A Guide to Assessing 

Urban Forests

INTRODUCTION

Urban forests provide numerous ecosystem services. To quantify these services and guide 

management to sustain these services for future generations, the structure or composition of the forest 

must be assessed. There are two basic ways of assessing the structure or composition of the urban 

forest:

Bottom-up approach. Field-based assessments to measure the physical structure of the forest (e.g., 

species composition, number of trees)—typically used for strategic resource management or advocacy 

by connecting forest structure, functions and values with management costs, risks,and needs.

Top-down approach. Assessments of canopy cover using 

aerial or satellite images—used to determine amount and 

distribution of tree cover, potential planting space and other 

cover types.

These two approaches provide different types of urban 

forest information. The purpose of this guide is to outline the 

advantages, disadvantages and costs associated with various 

common assessment alternatives under these two approaches.

United States

Department of Agriculture

Northern 

Research Station NRS-INF-24-13

Forest 

Service
2 A Guide to Assessing Urban Forests

THE BOTTOM-UP APPROACH: 

FIELD-BASED ASSESSMENTS

The bottom-up approach involves collecting field data on 

vegetation. It provides the most detailed information needed 

for urban forest management and to assess urban forest 

structure and its associated ecosystem services and values 

(Table 1). To aid in sampling or inventorying urban trees and 

forests, and for calculating their ecosystem 

services and values, the free i-Tree Eco 

and Streets models were developed 

(www.itreetools.org).

Advantages:

 Provides good estimates of basic 

forest information needed for 

management (e.g., number of trees and locations, 

species composition, tree sizes, tree health, risks)

 Provides estimates of numerous ecosystem services 

and their values

 Can be used for monitoring changes in forest 

composition and values

Disadvantages:

 Must collect accurate field data using technical metrics

 Cost of data collection

Cost:

Varies with size and scope of project. Volunteers, in-house 

crews and hired consultants have all been employed for 

collecting data. Hiring a consultant to carry out a typical i-Tree 

Eco sample of 200 plots could cost $40,000 at a contracted 

rate of $200 per plot. Costs would decrease with volunteers 

or student labor (e.g., $20,000 with students; even less with 

volunteers). Sampling intensity is determined by the user 

based on accuracy desired and resources available.

Accuracy:

Varies with sample size and accuracy of data collection; 200 

one-tenth acre plots typically produces a relative standard 

error less than 15 percent for the total population estimate.

THE TOP-DOWN APPROACH: 

URBAN TREE CANOPY COVER 

ASSESSMENTS

There are three common top-down approaches for assessing 

urban tree canopy cover and all three methods will produce 

estimates of tree and other cover types in an area, but with 

differing resolution, costs, and accuracy. The three methods are:

 NLCD analyses

 High-resolution image analyses 

 Aerial photo interpretation

NLCD analyses 

The National Land Cover Database (NLCD) has tree and 

impervious cover maps (30-m resolution) for the entire 

contiguous 48 states with percentage tree and percentage 

impervious cover estimated for each pixel. These maps 

and data are available for free and can be loaded into the 

free i-Tree Vue program to estimate tree cover and general 

ecosystem services.

Advantages

 Free

 Wall-to-wall 

coverage of lower 

48 states

 Maps ecosystem 

services in 

addition to tree 

cover distribution

Disadvantages

 Relatively course 

resolution (cannot see trees)

 Better suited for state or regional analyses rather than 

city scale or below

 Typically underestimates tree cover, on average, by 

about 10 percent. That is, if tree cover is 30 percent, 

NLCD tends to estimate 20 percent

 Data from circa 2001 (updated maps are being 

developed)

Cost:

Free

Accuracy:

Varies with mapping zone, but tends to underestimate tree 

cover by about 10 percent on average; user can adjust canopy 

cover percentage in individual pixels in i-Tree Vue to improve 

accuracy.

High-resolution land cover

With this approach, land cover features are extracted from 

high-resolution aerial or satellite imagery using automated 

techniques. This process yields a detailed map of tree and 

other cover types for a given area. This approach is used for 

Urban Tree Canopy (UTC) Assessments. For more information 

go to: http://www.nrs.fs.fed.us/urban/utc/

Advantages

 Produces accurate, high-resolution cover map

 Complete census of tree canopy locations

 Integrates well with GIS

High resolution (below) vs. 30-m imagery.
A Guide to Assessing Urban Forest 3

 Allows the data to be summarized at a broad range 

of scales (e.g., parcel to watershed), enabling tree 

canopy to be related to a host of demographic, 

planning, and biophysical data

 Locates potentially available spaces to plant trees

 Can be used to monitor locations of cover change

 The source imagery needed for the mapping is 

available for the entire United States free of charge 

from the USDA

Disadvantages

 Can be costly if the data are low quality or incomplete

 Requires highly trained personnel along with 

specialized software

 Significant effort and time needed to produce quality 

maps

 Change analyses can locate false changes due to map 

inaccuracies

 Does not include ecosystem services reporting

Cost:

Variable depending upon available data. Development of city 

cover maps are on the order of $5,000 to 40,000+ depending 

upon size of city and availability of source data.

Accuracy:

Depends on the processor and available data, but is typically 90 

percent accurate for tree cover. The incorporation of additional 

data, such as LiDAR, and/or the implementation of manual 

corrections can increase the accuracy to over 95 percent. 

Error matrix of map can detail actual accuracy of the map.

Photo-interpretation

Uses digital aerial images and a series of random points that 

are interpreted to determine the cover type at each point 

center. This process produces statistical estimates of cover 

with a known error of estimation. A free tool (i-Tree Canopy) 

can be used to photo-interpret cover across the globe using 

Google MapsTM. Photo interpretation has been used for 

accuracy assessments of the other top-down methods.

Advantages

 Low cost – most images can be 

acquired freely (e.g., Google 

Earth or from cities or counties)

 Cover assessment can be done 

quickly (e.g., available planting 

space, tree, impervious)

 Accuracy can be increased by 

adding more points and can be 

calculated quickly 

 Can produce sub-area analyses 

and maps (e.g., tree cover by 

neighborhood)

 Multi-date paired imagery can 

be used to assess change

Disadvantages

 Does not produce detailed cover map

 Photo-interpreters can create 

errors though 

misclassifications 

(training and 

quality checking are 

recommended)

 Leaf-off imagery can 

be difficult to interpret

 i-Tree Canopy 

interpretation 

limited to high 

quality Google images

 Poor image quality in some areas

 Resulting data cannot be summarized at multiple, 

user-defined scales

Cost:

At $10 per hour, cost is about 10 cents per point (e.g., 1,000 

points = $100). Costs involve set up and interpretation time.

Accuracy:

A sample of 100 points will produce an estimate with a 

standard error of about 4.6 percent (assuming 30 percent 

canopy cover) and can be interpreted in about 1 hour. A 

sample of 1,000 points will produce an estimate with a 

standard error of about 1.4 percent (assuming 30 percent 

canopy cover).

Photo-interpretation involves 

classifying randomly located points 

within preselected cover classes 

(e.g., tree, impervious, water).

Example of high-resolution land cover map.

Neighborhood tree cover in Toronto, Canada, 

determined through photo-interpretation.

Canopy cover

7% to 10%

11% to 20%

21% to 30%

31% to 40%

41% to 50%

51% to 62%
4 A Guide to Assessing Urban Forests

Table 1.—Summary of features of four types of urban forest analyses

Urban Forest Attribute

i-Tree

Ecoa

i-Tree

Vueb

i-Tree

Canopyc

Cover

Map (UTC)d

Cover

Amount or percent tree cover    

Specific locations and distribution of tree cover  

Amount or percent potential planting space    

Specific locations and distribution of plantable space  

Maps of tree cover and plantable space   

Urban Forest Composition and Management

Total number of trees / tree density 

Species composition 

Diameter / size distribution 

Species diversity 

Species importance values 

Leaf area and biomass 

Tree health 

Native vs. exotic composition 

Invasive trees 

Risk to insects and diseases 

Ground cover attributes   

Ecosystem Services and Values

Air pollution removal / human health   * *

Carbon storage and annual sequestration   * *

Effects on building energy use 

Rainfall interception 

Structural value 

Mapping of ecosystem services  * *

Monitoring

Change in tree cover   

Locations of tree cover change 

Change in species composition, services and values Change in species composition, services and values 

 - procedure calculates attribute

 - recommended procedure based on resolution, accuracy, and cost

* - broad estimates of services could be calculated based on procedures in i-Tree Vue a

i-Tree Eco – free program to assess ecosystem services and values from field data

b

i-Tree Vue – free program that uses NLCD cover data to map cover and estimate ecosystem services

c

i-Tree Canopy – free photo-interpretation tool to assess canopy cover and monitor change

d

Cover map - high-resolution cover maps generated as part of a UTC assessment

For more information contact:

David J. Nowak

U.S. Forest Service 

Northern Research Station 

Syracuse, NY

315-448-3212, dnowak@fs.fed.us

www.itreetools.org

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