Assessing Urban Forest 

Effects and Values: 

Douglas County, Kansas

David J. Nowak

Allison R. Bodine

Robert E. Hoehn III

Alexis Ellis

Kim Bomberger

Daniel E. Crane

Theodore A. Endreny

Thomas Taggert

Emily Stephan

United States Department of Agriculture

Forest 

Service

Resource Bulletin

NRS-91

Northern 

Research Station June 2014
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies:

U.S. FOREST SERVICE U.S. Forest Service

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073 359 Main Road

Delaware, OH 43015-8640

Fax: (740)368-0152

June 2014 Email: nrspubs@fs.fed.us

Abstract

An analysis of trees in Douglas County, Kansas, reveals that this area has about 

14,164,000 trees with tree and shrub canopy that covers 25.2 percent of the county. 

The most common tree species are American elm, northern hackberry, eastern 

redcedar, Osage-orange, and honeylocust. Trees in Douglas County currently store 

about 1.7 million tons of carbon (6.4 million tons CO2) valued at $124 million. In 

addition, these trees remove about 82,000 tons of carbon per year (300,000 tons 

CO2 per year valued at $5.8 million per year) and about 3,870 tons of air pollution per 

year ($17.7 million per year). Douglas County’s trees are estimated to reduce annual 

residential energy costs by $2.9 million per year. The compensatory value of the 

trees is estimated at $6.2 billion. Loss of the current tree cover in the Wakarusa River 

watershed in Douglas County would increase annual flow by an average of 2.6 percent 

(88.9 million ft3

). Information on the structure and functions of the regional forest can 

be used to inform forest management programs and to integrate regional forests within 

plans to improve environmental quality in Douglas County.

Cover Photo

The City of Lawrence took over the ownership of a Union Pacific Depot, turning it into 

a Visitor’s Center in the northern part of the city. Photo by Kim Bomberger, Kansas 

Forest Service, used with permission.

Manuscript received for publication 24 September 2013

Acknowledgments

Thanks go to Scott McCullough, Crystal Miles, Mary Miller, Chris Mullins, Erin 

O’Conner, Leah Rutledge, Sheila Stogsdill, Grant Thompson, and Renee Yocum for 

their technical support, help with this project, and review of the report. Thanks also to 

the land owners who allowed crews access to their property for the field data collection.
Assessing Urban Forest Effects and Values:

Douglas County, Kansas

David J. Nowak

Allison R. Bodine

Robert E. Hoehn III

Alexis Ellis

Kim Bomberger

Daniel E. Crane

Theodore A. Endreny

Thomas Taggert

Emily Stephan

The Authors

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

Service’s Northern Research Station at Syracuse, NY.

ALLISON R. BODINE is a research urban forester with the Davey Institute at 

Syracuse, NY. 

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

Research Station at Syracuse, NY.

ALEXIS ELLIS is a research urban forester with the Davey Institute at Syracuse, NY.

KIM BOMBERGER is a District Community Forester with the Kansas Forest Service 

at Manhattan, KS.

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

Service’s Northern Research Station at Syracuse, NY.

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

Science and Forestry at Syracuse, NY.

THOMAS TAGGERT is a Ph.D. student with the SUNY College of Environmental 

Science and Forestry at Syracuse, NY.

EMILY STEPHAN is a Ph.D. student with the SUNY College of Environmental 

Science and Forestry at Syracuse, NY.
Clinton Lake near Lawrence, Kansas.

Photo by Kim Bomberger, Kansas Forest Service, used with permission.
Contents

Project Overview 1

Executive Summary 2

I-Tree Eco Model and Field Measurements 4

Tree Characteristics of Douglas County’s Forest 7

Tree and Forest Cover and Leaf Area 11

Air Pollution Removal by Trees and Forests 13

Carbon Storage and Sequestration 16

Trees Affect Energy Use in Buildings 18

Structural and Functional Values 19

Potential Insect and Disease Impacts 20

Tree Canopy Cover Change 21

Wakarusa River Watershed Analysis 24

Conclusion 28

Appendix I. Species in Douglas County 29

Appendix II. Tree Species Distribution 32

Appendix III. Tree Structure and Functions by Land Use 36

Appendix IV. Population Information by Stem Diameter Class 39

Appendix V. Effects of Pollution Removal on Local Health 41

Appendix VI. General Recommendations for Air Quality Improvement 43

Appendix VII. Relative Tree Effects 44

Appendix VIII. Tree Planting Index Map 45

Appendix IX. Potential Insect and Disease Impacts 46

Appendix X. i-Tree Forecast Prototype Model Methods and Results 52

Appendix XI. i-Tree Hydro Model Methods and Calibration Graphs 61

References 66
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1

The project builds on 

the findings from the 

first i-Tree Eco project 

conducted in the 

Kansas City region and 

further strengthens a 

growing knowledge 

base that trees and 

forests are necessary 

for human health and 

well-being 

as the 

Kansas 

population 

grows.

Project Overview

Forest resources in Kansas occur across several ecoregions and comprise large blocks 

of forest, urban and community forests and trees, riparian forests, and agroforestry 

systems.1

 Douglas County resides in the Central Irregular Plains ecosystem, a Level 

III description by the U.S. Geological Survey.2

 This ecosystem supports vegetation 

from the tallgrass prairie to an oak-hickory forest. According to the U.S. Forest Service 

Forest Inventory and Analysis program, the oak-hickory forest that occurs in the 

eastern part of the State makes up 55 percent of Kansas forest land.3

Douglas County is adjacent to the Kansas City metropolitan area and within the 

Interstate-70 corridor, an area defined as a priority landscape in the Kansas Forest 

Action Plan.1

 According to the Mid-America Regional Council, growth in the ninecounty Kansas City region is expected to increase by half a million people by 2030. 

Between 2000 and 2010, Douglas County’s population increased by 10.9 percent 

and between 2010 and 2040, the county’s population is expected to expand by 37.6 

percent.4

 We assessed Douglas County forests because projected growth and land 

conversion threaten acreage and quality of forest and woodlands. Findings from this 

project will complement and encourage conservation practices and environmental 

planning within the county. By demonstrating the value of trees and forests in 

eastern Kansas, this project supports national priorities of conserving working forest 

landscapes, protecting forests from harm, and enhancing public benefits associated 

with trees and forests.

Results of this project will broaden the understanding of what trees and forests 

contribute to a growing Kansas population, such as the filtering of air pollutants, 

the reduction of power plant emissions, the sequestration (i.e., annual removal) 

and storage of carbon dioxide, energy benefits to residential structures, and positive 

impacts to water quality. Data will quantify the current composition and structure 

of the trees and forests in Douglas County and inform how this resource could be 

threatened by current and future insect and disease threats.

The project builds on the findings from the first i-Tree Eco project conducted in 

the Kansas City region and further strengthens a growing knowledge base that trees 

and forests are necessary for human health and well-being as the Kansas population 

grows. This report provides information on the current resource, but also necessary 

information to help plan for a better future.
2

Executive Summary

Trees in urban and rural areas contribute significantly 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 fulfill human life5

). To better understand the ecosystem services and values 

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

Urban Forest Effects (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 affect the environment and consequently enhance human health 

and environmental quality in urban and rural areas.

The 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 Douglas 

County, Kansas, a vegetation assessment was conducted during summer of 2012. For 

this assessment, 0.1-acre field plots (74.4 feet diameter circular plots) were sampled 

and analyzed using the i-Tree Eco model. This report summarizes (Table 1) results and 

values of:

•	 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

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

change based on tree data for Douglas County. Tree growth was based on various tree 

characteristics including species (growth rate, longevity, height at maturity), current 

tree size, crown competition, and tree condition. The model was used to consider 

several different scenarios to estimate the number of trees that need to be established 

to meet desired canopy goals in the future (Table 2). Ecosystem services under these 

scenarios are also summarized.

Benefits ascribed to 

urban trees include:

•	 Air pollution removal

•	 Air temperature 

reduction

•	 Reduced building 

energy use

•	 Absorption of 

ultraviolet radiation

•	 Improved water 

quality

•	 Reduced noise

•	 Improved human 

comfort

•	 Increased property 

value

•	 Improved 

physiological & 

psychological wellbeing

•	 Aesthetics

•	 Community 

cohesion
3

Table 1.—Summary of regional forest features, Douglas County, 2012

Feature Measure

Number of trees 14,164,000

Tree and shrub canopy cover 25.2%

Tree canopy cover 23.5%

Most common species American elm, northern hackberry, 

eastern redcedar, Osage-orange, 

honeylocust

Trees <6 inches diameter (%) 66.8%

Pollution removal, trees

Ozone 1,781 tons/year ($3.0 million/year)

Particulate matter (2.5-10μ) 1,269 tons/year ($8.0 million/year)

Nitrogen dioxide 436 tons/year ($149,000/year)

Sulfur dioxide 209 tons/year ($24,000/year)

Particulate matter (<2.5μ) 115 tons/year ($6.5 million/year)

Carbon monoxide 58 tons/year ($77,000/year)

All 6 pollutants 3,868 tons/year ($17.7 million/year)

Carbon storage 1.7 million tons ($124 million)

Carbon sequestration 82,000 tons/year ($5.8 million/year)

Building energy reduction $2.9 million/year

Reduced carbon emissions $534,300/year

Structural value $6.2 billion

Ton – short ton (U.S.) (2,000 lbs)

Table 2.—Number of trees needed to be established to meet cover goals, Douglas County, 2012a

Cover Goal

Land Use

Current cover 

(%)

Maintain 

cover

Increase cover 

5%

Increase cover 

10%

Increase cover 

20%

--------------------------------------trees---------------------------------------

Agriculture 10.5 201,000 325,000 468,000 774,000

Commercial 7.1 3,300 5,300 7,800 13,000

Park/Open 10.6 60,000 84,000 109,000 163,000

Residential 31.4 66,000 84,000 109,000 162,000

Vacant 81.9 86,000 129,000 176,000 262,000

Total 23.5 416,300 627,300 869,800 1,374,000

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 be established through natural regeneration.

A specialized analysis of the Wakarusa River watershed of eastern Kansas was also 

completed using the i-Tree Hydro model.6

 i-Tree Hydro is a semi-distributed, 

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

cover and elevation information, hourly weather data, and hourly stream flow data. 

This report details the stream flow response to changes in tree and/or impervious cover 

in the Wakarusa River watershed.
4

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 Douglas County’s urban and rural 

forests, data from 190 field plots located throughout the region (Fig. 1) were analyzed 

using the U.S. Forest Service’s i-Tree Eco model (formerly known as UFORE).7

The i-Tree Eco model uses standardized field data from randomly located plots and 

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

numerous effects, 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 for 

ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, particulate matter 

(2.5-10 microns), and particulate matter (<2.5 microns)

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

•	 Effects of trees on residential building energy use and consequent effects on 

carbon dioxide emissions from power sources

Figure 1.—Map of the study area and field plot distribution, Douglas County, 2012.
5

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

and carbon storage and sequestration

•	 Potential impact of infestations by insects and diseases, such as Asian 

longhorned beetle, oak wilt, thousand cankers disease, gypsy moth, or Dutch 

elm disease

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

density of approximately 1 plot for every 1,570 acres. The study is divided into smaller 

areas based on a land use classification recorded in the field. The plots were divided 

among the following land uses (Fig. 2): agriculture (124 plots, 71.4 percent of area); 

vacant (30 plots, 3.1 percent); commercial, including industrial, mixed and utility (12 

plots, 1.5 percent); residential, including single and multiple family (11 plots, 10.8 

percent); water (8 plots, 3.8 percent); and park/open, including institutional (5 plots, 

9.5 percent).

Field data were collected by the Kansas Forest Service. Data collection took place 

during the leaf-on season to properly assess tree canopies. Within each plot, data 

collected 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 feet, hereafter referred to as stem diameter), tree height, height to base of live 

crown, crown width, percentage crown canopy missing and dieback, and distance and 

direction to residential buildings.8

 Trees were recorded as woody plants with a stem 

diameter greater than or equal to 1 inch. As many species are classified 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.

Agriculture

71.4% 

Residential 

10.8% 

Park/Open 

9.5% 

Water 

3.8% 

Vacant

3.1% Commercial

1.5% 

Figure 2.—Land-use distribution, Douglas County, 2012, for inventoried plots.

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
6

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.9

 To 

adjust for this difference, biomass results for open-grown trees are multiplied by 0.8.9

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

biomass was converted to stored carbon by multiplying by 0.5.9

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), particulate matter 

greater than 2.5 microns and less than 10 microns 

(PM2.5-10, and particulate matter less than 2.5 microns 

(PM2.5). Estimates are derived from calculated hourly 

tree-canopy resistances for O3, SO2, and NO2 based on 

a hybrid of big-leaf and multi-layer canopy deposition 

models.10,11 As the removal of CO and PM2.5-10 by vegetation is not directly related 

to transpiration, removal rates (deposition velocities) for these pollutants were based 

on average measured values from the literature12,13 that were adjusted depending 

on leaf phenology and leaf area. Removal rates for PM2.5 varied with wind speed.14

Particulate removal incorporated a 50 percent resuspension rate of particles back to 

the atmosphere for PM2.5-10 and used variable resuspension rates that varied with 

windspeed for PM2.5.

14,15

Seasonal effects of trees on residential building energy use were calculated based on 

procedures described in the literature16 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 Appraisers17, which uses tree species, diameter, condition, and location 

information.17 Compensatory values represent compensation to owners for the loss of 

an individual tree and can be viewed as the value of the trees as a structural asset.

To learn more about i-Tree Eco methods7,18 refer to: http://nrs.fs.fed.us/tools/ufore/ 

or www.itreetools.org.

Data collector, Grant Thompson, measuring d.b.h.

Photo by Kim Bomberger, Kansas Forest Service, used with permission.
7

Tree Characteristics of Douglas County’s

Forest

Douglas County has an estimated 14,164,000 trees with a standard error (SE) of 

2,600,000. Tree and shrub cover is estimated from the photo-interpretation of Google 

Earth imagery of 500 random points (Table 3). Tree and shrub cover in Douglas 

County is estimated at 25.2 percent of the land area.19 As it is difficult to differentiate 

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

cover separately were used to differentiate between tree and shrub cover. Based on the 

field data in conjunction with photo-interpretation19, tree cover (without shrubs) in 

Douglas County is estimated to be 23.5 percent.

The five most common species in the region’s urban and rural forest were American 

elm (20.2 percent), northern hackberry (16.4 percent), eastern redcedar (13.7 

percent), Osage-orange (13.3 percent), and honeylocust (6.0 percent). The 10 most 

common species account for 86.1 percent of all trees; their relative abundance is 

illustrated in Figure 3. Thirty-nine tree species were sampled in Douglas County; these 

species and their relative abundance are presented in Appendix I. More information on 

species distribution by land use is given in Appendix II (Figures 25-32).

Table 3.—Land cover in Douglas County, 2010

Cover type % SE

Grass/herbaceous 66.2 2.1

Tree/shrub 25.2 1.9

Water 4.4 0.9

Impervious - building 1.8 0.6

Impervious - other 1.4 0.5

Impervious - road 0.6 0.3

Soil/bare ground 0.4 0.3

Figure 3.—Tree species composition, Douglas County, 2012.

American elm

20.2% 

Northern hackberry

16.4%

Eastern red cedar

Osage-orange 13.7% 

13.3% 

Honeylocust 

6.0% 

Black walnut

5.0% 

Red mulberry

3.7% 

Shagbark hickory

3.3% 

Eastern redbud

2.3% 

Black cherry

2.2% other species

13.9%

Tree and shrub 

cover in Douglas 

County is estimated 

at 25.2 percent of 

the land 

area.
8

The overall tree density for Douglas County is 47.5 trees/acre. The highest tree 

densities occur in vacant areas (328 trees/acre), followed by residential (43 trees/

acre), and agriculture land (41 trees/acre) (Fig. 4). Land uses that contain the greatest 

percentage of the total tree population are agriculture (62.1 percent), followed by 

vacant (21.4 percent), and residential (9.7 percent). More information on the tree 

species in each land use and their structure and functions is given in Appendix III.

Leaf area refers to the total one-sided area of a leaf and is typically summed for 

all leaves within any one classification. In Douglas County, leaf area is highest in 

agriculture (55.7 percent of total tree leaf area) and vacant (17.3 percent) land use 

types (Fig. 5). Leaf area index (LAI) is a ratio of the estimated total leaf area divided by 

land area. As each land use has a different land area, LAI standardizes the canopy extent 

on an equal area basis. Higher LAIs indicate a greater number of leaves per acre. Land 

uses that have the highest LAI are vacant (6.8), residential (1.7), and park/open (1.5).

0

50

100

150

200

250

300

350

0

1

2

3

4

5

6

7

8

9

Agriculture Vacant Residential Park/Open Commercial Water

Density (trees/acre) 

Number of Trees (millions) 

Land Use

Number of trees

Density

Figure 4.—Number of trees 

and tree density by land use, 

Douglas County, 2012.

0

1

2

3

4

5

6

7

8

0

50

100

150

200

250

Agriculture Commercial Park/Open Residential Vacant Water

Leaf Area Index (LAI)

Leaf Area (thousands of acres)

Land Use

Leaf area

Leaf area index

Figure 5.—Total leaf area and 

leaf area index by land use, 

Douglas County, 2012.
9

Trees that have diameters less than 6 inches account for 66.8 percent of the 

population. This diameter class also contains 21.5 percent of the total leaf area. Trees 

that have diameters greater than 18 inches account for 4.7 percent of the population 

and 29.0 percent of the total leaf area. Though these larger trees are a small percentage 

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

County. Leaf area has a strong correlation with benefits 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 6 and 7.

0

5

10

15

20

25

30

35

40

45

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

Totasl Population (percent)

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

Leaf area

Abundance

Figure 6.—Percent of total 

tree population (abundance) 

and leaf area by tree diameter 

class, Douglas County, 2012.

American elm

Northern hackberry

Eastern red cedar

Osage-orange

Honeylocust

Black walnut

Red mulberry

Shagbark hickory

Eastern redbud

Black cherry

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 (percnet)

Diameter Class

(d.b.h. in inches)

Figure 7.—Percent of species 

population by diameter class 

for 10 most common tree 

species, Douglas County, 2012.
10

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 land uses, while most of the large trees tend to be on 

agriculture and residential land (see Appendix IV). The small tree population tends 

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

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

of the small trees are American elms on agriculture lands. A few of the most common 

large trees demonstrate limited regeneration (i.e., they have a similar or greater number 

of large trees as they do small trees): black walnut, northern red oak, silver maple, and 

pin oak. This pattern may indicate poor regeneration or planting rates that may lead 

to a loss of these large tree populations in the future. Some other common large trees 

have substantially more small trees than large trees: American elm, northern hackberry, 

and Osage-orange. This pattern might be due to prolific regeneration and/or a limited 

ability of these species to reach large diameter classes (e.g., Dutch elm disease limiting 

the number of mature, large trees). Long-term monitoring can help determine how 

species populations are changing, and differences in diameter distributions illuminate 

potential problems with sustaining species populations through time.

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. 

Thus, 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-specific 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 Douglas County, about 84.0 percent of the trees are 

native to Kansas (Fig. 8). Trees with a native origin outside of North America are 

predominantly from Asia (1.6 percent of all species).

0

10

20

30

40

50

60

70

80

90

100

Kansas North America Asia Unknown Americas+* North

America+**

Total Population (percent)

Area of Native Origin

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

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

Figure 8.—Percent of total tree population by area of native origin, Douglas County, 2012.

Urban forests are 

a mix of native tree 

species that existed 

prior to development 

of the area and exotic 

species that were 

introduced by 

residents or 

other means.
11

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

reproductive capacity, and general lack of natural enemies. These characteristics 

enable them to displace native plants and threaten the native landscape.20 One of the 

tree species sampled in Douglas County, Russian olive, has been identified on the 

Kansas invasive species list.21 Though it contributes less than 0.1 percent to the total 

population of Douglas County, Russian olive is considered to be one of the most 

invasive plant species in the state of Kansas.

Tree and Forest Cover and Leaf Area

Trees cover about 23.5 percent of Douglas County; shrubs cover 1.7 percent of the 

area. Dominant ground-cover types include herbaceous (82.6 percent), water (7.9 

percent), and duff/mulch cover (3.3 percent) (Fig. 9).

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

the plant. In Douglas County, trees that dominate in terms of leaf area are northern 

hackberry, Osage-orange, and black walnut.

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, chinkapin oak, and Siberian elm. Smaller trees in the population 

are sandbar willow, American elm, and honeylocust (species with percent of leaf area 

much less than percent of total population). The 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.

Figure 9.—Percent of the county and land use areas covered by ground-cover classes, 

Douglas County, 2012.

Russian olive.

Photo by Leslie J. Mehrhoff, University of 

Connecticut, Bugwood.org.

0 10 20 30 40 50 60 70 80 90 100

Agriculture

Commercial

Park/Open

Residential

Vacant

Douglas County

Percent of Land Use 

Land Use

water bare soil herbaceous duff/mulch cover

impervious surfaces (excluding buildings) buildings

Ground Cover Classes
12

The importance value (IV) of a species is calculated using a formula that takes into 

account the relative leaf area and relative abundance (Fig. 10). The most important 

species in Douglas County, according to calculated IVs, are northern hackberry, 

Osage-orange, and American elm (Table 4). 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.

Table 4.—Percent of total population, percent of total leaf, and 

importance values of species with the greatest importance values, 

Douglas County, 2012

Common Name % Popa %LAb IVc

Northern hackberry 16.4 17.1 33.5

Osage-orange 13.3 16.7 30.0

American elm 20.2 9.6 29.8

Eastern redcedar 13.7 9.7 23.4

Black walnut 5.0 14.4 19.4

Honeylocust 6.0 3.6 9.6

Red mulberry 3.7 2.7 6.4

Shagbark hickory 3.3 2.7 6.0

Green ash 1.9 2.7 4.6

Silver maple 0.8 3.4 4.2

a %Pop - percent of total tree population

b %LA - percent of total leaf area

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

0

5

10

15

20

25

Northern

hackberry

OsageorangeBlack walnut Eastern red

cedar

American

elm

Honeylocust Silver maple American

sycamore

Red

mulberry

Green ash

Percent

Species

Leaf area

Abundance

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

tree species, Douglas County, 2012.

The most important 

species in Douglas 

County are northern 

hackberry, 

Osage-orange, 

and American 

elm.
13

Air Pollution Remova l by Trees and Forests

Poor air quality is a common problem in many urban areas. It can lead to human health 

problems, damage to landscape materials and ecosystem processes, and reduced visibility. 

The 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.22

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

externality values (c. 1994) for the United States for carbon monoxide (CO; $870/ton) 

and particulate matter less than 10 microns (PM10; $4,091/ton).23 These values have 

been adjusted to 2010 values (CO = $1,334/ton and PM10 = $6,271/ton) based on the 

producer’s price index.24 The value for PM10 was applied to the PM2.5-10 removal (i.e., 

particles between 2.5 and 10 microns in size). The U.S. EPA’s BenMAP program was 

used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) 

and associated monetary value that result from changes in NO2, O3, PM2.5 and SO2 

concentrations due to pollution removal by trees. BenMAP is a Windows-based 

computer program that uses geographic information system (GIS) data to estimate the 

health impacts and economic value when populations experience changes in air quality.25

See Appendix V for more details on the effect of pollution removal on local health.

Pollution removal by trees and shrubs (25.2 percent tree and shrub cover) in Douglas 

County was estimated using the i-Tree Eco model in conjunction with field data and 

hourly pollution and weather data for the year 2010. Pollution removal by trees and 

shrubs (Fig. 11) was greatest for O3 (2,580 tons), followed by PM2.5-10 (1,725 tons), 

NO2 (618 tons), SO2 (305 tons), PM2.5 (150 tons), and CO (88 tons). It is estimated 

In Douglas County 

tree and shrub cover 

combined remove 

approximately 5,466 

tons of pollution per 

year ($24.0 million/

year).

General urban 

forest management 

recommendations 

to improve air 

quality are given in 

Appendix 

VI.

Figure 11.—Annual air 

pollution removal and 

value by trees and shrubs, 

Douglas County, 2012.

0

2

4

6

8

10

12

0

500

1,000

1,500

2,000

2,500

3,000

CO NO O PM PM SO

Value (million dollars/year)

Pollution Removed (tons/year)

Pollutant

Pollution removed

Value

2 3 10* 2.5 2 
14

Table 5.—Annual air pollution removal and value by trees, Douglas County, 2012a

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

O3 1,781 (393-2,122) 2,978 (657-3,548)

PM2.5-10

b 1,269 (2,350-9,420) 7,952 (3,294-11,901)

NO2 436 (186-518) 149 (64-177)

SO2 209 (91-319) 24 (11-37)

PM2.5 115 (15-263) 6,529 (847-14,951)

CO 58 77

Total 3,868 (1,268-5,178) 17,710 (4,949-30,691)

a,b See explanation below Table 6

Table 6.—Annual air pollution removal and value by shrubs, Douglas County, 2012a

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

O3 799 (190-1,061) 1,362 (323-1,808)

PM2.5-10

b 456 (187-688) 2,861 (1,174-4,313)

NO2 182 (90-251) 63 (31-88)

SO2 96 (44-165) 11 (5-20)

PM2.5 35 (4-79) 1,951 (252-4,461)

CO 31 41

Total 1,599 (547-2,275) 6,290 (1,827-10,731)

a Estimated tons of pollution removed by trees/shrubs in Douglas County (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 values23 or health effect values from BenMAP14,25 for United States for each 

pollutant.

b Assumes 50 percent resuspension of particles

that trees remove 3,868 tons of air pollution (CO, NO2, O3, PM2.5-10, PM2.5, SO2) 

per year with an associated value of $17.7 million (Table 5). The effects of shrub cover 

(Table 6) in Douglas County would remove an additional estimated 1,599 tons/year 

($6.3 million/year). Thus, tree and shrub cover combined remove approximately 5,466 

tons of pollution per year ($24.0 million/year).

In 2012, trees in Douglas County emitted 682 tons of volatile organic compounds 

(VOCs) in the forms of isoprene (357 tons) and monoterpenes (325 tons). Land uses 

with the highest VOC emissions were agriculture, vacant, and residential. Seventy-one 

percent of Douglas County’s VOC emissions were from the Quercus (oak) and Juglans

(walnut) genera. Figure 12 illustrates the annual VOC emissions by genera.

These VOCs are a precursor chemical to O3 formation. Studies have estimated VOC 

emission costs. These costs are not included here as it is incorrect to add positive 

dollar estimates of ozone removal effects with negative dollar values of VOC emission 

effects to determine whether tree effects are positive or negative in relation to ozone. 

Estimates of VOC impacts on O3 formation (e.g., via photochemical models) 

should be contrasted with ozone removal by trees (i.e., O3 effects should be directly 

compared, not dollar estimates). In addition, air temperature reductions by trees have 
15

0 50 100 150 200 250 300 350 400

Mulberry

Willow

Elm

Hackberry

Hickory

Maple

Sycamore

Juniper

Walnut

Oak

VOCs Emitted (tons)

Genera

Isoprene Monoterpene

VOCs Emitted

Figure 12.—Annual isoprene and monoterpene volatile organic compound (VOC) 

emissions by genera with highest total emissions, Douglas County, 2012.

been shown to significantly reduce O3 concentrations26, but are not considered in this 

analysis. Modeling that integrates tree effects on air temperature, pollution removal, 

VOC emissions, and emissions from power plants can be used to determine the 

overall effect of trees on O3 concentrations. General recommendations for air quality 

improvement with trees are given in Appendix VI.

City of Lawrence downtown.

Photo by Kim Bomberger, Kansas Forest Service, used with permission.
16

Carbon Storage and Sequestrati on

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.27

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

new tissue growth every year. The amount of carbon annually sequestered is greater 

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

County is about 82,000 tons of carbon per year (300,000 tons/year of CO2) with an 

associated value of $5.8 million/year (Fig. 13).28 Net carbon sequestration in Douglas 

County is estimated at about 73,000 tons/year (266,000 tons/year of CO2) based on 

estimated carbon loss due to tree mortality and decomposition.

Carbon storage by trees is another way trees can influence 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. 

Thus, 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.29 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

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0

2

4

6

8

10

12

14

16

18

OsageorangeNorthern

hackberry

Honeylocust Black walnut American

elm

Eastern red

cedar

Chinkapin

oak

Red

mulberry

Shagbark

hickory

Silver maple

Value (million dollars/year)

Sequestration (thousand tons/year)

Carbon sequestration

Value

Figure 13.—Annual carbon sequestration and value for the tree species with the greatest 

total sequestration, Douglas County, 2012.

Regenerating American elm.

Photo by Kim Bomberger, Kansas Forest 

Service, used with permission.
17

Live and dead trees in Douglas County store an estimated 1.7 million tons of carbon 

(6.4 million tons of CO2) ($124 million). Of all the species sampled, Osage-orange 

stores the most carbon (approximately 25.2 percent of total carbon stored) and 

annually sequesters the most carbon (20.9 percent of all sequestered carbon). Total 

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

Figures 14 and 15.

0

2

4

6

8

10

12

0

50

100

150

200

250

300

350

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 (thousand tons)

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

Carbon storage

Carbon sequestration

0

20

40

60

80

100

120

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

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 sequestration

Figure 14.—Total carbon storage and sequestration by diameter class, Douglas County, 

2012.

Figure 15.—Average carbon storage and sequestration by diameter class, Douglas County, 

2012.
18

Trees Affect Energy Use in Buildings

Trees affect 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 Douglas County, many trees are, or may need 

to be, irrigated. Estimates of tree effects on energy use are based on field measurements 

of tree distance and direction to space-conditioned residential buildings.16

Based on average energy costs in 2009, trees in Douglas County reduce energy costs 

from residential buildings by an estimated $2.9 million annually. Trees also provide an 

additional $534,300 in value per year by reducing amount of carbon released by fossilfuel based power sources (a reduction of 7,500 tons of carbon emissions or 27,500 

tons of carbon dioxide). Energy savings are illustrated in Tables 7 and 8. This study 

did not attempt to estimate energy conservation benefits associated with commercial 

or institutional buildings or from urban heat island reduction.

Based on average energy costs in 

2009, trees in Douglas County reduced 

energy costs from residential buildings 

by an estimated $2.9 million annually.

Table 7.—Annual energy savings (MBTUs, MWHs, or tons) due to trees 

near residential buildings, Douglas County, 2012

Heating Cooling Total

MBTUa -10,600 n/a -10,600

MWHb -100 29,600 29,500

Carbon avoided (tons) -200 7,700 7,500

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

b MWH – Megawatt-hour

Table 8.—Annual monetary savingsc (dollars) in residential energy 

expenditures during heating and cooling seasons, Douglas County, 2012

Heating Cooling Total

MBTUa -138,600 n/a -138,600

MWHb -11,300 3,036,200 3,024,900

Carbon avoided -14,400 548,700 534,300

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

b MWH – Megawatt-hour

c Based on 2009 statewide energy costs30

Photo by Kim Bomberger, Kansas Forest Service, 

used with permission.
19

Structura l 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. The compensatory value17 of the trees and forests in Douglas 

County is about $6.2 billion (Fig. 16). The 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. There are many other functional values of the tree and forest resource, 

though they are not quantified here (e.g., reduction in air temperatures and ultraviolet 

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

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

benefits also can decrease as the amount of healthy tree cover declines.

Structural values:

•	 Compensatory value: $6.2 billion

•	 Carbon storage: $124 million

Annual functional values:

•	 Carbon sequestration: $5.8 million

•	 Pollution removal: $17.7 million 

•	 Reduced energy costs: $2.9 million

More detailed information on the trees and forests in Douglas County can be found at 

http://nrs.fs.fed.us/data/urban. Additionally, information on tree statistics by diameter class 

can be found in Appendix VII and priority planting areas are detailed in Appendix VIII.

Figure 16.—Tree species with 

the greatest compensatory 

value, Douglas County, 2012.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Osage-orange Northern

hackberry

Honeylocust Green ash Black walnut American elm Eastern red

cedar

Pin oak Chinkapin oak Red mulberry

Compensatory Value (bilions of dollars)
20

Potential Insect and Disease Impacts

Insects and diseases can infest urban and rural forests, potentially killing trees and 

reducing the health, value, and sustainability of the forest resource. Various pests have 

different tree hosts, so the potential damage or risk of each pest will differ. Thirty-one 

exotic insects/diseases were considered for their potential impact using range maps of 

these pests in the coterminous United States (www.foresthealth.info).31 For a complete 

analysis of the 31 exotic insects/diseases, see Appendix IX.

Although there are numerous pests that could impact Douglas County’s urban and 

rural forests, Asian longhorned beetle (ALB), Dutch elm disease (DED), gypsy moth 

(GM), thousand cankers disease (TCD), and oak wilt (OW) pose the most serious 

threats based on the number of trees 

at risk of infestation.

Dutch elm disease and OW pose a 

threat because they currently exist 

within Douglas County, while ALB, 

GM, and TCD can be found within 

750 miles of the county. Potential 

loss of trees from ALB is 3.7 million 

($1.2 billion in compensatory value), 

DED is 3.0 million ($434 million), 

GM is 797,000 ($733 million), TCD 

is 702,000 ($473 million), and OW 

is 531,000 ($719 million)(Fig. 17).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

ALB DED GM TCD OW

Compensatory Value (billions of dollars)

Number of Trees (millions) 

Pest/Disease

Trees at risk

Compensatory value

Figure 17.—Number of trees at risk and associated compensatory value for five most 

threatening insects/diseases, Douglas County, 2012.

Asian longhorned beetle.

Photo by Kenneth R. Law, USDA 

APHIS PPQ, www.invasive.org

Black walnut recently killed by thousand cankers disease. 

Photo by Curtis Utley, CSUE, bugwood.org
21

Tree Canopy Cover Change

Tree cover in Douglas County is 23.5 percent, but the canopy will 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 Douglas County. The model was used to consider 

several different scenarios to show the number of trees that need to be established to 

meet desired tree canopy goals in the future. For details on methods and results, see 

Appendix X.

For Douglas County, 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. The scenarios 

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

In Appendix X, additional scenarios 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 Douglas County.

Two other scenarios are detailed in Appendix X:

1) Annual tree establishment needed to sustain tree cover at the existing level 

given an emerald ash borer infestation that kills off all ash trees in 10 years

2) Annual tree establishment needed to sustain tree cover at the existing level 

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 natural regeneration.

Model Scenarios

In modeling tree establishment rates for Douglas County, each land use was modeled 

separately. Table 9 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 unsustainable canopy cover levels. Though planting 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 significant canopy cover loss in a relatively 

short time period as many of these trees can die within a relatively short time frame. 
22

Table 9.—Estimated number of trees to be established annually for various cover and 

year scenarios, Douglas County, 2012. These estimates assume a 4% annual mortality.

Agriculture Land Use

2012 canopy cover: 10.5%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 7,600 134,000 201,000

Increase cover by 5% 926,000 411,000 325,000

Increase cover by 10% 1,910,000 716,000 468,000

Increase cover by 20% 4,110,000 1,310,000 774,000

Commercial Land Use

2012 canopy cover: 7.1%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 4,800 3,600 3,300

Increase cover by 5% 20,000 8,100 5,300

Increase cover by 10% 42,000 14,000 7,800

Increase cover by 20% 82,000 24,000 13,000

Park/Open Land Use

2012 canopy cover: 10.6%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 74,000 62,000 60,000

Increase cover by 5% 209,000 109,000 84,000

Increase cover by 10% 341,000 152,000 109,000

Increase cover by 20% 640,000 256,000 163,000

Residential Land Use

2012 canopy cover: 31.4%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 153,000 96,000 66,000

Increase cover by 5% 287,000 134,000 84,000

Increase cover by 10% 431,000 180,000 109,000

Increase cover by 20% 780,000 283,000 162,000

Vacant Land Use

2012 canopy cover: 81.9%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 57,000 75,000 86,000

Increase cover by 5% 127,000 129,000 129,000

Increase cover by 10% 215,000 174,000 176,000

Increase cover by 20% 353,000 260,000 262,000

All Land Uses

2012 canopy cover: 23.5%

Scenario 10 Years 25 Years 50 Years

Maintain canopy cover 296,400 370,600 416,300

Increase cover by 5% 1,569,000 791,100 627,300

Increase cover by 10% 2,939,000 1,236,000 869,800

Increase cover by 20% 5,965,000 2,133,000 1,374,000
23

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

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). Thus, long-term estimates of 

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

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

need to be planted because of likely establishment through natural regeneration.

The 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 offsets 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 of trees. 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 

benefits for Douglas County. As an example, the benefits provided by the regional 

forest over the next 25 years were estimated for the model scenario of increasing 

canopy cover by 10 percent (increasing tree cover from 23.5 to 33.5 percent by 

establishing about 1.2 million trees per year with an average 4 percent mortality rate). 

Three ecosystem services/disservices were estimated: air pollution removal, carbon 

sequestration, and volatile organic compound (VOC) emissions

Air pollution removal was estimated by summing annual air pollution removal effects 

during the 25-year period. This simulation used the meteorological and pollution 

conditions of 2010 for all simulation years. Total pollution removal over the 25-year 

period is estimated at 119,000 tons ($544 million at nondiscounted current value), 

increasing from 3,868 tons/year ($17.7 million/year) in 2010 to 5,430 tons/year in 25 

years ($24.9 million/year).

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

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

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

Total carbon storage of live trees is estimated to increase from 1.49 million tons to 

1.54 million tons, for a 25-year sequestration amount of 47,000 tons ($3.4 million).

Annual VOC emissions over the 25 years were summed to estimate the total VOC 

emissions during the 25-year period. This simulation used the meteorological 

conditions of 2010 for all simulation years. Total isoprene and monoterpene emissions 
24

over the 25-year period are estimated at 20,950 tons, increasing from 682 tons/year in 

2010 to 957 tons/year in 25 years. These values will fluctuate annually based on local 

air temperatures, but assume constant 2010 meteorological conditions over the next 

25 years.

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

services should be considered rough estimates due to the numerous assumptions 

needed to attain these estimates. The numbers provided here are first-order estimates 

and will likely change through time as mortality, growth, and establishment rates in 

Douglas County differ from projected rates. However, these estimates provide a broad 

estimate of potential needs and impacts of attaining future canopy goals.

Wakarusa River Watershed Analysis

The Wakarusa River watershed (291,192 acres) in eastern Kansas was analyzed 

using the i-Tree Hydro model.6

 The i-Tree Hydro model (formerly known as 

UFORE-Hydro) simulates the stream flow hydrograph using hourly precipitation 

data, digital elevation data (Fig. 18), and land cover parameters. The model flow is 

calibrated against actual stream flow values recorded for a 1-year simulation period 

(see Appendix XI for details on methods). The flow of the Wakarusa watershed at the 

gauging station is largely controlled by the dam at Clinton Lake, and consequently 

this led to relatively low model calibration (Appendix XI); the effects illustrated by the 

model are likely more representative of flow changes in the river above the dam, not at 

the gauging station (i.e., the flow below the dam is based on water storage and release 

from the dam).

Figure 18.—Wakarusa River watershed.
25

-10.0

-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 (percent of all land)

Flow Change (percent) 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

10 20 30 40 50 60 70 80 90 100

Total Runoff

Pervious Area Flow

Base Flow

Impervious Area Flow

Flow (billion ft3/year)

Tree Cover (percent)

Figure 19.—Percent change in total flow with changes in canopy cover, Wakarusa River 

watershed.

Figure 20.—Changes in total runoff and components of total runoff (pervious area flow, 

impervious area flow, and base flow) with increases in percent tree cover in the Wakarusa 

River watershed (impervious cover maintained at 7.3 percent).

Tree Cover Effects

Loss of current tree cover in the Wakarusa River watershed results in a 2.6 percent 

(88.9 million ft3

) increase in estimated total flow for the simulation period. Based on 

national average costs of storm water control of $0.008936/gallon32, the current tree 

cover is valued at $5.9 million. Based on the simulation, increasing canopy cover from 

25.2 percent to 30 percent would result in a reduction of overall flow by another 0.5 

percent (15.8 million ft3

) during the year (Fig. 19). Increasing tree cover reduces flow 

mainly from pervious areas (Fig. 20).

Impervious Cover Effects

Removing all impervious cover reduces total flow by an average of 6.7 percent (234 

million ft3

) during the simulation period. Increasing impervious cover from 7.3 

percent to 10 percent of the watershed increases total flow another 2.8 percent (99 

million ft3

) during the period (Fig. 21). Increasing impervious cover in the simulation 

reduces base flow and pervious runoff while significantly increasing flow from 

impervious surfaces (Fig. 22).
26

Increasing tree cover will reduce stream flow, but the cover type most influencing 

stream flow is impervious surfaces. Under current cover conditions, the impact of 

increasing impervious cover on flow is 24 times greater relative to increasing tree cover. 

Increasing impervious cover by 1 percent averaged a 2.5 percent increase in stream 

flow, while increasing tree cover by 1 percent averaged only a 0.1 percent decrease 

in stream flow. The effects of manipulations of both tree and impervious cover are 

illustrated in Figures 23 (for total flow) and 24 (for changes in percent flow).

During the simulation period, 33.8 inches of rain were “recorded”. Since that amount 

is assumed to have fallen over the entire 291,192-acre watershed, a total of 35.7 billion 

ft3

 of rain fell on the watershed during the simulation period. The simulated flow in 

the Wakarusa River watershed for the base case scenario (no landscape change) was 

3.48 billion ft3

. The total flow is made up of surface runoff and baseflow (water that 

travels underground to the stream). Runoff from pervious surfaces contributes 98.7 

percent of total stream flow. Tree canopies intercepted about 10.7 percent of the total 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0 10 20 30 40 50 60 70 80 90 100

Total Runoff

Pervious Area Flow

Base Flow

Impervious Area Flow

Flow (billion ft3/year)

Impervious Cover (percent)

Figure 21.—Percent change in total flow with changes in percent impervious cover, 

Wakarusa River watershed.

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

impervious area runoff and base flow) with changes in percent impervious cover in the 

Wakarusa River watershed (Tree cover held at 25.2 percent).

-100

0

100

200

300

400

500

600

700

800

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

Impervious Cover (percent of all land)

Flow Change (percent) 
27

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

0

0.5

1

1.5

2

2.5

3

x 1010

Impervious Cover (%)

 original point

Tree Cover (%)

Total Flow (Cubic Feet)

0.5

1

1.5

2

2.5

x 1010 Billion

25

20

15

10

5 

Total Flow (billion ft3) 

30

25

20

15

10

5 

0 

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

-200

0

200

400

600

800

Impervious Cover (%)

 original point

Tree Cover (%)

Annual Flow Change (%)

0

100

200

300

400

500

600

700

Percent

Figure 23.—Changes in total flow during simulation period based on changes in percent 

impervious and percent tree cover, Wakarusa River watershed. Red star indicates current 

conditions.

Figure 24.—Percent change in total flow during simulation period based on changes 

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

indicates current conditions.

rainfall, but since only 25.2 percent of the watershed is under tree cover, interception of 

total precipitation in the watershed by trees was only 2.7 percent (966 million ft3

). Areas 

of short vegetation, including shrubs, intercepted about 5.4 percent of the total rainfall, 

but since only 66.8 percent of the watershed is under short vegetation, interception of 

total precipitation in the watershed by short vegetation was only 3.6 percent (1.3 billion 

ft3

). About 83.9 percent of total precipitation is estimated to re-enter the atmosphere 

through evaporation or evapotranspiration before it enters the stream.
28

Based on these changes in flow rates and national event mean concentration (EMC) 

values, the current tree cover is estimated to reduce total suspended solids during the 

simulation period by around 83 to 120 tons. Other chemical constituents are also 

reduced due to tree cover (Table 10).

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 Douglas County.

More information on trees in Douglas County can be found at: http://nrs.fs.fed.us/

data/urban.

Table 10.—Estimated reduction in chemical constituents in Wakarusa 

River watershed from existing tree cover during simulation period 

based on median and mean pooled event mean concentration values

Reduction (tons)

Constituent Median Mean

Total suspended solids 83.3 119.8

Biochemical oxygen demand 17.6 21.5

Chemical oxygen demand 68.3 80.7

Total phosphorus 0.4 0.5

Soluble phosphorus 0.2 0.2

Total Kjeldhal nitrogena

 2.2 2.6

Nitrite and nitrate 0.8 1.0

Copper 0.02 0.02

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

+

)

Arbor Day planting by local scouts along Burroughs Creek Trail in 

the City of Lawrence. Photo by Kim Bomberger, Kansas Forest Service, used 

with permission.
29

Appendix I. Species in Douglas County

Table 11.—Tree species sampled in the regional forest, Douglas County, 2012

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 6,160 0.0 0.0 0.0 1.5 1.5 134 0.0

Acer rubrum red maple 3,620 0.0 0.0 0.0 3.5 3.5 316 1.0

Acer saccharinum silver maple 117,340 0.8 3.4 4.2 14.7 15.6 237,114 144.9

Acer saccharum sugar maple 32,800 0.2 0.7 0.9 10.6 10.7 22,593 62.6

Aesculus glabra Ohio buckeye 3,080 0.0 0.0 0.0 2.5 2.5 151 0.1

Asimina triloba pawpaw 80,120 0.6 0.1 0.7 1.6 1.8 2,387 1.1

Carya cordiformis bitternut hickory 124,160 0.9 0.3 1.2 2.8 4.9 33,293 23.8

Carya ovata shagbark hickory 470,960 3.3 2.7 6.0 2.0 4.8 121,686 156.2

Celtis occidentalis northern hackberry 2,324,120 16.4 17.1 33.5 4.2 5.2 730,959 972.8

Cercis canadensis eastern redbud 323,600 2.3 1.4 3.7 3.6 5.1 89,979 125.3

Elaeagnus angustifolia Russian olive 3,080 0.0 0.0 0.0 2.5 2.5 151 0.0

Fraxinus americana white ash 36,980 0.3 0.0 0.3 1.7 1.8 1,143 0.7

Fraxinus pennsylvanica green ash 263,070 1.9 2.7 4.6 6.8 10.4 352,350 600.1

Gleditsia triacanthos honeylocust 843,740 6.0 3.6 9.6 8.2 8.8 561,140 610.4

Gymnocladus dioicus Kentucky coffeetree 17,170 0.1 0.0 0.1 1.5 1.5 375 0.7

Juglans nigra black walnut 701,860 5.0 14.4 19.4 10.1 10.3 595,109 473.1

Juniperus species juniper spp 7,250 0.1 0.0 0.1 6.0 6.0 1,680 3.5

Juniperus virginiana eastern redcedar 1,944,450 13.7 9.7 23.4 2.9 3.9 305,566 346.1

Maclura pomifera Osage-orange 1,882,360 13.3 16.7 30.0 4.9 8.1 1,388,494 1,188.2

Malus tschonoskii crabapple 3,620 0.0 0.0 0.0 8.5 8.5 1,601 4.2

Morus alba white mulberry 56,010 0.4 0.4 0.8 3.5 4.3 8,992 5.2

Morus rubra red mulberry 519,320 3.7 2.7 6.4 3.9 5.8 181,707 165.7

Ostrya virginiana eastern hophornbeam 46,230 0.3 0.2 0.5 3.3 3.2 3,614 1.5

Other species unknown species 3,080 0.0 0.0 0.0 1.5 1.5 67 0.0

Platanus occidentalis American sycamore 64,510 0.5 3.2 3.7 5.0 10.9 62,932 101.6

Populus deltoides eastern cottonwood 90,600 0.6 0.1 0.7 1.5 1.9 4,750 1.9

Prunus americana American plum 106,110 0.7 0.0 0.7 1.5 1.5 2,315 1.9

Prunus serotina black cherry 317,240 2.2 1.9 4.1 4.4 5.1 71,195 49.0

Quercus alba white oak 18,490 0.1 0.1 0.2 10.0 10.0 13,766 6.2

Quercus macrocarpa bur oak 128,680 0.9 1.3 2.2 4.1 9.0 129,400 158.2

Quercus muehlenbergii chinkapin oak 215,030 1.5 2.6 4.1 9.7 10.0 186,261 199.4

Quercus palustris pin oak 32,800 0.2 1.1 1.3 22.6 23.1 99,667 201.3

Quercus rubra northern red oak 135,710 1.0 1.5 2.5 14.0 10.8 133,969 153.9

Salix interior sandbar willow 145,870 1.0 0.1 1.1 2.5 2.5 7,479 2.5

Salix nigra black willow 36,980 0.3 0.0 0.3 1.8 3.1 3,681 1.3

Tilia americana American basswood 33,900 0.2 0.2 0.4 3.5 5.0 9,833 4.9

Ulmus americana American elm 2,854,730 20.2 9.6 29.8 3.0 4.5 637,222 348.1

Ulmus pumila Siberian elm 166,550 1.2 1.9 3.1 4.3 8.6 166,154 85.9

Ulmus rubra slippery elm 3,080 0.0 0.1 0.1 4.5 4.5 420 0.2

a

 IV = importance value (% population + % leaf area)
30

Table 12.—Shrub speciesa by land use, Douglas County, 2012 

Per Unit Area County Total

Land Useb Genus Species Common Name

Leaf Area 

(ft2

/ac)

Leaf 

Biomass 

(lb/ac)

Leaf Area 

(ac)

Leaf 

Biomass 

(tons)

Agriculture Symphoricarpos orbiculatus coralberry 6,728.64 76.99 32,891.48 8,194.54

Cornus drummondii roughleaf dogwood 3,510.22 41.92 17,158.62 4,462.62

Juniperus virginiana eastern redcedar 1,193.61 67.90 5,834.03 7,227.80

Rubus occidentalis black raspberry 1,015.80 7.76 4,964.24 826.29

Ribes missouriense Missouri gooseberry 286.46 4.39 1,401.06 467.61

Lonicera maackii amur honeysuckle 276.74 2.79 1,351.64 297.19

Rubus ostryifolius highbush blackberry 213.36 1.63 1,042.76 173.53

Prunus americana American plum 200.86 3.19 980.99 338.82

Zanthoxylum americanum common prickly ash 112.08 1.72 548.56 182.95

Maclura pomifera Osage-orange 109.38 2.25 533.74 239.70

Ulmus americana American elm 89.56 1.34 437.37 141.97

Rosa multiflora multiflora rose 55.02 0.85 269.34 89.84

Gleditsia triacanthos honeylocust 17.60 0.37 86.49 40.16

Celtis occidentalis northern hackberry 15.55 0.17 76.60 17.64

Morus rubra red mulberry 9.54 0.20 46.95 20.61

Carya cordiformis bitternut hickory 3.01 0.04 14.83 4.13

Total, Agriculture 13,837.43 213.51 67,638.68 22,725.38

Commercial Symphoricarpos orbiculatus coralberry 2,433.92 27.85 242.16 60.52

Cornus drummondii roughleaf dogwood 1,997.31 23.86 200.15 51.85

Morus rubra red mulberry 1,452.89 29.56 145.79 64.22

Juniperus virginiana eastern redcedar 744.89 42.38 74.13 92.09

Lonicera tatarica Tartarian honeysuckle 333.50 3.36 32.12 7.32

Syringa vulgaris common lilac 193.24 3.82 19.77 8.30

Maclura pomifera Osage-orange 107.38 2.21 9.88 4.80

Gleditsia triacanthos honeylocust 16.38 0.35 2.47 0.76

Total, Commercial 7,279.51 133.38 726.47 289.87

Park/Open Cornus drummondii roughleaf dogwood 13,405.61 160.12 8,767.11 2,280.26

Carya ovata shagbark hickory 2,830.76 42.46 1,850.78 604.68

Morus rubra red mulberry 1,311.92 26.69 857.44 380.05

Quercus muehlenbergii chinkapin oak 1,309.18 26.46 857.44 376.84

Aesculus glabra Ohio buckeye 593.56 8.90 387.95 126.69

Total, Park/Open 19,451.08 264.62 12,720.71 3,768.50

Residential Cornus drummondii roughleaf dogwood 9,009.62 107.62 6,637.11 1,726.27

Rhus glabra smooth sumac 1,255.47 14.18 924.15 227.44

Symphoricarpos orbiculatus coralberry 227.69 2.61 168.03 41.79

Ribes missouriense Missouri gooseberry 154.86 2.37 113.67 38.10

Total, Residential 10,647.64 126.77 7,842.95 2,033.61
31

Vacant Symphoricarpos orbiculatus coralberry 48,747.44 557.78 10,346.08 2,577.64

Cornus drummondii roughleaf dogwood 31,816.97 380.04 6,753.24 1,756.25

Juniperus virginiana eastern redcedar 10,409.84 592.24 2,209.07 2,736.95

Ribes missouriense Missouri gooseberry 7,570.45 116.10 1,606.15 536.54

Asimina triloba pawpaw 1,681.67 57.78 355.82 267.03

Rubus occidentalis black raspberry 1,326.56 10.14 281.69 46.85

Rosa multiflora multiflora rose 1,207.42 18.51 256.98 85.57

Carya ovata shagbark hickory 931.51 13.97 197.68 64.57

Lonicera maackii amur honeysuckle 799.21 8.07 170.50 37.27

Cercis canadensis eastern redbud 509.36 6.68 108.72 30.88

Rubus ostryifolius highbush blackberry 357.94 2.74 76.60 12.64

Zanthoxylum americanum common prickly ash 331.45 5.09 69.19 23.49

Aesculus glabra Ohio buckeye 287.94 4.32 61.78 19.95

Fraxinus americana white ash 245.55 2.86 51.89 13.20

Salix nigra black willow 231.48 3.01 49.42 13.89

Juglans nigra black walnut 225.04 3.69 46.95 17.07

Quercus muehlenbergii chinkapin oak 203.95 4.12 44.48 19.05

Ulmus americana American elm 195.76 2.92 42.01 13.48

Rhus glabra smooth sumac 177.42 2.01 37.07 9.26

Quercus rubra northern red oak 173.50 2.83 37.07 13.09

Morus rubra red mulberry 172.50 3.51 37.07 16.22

Gleditsia triacanthos honeylocust 148.54 3.19 32.12 14.72

Celtis occidentalis northern hackberry 36.46 0.39 7.41 1.80

Fraxinus pennsylvanica green ash 25.27 0.34 4.94 1.55

Total, Vacant 107,813.16 1,802.29 22,881.46 8,328.93

Douglas County Total 16,324.32 249.06 111,812.75 37,146.28 a Shrubs are defined as woody plants with stem diameter at 4.5 feet less than 1 inch.

b Water land use did not have any shrubs.

Per Unit Area County Total

Land Useb Genus Species Common Name

Leaf Area 

(ft2

/ac)

Leaf 

Biomass 

(lb/ac)

Leaf Area 

(ac)

Leaf 

Biomass 

(tons)

Table 12.—continued
32

Appendix II. Tree Species Distribution

Species distributions for each land use are illustrated (Figs. 25-32) for up to 20 most common 

species in each land use category. More detailed information on species by land use can be 

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

Figure 25.—The 20 most common tree species as a percent of the total tree population, Douglas 

County, 2012.

Figure 26.—The percent land use population occupied by the 10 most common tree species, Douglas 

County, 2012. For example, American elm comprises 23.2 percent of the tree population on agriculture 

land.

0 5 10 15 20 25

Eastern cottonwood

American plum

Silver maple

Bitternut hickory

Bur oak

Northern red oak

Sandbar willow

Siberian elm

Chinkapin oak

Green ash

Black cherry

Eastern redbud

Shagbark hickory

Red mulberry

Black walnut

Honeylocust

Osage-orange

Eastern red cedar

Northern hackberry

American elm

Total Population (percent)

Species

0

5

10

15

20

25

30

35

40

45

American elm Northern

hackberry

Eastern red

cedar

Osage-orange Honeylocust Black walnut Red mulberry Shagbark

hickory

Eastern

redbud

Black cherry

Land Use Population (percent)

Species

Agriculture Commercial Park/Open Residential Vacant

Land Use Category
33

0 5 10 15 20 25

Northern red oak

Kentucky coffeetree

Silver maple

Chinkapin oak

Bur oak

Bitternut hickory

Black walnut

American plum

Green ash

Eastern redbud

Siberian elm

Black cherry

Red mulberry

Honeylocust

Eastern red cedar

Osage-orange

Northern hackberry

American elm

Percent of Trees

Species

0

10

20

30

40

50

60

70

80

90

100

American elm Northern

hackberry

Eastern red

cedar

Osage-orange Honeylocust Black walnut Red mulberry Shagbark

hickory

Eastern

redbud

Black cherry

Species Population (percent)

Species

Agriculture Commercial Park/Open Residential Vacant

Land Use Category

Figure 27.—The percent species population occupied by the 10 most common tree species, 

Douglas County, 2012. For example, 71.6 percent of American elm are found on agriculture land.

Figure 28.—Percent of trees in agriculture land use category, Douglas County, 2012.
34

0 5 10 15 20 25 30 35

Pin oak

Red mulberry

White mulberry

Crabapple

Eastern red cedar

Eastern redbud

Sugar maple

Red maple

Juniper spp

Honeylocust

Percent of Trees

Species

0 10 20 30 40 50

Chinkapin oak

Eastern red cedar

Honeylocust

Northern hackberry

Shagbark hickory

Black walnut

Percent of Trees

Species

Figure 29.—Percent of trees in commercial land use category, Douglas County, 2012.

Figure 30.—Percent of trees in park/open land use category, Douglas County, 2012.
35

Figure 32.—Percent of trees in vacant category of land use category, Douglas County, 2012.

0 5 10 15 20 25 30

Siberian elm

Northern red oak

Pin oak

Bur oak

Red mulberry

Osage-orange

Black walnut

Sugar maple

Black cherry

American sycamore

Green ash

Silver maple

Eastern cottonwood

Eastern red cedar

Honeylocust

Eastern redbud

Northern hackberry

Shagbark hickory

Sandbar willow

American elm

Percent of Trees

Species

0 10 20 30 40 50

Bur oak

American basswood

Black willow

White ash

Red mulberry

Eastern hophornbeam

White mulberry

Bitternut hickory

Honeylocust

Pawpaw

Northern red oak

Chinkapin oak

Green ash

Shagbark hickory

Eastern redbud

Black walnut

Osage-orange

Northern hackberry

American elm

Eastern red cedar

Percent of Trees

Species

Figure 31.—Percent of trees in residential land use category, Douglas County, 2012.
36

Appendix III. Tree Structure and Functions by Land Use

This appendix provides details of various structural and functional values for each species by land use class and overall 

values for each land use class. More information can be found at: http://nrs.fs.fed.us/data/urban.

Table 13.—Tree structure and functions by land use, Douglas County, 2012

Common Name

Number of 

Trees

Carbon 

Storage 

(tons)

Carbon 

Sequestration 

(tons/year)

Net Carbon 

Sequestration 

(tons/year)

Leaf Area 

(acres)

Leaf 

Biomass 

(tons)

Structural Value 

($US)

----------------------------------------------------------------------------------Agriculture--------------------------------------------------------------------------------

American elm 2,043,422 93,725 4,916 2,768 22,597 7,331 267,763,181

Northern hackberry 1,854,534 182,189 9,020 8,708 49,344 11,453 809,088,248

Osage-orange 1,631,303 367,163 14,367 13,232 52,266 23,438 1,006,907,390

Eastern redcedar 1,167,670 22,594 1,633 1,597 18,689 23,158 178,718,001

Honeylocust 601,006 72,302 4,260 3,880 6,716 3,137 275,469,382

Red mulberry 446,462 46,139 2,516 2,348 9,221 4,085 162,621,043

Black cherry 240,403 16,301 1,484 1,448 6,811 2,356 46,548,331

Siberian elm 137,373 3,818 420 413 1,344 408 12,529,091

Eastern redbud 103,030 2,067 291 286 765 219 15,355,265

Green ash 103,030 4,622 327 316 2,610 759 38,101,177

American plum 103,030 179 99 98 157 54 1,912,918

Black walnut 85,858 77,685 2,557 2,503 19,489 6,967 162,893,096

Bitternut hickory 68,686 5,035 406 402 337 94 17,458,665

Kentucky coffeetree 17,172 29 16 16 34 11 686,865

Northern red oak 17,172 9,961 463 455 1,347 479 41,023,412

Total 8,791,866 989,116 46,105 41,739 203,076 88,593 3,334,144,427

-------------------------------------------------------------------------------Commercial------------------------------------------------------------------------------

Honeylocust 18,115 2,635 224 213 265 124 11,236,437

Juniper spp 7,246 191 32 31 40 50 3,519,031

Red maple 3,623 61 22 22 33 10 988,332

Sugar maple 3,623 1,053 86 82 398 107 8,456,213

Eastern redbud 3,623 238 38 37 27 8 3,385,275

Eastern redcedar 3,623 800 43 40 201 249 6,740,515

Crabapple 3,623 334 47 45 113 44 4,229,126

White mulberry 3, 623 83 25 24 75 24 801,370

Red mulberry 3,623 5 4 4 8 4 101,999

Pin oak 3,623 6,880 283 256 694 280 27,655,549

Total 54,344 12,281 803 754 1,856 899 67,113,846

----------------------------------------------------------------------------------Park/Open------------------------------------------------------------------------------

Black walnut 398,846 63,636 3,541 2,265 21,557 7,707 259,184,242

Shagbark hickory 227,912 26,445 2,117 2,054 8,400 2,744 148,243,054

Northern hackberry 113,956 7,580 812 794 4,665 1,083 85,255,860

Honeylocust 56,978 17,531 1,016 975 2,712 1,267 106,497,731

Eastern redcedar 56,978 7,189 341 324 4,321 5,354 86,131,204

Chinkapin oak 56,978 49 37 36 74 32 3,328,940

Total 911,648 122,431 7,864 6,448 41,729 18,187 688,641,032

continued
37

----------------------------------------------------------------------------------Residential-----------------------------------------------------------------------------

American elm 204,215 2,688 666 653 2,260 733 30,125,011

Sandbar willow 145,868 817 289 241 364 103 2,456,978

Shagbark hickory 116,695 167 143 141 460 150 3,255,778

Eastern redbud 87,521 17,073 1,338 1,268 3,163 903 102,184,443

Northern hackberry 87,521 1,808 548 539 2,186 507 24,120,629

Honeylocust 87,521 46,569 2,499 2,464 2,296 1,072 188,905,309

Eastern redcedar 87,521 504 103 100 577 715 5,718,690

Eastern cottonwood 87,521 155 68 52 102 33 875,209

Silver maple 58,347 49,747 1,956 1,764 9,938 2,333 126,362,056

Green ash 58,347 45,093 1,435 1,262 5,285 1,538 543,886,102

American sycamore 58,347 19,545 1,243 1,165 11,234 2,428 100,618,637

Black cherry 58,347 690 141 32 81 28 1,935,524

Sugar maple 29,174 6,436 666 639 2,195 590 54,132,190

Black walnut 29,174 543 176 173 540 193 3,851,947

Osage-orange 29,174 40,881 1,718 1,689 2,921 1,310 140,939,365

Red mulberry 29,174 30 30 30 105 47 1,232,025

Bur oak 29,174 162 0 -45 0 0 0

Pin oak 29,174 32,334 1,579 1,452 3,230 1,304 173,664,116

Northern red oak 29,174 14,241 930 873 2,511 892 91,407,525

Siberian elm 29,174 45,207 1,666 1,238 5,489 1,668 73,405,479

Total 1,371,161 324,690 17,195 15,731 54,935 16,547 1,669,077,013

-------------------------------------------------------------------------------------Vacant--------------------------------------------------------------------------------

Eastern redcedar 628,660 23,588 909 876 11,744 14,553 68,801,291

American elm 607,088 35,021 1,469 1,144 10,143 3,291 50,215,874

Northern hackberry 268,105 24,506 1,146 1,059 6,300 1,462 54,372,956

Osage-orange 221,880 28,378 1,024 870 5,588 2,506 40,381,096

Black walnut 187,982 45,162 1,322 1,047 10,873 3,887 47,174,487

Eastern redbud 129,430 1,829 184 168 1,266 362 4,384,641

Shagbark hickory 126,348 1,954 206 203 997 326 4,702,297

Green ash 101,695 6,941 300 278 1,854 539 18,066,607

Chinkapin oak 89,368 37,120 1,111 943 3,653 1,608 56,223,765

Northern red oak 89,368 23,717 396 -85 1,722 612 21,481,070

Pawpaw 80,123 256 49 45 303 227 1,058,265

Honeylocust 80,123 24,868 620 450 1,133 530 28,323,960

Bitternut hickory 55,470 4,540 224 221 837 235 6,361,669

White mulberry 52,388 1,950 142 138 1,230 401 4,400,071

Eastern 

hophornbeam

46,225 483 71 70 686 200 1,510,036

Red mulberry 40,062 1,424 61 40 417 185 1,708,613

White ash 36,980 120 31 30 161 41 716,547

Table 13.—continued

Common Name

Number of 

Trees

Carbon 

Storage 

(tons)

Carbon 

Sequestration 

(tons/year)

Net Carbon 

Sequestration 

(tons/year)

Leaf Area 

(acres)

Leaf 

Biomass 

(tons)

Structural Value 

($US)

continued
38

Table 13.—continued

Common Name

Number of 

Trees

Carbon 

Storage 

(tons)

Carbon 

Sequestration 

(tons/year)

Net Carbon 

Sequestration 

(tons/year)

Leaf Area 

(acres)

Leaf 

Biomass 

(tons)

Structural Value 

($US)

Table 14.—Summary of tree structure and functions by land use, Douglas County, 2012

Trees

Carbon Storage 

(tons)

Carbon Seq 

(tons/yr)

Net Carbon Seq 

(tons/yr)

Leaf Area 

(acres)

Leaf Biomass 

(tons)

Tree Value 

($US billion)

Land Use N % N % N % N % N % N % N %

Agriculture 8,791,866 62.1 989,116 57.0 46,105 56.3 41,739 57.5 203,076 55.7 88,593 56.7 3.33 53.7

Commercial 54,344 0.4 12,281 0.7 803 1.0 754 1.0 1,856 0.5 899 0.6 0.07 1.1

Park/Open 911,648 6.4 122,431 7.1 7,864 9.6 6,448 8.9 41,729 11.4 18,187 11.6 0.69 11.1

Residential 1,371,161 9.7 324,690 18.7 17,195 21.0 15,731 21.7 54,935 15.1 16,547 10.6 1.67 26.9

Vacant 3,035,442 21.4 286,752 16.5 9,966 12.2 7,973 11.0 63,018 17.3 32,077 20.5 0.44 7.2

Water 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0.00 0.0

Total 14,164,460 1,735,269 81,933 72,645 364,613 156,303 6.20

Black willow 36,980 723 35 20 77 22 1,309,541

American basswood 33,898 1,991 79 75 901 117 4,939,245

Bur oak 30,817 9,172 274 267 956 421 16,678,312

Silver maple 24,653 6,166 97 37 677 159 2,748,680

Black cherry 18,490 691 20 9 71 25 550,931

White oak 18,490 4,929 118 -8 378 123 6,205,963

Boxelder 6,163 13 2 2 8 3 14,638

American sycamore 6,163 407 27 27 500 108 1,019,265

Ohio buckeye 3,082 19 4 4 20 7 62,394

Russian olive 3,082 12 3 3 27 9 43,984

Unknown species 3,082 5 2 2 18 6 41,211

Eastern cottonwood 3,082 685 29 29 154 50 976,520

American plum 3,082 5 2 2 1 0 25,270

Slippery elm 3,082 76 8 8 320 64 161,410

Total 3,035,442 286,752 9,966 7,973 63,018 32,077 444,660,609

-----------------------------------------------------------------------------------Water----------------------------------------------------------------------------------

Total 0 0 0 0 0 0 0

Douglas County Total 14,164,460 1,735,269 81,933 72,645 364,613 156,303 6,203,636,926
39

Appendix IV. Population Information by Stem Diameter Class

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)

Agriculture Commercial Park/Open Residential Vacant

Land Use Category

Figure 33.—Percent of tree population by diameter class and land use category, Douglas 

County, 2012. For example, of the trees that have diameters between 27 and 30 inches, 70 

percent are found in agriculture land uses and 24 percent are found in residential areas.

Figure 34.—Percent of smallest and largest diameter classes (less than 3 inches and greater than 

18 inches) by species for the most common tree species in those classes, Douglas County, 2012.

0

5

10

15

20

25

30

Diameter Class Population (percent)

Species

Less than 3 Greater than 18

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

This appendix details how trees in each diameter class are distributed among land uses (Fig. 33); 

whether the most common species in the diameter classes less than 3 inches or greater than 18 inches 

are gaining or losing prominence (Figs. 34 and 35); and how trees less than 3 inches in diameter are 

distributed by land use (Fig. 36).
40

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Number of Trees (millions) 

Species

Less than 3 Greater than 18

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

0

2

4

6

8

10

12

14

16

18

American elm Eastern red

cedar

Northern

hackberry

Osage-orange Shagbark

hickory

Honeylocust Red mulberry Eastern

redbud

Black cherry Sandbar

willow

Diameter Class < 3 inches (percent)

Species

Agriculture Commercial Park/Open Residential Vacant

Land Use Category

Figure 35.—Number of trees in smallest and largest diameter classes for the most common tree 

species in those classes, Douglas County, 2012.

Figure 36.—Percent of trees in less than 3 inch diameter class for the most common species in that 

diameter class, by land use category, Douglas County, 2012. For example, 17.8 percent of the trees 

less than 3 inches in diameter are American elm within agriculture land uses.
41

Table 15.—Number of incidences of avoided health effects from pollutants, Douglas County, 2012a 

Health Effect NO2 SO2 O3 PM2.5

---------------------------------------------------Trees only-----------------------------------------------------

Hospital admissions 2.1 0.6 1.0

Emergency room visits 1.0 0.5 0.5 0.4

Asthma exacerbation 834.3 84.9 284.8

Acute respiratory symptoms 54.9 9.2 1181.2 525.3

Mortality 0.4 0.8

School loss days 295.8

Acute bronchitis 0.6

Acute myocardial infarction 0.4

Chronic bronchitis 0.3

Hospital admissions (cardiovascular) 0.1

Hospital admissions (respiratory) 0.1

Lower respiratory symptoms 7.3

Upper respiratory symptoms 5.8

Work loss days 91.0

--------------------------------------------------Shrubs only----------------------------------------------------

Hospital admissions 0.9 0.3 0.5

Emergency room visits 0.4 0.2 0.3 0.1

Asthma exacerbation 354.8 39.4 85.2

Acute respiratory symptoms 23.3 4.2 543.8 157.1

Mortality 0.2 0.2

School loss days 136.0

Acute bronchitis 0.2

Acute myocardial infarction 0.1

Chronic bronchitis 0.1

Hospital admissions (cardiovascular) 0.03

Hospital admissions (respiratory) 0.03

Lower respiratory symptoms 2.2

Upper respiratory symptoms 1.7

Work loss days 27.2

a The same health effects are not analyzed for each pollutant. Blank cells indicate that the incidence is not estimated 

for that pollutant and health effect.

Appendix V. Effects of Pollution Remova l on Local Health

Air pollution is a common problem in many urban areas and can have far-reaching effects, impacting 

human health, ecosystem health, and landscape materials. To calculate the effects of pollution removal 

on local health in Douglas County, we used the U.S. Environmental Protection Agency’s Environmental 

Benefits Mapping and Analysis Program (BenMAP)25 along with 2010 population statistics from the U.S. 

Census Bureau.33 The model estimates the incidence of adverse health effects and associated monetary 

value that result from changes in pollution concentrations due to pollution removal by trees and shrubs.

The number of adverse health effects and associated economic value is calculated for ozone (O3), sulfur 

dioxide (SO2), nitrogen dioxide (NO2), and particulate matter <2.5 microns (PM2.5) in Douglas County 

(Table 15 and 16).
42

Table 16.—Associated value ($) of avoided health effects from pollutants, Douglas County, 2012a

Health Effect NO2 SO2 O3 PM2.5

---------------------------------------------------Trees, only-----------------------------------------------------

Hospital admissions 73,300 17,200 24,800

Emergency room visits 400 200 200 200

Asthma exacerbation 73,700 6,700 23,200

Acute respiratory symptoms 1,700 300 101,000 51,500

Mortality 2,823,000 6,314,800

School loss days 29,000

Acute bronchitis 50

Acute myocardial infarction 32,900

Chronic bronchitis 84,200

Hospital admissions (cardiovascular) 4,000

Hospital admissions (respiratory) 3,000

Lower respiratory symptoms 400

Upper respiratory symptoms 300

Work loss days 14,500

Total value (trees) 149,200 24,400 2,978,000 6,529,000

--------------------------------------------------Shrubs, only----------------------------------------------------

Hospital admissions 31,100 8,100 11,300

Emergency room visits 200 100 100 50

Asthma exacerbation 31,300 3,100 6,900

Acute respiratory symptoms 700 100 46,500 15,400

Mortality 1,290,400 1,887,400

School loss days 13,400

Acute bronchitis 20

Acute myocardial infarction 9,800

Chronic bronchitis 25,200

Hospital admissions (cardiovascular) 1,200

Hospital admissions (respiratory) 900

Lower respiratory symptoms 100

Upper respiratory symptoms 80

Work loss days 4,300

Total value (shrubs) 63,400 11,500 1,361,600 1,951,400

Total (trees and shrubs) 212,600 35,800 4,339,600 8,480,400

a The same health effects are not analyzed for each pollutant. Blank cells indicate that the value is not estimated 

for that pollutant and health effect.
43

Appendix VI. General Recommendations for Air Quality 

Improvement

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

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

Temperature reduction and other microclimatic effects

Removal of air pollutants

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

Energy conservation on buildings and consequent power plant emissions

The cumulative and interactive effects of trees on climate, pollution removal, and VOC and power plant 

emissions determine the overall impact of trees on air pollution. Cumulative studies involving urban tree 

impacts on ozone have revealed that increased urban canopy cover, particularly with low VOC emitting 

species, leads to reduced ozone concentrations in cities. Local urban forest management decisions also can 

help improve air quality.

Urban forest management strategies to help improve air quality include:

Strategy Reason

Increase the number of healthy trees 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 effects

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 benefits

Avoid pollutant-sensitive species Improve tree health

Utilize evergreen trees for particulate matter Year-round removal of particles
44

Appendix VII. Relative Tree Effects

The tree and forest resource in Douglas County provides benefits that include carbon storage and 

sequestration and air pollutant removal. These benefits vary by stem diameter class (Table 17). To estimate 

a relative value of these benefits, tree benefits were compared to estimates of average carbon emissions in the 

region34, average passenger automobile emissions35, and average household emissions.36

General tree information:

Average tree stem diameter = 6.1 in.

Median tree stem diameter = 4.0 in.

Number of trees sampled = 1,387

Number of species sampled = 39

Table 17.—Average tree effects by stem diameter class (d.b.h.), Douglas County, 2012

D.b.h.

(inches)a

Carbon storage Carbon sequestration Pollution removal

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

1-3 6 0.23 20 1.7 0.06 6 0.09 0.20

3-6 42 1.48 150 5.7 0.20 21 0.3 0.69

6-9 137 4.88 500 10.7 0.38 39 0.5 1.24

9-12 316 11.27 1,160 19.4 0.69 71 1.1 2.45

12-15 570 20.30 2,090 28.0 1.00 103 1.4 3.13

15-18 934 33.25 3,420 40.3 1.43 147 1.6 3.66

18-21 1,359 48.40 4,980 46.7 1.66 171 2.6 5.96

21-24 2,047 72.89 7,500 75.0 2.67 275 2.2 5.07

24-27 2,882 102.66 10,560 99.1 3.53 363 2.5 5.67

27-30 3,215 114.50 11,770 96.3 3.43 353 4.0 9.10

30+ 4,945 176.14 18,110 101.7 3.62 373 5.7 12.96

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

The trees in Douglas County provide: 

Carbon storage equivalent to:

Amount of carbon (C) emitted in region in 943 days or 

annual carbon emissions from 1,041,000 automobiles or 

annual C emissions from 522,900 single family houses

Carbon monoxide removal equivalent to:

Annual carbon monoxide emissions from 351 automobiles or

annual carbon monoxide emissions from 1,500 family 

houses

Nitrogen dioxide removal equivalent to:

Annual nitrogen dioxide emissions from 39,000 

automobiles or

annual nitrogen dioxide emissions from 26,000 single family 

houses 

Sulfur dioxide removal equivalent to:

Annual sulfur dioxide emissions from 445,900 automobiles 

or

annual sulfur dioxide emissions from 7,500 single family 

houses 

Particulate matter less than 10 micron (PM10) removal 

equivalent to:

Annual PM10 emissions from 4,994,400 automobiles or

annual PM10 emissions 482,100 single family houses

Annual C sequestration equivalent to:

Amount of C emitted in region in 45 days or

annual C emissions from 49,200 automobiles or

annual C emissions from 24,700 single family homes 
45

Appendix VIII. Tree Planting Index Map

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

Cover Data37 were used in conjunction with 2010 U.S. Census Bureau data to produce an index of priority 

planting areas for Douglas County. Index values were produced for each census block group; the higher the 

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

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

value. The criteria for 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 standardized38 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 37.—Priority planting 

areas, Douglas County, 

2012. Higher index scores 

indicate higher priority areas 

for planting.

Douglas County, Kansas

0 2.5 5 7.5 10 1.25

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
46

Appendix IX. Potential Insect and Disease Impacts

The following insects and diseases were analyzed to quantify their potential impact on Douglas 

County’s rural and urban forests:

•	 Aspen leafminer (AL)—An insect that causes damage primarily to trembling or small tooth 

aspen by larval feeding of leaf tissue. While outbreaks of the aspen leafminer have been 

recorded throughout parts of Alaska, Canada, and the western United States, the pest is 

relatively uncommon in eastern North America.39

•	 Asian longhorned beetle (ALB)—A beetle40 that bores into and kills a wide range of 

hardwood species. This 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, NY (2002), Toronto/Vaughan, Ontario (2003), and Middlesex/Union 

counties, NJ (2004). In 2007, the beetle was found on Staten and Prall’s Islands, NY. Most 

recently, beetles were detected in Worcester, MA (2008) and Bethel, OH (2011). In addition 

to the eradication in Chicago, successful eradication has since occurred in Hudson County, 

NJ (2008) and Islip, NY (2011).

•	 Beech bark disease (BBD)—An insect- disease complex that primarily impacts American 

beech. It is caused by the infestation of several different species. First, the insect, Cryptococcus 

fagisuga, feeds on the sap of the beech trees. These affected trees can become hosts to the 

nectria fungi. The two primary species of nectria fungi in North America are N. coccinea var.

faginata and N. gallifena.

41

•	 Butternut canker (BC)—Caused by a fungus that infects butternut trees. The disease was 

first discovered in 1967 in Wisconsin and has since caused significant declines in butternut 

populations in the United States.42

•	 Chestnut blight (CB)—The most common hosts of the fungus that cause chestnut blight are 

American and European chestnut. This disease causes canker formation in host trees resulting 

in dead limbs, brown or yellowing leaves, or mortality.43

•	 Dogwood anthracnose (DA)—A disease that affects dogwood species, specifically flowering 

and Pacific dogwood. It is caused by a fungus that produces leaf spots and necrotic blotches 

and canker formation on twigs, branches, and the main stem of infected trees.44

•	 Dutch elm disease (DED)—American elm, one of the most important street trees in the 20th

century, has been devastated by the Dutch elm disease. Since first reported in the 1930s, it 

has killed more than 50 percent of the native elm population in the United States.45

•	 Douglas-fir beetle (DFB)—A bark beetle that infests Douglas-fir trees. Infestations of the 

Douglas-fir beetle have been seen throughout the western United States, British Columbia, 

and Mexico often resulting in tree mortality.46

•	 Emerald ash borer (EAB)—Since being discovered in Detroit in 2002, emerald ash borer47

has killed millions of ash trees in Connecticut, Illinois, Indiana, Iowa, Kansas, Kentucky, 

Massachusetts, Maryland, Michigan, Minnesota, Missouri, New York, Ohio, Ontario, 

Pennsylvania, Quebec, Virginia, Tennessee, West Virginia, and Wisconsin. With the recent 

detection of emerald ash borer in Wyandotte County, Kansas in 2012, millions of ash trees 

in Kansas are now at a greater risk of infestation, including the 300,000 ash trees in Douglas 

County.
47

•	 Fir Engraver (FE)—One common pest of white fir, grand fir, and red fir trees is the fir 

engraver. This bark beetle is distributed primarily in the western United States.48

•	 Fusiform rust (FR)—A fungal disease that is distributed in the southern United States. It 

is particularly damaging to slash pine and loblolly pine because it infects the living tissue of 

the host’s stems and branches. Pine trees affected by the fungus can develop fatal galls and 

cankers.49

•	 Goldspotted oak borer (GSOB)—Infestations of the goldspotted oak borer have been a 

growing problem in southern California. This forest pest is native to southeastern Arizona 

and Mexico and believed to have been transported to California by the movement of 

firewood. The three known host species for GOB are coast live oaks, California black oaks, 

and canyon live oaks.50

•	 Gypsy moth (GM)—The larvae of the gypsy moth51 feed on the leaves of many species 

causing widespread defoliation and tree death if outbreak conditions last several years.

•	 Hemlock woolly adelgid (HWA)—As one of the most damaging pests to eastern hemlock 

and Carolina hemlock, hemlock woolly adelgid has played a large role in hemlock mortality 

in the United States. Since the pest was first discovered in 1951, infestations have expanded 

to cover about half of the range of hemlock in the eastern United States.52

•	 Jeffrey pine beetle (JPB)—Native to North America, the beetle is distributed across 

California, Nevada, and Oregon where its only host, Jeffrey pine, also occurs. 53

•	 Large aspen tortrix(LAT)—Quaking aspen is a principal host for the defoliator, large aspen 

tortrix. The insect has been found across much of the northeastern, north central, and 

western United States, as well as Alaska and Canada. Large aspen tortrix can reach outbreak 

levels where quaking aspen are abundant and will potentially strip hosts of all of their 

foliage.54

•	 Laurel wilt disease (LWD)—A fungus-caused disease that is introduced to host trees by 

the redbay ambrosia beetle. Redbay, as well as other tree species in the Laurel family, are 

common hosts for laurel wilt disease which has been observed in North Carolina, South 

Carolina, Georgia, Alabama, Mississippi, and Florida.55

•	 Mountain pine beetle (MPB)—This bark beetle primarily attacks pine species in the western 

United States. The major host species of the mountain pine beetle, lodgepole pine, ponderosa 

pine, western white pine, sugar pine, limber pine, and whitebark pine, have a similar 

distribution as this pest.56

•	 Northern spruce engraver (NSE)—This insect has had a significant impact on the boreal and 

sub-boreal forests of North America where the pest’s distribution overlaps with the range of 

its major hosts, white spruce, Englemann spruce, and Lutz’s spruce. This forest pest has been 

found in Alaska, Maine, Michigan, Minnesota, and Montana within the United States and 

in most of the provinces of Canada.57

•	 Oak wilt (OW)—Caused by a fungus and is a prominent disease among oak trees producing 

leaf wilting and discoloration, heavy defoliation, or fungal mats beneath the bark. The disease 

has been found in 21 states throughout most of the midwestern United States and it is still 

unknown whether any species of oak are immune to it.58

•	 Port-Orford-cedar root disease (POCRD)—Caused by a fungus and is most damaging to 

Port-Orford cedar and Pacific yew species.59
48

•	 Pine shoot beetle (PSB)—A wood borer that attacks various pine species, though Scotch 

pine is the preferred host in North America. The beetle has an international geographic 

distribution. In the United States it has been discovered in Illinois, Indiana, Maine, 

Maryland, Michigan, New Hampshire, New York, Ohio, Pennsylvania, Vermont, West 

Virginia, and Wisconsin, as well as in Ontario and Quebec in Canada.60

•	 Spruce beetle (SB)—All species of spruce that fall within the spruce beetle’s range are 

suitable hosts for attack. This bark beetle causes significant mortality and covers large areas of 

Alaska, Canada, and the northern United States, as well as some patches through the Rocky 

Mountain range.61

•	 Spruce budworm (SBW)—An insect that causes severe damage to balsam fir. During the 

larval stage of the budworm’s life, it feeds primarily on the needles or expanding buds of its 

hosts. Years of heavy defoliation can ultimately lead to tree mortality. Other hosts for the 

spruce budworm include white, red, and black spruce.62

•	 Sudden oak death (SOD)—A fungus-caused disease most common in British Columbia, 

Washington, Oregon, and California. It impacts many different species including, southern 

red oak, California black oak, northern red oak, pacific madrone, tanoak, and coastal live oak.63

•	 Southern pine beetle (SPB)—Although the southern pine beetle will attack most pine 

species, its preferred hosts are loblolly, Virginia, pond, spruce, shortleaf, and sand pines. The 

range of this particular bark beetle covers much of the southeastern United States.64

•	 Sirex woodwasp (SW)—A wood borer that primarily attacks pine species. It is not native 

to the United States, but is known to cause high amounts of tree mortality among North 

American species that have been planted in countries of the southern hemisphere.65

•	 Thousand cankers disease (TCD)—An 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. 

Tennessee was the first state in the east where thousand cankers disease was been found. 

Tree mortality is the result of attacks by the walnut twig beetle and subsequent canker 

development caused by associated fungi.66 Black walnut, the most important commerciallyharvested tree in Kansas, constitutes 5% of the tree population in Douglas County.

•	 Western pine beetle (WPB)—This beetle aggressively attacks ponderosa and Coulter pines 

and has caused significant swaths of damage in California, Oregon, Washington, Idaho, 

British Columbia, Montana, Nevada, Utah, Colorado, Arizona, New Mexico, Texas, and 

parts of northern Mexico.67

•	 White pine blister rust (WPBR)—Since its introduction to the United States in 1900, white 

pine blister rust has had a detrimental effect on white pines, particularly in the Lake States.68

•	 Western spruce budworm (WSB)—An insect that causes defoliation in western conifers. 

It has been found in Arizona, New Mexico, Colorado, Utah, Wyoming, Montana, Idaho, 

Oregon, and Washington in the United States and British Columbia and Alberta in Canada. 

The western spruce budworm feeds on new foliage of its hosts. Common host species include 

Douglas-fir, grand fir, white fir, subalpine fir, corkbark fir, blue spruce, Engelmann spruce, 

white spruce, and western larch.69
49

Code Scientific Name Common Name

Trees at 

Risk

#

Compensatory 

Value

($ millions)

AL Phyllocnistis populiella Aspen leafminer 182,848 3.8

ALB Anoplophora glabripennis Asian longhorned beetle 3,723,887 1,248

BBD Cryptococcus fagisuga Beech bark disease - -

BC Sirococcus clavigignentijuglandacearumButternut canker - -

CB Cryphonectria parasitica Chestnut blight - -

DA Discula destructive Dogwood anthracnose - -

DED Ophiostoma novo-ulmi Dutch elm disease 3,024,355 434

DFB Dendroctonus pseudotsugae Douglas-fir beetle - -

EAB Agrilus planipennis Emerald ash borer 300,052 601

FE Scotylus ventralis Fir engraver - -

FR Cronartium fusiforme Fusiform rust - -

GSOB Agrilus auroguttatus Goldspotted oak borer - -

GM Lymantria dispar Gypsy moth 797,305 733

HWA Adelges tsugae Hemlock woolly adelgid - -

JPB Dendroctonus jeffreyi Jeffrey pine beetle - -

LAT Choristoneura conflictana Large aspen tortrix 182,848 3.8

LWD Raffaelea lauricola Laurel wilt disease - -

MPB Dendroctonus ponderosae Mountain pine beetle - -

NSE Ips perturbatus Northern spruce engraver - -

OW Ceratocystis fagacearum Oak wilt 530,711 719

POCRD Phytophthora lateralis Port-Orford-cedar root disease - -

PSB Tomicus piniperda Pine shoot beetle - -

SB Dendroctonus rufipennis Spruce beetle - -

SBW Choristoneura fumiferana Spruce budworm - -

SOD Phytophthora ramorum Sudden oak death 168,511 355

SPB Dendroctonus frontalis Southern pine beetle - -

SW Sirex noctilio Sirex woodwasp - -

TCD Pityophthorus juglandis & 

Geosmithia spp.

Thousand canker disease 701,859 473

WPB Dendroctonus brevicomis Western pine beetle - -

WPBR Cronartium ribicola White pine blister rust - -

WSB Choristoneura occidentalis Western spruce budworm - -

Table 18.—Potential risk to trees by insect or disease, Douglas County, 2012

As each insect/disease is likely to attack different host tree species, the implications for Douglas 

County will vary. The number of trees at risk reflects only the known host species that could 

experience mortality (Table 18). The species host lists used for these insects/diseases can be found at 

http://nrs.fs.fed.us/data/urban.
50

Pest range maps from the Forest Health Technology Enterprise Team (FHTET)27 were used to 

determine the proximity of each pest to the county. For Douglas County, proximity was classified 

as whether the insect/disease occurs within the county, is within 250 miles of the county edge, is 

between 250 and 750 miles away, or is greater than 750 miles away (Fig. 38). FHTET did not have 

pest range maps for Dutch elm disease and chestnut blight. The range of these pests was based on 

known occurrence and the host range, respectively. 31

For more information on these pests and to access pest range maps, please visit www.foresthealth.info.

Based on the host tree species for each pest and the current range of the pest, it is possible to 

determine what the risk is that each tree species sampled in Douglas County could be attacked by an 

insect or disease. In Table 19, species risk is designated as one of the following: 

•	 Red - tree species is at risk to at least one pest within county

•	 Orange - tree species has no risk to pests within county, but has a risk to at least one pest 

within 250 miles from the county

•	 Yellow - tree species has no risk to pests within 250 miles of county, but has a risk to at least 

one pest that is 250 to 750 miles from the county

•	 Green - tree species has no risk to pests within 750 miles of county, but has a risk to at least 

one pest that is greater than 750 miles from the county

Species that were sampled in Douglas County, but that are not listed in this matrix, are not known to 

be hosts to any of the 31 exotic insects/diseases analyzed. Tree species at the greatest risk to existing 

pest infestations in Douglas County are oaks and elms. 

Figure 38.—Number of trees at risk and associated compensatory value of insect/disease effects, 

Douglas County, 2012.

0

200

400

600

800

1,000

1,200

1,400

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

DED

OW

BC

FR

PSBSPB

ALB

GM

TCD

EAB

AL

BBD

CBDA

DFB

FE

HWA

LWD

MPB

SB

SBWWPBWSB

LAT

SOD

GOB

JPB

NSE

POCRD

SW

WPBR

Compensatory Value (dollars) 

Number of Trees (millions) 

Pest/Disease Trees at risk

Compensatory value

Red indicates pest/disease is within county

Orange indicates pest/disease is within 250 miles of county

Yellow indicates pest/disease is within 750 miles of county

Green indicates pest/disease is outside of these ranges
51

Table 19.—Potential insect and disease risk for tree species, Douglas County, 2012

Pestc

Spp. Riska

Risk weightb

Common 

Name

DEDOWEABBCFRPSBSPBALBGMTCDALBBDCBDADFBFEHWALWDMPBSBSBWWPBWSBLATSODGOBJPBNSEPOCRDSWWPBR

7

Northern red 

oak 3

7 Pin oak 3

6 American elm 3

6

Chinkapin 

oak 3

6 Siberian elm 3

6 Bur oak 3

6 White oak 3

6 Slippery elm 3

5 Green ash 2 3

3 White ash 2

7

Sandbar 

willow 3 3 3

7 Black willow 3 3 3

2 Black walnut 3

2 Silver maple 3

2

Eastern 

cottonwood 3

2

Eastern

hophornbeam 3

2

American 

basswood 3

2 Sugar maple 3

2 Boxelder 3

2 Red maple 3

2 Crabapple 3

2 Ohio buckeye 3

a

Species Risk

Red indicates that tree species is at risk to at least one pest currently present within the county

Orange indicates that tree species has no risk to pests in the county, but has a risk to at least one pest within 250 miles from the county

Yellow indicates that tree species has no risk to pests within 250 miles of the county, but has a risk to at least pest that is 250 to 750 miles from the 

county

Green indicates that tree species has no risk to pests within 750 miles of the county, but has a risk to at least pest that is greater than 750 miles 

from the county

b

Risk weight

Numerical scoring system based on sum of points assigned to pest risks for species. Each pest that could attack tree species is scored as 4 points if 

red, 3 points if orange, 2 points if yellow and 1 point if green. 

c

Pest Color Codes

Red indicates pest is within Douglas County

Orange indicates pest is within 250 miles of Douglas County

Yellow indicates pest is within 750 miles of Douglas County

Green indicates pest is outside of these ranges
52

Appendix X. i-Tree Forecast Prototype Model 

Methods and Results

The 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 from the current 

forest structure data. There are three main components of the model:

1) Tree growth—projects annual growth for tree diameter, crown size, and leaf area for each 

tree

2) Tree mortality—projects annual mortality based on user defined mortality rates

3) Tree establishment—projects new trees; can be used to illustrate the effect of the new trees 

or determine how many new trees need to be added annually to sustain a certain level of tree 

cover or benefits.

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.

Length of growing season - To determine a base growth rate based on length of growing season, 

growth measurement for urban street trees, park trees, and forest trees were standardized 

to growth rates for 153 frost-free days based on: Standardized growth = measured growth x 

(number of frost-free days of measurement/153).7

 Growth rates of trees of the same species or 

genera were also compared to determine the average difference between standardized street tree 

growth and standardized park tree and forest tree growth rates. Park growth was 1.78 times less 

than street trees, on average, and forest growth was 2.26 times less than street tree growth.

Species growth rates - Based on these data, average standardized growth rates for open-grown 

trees were set at 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. There are limited measured data on urban tree growth 

for slow, moderate or fast-growing tree species, so the growth rates used are estimates. These 

growth rates, by species growth-rate class, were estimated such that the entire population’s 

average growth rate was comparable to the measured growth rates for trees standardized to the 

number of frost-free days.

Tree competition - Crown light exposure (CLE) measurements were used to represent tree 

competition the CLE of 0-1 representing forest conditions; 2-3 for park conditions; and 4-5 for 

open-grown conditions. Thus, 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 canopy 

cover increased or decreased, the CLE 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 decreased, the CLE 

adjustment factor increased).
53

Tree condition - Base growth rates are also adjusted depending on tree condition. These 

adjustment factors are based on percent crown dieback and the assumption that less than 

25 percent crown dieback had a limited effect on diameter growth rates. For trees in fair to 

excellent condition (less than 25 percent dieback), base growth rates are multiplied by 1 (no 

adjustment), trees in poor condition (crown dieback of 26 to 50 percent) by 0.76, critical trees 

(crown dieback of 51 to 75 percent) by 0.42, dying trees (crown dieback of 76 to 99 percent) 

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

Tree height - As trees approach maximum height, growth rates decrease. Thus 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 more than 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. The 

growth rate is maintained at one-half growth until the tree is 125 percent of maximum height, 

when the growth rate is then reduced to 0.

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

for each year. Height, crown height, and crown width are calculated using species, genus, 

order, and family specific 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, we use an equation for all trees to estimate these parameters. Leaf area is calculated 

from the crown height, tree height, and crown width estimates based on i-Tree methods.7

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

the population. This 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. Thus 

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 projections predict an increase in percent tree cover, the percent crown overlap is held 

constant. However, when 100 percent tree cover is attained, all new canopy added is considered 

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

crown overlap decreases in proportion to the increase in the amount of available greenspace 

(i.e., as tree cover decreases and available greenspace increases—the crown overlap decreases).
54

Tree Mortality Rate

Canopy dieback is the first determinant for tree mortality. Trees with 50 to 75 percent 

crown dieback have an annual mortality rate of 13.1 percent; trees with 76 to 99 percent 

dieback have a 50 percent annual mortality rate, and trees with 100 percent dieback have 

a 100 percent annual mortality rate.70 Trees with less than 50 percent dieback have a userdefined 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 41to 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 are assigned based on measured tree 

mortality by diameter class (Fig. 39).70 The same distribution of mortality by diameter class 

was used for all tree size classes, but the diameter range of the classes differed by size class. 

The 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 the user. That 

is, the relative curve of mortality stayed the same among diameter classes, but the actual 

values would change based on the user-defined overall average rate.

Small

Medium

Large

(A)

(B)

Figure 39.—A) Mortality rate 

distribution by diameter class 

(d.b.h.) with d.b.h. range 

classified by the percent 

of maximum d.b.h. for the 

species; and B) for actual d.b.h. 

classes for small, medium, and 

large tree species.
55

Tree Establishment

Based on the desired level of tree cover and the number of years desired to reach that canopy 

goal, the model calculates the number of trees needing to be established annually to reach that 

goal given the model growth and mortality rate. In simulating the addition of 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 given 

a starting stem diameter of 1 inch.

Model Scenarios

Numerous model scenarios were run for Douglas County. 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 with land use tree cover based on field data estimates.

For cases seeking to maintain current tree cover, two additional scenarios were run taking into 

account:

1) Ash mortality: the entire ash population dies in 10 years. The mortality rate is 10 percent of 

the initial population per year.

2) Black walnut mortality: the entire black walnut population dies in 10 years. The mortality 

rate is 10 percent of the initial population per year.

Land Use—Agriculture

Current Conditions

Area: 212,935 ac 

Current Canopy Cover: 10.5%

Tree-size Classes (height)

Small Trees – 21.5%

Medium Trees – 14.7%

Large Trees – 63.8%

Scenarios

The 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 canopy cover of 10.5 percent

Number of Trees

no additional mortality ash kill in 10 years black walnut kill in 10 years

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 0 0 0 0 0 0 0 0 0

3 0 0 63,000 0 0 75,000 0 0 73,000

4 7600 134,000 201,000 52,000 151,000 210,000 95,000 170,000 210,000

5 321,000 378,000 340,000 373,000 373,000 347,000 439,000 392,000 346,000
56

Increase agriculture canopy cover by 5 to 15.5 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 225,000 0 15,000

3 667,000 164,000 155,000

4 926,000 411,000 325,000

5 1,440,000 680,000 519,000

Increase agriculture canopy cover by 10 to 20.5 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 1,080,000 157,000 78,000

3 1,450,000 416,000 261,000

4 1,910,000 716,000 468,000

5 2,500,000 1,000,000 689,000

Increase agriculture canopy cover by 20 to 30.5 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 2,930,000 590,000 235,000

3 3,480,000 930,000 483,000

4 4,110,000 1,310,000 774,000

5 4,720,000 1,760,000 1,133,000

Land Use—Commercial

Current Conditions

Area: 4,347 ac 

Current Canopy Cover: 7.1%

Tree Size Classes (height)

Small Trees – 20.0%

Medium Trees – 20.0%

Large Trees – 60.0%

Scenarios

The 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 canopy cover of 7.1 percent 

(Note: there were no ash or walnut trees in this land use category)

Number of Trees

No Additional Mortality

Mortality (%) 10 years 25 years 50 years

2 0 0 600

3 1,200 1,500 2,000

4 4,800 3,600 3,300

5 8,500 5,700 4,900
57

Increase commercial canopy cover by 5 to 12.1 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 11,000 3,000 1,800

3 16,000 5,300 3,400

4 20,000 8,100 5,300

5 26,000 12,000 8,000

Increase commercial canopy cover by 10 to 17.1 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 27,000 6,300 3,000

3 33,000 9,300 5,200

4 42,000 14,000 7,800

5 47,000 17,000 11,300

Increase commercial canopy cover by 20 to 27.1 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 59,000 14,000 5,900

3 70,000 19,400 9,200

4 82,000 24,000 13,000

5 87,000 33,000 19,000

Land Use—Park/Open

Current Conditions

Area: 28,489 ac 

Current Canopy Cover: 10.6%

Tree Size Classes (height)

Small Trees – 0.0%

Medium Trees – 7.1%

Large Trees – 92.9%

Scenarios

The 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 park/open canopy cover of 10.6 percent (Note: there were no 

ash trees in this land use category)

Number of Trees

no additional mortality black walnut kill in 10 years

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 0 0 7,800 292,000 69,000 32,000

3 3,900 24,000 34,000 344,000 99,000 54,000

4 74,000 62,000 60,000 394,000 134,000 82,000

5 112,000 99,000 90,000 443,000 180,000 120,000
58

Increase park/open canopy cover by 5 to 15.6 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 73,000 19,000 19,000

3 135,000 64,000 48,000

4 209,000 109,000 84,000

5 176,000 159,000 129,000

Increase park/open canopy cover by 10 to 20.6 percent 

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 195,000 50,000 29,000

3 269,000 96,000 65,000

4 341,000 152,000 109,000

5 412,000 215,000 161,000

Increase park/open canopy cover by 20 to 30.6 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 429,000 117,000 55,000

3 513,000 179,000 102,000

4 640,000 256,000 163,000

5 711,000 337,000 243,000

Land Use—Residential

Current Conditions

Area: 32,092 ac 

Current Canopy Cover: 31.4%

Tree Size Classes (height)

Small Trees – 9.3%

Medium Trees – 16.3%

Large Trees – 74.4%

Scenarios

The 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

Number of Trees

no additional mortality ash kill in 10 years black walnut kill in 10 years

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 0 0 8,800 80,000 14,000 12,000 0 4,000 10,000

3 78,000 48,000 37,000 152,000 61,000 39,000 95,000 52,000 38,000

4 153,000 96,000 66,000 237,000 105,000 68,000 178,000 98,000 68,000

5 265,000 152,000 101,000 325,000 159,000 103,000 264,000 154,000 104,000
59

Increase residential canopy cover by 5 to 36.4 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 95,000 32,000 20,000

3 194,000 82,000 50,000

4 287,000 134,000 84,000

5 398,000 194,000 125,000

Increase residential canopy cover by 10 to 41.4 percent 

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 225,000 58,000 30,000

3 336,000 117,000 67,000

4 431,000 180,000 109,000

5 559,000 248,000 155,000

Increase residential canopy cover by 20 to 51.4 percent 

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 490,000 128,000 56,000

3 620,000 203,000 105,000

4 780,000 283,000 162,000

5 890,000 374,000 232,000

Land Use—Vacant

Current Conditions

Area: 9,244 ac 

Current Canopy Cover: 81.9%

Tree Size Classes (height)

Small Trees – 13.3%

Medium Trees – 26.5%

Large Trees – 60.2%

Scenarios

The 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 vacant canopy cover of 81.9 percent

Number of Trees

no additional mortality ash kill in 10 years black walnut kill in 10 years

Mortality (%) 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs 10 yrs 25 yrs 50 yrs

2 0 0 0 0 0 0 0 0 0

3 0 4,800 39,000 9,100 16,000 42,000 108,000 33,000 47,000

4 57,000 75,000 86,000 109,000 89,000 101,000 203,000 105,000 96,000

5 170,000 150,000 141,000 225,000 167,000 150,000 326,000 185,000 153,000
60

Increase vacant canopy cover by 5 to 86.9 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 0 0 0

3 9,700 34,000 65,000

4 127,000 129,000 129,000

5 245,000 215,000 212,000

Increase vacant canopy cover by 10 to 91.9 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 0 0 0

3 73,000 60,000 85,000

4 215,000 174,000 176,000

5 358,000 292,000 278,000

Increase vacant canopy cover by 20 to 100.0 percent

Number of Trees

Mortality (%) 10 years 25 years 50 years

2 60,000 0 18,000

3 193,000 121,000 131,000

4 353,000 260,000 262,000

5 509,000 415,000 410,000

A “0” in several columns of these tables illustrate that at low mortality rates, the projected tree 

growth more than compensates for the loss of tree cover due to mortality. This 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 percent as no trees would die 

and existing trees would continue to grow.

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 influence these rates.
61

Appendix XI. i-Tree Hydro Model Methods and

Calibration Graphs

Data and Model Calibration

The hourly weather data were derived from a local weather station in Lawrence, KS (WBAN: 

724508). Tree and impervious cover parameters (Table 20) were derived for the watershed from 

photo-interpretation of Google Earth imagery (2013) using 301 randomly located points.

Table 20.—Cover estimates for the Wakarusa River watershed, 2013

Percent Cover

Area Impervious Tree Grass/shrub Bare Soil

Wakarusa River Watershed 7.3% 25.2% 66.8% 0.7%

The model was calibrated using hourly stream flow data collected at the “Wakarusa River near 

Lawrence, KS” gauging station (USGS 06891500) from Jan. 1, 2005 to Jan. 1, 2006. Model results 

were calibrated against measured stream flow to yield the best fit between modeled and measured 

stream flow results. Calibration coefficients (-1 to +1 with +1.0 = perfect fit) were calculated for peak 

flow, base flow, and balance flow (peak and base) (Table 21). A coefficient of +1 indicates a perfect fit, 

0 indicates the model predicts the same as using the mean value, and negative values indicate using 

the mean is a better predictor than the model.71 Differences between measured and estimated flow can 

be substantially different, particularly for peak flows, due to mismatching of stream flow 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 field studies; the percent 

of impervious cover directly connected to the stream varied with percent impervious (2 percent 

connected at 7.3 percent impervious). The percentage of directly connected impervious cover 

represents the portion of impervious cover that drains directly to the modeled stream or any of its 

tributaries. The phrase “drains directly” describes a situation where precipitation that falls on a portion 

of the watersheds impervious cover is conveyed, overland or through a storm sewer network, directly 

into the stream or its tributaries.

Table 21.—Calibration coefficients for model estimates and gauging station data

Calibration Coefficients

Watershed Peak Flow Base Flow Balanced Flow

Wakarusa River -0.52 0.25 0.20

Model calibration procedures adjust several model parameters (mostly related to soils) to find the 

best fit between the observed flow and the model flow 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 differing amounts of 

precipitation than observed at the measurement station. These differences in precipitation can lead 

to poorer fits between the observed and predicted estimates of flow as the precipitation is a main 

driver of the stream flow. As can be seen in Figure 40, the observed and simulated results can diverge, 

which is often an artifact of the precipitation data. For example, observed flow will rise sharply, but 
62

predicted flow does not, which is an indication of rain in the watershed, but not at the precipitation 

measurement station. Conversely, the simulated flow may rise, but the observed flow does not, which 

is an indication of rain at the precipitation station, but not in the watershed.

As the flow of the Wakarusa watershed at the gauging station is largely controlled by the dam at 

Clinton Lake, which led to relatively low model calibration, the effects illustrated by the model are 

likely more representative of flow changes in the river above the dam, not at the gauging station (i.e., 

the flow below the dam is based on water storage and release from the dam).

Since the model simulations are comparisons between the base simulation flows and another 

simulated flow with changed surface cover (e.g., increase or decrease in tree cover), both model runs 

are using the same simulation parameters. This means that the effects of changes in cover types are 

comparable, but may not exactly match the flow of the stream. Stated in another way, the estimates of 

the changes in flow are reasonable (e.g., the relative amount of increase or decrease in flow is sound as 

both are using the same model parameters and precipitation data), but the absolute estimate of flow 

may be incorrect. Thus the model results can be used to assess the relative differences in flow due to 

changes in cover parameters, but should not be used to predict the actual effects on stream flow due 

to precipitation and calibration imperfections. The model can be used to compare the changes in flow 

(e.g., increased tree cover leads to an X% change in stream flow), but will likely not exactly match 

the flow observed in the stream. The model is more diagnostic of cover change effects than predictive 

of actual stream flow due to imperfections of models and data used in the model. Due in part to the 

dam, the model had a difficult time matching the flow, but did a reasonable job in simulating flow 

(Fig. 41).

Model Scenarios

After calibration, the model was used under various conditions to determine stream flow 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 fill grass and shrub covered areas first, followed by bare 

Figure 40.—Comparison of simulated model flow (black) vs. observed flow (red) in Wakarusa River watershed.
63

soil spaces next, and then finally impervious covered land. At 100 percent tree cover, all impervious 

land is covered by trees. This 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 with impervious cover varying between 

0 and 100 percent. Increasing impervious cover was assumed to fill grass and shrub covered areas first, 

followed by bare soil spaces, and then under tree canopies. The assumption 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 directly connected 

to the stream, following equations by Sutherland72, such that at 100 percent impervious cover, all 

impervious cover is connected to the stream. Reductions in impervious cover were assumed to be filled 

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 flow-proportional average concentration of a given parameter 

during a storm event and is defined as the total constituent mass divided by the total runoff volume. 

EMC estimates are usually obtained from a flow-weighted composite of concentration samples taken 

during a storm. Mathematically73,74:

EMC C M

V

C t Q t dt

Q t dt

C t Q t t

Q t t = = = ≈ ∫

∫

∑

∑

() ()

( )

() ()

( )

∆

∆ (1)

where C t( ) and Q t( ) are the time-variable concentration and flow measured during the runoff event, 

and M and V are pollutant mass and runoff volume as defined in Equation 1. It is clear that the EMC 

results from a flow-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).

Figure 41.—Comparison observed vs. simulated flow, Wakarusa River watershed.

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50

Predicted flow (million ft

3/hr)

Observed Flow (million ft3/hr)
64

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

L = = EMC Q* * EMC dr * A (2)

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, ...), dr 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.

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

receiving water is provided. The instantaneous concentration during a storm can be higher or lower 

than the EMC, but the use of the EMC as an event characterization replaces the actual time variation 

of C versus t in a storm with a pulse of constant concentration having equal mass and duration as the 

actual event. This process ensures that mass loadings from storms will be correctly represented. EMCs 

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

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

the most useful means for quantifying the level of pollution resulting from a runoff event.75 Figure 42 

illustrates the inter-storm variation of pollutographs and EMC.

Since collecting the data necessary for calculating site-specific EMCs can be cost prohibitive, 

researchers or regulators will often use values that are already available in the literature. If site-specific 

numbers are not available, regional or national averages can be used, although the accuracy of using 

these numbers is questionable. Due to the specific climatological and physiographic characteristics 

of individual watersheds, agricultural and urban land uses can exhibit a wide range of variability in 

nutrient export.76

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). The U.S. Environmental Protection Agency developed the NURP to expand the state 

knowledge of urban runoff pollution by applying research projects and instituting data collection in 

selected urban areas throughout the country.

In 1983, the U.S. Environmental Protection Agency77 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.

Figure 42.—Inter-storm variation of pollutographs and event mean concentrations (EMC).
65

Subsequently, the U.S. Geological Survey (USGS) created another urban stormwater runoff base78, 

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 collect urban runoff 

quality data as part of the application requirements for stormwater discharge permits under the 

National Pollutant Discharge Elimination System (NPDES). The NPDES data are from over 30 cities 

and more than 800 station-storms for more than 150 paramaters.72

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

EMC population means and medians for the 10 pollutants with many more degrees of freedom than 

were available to the NURP investigators.79 A “pooled” mean was calculated representing the mean 

of the total population of sample data. The NURP and pooled mean EMCs for the 10 pollutants or 

constituents are listed in Table 22.79 NURP or pooled mean EMCs were selected because they are 

based on field data collected from thousands of storm events. However, these estimates are based on 

nationwide data, so they do not account for regional variation in soil types, climate, and other factors.

Table 22.—National pooled EMCs and NURP EMCs

EMCs (mg/l)

Pollutant/Constituent (Abbrevation) Data Source 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

0.0135

0.0666

0.0111

0.0548

1657

849

Lead (Pb) Pooled

NURP

0.0675

0.175

0.0507

0.131

2713

1579

Zinc (Zn) Pooled

NURP

0.162

0.176

0.129

0.140

2234

1281

Note:

(1) No BOD5 data available in the USGS dataset - pooled data includes NURP+NPDES

(2) No TSP data available in NPDES dataset - pooled data 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 flow, not the base flow values, to estimate 

effects 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 affect these values. However, across the entire 

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

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

derived coefficients. It is not known how well the national EMC values represent local conditions.
66

References

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learning_center/manuals_workbooks.shtm

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Station: 83-94.
67

10 Baldocchi, D. 1988. A multi-layer model for estimating sulfur dioxide deposition to a 

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11 Baldocchi, D.D.; Hicks, B.B.; Camara, P. 1987. A canopy stomatal resistance model for 

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12 Bidwell, R.G.S.; Fraser, D.E. 1972. Carbon monoxide uptake and metabolism by leaves. 

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13 Lovett, G.M. 1994. Atmospheric deposition of nutrients and pollutants in North 

America: an ecological perspective. Ecological Applications. 4: 629-650.

14 Nowak, D.J., Hirabayshi, S., Bodine, A., Hoehn, R. 2013. Modeled PM2.5 removal by 

trees in ten U.S. cities. Environmental Pollution. 178: 395-402.

15 Zinke, P.J. 1967. Forest interception studies in the United States. In: Sopper, W.E.; Lull, 

H.W., eds. Forest hydrology. Oxford, UK: Pergamon Press: 137-161.

16 McPherson, E.G.; Simpson, J.R. 1999. Carbon dioxide reduction through urban 

forestry: guidelines for professional and volunteer tree planters. Gen. Tech. Rep. 

PSW-171. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 237 p.

17 Nowak, D.J.; Crane, D.E.; Dwyer, J.F. 2002. Compensatory value of urban trees in the 

United States. Journal of Arboriculture. 28(4): 194-199.

18 Nowak, D.J.; Crane, D.E.; Stevens, J.C.; Ibarra, M. 2002. Brooklyn’s urban forest. Gen. 

Tech. Rep. NE-290. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Research Station. 107 p.

19 Based on photo-interpretation of Google Earth imagery (image dates circa 2012) of 500 

random points across the region. Standard error of estimate is 1.9 percent for Douglas 

County. Tree cover was estimated using field plot estimates of tree (14.5%) and shrub 

(7.7%) cover. Plot cover estimates were pro-rated to equal photo-interpretation estimates of 

combined tree and shrub cover.

20 U.S. Department of Agriculture, National Invasive Species Information Center. 2011. 

Plants. Beltsville, MD: U.S. Department of Agriculture, National Agriculture Library. 

http://www.invasivespeciesinfo.gov/plants/main.shtml

21 State list of invasive plant species was obtained from:

Kansas Native Plant Society. 2006. Invasive plant fact sheet. Lawrence, KS: University 

of Kansas, R.L. McGregor Herbarium. 1 p. http://www.kansasnativeplantsociety.org/

documents/InvasivePlantFactSheet.pdf
68

22 Nowak D.J.; Dwyer, J.F. 2000. Understanding the benefits and costs of urban forest 

ecosystems. In: Kuser, John E., ed. Handbook of urban and community forestry in the 

northeast. New York, NY: Kluwer Academics/Plenum: 11-22.

23 Murray, F.J.; Marsh L.; Bradford, P.A. 1994. New York state energy plan, vol. II: issue 

reports. Albany, NY: New York State Energy Office. 

Ottinger, R.L.; Wooley D.R.; Robinson N.A.; Hodas D.R.; Babb S.E.; Buchanan S.C.; 

Chernick P.L.; Caverhill E; Krupnick A.; Fritsche U. 1990. Environmental costs of 

electricity. White Plains, NY: Oceana Publications. 769 p. 

These values were updated to 2010 dollars based on the producer price index from U.S 

Department of Labor, Bureau of Labor Statistics n.d. www.bls.gov/ppi 

24 The Capital District Regional Commission. 2008. Consumer and producer price indices.

www.cdrpc.org/CPI_PPI.html

25 U.S. Environmental Protection Agency. 2012. Environmental benefits mapping and 

analysis program (BenMAP). Washington, DC: U.S. Environmental Protection Agency. 

http://www.epa.gov/air/benmap/. (Accessed May 24, 2012).

26 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.

27 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.

28 Carbon value estimated at $78.5/metric ton of carbon based on:

Interagency Working Group on Social Cost of Carbon, United States Government. 2010. 

Technical support document: social cost of carbon for regulatory impact analysis under 

Executive Order 12866. http://www.epa.gov/oms/climate/regulations/scc-tsd.pdf

29 Nowak, D.J.; Stevens, J.C.; Sisinni, S.M.; Luley, C.J. 2002. Effects of urban tree 

management and species selection on atmospheric carbon dioxide. Journal of 

Arboriculture. 28(3): 113-122.

30 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/
69

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.

31 Insect/disease proximity to study area was completed using the U.S. Forest Service’s Forest 

Health Technology Enterprise Team (FHTET) database. Data includes distribution of pest 

by county FIPs code for 2004-2009. FHTET range maps are available at www.foresthealth.

info for 2006-2010. 

FHTET pest range data was supplemented by: 

For DED – Eastern Forest Environmental Threat Assessment Center. Dutch Elm Disease. 

http://threatsummary.forestthreats.org/threats/threatSummaryViewer.cfm?threatID=43 

For CB – Worrall, J.J. Chestnut Blight. Forest and Shade Tree Pathology. 

http://www.forestpathology.org/dis_chestnut.html

32 U.S. Forest Service. Tree guides [a series of publications]. Albany, CA: U.S. Department of 

Agriculture, Forest Service, Pacific Southwest Research Station. http://www.fs.fed.us/psw/

programs/uesd/uep/tree_guides.php

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Xiao, Q. 1999. Tree guidelines for San 

Joaquin Valley Communities. Sacramento, CA: Local Government Commission. 64 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Scott, K.I.; Xiao, Q. 2000. Tree guidelines 

for Coastal Southern California Communities. Sacramento, CA: Local Government 

Commission. 98 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Xiao, Q.; Pittenger, D.R.; Hodel, D.R. 2001. 

Tree guidelines for Inland Empire Communities. Sacramento, CA: Local Government 

Commission. 116 p. 

McPherson, E.G.; Maco, S.E.; Simpson, J.R.; Peper, P.J.; Xiao, Q.; VanDerZanden, A.M.; 

Bell, N. 2002. Western Washington and Oregon community tree guide: benefits, costs, 

and strategic planting. Silverton, OR: International Society of Arboriculture, Pacific 

Northwest Chapter. 76 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Xiao, Q.; Maco, S.E.; Hoefer, P.J. 2003. 

Northern mountain and prairie community tree guide: benefits, costs, and strategic 

planting. Davis, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station, Center for Urban Forest Research. 88 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, S.E.; Xiao Q.; Mulrean, E. 2004. 

Desert southwest community tree guide: benefits, costs, and strategic planting. Phoenix, 

AZ: Arizona State Land Department Natural Resources Division, Urban & Community 

Forestry Section & Arizona Community Tree Council, Inc. 81 p. 
70

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Maco, S.E.; Xiao, 

Q. 2006. Coastal plain community tree guide: benefits, costs, and strategic planting.

PSW-GTR-201. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 105 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, S.E.; Gardner, S.L.; Vargas, K.E.; Xiao, 

Q. 2006. Piedmont community tree guide: benefits, costs, and strategic planting. PSWGTR 200. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 99 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, S.E.; Gardner, S.L.; Cozad, S.K.; Xiao, 

Q. 2006. Midwest community tree guide: benefits, costs, and strategic planting. PSWGTR-199. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 99 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Xiao, Q. 2007. 

Northeast community tree guide: benefits, costs, and strategic planting. Gen. Tech. 

Rep. PSW-GTR-202. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 106 p. 

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Crowell, A.M.N.; Xiao, Q. 2010. Northern 

California coast community tree guide: benefits, costs, and strategic planting. Gen. 

Tech. Rep. PSW-GTR-228. Albany, CA: U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station. 118 p. 

Peper, P.J.; McPherson, E.G.; Simpson, J.R.; Vargas, K.E.; Xiao Q. 2009. Lower Midwest 

community tree guide: benefits, costs, and strategic planting. Gen. Tech. Rep. PSWGTR-219. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 115 p. 

Peper, P.J.; McPherson, E.G.; Simpson, J.R.; Albers, S.N.; Xiao, Q. 2010. Central Florida 

community tree guide: benefits, costs, and strategic planting. Gen. Tech. Rep. PSWGTR-230. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 118 p. 

Vargas K.E.; McPherson E.G.; Simpson J.R.; Peper P.J.; Gardner S.L.; Xiao Q. 2007. 

Temperate interior west community tree guide: benefits, costs, and strategic planting.

Gen. Tech. Rep. PSW-GTR-206. Albany, CA: U.S. Department of Agriculture, Forest 

Service, Pacific Southwest Research Station. 108 p. 

Vargas K.E.; McPherson E.G.; Simpson J.R.; Peper P.J.; Gardner S.L.; Xiao Q. 2007. 

Interior west community tree guide: benefits, costs, and strategic planting. Gen. Tech. 

Rep. PSW-GTR-205. Davis, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 105 p. 
71

Vargas, K.E.; McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Xiao Q. 2008. 

Tropical community tree guide: benefits, costs, and strategic planting. Gen. Tech. 

Rep. PSW-GTR-216. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 109 p.

33 U.S. Census Bureau. 2010. Census summary file 1, QT-P2 - single years of age and sex: 

2010. Washington, DC: U.S. Department of Commerce, Census Bureau. http://factfinder2.

census.gov (Accessed February 2013)

34 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.

35 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 refinement and transportation included (Graham, R.L.; 

Wright, L.L.; Turhollow, A.F. 1992. The potential for short-rotation woody crops to 

reduce U.S. CO2 emissions. Climatic Change. 22: 223-238.)

36 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. 2000. U.S. power plant emissions total by year.

www.epa.gov/cleanenergy/egrid/samples.htm. 

CO emission per kWh assumes one-third of 1 percent of C emissions is CO based on: 
72

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 fine 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.

37 National Land Cover Data are available at: www.epa.gov/mrlc/nlcd-2001.html 

38 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 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 
73

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).

39 Kruse, J.; Ambourn, A.; Zogas, K. 2007. Aspen leaf miner. Forest Health Protection leaflet. 

R10-PR-14. Juneau, AK: U. S. Department of Agriculture, Forest Service, Alaska Region. 

http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_038064.pdf (Accessed 

April 2012).

40 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 (Accessed April 2012). 

Natural Resources Canada. 2011. Trees, insects, and diseases of Canada’s forests - Asian 

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https://tidcf.nrcan.gc.ca/insects/factsheet/1000095 (Accessed April 2012).

41 Houston, D.R.; O’Brien, J.T. 1983. Beech bark disease. Forest Insect & Disease Leaflet 

75. Washington, DC: U. S. Department of Agriculture, Forest Service. 8 p. http://www.

fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_043310.pdf

42 Ostry, M.E.; Mielke, M.E.; Anderson, R.L. 1996. How to identify butternut canker and 

manage butternut trees. St. Paul, MN: U.S. Department of Agriculture, Forest Service, 

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ht_but/ht_but.htm

43 Diller, J.D. 1965. Chestnut blight. Forest Pest Leaflet 94. U.S. Department of Agriculture, 

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44 Mielke, M.E.; Daughtrey, M.L. How to identify and control dogwood anthracnose. NAGR-18. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Area 

State and Private Forestry. http://na.fs.fed.us/spfo/pubs/howtos/ht_dogwd/ht_dog.htm

45 The Plant Disease Diagnostic Clinic, Cornell University. 2011. Dutch elm disease. Ithaca, 

NY: Cornell University. http://plantclinic.cornell.edu/factsheets/dutchelmdisease.pdf

46 Schmitz, R.F.; Gibson, K.E. 1996. Douglas-fir beetle. Forest Insect & Disease Leaflet 5. 

R1-96-87. Washington, DC: U.S. Department of Agriculture, Forest Service. 8 p. http://

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47 Michigan State University. 2013. Emerald ash borer. East Lansing, MI: Michigan State 

University [and others]. http://www.emeraldashborer.info/
74

48 Ferrell, G.T. 1986. Fir engraver. Forest Insect & Disease Leaflet 13. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 8 p. http://www.fs.usda.gov/Internet/FSE_

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49 Phelps, W.R.; Czabator, F.L. 1978. Fusiform rust of southern pines. Forest Insect & 

Disease Leaflet 26. Washington, DC: U.S. Department of Agriculture, Forest Service. 7 p. 

http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_043078.pdf

50 Society of American Foresters. 2011. Gold spotted oak borer hitches ride in firewood, 

kills California oaks. Forestry Source. 16(10). 

51 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/ (Accessed April 2012).

52 U.S. Forest Service. 2005. Hemlock woolly adelgid. Pest Alert NA-PR-09-05. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Area State and 

Private Forestry. http://na.fs.fed.us/spfo/pubs/pest_al/hemlock/hwa05.htm

53 Smith, S.L.; Borys, R.R.; Shea, P.J. 2009. Jeffrey pine beetle. Forest Insect & Disease 

Leaflet 11. Washington, DC: U.S. Department of Agriculture, Forest Service. 8 p. http://

www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_043542.pdf

54 Ciesla, W.M.; Kruse, J.J. 2009. Large aspen tortrix. Forest Insect & Disease Leaflet 139. 

Washington, DC: U.S. Department of Agriculture, Forest Service. 8 p. http://www.fs.usda.

gov/Internet/FSE_DOCUMENTS/fsbdev2_042981.pdf

55 U.S. Forest Service, Forest Health Protection. 2011. Laurel wilt. Atlanta, GA: U.S. 

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56 Gibson, K.; Kegley, S.; Bentz, B. 2009. Mountain pine beetle. Forest Insect & Disease 

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57 Burnside, R.E. et al. 2011. Northern Spruce Engraver. Forest Insect & Disease Leaflet 

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58 Rexrode, C.O.; Brown, H.D. 1983. Oak wilt. Forest Insect & Disease Leaflet 29. 

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59 Liebhold, A. 2010 . Personal communication on the geographical distribution of forest pest 

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75

60 Ciesla, W.M. 2001. Tomicus piniperda. Washington, DC: North American Forest 

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61 Holsten, E.H.; Thier, R.W.; Munson, A.S.; Gibson, K.E. 1999. The spruce beetle. Forest 

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62 Kucera, D.R.; Orr, P.W. 1981. Spruce budworm in the eastern United States. Forest Pest 

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63 Kliejunas, John. 2005. Phytophthora ramorum. Washington, DC: North American Forest 

Commission, Exotic Forest Pest Information System for North America (ExFor). http://

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64 Clarke, Stephen R.; Nowak, J.T. 2009. Southern pine beetle. Forest Insect & Disease 

Leaflet 49. Washington, DC: U.S. Department of Agriculture, Forest Service. 8 p. http://

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65 Haugen, D.A.; Hoebeke, R.E. 2005. Sirex woodwasp – Sirex noctilio F. (Hymenoptera: 

Siricidae). Pest Alert NA-PR-07-05. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Area State and Private Forestry. http://na.fs.fed.us/

spfo/pubs/pest_al/sirex_woodwasp/sirex_woodwasp.htm

66 Seybold, S.; Haugen, D.; Graves, A. 2010. Thousand cankers disease. Pest Alert NAPR-02-10. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Area State and Private Forestry. http://na.fs.fed.us/pubs/palerts/cankers_

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Printed on Recycled Paper

Nowak, David J.; Bodine, Allison R.; Hoehn, Robert E., III; Ellis, Alexis; Bomberger, Kim; 

Crane, Daniel E.; Endreny, Theodore A.; Taggert, Thomas; Stephan, Emily. 2014. 

Assessing urban forest effects and values: Douglas County, Kansas. Resour. Bull. 

NRS-91. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 76 p.

An analysis of trees in Douglas County, Kansas, reveals that this area has about 

14,164,000 trees with tree and shrub canopy that covers 25.2 percent of the county. The 

most common tree species are American elm, northern hackberry, eastern redcedar, 

Osage-orange, and honeylocust. Trees in Douglas County currently store about 1.7 million 

tons of carbon (6.4 million tons CO2) valued at $124 million. In addition, these trees remove 

about 82,000 tons of carbon per year (300,000 tons CO2 per year valued at $5.8 million 

per year) and about 3,870 tons of air pollution per year ($17.7 million per year). Douglas 

County’s trees are estimated to reduce annual residential energy costs by $2.9 million per 

year. The compensatory value of the trees is estimated at $6.2 billion. Loss of the current 

tree cover in the Wakarusa River watershed in Douglas County would increase annual flow 

by an average of 2.6 percent (88.9 million ft3

). Information on the structure and functions 

of the regional forest can be used to inform forest management programs and to integrate 

regional forests within plans to improve environmental quality in Douglas County.

KEY WORDS: urban forestry; ecosystem services; air pollution removal; carbon 

sequestration; tree value

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on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, 

familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, 

or because all or part of an individual’s income is derived from any public assistance program. (Not 

all prohibited bases apply to all programs.) Persons with disabilities who require alternate means for 

communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s 

TARGET Center at (202)720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call 

(800)795-3272 (voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer. 
Northern Research Station

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