARC) at Kansas City, KS.

KASSIE SHELTON is an environmental and watershed planner at MidAmerica Regional Council (MARC) at Kansas City, KS.
Jacob L. Loose Park, Kansas City MO. Photo by Mid-America Regional Council, used with permission.
1

Urban forests provide 

numerous benefi ts to 

society, yet relatively 

little is known about 

this important resource 

in the Kansas City 

region.

In 2010, the i-Tree Eco 

model was used to 

survey and analyze the 

Kansas City 

region’s urban 

forest.

The calculated 

environmental 

benefi ts of the 

urban forest are 

signifi cant, yet many 

environmental and 

social benefi ts still 

remain to be quantifi ed.

EXECUTIVE SUMMARY

Trees in urban and rural areas contribute signifi cantly to human health and 

environmental quality by providing various ecosystem services (i.e., the conditions and 

processes through which natural ecosystems, and the species which make them up, 

sustain and fulfi ll human life1

). To better understand the ecosystem services and values 

provided by trees, the U.S. Forest Service, Northern Research Station, developed the 

Urban Forest Eff ects (UFORE) model, which is now known as i-Tree Eco. Results 

from i-Tree models are used to advance the understanding of tree and forest resources; 

improve urban and rural forest policies, planning and management; provide data 

to support the potential inclusion of trees within environmental regulations; and 

determine how trees aff ect the environment and consequently enhance human health 

and environmental quality in urban and rural areas.

Th e i-Tree Eco model is used to help quantify forest structure, function, and values. 

Forest structure is a measure of various physical attributes of the vegetation, including 

tree species composition, number of trees, tree density, tree health, leaf area, biomass, 

and species diversity. Forest functions, which are determined by forest structure, 

include a wide range of environmental and ecosystem services such as air pollution 

removal and cooler air temperatures. Forest values are an estimate of the economic 

worth of the various forest functions.

To help determine the vegetation structure, functions, and values of trees in the 

greater Kansas City region (hereafter referred to as the Kansas City region), a 

vegetation assessment was conducted during autumn of 2010. For this assessment, 

0.1-acre fi eld plots were sampled and analyzed using the i-Tree Eco model. Th is report 

summarizes results and values of (Table 1):

• Forest structure

• Potential risk to trees from various insects or diseases

• Air pollution removal

• Carbon storage

• Annual carbon removal (sequestration)

• Changes in building energy use
2

Table 1.—Summary of regional forest features, Kansas City region, 2010

Feature Measure

Number of trees 249,450,000

Tree and shrub canopy cover 28.3%

Tree canopy cover 18.6%

Most common species American elm, northern hackberry, Osageorange, honeylocust, eastern redcedar

Trees < 6 inches diameter (%) 71.0%

Pollution removal, trees

All 5 pollutants 25,940 tons/year ($198 million/year)

Ozone 15,850 tons/year ($142million/year)

Particulate matter 6,030 tons/year ($36 million/year)

Sulfur dioxide 2,260 tons/year ($5.0 million/year)

Nitrogen dioxide 1,610 tons/year ($14.4 million/year)

Carbon monoxide 200 tons/year ($257,000/year)

Carbon storage 19.9 million tons ($411 million)

Carbon sequestration 1.0 million tons/year ($20.7 million/year)

Building energy reduction $14.0 million/year

Reduced carbon emissions $500,800/year

Structural value $93.4 billion

Ton – short ton (U.S.) (2,000 lbs)

In addition, a tree growth projection model was used to estimate annual tree canopy 

change based on tree data for the Kansas City region. Tree growth was based on 

various tree characteristics including species (growth rate, longevity, height at 

maturity), current tree size, crown competition, and tree condition. Th e model was 

used to consider several diff erent scenarios to estimate the number of trees that need 

to be established to meet desired canopy goals in the future. For several scenarios, the 

ecosystem services of these canopy goals were also summarized (Table 2).

A specialized analysis of the Blue River watershed of the Kansas City region was 

also completed using the i-Tree Hydro model.2

 i-Tree Hydro is a semi-distributed, 

physical-based model created to simulate tree eff ects on stream hydrology using local 

cover and elevation information, hourly weather data, and hourly stream fl ow data. 

Th is report details the stream fl ow response to changes in tree and/or impervious cover 

in the Blue River watershed.

Table 2.—Number of trees to be established to meet cover goals, Kansas City region, 2010a

Cover Goal

Land Use

Current 

cover (%)

Maintain 

cover

Increase 

cover 5%

Increase 

cover 10%

Increase 

cover 20%

Residential 31.4 1,250,000 1,800,000 2,200,000 3,100,000

Golf, Park, Institutional 34.3 350,000 400,000 460,000 610,000

Comm., Utilities, Transport. 9.6 18,000 75,000 138,000 265,000

Agriculture, Vacant 11.8 960,000 1,700,000 2,500,000 4,200,000

Water, Other 49.2 350,000 480,000 640,000 1,000,000

Total 18.6 2,928,000 4,555,000 5,938,000 9,175,000

a Estimated number of trees needed to be established annually by land use to achieve various canopy coverage goals 

in 50 years with 4 percent annual mortality. Most of these trees will likely occur through natural regeneration.
3

I-TREE ECO MODEL AND FIELD MEASUREMENTS

Urban trees and forests have many functions and values, but currently only a few of 

these attributes can be assessed due to a limited ability to quantify all of these values 

through standard data analyses. To help assess the Kansas City region’s urban and 

rural forests, data from 340 fi eld plots located throughout the region were analyzed 

using the U.S. Forest Service’s i-Tree Eco model (formerly known as UFORE).3

 Th is 

region was defi ned as the nine-county area surrounding the city of Kansas City: Cass, 

Clay, Jackson, Platte, and Ray counties in Missouri, and Johnson, Leavenworth, 

Miami, and Wyandotte counties in Kansas (Fig. 1).

Figure 1.—Map of the study area and fi eld plot distribution, Kansas City 

region, 2010.

Th e i-Tree Eco model uses standardized fi eld data from randomly located plots and 

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

numerous eff ects, including: 

• Forest structure (e.g., species composition, tree density, tree health, leaf area, 

leaf and tree biomass, species diversity, etc.)

• Amount of pollution removed hourly by the forest, and its associated percent 

air quality improvement throughout a year. Pollution removal is calculated 
4

Field Survey Data

Plot Information

• Land use

• Percent tree cover

• Percent shrub 

cover

• Percent plantable

• Percent ground 

cover types

• Shrub species/

dimensions

Tree parameters

• Species

• Stem diameter

• Total height

• Height to crown 

base

• Crown width

• Percent foliage 

missing

• Percent dieback

• Crown light 

exposure

• Distance and 

direction to 

buildings from trees

for ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, and particulate 

matter (<10 microns).

• Total carbon stored and net carbon annually sequestered by the urban forest.

• Eff ects of trees on residential building energy use and consequent eff ects on 

carbon dioxide emissions from power sources.

• Compensatory value of the forest, as well as the value of air pollution removal 

and carbon storage and sequestration.

• Potential impact of infestations by Asian longhorned beetles, emerald ash 

borers, gypsy moth, Dutch elm disease, or thousand cankers disease.

In the fi eld, 0.1-acre plots were selected based on a randomized grid with an average 

density of approximately one plot for every 8,326 acres. Th e study is divided into 

smaller areas based on a land-use classifi cation recorded in the fi eld. Th e plots were 

divided among the following land uses (Fig. 2): agriculture and vacant (194 plots, 57.1 

percent of area); residential (86 plots, 25.3 percent); “other” (21 plots, 6.2 percent); 

commercial/utility/transportation (20 plots, 5.9 percent); and golf/park/institutional 

(19 plots, 5.6 percent). Th e land use “other” includes water/wetland land-use types as 

well as those that do not fall into one of the previously mentioned categories.4

Ag/Vacant Residential 57.1% 

25.3% 

Other 

6.2% 

Comm/Util/Trans 

5.9% 

Golf/Park/Inst 

5.6% 

Figure 2.—Land-use distribution, Kansas City region, 

2010, for inventoried plots.

Field data were collected by two, two-person teams. Each team consisted of a Davey 

Resource Group project manager and a Mid-America Regional Council (MARC) 

intern. Data collection occurred during the leaf-on season of autumn 2010 to properly 

assess tree canopies. Within each plot, data included land use, ground and tree cover, 

shrub characteristics, and individual tree attributes of species, stem diameter at breast 

height (d.b.h.; measured at 4.5 ft, hereafter referred to as stem diameter), tree height, 
5

height to base of live crown, crown width, percentage crown canopy missing and 

dieback, and distance and direction to residential buildings.4

 Trees were recorded as 

woody plants with a stem diameter greater than or equal to 1 inch. As many species 

are classifi ed as small tree/large shrub, the 1-inch minimum stem diameter of all 

species means that many species commonly considered as shrubs will be included in 

the species tallies when they meet the minimum diameter requirement.

To estimate current carbon storage, biomass for each tree was calculated using 

equations from the literature and measured tree data. Open-grown, maintained trees 

tend to have less biomass than predicted by forest-derived biomass equations.5

 To 

adjust for this diff erence, biomass results for open-grown trees are multiplied by 0.8.5

No adjustment was made for trees found in natural stand conditions. Tree dry-weight 

biomass was converted to stored carbon by multiplying by 0.5.5

To estimate the gross amount of carbon sequestered annually, average diameter 

growth from appropriate genera and 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.

Air pollution removal estimates were calculated for carbon monoxide (CO), ozone 

(O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter less 

than 10 microns (PM10). 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.6,7 As the removal of CO and particulate matter by vegetation is 

not directly related to transpiration, removal rates (deposition velocities) for these 

pollutants were based on average measured values from the literature8,9 that were 

adjusted depending on leaf phenology and leaf area. Particulate removal incorporated 

a 50 percent resuspension rate of particles back to the atmosphere10.

Seasonal eff ects of trees on residential building energy use was calculated based on 

procedures described in the literature11 using distance and direction of trees from 

residential structures, tree height, and tree condition data.

Compensatory values were based on valuation procedures of the Council of Tree and 

Landscape Appraisers12, which uses tree species, diameter, condition, and location 

information.12

To learn more about i-Tree Eco methods3,13 refer to: http://nrs.fs.fed.us/tools/ufore/, 

or www.itreetools.org.
6

TREE CHARACTERISTICS OF KANSAS CITY’S 

REGIONAL FOREST

Th e Kansas City region has an estimated 249,450,000 trees with a standard error (SE) 

of 25,410,000. Tree and shrub cover is estimated from the photo-interpretation of 

Google Earth imagery of 3,000 random points. Tree and shrub cover in the Kansas 

City region is estimated to cover 28.3 percent of land area.14 As it is diffi cult to 

diff erentiate between trees and shrubs from aerial imagery, the plot estimates of tree 

and shrub cover separately were used to diff erentiate between tree and shrub cover. 

Based on the fi eld data in conjunction with photo-interpretation14, tree cover in the 

Kansas City region is estimated to be 18.6 percent. Th e number of trees and percent 

cover by county is displayed in Tables 3 and 4 and Figure 3.

Table 3.—Number of trees by county, Kansas City region, 2010

Trees

County Number %Popa Trees/Acre

Cass 43,740,000 17.5 97.3

Clay 26,940,000 10.8 103.1

Jackson 32,540,000 13.1 82.5

Johnson 25,490,000 10.2 83.0

Leavenworth 33,210,000 13.3 110.6

Miami 38,700,000 15.5 102.4

Platte 19,590,000 7.9 71.7

Ray 22,710,000 9.1 61.8

Wyandotte 6,530,000 2.6 65.4

Total 249,450,000 100.0 88.1

 a Percent of total population

Table 4.—Percent tree/shrub and impervious cover in each county, 

Kansas City region, 2010

Tree/Shruba Imperviousa Tree Coverb

County % SE % SE %

Cass 24.8 1.9 3.4 0.8 17.3

Clay 30.1 2.7 9.2 1.7 19.5

Jackson 36.0 2.3 14.1 1.7 21.5

Johnson 23.7 2.4 16.0 2.0 15.7

Leavenworth 31.1 2.7 3.7 1.1 20.7

Miami 23.6 2.1 2.8 0.8 16.8

Platte 30.4 2.7 4.7 1.2 19.5

Ray 26.0 2.3 3.0 0.9 15.2

Wyandotte 35.0 4.7 24.3 4.2 27.8

Region 28.3 0.8 7.6 0.5 18.6

a Based on photo-interpretation13

b Based on photo-interpretation in conjunction with fi eld estimates13

There are an 

estimated 249,450,000 

trees in the Kansas 

City region with tree 

and shrub canopy that 

covers 28.3% of the 

region.

Trees alone cover 

18.6% of the region.
7

Th e fi ve most common species in the region’s urban and rural forest were American 

elm (28.9 percent), northern hackberry (14.0 percent), Osage-orange (7.2 percent), 

honeylocust (6.7 percent), and eastern redcedar (5.0 percent). Th e 10 most common 

species account for 77.9 percent of all trees; their relative abundance is illustrated in 

Figure 4. Th e fi ve most common species by county are shown in Table 5. Fifty-one 

tree species were sampled in the Kansas City region; these species and their relative 

abundance are presented in Appendix I. More information on species distribution by 

land use is given in Appendix II.

0

5

10

15

20

25

30

35

40

Percent Tree and Shrub Cover 

Figure 3.—Percent tree and shrub cover by county and for Kansas City region, 2010.

American elm 

28.9% 

Northern hackberry 

14.0% 

Osage-orange 

7.2% 

Honeylocust 

6.7% 

Eastern redcedar 

5.0% 

Black walnut 

4.1% 

Bitternut hickory 

3.7% 

White mulberry 

3.0% 

Boxelder 

2.9% 

Shagbark hickory 

2.4% 

other species 

22.1% 

Figure 4.—Tree species composition, Kansas City region, 2010.
8

Table 5.—Five most common species in each county, Kansas City region, 2010

County Common Name Percent of County Population

Cass American elm 32.5

Northern hackberry 19.9

Honeylocust 6.4

Bitternut hickory 5.5

Osage-orange 5.5

Clay American elm 26.5

Ohio buckeye 11.3

Northern hackberry 8.5

Bitternut hickory 6.4

Osage-orange 5.2

Jackson American elm 32.3

Northern hackberry 17.7

Honeylocust 9.5

White mulberry 5.7

White ash 3.5

Johnson American elm 33.2

Northern hackberry 16.6

Osage-orange 16.1

Green ash 3.9

Black walnut 3.4

Leavenworth American elm 38.3

Eastern redcedar 12.3

Honeylocust 7.9

Osage-orange 6.9

Boxelder 6.6

Miami American elm 22.7

Honeylocust 15.5

Osage-orange 12.2

Eastern redcedar 11.6

Northern hackberry 9.6

Platte American elm 22.8

Pawpaw 12.9

Black walnut 12.3

Northern hackberry 10.5

Chinkapin oak 7.0

Ray Northern hackberry 23.0

American elm 15.5

Bitternut hickory 7.5

Shagbark hickory 6.3

Osage-orange 6.0

Wyandotte Eastern redcedar 22.0

American elm 22.0

Northern hackberry 16.5

Black willow 14.3

Black walnut 7.7

The most common species 

in the Kansas City region:

American elm (28.9%)

Northern hackberry (14.0%)

Osage-orange (7.2%)

Honeylocust (6.7%)

Eastern redcedar (5.0%).

Examining the distribution 

by county, American elm 

was the most common 

species in 7 of 9 counties.
9

Th e highest densities of trees occur in “other” areas (320 trees/ac), followed by golf/

park/institutional (135 trees/ac), and residential land (122 trees/ac) (Fig. 5). Th e tree 

density for the entire Kansas City region is 88.1 trees/ac. Th e highest densities of 

trees occur in Leavenworth (111 trees/ac), Clay (103 trees/ac), and Miami (102 trees/

ac) counties (Fig. 6). Land uses that contain the greatest percentage of the total tree 

population are residential (34.9 percent), followed by agriculture and vacant (32.5 

percent) and “other” (22.4 percent). More information on the tree species in each land 

use and their structure and functions is given in Appendix III. Cass County contains 

the greatest percentage of the total tree population (17.5 percent), followed by Miami 

(15.5 percent) and Leavenworth (13.3 percent).

Figure 5.—Number of trees and tree density by land use, Kansas City region, 2010.

Figure 6.—Number of trees and tree density by county, Kansas City region, 2010.

0

50

100

150

200

250

300

350

0

10

20

30

40

50

60

70

80

90

Residential Ag/Vacant Other Golf/Park/Inst Comm/Util/Trans

Density (trees per acre) 

Number of Trees (millions) 

Land Use 

Number of trees

Density

0

20

40

60

80

100

120

0

5

10

15

20

25

30

35

40

45

50

Cass Clay Jackson Johnson Leavenworth Miami Platte Ray Wyandotte

Density (trees per acre) 

Number of Trees (millions) 

County 

Number of trees

Density
10

Leaf area is highest in residential (42.7 percent of total tree leaf area) and agriculture 

and vacant (33.0 percent) land-use types (Fig. 7). Leaf area index (LAI) is an estimate 

of the total leaf area (one-sided) divided by land area. As each land use has a diff erent 

land area, LAI standardizes the canopy depth on an equal area basis. Higher LAIs 

indicate a greater number of leaves per acre. Land uses that have the highest LAI are 

“other” (2.7), residential (2.0), and golf/park/institutional (1.7).

Trees that have diameters less than 6 inches account for 71.0 percent of the population. 

Th is diameter class also contains 26.3 percent of the total leaf area. Trees that have 

diameters greater than 18 inches account for 2.8 percent of the population and comprise 

19.4 percent of the total leaf area. Th ough these larger trees are a small percentage of 

the population, they are an important part of the urban and rural forests in the Kansas 

City region. Leaf area has a strong correlation with benefi ts that the trees produce for 

the ecosystem, such as pollution removal; the percent of abundance and leaf area 

contributed by each tree diameter class and species are illustrated in Figures 8 and 9.

Tree populations vary in diameter class distribution between the small (<3 inches stem 

diameter) and large trees (>18 inches stem diameter). Most of the small trees tend to 

be on agriculture and vacant, residential, or “other” land uses, while most of the large 

trees tend to be on residential land (see Appendix IV). Th e small tree population tends 

to be dominated by American elm, northern hackberry and eastern red cedar, with a 

distribution that varies among the land-use classes (see Appendix IV). Fourteen percent 

of the small trees are American elms on “other” lands. Two of the 10 most common 

small trees are classifi ed as invasive: white mulberry (ninth most common) and Osageorange (10th). However, in comparing the large versus small tree distribution by species 

(Appendix IV), these two species had a greater percentage of large trees than small trees, 

indicating that these species may not be regenerating very well given the number of 

large trees (or various eradication procedures may be reducing the number of small 

trees). Two other common species, bur oak and black walnut, had a greater percent 

of large trees than small trees, which is an indication that there is likely not enough 

regeneration of these species to sustain the current species population through time.

0

0.5

1

1.5

2

2.5

3

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Ag/Vacant Comm/Util/Trans Residential Golf/Park/Inst Other

Leaf Area Index (LAI) 

Leaf Area (million acres) 

Land Use 

Leaf area

Leaf area index

Figure 7.—Total leaf area and leaf area index by land use, Kansas City region, 2010.
11

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 (d.b.h. in inches) 

Leaf area

Abundance

American elm

Northern hackberry

Osage-orange

Honeylocust

Eastern redcedar

Black walnut

Bitternut hickory

White mulberry

Boxelder

Shagbark hickory

0

10

20

30

40

50

60

70

1-3

3-6

6-9

9-12

12-15

15-18

18-21

21-24

24-27

27-30

30+

Species 

Species Population (percent) 

Diameter Class 

(d.b.h. in inches) 

Figure 8.—Percent of total tree population (abundance) and leaf area by tree diameter 

class, Kansas City region, 2010.

Figure 9.—Percent of species population by diameter class for 10 most common 

tree species, Kansas City region, 2010.
12

Urban forests are a mix of native tree species that existed prior to the development 

of the area and exotic species that were introduced by residents or other means. 

Th us, urban forests often have a tree diversity that is higher than surrounding native 

landscapes. Increased tree diversity can minimize the overall impact or destruction 

by a species-specifi c insect or disease, but the increase in the number of exotic plants 

can also pose a risk to native plants if exotic species are invasive and out-compete and 

displace native species. In the Kansas City region, about 87.9 percent of the trees are 

native to Missouri or Kansas (Fig. 10). Trees with a native origin outside of North 

America are mostly from Asia (3.6 percent of species).

0

10

20

30

40

50

60

70

80

90

100

Missouri or

Kansas

North

America

Asia Eurasia North

America +*

Unknown Europe

Total Population (percent) 

Area of Native Origin 

* native to North America and one other continent, excluding South America 

** native to North America and South America, and one other continent 

Figure 10.—Percent of tree population by area of native origin, Kansas City region, 2010.

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

reproductive capacity, and general lack of natural enemies. Th ese abilities enable them 

to displace native plants and make them a threat to the native landscape.15 Several of 

the tree species sampled in the Kansas City region have been identifi ed on the Missouri 

invasive species list.16 Th ese species comprise 10.3 percent of the population: Osageorange (7.2 percent of the population), white mulberry (3.0 percent), and Siberian elm 

(0.1 percent). Osage-orange, white mulberry, and Siberian elm are all considered to be 

potentially invasive (they are invasive in surrounding states) or invasive to the state of 

Missouri though they may only cause a minimal level of impact. None of the species 

sampled in the Kansas City region were identifi ed on the Kansas invasive species list.16

Amur honeysuckle and other honeysuckle species are nonnative shrub species in Missouri 

and Kansas. Th ey are present in the Kansas City region, most notably in residential areas 

and demonstrate invasive characteristics. Due to the fact that these woody shrubs tend 

to be smaller than 1 inch in stem diameter, they were not included in the tree analysis. 

However, Amur honeysuckle and other honeysuckle species together make up 3.3 percent 

of total shrub leaf area and 2.6 percent of total shrub leaf biomass.

Urban forests are a mix 

of native tree species 

that existed prior to 

development and exotic 

species that were 

introduced by residents 

or other 

means.
13

TREE AND FOREST COVER AND LEAF AREA

Trees cover about 18.6 percent of the Kansas City region; shrubs cover 9.7 percent of 

the area. Dominant ground-cover types include herbaceous (79.7 percent), duff /mulch 

cover (8.2 percent), and bare soil (3.4 percent) (Fig. 11).

Many tree benefi ts are linked directly to the amount of healthy leaf surface area of the 

plant. In the Kansas City region, trees that dominate in terms of leaf area are American 

elm, northern hackberry, and Osage-orange.

Tree species with relatively large individuals contributing leaf area to the population 

(species with percentage of leaf area much greater than percentage of total population) 

are black walnut, green ash, and Osage-orange. Smaller trees in the population are 

Ohio buckeye, pawpaw, and honeylocust (species with percent of leaf area much less 

than percent of total population). Th e species must also have constituted at least 1 

percent of the total population to be considered as relatively large or small trees in the 

population.

Th e importance value (IV) is calculated using a formula that takes into account the 

relative leaf area and relative abundance (Fig. 12). Th e most important species in the 

urban forest, according to calculated IVs, are American elm, northern hackberry, and 

Osage-orange (Table 6). High importance values do not mean that these trees should 

necessarily be used in the future, rather that these species currently dominate the urban 

and rural forest structure.

Figure 11.—Percent of the region and land-use areas covered by ground-cover classes, 

Kansas City region, 2010.

0 20 40 60 80 100

Ag/Vacant

Comm/Util/Trans

Residential

Golf/Park/Inst

Other

Kansas City Region

Percent of Land Use 

Land Use 

water bare soil

herbaceous duff/mulch cover

impervious surfaces (excluding buildings) buildings

Ground Cover Class
14

0

5

10

15

20

25

30

American

elm

Northern

hackberry

OsageorangeBlack walnut Eastern

cottonwood

Eastern 

UHGcedar

American

basswood

Silver maple White

mulberry

Bitternut

hickory

Percent 

Species 

Leaf area

Abundance

Figure 12.—Percent of total population (abundance) and leaf area for 10 most common 

tree species, Kansas City region, 2010.

Table 6.—Percent of total population, percent of total leaf and 

importance values of species with the greatest importance 

values, Kansas City region, 2010

Common Name %Popa %LAb IVc

American elm 28.9 17.7 46.6

Northern hackberry 14.0 15.8 29.8

Osage-orange 7.2 10.7 17.9

Black walnut 4.1 10.4 14.5

Honeylocust 6.7 2.2 8.9

Eastern redcedar 5.0 3.4 8.4

Bitternut hickory 3.7 2.6 6.3

White mulberry 3.0 2.7 5.7

Boxelder 2.9 2.3 5.2

Bur oak 2.1 2.6 4.7

a %Pop - percent of total tree population

b %LA - percent of total leaf area

c Importance Value (IV) = %Pop + %LA

American elm has the 

greatest importance 

in the Kansas City 

region’s urban forest 

based on relative 

leaf area and relative 

population.
15

AIR POLLUTION REMOVAL BY TREES AND FORESTS

Poor air quality is a common problem in the Kansas City region as well as in many 

other urban areas. It can lead to human health problems, damage to landscape materials 

and ecosystem processes, and reduced visibility. Th e urban forest can help improve 

air quality by reducing air temperature, directly removing pollutants from the air, and 

reducing energy consumption in buildings, which consequently reduce air pollutant 

emissions from power sources. Trees also emit volatile organic compounds that can 

contribute to ozone formation. However, integrative studies have revealed that an 

increase in tree cover leads to reduced ozone formation.17

Th e monetary value of pollution removal by trees is estimated using the median 

externality values for the United States for each pollutant. Th ese values are: nitrogen 

dioxide (NO2) = $6,127/ton, particulate matter less than 10 microns (PM10) = $4,091/

ton, sulfur dioxide (SO2) = $1,500/ton, and carbon monoxide (CO) = $870/ton.18

Recently, these values were adjusted to 2007 values based on the producer’s price 

index19 and are now: NO2 = $8,989/ton, PM10 = $6,002/ton, SO2 = $2,201/ton, and 

CO = $1,277/ton. Externality values for ozone (O3) are set to equal the value for NO2.

3

Pollution removal by trees and shrubs (28.3 percent tree and shrub cover) in the 

Kansas City area was estimated using the i-Tree Eco model in conjunction with fi eld 

data and hourly pollution and weather data for the year 2009. Pollution removal by 

trees and shrubs (Fig. 13) was greatest for O3 (23,040 tons), followed by PM10 (8,381 

tons), SO2 (3,302 tons), NO2 (2,297 tons), and CO (307 tons). It is estimated that trees 

only remove 25,900 tons of air pollution (CO, NO2, O3, PM10, SO2) per year with an 

associated value of $198.3 million (Table 7). Th e eff ects of shrub cover (Table 8) in the 

Kansas City region (9.7 percent cover including shrubs beneath canopies) would remove 

an additional estimated 11,400 tons/year ($87.4 million/year). Th us, tree and shrub 

cover combined remove approximately 37,300 tons of pollution/year ($286 million/year).

0

50

100

150

200

250

0

5

10

15

20

25

CO NO2 O3 PM10 SO2

Value (million dollars / year) 

Pollution Removed (thousand tons / year) 

Pollutant 

Pollution removed

Value

Figure 13.—Annual air pollution removal and value by trees and shrubs, Kansas City 

region, 2009.

The trees and 

shrubs in the 

Kansas City 

region remove 

approximately 

37,300 tons of 

air pollution 

each year, with a 

societal value of 

$286 million/year.

General urban 

forest management 

recommendations 

to improve air 

quality are given in 

Appendix V.
16

Table 7.—Annual air pollution removal and value by trees, Kansas City region, 2009a

Pollutant Removal (tons) Value (U.S. $1,000)

Ozone 15,850 (3,250-19,000) 142,438 (29,216-170,761)

Particulate matter < 10 micronsb 6,030 (2,350-9,420) 36,167 (14,128-56,511)

Sulfur dioxide 2,260 (840-3,550) 4,968 (1,852-7,803)

Nitrogen dioxide 1,610 (530-1,790) 14,436 (4,762-16,134)

Carbon monoxide 200 257

Total 25,940 (7,180-33,960) 198,265 (50,214-251,465)

a,b See explanation below Table 8

Table 8.—Annual air pollution removal and value by shrubs, Kansas City region, 2009a

Pollutant Removal (tons) Value (U.S. $1,000)

Ozone 7,190 (1,580-9,800) 64,612 (14,185-88,099)

Particulate matter < 10 micronsb 2,350 (920-3,680) 14,122 (5,516-22,066)

Sulfur dioxide 1,040 (420-1,830) 2,297 (923-4,031)

Nitrogen dioxide 690 (260-920) 6,207 (2,321-8,259)

Carbon monoxide 110 134

Total 11,380 (3,280-16,340) 87,371 (23,080-122,588)

a Estimated tons of pollution removed by trees in the Kansas City region (2009) and associated monetary value 

(thousands of dollars); numbers in parentheses represent expected range of values (no range determined for carbon 

monoxide). Monetary value of pollution removal by trees estimated using median externality values for United States 

for each pollutant.18

b

Assumes 50 percent resuspension of particles

Pollution removal by tree and shrub cover in 2009 (37,300 tons/year) was 25.2 

percent less than pollution removal by tree and shrub cover in 2005 (50,000 tons/

year). Th ese data refl ect the diff erences in pollution concentration and meteorology 

in 2005 and 2009. In general, air temperatures and concentrations of pollutants were 

higher in the Kansas City region in 2005 than they were in 2009.

In 2005 and 2009, trees in the Kansas City region emitted 260,310 tons of volatile 

organic compounds (VOCs; 118,348 tons of isoprene, 39,117 tons of monoterpene, 

and 102,846 tons of other VOCs) and 91,221 tons of VOCs (39,213 tons of 

isoprene, 14,327 tons of monoterpene, and 37,682 tons of other VOCs), respectively. 

Th e diff erences in emissions between years being due to diff erences in meteorology 

(e.g., air temperatures, sunshine). Land uses with the highest VOC emissions were 

residential, agriculture, and vacant, and “other” for both years. Forty percent of 

the Kansas City region’s VOC emissions were from the Quercus (oak) and Maclura

(Osage-orange) genera in 2005, while the same genera accounted for 39 percent of the 

region’s VOC emissions in 2009. Figure 14 illustrates the annual VOC emissions by 

genera in 2009.
17

Th ese VOCs are a precursor chemical to O3 formation. Some economic studies have 

estimated VOC emission costs. Th ese costs are not included here as there is a tendency 

to add positive dollar estimates of ozone removal eff ects with negative dollar values 

of VOC emission eff ects to determine whether tree eff ects are positive or negative in 

relation to ozone. Combining dollar values to determine overall eff ects should not be 

done; instead, estimates of VOC impacts on O3 formation (e.g., via photochemical 

models) should be contrasted with ozone removal by trees (i.e., O3 eff ects should 

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

by trees have been shown to signifi cantly reduce O3 concentrations20, but are not 

considered in this analysis. Modeling that integrates tree eff ects on air temperature, 

pollution removal, VOC emissions, and emissions from power plants can be used to 

determine the overall eff ect of trees on O3 concentrations. General recommendations 

for air quality improvement with trees are given in Appendix V.

0 5 10 15 20 25 30

Carya

Acer

Platanus

Celtis

Ulmus

Juniperus

Populus

Juglans

Maclura

Quercus

VOCs Emitted (thousand tons) 

Genera 

Isoprene

Monoterpene

Other VOCs

Figure 14.—Annual volatile organic compound (VOC) emissions by genera with highest 

total emissions, Kansas City region, 2009.
18

CARBON STORAGE AND SEQUESTRATION

Climate change is an issue of global concern to many people. Tree and forest resources 

can help mitigate climate change by sequestering atmospheric carbon (from carbon 

dioxide) in tissue and by reducing energy use in buildings, and consequently 

reducing carbon dioxide emissions from fossil-fuel and wood-based power sources.21

Trees reduce the amount of carbon in the atmosphere by sequestering carbon 

in new tissue growth every year. Th e amount of carbon annually sequestered is 

greater for healthier trees and larger diameter trees. Gross sequestration by trees in 

the Kansas City region is about 1.0 million tons of carbon/yr (3.7 million tons/

year of CO2) with an associated value of $20.7 million/year (Fig. 15).22 Net carbon 

sequestration in the Kansas City region is estimated at about 667,000 tons/year (2.4 

million tons/yr of CO2) based on estimated carbon loss due to tree mortality and 

decomposition.

Carbon storage by trees is another way trees can infl uence global climate change. As 

trees grow, they store more carbon by holding it in their accumulated tissue. When 

trees die and decay, they release much of the stored carbon back to the atmosphere. 

Th us, carbon storage is an indication of the amount of carbon that could 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.23 When trees 

die, using the wood in long-term wood products or using wood to heat buildings or 

produce energy will help reduce carbon emissions from wood decomposition or from 

fossil-fuel or wood-based power sources.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0

20

40

60

80

100

120

140

160

American

elm

Northern

hackberry

OsageorangeBlack walnut Honeylocust White

mulberry

Boxelder Shagbark

hickory

Bur oak Bitternut

hickory

Value (million dollars / year) 

Sequestration (thousand tons / year)

Carbon sequestration

Value

Figure 15.—Annual carbon sequestration and value for the tree species with the greatest 

total sequestration, Kansas City region, 2010.

Urban forests may play 

important roles 

in capturing 

and storing 

carbon dioxide 

from the 

atmosphere. 

Net carbon 

sequestration is 

positive in healthy and 

actively growing trees, 

but can be negative 

if emission of carbon 

from decomposition 

is greater than 

sequestration by 

healthy trees.
19

Trees in the Kansas City region store an estimated 19.9 million tons of carbon (72.8 

million tons of CO2) ($411 million). Of all the species sampled, Osage-orange stores 

the most carbon (approximately 18.7 percent of total carbon stored) and American 

elm annually sequesters the most carbon (15.4 percent of all sequestered carbon). 

Total and average carbon storage and sequestration by diameter class are illustrated in 

Figures 16 and 17.

0

10

20

30

40

50

60

70

80

90

100

0

1

2

3

4

5

6

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 (d.b.h. in inches) 

Carbon storage

Carbon sequesteration

Figure 16.—Total carbon storage and sequestration by diameter class, Kansas City 

region, 2010.

Figure 17.—Average carbon storage and sequestration by diameter class, Kansas City 

region, 2010.

0

20

40

60

80

100

120

140

160

180

0.0

0.5

1.0

1.5

2.0

2.5

3.0

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

Total Sequestration (thousand tons / year) 

Total Storage (million tons) 

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

Carbon storage

Carbon sequestration

Carbon storage:

Carbon currently held 

in tree tissue (roots, 

stems, and branches) 

in the Kansas City 

region’s urban forest 

is 19.9 million tons 

valued at $411 million.

Carbon 

sequestration:

The estimated amount 

of carbon removed 

annually by the Kansas 

City region’s trees is 

1.0 million tons/year 

with a value of $20.7 

million annually.
20

TREES AFFECT ENERGY USE IN BUILDINGS

Trees aff ect energy consumption 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. To enhance 

or sustain evaporative cooling by trees in the Kansas City region, many trees are 

or may need to be irrigated. Estimates of tree eff ects on energy use are based on 

fi eld measurements of tree distance and direction to space-conditioned residential 

buildings.11

Based on average energy costs in 2009, trees in the Kansas City region reduce energy 

costs from residential buildings by an estimated $14.0 million annually. Trees also 

provide an additional $500,800 in value per year by reducing amount of carbon 

released by fossil-fuel based power sources (a reduction of 24,100 tons of carbon 

emissions or 88,500 tons of carbon dioxide). Energy savings are illustrated in Tables 9 

and 10. Th is study did not attempt to estimate energy conservation benefi ts associated 

with commercial or institutional buildings or from urban heat island reduction.

Table 9.—Annual energy savings (MBTUs, MWHs, or tons) due to trees near 

residential buildings, Kansas City region, 2010

Heating Cooling Total

MBTUa 572,800 n/a 572,800

MWHb 8,800 53,600 62,400

Carbon avoided (tons) 11,100 13,000 24,100

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

b MWH – Megawatt-hour

Table 10.—Annual monetary savingsc (dollars) in residential energy 

expenditures during heating and cooling seasons, Kansas City region, 2010

Heating Cooling Total

MBTUa 7,716,000 n/a 7,716,000

MWHb 880,000 5,377,000 6,257,000

Carbon avoided 231,000 269,800 500,800

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

b MWH – Megawatt-hour

c

 Based on 2009 statewide energy costs24

Trees affect energy consumption by shading buildings, providing 

evaporative cooling, and blocking winter winds.

Interactions between buildings and trees save an estimated $14.0 

million/year in heating and cooling costs.

Lower energy use in residential buildings reduced carbon emissions 

from power plants by 24,100 tons/year ($500,800/year).

Photo by Mid-America Regional Council, 

used with permission.
21

STRUCTURAL AND FUNCTIONAL VALUES

Urban and rural forests have a structural value based on the tree itself, including 

compensatory value (e.g., the cost of having to replace the tree with a similar tree) 

and a carbon storage value. Th e compensatory value12 of the trees (Fig. 18) and forests 

in the Kansas City region is about $93.4 billion. Th e structural value of an urban or 

rural forest tends to increase with a rise in the number and size of healthy trees.

Urban and rural forests also have functional values (either positive or negative) based 

on the functions the tree performs. 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. Th ere are many other functional values of the tree and forest 

resource, though they are not quantifi ed here (e.g., reduction in air temperatures and 

ultraviolet radiation, improvements in water quality, aesthetics, wildlife habitat, etc.). 

Th rough proper management, tree and forest values can be increased. However, the 

values and benefi ts also can decrease as the amount of healthy tree cover declines.

Structural values:

• Compensatory value: $93.4 billion

• Carbon storage: $411 million

Annual functional values:

• Carbon sequestration: $20.7 million

• Pollution removal: $198 million 

• Reduced energy costs: $14.0 million

More detailed information on the trees and forests in the Kansas City region can be 

found at http://nrs.fs.fed.us/data/urban, http://nrs.fs.fed.us/data/urban/state/?state=KS 

and http://nrs.fs.fed.us/data/urban/state/?state=MO. Additionally, information on tree 

statistics by diameter class can be found in Appendix VI and priority planting areas are 

detailed in Appendix VII.

Figure 18.—Tree species with the greatest compensatory value, Greater Kansas City region, 2010.

0

2

4

6

8

10

12

14

Compensatory Value (billions of dolalrs) 

Osage-orange

Northern hackberryAmerican elm

Black walnut

Honeylocust

Bur oak

Green ash

White mulberry

Northern red oak

Silver maple

Urban forests 

have a structural 

value based on 

the characteristics 

of the trees 

themselves.

Urban forests also 

have functional 

values based on 

the ecosystem 

functions the trees 

perform.

Large, healthy, 

long-lived trees 

provide the greatest 

structural and 

functional values.
22

POTENTIAL INSECT AND DISEASE IMPACTS

Various insects and diseases can infest urban and rural forests, potentially killing trees 

and reducing the health, value, and sustainability of the forest resource. As various pests 

have diff ering tree hosts, the potential damage or risk of each pest will diff er. Five exotic 

pests/diseases were analyzed for their potential impact: Asian longhorned beetle, gypsy 

moth, emerald ash borer, Dutch elm disease, and thousand cankers disease (Fig. 19). 

Species host lists used for these pests/diseases can be found at: http://nrs.fs.fed.us/tools/

ufore/.

Th e Asian longhorned beetle (ALB)25 is an insect that bores into and kills a wide range 

of hardwood species. Th is beetle was discovered in 1996 in Brooklyn, NY, and has 

subsequently spread to Long Island, Queens, and Manhattan. In 1998, the beetle was 

discovered in the suburbs of Chicago, IL, and successfully declared eradicated in 2006. 

Beetles have also been found in Jersey City, NJ (2002), Toronto/Vaughan, Ontario 

(2003), and Middlesex/Union counties, NJ (2004). In 2007, the beetle was found on 

Staten and Prall’s Island, NY. Most recently, beetles were detected in Worcester, MA 

(2008). While this beetle is not currently in the Kansas City region, it does represent a 

potential loss to the area of $28.6 billion in compensatory value (42.1 percent of live tree 

population).

Th e gypsy moth (GM)26 is a defoliator that feeds on many species causing widespread 

defoliation and tree death if outbreak conditions last several years. Currently GM is 

located in northeast Missouri and is moving further west annually. Th is pest could 

potentially result in damage to or a loss of $16.8 billion in compensatory value of the 

Kansas City region’s trees (11.4 percent of live tree population).

Asian longhorned 

beetle

Photo by Kenneth R. Law 

USDA APHIS PPQ, www.invasive.org

0

5

10

15

20

25

30

35

0

10

20

30

40

50

60

70

80

90

100

ALB GM EAB DED TCD

Compensatory Value (billions of dollars) 

Number of Trees (millions) 

Insect/Disease 

Trees at risk

Compensatory value

Figure 19.—Number of trees at risk and associated compensatory value for various 

insects/diseases, Kansas City region, 2010.
23

Since being discovered in Detroit in 2002, emerald ash borer (EAB)27 has killed 

millions of ash trees in Illinois, Indiana, Kentucky, Maryland, Michigan, Minnesota, 

Missouri, New York, Ohio, Ontario, Pennsylvania, Quebec, Virginia, West Virginia, 

and Wisconsin. EAB has only been detected in southeastern Missouri but could already 

be in the Kansas City region or transferred anytime. EAB has the potential to aff ect 2.6 

percent of the area’s live tree population ($4.6 billion in compensatory value).

American elm, one of the most important street trees in the 20th century, has been 

devastated by the Dutch elm disease (DED). Since fi rst reported in the 1930s, it has 

killed over 50 percent of the native elm population throughout the entire United 

States.28 Although some elm species have shown varying degrees of resistance, the Kansas 

City region possibly could lose 27.9 percent of its trees to this disease ($10.9 billion in 

structural value).

Th e thousand cankers disease is a recently discovered insect-disease complex that kills 

several species of walnuts, including black walnut. It is known to occur primarily in the 

western states of Washington, Oregon, California, Idaho, Utah, Arizona, New Mexico, 

and Colorado. It also has been recently discovered in Tennessee29, the fi rst state in the 

east where thousand cankers disease has been found. Trees often are killed within 3 

years after initial symptoms are noticed. Tree mortality is the result of attack by the 

walnut twig beetle and subsequent canker development around beetle galleries caused 

by associated fungi.30 In the urban and rural forests of the Kansas City region there are 

10.2 million black walnuts (compensatory value of $8.4 billion) that could be lost to 

this disease.

Emerald ash borer

Photo by David Cappaert

Michigan State University,

www.invasive.org

Black walnuts recently killed by thousand cankers disease. Photo by Curtis Utley, CSUE, Bugwood.org
24

TREE CANOPY COVER CHANGE

Tree cover in the Kansas City region is 18.6 percent, but the canopy will continually 

change based on numerous factors, including tree mortality, tree growth, and new tree 

establishment. A tree population projection model was used to estimate annual tree 

canopy change based on current tree data for the Kansas City region. Th e model was 

used to consider several diff erent scenarios to show the number of trees that need to be 

established to meet desired tree canopy goals in the future. For several scenarios, the 

benefi ts of these canopy goals were also estimated. For details on methods and more 

detailed results, see Appendix VIII.

For the Kansas City region, the population projection model was used to estimate the 

number of new trees required annually to maintain existing tree canopy cover or to 

increase canopy cover by 5, 10, or 20 percent over 10, 25, or 50 years. Th e scenarios 

below are based on an average population mortality of 4 percent.

In Appendix VIII, additional scenario results are detailed that cover results for average 

mortality rates of 2, 3, and 5 percent. Multiple mortality rates are estimated as the 

true average mortality rate for the region is unknown and the multiple estimates will 

illustrate a range of possible values. Long-term monitoring of tree populations can help 

determine actual average mortality rates for the Kansas City region. 

Two other scenarios are detailed in Appendix VIII:

1) Annual tree establishment needed to sustain tree cover given an emerald 

ash borer infestation that kills off all ash trees in 10 years 

2) Annual tree establishment needed to sustain tree cover given a thousand 

cankers disease outbreak that kills off all black walnut trees in 10 years

Note that the estimated number of trees required is not the number of trees needed to 

be planted as many new trees are established annually through natural regeneration, 

particularly in more rural areas. Human activities in urban areas (development, 

mowing) often preclude the establishment of tree cover. Decreasing activities 

such as mowing, as well as sustaining pervious surfaces can facilitate more natural 

regeneration.

Model Scenarios

In modeling tree establishment rates for the Kansas City region, each land use was 

modeled separately. Table 11 details the estimated annual tree establishment rates 

needed (number of trees per year) in each land use to either maintain or increase tree 

cover 10, 25, or 50 years in the future given an average 4 percent tree mortality rate. 

It should be noted that increasing canopy cover in too short of a time frame can lead 

to nonsustainable canopy cover levels. Th ough planting too many trees in a short time 

can reach a canopy goal, canopy cover will surpass the goal through time. Having too 

many trees in one age class can lead to a signifi cant canopy cover loss in a relatively 

short time period as many of these trees can die within a relatively short time frame. 

Also, for shorter time frames, estimated canopy growth can off set the need to establish 

Having too many trees 

in one age class can 

lead to a signifi cant 

canopy cover loss 

in a relatively short 

time period as many 

of these trees can 

die within a relatively 

short time frame.
25

new trees, thus tree planting estimates will be relatively low. However, if no new trees 

are established, the population will become unstable in the long run as there will be 

missing age classes in the future to sustain canopy cover (e.g., if no new trees were 

ever established, tree cover would be sustained for a while due to canopy growth, 

but eventually the tree cover would drop to zero). Th us, long-term estimates of 

establishment (e.g., 50-year estimates) are likely the most reasonable estimates for tree 

establishment. However, most of the trees to be established in the region will likely not 

need to be planted, but will occur through natural regeneration.

Table 11.—Estimated number of trees to be established annually for various cover 

and year scenarios, Kansas City region, assuming a 4 percent annual mortality

Scenario 10 Years 25 Years 50 Years

Agriculture & Vacant Land Use

2010 canopy cover: 11.8%

Maintain canopy cover 0 40,000 960,000

Increase cover by 5% 1,900,000 1,400,000 1,700,000

Increase cover by 10% 5,500,000 2,500,000 2,500,000

Increase cover by 20% 13,000,000 5,400,000 4,200,000

Commercial, Utilities, & Transportation Land Use

2010 canopy cover: 9.6%

Maintain canopy cover 0 0 18,000

Increase cover by 5% 130,000 40,000 75,000

Increase cover by 10% 600,000 160,000 138,000

Increase cover by 20% 1,450,000 435,000 265,000

Golf, Park, & Institutional Land Use

2010 canopy cover: 34.3%

Maintain canopy cover 0 28,000 350,000

Increase cover by 5% 0 180,000 400,000

Increase cover by 10% 300,000 280,000 460,000

Increase cover by 20% 760,000 480,000 610,000

Residential Land Use

2010 canopy cover: 31.4%

Maintain canopy cover 0 43,000 1,250,000

Increase cover by 5% 400,000 900,000 1,800,000

Increase cover by 10% 2,200,000 1,650,000 2,200,000

Increase cover by 20% 6,400,000 3,300,000 3,100,000

Water & Other Land Use

2010 canopy cover: 49.2%

Maintain canopy cover 0 0 350,000

Increase cover by 5% 0 0 480,000

Increase cover by 10% 0 0 640,000

Increase cover by 20% 500,000 470,000 1,000,000

All Land Uses

2010 canopy cover: 18.6%

Maintain canopy cover 0 111,000 2,928,000

Increase cover by 5% 2,165,000 2,520,000 4,555,000

Increase cover by 10% 8,500,000 4,590,000 5,938,000

Increase cover by 20% 22,090,000 10,085,000 9,175,000
26

Th e projections of annual tree establishment are rough estimates based on average 

growth and mortality rates. As growth rates increase, the number of trees needed to 

be established decreases as canopy growth off sets the need for more new trees. Given 

the existing assumptions about growth and mortality, canopy cover can be sustained 

for about 10 years with no new trees added as existing growth can compensate for 

the loss of canopy due to mortality during this period. However, if no new trees are 

established during this period, the canopy cover will decline more rapidly in the future 

due to a lack of trees in this age class. No new trees being established during any 

year is not an issue as many trees are established due to natural regeneration, but this 

regeneration varies by land-use type. 

Ecosystem Services from Increased Tree Cover

Increasing or sustaining tree canopy cover through time will produce environmental 

benefi ts for the Kansas City region. As an example, the benefi ts provided by the 

regional forest over the next 25 years was estimated for the model scenario of 

increasing canopy cover by 10 percent (increasing tree cover from 18.6 to 28.6 

percent by establishing about 4.6 million trees per year with an average 4 percent 

mortality rate). Th ree ecosystem services were estimated: air pollution removal, carbon 

sequestration, and volatile organic compound (VOC) emissions.

For air pollution removal, annual air pollution removal over the 25 years was summed 

to estimate the total air pollution removal eff ects during the 25-year period. Th is 

simulation used the meteorological and pollution conditions of 2009 for all simulation 

years. Total pollution removal over the 25-year period is estimated at 1.0 million tons 

($7.8 billion at nondiscounted current value), increasing from 26,000 tons per year 

($198 million/year) in 2009 to 46,000 ton per year in 25 years ($349 million/year).

For carbon sequestration, the estimated tree population and diameter distribution in 

year 25 was used to estimate future carbon storage. Th is result was contrasted with the 

current carbon estimate to determine carbon sequestration over the 25 year period. 

Total carbon storage was estimated to increase from 19.9 million tons to 29.3 million 

tons, for a 25-year sequestration rate of 9.4 million tons ($194 million). 

For VOC emissions, annual emissions over the 25 years were summed to estimate the 

total VOC emissions during the 25-year period. Th is simulation used the meteorological 

conditions of 2009 for all simulation years. Total VOC emissions over the 25-year 

period are estimated at 3.1 million tons, increasing from 91,000 ton per year in 2009 to 

135,000 ton per year in 25 years. Th ese values will fl uctuate annually based on local air 

temperatures, but assume constant 2009 meteorological conditions over the next 25 

years.

Werner Park, Merriam KS. 

Photo by Mid-America Regional Council, used with 

permission.
27

All of the projected estimates of the number of new trees required and their associated 

ecosystem services should be considered rough estimates due to the numerous 

assumptions needed to attain these estimates. Th e numbers provided here are 

fi rst-order estimates and will likely change through time as mortality, growth, and 

establishment rates in the Kansas City region diff er from projected rates. However, 

these estimates provide a broad estimate of potential needs and impacts of attaining 

future canopy goals.

BLUE RIVER WATERSHED ANALYSIS

Th e Blue River watershed (184,958 acres) in the Kansas City region was analyzed 

using the i-Tree Hydro model.2

 Th e i-Tree Hydro model (formerly known as 

UFORE-Hydro) simulates the stream fl ow hydrograph using hourly precipitation 

data, digital elevation data (Fig. 20), and land-cover parameters. Th e model fl ow 

is calibrated against actual stream fl ow values. Model methods and calibration 

information are detailed in Appendix IX. 

Figure 20.—Elevation (feet) 

of the Blue River watershed, 

Kansas City region.
28

Tree Cover Effects

Loss of current tree cover in the Blue River watershed would increase total fl ow during 

the simulation period by an average of 2.3 percent (63.4 million ft3

). Increasing 

canopy cover from 34.6 percent to 40 percent would reduce overall fl ow by another 

0.4 percent (10,800,000 ft3

) during this 6.5-month period (Fig. 21). Increasing tree 

cover reduces fl ow from both pervious and impervious areas (Fig. 22).

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

0 10 20 30 40 50 60 70 80 90 100

Total Runoff

Pervious Area Flow

Base Flow

Impervious Area Flow

 Water Flow (ft3) 

Tree Cover (%) 

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Tree Cover (%) 

Change in Water Flow (%) 

Figure 21.—Percent change in total fl ow with changes in percent tree cover, Blue River 

watershed.

Figure 22.—Changes in total runoff and components of total runoff (pervious area fl ow, 

impervious area fl ow, and base fl ow) with changes in percent tree cover in the Blue 

River watershed (Impervious cover held at 31.5 percent).

Loss of the current 

tree cover in the Blue 

River watershed of the 

greater Kansas City 

region would increase 

fl ow over a 6.5-month 

period by an average 

of 2.3 percent (63.4 

million ft3

).
29

Impervious Cover Effects

Removal of current impervious cover would reduce total fl ow during the simulation 

period by an average of 65.4 percent (1.78 billion ft3

). Increasing impervious cover 

from 31.5 percent to 40 percent of the watershed would increase total fl ow another 

53.7 percent (1.46 billion ft3

) during this 6.5-month period (Fig. 23). Increasing 

impervious cover reduces base fl ow and pervious runoff while signifi cantly increasing 

fl ow from impervious surfaces (Fig. 24).

Figure 23.—Percent change in total fl ow with changes in percent impervious cover, 

Blue River watershed.

Figure 24.—Changes in total runoff and components of total runoff (pervious area 

runoff, impervious area runoff and base fl ow) with changes in percent impervious cover 

in the Blue River watershed (tree cover maintained at 34.6 percent).

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

0 10 20 30 40 50 60 70 80 90 100

Total Runoff

Pervious Area Flow

Base Flow

Impervious Area Flow

Flow (Ĩƚϯ) 

Impervious Cover (%) 

-100.0

0.0

100.0

200.0

300.0

400.0

500.0

10 20 30 40 50 60 70 80 90 100

Impervious Cover;йͿ

Percent Change (%) 
30

Increasing tree cover will reduce stream fl ow, but the dominant cover type infl uencing 

stream fl ow is impervious surfaces. Under current cover conditions, increasing 

impervious cover had a 32 times greater impact on fl ow relative to tree cover. 

Increasing impervious cover by 1 percent averaged a 3.2 percent increase in stream 

fl ow, while increasing tree cover by 1 percent averaged only a 0.1 percent decrease in 

stream fl ow. Th e interactions between changing both tree and impervious cover are 

illustrated in Figures 25 and 26 for total fl ow during the simulation period and for 

changes in percent fl ow.

Figure 25.—Changes in total fl ow during simulation period based on changes 

in percent impervious and percent tree cover, Blue River watershed. Red star 

indicates current conditions.

Figure 26.—Percent change in total fl ow during simulation period based on 

changes in percent impervious and percent tree cover, Blue River watershed. Red 

star indicates current conditions. Total Flow (ft3

)
31

During the simulation period, the total rainfall recorded was 26.4 inches. Since that 

amount is assumed to have fallen over the entire 184,958 acre watershed, a total of 17.7 

billion cubic feet of rain fell on the watershed during the simulation time. Th e total 

modeled fl ow in the Blue River watershed throughout the simulation time for the base 

case scenario (no landscape change) was 2.72 billion ft3

. Th e total fl ow is made up of 

surface runoff and basefl ow (water that travels underground to the stream). Runoff from 

impervious and pervious areas are the biggest contributors to stream fl ow with 68.1 

percent and 28.8 percent of total fl ow generated from impervious and pervious runoff 

respectively. Base fl ow was estimated to generate 3.1 percent of total fl ow. Areas of tree 

canopies intercepted about 9.5 percent of the total rainfall, but as only 34.6 percent of 

watershed is under tree cover, interception of total precipitation in the watershed by 

trees was only 3.3 percent (581 million ft3

). Areas of short vegetation, including shrubs, 

intercepted about 3.7 percent of the total rainfall, but as only 37.6 percent of watershed 

in under short vegetation, interception of total precipitation in the watershed by short 

vegetation was only 1.4 percent (245 million ft3

). About 80 percent of total precipitation 

is estimated to re-enter the atmosphere through evaporation or evapotranspiration before 

it enters the stream.

Based on these changes in fl ow rates and national EMC values, the current tree cover is 

estimated to reduce total suspended solids during the simulation period by around 108 

to 155 tons. Other chemical constituents are also reduced (Table 12).

CONCLUSION

Data from this report provide the basis for a better understanding of the urban and rural 

forest resource and the ecosystem services and values provided by this resource. Managers 

and citizens can use these data to help improve long-term management plans and policies 

to sustain a healthy tree and forest population and ecosystem services for future generations. 

Improved planning and management to sustain healthy tree populations can lead to 

improved environmental quality and quality of life for residents of the Kansas City region.

More information on trees in the Kansas City region can be found at: http://nrs.fs.fed.

us/data/urban.

Table 12.—Estimated reduction in chemical constituents in Blue River 

watershed due to existing tree cover during simulation period based on 

median and mean pooled event mean concentration values

Reduction (t)

Constituent Median Mean

Total suspended solids 107.8 155.0

Biochemical oxygen demand 22.7 27.9

Chemical oxygen demand 88.4 104.4

Total phosphorus 0.5 0.6

Soluble phosphorus 0.2 0.3

Total Kjeldhal nitrogena 2.9 3.4

Nitrite and nitrate 1.1 1.3

Copper 21.9 26.7

a Sum of organic nitrogen, ammonia (NH3), and ammonium (NH4

+

)

Indian Creek, a major tributary of 

the Blue River. 

Photo by Mid-America Regional Council, used with 

permission.
32

APPENDIX I. SPECIES SAMPLED IN THE KANSAS CITY REGION

Table 13.—Tree species sampled in the regional forest, Kansas City region, 2010

Genus Species Common Name

Number

of Trees

Pop

%

Leaf 

Area

% IVa

Med 

Dbh

(in)

Ave 

Dbh

(in)

Basal

Area

(ft2)

Value

($ Millions)

Acer negundo boxelder 7,160,350 2.9 2.3 5.2 5.1 6.0 2,322,715 2,133.9

Acer platanoides Norway maple 166,520 0.1 0.8 0.9 4.0 13.0 247,492 496.7

Acer rubrum red maple 582,820 0.2 0.5 0.7 5.8 7.1 222,970 478.0

Acer saccharinum silver maple 2,331,280 0.9 2.8 3.7 9.0 11.6 3,196,224 2,461.7

Acer saccharum sugar maple 666,080 0.3 0.3 0.6 5.5 4.9 128,514 243.0

Aesculus glabra Ohio buckeye 3,996,480 1.6 0.0 1.6 1.7 1.9 142,969 188.2

Asimina triloba pawpaw 3,496,920 1.4 0.3 1.7 1.5 1.6 88,544 172.4

Betula nigra river birch 1,082,380 0.4 0.1 0.5 2.3 2.4 55,402 90.1

Carya alba mockernut hickory 666,080 0.3 0.1 0.4 2.5 2.5 38,146 34.3

Carya cordiformis bitternut hickory 9,158,590 3.7 2.6 6.3 2.7 3.9 1,486,742 1,742.3

Carya illinoinensis pecan 333,040 0.1 0.1 0.2 5.0 5.3 66,755 82.5

Carya laciniosa shellbark hickory 499,560 0.2 0.1 0.3 1.7 3.4 78,562 64.2

Carya ovata shagbark hickory 6,077,970 2.4 2.2 4.6 3.9 5.4 1,753,518 1,859.6

Catalpa speciosa northern catalpa 83,260 0.0 0.1 0.1 16.5 16.5 131,239 87.4

Celtis occidentalis northern hackberry 34,969,160 14.0 15.8 29.8 3.5 4.8 8,090,512 12,669.4

Crataegus phaenopyrum Washington hawthorn 166,520 0.1 0.0 0.1 2.0 2.5 9,082 9.1

Diospyros virginiana common persimmon 249,780 0.1 0.3 0.4 7.5 7.5 109,896 174.7

Fraxinus americana white ash 2,248,020 0.9 1.2 2.1 3.5 5.5 659,280 894.2

Fraxinus pennsylvanica green ash 3,996,480 1.6 2.5 4.1 5.3 7.1 1,988,200 3,662.4

Gleditsia triacanthos honeylocust 16,651,980 6.7 2.2 8.9 3.1 5.0 5,183,017 6,255.8

Gymnocladus dioicus Kentucky coffeetree 499,560 0.2 0.3 0.5 5.0 7.2 225,241 505.0

Juglans nigra black walnut 10,157,710 4.1 10.4 14.5 7.9 8.8 6,400,426 8,377.8

Juniperus virginiana eastern redcedar 12,488,990 5.0 3.4 8.4 2.3 3.1 1,210,884 1,558.6

Liriodendron tulipifera tulip tree 83,260 0.0 0.4 0.4 11.5 11.5 65,392 138.1

Maclura pomifera Osage-orange 17,900,880 7.2 10.7 17.9 7.4 8.7 12,625,269 13,825.0

Magnolia x soulangiana saucer magnolia 166,520 0.1 0.2 0.3 8.5 8.5 73,566 129.6

Malus species apple spp 915,860 0.4 0.4 0.8 2.8 4.3 161,665 262.6

Morus alba white mulberry 7,576,650 3.0 2.7 5.7 3.6 5.2 2,221,671 2,971.3

Ostrya virginiana eastern hophornbeam 2,914,100 1.2 0.6 1.8 2.5 2.8 197,079 272.9

Other species other species 832,600 0.3 0.0 0.3 4.0 7.1 419,601 0.0

Pinus strobus eastern white pine 249,780 0.1 0.1 0.2 5.5 5.5 49,953 100.6

Platanus occidentalis American sycamore 1,082,380 0.4 2.6 3.0 7.5 12.0 1,307,847 1,376.6

Populus deltoides eastern cottonwood 1,581,940 0.6 3.6 4.2 19.5 16.9 3,201,952 1,964.8

Prunus cerasifera cherry plum 166,520 0.1 0.0 0.1 6.5 6.5 44,503 91.5

Prunus persica peach 166,520 0.1 0.0 0.1 4.0 6.0 44,049 48.9

Prunus serotina black cherry 2,830,840 1.1 0.6 1.7 4.0 4.8 581,445 559.4

Prunus serrulata Kwanzan cherry 249,780 0.1 0.2 0.3 9.5 9.8 147,587 183.9

Pyrus calleryana callery pear 333,040 0.1 0.1 0.2 7.0 6.8 103,992 174.6

Quercus alba white oak 1,248,900 0.5 0.5 1.0 8.5 9.4 896,406 1,641.7

Quercus bicolor swamp white oak 166,520 0.1 0.0 0.1 3.0 5.0 33,150 66.0

Quercus imbricaria shingle oak 1,248,900 0.5 0.8 1.3 9.3 8.0 643,933 922.8

continued
33

Quercus macrocarpa bur oak 5,328,630 2.1 2.6 4.7 3.4 6.2 2,596,484 4,558.8

Quercus muehlenbergii chinkapin oak 5,495,150 2.2 1.0 3.2 2.5 3.9 869,880 1,194.9

Quercus palustris pin oak 1,332,160 0.5 1.3 1.8 8.0 10.0 1,281,509 2,198.4

Quercus rubra northern red oak 2,248,020 0.9 1.6 2.5 4.7 8.8 2,046,024 2,916.5

Quercus stellata post oak 83,260 0.0 0.0 0.0 3.5 3.5 7,266 6.9

Quercus velutina black oak 166,520 0.1 0.2 0.3 3.0 10.0 151,220 265.9

Salix alba white willow 83,260 0.0 0.0 0.0 19.5 19.5 181,646 72.9

Salix nigra black willow 2,830,840 1.1 1.0 2.1 2.9 4.3 531,313 392.6

Tilia americana American basswood 1,998,240 0.8 2.9 3.7 7.0 8.4 1,361,145 1,940.5

Ulmus americana American elm 72,103,080 28.9 17.7 46.6 2.8 3.9 11,717,067 10,894.0

Ulmus pumila Siberian elm 333,040 0.1 0.1 0.2 2.0 4.0 51,769 34.9

a

 IV = importance value (% population + % leaf area)

Table 13.—continued

Genus Species Common Name

Number

of Trees

Pop

%

Leaf 

Area

% IVa

Med 

Dbh

(in)

Ave 

Dbh

(in)

Basal

Area

(ft2

)

Value

($ Millions)

Table 14.—Shrub speciesa by land use, Kansas City region, 2010

Per Unit Area City Total

Land Use Genus Species Common Name

Leaf Area

(ft2

/ac)

Leaf Biomass 

(lb/ac)

Leaf Area

(ac)

Leaf Biomass 

(tons)

Ag / Vacant Cornus racemosa gray dogwood 4,901.69 47.92 181,756.88 38,691.54

Cercis canadensis eastern redbud 1,090.81 14.31 40,447.80 11,551.32

Rosa multifl ora multifl ora rose 934.29 14.31 34,643.42 11,551.63

Symphoricarpos orbiculatus coralberry 749.94 8.58 27,808.63 6,928.38

Juniperus virginiana eastern redcedar 303.23 17.25 11,245.52 13,929.70

Toxicodendron radicans poison ivy 208.53 3.19 7,731.76 2,578.21

Ostrya virginiana eastern hophornbeam 207.79 2.77 7,704.58 2,242.88

Asimina triloba pawpaw 172.72 5.93 6,404.83 4,792.16

Elaeagnus angustifolia Russian olive 138.92 2.12 5,152.04 1,717.81

Maclura pomifera Osage-orange 87.47 1.80 3,244.42 1,454.17

Lonicera maackii amur honeysuckle 71.00 0.71 2,631.62 578.41

Carya ovata shagbark hickory 66.47 1.00 2,466.06 805.22

Carya cordiformis bitternut hickory 52.10 0.67 1,932.32 541.64

Celtis occidentalis northern hackberry 49.49 0.53 1,835.95 425.86

Rhus glabra smooth sumac 48.31 0.54 1,791.48 440.46

Morus alba white mulberry 37.77 0.56 1,401.06 456.88

Ulmus americana American elm 27.14 0.40 1,008.17 326.61

Gleditsia triacanthos honeylocust 25.66 0.55 951.34 444.50

Alnus serrulata hazel alder 17.42 0.26 647.40 210.18

Quercus alba white oak 10.59 0.16 392.89 127.12

Acer saccharinum silver maple 9.54 0.11 353.35 82.91

Prunus serotina black cherry 8.06 0.12 298.99 103.43

Quercus muehlenbergii chinkapin oak 7.14 0.14 264.40 116.24

Ribes missouriense Missouri gooseberry 5.49 0.08 202.62 67.60

Ulmus pumila Siberian elm 1.18 0.02 42.01 13.01

continued
34

Aesculus glabra Ohio buckeye 0.00 0.00 0.00 0.00

Total 9,232.69 124.08 342,354.58 100,177.91

Comm/Util/Trans Wisteria species wisteria spp 15,357.92 235.18 58,708.49 19,576.12

Cornus racemosa gray dogwood 2,315.09 22.63 8,851.12 1,883.95

Cercis canadensis eastern redbud 654.33 8.58 2,500.65 714.36

Ligustrum species privet spp 526.04 9.80 2,011.39 815.29

Juniperus virginiana eastern red cedar 235.23 13.38 899.44 1,113.97

Buxus species boxwood spp 74.36 1.14 284.17 94.79

Symphoricarpos orbiculatus coralberry 21.08 0.24 81.54 20.09

Carya ovata shagbark hickory 14.68 0.22 56.83 18.34

Total 19,198.77 291.17 73,391.17 24,236.89

Residential Cornus racemosa gray dogwood 10,725.52 104.86 176,303.38 37,530.64

Lonicera species honeysuckle spp 3,380.98 34.11 55,575.26 12,209.60

Cercis canadensis eastern redbud 3,012.11 39.51 49,511.43 14,140.19

Symphoricarpos orbiculatus coralberry 2,553.28 29.22 41,969.94 10,456.69

Lonicera maackii amur honeysuckle 2,387.22 24.09 39,239.48 8,620.82

Rosa multifl ora multifl ora rose 1,134.28 17.37 18,646.17 6,217.01

Juniperus virginiana eastern redcedar 542.46 30.86 8,917.84 11,046.83

Celtis occidentalis northern hackberry 530.09 5.65 8,712.75 2,021.84

Taxus species yew spp 414.74 13.30 6,817.49 4,761.55

Elaeagnus angustifolia Russian olive 342.35 5.25 5,626.47 1,876.45

Staphylea species bladdernut spp 214.49 3.28 3,526.12 1,175.64

Carya cordiformis bitternut hickory 155.77 2.01 2,559.96 717.88

Crataegus phaenopyrum Washington hawthorn 152.77 2.36 2,510.54 843.49

Morus alba white mulberry 130.73 1.96 2,149.77 700.97

Maclura pomifera Osage-orange 108.90 2.24 1,791.48 802.62

Ribes missouriense Missouri gooseberry 63.82 0.98 1,050.18 349.76

Picea glauca white spruce 63.21 2.08 1,040.29 744.57

Ligustrum obtusifolium border privet 58.94 1.10 968.63 392.75

Viburnum prunifolium black haw 55.28 0.85 909.33 302.92

Syringa vulgaris common lilac 53.84 1.06 884.62 380.81

Quercus palustris pin oak 48.66 0.90 800.60 322.68

Asimina triloba pawpaw 47.31 1.62 778.37 581.79

Picea abies Norway spruce 43.74 1.49 719.06 534.25

Carya ovata shagbark hickory 33.11 0.50 543.62 177.69

Ilex glabra inkberry 32.54 0.89 536.21 319.03

Ulmus americana American elm 31.28 0.46 513.97 166.79

Acer saccharum sugar maple 28.53 0.35 469.49 125.93

Betula nigra river birch 17.03 0.27 279.22 96.77

Berberis species barberry spp 15.42 0.23 254.51 84.49

Fraxinus americana white ash 14.16 0.16 232.27 58.97

Table 14.—continued

Per Unit Area City Total

Land Use Genus Species Common Name

Leaf Area

(ft2

/ac)

Leaf Biomass 

(lb/ac)

Leaf Area

(ac)

Leaf Biomass 

(tons)

continued
35

Salix nigra black willow 13.20 0.17 217.45 61.34

Gymnocladus dioicus Kentucky coffeetree 12.68 0.20 207.56 69.41

Pyrus species pear spp 11.89 0.18 195.21 65.15

Rosa rugosa rugosa rose 10.54 0.16 172.97 57.70

Gleditsia triacanthos honeylocust 9.45 0.21 155.67 72.49

Rhus glabra smooth sumac 4.88 0.05 79.07 19.69

Physocarpus species ninebark spp 3.83 0.06 61.78 20.92

Euonymus kiautschovica creeping strawberry bush 3.31 0.05 54.36 18.03

Acer palmatum Japanese maple 1.96 0.03 32.12 8.00

Prunus serotina black cherry 1.48 0.03 24.71 8.39

Acer saccharinum silver maple 1.39 0.02 22.24 5.38

Fraxinus pennsylvanica green ash 1.22 0.02 19.77 5.83

Rhododendron azalea azalea 1.18 0.04 19.77 17.26

Hibiscus syriacus rose-of-sharon 0.00 0.00 0.00 0.00

Total 26,469.44 330.21 435,098.62 118,190.98

Golf/Park/Inst Symphoricarpos orbiculatus coralberry 8,073.62 92.38 29,320.89 7,305.05

Elaeagnus angustifolia Russian olive 5,159.35 79.00 18,737.59 6,247.57

Cornus racemosa gray dogwood 3,874.13 37.87 14,069.87 2,994.99

Wisteria species wisteria spp 3,229.87 49.46 11,729.84 3,911.10

Staphylea species bladdernut spp 2,448.99 37.50 8,893.13 2,965.57

Carya cordiformis bitternut hickory 290.16 3.74 1,052.65 295.41

Rosa multifl ora multifl ora rose 184.26 2.82 669.64 223.13

Carya illinoinensis pecan 40.64 0.58 148.26 45.78

Lonicera maackii amur honeysuckle 11.28 0.12 42.01 9.00

Total 23,312.27 303.47 84,661.40 23,997.59

Other Cornus racemosa gray dogwood 13,526.14 132.24 54,292.81 11,557.47

Cercis canadensis eastern redbud 6,998.93 91.80 28,092.80 8,023.07

Rosa multifl ora multifl ora rose 4,663.37 71.41 18,717.83 6,241.40

Lonicera maackii amur honeysuckle 2,812.07 28.37 11,287.53 2,479.74

Alnus serrulata hazel alder 1,724.27 25.75 6,921.27 2,250.22

Symphoricarpos orbiculatus coralberry 1,688.42 19.32 6,777.95 1,688.48

Asimina triloba pawpaw 1,496.93 51.43 6,009.47 4,495.29

Carya cordiformis bitternut hickory 1,300.16 16.74 5,218.75 1,463.02

Ostrya virginiana eastern hophornbeam 1,087.32 14.53 4,363.79 1,270.62

Juniperus virginiana eastern redcedar 560.06 31.86 2,248.61 2,784.92

Fraxinus pennsylvanica green ash 548.17 7.32 2,199.19 640.01

Ulmus americana American elm 322.13 4.80 1,292.33 419.41

Ribes missouriense Missouri gooseberry 203.08 3.11 815.43 271.81

Carya illinoinensis pecan 201.25 2.86 808.02 250.55

Quercus macrocarpa bur oak 189.19 3.83 758.60 334.18

Rhus aromatica fragrant sumac 188.84 3.94 758.60 344.84

Table 14.—continued

Per Unit Area City Total

Land Use Genus Species Common Name

Leaf Area

(ft2

/ac)

Leaf Biomass 

(lb/ac)

Leaf Area

(ac)

Leaf Biomass 

(tons)

continued
36

Quercus rubra northern red oak 184.92 3.02 741.30 263.75

Crataegus species hawthorn spp 161.70 1.20 649.87 104.13

Celtis occidentalis northern hackberry 157.91 1.69 632.58 147.06

Acer negundo boxelder 100.89 1.89 405.24 165.21

Rhus glabra smooth sumac 62.64 0.70 252.04 61.82

Fraxinus americana white ash 50.18 0.58 202.62 51.03

Other species other species 4.62 0.07 19.77 6.18

Total 38,233.19 518.47 153,463.93 45,314.22

CITY TOTAL 16,756.84 220.42 1,088,969.70 311,917.57

a

 shrubs are defi ned as woody plants with stem diameter at 4.5 feet less than 1-inch.

Table 14.—continued

Per Unit Area City Total

Land Use Genus Species Common Name

Leaf Area

(ft2

/ac)

Leaf Biomass 

(lb/ac)

Leaf Area

(ac)

Leaf Biomass 

(tons)
37

APPENDIX II. TREE SPECIES DISTRIBUTION

Th is appendix illustrates species distributions. Th e species distributions for each land 

use are illustrated (Figs. 27-34) for up to 20 most common species in the land-use 

category. More detailed information on species by land use can be found at: 

http://nrs.fs.fed.us/data/urban.

Figure 27. —The 20 most common tree species as a percent of the total tree population, 

Kansas City region, 2010.

0 5 10 15 20 25 30

Northern red oak

Silver maple

Black cherry

Black willow

Eastern hophornbeam

Pawpaw

Green ash

Ohio buckeye

Bur oak

Chinkapin oak

Shagbark hickory

Boxelder

White mulberry

Bitternut hickory

Black walnut

Eastern red cedar

Honeylocust

Osage-orange

Northern hackberry

American elm

Tree Population (percent) 

Species 

0

5

10

15

20

25

30

35

40

45

American

elm

Northern

hackberry

OsageorangeHoneylocust Eastern

UHGcedar

Black walnut Bitternut

hickory

White

mulberry

Boxelder Shagbark

hickory

Land-use Population (percent) 

Species 

Ag/Vacant Comm/Util/Trans

Residential Golf/Park/Inst

Other

Land-use Category 

Figure 28.—The percent land-use population occupied by the 10 most common tree 

species, Kansas City region, 2010.

For example, American elm comprises 42 percent of the tree population on the 

commercial/utility/transportation land use.
38

0

10

20

30

40

50

60

70

American

elm

Northern

hackberry

OsageorangeHoneylocust Eastern red

cedar

Black walnut Bitternut

hickory

White

mulberry

Boxelder Shagbark

hickory

Species Population (percent) 

Species 

Ag/Vacant Comm/Util/Trans

Residential Golf/Park/Inst

Other

Land-use Category

Figure 29.—The percent county population occupied by the 10 most common tree species, 

Kansas City region, 2010.

For example, 32 percent of American elm are found within the “other” land use.

0 5 10 15 20 25 30

Apple spp

Black cherry

Green ash

White ash

Ohio buckeye

Bur oak

Black willow

Shagbark hickory

Northern red oak

Silver maple

Boxelder

Chinkapin oak

White mulberry

Black walnut

Bitternut hickory

Eastern redcedar

Honeylocust

Northern hackberry

Osage-orange

American elm

Percent of Trees 

Species 

Figure 30.—Percent of trees in agriculture and vacant category of land use, Kansas City 

region, 2010.
39

0 10 20 30 40 50

Siberian elm

Black cherry

Osage-orange

Shagbark hickory

Norway maple

Eastern white pine

White mulberry

Northern hackberry

Silver maple

Eastern red cedar

Boxelder

American elm

Percent of Trees 

Species 

Figure 31.—Percent of trees in commercial/utility/transportation category of land use, 

Kansas City region, 2010.

0 5 10 15 20 25

Shingle oak

River birch

White ash

Boxelder

American basswood

Eastern hophornbeam

Chinkapin oak

Bur oak

Honeylocust

Black cherry

Green ash

Ohio buckeye

Osage-orange

Bitternut hickory

White mulberry

Eastern redcedar

Shagbark hickory

Black walnut

Northern hackberry

American elm

Percent of Trees 

Species 

Figure 32.—Percent of trees in residential category of land use, Kansas City region, 2010.
40

0 5 10 15 20 25 30

Silver maple

Black cherry

Kentucky coffeetree

Pawpaw

Bur oak

Shagbark hickory

Pecan

Other species

Eastern redcedar

Honeylocust

White mulberry

Pin oak

Green ash

Black walnut

Bitternut hickory

Osage-orange

Chinkapin oak

Boxelder

Northern hackberry

American elm

Percent of Trees 

Species 

0 10 20 30 40 50

White ash

Eastern cottonwood

Shagbark hickory

Green ash

American basswood

Black willow

American sycamore

Black walnut

White mulberry

White oak

Bitternut hickory

Osage-orange

Eastern hophornbeam

Boxelder

Bur oak

Pawpaw

Honeylocust

Eastern redcedar

Northern hackberry

American elm

Percent of Trees 

Species 

Figure 33.—Percent of trees in golf/park/institutional category of land use, Kansas City 

region, 2010.

Figure 34.—Percent of trees in “other” category of land use, Kansas City region, 2010.
41

APPENDIX III. TREE STRUCTURE AND FUNCTIONS BY LAND USE

Th is appendix details various structural and functional values for each species by land-use class (Table 15) and 

overall values for each land use class (Table 16). More information can be found at: http://nrs.fs.fed.us/data/urban. continued Table 15.—Tree structure and functions by land use, Kansas City region, 2010 Common Name Number of Trees Carbon Storage (tons) Carbon Sequestration (tons/year) Net Carbon Seuestration (tons/year) Leaf Area (ac) Leaf Biomass (tons) Structural Value ($US) Agriculture and Vacant American elm 20,315,415 595,034 41,005 38,382 186,247 60,424 2,631,810,452 Osage-orange 11,989,425 2,067,386 75,001 69,063 232,469 104,247 6,957,891,480 Northern hackberry 11,739,645 858,783 44,773 42,382 217,167 50,403 4,123,999,544 Honeylocust 11,406,606 482,099 29,919 28,240 39,729 18,558 2,383,879,835 Eastern redcedar 3,580,176 69,244 4,201 4,145 45,940 56,925 482,652,938 Bitternut hickory 3,496,916 89,473 8,768 8,664 25,186 7,063 468,581,508 Black walnut 2,414,537 651,012 24,460 21,904 123,858 44,279 2,060,144,290 White mulberry 2,248,017 124,184 7,258 7,008 24,788 8,089 545,075,325 Chinkapin oak 1,748,458 34,379 3,043 2,613 8,779 3,865 205,358,193 Boxelder 1,415,418 131,831 5,454 5,293 11,800 4,815 233,808,826 Silver maple 999,119 305,742 5,886 2,527 19,258 4,521 359,280,597 Northern red oak 999,119 479,218 9,130 5,910 28,275 10,050 1,258,653,533 Shagbark hickory 915,859 135,427 6,275 5,558 14,841 4,848 352,165,069 Black willow 915,859 78,612 5,170 5,113 18,245 5,156 291,933,289 Bur oak 832,599 48,090 3,473 3,434 19,667 8,658 324,217,544 Ohio buckeye 749,339 1,813 563 560 - - 25,411,717 White ash 666,079 109,685 5,398 5,277 24,357 6,174 458,084,950 Green ash 666,079 49,223 2,316 2,281 11,539 3,357 360,954,356 Black cherry 666,079 49,020 3,314 3,171 8,386 2,901 164,764,667 Apple spp 582,819 2,526 604 600 844 281 19,971,163 Pawpaw 499,559 1,687 428 426 1,690 1,265 18,224,010 Shellbark hickory 416,299 18,096 1,196 1,183 4,124 964 61,711,928 Eastern hophornbeam 333,040 427 165 164 944 275 9,848,222 Eastern cottonwood 249,780 370 131 131 935 301 3,122,246 Other species 166,520 8,666 - -433 - - - White oak 166,520 169,377 4,715 4,597 8,107 2,631 645,862,752 Shingle oak 166,520 43,598 1,659 1,629 2,830 1,246 139,542,543 Pin oak 166,520 244,681 4,899 4,730 5,616 2,267 698,757,172 Siberian elm 166,520 6,504 503 498 2,439 741 31,654,795 Sugar maple 83,260 11,568 723 715 1,379 370 58,366,817
42Table 15.—continued

Common Name

Number of 

Trees

Carbon 

Storage (tons)

Carbon Sequestration 

(tons/year)

Net Carbon 

Seuestration (tons/year)

Leaf Area 

(ac)

Leaf Biomass 

(tons)

Structural Value 

($US)

continued

Pecan 83,260 5,919 491 487 744 231 31,074,978Washington hawthorn 83,260 826 146 145 148 50 5,473,407Post oak 83,260 778 147 146 130 50 6,935,489White willow 83,260 51,381 1,600 1,564 1,221 345 72,908,702Total 81,095,140 6,926,659 302,814 278,107 1,091,682 415,351 25,492,122,336

Commercial-Utility-Transportation 

American elm 1,665,198 69,580 7,206 6,751 24,472 7,940 416,347,407Boxelder 582,819 27,844 3,201 3,162 4,781 1,951 89,142,946Eastern redcedar 333,040 1,027 181 180 1,174 1,455 9,887,113Silver maple 249,780 32,979 1,565 1,542 7,994 1,877 128,503,805Northern hackberry 249,780 16,402 536 -129 795 185 33,003,248White mulberry 249,780 1,317 336 334 1,380 450 10,576,988Eastern white pine 249,780 5,201 771 766 2,925 839 100,585,101Norway maple 83,260 1,462 345 343 540 130 13,688,995Shagbark hickory 83,260 46,927 2,195 2,162 4,898 1,600 126,206,890Osage-orange 83,260 34,434 - -9,468 - - -Black cherry 83,260 13,818 1,214 1,204 940 325 55,265,372Siberian elm 83,260 48 42 42 204 62 1,300,936Total 3,996,475 251,040 17,592 6,889 50,105 16,814 984,508,799

 Residential 

American elm 20,898,237 951,407 68,268 25,150 241,182 78,247 4,875,883,090Northern hackberry 14,653,744 575,084 64,715 62,090 196,776 45,671 5,227,860,209Black walnut 5,994,713 833,507 53,061 48,714 163,656 58,508 4,632,215,586Shagbark hickory 4,246,255 181,253 17,308 6,519 41,617 13,596 889,326,900Eastern redcedar 3,663,436 45,474 5,008 4,791 34,882 43,224 496,414,537White mulberry 3,580,176 314,344 20,528 18,265 51,110 16,678 1,833,170,144Bitternut hickory 3,496,916 135,509 14,061 13,412 33,516 9,399 709,267,452Osage-orange 3,496,916 1,283,797 42,903 32,227 86,657 38,860 5,145,590,265Ohio buckeye 3,247,136 12,820 3,314 2,513 195 64 162,780,668

Green ash 1,831,718 207,331 10,410 9,595 48,960 14,245 2,032,582,484Black cherry 1,748,458 69,070 7,338 3,185 8,406 2,908 290,365,372Honeylocust 1,665,198 644,847 15,156 -64,952 15,110 7,058 1,826,734,629
43

Table 15.—continuedCommon NameBur oak 1,498,678 394,341 14,157 -4,817 38,895 17,123 2,080,875,431Chinkapin oak 1,415,418 114,468 7,933 -4,793 13,041 5,741 597,130,685Eastern hophornbeam 1,332,159 12,312 2,494 1,649 10,407 3,030 122,437,539American basswood 1,248,899 172,688 6,314 -10,840 32,789 4,270 1,003,160,382Boxelder 1,165,639 83,974 7,106 6,403 8,031 3,277 369,604,161White ash 1,165,639 8,272 2,373 2,325 7,014 1,778 107,642,633River birch 1,082,379 6,182 2,074 2,038 3,855 1,333 90,071,957Shingle oak 1,082,379 133,009 10,692 10,395 22,877 10,071 783,216,221Black willow 1,082,379 5,368 1,776 1,744 6,951 1,964 24,236,644Silver maple 749,339 353,558 11,965 50,730 11,911 10,287 1,417,843,560Eastern cottonwood 749,339 645,203 19,814 3,815 81,186 26,132 1,436,840,862Northern red oak 749,339 301,263 11,122 5,136 23,200 8,247 1,612,993,642Mockernut hickory 666,079 4,059 1,293 1,272 2,271 580 34,293,141Red maple 582,819 57,707 5,035 4,801 15,131 4,546 477,990,184Sugar maple 582,819 21,879 3,278 3,228 10,143 2,726 184,613,181Kentucky coffeetree 333,040 29,318 2,459 2,340 5,078 1,694 241,850,918Apple spp 333,040 27,876 2,902 2,787 11,173 3,726 242,613,732Callery pear 333,040 19,024 2,248 2,169 3,420 1,141 174,639,592Pin oak 333,040 174,116 6,821 6,141 22,017 8,888 864,867,673Common persimmon 249,780 26,088 2,025 1,919 10,168 3,391 174,697,581American sycamore 249,780 240,722 7,780 6,854 51,298 11,086 795,649,069Kwanzan cherry 249,780 38,524 3,353 3,132 5,974 2,062 183,888,409Saucer magnolia 166,520 13,900 1,466 1,409 5,028 1,499 129,576,283Other species 166,520 48,251 - -13,267 - - -Peach 166,520 9,795 1,234 1,134 660 228 48,931,218Swamp white oak 166,520 6,256 850 823 1,330 586 66,049,009Norway maple 83,260 78,361 2,453 2,152 24,808 5,973 483,022,208Pawpaw 83,260 55 47 47 411 308 3,671,762Northern catalpa 83,260 37,344 1,731 1,585 2,058 559 87,438,757Washington hawthorn 83,260 98 63 63 114 38 3,671,762Tulip tree 83,260 13,718 1,004 949 13,390 3,521 138,066,232Cherry plum 83,260 4,971 657 636 896 243 41,495,675

Number of 

Trees

Carbon 

Storage (tons)

Carbon Sequestration 

(tons/year)

Net Carbon 

Seuestration (tons/year)

Leaf Area 

(ac)

Leaf Biomass 

(tons)

Structural Value 

($US)

continued
44

White oak 83,260 9,764 959 919 433 141 91,239,196Black oak 83,260 66,379 2,988 2,730 6,228 1,964 261,532,635Total 87,089,863 8,413,287 470,541 218,672 1,413,072 478,230 42,498,043,272

 Golf-Park-Institutional 

Table 15.—continuedCommon NameAmerican elm 5,911,453 165,089 11,434 8,557 47,241 15,327 1,076,575,038Northern hackberry 2,830,836 147,433 8,379 3,042 46,856 10,875 1,295,092,803Boxelder 2,248,017 219,980 9,735 -5,083 31,289 12,768 998,577,070Chinkapin oak 2,081,497 14,484 2,512 2,451 6,352 2,796 241,611,887Osage-orange 1,165,639 213,578 8,619 6,620 21,304 9,553 1,183,137,915Bitternut hickory 1,082,379 35,789 2,610 2,469 10,601 2,973 226,129,3 64Black walnut 915,859 145,783 6,810 6,200 35,226 12,593 1,054,738,446Green ash 832,599 68,566 1,731 1,316 15,996 4,654 1,076,355,876Pin oak 666,079 73,885 4,394 4,221 11,638 4,698 594,086,667White mulberry 582,819 27,565 2,010 1,913 6,116 1,996 237,203,011Honeylocust 499,559 209,936 5,803 5,015 7,430 3,471 1,204,829,532Eastern red cedar 499,559 29,114 1,336 1,248 12,987 16,093 325,220,498Other species 333,040 2,141 - -513 - - -Pecan 249,780 5,784 687 672 1,706 529 51,384,976Shagbark hickory 249,780 13,036 1,207 1,154 1,299 424 136,469,830Bur oak 249,780 65,558 2,103 1,954 5,508 2,425 419,597,046Pawpaw 166,520 181 84 83 612 458 9,179,403Kentucky coffeetree 166,520 28,660 1,241 1,128 3,785 1,262 263,106,803Black cherry 166,520 5,580 651 638 333 115 46,495,070Silver maple 83,260 81 38 37 94 22 2,185,572American sycamore 83,260 2,025 242 234 152 33 18,872,694Eastern cottonwood 83,260 5,533 406 384 2,989 962 56,546,236Cherry plum 83,260 5,154 455 434 487 132 49,998,190Northern red oak 83,260 124 54 53 310 110 7,077,091Black willow 83,260 24,836 1,050 993 5,591 1,580 70,764,110Total 21,397,793 1,509,894 73,592 45,219 275,901 105,850 10,645,235,127

Number of 

Trees

Carbon 

Storage (tons)

Carbon Sequestration 

(tons/year)

Net Carbon 

Seuestration (tons/year)

Leaf Area 

(ac)

Leaf Biomass 

(tons)

Structural Value 

($US)

continued
45

Table 15.—continuedCommon Name

Number of 

Trees

Carbon 

Storage (tons)

Carbon Sequestration 

(tons/year)

Net Carbon 

Seuestration (tons/year)

Leaf Area 

(ac)

Leaf Biomass 

(tons)

Structural Value 

($US)

 Other 

American elm 23,312,773 368,826 26,699 18,156 86,336 28,010 1,893,421,513Northern hackberry 5,495,154 267,981 16,758 16,540 63,024 14,627 1,989,415,002Eastern redcedar 4,412,775 20,886 3,524 3,417 18,900 23,419 244,411,985Honeylocust 3,080,616 112,322 10,481 8,713 9,781 4,569 840,333,875Pawpaw 2,747,577 4,484 1,895 1,880 7,142 5,344 141,309,779Bur oak 2,747,577 312,315 10,233 10,000 21,847 9,618 1,734,0 71,605Boxelder 1,748,458 130,529 6,426 5,964 18,943 7,730 442,735,169Eastern hophornbeam 1,248,899 10,027 1,664 1,655 6,916 2,014 140,663,685Osage-orange 1,165,639 123,905 5,807 5,705 13,201 5,920 538,342,819Bitternut hickory 1,082,379 59,327 4,173 4,129 16,453 4,614 338,345,034White oak 999,119 104,977 6,441 6,361 9,404 3,051 904,601,103White mulberry 915,859 92,222 2,994 1,564 4,501 1,469 345,314,913Black walnut 832,599 125,131 5,422 5,333 20,881 7,465 630,673,527American sycamore 749,339 235,859 6,897 6,726 34,249 7,402 562,029,765Black willow 749,339 5,635 654 645 1,877 530 5,665,613American basswood 749,339 79,483 4,731 4,414 62,316 8,116 937,318,655Green ash 666,079 26,723 1,003 573 4,833 1,406 192,504,754Shagbark hickory 582,819 73,942 4,482 4,387 9,073 2,964 355,435,545Eastern cottonwood 499,559 259,811 6,592 6,411 35,003 11,267 468,265,237White ash 416,300 54,741 3,116 2,230 9,772 2,477 328,452,625Northern red oak 416,300 6,620 693 688 2,714 965 37,727,257Silver maple 249,780 196,269 3,422 2,911 14,344 3,368 553,932,951Chinkapin oak 249,780 23,898 1,664 1,602 4,307 1,896 150,768,437Other species 166,520 57,524 - -2,876 - - -Black cherry 166,520 4,345 70 -139 216 75 2,497,797Pin oak 166,520 4,440 465 462 3,521 1,421 40,705,526Shellbark hickory 83,260 120 51 51 61 14 2,497,797Black oak 83,260 324 98 98 200 63 4,371,145Siberian elm 83,260 1,667 140 138 737 224 1, 948,669Total 55,867,396 2,764,331 136,594 117,738 480,550 160,038 13,827,761,782Total for Kansas City Region 249,446,667 19,865,210 1,001,132 666,625 3,311,311 1,176,282 93,447,671,317
46Table 16.—Summary of tree structure and functions by land use, Kansas City region, 2010

Trees Carbon Storage 

(tons)

Carbon Seq 

tons/yr)

Net Carbon Seq 

(tons/yr)

Leaf Area 

(ac)

Leaf Biomass 

(tons) 

Tree Value 

($US billion)

LAND USE N % N % N % N % N % N % N %Agriculture & Vacant 81,095,000 32.5 6,927,000 34.9 303,000 30.3 278,000 41.7 1,092,000 33.0 415,000 35.3 25.5 27.3Comm-Utility-Trans 3,996,000 1.6 251,000 1.3 18,000 1.8 7,000 1.0 50,000 1.5 17,000 1.4 1.0 1.1Residential 87,090,000 34.9 8,413,000 42.4 471,000 47.1 219,000 32.8 1,413,000 42.7 478,000 40.6 42.5 45.5Golf-Park-Inst 21,398,000 8.6 1,510,000 7.6 74,000 7.4 45,000 6.7 276,000 8.3 106,000 9.0 10.6 11.3Other 55,867,000 22.4 2,764,000 13.9 137,000 13.7 118,000 17.7 481,000 14.5 160,000 13.6 13.8 14.8Total 249,447,000 19,865,000 1,001,000 667,000 3,311,000 1,176,000 93.4
47

0

10

20

30

40

50

60

70

80

90

100

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

Diameter Class Population (percent) 

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

Other Golf/Park/Inst Residential Comm/Util/Trans Ag/Vacant

Land-use Category 

APPENDIX IV. POPULATION INFORMATION BY STEM 

DIAMETER CLASS

Th is appendix details how trees in each diameter class are distributed among land uses 

(Fig. 35); whether the most common species in the diameter classes less than 3 inches 

or greater than 18 inches are gaining or losing prominence (Fig. 36); and how trees 

less than 3 inches in diameter are distributed by land use (Fig. 37).

Figure 35.—Percent of diameter class population in each land-use category, 

Kansas City region, 2010

For example, of the trees that have diameters between 27 and 30 inches, 75 percent are 

found in residential land uses and 25 percent are found in golf/park/institutional areas.

0

5

10

15

20

25

30

35

American elm Northern

hackberry

Eastern red

cedar

Honeylocust Bitternut

hickory

Ohio buckeye Chinkapin oak Pawpaw White

mulberry

Osage-orange Bur oak Black walnut

Diameter Class Population (percent) 

Species 

Less than 3

Greater than 18

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

Figure 36.—Percent of diameter class (<3 inches or >18 inches) population occupied by the 

most common tree species in those classes, Kansas City region, 2010 

For example, American elm comprises 34 percent of the trees with diameters less than 

3 inches and 6 percent of the trees with diameters greater than 18 inches.
48

Figure 37.—Percent of diameter class (<3 inches) occupied by the most common species in 

that diameter class, Kansas City region, 2010

For example, 2.6 percent of the trees less than 3 inches in diameter are Ohio buckeye 

within residential land uses.

0

2

4

6

8

10

12

14

American elm Northern

hackberry

Eastern

UHGcedar

Honeylocust Bitternut

hickory

Ohio buckeye Chinkapin oak Pawpaw White

mulberry

Osage-orange

Diameter Class <3 inches (percent) 

Species 

Ag/Vacant Comm/Util/Trans

Residential Golf/Park/Inst

Other

Land-use Category 
49

APPENDIX V. GENERAL RECOMMENDATIONS FOR AIR QUALITY 

IMPROVEMENT

Urban vegetation can directly and indirectly aff ect local and regional air quality by altering the urban 

atmospheric environment. Four main ways that urban trees aff ect air quality are:

 Temperature reduction and other microclimatic eff ects

Removal of air pollutants

Emission of volatile organic compounds (VOC) and tree maintenance emissions

Energy conservation on buildings and consequent power plant emissions

Th e cumulative and interactive eff ects 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 Increase pollution removal

Sustain existing tree cover Maintain 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 eff ects

Use long-lived trees Reduce long-term pollutant emissions from planting 

 and removal

Use low maintenance trees Reduce pollutants emissions from maintenance activities

Reduce fossil fuel use in maintaining vegetation Reduce pollutant emissions

Plant trees in energy conserving locations Reduce pollutant emissions from power plants

Plant trees to shade parked cars Reduce vehicular VOC emissions

Supply ample water to vegetation Enhance pollution removal and temperature reduction

Plant trees in polluted or heavily populated areas Maximizes tree air quality benefi ts

Avoid pollutant-sensitive species Improve tree health

Utilize evergreen trees for particulate matter Year-round removal of particles
50

APPENDIX VI. RELATIVE TREE EFFECTS

Th e tree and forest resource in the Kansas City region provides benefi ts that include carbon storage and 

sequestration and air pollutant removal. To estimate a relative value of these benefi ts, tree benefi ts were compared 

to estimates of average carbon emissions in the region31, average passenger automobile emissions32, and average 

household emissions.33

General tree information:

Average tree stem diameter = 5.2 inches

Median tree stem diameter = 3.4 inches

Number of trees sampled = 2,756

Number of species sampled = 51

Table 17.—Average tree effects by stem diameter class (d.b.h.), Kansas City region, 2010

D.b.h.

(inches)a

Carbon storage Carbon sequestration Pollution removal

(lbs) ($) (miles) a (lbs/yr) ($/yr) (miles) b (lbs) ($)

1-3 6 0.06 20 1.6 0.02 6 0.05 0.19

3-6 38 0.39 140 5.6 0.06 21 0.2 0.61

6-9 136 1.41 500 10.9 0.11 40 0.3 1.24

9-12 314 3.25 1,150 17.9 0.19 66 0.6 1.97

12-15 557 5.76 2,040 23.5 0.24 86 0.8 2.95

15-18 914 9.46 3,350 32.0 0.33 117 1.1 3.98

18-21 1,336 13.82 4,890 40.5 0.42 148 1.3 4.57

21-24 1,824 18.87 6,680 53.2 0.55 195 1.7 6.09

24-27 2,488 25.73 9,110 59.5 0.62 218 1.8 6.25

27-30 2,826 29.24 10,350 76.1 0.79 279 3.0 10.55

30+ 5,806 60.06 21,260 92.6 0.96 339 2.8 9.90

a lower limit of the diameter class (d.b.h.) 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

Th e trees in the Kansas City region provide:

Carbon storage equivalent to:

Amount of carbon (C) emitted in region in 613 days or 

annual carbon emissions from 11,922,000 automobiles or

annual C emissions from 5,986,400 single family houses 

Carbon monoxide removal equivalent to:

Annual carbon monoxide emissions from 2,582 automobiles 

or

annual carbon monoxide emissions from 10,700 family 

houses 

Nitrogen dioxide removal equivalent to:

Annual nitrogen dioxide emissions from 287,200 

automobiles or

annual nitrogen dioxide emissions from 191,500 single 

family houses

Sulfur dioxide removal equivalent to:

Annual sulfur dioxide emissions from 3,342,900 automobiles 

or

annual sulfur dioxide emissions from 56,000 single family 

houses 

Particulate matter less than 10 micron (PM10) removal 

equivalent to:

Annual PM10 emissions from 63,376,300 automobiles or 

annual PM10 emissions 6,118,000 single family houses

Annual C sequestration equivalent to:

Amount of C emitted in region in 31 days or

annual C emissions from 600,800 automobiles or

annual C emissions from 301,700 single family homes
51

APPENDIX VII. TREE PLANTING INDEX MAP

To determine the best locations to plant trees, tree canopy and impervious cover maps from National Land 

Cover Data34 were used in conjunction with 2000 U.S. Census data to produce an index of priority planting 

areas for the Kansas City region. Index values were produced for each census block group; the higher the index 

value, the higher the priority of the area for tree planting. Th is index is a type of “environmental equity” index 

with areas with higher human population density and lower tree cover tending to get the higher index value. 

Th e criteria used to make the index were:

• Population density: the greater 

the population density, the 

greater the priority for tree 

planting

• Tree stocking levels: the lower 

the tree stocking level (the 

percent of available greenspace 

[tree, grass, and soil cover 

areas] that is occupied by 

tree canopies), the greater the 

priority for tree planting

• Tree cover per capita: the lower 

the amount of tree cover per 

capita (m2

/capita), the greater the 

priority for tree planting

Each criteria was standardized35 on a 

scale of 0 to 1 with 1 representing the 

census block group with the highest 

value in relation to priority of tree 

planting (i.e., the census block group 

with highest population density, 

lowest stocking density or lowest tree 

cover per capita were standardized to 

a rating of 1). Individual scores were 

combined and standardized based on 

the following formula to produce an 

overall priority planting index (PPI) 

value between 0 and 100:

PPI = (PD * 40) + (TS * 30) + (TPC * 30)

Where PPI = index value, PD is standardized population density, TS is standardized tree stocking, and TPC is 

standardized tree cover per capita.

Figure 38.—Priority planting areas, Kansas City region.

Higher index scores indicate higher priority areas for planting.

.

0 8 16 24 32 4

Miles

- Kansas City Boundary

Priority

Planting Index

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
52

Figure 39.—Priority planting areas, Kansas City.

Higher index scores indicate higher priority areas for planting.

.

0 3 6 9 12 1.5

Miles

Priority

Planting Index

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
53

APPENDIX VIII. i-TREE FORECAST PROTOTYPE MODEL 

METHODS AND RESULTS

Th e i-Tree Forecast Prototype Model was built to simulate future forest structure (e.g., number of trees 

and sizes) and various ecosystem services based on annual projections of the current forest structure data. 

Th ere are three main components of the model:

1) Tree growth—simulates tree growth to annually project tree diameter, crown size, and leaf area 

for each tree

2) Tree mortality—annually removes trees from the projections based on user defi ned mortality rates

3) Tree establishment—annually adds new trees to the projection. Th ese inputs can be used 

to illustrate the eff ect of the new trees or determine how many new trees need to be added 

annually to sustain a certain level of tree cover or benefi ts.

Tree Growth

Annual tree diameter growth is estimated for the region based on: 1) the length of growing season; 2) 

species average growth rates; 3) tree competition; 4) tree condition; and 5) current tree height relative to 

maximum tree height.

To determine a base growth rate based on length of growing season, urban street tree, park tree, 

and forest growth measurements were standardized to growth rates for 153 frost-free days based on: 

Standardized growth = measured growth × (number of frost-free days of measurement/153).3

 Growth 

rates of trees of the same species or genera were also compared to determine the average diff erence 

between standardized street tree growth and standardized park tree and forest tree growth rates. Park 

growth averaged 1.78 times less than street trees, and forest growth averaged 2.26 times less than street 

tree growth.

For this study, average standardized growth rates for open-grown trees was input as 0.26 in/yr for slow 

growing species, 0.39 in/yr for moderate growing species and 0.52 in/yr for fast growing species. Th ere 

are limited measured data on urban tree growth for slow, moderate or fast-growing tree species, so the 

growth rates used here are estimates. Th ese growth rates by species growth-rate class were estimated such 

that the entire population average growth rate was comparable to the measured growth rates for trees 

standardized to the number of frost free days. 

Crown light exposure (CLE) measurements of 0-1 were used to represent forest growth conditions; 

2-3 for park conditions; and 4-5 for open-grown conditions. Th us, for: CLE 0-1: Base growth = 

Standardized growth (SG) / 2.26; CLE 2-3: Base growth = SG / 1.78; and CLE 4-5: Base growth = 

SG. However, as the percent canopy cover increased or decreased, the CLE correction factors were 

adjusted proportionally to the amount of available greenspace (i.e., as tree cover decreased and available 

greenspace increased—the CLE adjustment factor decreased; as tree cover increased and available 

greenspace decreaseed—the CLE adjustment factor increased).

Base growth rates are also adjusted based on tree condition. Th ese adjustments factors are based on 

percent crown dieback and the assumption that less than 25 percent crown dieback had a limited eff ect 

on diameter growth rates. For trees in fair to excellent condition (less than 25 percent dieback), base 
54

growth rates are multiplied by 1 (no adjustment), trees in poor condition (crown dieback of 26-50 

percent) by 0.76, critical trees (crown dieback of 51-75 percent) by 0.42, dying trees (crown dieback of 

76-99 percent) by 0.15, and dead trees (crown dieback of 100 percent) by 0.

As trees approach their estimated maximum height, growth rates are reduced. Th us the species growth 

rates as described above were adjusted based on the ratio between the current height of the tree and the 

average height at maturity for the species. When a tree’s height is over 80 percent of its average height at 

maturity, the annual diameter growth is proportionally reduced from full growth at 80 percent of height 

to one-half growth rate at height at maturity. Th e growth rate is maintained at one-half growth until the 

tree is 125 percent past maximum height, when the growth rate is then reduced to 0.

Tree height, crown width, crown height, and leaf area were then estimated based on tree diameter each 

year. Height, crown height, and crown width are calculated using species, genus, order, and family 

specifi c equations that were derived from measurements from urban tree data (unpublished equations). 

If there is no equation for a particular species, then the genus equation is used, followed by the family 

and order equations, if necessary. If no order equation is available, one average equation for all trees 

is used to estimate these parameters. Leaf area was calculated from the crown height, tree height, and 

crown width estimates based on i-Tree methods.3

Total canopy cover was calculated by summing the two-dimensional crown area of each tree in the 

population. Th is calculated estimate of crown area was adjusted to attain the actual tree cover of the 

study area based on photo-interpretation. Trees often have overlapping crowns, so the sum of the crown 

areas will often over estimate total tree cover as determined by aerial estimates. Th us the crown overlap 

can be determined by comparing the two estimates:

% crown overlap = (sum of crown area – actual tree cover area)/sum of crown area

When future projections predicted an increase in percent tree cover, the percent crown overlap was 

held constant However, when 100 percent tree cover was attained all new canopy added was considered 

as overlapping canopy. When there was a projected decrease in percent tree cover, the percent crown 

overlap decreased in proportion to the increase in the amount of available greenspace (i.e., as tree cover 

dropped and available greenspace increased – the crown overlap decreased).

Tree Mortality Rate

Canopy dieback is the fi rst determinant for tree mortality. Trees with 50-75 percent crown dieback 

having an annual mortality rate of 13.1 percent; trees with 76-99 percent dieback have a 50 percent 

annual mortality rate, and trees with 100% dieback have a 100 percent annual mortality rate.36 Trees 

with less than 50 percent dieback have a user defi ned mortality rate that is adjusted based on the tree 

size class and diameter.

Trees are assigned to species size classes: small trees have an average height at maturity of less than or 

equal to 40 ft (maximum diameter class = 20+ inches); medium trees have mature tree height of 41- 60 

ft (maximum diameter = 30+ inches); large trees have a mature height of greater than 60 ft (maximum 

diameter = 40+ inches). Each size class has a unique set of seven diameter ranges to which base mortality 

rates area assigned based on measured tree mortality by diameter class (Fig. 40).36 Th e same distribution 
55

of mortality by diameter class was used for all tree size classes, but the diameter range of the classes 

diff ered by size class. Th e actual mortality rate for each diameter class was adjusted so that the overall 

average mortality rate for the base population equaled the mortality rates assigned by user. Th at is, the 

relative curve of mortality stayed the same among diameter classes, but the actual values would change 

based on the user-defi ned overall average rate.

0

0.01

0.02

0.03

0.04

0.05

0.06

0-10% 10-20% 20-40% 40-60% 60-80% 80-100% Max.

Base Mortality Rate 

Diameter Range (A) 

0

0.01

0.02

0.03

0.04

0.05

0.06

0 - 2 2 - 4 4 - 8 8 - 12 12 - 16 16 - 20 20 +

Base Mortality Rate 

Diameter Range 

 0 - 3 3 - 6 6 - 12 12 - 18 18 - 24 24 - 30 30 + 

 0 - 4 4 - 8 8 - 16 16 - 24 24 - 32 32 - 40 40 + 

Small 

Medium 

Large 

(B) 

Figure 40.—A) Mortality rate distribution by diameter class (d.b.h.) with d.b.h. range 

classifi ed by maximum d.b.h. for the species, and B) for actual d.b.h. classes for small, 

medium, and large tree species.
56

Tree Establishment

Based on the desired tree cover level and the number of years desired to reach that canopy goal, the 

program calculates the number of trees needing to be established annually to reach that goal given the 

model growth and mortality rate. In adding new trees to the model each year, the species composition 

of new trees was assumed to be proportional to the current species composition. Crown light exposure 

of newly established trees was also assumed to be proportional to the current growth structure of the 

canopy. Newly established trees were input with a starting stem diameter of 1 inch. 

Model Scenarios

Numerous model scenarios were run for the Kansas City region. All scenarios were run with 2, 3, 4, 

or 5 percent average annual mortality rates for projections 10, 25 and 50 years in the future. All model 

scenarios were run by land use class.

For cases of maintaining current tree cover, two additional scenarios were run:

1) Ash mortality: the entire ash population dies in 10 years. Th e mortality rate is 10 percent of the 

initial population per year.

2) Black walnut mortality: the entire black walnut population dies in 10 years. Th e mortality rate 

is 10 percent of the initial population per year.

Land Use—Agriculture & Vacant

Area: 1,615,263 ac 

Current Canopy Cover: 11.8%

Tree-size Classes (height)

 Small Trees – 14.7%

 Medium Trees – 8.2%

 Large Trees – 77.1%

Agriculture & Vacant Land Use Scenarios:

Th e following tables illustrate the number of trees that must be established annually to attain the desired 

canopy cover goal within the 10-, 25- and 50-year periods.

Maintain existing agriculture & vacant canopy cover of 11.8 percent 

Trees

No Additional Mortality Ash Kill in 10 Years Black Walnut Kill in 10

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 00 0 00 0 0 0 0

3 0 0 74,000 0 0 160,000 0 0 260,000

4 0 40,000 960,000 0 100,000 1,100,000 450,000 300,000 1,200,000

5 0 1,200,000 2,100,000 130,000 1,500,000 2,300,000 600,000 1,700,000 2,400,000
57

Increase agriculture & vacant canopy cover by 5 to 16.8 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 0 760,000

4 1,900,000 1,400,000 1,800,000

5 3,650,000 2,900,000 2,900,000

Increase agriculture & vacant canopy cover by 10 to 21.8 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 1,200,000 0 0

3 3,400,000 1,400,000 1,200,000

4 5,500,000 2,500,000 2,500,000

5 7,500,000 4,200,000 4,000,000

Increase agriculture & vacant canopy cover by 20 to 31.8 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 8,800,000 1,800,000 900,000

3 11,000,000 3,300,000 2,440,000

4 13,000,000 5,400,000 4,200,000

5 16,000,000 7,300,000 6,400,000

Land Use – Commercial, Utilities & Transportation

Area: 166,523 ac 

Current Canopy Cover: 9.6%

Tree Size Classes (height)

 Small Trees – 0.0%

 Medium Trees – 18.2%

 Large Trees – 81.8%

Commercial, Utilities & Transportation Land Use Scenarios:

Th e following tables illustrate the number of trees that must be established annually to attain the desired 

canopy cover goal within the 10, 25 and 50 year periods.

Maintain existing commercial, utilities, transportation canopy cover 

of 9.6 percent (Note - there were no ash or walnut trees in this land 

use category)

Trees

No Additional Mortality

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 00

4 0 0 18,000

5 0 0 75,000
58

Increase commercial, utilities, transportation canopy cover by 5 to 

14.6 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 30,000 0 15,000

4 130,000 40,000 75,000

5 250,000 120,000 140,000

Increase commercial, utilities, transportation canopy cover by 10 to 

19.6 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 300,000 0 0

3 450,000 80,000 60,000

4 600,000 160,000 138,000

5 750,000 260,000 230,000

Increase commercial, utilities, transportation canopy cover by 20 to 

29.6 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 1,050,000 190,000 65,000

3 1,250,000 285,000 155,000

4 1,450,000 435,000 265,000

5 1,600,000 580,000 400,000

Land Use—Golf, Park & Institutional

Area: 158,196 ac 

Current Canopy Cover: 34.3%

Tree Size Classes (height)

 Small Trees – 6.5%

 Medium Trees – 5.6%

 Large Trees – 87.9%

Golf, Park & Institutional Land Use Scenarios:

Th e following tables illustrate the number of trees that must be established annually to attain the desired 

canopy cover goal within the 10, 25 and 50 year periods.

Maintain existing golf, park & institutional canopy cover of 34.3 percent

Trees

No Additional Mortality Ash Kill in 10 Years Black Walnut Kill in 10

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 00 0 00 0 0 0 0

3 0 0 50,000 0 0 54,000 0 0 80,000

4 0 28,000 350,000 0 110,000 355,000 80,000 170,000 370,000

5 50,000 400,000 630,000 400,000 490,000 635,000 570,000 550,000 650,000
59

Increase golf, park & institutional canopy cover by 5 to 39.3 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 0 95,000

4 0 180,000 400,000

5 500,000 600,000 750,000

Increase golf, park & institutional canopy cover by 10 to 44.3 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 0 130,000

4 200,000 280,000 460,000

5 800,000 750,000 800,000

Increase golf, park & institutional canopy cover by 20 to 54.3 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 200,000 270,000 230,000

4 760,000 480,000 610,000

5 1,250,000 950,000 1,000,000

Land Use—Residential

Area: 716,029 ac 

Current Canopy Cover: 31.4%

Tree Size Classes (height)

 Small Trees – 5.3%

 Medium Trees – 12.4%

 Large Trees – 82.3%

Residential Land Use Scenarios:

Th e following tables illustrate the number of trees that must be established annually to attain the desired 

canopy cover goal within the 10, 25 and 50 year periods.

Maintain existing residential canopy cover of 31.4 percent

Trees

No Additional Mortality Ash Kill in 10 Years Black Walnut Kill in 10

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 00 0 00 0 0 0 0

3 0 0 165,000 0 0 320,000 0 0 450,000

4 0 43,000 1,250,000 0 340,000 1,500,000 450,000 900,000 1,650,000

5 72,000 1,700,000 2,500,000 800,000 1,800,000 2,750,000 2,500,000 240,000 2,900,000
60

Increase residential canopy cover by 5 to 36.4 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 0 570,000

4 400,000 900,000 1,800,000

5 2,000,000 2,600,000 3,100,000

Increase residential canopy cover by 10 to 41.4 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 39,000 0 830,000

4 2,200,000 1,650,000 2,200,000

5 4,400,000 3,350,000 3,700,000

Increase residential canopy cover by 20 to 51.4 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 1,200,000 0 0

3 3,700,000 1,200,000 1,500,000

4 6,400,000 3,300,000 3,100,000

5 8,900,000 5,400,000 5,000,000

Land Use—Water & Other

Area: 174,848 ac 

Current Canopy Cover: 49.2%

Tree Size Classes (height)

 Small Trees – 7.7%

 Medium Trees – 12.6%

 Large Trees – 79.7%

Water & Other Land Use Scenarios:

Th e following tables illustrate the number of trees that must be established annually the desired canopy 

cover goal within the 10, 25 and 50 year periods.

Maintain existing water & other canopy cover of 49.2 percent

Number of Trees

No Additional Mortality Ash Kill in 10 Years Black Walnut Kill in 10

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 00 0 00 0 0 0 0

3 00 0 00 0 0 0 0

4 0 0 350,000 0 0 410,000 0 0 420,000

5 0 190,000 950,000 0 300,000 1,000,000 0 350,000 1,010,000
61

Increase water & other canopy cover by 5 to 54.2 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 00

4 0 0 480,000

5 0 610,000 1,180,000

Increase water & other canopy cover by 10 to 59.2 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 00

4 0 0 640,000

5 380,000 880,000 1,400,000

Increase water & other canopy cover by 20 to 69.2 percent

Number of Trees

Mortality (%) 10 Years 25 Years 50 Years

2 0 00

3 0 0 106,000

4 480,000 470,000 1,000,000

5 1,700,000 1,500,000 2,000,000

Th e results illustrate that at low mortality rates, the projected tree growth more than compensates for 

the loss of tree cover due to mortality. Th is result may be an artifact of mortality rates that are too low 

relative to actual mortality rates, or that growth rates are too high. However, even though tree cover can 

be sustained without new trees, the total tree population will decline through time and eventually the 

tree cover of the area will decline rapidly. With very low mortality rates, which are unreasonable (e.g., 

0%), no new trees would ever need to be established and tree cover would increase to 100% as no trees 

would die and existing trees would continue to grow.

Th us, these population projections are estimates that are based on the assumptions that drive canopy 

cover and tree population estimates (i.e., tree growth and mortality rates). Some information is known 

on urban tree and forest growth rates, but very little is known on actual tree mortality rates. Long-term 

urban forest monitoring studies are needed to help determine actual urban tree mortality rates and 

factors that infl uence these rates.
62

APPENDIX IX. I-TREE HYDRO MODEL METHODS AND 

CALIBRATION GRAPHS

Data and Model Calibration

Th e hourly weather data were derived from a local weather station at the Charles B. Wheeler Airport 

in Kansas City (USAF: 724463, WBAN: 99999). Tree and impervious cover parameters (Table 18) 

were derived for the watershed from photo-interpretation of Google Earth imagery (image date circa 

May 2010) using 800 randomly located points.

Table 18.—Cover estimates for the Blue River watershed, 2010

Percent Cover

Area Impervious Tree Grass/shrub Bare Soil

Blue River Watershed 31.5% 34.6% 37.6% 1.3%

Th e model was calibrated using hourly stream fl ow data collected at the “Blue River at 12th Street 

in Kansas City, MO” gauging station (USGS 06893590) from 04/15/2000 – 10/31/2000. Model 

results were calibrated against measured stream fl ow to yield the best fi t between model and measured 

stream fl ow results. Calibration coeffi cients (0-1 with 1.0 = perfect fi t) were calculated for peak fl ow, 

base fl ow, and balance fl ow (peak and base) (Table 19). Diff erences between measured and estimated 

fl ow can be substantially diff erent, particularly for peak fl ows, due to mismatching of steam fl ow 

and weather data as the weather stations are often outside of the watershed area. For example, it may 

be raining at the weather station and not in the watershed or vice versa. Tree canopy leaf area index 

(LAI) was estimated at 5 based on various fi eld studies and the amount of percent of impervious 

cover connected to the stream was estimated at 65 percent.

Table 19.—Calibration coefficients for model estimates and gauging station data

Calibration Coefficients

Watershed Peak Flow Base Flow Balanced Flow

Blue River 0.62 0.53 0.68

Model calibration procedures adjust several model parameters (mostly related to soils) to fi nd the 

best fi t between the observed fl ow and the model fl ow on an hourly basis. However, there are often 

mismatches between the precipitation data, which are often collected outside of the watershed, and 

the actual precipitation that occurs in the watershed. Even if the precipitation measurements are 

within the watershed, local variations in precipitation intensity can lead to diff ering amounts of 

precipitation than observed at the measurement station. Th ese diff erences in precipitation can lead 

to poorer fi ts between the observed and predicted estimates of fl ow as the precipitation is a main 

driver of the stream fl ow. As can be seen in Figure 41, the observed and simulated results can diverge, 

which is often an artifact of the precipitation data. For example, observed fl ow will rise sharply, but 

predicted fl ow does not, which is an indication of rain in the watershed, but not at the precipitation 

measurement station. Conversely, the simulated fl ow may rise, but the observed fl ow does not, which 

is an indication of rain at the precipitation station, but not in the watershed.

As the model simulations are comparisons between the base simulation fl ows and another simulated 

fl ow where surface cover is changed (e.g., increase or decrease in tree cover), both model runs are 

using the same simulation parameters. What this means is that the eff ects of changes in cover types 
63

are comparable, but may not exactly match the fl ow of the stream. Th at is the estimates of the 

changes in fl ow are reasonable (e.g., the relative amount of increase or decrease in fl ow is sound as 

both are using the same model parameters and precipitation data), but the absolute estimate of fl ow 

may be incorrect. Th us the model results can be used to assess the relative diff erences in fl ow due to 

changes in cover parameters, but should not be used to predict the actual eff ects on stream fl ow due 

to precipitation and calibration imperfections. Th e model can be used to compare the changes in 

fl ow (e.g., increased tree cover leads to an x percent change in stream fl ow), but will likely not exactly 

match the fl ow observed in the stream. Th e model is more diagnostic of cover change eff ects than 

predictive of actual stream fl ow due to imperfections of models and input data used in the model.

Th e model tends to underestimate observed peak fl ows, particularly fl ows over about 8 million cubic 

feet per hour in Blue River (Fig. 42), which did not occur too often during the simulation period.

Model Scenarios

After calibration, the model was used under various conditions to determine stream fl ow response 

given varying tree and impervious cover values for the watershed area. For tree cover simulations, 

impervious cover was held constant at the original value with tree cover varying between 0 and 100 

percent. Increasing tree cover was assumed to fi ll bare soil spaces fi rst, followed by grass and shrub 

covered areas, and then fi nally impervious covered land. At 100 percent tree cover, all impervious 

land is covered by trees. Th is assumption is unreasonable as all buildings, roads, and parking lots 

would be covered by trees, but the results illustrate the potential impact. Tree cover reductions 

assumed that trees were replaced with grass and shrub cover. 

For impervious cover simulations, tree cover was held constant at the original value with impervious 

cover varying between 0 and 100 percent. Increasing impervious cover was assumed to fi ll bare soil 

spaces fi rst, then grass and shrub covered areas, and then fi nally under tree canopies. Th e assumption 

Figure 41.—Comparison of simulated model fl ow vs. observed fl ow in Blue River watershed.

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

1 1001 2001 3001 4001

Flow (cubic feet per hour) 

Hour 

Simulated flow

Observed Flow
64

of 100 percent impervious cover is unreasonable, but the results illustrate the potential impact. In 

addition, as impervious increased from the current conditions, so did the percent of the impervious 

cover connected to the stream such that at 100% impervious cover, all (100%) impervious cover is 

connected to the stream. Reductions in impervious cover were assumed to be fi lled with grass and 

shrub cover.

Water Quality Effects—Event Mean Concentration to Calculate 

Pollution Load

Event mean concentration (EMC) data is used for estimating pollutant loading into watersheds. 

EMC is a statistical parameter representing the fl ow-proportional average concentration of a given 

parameter during a storm event and is defi ned as the total constituent mass divided by the total 

runoff volume. EMC estimates are usually obtained from a fl ow-weighted composite of concentration 

samples taken during a storm. Mathematically37,38:

¦

¦

3

3

'

' | Q t t

C t Q t t

Q t dt

C t Q t dt

V

M EMC C

( )

( ) ( )

( )

( ) ( )

 (1)

where C(t) and Q(t) are the time-variable concentration and fl ow measured during the runoff event, 

and M and V are pollutant mass and runoff volume as defi ned in Equation 1. It is clear that the EMC 

results from a fl ow-weighted average, not simply a time average of the concentration. EMCs are 

reported as a mass of pollutant per unit volume of water (usually mg/L).

Th e pollution load () calculation from the EMC method is

L EMC *Q EMC * dr * A (2)

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

0 5,000,000 10,000,000 15,000,000 20,000,000

Observed Flow (Cubic feet per hour) 

Simulated Flow (Cubic feet per hour) 

Figure 42.—Comparison observed vs. simulated fl ow, Blue River watershed.
65

Where EMC is event mean concentration (mg/l, mg/m3

, lbs/ft3,...), Q is runoff of a time period 

associated with EMC (l/h, m3

 /day, ft3/day, ...), r d is runoff depth of unit area (mm/h, in/h, ...), A

is the land area (m2

, ft2...) which is catchment area in i-Tree Hydro.

Th us, when the EMC is multiplied by the runoff volume, an estimate of the loading to the receiving 

water is provided. Th e 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. Th is process ensures that mass loadings from storms will be correctly represented. EMCs 

represent the concentration of a specifi c 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.39 Figure 43 

illustrates the inter-storm variation of pollutographs and EMC.

Since collecting the data necessary for calculating site-specifi c EMCs can be cost-prohibitive, 

researchers or regulators will often use values that are already available in the literature. If site-specifi c 

numbers are not available, regional or national averages can be used, although the accuracy of using 

these numbers is questionable. Due to the specifi c climatological and physiographic characteristics 

of individual watersheds, agricultural and urban land uses can exhibit a wide range of variability in 

nutrient export.40

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 to the Clean Water Act 

(PL 95-217). Th e 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 Agency41 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.

Subsequently, the USGS created another urban stormwater runoff base42, based on data measured 

through mid-1980s for more than 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 

Figure 43.—Inter-storm variation of pollutographs and event mean concentrations (EMC).
66

Elimination System (NPDES). Th e NPDES data are from over 30 cities and more than 800 stationstorms for over 150 paramaters.43

Th e 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.43 A “pooled” mean was calculated representing 

the mean of the total population of sample data. Th e NURP and pooled mean EMCs for the 10 

pollutants or constituents are listed in the Table 20.43 NURP or pooled mean EMCs were selected 

because they are based on fi eld data collected from thousands of storm events. Th ese estimates are 

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

climate, and other factors.

Table 20.—National pooled EMCs and NURP EMCs

Pollutant/Constituent (Abbrevation) Data Source

EMCs (mg/l)

Mean Median No. of Events

Total Suspended Solids (TSS) Pooled

NURP

78.4

17.4

54.5

113

3047

2000

Biochemical Oxygen Demand (BOD5) Pooled

NURP

14.1

10.4

11.5

8.39

1035

474

Chemical Oxygen Demand (COD) Pooled

NURP

52.8

66.1

44.7

55

2639

1538

Total phosphorus (TP) Pooled

NURP

0.315

0.337

0.259

0.266

3094

1902

Soluble phosphorus (Soluble P) Pooled

NURP

0.129

0.1

0.103

0.078

1091

767

Total Kjeldhal nitrogen (TKN) Pooled

NURP

1.73

1.67

1.47

1.41

2693

1601

Nitrite and Nitrate (NO2 and NO3) Pooled

NURP

0.658

0.837

0.533

0.666

2016

1234

Copper (Cu) Pooled

NURP

13.5

66.6

11.1

54.8

1657

849

Lead (Pb) Pooled

NURP

67.5

175

50.7

131

2713

1579

Zinc (Zn) Pooled

NURP

162

176

129

140

2234

1281

Note: (1) Polled data sources include: NURP, USGS, NPDES

 (2) No BOD5 data available in the USGS dataset - polled includes NURP+NPDES

 (3) NO TSP data available in NPDES dataset - polled includes NURP+USGS

For i-Tree Hydro, the pooled median and mean EMC value for each pollutant were applied to the 

runoff regenerated from pervious and impervious surface fl ow, not the base fl ow values, to estimate 

eff ects on pollutant load across the entire modeling period. All rain events are treated equally using 

the EMC value, which mean some events may be overestimated and others underestimated. In 

addition, local management actions (e.g., street sweeping) can aff ect these values. However, across the 

entire season, if the EMC value is representative of the watershed, the estimate of cumulative eff ects 

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

using locally derived coeffi cients. It is not known how well the national EMC values represent local 

conditions.
67

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14 Based on photo-interpretation of Google Earth imagery (image dates circa 2010) of 

3,000 random points across the region. Standard error of tree and shrub cover estimate 

is 0.8%. Tree cover was estimated photo-interpretation estimates using fi eld plot 

estimates of tree (20.2%) and shrub (10.5%) cover. Plot cover estimates were prorated to equal photo-interpretation estimates of combined tree and shrub cover.

15 U.S. Department of Agriculture, National Invasive Species Information Center. 2011. 

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16 State lists of invasive plant species were obtained from:

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KS: University of Kansas, R.L. McGregor Herbarium. http://www.

kansasnativeplantsociety.org/documents/InvasivePlantFactSheet.pdf

Missouri Botanical Gardens. 2002. Missouri exotic pest plants. St. Louis, MO: 

Missouri Botanical Gardens. http://www.mobot.org/mobot/research/mepp/alphalist.

shtml

17 Nowak D.J.; Dwyer, J.F. 2000. Understanding the benefi ts and costs of urban 

forest ecosystems. In: Kuser, John E., ed. Handbook of urban and community 

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18 Murray, F.J.; Marsh L.; Bradford, P.A. 1994. New York state energy plan, vol. II: 

issue reports. Albany, NY: New York State Energy Offi ce. Th ese values were updated 

to 2007 dollars based on the producer price index from U.S Department of Labor, 

Bureau of Labor Statistics n.d. www.bls.gov/ppi 

19 Th e Capital District Regional Commission. 2008. Consumer and Producer Price 

Indices. www.cdrpc.org/CPI_PPI.html
69

20 Cardelino, C.A.; Chameides, W.L. 1990. Natural hydrocarbons, urbanization, and 

urban ozone. Journal of Geophysical Research. 95(D9): 13,971-13,979

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.

21 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.

22. Carbon value estimated at $22.8/metric ton of carbon based on Faukhauser, S. 1994. 

Th e social costs of greenhouse gas emissions: an expected value approach. Th e 

Energy Journal. 15(2): 157-184.

23 Nowak, D.J.; Stevens, J.C.; Sisinni, S.M.; Luley, C.J. 2002. Eff ects of urban tree 

management and species selection on atmospheric carbon dioxide. Journal of 

Arboriculture. 28(3): 113-122.

24 Energy costs are derived from the US Energy Information Administration based on 

2009 state average costs for natural gas (http://tonto.eia.doe.gov/dnav/ng/ng_pri_

sum_a_EPG0_PRS_DMcf_a.htm); 2010/2011 heating season fuel oil costs (http://

tonto.eia.doe.gov/OOG/INFO/HOPU/hopu.asp); 2009 residential electricity costs 

(http://www.eia.doe.gov/cneaf/electricity/epm/table5_3.html) and 2008 costs of wood 

(http://www.eia.doe.gov/emeu/states/sep_sum/html/sum_pr_tot.html).Energy costs 

were averaged for the states of Kansas and Missouri.

25 Animal and Plant Health Inspection Service. 2010. Plant health – Asian longhorned 

beetle. Washington, DC: U.S. Department of Agriculture, Animal and Plant Health 

Inspection Service. http://www.aphis.usda.gov/plant_health/plant_pest_info/asian_

lhb/index.shtml;

Natural Resources Canada. 2011. Trees, insects, and diseases of Canada’s forests - 

Asian longhorned beetle. https://tidcf.nrcan.gc.ca/insects/factsheet/1000095

26 Northeastern Area State and Private Forestry. 2005. Gypsy moth digest. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Area State 

and Private Forestry. http://www.na.fs.fed.us/fhp/gm/

27 Michigan State University. 2010. Emerald ash borer. East Lansing, MI: Michigan 

State University [and others]. http://www.emeraldashborer.info/

28 Stack, R.W.; McBride, D.K.; Lamey, H.A. 1996. Dutch elm disease. PP-324 

(revised). Fargo, ND: North Dakota State University, Cooperative Extension Service.
70

29 Seybold, Steven ; Haugen, Dennis; Graves, Andrew. 2010. Th ousand cankers diseasePest alert. NA-PR-02-10. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Area State and Private Forestry.

30 Cranshaw, W.; Tisserat, N. 2009. Walnut twig beetle and the thousand cankers 

disease of black walnut. Pest Alert. Fort Collins, CO: Colorado State University. 

http://www.ext.colostate.edu/pubs/insect/0812_alert.pdf

31 Total carbon emissions were based on 2003 U.S. per capita carbon emissions, 

calculated as total U.S. carbon emissions (Energy Information Administration, 2003, 

Emissions of Greenhouse Gases in the United States 2003. http://www.eia.doe.gov/

oiaf/1605/1605aold.html ) divided by 2003 total U.S. population (www.census.gov). 

Per capita emissions were multiplied by study population to estimate total city carbon 

emissions.

32 Average passenger automobile emissions per mile were based on dividing total 2002 

pollutant emissions from light-duty gas vehicles (National Emission Trends http://

www.epa.gov/ttn/chief/ trends/index.html) by total miles driven in 2002 by passenger 

cars (National Transportation Statistics http://www.bts.gov/publications/national_

transportation_statistics/2004/).

Average annual passenger automobile emissions per vehicle were based on dividing 

total 2002 pollutant emissions from light-duty gas vehicles by total number of 

passenger cars in 2002 (National Transportation Statistics http://www.bts.gov/

publications/national_transportation_statistics/2004/).

Carbon dioxide emissions from automobiles assumed 6 pounds of carbon per gallon of 

gasoline with energy costs of refi nement and transportation included (Graham, R.L.; 

Wright, L.L.; Turhollow, A.F. 1992. Th e potential for short-rotation woody crops to 

reduce U.S. CO2 emissions. Climatic Change. 22: 223-238.)

33 Average household emissions 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 from: 

Energy Information Administration. Total Energy Consumption in U.S. Households 

by Type of Housing Unit, 2001 www.eia.doe.gov/emeu/recs/recs2001/detailcetbls.

html. 

CO2, SO2, and NOx power plant emission per KWh from:

U.S. Environmental Protection Agency. U.S. power plant emissions total by year www.

epa.gov/cleanenergy/egrid/samples.htm.
71

CO emission per kWh assumes one-third of 1 percent of C emissions is CO based on:

Energy Information Administration. 1994. Energy use and carbon emissions: nonOECD countries. DOE/EIA-0579(94). Washington, DC: Department of Energy, 

Energy Information Administration. http://tonto.eia.doe.gov/bookshelf

PM10 emission per kWh from: 

Layton, M. 2004. 2005 Electricity environmental performance report: 

electricity generation and air emissions. Sacramento, CA: California Energy 

Commission. ttp://www.energy.ca.gov/2005_energypolicy/documents/2004-11-15_

workshop/2004-11-15_03- A_LAYTON.PDF

CO2, NOx, SO2, PM10, 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:

Abraxas energy consulting. http://www.abraxasenergy.com/emissions/

CO2 and fi ne particle emissions per Btu of wood from:

Houck, J.E.; Tiegs, P.E.; McCrillis, R.C.; Keithley, C.; Crouch, J. 1998. Air 

emissions from residential heating: the wood heating option put into 

environmental perspective. In: Proceedings of U.S. EPA and Air and Waste 

Management Association conference: living in a global environment, V.1: 373-384.

CO, NOx and SOx emission per Btu of wood based on total emissions from wood 

burning (tonnes) from:

Residential Wood Burning Emissions in British Columbia. 2005. http://www.env.gov.

bc.ca/air/airquality/pdfs/wood_emissions.pdf.

Emissions per dry tonne of wood converted to emissions per Btu based on average dry 

weight per cord of wood and average Btu per cord from:

Kuhns, M.; Schmidt, T. 1988. Heating with wood: species characteristics and 

volumes I. NebGuide G-88-881-A. Lincoln, NE: University of Nebraska, Institute of 

Agriculture and Natural Resources, Cooperative Extension.

34 National Land Cover Data are available at: www.epa.gov/mrlc/nlcd-2001.html

35 Standardized value for population density was calculated as PD = (n – m)/r, where 

PD is the value (0-1), n is the value for the census block (population/km2

), m is the 

minimum value for all census blocks, and r is the range of values among all census 
72

blocks (maximum value – minimum value). Standardized value for tree stocking was 

calculated as TS = [1 – (t/(t+g)], where TS is the value (0-1), t is percent tree cover, 

and g is percent grass cover. Standardized value for tree cover per capita was calculated 

as TPC = 1 – [(n – m)/r], where TPC is the value (0-1), n is the value for the census 

block (m2

/capita), m is the minimum value for all census blocks, and r is the range of 

values among all census blocks (maximum value – minimum value).

36 Nowak, D.J. 1986. Silvics of an Urban Tree Species: Norway Maple (Acer 

platanoides L.). M.S. Th esis. SUNY Coll. of Environ. Sci. and Forestry, Syracuse, 

NY. 148p.

37 Sansalone J.J.; Buchberger S.G. 1997. Partitioning and fi rst fl ush of metals in urban 

roadway storm water. Journal of Environmental Engineering ASCE. 123: 134 -143.

38 Charbeneau R.J.; Barretti M. 1998. Evaluation of methods for estimating 

stormwater pollutant loads. Water Environment Research. 70: 1295 -1302.

39 U.S. Environmental Protection Agency. 2002. Urban stormwater BMP performance 

monitoring. A guidance manual for meeting the national stormwater BMP 

database requirements. Washington, DC: U.S. Environmental Protection Agency. 

http://water.epa.gov/scitech/wastetech/guide/stormwater/upload/2009-StormwaterBMP-Monitoring-Manual.pdf

40 Beaulac, M.N.; Reckhow, K.H. 1982. An examination of land use-nutrient export 

relationships. Water Resources Bulletin. 18(6): 1013-1024.

41 U.S. Environmental Protection Agency. 1983. Results of the nationwide urban 

runoff program: Volume I – fi nal report. EPA No. 832R83112. Washington, DC: 

U.S. Environmental Protection Agency. http://www.epa.gov/npdes/pubs/sw_nurp_

vol_1_fi nalreport.pdf

42 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. Denver, CO: 

U.S. Department of Interior, Geological Survey. 219 p. http://pubs.usgs.gov/

of/1985/0337/report.pdf

43 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.
Brush Creek, Kansas City MO. Photo by Mid-America Regional Council, used with permission.
Printed on Recycled Paper

Nowak, David J.; Bodine, Allison R.; Hoehn, Robert E. III; Crane, Daniel E.; Ellis, 

Alexis; Endreny, Theodore A.; Yang, Yang; Jacobs, Tom; Shelton, Kassie. 2013. 

Assessing urban forest effects and values: the greater Kansas City region. 

Resour. Bull. NRS-75. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 72 p.

An analysis of trees in the greater Kansas City region of Missouri and Kansas 

reveals that this area has about 249,450,000 trees with tree and shrub canopy that 

covers 28.3 percent of the region. The most common tree species are American elm, 

northern hackberry, Osage-orange, honeylocust, and eastern redcedar. Trees in the 

greater Kansas City region currently store about 19.9 million tons of carbon (72.8 

million tons CO2) valued at $411 million. In addition, these trees remove about 1.0 

million tons of carbon per year (3.7 million tons CO2 per year valued at $20.7 million 

per year) and about 26,000 tons of air pollution per year ($198.3 million per year). The 

greater Kansas City region’s trees are estimated to reduce annual residential energy 

costs by $14.0 million per year. The compensatory value of the trees is estimated at 

$93.4 billion. Loss of the current tree cover in the Blue River watershed of the greater 

Kansas City region would increase total fl ow over a 6.5-month period by an average 

of 2.3 percent (63.4 million ft3

). Information on the structure and functions of the urban 

forest can be used to inform urban forest management programs and to integrate 

urban forests within plans to improve environmental quality in the greater Kansas City 

region.

KEY WORDS: urban forestry, ecosystem services, air pollution removal, carbon 

sequestration, tree value

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