United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-921

January

2016

Urban Forest Restoration 

Cost Modeling: A Seattle 

Natural Areas Case Study

Jean M. Daniels, Weston Brinkley, and Michael D. Paruszkiewicz
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Authors

Jean M. Daniels is a research forester, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW 

Main St., Suite 400, Portland, OR 97205; Weston Brinkley was the research and 

development program manager with Forterra, 901 5th Ave., Suite 2200, Seattle, WA 

98164. Brinkley is currently principal and owner, Street Sounds Ecology, LLC, Seattle, WA; Michael D. Paruszkiewicz is an economist, Northwest Economic Research 

Center, Portland State University, Post Office Box 751, Portland, OR 97207.

Cover photographs: clockwise from top left—Forterra, EarthCorps Seattle, and Green 

Seattle Partnership.
Abstract

Daniels, Jean M.; Brinkley, Weston; Paruszkiewicz, Michael D. 2015. Urban 

forest restoration cost modeling: a Seattle natural areas case study. Gen. Tech. 

Rep. PNW-GTR-921. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 28 p.

Cities have become more committed to ecological restoration and management 

activities in urban natural areas. Data about costs are needed for better planning 

and reporting. The objective of this study is to estimate the costs for restoration 

activities in urban parks and green space in Seattle, Washington. Stewardship activity data were generated from a new database designed to track restoration efforts 

of the Green Seattle Partnership, an urban forest restoration group based in Seattle, 

Washington. Cost data were estimated and combined with activity data to arrive at 

per-acre costs for invasive removal, planting, monitoring, and trail work performed 

at restoration events between January 1, 2011, and June 30, 2013. Events where 

labor was performed by paid contractors were consistently the most costly, but the 

most productive in terms of acres treated per event. Cost modeling suggests that all 

else equal, event leader type and steep slopes contribute to greater costs, but costs 

decline with greater site size and phase of restoration. Limitations of the analysis as 

well as suggestions for improving restoration and cost data quality are discussed.

Keywords: Urban forestry, restoration costs, natural areas, Seattle, cost modeling.
Contents

1 Introduction

2 Literature Review

4 Data and Methods

4 Tree-iage Prioritization System

5 Phases of Restoration

6 Event Leader Types

6 Physical Site Characteristics

6 Restoration Data 

7 Tree-iage and Site Data

8 Estimating Restoration Costs

8 Labor Costs

8 Staff Costs

9 Plant Costs

9 Materials Costs

9 Tool Costs

10 Preparation Costs

11 The Economic Model

13 General Results

14 Restoration Costs

16 Seattle Results

18 Model Results

20 Interpreting the Model

20 Discussion

20 General Findings

21 Seattle Area Findings

22 Performance of the Model

23 Looking Ahead

24 Acknowledgments

24 Metric Equivalents

24 References
1

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Introduction

Knowing the cost of restoring and maintaining urban forests and green space is 

essential for long-term planning and lasting conservation results. Forest restoration is a multimillion dollar annual activity across the Puget Sound region. Yet the 

costs of forest restoration in urban areas are not well understood, as the management and ecology of these areas are considerably different from traditional forest 

management. Additionally, urban forest restoration is completed by county and city 

governments as well as dozens of community groups and nonprofit organizations 

working as volunteers or on small budgets with little margin for cost overruns or 

unexpected expenses.

As restoration and management activities in urban natural areas have gained 

more support, the need to gather and provide treatment data to accurately report 

and predict costs has also increased. Holl and Howarth (2000) described issues 

surrounding funding restoration projects and identified the lack of cost reporting by 

restoration practitioners as a barrier. Cost data for specific projects are difficult to 

obtain and rarely reported in the literature. Without an adequate understanding of 

restoration costs, how can practitioners plan and budget for restoration activities? 

The city of Seattle defines urban forested natural areas as sections of urban 

parks, green belts, and other green spaces that contain a forested canopy and understory vegetation. These areas are particularly susceptible to degradation owing 

to their isolation from other natural areas and seed sources, and their proximity 

to human impacts. Of particular concern is the pervasiveness of invasive species 

and their displacement of native plants. Forest restoration is typically the physical 

activity required to manage these lands, including removal of invasive species and 

planting and maintenance of native plants. 

The objective of this study is to estimate the costs for forest restoration activities in urban parks and green spaces in the Seattle area, and develop methodology 

that may be applied to urban forest restoration in the Pacific Northwest more 

broadly. The analysis combines a dataset of stewardship activities from 2.5 years of 

restoration events in Seattle with estimated hourly activity costs. Restoration activity data were generated from a new database designed and implemented to track 

restoration efforts of the Green Seattle Partnership (GSP), a partnership between 

Forterra, a regional conservation organization, the city of Seattle, and the stewardship community. 

The city of Seattle manages over 6,200 ac of parks and natural areas within the 

city. Seattle parks are organized into subunits called park sites that have similar characteristics and management needs. Most parks contain multiple sites; for the purposes 

of this study, we considered the park site as the unit of analysis of restoration work. 

Without an adequate 

understanding of 

restoration costs, 

how can practitioners 

plan and budget for 

restoration activities? 
2

GENERAL TECHNICAL REPORT PNW-GTR-921

Forest restoration work on park sites takes place during restoration events and could 

be conducted by paid city staff, paid contractors, or volunteers. Additionally, for some 

events, both volunteers and paid professionals work together. Costs associated with 

each of these event leaders differ, and all were considered in this analysis.

Forterra, the city, and other restoration partners would like to estimate the cost 

per acre to treat sites based on an initial site condition rating, restoration activity, 

and site characteristics. This link between restoration costs and the restoration 

activities occurring in city park sites has yet to be established. The hope is that 

combining costs with restoration accomplishments will lead to the development 

of a model to estimate the time and budget necessary to restore parks around the 

city. In addition, this information could increase the effectiveness and efficiency of 

restoration efforts, as it supports forecasting, realistic budget goals, and effective 

resource allocation, while providing valuable cost information to share among the 

urban restoration community. Steps involved include estimating restoration costs, 

compiling restoration activities and site characteristics, and constructing a cost 

model using common statistical techniques. Initially we looked to the literature for 

guidance on methods.

Literature Review

Little research has been published on the costs of urban ecological restoration in 

terrestrial systems, although the body of work regarding aquatic systems is growing 

(Kenney et al. 2012, Neal 2011). The following describes general approaches of cost 

accounting and modeling, then presents their application to ecological restoration 

efforts to analyze costs and (in a few cases) benefits of those efforts.

Cost accounting has long been important for the planning and design of development projects, whether public sector infrastructure, parks and recreation facilities, 

or commercial and residential developments. The most common approach has been 

historical cost accounting, which are cost estimates based on past cost performance. 

In restoration, this method is commonly seen in bids for proposed projects or impact 

assessments of projected costs (LeBoon 2007, Palmer et al. 2007). Another method, 

cost engineering, is commonly used for planning and management for construction 

projects, particularly for estimating the costs of different project designs, materials 

selection, and labor requirements (Hendrickson 2008). Related efforts have focused 

on developing procedures, guides, and manuals that provide technical resources for 

estimating costs involved in permitting, designing, or constructing stream channel 

and bank stabilization and restoration projects (Pennsylvania Lake Management 

Society 2004, Templeton et al. 2008, U.S. Army Corps of Engineers 2005, Virginia 

Department of Conservation and Recreation 2004). 

Cost accounting has 

long been important 

for the planning 

and design of 

development projects, 

whether public 

sector infrastructure, 

parks and 

recreation facilities, 

or commercial 

and residential 

developments.
3

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Only a few examples of restoration cost estimation and modeling are found in 

the literature. Bullock et al. (2011) described how the Economics of Ecosystems and 

Biodiversity project reviewed over 20,000 restoration case studies in 2011 and found 

only 96 studies providing meaningful costs data. Most represented aquatic ecosystems in landscapes such as the Willamette River basin (Michie 2010) in Oregon, the 

Wind River watershed in Washington state (Bair 2004, Wohl et al. 2005), and the 

Chesapeake Bay basin in Maryland (Center for Watershed Protection 2005). Costs 

analyses have also been conducted for fisheries management conservation plans 

(Snohomish County 2005) and oil spill response and damage costs (Etkin 2004). 

Related research examined the substitutability of wetlands mitigation for natural 

sites (Gutrich and Hitzhusen 2004) and the cost effectiveness of different strategies 

for restoring the vegetative cover and ecological functions of mangrove forests 

using a case study analyzing tradeoffs among actual costs and ecological outcomes 

(Lewis 2001). 

Less information is available for terrestrial systems. Most studies have focused 

on invasive species (e.g., honeysuckle spread, Hartman and McCarthy 2004; tamarisk removal, Zavaleta 2000) and fire (e.g., ponderosa pine forest restoration, Lynch 

2001, 2003; Macmillan et al. 1998). In one analysis, Zentner et al. (2003) estimated 

baseline costs for three types of wetland restoration costs (salt marsh, freshwater 

marsh, and riparian woodlands) and estimated that riparian woodland restoration 

costs for 1 ac totaled about $40,000.

A number of studies recommend full assessment of the benefits of restoration treatments as well as their costs. Stinchfield et al. (2008) discussed this literature as it relates 

to watershed restoration and salmon recovery in the Puget Sound area. Hurd (2009) 

assessed the analysis of costs and benefits in the broader context of the political economy of watershed restoration. Earlier, Zhao and Zilberman (1999) applied real option 

theory and the concepts of irreversibility and uncertainty to model restoration projects 

that may result in negative environmental benefits. Similarly, Ferraro (2004) applied a 

cost-efficiency framework to prioritize restoration treatments in the Chickasawhatchee 

Swamp in Georgia, emphasizing that decisionmakers need to consider costs as well 

as benefits when evaluating projects. Goldstein et al. (2008) also implicitly addressed 

benefits as well as costs with an approach that assessed the cost effectiveness of various 

restoration practices. They developed a general framework based on maximizing 

return-on-investment (ROI) then applied it to assess ROI profiles for potential projects 

to reforest montane pastureland in Hawaii, concluding that their approach could be 

applied to develop policies promoting more cost-effective restoration practices. De 

Groot et al. (2013) found that most restoration investments can achieve a rate of return 

of 10 percent for every dollar spent when benefits are assessed correctly.
4

GENERAL TECHNICAL REPORT PNW-GTR-921

Although reports of urban ecological restoration efforts are readily found in 

the peer-reviewed literature, research focusing on development of cost estimates 

for urban ecological restoration projects is rare (Neal 2004, Purcell et al. 2002). 

Daniels et al. (2014) estimated the cost of utilizing volunteers for restoration 

activities. Miller et al. (2009) applied optimization modeling to assess spatially 

explicit habitat acquisition and restoration strategies for 19 species of butterfly in 

a rapidly urbanizing county near Chicago, Illinois. They identified parcels in the 

immediate area that could be acquired and restored to provide habitat and then 

analyzed the tradeoffs between distance to sites, site acquisition, and restoration 

treatment costs.

In summary, peer-reviewed literature provides some examples of cost models 

applied to restoration activities. However, published research is generally less applicable for projects in urban areas and provides little guidance for model development 

or comparable results. 

Data and Methods

The GSP would like to estimate the cost per acre to treat sites based on the 

following factors: initial site condition, restoration intensity, event leader, and 

physical characteristics. Each factor was believed to affect the cost of restoring 

any one site, and thus was a variable of interest for the cost model for urban 

forest restoration.

Steps involved include compiling restoration costs associated with each 

restoration phase, compiling site characteristics, and constructing a cost-per-acre 

model for all tree-iage categories. Developing a model was the focus of this work 

because of the wide applicability and practicality of models. Results may be generalizable to the greater restoration community, even though the analysis focuses 

on Seattle. Additionally, we undertook this work knowing that data limitations 

could be a challenge. 

Tree-iage Prioritization System

The GSP prioritizes areas for restoration using a rapid assessment method developed by the city of Seattle called “tree-iage” (Green Seattle Partnership 2005). This 

method has its origins in medical emergency situations where patients are treated 

according to severity of injury and the likelihood that intervention will be effective. 

The GSP has used this nine-part classification system for prioritizing restoration 

activities since 1999. Forested parks and open space are assigned a tree-iage classification level based on native tree canopy cover and composition, and degree of 

threat from invasive plants (fig. 1). 

Peer-reviewed 

literature provides 

some examples of 

cost models applied to 

restoration activities. 

However, published 

research is generally 

less applicable for 

projects in urban 

areas and provides 

little guidance for 

model development or 

comparable results.

The GSP prioritizes 

areas for restoration 

using a rapid 

assessment method 

developed by the 

city of Seattle called 

“tree-iage”
5

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Using this system, a site rated with a 1 would require the least effort to restore, 

as tree composition value is high and invasive threat is low. A site rated 9 would 

be the most difficult to restore, as it would likely be overrun with invasive plants 

with little tree canopy. Activities involving volunteers tend to concentrate most on 

planting and invasive plant removal in site classes 4, 5, 7, and 8. Sites with high 

invasive cover are more likely to require the skills of a paid contractor for herbicide 

application, heavy equipment operation, and erosion control, all of which has a 

direct impact on costs. The full cost of restoring a site based on its initial tree-iage 

classification is unknown, but all things equal, one would expect that restoration 

costs would increase with the intensity of the work required.

Phases of Restoration

In addition to the tree-iage system, the GSP has developed a four-phase approach to 

scheduling restoration field work and categorizing activities. These phases are:

• Phase 1: invasive species removal 

• Phase 2: planting of trees, shrubs, or ground cover 

• Phase 3: secondary, or followup, invasive species removal 

• Phase 4: long-term monitoring on restored sites

• Phase 5: trail maintenance

Tree co

m

p

ostion value

Threat

High

>25% native tree cover, 

>50% canopy is evergreen

Medium 

>25% native tree cover, 

<50% canopy is evergreen

Low 

<25% native tree

canopy cover 

Low

Less than 5%

invasive cover

Medium

5% - 50%

invasive cover

High 

More than 5% 

invasive cover 

Figure 1—Tree-iage restoration prioritization categories (Green Seattle Partnership 2006).

The full cost of 

restoring a site based 

on its initial treeiage classification is 

unknown, but all things 

equal, one would 

expect that restoration 

costs would increase 

with the intensity of the 

work required.
6

GENERAL TECHNICAL REPORT PNW-GTR-921

Trail maintenance is not a restoration activity per se, but trail activity data 

are collected so we include them here. We use the term treatment to describe any 

restoration activity, similarly to how it is used to describe wildland restoration. 

Treatments can occur during any of the four phases of forest restoration or trail 

maintenance work. Not all sites require all four phases; site conditions dictate which 

restoration activities are necessary. Although sites are assigned a tree-iage rating, 

budgeting for restoration work is done by phase. We expected that restoration costs 

would decrease as sites moved through the phases of work over time.

Event Leader Types

Green Seattle Partnership events are led by three possible leader types, and the 

leader type was hypothesized to affect treatment costs. Volunteer leaders were 

present at all-volunteer events, which were presumably the least expensive. Mixed 

volunteer-paid staff events, where paid staff supervised volunteers in restoration 

activities, were thought to be the next most expensive. Events using strictly paid 

contractors were likely the most expensive. 

Physical Site Characteristics

Site size and steepness of terrain were hypothesized to affect treatment costs. 

The model variables slope and acres were added to account for these site-specific 

landscape factors. Slope is an important consideration because volunteers were not 

allowed on slopes greater than 50 percent because of safety concerns. Steep sites 

may also be much more costly for professional crews owing to the difficulty of 

working on the terrain. We wanted to test whether to expect a park site’s size (in 

acres) to significantly affect restoration costs. Cases could be made for constant or 

increasing returns to scale for this type of work, meaning that the cost to treat a site 

could remain constant or decline for larger sites. 

Restoration Data 

Forterra collects information for the GSP on restoration events using a proprietary 

database called the Centralized Data Repository (CEDAR). The CEDAR database 

is used by staff, contractors, and volunteers to record information about sponsored 

restoration activities occurring in Seattle parks. Restoration treatment data for 

events occurring between January 1, 2011, and June 30, 2013, were obtained from 

the CEDAR system. Parks in Seattle are divided into multiple sites for planning 

purposes, so we chose the site as the unit of analysis. For every event held, whether 

volunteer or contracted work was used, the date, park name, site name, and lead 

agency were used along with the count and hours of paid personnel and volunteers 

(youth and adult) present. Event duration was known, but only the total number of 

Slope is an important 

consideration because 

volunteers were not 

allowed on slopes 

greater than 50 percent 

because of safety 

concerns.

The CEDAR database 

is used by staff, 

contractors, and 

volunteers to record 

information about 

sponsored restoration 

activities occurring in 

Seattle parks.
7

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

people present at the park for the event was reported. So if the actual restoration 

work occurred across multiple sites in the same park, there was no way to directly 

measure how many hours were spent at each site. In such cases, hours were allocated using a weighted average of restoration activity to apportion total event time 

among sites and phases. The CEDAR data also included restoration work accomplished during each event. Multiple types of restoration could occur during the 

same event, including invasive removal, planting, watering, herbicide application, 

placement of cardboard and other weed blockers, and trail work. These activities 

were organized by phase, and events with multiple phases had hours assigned using 

a similar weighting process to allocate hours to sites. This study is the first to use 

restoration data from CEDAR for research purposes, which presented multiple 

challenges for our cost analysis.

Tree-iage and Site Data

The tree-iage category, slope, and acreage of each park site were acquired from 

city of Seattle staff (Bazinet 2013). Some park sites were missing initial tree-iage 

categories. Because tree-iage is a key variable for the model, sites lacking tree-iage 

classification were not included, which meant 477 records (15 percent) were omitted. The remainder of the analysis was performed and results are reported only for 

sites with an initial tree-iage condition assessment. Table 1 shows the frequency that 

each tree-iage category appears in the dataset, which contained 2,668 observations.

Slope and acres of each park site were provided by the city of Seattle. Slope 

was measured in percentage of park site with slope greater than 50 percent. About 

20 percent of sites treated had a significant amount of steep ground. The 50 

percent slope was selected because safety concerns prohibit volunteers from working on steeper sites. The largest site was just over 19 ac, and the smallest was 0.04 

ac, or 1,577 ft2.

Table 1—Frequency distribution of each tree-iage category

Tree-iage Frequency Percent

Cumulative

frequency

Cumulative

percent

1 260 9.75 260 9.75

2 798 29.91 1,058 39.66

3 406 15.22 1,464 54.87

4 316 11.84 1,780 66.72

5 416 15.59 2,196 82.31

6 187 7.01 2,383 89.32

7 203 7.61 2,586 96.93

8 39 1.46 2,625 98.39

9 43 1.61 2,668 100.00

Multiple types of 

restoration could occur 

during the same event, 

including invasive 

removal, planting, 

watering, herbicide 

application, placement 

of cardboard and other 

weed blockers, and 

trail work.
8

GENERAL TECHNICAL REPORT PNW-GTR-921

Estimating Restoration Costs

Costs were organized into six categories: labor and staff costs (counted as two categories), plants, materials, tools, and event preparation. These costs were estimated 

by site based on the recorded activity occurring during each restoration event. Total 

costs were divided by site acres to arrive at the cost per acre. The cost data were 

then combined with the treatment and site data to estimate the cost model. Next is a 

discussion of each cost and how it was calculated

Labor Costs

Labor costs included the wages of paid contractors and crews on site during restoration events. They accrued during all phases of restoration. Hourly wage rates are 

listed in table 2; contractor names are not disclosed to protect privacy and proprietary wage information. Note that contractor 2 has two entries, an hourly labor rate 

for events without volunteers and a per-volunteer rate for leading mixed events with 

volunteers present. 

The city of Seattle Park’s Department crew on site during events was the 

Natural Area Crew (NAC), who also helped with event preparation (discussed 

later). Salaries for the city of Seattle employees are posted online (Bloom 2012). 

These include the salaries of the five NAC members, which were averaged to arrive 

at one hourly rate of $22.30. An average value was used because only the number of 

crew members and total hours spent onsite are included in CEDAR entries. Annual 

salary is converted to hourly wage by assuming 261 work days in a year. 

Staff Costs

Staff costs were the costs associated with professional staff in organizing, preparing, 

and followup after events—not conducting the physical restoration work in the forest. These costs were accrued for all phases by Forterra and city of Seattle staff for 

Table 2—Labor costs for paid contractors

Labor information Hourly rate ($)

Paid contractor 1 39.43

Paid contractor 2, no volunteers 32.31

Paid contractor 2, mixed events 34.67

Paid contractor 3

Actual invoiced value or

37.28

Paid contractor 4 40.00

Paid contractor 5 12.70

Labor costs included 

the wages of paid 

contractors and 

crews on site during 

restoration events.
9

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

time spent in support of restoration events. Not included were staff costs not directly 

related to the events themselves, such as planning, administration, and contracting.

In many cases, the CEDAR database contained the names of Seattle professional staff present at events. In these cases, actual salaries were used to estimate 

staff costs from the online source described above. Annual salaries ranged from 

$45,403 to $58,015, which corresponds to hourly wages of $21.74 to $27.78 again 

assuming 261 work days in a year. Staff costs for events where the individuals 

present were not identified were estimated based an average salary value of $22.30 

per hour. A $50-hourly cost was assigned to all Forterra and other nongovernmental 

agency staff to reflect average salaries and overhead costs incurred by the organizations based on personal communication (Brinkley 2013). 

Plant Costs

The number of plants by planting stock, including containers, bare root, plugs, 

and stakes was contained in the CEDAR data. Plant costs were estimated from 

two sources and were organized according to event-leader type: volunteer, mixedleader, and paid contractor. First, we had to determine which individuals and 

groups actually received plants for volunteer and mixed-leader events. Eligible 

events were assigned per-plant prices based on expenditure data provided by Forterra: $2.50 for plants in gallon containers, $1.50 for bare root and plugs, and $0.50 

for stakes. These prices were applied to all plant material from trees to understory 

ground cover. Paid contractor planting events were assigned plant costs either 

according to actual invoiced values or the Forterra prices described above when no 

invoice was available. 

Materials Costs

These costs were provided directly in contractor bids and included things like fabric 

and coir logs purchased for erosion control. Only 15 events had materials costs 

listed in contractor invoices. Materials like gravel and wood chips were provided for 

free by the city for many events; crew time for delivery and distribution of materials 

was accounted for as a preparation cost.

Tool Costs

Tools were the most challenging cost to estimate and represent the greatest source 

of uncertainty. Forterra does not track the number of tools like shovels, rakes, and 

pruners they provide to support GSP volunteer events. In addition, the number and 

type of tools that Forterra and their community partners have in stock, purchase 

in a year, and provide for restoration events are all unknown. Because tool cost 

data are lacking, traditional methods for estimating tool costs could not be applied. 

Tools were the most 

challenging cost to 

estimate and represent 

the greatest source of 

uncertainty. 
10

GENERAL TECHNICAL REPORT PNW-GTR-921

Rather than exclude tool costs from the analysis, a valuation method was devised 

from conversations with Forterra staff.

First, a list of all possible tools was compiled from tool and materials request 

forms. An hourly rental rate for each tool was assigned based on an Internet search 

of rates advertised by rental companies in the Puget Sound area. Next, lists of tools 

typically used for restoration activities were developed only for phases 1, 2, 3, and 

trails (phase 5). We noted that tools provided to youth volunteers were different 

from those provided to adults, and adjusted the tool list accordingly. Then, for 

each restoration event, an average rental rate was computed and multiplied by the 

number of hours each volunteer spent there. An example for planting events appears 

in table 3. The decision about which events were assigned tool costs was made 

similarly to the distribution of plants described above. The resulting hourly and 

total tool costs are provided in table 4. 

Preparation Costs

Events with preparation time were identified using information in the comments 

sections of the CEDAR database, which included crew time for delivering mulch, 

gravel, and burlap ahead of events. Time associated with staging and replanting 

Table 3—Tool costs for adult and youth volunteers at planting (phase 2) events

Tool Quantity Volunteer type

Rental rate 

(dollars per 

hour)

Tool cost 

(dollars per 

hour per 

volunteer)

Gloves Per volunteer Youth and adult 2.50 2.50 

Shovel (digging) Per 3 volunteers Youth and adult 7.33 2.44 

Bucket Per 6 volunteers Youth and adult 5.00 0.83 

Push broom Per 6 volunteers Adult 7.00 1.17 

Wheelbarrow Per 6 volunteers Adult 14.33 2.39 

Adult hourly 

tool cost: $9.33 

Youth hourly 

tool cost: $5.78 

Table 4—Hourly tool cost rates by phase summary tablea

Volunteer type

Phase 1

(invasive 

removal)

Phase 2

(planting)

Phase 3

(followup 

invasive 

removal)

Phase 5

trails

Adult $14.69 $9.33 $13.43 $5.53

Youth $9.83 $5.78 $9.88 $2.50

a Note: no tools used in phase 4.
11

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

poorly planted plants before and after planting events was also included. Preparation costs differ from staff costs because they did not accrue during the events, but 

rather in support of events.

The last step to prepare the data was to combine the restoration treatment data, 

site data, and cost estimates at the site level. After omitting events with zero costs 

or no tree-iage category, the site cost database contained 2,668 records. Descriptive 

statistics for the costs estimated for each restoration event are presented later in 

results. The dataset was entered into an economic model, which is described next.

The Economic Model

In economics, a model is a theoretical construct representing economic processes 

by a set of variables and the relationships between them. Fundamentally, a model is 

a simplified framework designed to illustrate a complex process. Empirical models 

are designed to be used with data to generate estimates for model variables using 

statistical techniques. Models are quite useful, in fact indispensable, in economic 

analysis, but they have limitations that can reduce their reliability. Models are based 

on assumptions that influence results, conclusions, and insights. If those initial 

assumptions are wrong, misleading, or incomplete, conclusions will be in error. 

Also, models are used because they provide a simplified version of reality, yet 

oversimplification can also lead to erroneous results. Selecting the wrong variables 

or omitting variables that should be in the model introduces error. Last, the quality 

of data for each variable will influence the quality of model results. 

In this analysis, we developed an empirical model to estimate the cost per acre 

to restore natural area sites in Seattle. We assumed that this cost could be explained 

using the variables initial tree-iage rating, phase of restoration, event leader type 

(paid contractor, volunteer, or mix of the two), and the physical size and slope of the 

restoration site. Thus the empirical model estimated the per-acre cost to restore a 

site as a function of these five explanatory variables:

Ci = f(E, T, P, S, A)

where

i = the restoration site, 

Ci = cost per acre to restore site i,

E = event leader type, 

T = tree-iage rating, 

P = phase of restoration, 

S = site slope (percent), and

A = site size (acres).

Preparation costs 

differ from staff costs 

because they did not 

accrue during the 

events, but rather in 

support of events.
12

GENERAL TECHNICAL REPORT PNW-GTR-921

In regression analysis, the left-hand side variable (Ci, above) is called the dependent 

variable and the set of variables on the right hand side of the equation are the independent 

variables. Regression analysis is a set of statistical techniques for estimating and modeling the relationship between a dependent variable and one or more independent variables. 

Initially, we estimated coefficients for the cost equation using a simple linear equation: 

Ci = (α + β1Ei + β2Ti + β3Pi + β4Si + β5Ai + ε),

where α is the intercept and ε is an error term, assumed to be identically and 

independently distributed. Once estimated, the beta coefficients βi indicate the magnitude and direction of each variable’s marginal effect on restoration cost, holding 

the other variables constant.

The structure of the dataset required modifying the initial regression model. 

Data were reorganized as a panel because they are longitudinal—meaning they 

track the same type of information on the same variable at multiple points in 

time. We identified a fixed-effects regression approach as the most appropriate 

for our model. Fixed-effects models control for the time-invariant characteristics 

in the longitudinal data to estimate the marginal effects of the independent 

variables on the dependent variable. Recall the tree-iage level of a site undergoing restoration does not change over time, so its expected relationship to the cost 

to restore that site is ambiguous. For instance, one could expect that a site with a 

tree-iage rating of six would correspond with a higher cost than a site rated with 

a one, given the labor-intensive work required to restore the site. But because the 

initial tree-iage rating was never updated, its relationship to the cost of restoring 

the site diminishes over time. However, there may be time-invariant characteristics associated with sites in a given tree-iage category that do correlate with the 

other independent variables—for instance, steep sites may be more likely to be 

assigned a higher tree-iage level because they are more difficult to maintain. 

Ideally, our panel dataset would be balanced, with data covering each tree-iage 

equally over time. The panel dataset constructed here was unbalanced; sites within 

each tree-iage category were treated with irregular frequency over time, often over 

the course of varying numbers of events. An event’s date was not a unique identifier 

of observations in the dataset as several events at different parks could occur on the 

same day. The resulting unbalanced panel thus has no time dimension; the fixedeffects model explains the variation in cost for sites that began at each tree-iage level. 

The event leader data were categorical and entered the model as binary or dummy 

variables. In our case, we had three binary variables. Mathematically, only two of the 

three categories can appear in the estimation equation. We used volunteer-led events 

as the base for the equation and created dummy variables for mixed and paid events. 

We identified a fixedeffects regression 

approach as the 

most appropriate for 

our model.
13

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Algebraically, the fixed-effects model of the cost of treating site i and tree-iage 

level j is given by:

Cij = α + β1Paidij + β2Mixedij + β3Phaseij + β4Steepnessij + β5Sizeij + ui + εij,

where

Cij = cost per acre of site i of initial tree-iage level j. 

α = common intercept term.

Paidij = binary variable; equals 1 if an event consisted of all paid labor.

Mixedij = binary variable; equals 1 if an event consisted of both paid and volunteer labor.

Steepnessij = the steepness of the event site, in percent greater than 50 percent slope.

Sizeij = the size of the site, in acres.

ui = tree-age rating fixed effect; site-invariant within a rating.

εij = error term with usual assumptions.

General Results

Table 5 lists the restoration work accomplished at GSP events from January 1, 2011, 

to June 30, 2013, by phase and event leader type. Accomplishments were measured 

in different units, depending on the activity. The majority of work was performed 

by paid contractors in all categories except post restoration monitoring and trail 

maintenance. Most of the area treated was in restoration phase 3, followup weeding 

Table 5—Restoration accomplishments by phase and event type

Accomplishments by event-leader type

Phase Treatment type Treatment unit Mixed Contractor Volunteer

1 Invasives removed Square feet 904,435 8,605,963 775,762

1 Survival rings Count 389 505 1,320

1 Erosion control fabric Square feet 300 119,908 3,285

1 Cut stumps Count 45 29,265 0

1 Foliar herbicide Square feet 0 513,096 0

2 Plants planted Count 31,823 98,876 46,596

3 Weeding Square feet 2,995,281 24,417,212 3,202,335

3 Cardboard Square feet 140,755 272,786 110,663

3 Mulching Square feet 521,297 850,419 585,815

3 Watering Count 1,843 135,965 12,247

3 Erosion control fabric Square feet 6,005 18,300 4,350

3 Cut stumps Count 3,510 20,860 0

3 Foliar herbicide Square feet 0 15,000 0

4 Monitoring plot Count 118 29 8

5 Trail maintained Feet 10,083 11,166 46,639

5 Trail structure Count 336 168 74

The majority of work 

was performed by 

paid contractors in all 

categories except post 

restoration monitoring 

and trail maintenance.
14

GENERAL TECHNICAL REPORT PNW-GTR-921

and maintenance. Presumably, sites in phase 3 were better suited for volunteers 

than those in phase 1, but whether this is owing to site conditions or the nature of 

the work is unknown. 

Restoration Costs

Labor, materials, staff, plants, preparation, and tool costs were calculated for each 

site (fig. 2). Labor represents the greatest restoration cost, followed by tools provided to volunteers. Materials and preparation time had little influence on costs. 

Overall, the total cost of 2.5 years of GSP-sponsored restoration events was calculated at just over $4 million, an average of about $540 per acre. 

Table 6 shows descriptive statistics for the six categories of treatment costs, 

total costs, and costs per acre. Statistics were calculated after omitting zero values; 

there was no event that incurred every type of cost. Thus the count of the cost 

categories ranged from 13 to 2,339 while the total and average cost had 2,668 

observations. Minimum and maximum values show wide variation in costs across 

sites. Note the standard deviation and gaps between the mean and median values 

for all cost categories. The average total cost and cost per acre for the entire sample 

of events and sites was $1,542 and $1,126, respectively. The mean cost for tools and 

labor was highest and similar at about $1,000, followed by plants with a mean of 

$475. Confidence intervals were calculated at the 95 percent significance level. 

Table 7 shows treatment costs broken down by leader type: paid contractor, 

volunteer, and mixed leader. On average, paid contractor events were about six 

times more costly than volunteer-led events. The greatest single cost was contracted 

labor, which was estimated at $1.7 million. Plant costs were also greatest for paid 

contractors. Mixed events were the most costly; owing to wages for contractors and 

0

500

1,000

1,500

2,000

2,500

Staff Materials Plants Labor Preparation Tools

Thousand

U.S. d

ollars

Cost category

Total cost: $4,113,840

Acres treated: 7,628

Average cost per acre: $539

Figure 2—Total costs for Seattle parks restoration events held between January 1, 2011, and June 30, 2013.

Overall, the total cost 

of 2.5 years of GSPsponsored restoration 

events was calculated 

at just over $4 million, 

an average of about 

$540 per acre.

The greatest single cost 

was contracted labor, 

which was estimated at 

$1.7 million.
15

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Table 6—Descriptive statistics for estimated restoration costs

Staff Materials Plants Labor Preparation Tools Total cost Cost per acre

Calculations excluding zeroes:

Count 545.00 13.00 303.00 2,339.00 72.00 1,212.00 2,668.00 2,668.00Minimum 6.30 22.00 4.00 1.19 33.00 2.00 2.00 0.42Maximum 7,331.06 264.00 9,125.00 40,412.38 3,489.00 13,083.00 40,412.38 37,394.22Mean 738.86 71.54 475.07 999.53 241.04 999.21 1,541.92 1,126.60Median 486.93 39.00 210.00 316.75 89.00 508.50 624.74 312.97Standard deviation 853.97 70.74 969.79 2,125.08 457.04 1,428.78 2,663.87 2,473.77Standard error 36.58 19.62 55.71 43.94 53.86 41.04 51.57 47.89Sum 402,679.35 930.00 143,945.00 2,337,892.51 17,355.00 1,211,038.00 4,113,839.87 3,005,771.3295% confidence interval 71.86 42.75 109.63 86.17 107.40 80.52 101.13 93.91

Table 7—Restoration costs by leader type at Green Seattle Partnership sponsored events from January 1, 2011, and June 30, 2013Event leader type Acres Staff Materials Plants Labor Preparation Tools Total Cost per acreMixed 3,769 348,229 0 64,428 635,044 741 1,097,782 2,146,224 569 Paid 2,792 54,450 930 69,814 1,702,848 16,614 0 1,844,657 661 Volunteer 1,068 0 0 9,703 0 0 113,256 122,959 115 

Total 7,629 402,679 930 143,945 2,337,893 17,355 1,211,038 4,113,840 539 
16

GENERAL TECHNICAL REPORT PNW-GTR-921

volunteer support staff and tool costs incurred for volunteers at particularly large 

events. At one particularly costly event, a paid staff of 20 was present to oversee 

188 volunteers from a local corporation. Another mixed event had 17 paid contractors leading 179 adult and 24 youth volunteers. Preparation costs were mostly 

incurred by paid staff in support of restoration events. 

Seattle Results

These next results largely pertain to the city of Seattle but could be generalizable 

to any city that uses a systematic approach to scheduling restoration and keeping 

records. Restoration costs by site were combined with data obtained from the city 

of Seattle for each site’s tree-iage rating, phase of restoration, acreage, and slope. 

These costs can be highlighted in a variety of ways. Table 8 shows costs broken out 

by each of the five possible phases of work. Recall that phase 1 is invasive removal, 

phase 2 is planting, phase 3 is followup and maintenance, phase 4 is monitoring, 

and phase 5 is trail work. Most of the acres treated were in phase 3, meaning they 

already had invasive species removed and plantings established. Costs for phase 3 

were the greatest for every cost category except plants, because of labor and staff 

costs for large mixed events and the tool costs associated with phase 3 volunteers. 

Table 9 shows costs broken out by each of the nine possible tree-iage categories. 

Recall from figure 1 that tree-iage ratings combine invasive species threat with 

native tree canopy cover in a matrix of values that represent difficulty in restoring 

the site. Presumably, sites having fewer invasives and more tree canopy (rating of 

1, 2, 3, 4, 5, and 7) would need less intensive restoration than sites rated 6, 8, and 

9 with thick invasives and little tree canopy. Although total costs were greatest for 

sites in category 2, costs per acre are greatest for tree-iage category 1, followed by 

category 5. Category 3 had the most acres treated and the lowest cost per acre at 

$279. Acres in categories 8 and 9 sites were treated the least often, which makes 

sense as they are the most difficult sites to restore. Notice that most costs decline 

for tree-iage categories 6, 8, and 9, which likely reflects that few events are held on 

these sites, not that the sites are less expensive to restore. 

Figure 3 shows restoration costs per acre by event leader and tree-iage rating. 

Overall, sites with a tree-iage rating of 9 treated by paid contractors were the most 

expensive at almost $1,800 per acre. In fact, per-acre costs were consistently greatest when only paid staff were working with one exception. Mixed-leader events 

were the most costly on tree-iage 1 and tree-iage 2 sites, with costs of $1,510 and 

$1,389 per acre respectively. Only tree-iage 2 sites were more expensive to restore 

using mixed-leader events, although per acre costs were similar to paid contractor 

costs for tree-iage 5 and 8. Volunteer-led events were the least expensive compared 

to other leaders, ranging from $115 per ac on sites rated tree-iage 3 to almost $450 

Restoration costs by 

site were combined 

with data obtained 

from the city of Seattle 

for each site’s treeiage rating, phase of 

restoration, acreage, 

and slope.
17

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

Table 8—Restoration costs by phase of work at Green Seattle Partnership sponsored events from January 1, 2011, and June 30, 2013Phase Acres Staff Materials Plants Labor Preparation Tool Total Cost per acre1 1,761 116,119 324 0 713,839 2,970 347,072 1,180,234 6702 1,574 41,480 0 143,945 338,360 10,408 132,561 666,755 4243 3,534 245,170 606 0 1,223,006 2,652 715,566 2,187,000 6194 354 0 0 0 14,676 0 0 14,676 415 406 0 0 0 48,011 1,325 15,839 65,175 160

Total 7,629 402,679 930 143,945 2,337,893 17,355 1,211,038 4,113,840 539

Table 9—Restoration costs by tree-iage category at Green Seattle Partnership sponsored events from January 1, 2011, and June 30, 2013Tree-iage category Acres Staff Materials Plants Labor Preparation Tool Total Cost per acre1 593 59,450 24 21,156 329,720 2,050 137,208 549,608 9282 1,911 108,355 519 39,607 725,290 5,646 380,871 1,260,288 6603 1,966 43,286 0 28,565 255,927 122 219,956 547,856 2794 957 46,555 90 12,838 238,297 1,100 131,433 430,314 4505 887 62,065 229 18,100 407,654 1,407 166,511 655,966 7406 589 32,271 0 9,772 178,742 1,597 72,501 294,883 5007 495 37,231 0 9,299 147,800 5,032 69,523 268,885 5448 83 2,497 68 731 19,831 0 9,793 32,920 3979 148 10,968 0 3,877 34,632 401 23,242 73,120 493

Total 7,629 402,679 930 143,945 2,337,893 17,355 1,211,038 4,113,840 539
18

GENERAL TECHNICAL REPORT PNW-GTR-921

per ac on sites rated tree-iage 5. Note that no volunteers were present at events on 

sites rated with an 8 or 9. Figure 3 suggests that costs per acre by tree-iage rapid 

assessment rating were not strongly related to the type of event leader.

Figure 4 shows average cost per acre by tree-iage category and phase of work. All 

tree-iage categories were undergoing some phase of work. The most expensive sites to 

restore were receiving followup maintenance (phase 3) after initially being assigned a 

tree-iage rating of 8. Sites in the easier to treat tree-iage categories incurred most of their 

costs in phase 1 (invasive plant removal). Phase 1 and phase 3 activities were the most 

costly. Presumably the costs observed for the most difficult to treat acres (tree-iage 6, 8, 

and 9) resulted from prohibiting the use of volunteers for restoration work on those sites.

Model Results

Recall that a fixed-effects specification was used for the regression model and that 

tree-iage rating was used as the longitudinal variable. The model performed best 

in log-linear form, so the cost-per-acre values of the dependent variable were transformed into their natural logarithms. This made the interpretation somewhat more 

complicated, but provided better results. Table 10 shows the model estimation output. 

The key results from the estimation output can be briefly summarized. First, 

coefficients are statistically significant and estimates have the expected sign. This 

means that paid staff and terrain steepness add to an event’s cost. The sign on the 

acreage variable is negative, reflecting an economy of scale as costs decline with 

larger sites. The phase coefficient is negative, which suggests that as sites progress 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1 2 3 4 5 6 7 8 9

Restoration cost per acre (U.S. d

ollars)

Tree-iage rating

Mixed

Paid

Volunteer

Figure 3—Restoration per-acre costs tree-iage category and event leader type.

The most expensive 

sites to restore were 

receiving followup 

maintenance (phase 3) 

after initially being 

assigned a tree-iage 

rating of 8.
19

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

through the phases of restoration (or start from an advanced phase), the cost of the 

next event decreases. The F-test shows that the fixed effects (the unobserved characteristics of each tree-iage category) are jointly significant, verifying the validity of 

the chosen model form. Using the coefficients from the estimation output in table 

10, the estimated cost equation can be written as:

Natural log(cost per acre) = 5.98 + 1.15Paid + 1.02Mixed – 

0.31Phase +0.52Steepness – 0.205Size

0

500

1,000

1,500

2,000

2,500

1 2 3 4 5 6 7 8 9

Phase 1

Phase 2

Phase 3

Phase 4

Phase 5

Restoration cost per acre (U.S. d

ollars)

Tree-iage rating

Figure 4—Restoration per-acre costs by tree-iage category and phase of work.

Table 10—Estimation results from the restoration cost-per-acre model

Variable Coefficient Standard error P-value 95% confidence interval

Intercept 5.989 0.135 0.000 5.725 6.253

Event type

Mixed 1.017 0.113 0.000 0.796 1.239

Paid 1.152 0.116 0.000 0.925 1.380

Phase -0.305 0.028 0.000 -0.360 -0.250

Acres -0.196 0.009 0.000 -0.215 -0.178

Steep 0.523 0.132 0.000 0.263 0.782

R-squared: within = 0.2171

between = 0.3533

overall = 0.2294

F(8, 2636) = 6.51 Prob > F = 0.000
20

GENERAL TECHNICAL REPORT PNW-GTR-921

Interpreting the Model

The estimated coefficients in a log-linear equation are commonly interpreted as a 

percentage change in the dependent variable given a one-unit change in an independent variable, holding all else constant. These are called marginal impacts; for 

example, a 2-ac increase in size results in a 40 percent reduction in the per-acre cost 

to restore the site. Per-acre costs declined by 31 percent each time a site advances to 

the next phase of restoration. Steepness had the greatest impact of the site variables; 

for every additional percentage of a site with slope greater than 50 percent, cost-per 

acre rose by about 50 percent. 

In contrast, the coefficients on the dummy variables (event types 2 and 3— 

mixed and paid staff, respectively) cannot be interpreted as percentage changes 

because of their discrete nature. Instead, the coefficient on say, the variable Paid 

contractors in the log-linear equation is interpreted as (100 X (e(.1.152) – 1), or 216 

percent change in cost relative to the base case of an all-volunteer event. 

Discussion

The objective of this analysis was to combine restoration activity data from a 

proprietary database with estimated site treatment costs to generate a cost-per-acre 

model for restoring forested urban green spaces in Seattle, Washington. Overall, the 

venture met with varying success. 

General Findings

The large variation in the restoration cost estimates could be addressed by collapsing the data into fewer categories. The variables we selected were of particular 

interest to practitioners in the Seattle area. The majority of costs were incurred for 

four things: event preparation by professional staff, labor that physically performed 

the restoration work, tools, and plants. We believe these are the most basic treatment costs necessary to provide a defensible cost model; efforts to improve the data 

collected for these costs could pay off with improved results.

Tool costs were the most difficult to estimate and represent the greatest 

source of uncertainty in the cost estimates. The lack of tool expenditure data was 

pervasive across multiple organizations that we approached once we ascertained 

that Forterra did not track tool purchases. We spoke to organizations such as 

Friends of Trees, a Portland, Oregon, tree planting nonprofit and the Atlanta Tool 

Library, which stores, maintains, and loans tools for ecological restoration around 

Atlanta, Georgia. None of these organizations regularly inventory their tools, or 

could provide a definitive answer about how long a particular tool lasts or how 

often tools are replaced. One recommendation to improve the data for tools is to 

Steepness had the 

greatest impact of the 

site variables; for every 

additional percentage 

of a site with slope 

greater than 50 percent, 

cost-per acre rose by 

about 50 percent.

The lack of tool 

expenditure data was 

pervasive across 

multiple organizations 

that we approached 

once we ascertained 

that Forterra did not 

track tool purchases.
21

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

decide how many of each tool to stock, count the tools in stock at the beginning 

of each year, and replace any missing or broken tools at one time and record the 

cost. At least, event leaders could record the number and type of tools provided at 

restoration events. 

Costs by event-leader type were of particular interest and table 6 showed the 

majority of work was performed by paid contractors, followed by mixed- and 

volunteer-led events. Calculated values showed that sites treated by paid contractors cost an average of $1,490 per acre, but ranged from nearly $22,000 to $1.66 

per acre. Mixed-leader events had the highest costs, largely owing to supervising 

staff wages and the cost of tools provided to volunteers. Costs per acre ranged 

from $24,000 to treat one steep site to $1.46, for planting five trees. Volunteer-led 

events were the least costly, averaging $435 per acre, but they tended to achieve 

the least in terms of restoration work performed per person. These events did, 

however, tend to draw participants from a variety of community partners; strong 

public participation may be one way to garner support for restoration work from 

municipal policymakers.

Seattle Area Findings

Sites in tree-iage categories 2, 5, and 7 were treated the most often. Figure 1 

reveals that these sites vary with respect to native tree canopy cover, but all have 

a low to medium amount of invasive cover. These types of sites (along with category 1 and 4 sites) may be perceived as having a higher likelihood of restoration 

success. The selection of a site for restoration would ideally be determined by its 

initial tree-iage rating. Unfortunately, not all sites had been assigned a tree-iage 

category, and 15 percent of the data obtained from the CEDAR database had to be 

omitted from the analysis. Also, recall that tree-iage categories have never been 

updated to reflect work completed or restoration progress, so documenting success was problematic. For example, if $1,000 and 100 hours were spent removing 

invasive plants on a site in tree-iage category 5, unless the site was assigned an 

updated phase and tree-iage category afterwards, the cost to move between treatment categories cannot be determined. Two suggestions regarding the tree-iage 

rapid assessment system are to identify all sites lacking a tree-iage rating and 

assign them one and to periodically revise the tree-iage rating for sites that are 

undergoing restoration to reflect progress.

More issues with the CEDAR data became apparent during our analysis. 

Hours of work occurring on multiple sites in the same park are not recorded 

separately, so apportioning hours worked among sites was challenging. Similarly, the total number of participants at each event was recorded, but restoration activities often occurred on multiple sites during the same event, with 

These events did, 

however, tend to draw 

participants from a 

variety of community 

partners; strong public 

participation may be 

one way to garner 

support for restoration 

work from municipal 

policymakers.

Two suggestions 

regarding the 

tree-iage rapid 

assessment system 

are to identify all 

sites lacking a 

tree-iage rating and 

assign them one 

and to periodically 

revise the tree-iage 

rating for sites that 

are undergoing 

restoration to reflect 

progress.
22

GENERAL TECHNICAL REPORT PNW-GTR-921

no way to allocate the people among sites. In addition, there was no way to 

determine the proportion of a site that was treated during each event. One way 

to improve cost estimates is to record the number of participants by site rather 

than by event, but that could mean more work for event leaders, many of whom 

were volunteers.

Performance of the Model

In general, the accuracy or “fit” of a statistical model—such as the fixed-effects 

model described above—can be measured by the fraction of the dependent variable’s variance that is explained by the independent variables. In our case, this 

refers to how much of the variation in cost per acre of restoration projects within 

each tree-iage level is explained by an event’s phase, size, leadership type, and 

steepness of terrain. 

The model’s fit was modest. The “within” r-squared value in table 10 indicates that only 21 percent of the variation of cost per acre was explained by the 

model. In other words, most of that variation is owing to other factors that have 

not been identified. Nevertheless, the model does provide some useful information to event planners. Interpreted correctly, it gives a rough estimate of the 

marginal effect of any one variable, holding all others constant. It also strongly 

confirms the direction and significance of those effects—for instance, the addition of paid staff to an event’s leadership adds a statistically significant amount 

to its cost per acre. 

We tested the predictive ability of the cost equation by comparing the cost 

per acre predicted by the model against the actual cost of events at three sites 

selected from the dataset. The size of the discrepancy underlines the limitations 

of our results. Test site one held a planting event that occurred in the spring of 

2011 in Pigeon Point Park at the Pigeon Point 04 site. The event was volunteer 

led, in phase 2 (planting) of restoration, and lasted 4 hours. One youth and 

seven adult volunteers installed 208 bareroot plants. The site had a tree-iage 

classification of 5 and contained 4.75 ac of relatively flat terrain as only 0.3 percent of the site is over 50 percent steep. Using these values, the model predicts 

the cost per acre of $257. Total estimated cost of treating the site was $428, a 

cost per acre of $90.

Test site two is the Rhododendron Trail in Frink Park, where a phase 3 weeding event occurred in the fall of 2011. The 3.5-hour event was mixed leader, 

with two paid staff supervising a volunteer pool of 26 adults and 2 youth as they 

weeded 15,000 ft2 of ground. The site had a tree-iage classification of 6, contains 

2.66 ac where 28 percent of the site is over 50 percent steep. Using these values, 

We tested the 

predictive ability of 

the cost equation by 

comparing the cost 

per acre predicted by 

the model against the 

actual cost of events 

at three sites selected 

from the dataset.
23

Urban Forest Restoration Cost Modeling: a Seattle Natural Areas Case Study

the model predicts the cost per acre of $902. We estimated total cost of treating 

the site at that event was $2,938, a cost per acre of almost $1,109. The model 

performed best for this site.

Site three was also in Frink Park, at the 1.70-ac Upper Leschi site, at an event 

held in the spring of 2011. This phase 1-event was staffed by paid contractors, who 

removed 46,277 ft2 of invasive plants, installed 800 ft2 of erosion control fabric, and 

cut 600 undesirable trees. The site was quite difficult, with 70 percent of the site 

over 50 percent steep and an initial tree-iage rating of 6. Plugging these values into 

the model yields a cost per acre of $2,838. Total estimated cost of treating the site 

was $9,544, a cost per acre of $5,614.

Looking Ahead

By itself, the CEDAR database does not provide enough information to directly 

calculate the cost to complete a particular phase of restoration. Nor does it combine 

easily with tree-iage rapid assessment. The value in this work is its contribution 

to published literature on estimating urban forest and park restoration treatment 

and relative costs. The methods and results presented here depict the best possible 

results with the data given. The model was intended to be used by practitioners; 

findings may be generalizable if data limitations can be overcome. 

Efforts to improve consistency and reliability in reporting among users would 

improve the usability of the data. The CEDAR system was new and training was 

ongoing, and initially its many users were entering data differently, which may 

have led to incomplete or inaccurate data (Brinkley 2013). We expect that data 

quality has improved over time. Data are likely to become more robust as training and additional experience will lead users of the CEDAR system to fill out 

more complete restoration logs. As the quality of the CEDAR data improves, this 

analysis and its methods may become a more powerful tool for predicting urban 

forest restoration costs.

Aside from tools, Forterra does track its own expenditures comprehensively 

and in detail, but their internal budgeting and accounting systems are decoupled 

from systems tracking site conditions, work completed, volunteer hours, and forest 

restoration accomplishments. Additionally, Forterra is one of many entities conducting forest restoration throughout Seattle and using the CEDAR system to record 

treatment activity. Building a viable bridge between existing financial accounting systems employed by municipalities and their partner organizations, and the 

restoration tracking systems on the ground could create a valuable tool to predict 

the costs associated with urban forest restoration.

Building a viable bridge 

between existing 

financial accounting 

systems employed by 

municipalities and their 

partner organizations, 

and the restoration 

tracking systems 

on the ground could 

create a valuable tool 

to predict the costs 

associated with urban 

forest restoration.
24

GENERAL TECHNICAL REPORT PNW-GTR-921

Acknowledgments

Major funding for this project was provided by the U.S. Department of Agriculture 

Forest Service, State and Private Forestry, Urban and Community Forestry Program. The authors are grateful for suggestions, comments, and statistical support 

provided by four reviewers. We thank the city of Seattle and Forterra staff for providing data and other valuable assistance over the course of the study. Dr. Charles 

Harris made contributions to early drafts of this work.

Metric Equivalents

When you know: Multiply by: To find:

Feet (ft) 0.305 Meters

Square feet (ft2) 0.0929 Square meters

Acres (ac) 0.405 Hectares

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Brinkley, W. 2013. Unpublished CEDAR and staff cost data. On file with: Forterra 

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