United States Department of Agriculture

Urban Tree Monitoring: A Resource Guide

General Technical

Report PSW-266

Pacific Southwest

Research Station August 2020

Forest

Service
Visit our homepage at: https://www.fs.fed.us/psw/

Manuscript received for publication November 2019

Published by: For additional copies, email:

USDA FOREST SERVICE sm.fs.psw-comms@usda.gov

800 BUCHANAN STREET

ALBANY, CA 94710

August 2020

Abstract

The guidelines proposed in Urban Tree Monitoring: A Resource Guide (hereafter 

referred to as the Resource Guide) were developed and refined over many 

years to address the need for standardized urban tree monitoring protocols. 

The Resource Guide provides in-depth guidance for urban forest managers 

and researchers who want to design and implement a tree monitoring project. 

This Resource Guide is a companion to Urban Tree Monitoring: A Field Guide; 

however, the Resource Guide can also be used on its own. The Resource Guide 

is divided into three parts. In Part I, we discuss (1) the varied goals of monitoring 

projects and how to match data collection to those goals, (2) the development of 

these urban tree monitoring standards, (3) types of monitoring projects, and (4) 

connections to other protocols for urban tree data collection. We offer guidance 

on methods for recording tree location, developing tree record identifiers, 

organizing spreadsheets and databases, choosing data collection systems, 

fostering research-practice partnerships, training crews, and managing fieldwork. 

In Part II, we present five monitoring data sets: Minimum Data Set, Tree Data 

Set, Site Data Set, Young Tree Management Data Set, and Community Data Set. 

We list study goals that could be addressed with each data set and descriptions 

of relevant variables. We also provide guidance regarding which variables 

are best suited for beginner and advanced crews. Lastly, in Part III we include 

appendices with additional resources for designing and implementing tree 

monitoring projects.

Cover photo

Indian laurel fig trees (Ficus microcarpa) line streets in Santa Monica, CA. Photo 

by Natalie S. van Doorn, USDA Forest Service.

The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
Authors

NATALIE S. van DOORN is a research urban ecologist, U.S. Department of 

Agriculture, Forest Service, Pacific Southwest Research Station, 

800 Buchanan Street, Albany, CA 94710.

LARA A. ROMAN is a research ecologist, U.S. Department of Agriculture, 

Forest Service, Northern Research Station, Philadelphia Field Station, 

100 N 20th Street Suite 205, Philadelphia, PA 19103.

E. GREGORY McPHERSON is a research forester (retired) and PAULA J. PEPER

is an urban ecologist (retired), U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station, 1731 Research Park Drive, Davis, CA 95616.

BRYANT C. SCHARENBROCH is an assistant professor of soil science, University 

of Wisconsin-Stevens Point, College of Natural Resources, 800 Reserve Street, 

Stevens Point, WI 54481, and research fellow at The Morton Arboretum, 

4100 Illinois Route 53, Lisle, IL 60532. 

JASON G. HENNING is a research forester, Davey Institute, a division of the Davey 

Tree Expert Company, and a contractor with U.S. Department of Agriculture, Forest 

Service, Philadelphia Field Station, 100 N 20th Street, Suite 205, Philadelphia, PA 

19103.

JOHAN P.A. ÖSTBERG is a researcher, Swedish University of Agricultural Sciences 

in Alnarp, Department of Landscape Architecture, Planning and Management, 

Box 66, 23053, Alnarp, Sweden. 

LEE S. MUELLER is a forestry specialist, the Davey Resource Group, Inc., 

1500 North Mantua, Kent, OH 44240 and formerly the program director, 

Urban Forest Project for Friends of Grand Rapids Parks in Grand Rapids, Michigan. 

ANDREW K. KOESER is an assistant professor, University of Florida Gulf Coast 

Research and Education Center, 14625 County Road 672, Wimauma, FL 33598; 

JOHN R. MILLS is a research forester (retired), U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station, 620 SW Main, Suite 400, 

Portland, OR 97205. 

RICHARD A. HALLETT is a research ecologist, U.S. Department of Agriculture, 

Forest Service, Northern Research Station, New York City Urban Field Station, 

431 Walter Reed Road, Bayside, NY 11359. 

JOHN E. SANDERS is a research specialist and JOHN J. BATTLES is a 

professor, University of California, Department of Environmental Science, Policy and 

Management, Berkeley, 130 Mulford Hall, Berkeley, CA 94720. 

DEBORAH J. BOYER is a project manager, Azavea, 990 Spring Garden Street, 

Fifth Floor, Philadelphia, PA 19123. 

JASON P. FRISTENSKY is a project manager, the Berger Partnership P.S., 

1721 8th Avenue N, Seattle, WA 98109 and formerly a research assistant, 

U.S. Department of Agriculture, Forest Service, Philadelphia Field Station, 

100 N 20th Street Suite 205, Philadelphia, PA 19103. 

SARAH K. MINCEY is an associate director, Indiana University-Bloomington, 

Integrated Program in the Environment, Multidisciplinary Science Building II 134, 

702 N Walnut Grove Avenue, Bloomington, IN 47405. 

JESS VOGT is an assistant professor, DePaul University, Environmental Science 

and Studies, 1 E Jackson Boulevard, Chicago, IL 60604. 
Contents

Part I: General Strategies for Urban Tree Monitoring .

1. Introduction .

1.1. Why Monitor Urban Trees? .

1.2. Monitoring Project Types .

1.3. Connecting Monitoring Goals to Field Methods .

1.4. Background . 

2. Getting Started . 

2.1. Planning Ahead for Data Collection, Management, 

 and Analysis . 

2.2. What Is a Tree? . 

2.3. Choosing the Appropriate Location Method . . . . . . . . . 30

2.4. Site Type and Land Use . 

2.5. Longitudinal Database Considerations . 

3. Managing Fieldwork . 

3.1. Tips for Training Field Crews . 

3.2. Tips for Managing and Supporting Field Crews . 

3.3. Field Equipment Suggestions . 

4. Conclusion . 

Part II: Data Sets for Urban Tree Monitoring . 

5. Data Sets Framework . 

6. Minimum Data Set . 

6.1. Basic Record Information . 

6.2. Location and Site . 

6.3. Tree Data . 

6.4. Background on Mortality Status . 

6.5. Background on Crown Vigor . 

6.6. Background on Trunk Diameter . 

7. Tree Data Set . 

7.1. Tree Size Measurements . 

7.2. Tree Condition and Maintenance . 

7.3. Images . 

7.4. Background on Fine Twig Dieback . 
8. Site Data Set . 

8.1. Roads . 

8.2. Buildings . 

8.3. Hardscapes . 

8.4. Overhead Conflicts . 

8.5. Soil . 

9. Young Tree Management Data Set . 

9.1. Program Information . 

9.2. Tree Steward Information . 

9.3. Stewardship Actions . 

10. Community Data Set . 

10.1. High-Priority National Data . 

10.2. Local Data . 

10.3. Community Surveys and Interviews . 

11. Acknowledgments . 

Part III: Supporting Documentation . 

Literature Cited . .

Appendix 1: Site Type and Land Use Examples . .

Appendix 2: Species Identification Resources . .

Web Sites and Apps . .

Books . .

Appendix 3: Other Protocols . .

Appendix 4: Field Crew Training Agendas and Activities . .

Agenda No. 1: Full-Day (7 Hours) Field Crew 

Training Agenda for Interns or Citizen Scientists . .

Agenda No. 2: Short (2.5 hours) Training Agenda 

for Interns or Citizen Scientists . .

Glossary . .

vii

Foreword

We wrote Urban Tree Monitoring: A Resource Guide (hereafter Resource Guide) 

to assist managers, researchers, and students of urban forestry and urban ecology in 

field-based tree monitoring projects. We have each been involved with numerous tree 

monitoring projects conducted in settings ranging from urban to rural, for research 

and management applications, with field crews including volunteers, students, 

interns, and professionals. The Resource Guide encompasses best practices we have 

learned through years of fieldwork and associated data management. We briefly 

describe our relevant experiences below, which have informed the many suggestions 

found throughout the Resource Guide.

Natalie S. van Doorn supervised field crews and managed data collection for 

permanent, rural forest plots for the National Science Foundation’s Long-Term 

Ecological Research program at Hubbard Brook Experimental Forest, New 

Hampshire, for 12 years and has been working with long-term urban tree data for 

5 years. John J. Battles, John E. Sanders, and Richard A. Hallett have also worked 

with permanent, rural forest plots in experimental forests at Hubbard Brook and 

elsewhere, for 34, 10, and 21 years, respectively. Lara A. Roman has collected data 

and supervised field crews for yard and street tree monitoring in northern California 

and Philadelphia, PA, for 13 years, and has trained citizen scientists. E. Gregory 

McPherson and Paula J. Peper have been involved with collecting, managing, and 

analyzing street tree inventories across the United States for more than 20 years. 

Bryant C. Scharenbroch has worked on urban soils and tree inventories in Chicago, 

IL, and elsewhere for 9 years. Jason G. Henning has taught forest measurements and 

collected rural forestry data for 12 years and worked with urban forestry data related 

to i-Tree from across the United States for 6 years. Johan P.A. Östberg has been 

doing urban forestry field inventories for 11 years and developed urban tree inventory 

standards for Sweden. Lee S. Mueller has recruited, managed, and supported 

volunteers for tree inventory and assessment projects in Detroit and Grand Rapids, 

MI, for 7 years. Deborah J. Boyer has worked with urban forest inventory software 

for municipal, nonprofit, and state organizations for 7 years. Andrew K. Koeser has 

supervised tree inventories and post-hurricane assessments across Florida for 7 years, 

using the data collected to assess past management efforts and guide best practices. 

Jess Vogt and Sarah K. Mincey have supervised street tree data collecting in 

Indianapolis, Indiana, and other cities for 8 years, and have developed protocols for 

monitoring recently planted trees, and Jess Vogt has also taught inventory methods 

for 4 years. Jason P. Fristensky worked as a field crew member and database manager 

for urban tree inventories for 1 year and has been a landscape architect for 6 years.
viii GTR-PSW-266

We sincerely hope that readers find the Resource Guide and the companion Urban 

Tree Monitoring: A Field Guide useful when implementing urban tree monitoring 

projects, as these documents contain the kinds of information that we all would have 

benefited from when we began long-term field studies of urban trees. Our experiences 

and examples in this Resource Guide are mostly from the United States, but the 

strategies and suggestions we offer are broadly applicable to urban tree monitoring 

worldwide.
1

Part I: General Strategies for Urban Tree Monitoring
2

1. Introduction

Urban Tree Monitoring: A Resource Guide (hereafter Resource Guide) is a 

companion to Urban Tree Monitoring: A Field Guide (hereafter Field Guide) 

(Roman et al. 2020). Whereas the Field Guide provides detailed protocols for 

a core set of variables and can be used by professionals, researchers, interns, 

and volunteers in the field, the Resource Guide provides in-depth guidance 

for urban forest managers and researchers who want to design and implement 

a monitoring project. Throughout the Resource Guide, we discuss various 

goals of monitoring projects and how to match data collection methods 

to those goals, the background of the development of these urban tree 

monitoring guidelines, types of monitoring projects, connections to other 

protocols for urban tree data collection, tips for training crews and managing 

fieldwork, guidance on selecting tree location methods and tree record 

identifiers, and detailed explanations of monitoring data sets. 

Notably, while the term urban forest is generally used in the United States 

to refer to all trees in cities and urbanized areas, both public and private, 

including street, yard, and park trees (Konijnendijk et al. 2006, Nowak et al. 

2010, Piana and Troxel 2014), our Field Guide and Resource Guide focus on 

trees along streets and in lawns (including residential lawns, park lawns, as 

well as other landscaped spaces). We do not cover trees in wooded or natural 

areas of urban parks because these would require a different monitoring 

approach. For further reading about field methods for inventories and 

monitoring in rural forest systems, see Avery and Burkhart (2001), Brassel 

and Lischke (2001), Husch et al. (2002), Van Laar and Akca (2007), and West 

(2009). It is important to recognize that trees in different parts of the urban 

ecosystem have different environmental conditions and population dynamics, 

which can require different approaches to inventory and monitoring methods.

Throughout this document, terms in bold are defined in the Glossary 

(page 127). 

Monitoring is a key element of sustainable urban forest management. 

As suggested by Clark et al. (1997), assessing urban forest resources for 

sustainability entails “collect[ing] information about the urban forest on 

a routine basis.” This is more than a static “snapshot” inventory. A single, 

static inventory can be used to understand structure, function, and ecosystem 

services at a given point in time—such as tree size class distribution, species 

composition, and associated environmental benefits—but an inventory 

can quickly become outdated in the dynamic urban landscape. Indeed, 

Urban Tree Monitoring: A 

Resource Guide focuses 

on trees along streets and 

in lawns.

1.1. Why Monitor 

Urban Trees?
Urban Tree Monitoring: A Resource Guide 3

recognizing the value of long-term field data, many municipal arborists, 

urban greening nonprofits, and states are already engaged in ongoing 

monitoring (Roman et al. 2013). Additionally, many research articles about 

monitoring urban tree mortality (and its flip side, survival), growth, and 

health have been published in the past few years, demonstrating increasing 

interest in the topic from scholars (e.g., Breger et al. 2019; Hallett et al. 2018; 

Hilbert et al. 2019; Ko et al. 2015a, 2015b; Koeser et al. 2014; Lima et al. 

2013; Martin et al. 2016; Roman et al. 2014a, 2014b; Roman et al. 2015; 

Vogt et al. 2015a; van Doorn and McPherson 2018; Widney et al. 2016). 

While a single inventory can describe structure and spatial patterns in an 

urban forest, only monitoring data can describe change over time.

In this report, we focus on long-term field monitoring, although there are 

certainly other kinds of monitoring relevant to urban forests (Leff 2016), 

including using aerial imagery, LiDAR, and remote sensing to detect 

changes in tree cover. One could also assess changes over time in the human 

communities and institutions that steward trees. But, in this report, we 

specifically focus on field monitoring of urban trees to produce longitudinal 

data: repeated observations of the same individual trees. Longitudinal studies 

are essential for research on tree demography—the study of population 

dynamics—including analysis of change over time in mortality, growth, and 

health. While such studies are relatively new to urban forestry, longitudinal 

monitoring studies are more widespread in rural forests (Roman et al. 2016). 

Studies of tree demography in rural forests can include analysis of tree 

mortality, growth, ingrowth, and regeneration within plots or stands, and 

likewise, tree demography studies of yard or street trees can include analysis 

of tree mortality (including removal), growth, and planting (with some 

natural regeneration possible depending on site conditions). The urban tree 

monitoring standards presented in the Field Guide, and tips offered in the 

Resource Guide, support structured longitudinal studies of urban trees. Our 

urban tree monitoring standards are focused on trees whose population cycles 

are anthropogenically controlled (Roman et al. 2016). Although several 

dozen urban forestry programs in the United States already engage in tree 

monitoring (Roman et al. 2013), methods differ widely, and it is important 

to have coordinated efforts to collect monitoring data across many cities (for 

examples of coordinated monitoring projects across cities, see McPherson et 

al. 2016, Widney et al. 2016). The variation in methods presents challenges 

for managers and researchers to compare data across cities and programs. 

Such inter- and intra-city comparisons using standard data are important to 

illuminate typical rates of mortality and growth across regions and species 

and to facilitate studies concerning which factors influence those outcomes.

Urban tree field monitoring can serve several management and research 

goals, whether for inter- or intra-city studies. However, no single project can 

achieve all these goals, making it important to decide the intended objectives 

at the beginning of a monitoring project. Some possible goals (listed in no 

particular order) are offered below.

Monitoring studies describe 

change over time.

Longitudinal data consist of 

repeated observations of the 

same individual trees.

The intended objectives 

should be decided at the 

beginning of a monitoring 

project.
4 GTR-PSW-266

• Evaluating planting program performance. Tracking trees planted 

through a particular program can yield information about tree 

performance outcomes—such as growth, survival, health, and associated 

ecosystem services. For many municipalities and nonprofits that conduct 

tree monitoring, tree performance outcomes serve as metrics of program 

success. For example, New York City Parks & Recreation (New York, 

NY) collected data on street tree survival in relation to social and 

ecological factors (Lu et al. 2010). Researchers collaborating with the 

Sacramento Tree Foundation and Sacramento Municipal Utility District 

in Sacramento, CA, monitored the survival and growth of residential 

shade trees to determine how well the initial energy-saving projections 

matched observed performance (Ko et al. 2015a, 2015b; Roman et 

al. 2014a). In San Francisco, CA, researchers collaborated with the 

nonprofit organization Friends of the Urban Forest to monitor growth and 

vigor of commonly planted ornamental trees (Martin et al. 2016).

• Understanding the ecological and social factors that predict tree 

mortality, growth, and health. Studies that track factors that might 

be associated with tree mortality, growth, and health can improve our 

scientific and practical understanding of how urban tree systems change 

through time. Studies that incorporate long-term monitoring can also 

suggest potential areas for program enhancement and identify trees 

at higher risk for decline. For example, the aforementioned studies 

of residential shade trees in Sacramento showed that tree mortality 

was linked with homeowner instability (i.e., foreclosures, home sales, 

and renter-occupied properties), and that homeowner instability was 

connected to poor maintenance practices by residents (Ko et al. 2015a, 

Roman et al. 2014a). This suggested the need to support residential tree 

maintenance over time, even with new owners and tenants. Street tree 

studies have also shown the importance of maintenance and especially 

watering regimes on tree survival (Breger et al. 2019; Koeser et al. 2014; 

Mincey and Vogt 2014; Vogt et al. 2015a). Additionally, some urban tree 

monitoring studies have shown how mortality and health outcomes differ 

by species or groups of species, in relation to ecological factors such as 

drought tolerance, city microclimates, and coastal flooding (Hallett et 

al. 2018, Ko et al. 2015a, Koeser et al. 2014, Martin et al. 2016, Roman 

et al. 2014a, ). In the same vein, qualitative evaluations of street tree 

planting programs with unusually high or low performance (e.g., very 

high or very low establishment survival) can suggest elements essential 

for tree survival (Breger et al. 2019, Roman et al. 2015, Yang and 

McBride 2003). Studies based on repeated plot inventories can similarly 

be used to understand factors associated with urban tree mortality, 

growth, and health. Such plot-based studies have also assessed changes 

in urban forest structure and ecosystem services over time and the 

impacts of extreme weather events (e.g., Lawrence et al. 2012, Lima et 

al. 2013, Nowak et al. 2013b, Staudhammer et al. 2011). Notably, some 

Tree performance outcomes 

may include growth, survival, 

health, and associated 

ecosystem services.
Urban Tree Monitoring: A Resource Guide 5

of the plot-based studies span many different land uses and site types in 

the urban forest, including forested stands in parks, residential trees, and 

street trees.

• Engaging local communities. Community engagement in urban 

forestry can involve citizen science (Roman et al. 2017, 2018a). Citizen 

science engages the public in ecological research and natural resource 

management (Dickinson et al. 2010, 2012; Tulloch et al. 2013), typically 

using volunteers to collect field data (Silvertown 2009). For example, 

some nonprofits recruit volunteers to track recently planted trees, 

including Friends of the Urban Forest (San Francisco, CA), Canopy (Palo 

Alto, CA), and the Pennsylvania Horticultural Society (Philadelphia, 

PA). When volunteers observe tree maintenance issues, that information 

can be used to inform residents about tree condition and provide 

recommendations for improved stewardship. Additionally, volunteers 

have been engaged in citywide street tree inventories, including Portland, 

OR, and New York, NY (Crown et al. 2018, Silva et al. 2013, St. John 

2011). Many of these citizen science programs operate as event-based 

field campaigns, with volunteers going out on specific days, sometimes 

organized as “mapping parties.” This contrasts with crowdsourcing

approaches to urban tree data collection, in which large numbers of 

volunteers submit data when and where they wish. While citizen science 

has been used in these varied urban forestry examples, data quality can 

be a concern. Recent studies that have assessed volunteer data quality 

in street tree inventories include Roman et al. (2017, 2018b), Bancks et 

al. (2018), Hallett and Hallett (2018), and Hamilton et al. (2018). Those 

studies suggest that project supervisors should connect data quality 

needs to field crew training and appropriateness of utilizing volunteers. 

For certain variables and certain uses of the data, volunteer-generated 

data quality is appropriate (see sections 1.3, 1.4.3, and 2.1.9 for further 

discussion about data quality and volunteers).

• Managing pruning cycles and tree risk. This is especially relevant 

for street trees, which require frequent inspections to guide pruning 

and removal of aging and hazardous trees (Harris et al. 2004, Pokorny 

2003). Repeated street tree inventories can be integrated with a municipal 

forester’s regular inspection and pruning cycle. For instance, a municipal 

arborist could inspect one-fifth of the town’s street trees each year and 

update the inventory each year with tree mortality and new planting data, 

such that inspections and inventories are completed in a 5-year cycle. 

This kind of systematic inventory/inspection cycle ensures that records 

are up to date for proactive management and allows for analysis of 

mortality and tree risk (Hauer and Peterson 2016, Roman et al. 2013). 

• Producing empirical data for population projection and ecosystem 

services models. Incomplete information about tree mortality is 

acknowledged as a major source of uncertainty in models that project 

urban forest populations into the future to estimate ecosystem services 

To evaluate the 

appropriateness of using 

volunteers for urban tree 

monitoring, consider data 

quality needs and field 

crew training.
6 GTR-PSW-266

(McPherson 2014, McPherson et al. 2008, Morani et al. 2011, Roman 

et al. 2016). Projected ecosystem services are also sensitive to changes 

in tree growth (Ko et al. 2015b, Widney et al. 2016), yet there is very 

little empirical data about tree growth in urban environments (Roman 

et al. 2015). Urban tree allometry—the sizing relationships of trees 

(e.g., equations to estimate height, crown dimensions, and biomass from 

trunk size)— is another essential component of ecosystem services 

and population projection models (Blood et al. 2016, McHale et al. 

2009, McPherson et al. 2016, Troxel et al. 2013). Generating more tree 

mortality, growth, and allometry data—across cities, programs, species, 

and site conditions—is critical to improve model accuracy.

• Detecting emerging threats from pests and diseases. Monitoring for 

tree pests and diseases in the urban environment can provide valuable 

early warnings about threats to both urban and natural forest systems. 

Field methods designed for pest detection and tree health monitoring 

have been developed by the U.S. Forest Service and the Healthy Trees, 

Healthy Cities initiative of The Nature Conservancy. There is also an 

i-Tree module for recording pest detection data (USDA Forest Service 

2017c). These tools are similar to pest detection systems for rural forests 

(Bargeron and Moorhead 2007), and such monitoring can be geared 

toward professionals or citizen scientists.

• Evaluating outcomes of experimental tree plantings. Experimental 

tree plantings can be used to evaluate the performance of new species, 

new cultivars, or responses to changing climate conditions. For example, 

field tests of serviceberry (Amalanchier spp.), crabapple (Malus spp.), 

and callery pear (Pyrus calleryana) indicated varying growth rates, 

condition, and site suitability for different cultivars (Gerhold 2007a, 

2007b, 2008). Other studies have experimentally planted species 

previously untested in a particular climate zone (McPherson and 

Albers 2014). One such experiment, the Climate-ready Trees project 

(McPherson et al. 2018), is planting species thought to be adaptable to 

the predicted future conditions in California climate zones. To determine 

success or failure of species or cultivars that are new to a city or region, 

it is important to track their mortality, growth, and health over a number 

of years, potentially decades. 

1.1.1. A brief note about mortality and survival terms

Because mortality is often one of the key outcomes of interest in urban 

tree monitoring studies, we provide here a brief note about the meaning of 

mortality and survival terms. We define urban tree mortality as a combination 

of trees observed dead and those that were removed. Mortality observations 

can be expressed as the annual mortality rate, which is the proportion 

of trees dying (or removed) in a given year. The annual survival rate is 

the flip side of mortality, the proportion of trees surviving in a given year 

(annual survival = 1 – annual mortality). When tracking trees from a planting 

Generating more tree 

mortality, growth, and 

allometry data is critical to 

improve the accuracy of 

population projection and 

ecosystem service models.

We define urban tree 

mortality as a combination 

of trees observed dead and 

those that were removed.
Urban Tree Monitoring: A Resource Guide 7

project, survivorship is the proportion of trees surviving from planting to a 

particular time (e.g., survivorship to 10 years post-planting). These terms are 

all rooted in standard definitions from demography and population biology. 

Survivability, a term often used among urban forestry professionals, does 

not have a standard definition. See Roman et al. (2016) for more information 

about how to calculate annual mortality, annual survival, and survivorship, 

and other applications of demographic concepts to urban forests.

There are two basic types of field-based urban tree monitoring projects dealt 

with in the Field Guide and Resource Guide: (1) planting cohort monitoring 

of relatively even-aged trees, and (2) multi-age inventory monitoring. 

• Planting cohort monitoring of relatively even-aged trees (hereafter 

referred to as planting cohort monitoring). A planting cohort is a group 

of trees planted around the same time (e.g., same planting season or 

same calendar year). Cohort monitoring is usually intended to track trees 

planted within the same program or initiative. With a cohort study, the 

total number of originally planted trees decreases over time as trees die 

or are removed. If replacement trees are planted, then those new cohorts 

can also be monitored.

• Multi-age inventory monitoring. Monitoring trees within a given 

geographic area (e.g., city, neighborhood), regardless of who planted or 

when, is an inventory remeasurement. Inventories may span multiple age 

classes as a result of different planting campaigns, disturbance intensities 

(e.g., past storms), and management regimes. Multi-age inventory 

monitoring could target specific land uses or site types (e.g., residential 

yards, street trees, neighborhood parks). With this project type, trees can 

be both added to (via planting or natural regeneration) and removed from 

(via death in place or human removal) the inventory. The total number of 

trees monitored may therefore increase or decrease over time. New trees 

added to the inventory (via planting or regeneration) can be analyzed 

as ingrowth. Changes in the total population size can be reflected in 

the population growth rate (not to be confused with tree growth rate). 

Replacement trees can also be monitored to understand the dynamics of 

the tree population. Changes in species composition and diversity can 

also be analyzed for multi-age inventory monitoring projects.

Each of the two monitoring types described above can be further categorized 

based on what portion of trees within the project or geographic area are 

monitored. In a census, all trees in the planting project or geographic area are 

inventoried, whereas in a sample, only a selection of trees is measured. For 

instance, a repeated street tree inventory that covers all streets in a particular 

city would be a multi-age inventory monitoring project that constitutes 

a citywide census. But a repeated street tree inventory that involves only 

selected blocks would be a sample multi-age inventory monitoring project.

1.2. Monitoring Project 

Types

Planting cohort monitoring 

tracks trees planted within 

the same program 

or initiative.

Multi-age inventory 

monitoring tracks trees 

of all ages within a given 

geographic area.
8 GTR-PSW-266

For both planting cohort monitoring and multi-age inventory monitoring, 

tree mortality, growth and replacement rates, as well as health changes, can 

be analyzed. However, it is essential that all tree remeasurements are linked 

to the first record of that tree or planting site in the database; this is the 

essence of longitudinal database structures to track individuals over time (see 

section 2.5 about longitudinal database considerations). If it is not possible 

to link the remeasurement or loss of the same tree across time, it will not be 

possible to analyze the data for individual tree mortality, growth, or health, 

and summarize outcomes across all the trees studied. For some projects 

seeking to summarize stocking levels (the extent to which growing space is 

filled with trees) and planting needs over time, the planting site (not the tree) 

should be the basic organizational unit of the longitudinal database. When 

tree planting sites (including potential sites) are the central unit of interest 

in a monitoring project, then vacant sites are part of the inventory and can 

have different trees residing through the years, but their geographic location 

remains constant. Tracking planting sites is particularly relevant to street 

tree planning and management (van Doorn and McPherson 2018, see also 

section 2.1.14). See section 2.5.3 for example database structures that involve 

tracking sites and replacement trees.

Each of the goals listed above has associated data needs, and it is essential 

to link monitoring goals to data collection strategies. Neglecting to articulate 

up-front clear monitoring objectives, variables to be collected to meet 

those objectives, and plans for data analysis can lead to the “data-rich but 

information-poor” scenario of environmental monitoring (Ward et al. 1986) 

in which analysts become “snowed by a blizzard of ecological details” 

(Lindenmayer and Likens 2010a). This can sometimes result from poorly 

articulated goals, leading to an overload of variables without clear uses for 

each piece of data collected.

As an example of appropriate links between goals and methods, consider the 

two types of monitoring projects (section 1.2): planting cohort monitoring 

and multi-age inventory monitoring. Monitoring trees from a planting cohort 

is a way to evaluate the performance of a planting program. For example, 

a neighborhood planting program seeks to understand losses during the 

establishment phase (the first few years after planting [Richards 1979]) 

and determine potential program alterations for enhanced tree survival 

and vigor. The program also seeks to understand the impacts of tree care 

and site conditions on survival. Such monitoring of recently planted trees 

should include data collection for mortality, vigor, and stewardship (e.g., 

check whether tree care recommendations are being followed, such as 

irrigation, mulching, staking, and pruning). The program may also choose 

to record site characteristics for each tree. If volunteers or interns with little 

prior experience are collecting the data, only a select few variables for site 

characteristics and tree maintenance observations are appropriate. Additional 

tree growth and health outcomes may also be relevant to the project, 

The basic organizational unit 

of the longitudinal database 

can be the planting site or 

the tree.

Tree remeasurements should 

be linked to the first record 

of that tree or tree site in the 

database.

1.3. Connecting 

Monitoring Goals 

to Field Methods

Monitoring goals should 

be linked to data collection 

strategies.
Urban Tree Monitoring: A Resource Guide 9

depending on the program’s objectives and field crew skills or training. 

Planting cohort monitoring can also be used to evaluate growth and vigor 

of experimental tree planting of new species or cultivars or of commonly 

planted species across varying site conditions. Such projects may also 

evaluate species’ tolerance for changing climate conditions or pest resistance. 

Extending monitoring goals beyond mortality outcomes requires collecting 

additional tree and site variables (see Tree Data Set, section 7, and Site Data 

Set, section 8).

Next, consider multi-age inventory monitoring. Just as the U.S. Census 

tracks changes in the human population, a city tree inventory can be used 

to describe patterns in the urban forest over time. A repeated census or reinventory can be used to understand whether new plantings are on pace with 

losses from death and removal. For example, a municipality seeks to assess 

the extent to which a major street tree planting campaign is influencing 

overall population size and size class distribution, considering ongoing 

tree mortality across the city (in other words, determining whether planting 

rates are keeping up with losses). The municipality is also concerned with 

identifying trees that need to be pruned or removed to protect public safety 

and infrastructure. The municipality should do a repeated inventory of all 

street trees, both existing and newly planted, potentially visiting a fraction 

of the trees each year in conjunction with an inspection cycle. Analysis and 

conclusions about mortality and planting rates would be possible after the 

second census (e.g., an initial census, and then a second census to remeasure 

the same trees a few years later). A repeated tree census can also be used for 

pest and disease detection or to track impacts of extreme weather events on 

tree growth, health, and mortality. Monitoring goals may drive the need for 

additional variables (e.g., noting whether a tree is a replacement) or change 

how a variable is analyzed (e.g., basal sprout counted as a new tree) (see 

sections 2.1.15, 2.1.16, 2.1.17). Whatever the specific project objectives are, 

they should connect to and inform the data collection methods and analysis 

plans. 

Different monitoring goals and intended uses of the data also have different 

data quality needs, with implications for how skilled field crews need to 

be. For example, evaluations of wood condition, tree risk, and pruning 

needs should be done by professional arborists, not volunteers, based on 

evidence of low data quality for such variables in citizen science inventories 

(Bloniarz and Ryan 1996, Cozad 2005, Roman et al. 2017). There may 

also be liability concerns for municipal urban forest managers that require 

them to use certified arborists for evaluations of tree maintenance and risk. 

However, data collected by volunteers may be suitable for other management 

purposes. Urban forestry volunteers can provide reasonable accuracy for 

genus identification, particularly for common genera (Bancks et al. 2018, 

Bloniarz and Ryan 1996, Hallett and Hallett 2018, Hamilton et al. 2018, 

Roman et al. 2017). If more accurate species-level identification is required, 

professional field crews with excellent identification skills should be used. 

Project objectives should 

connect to and inform the 

data collection methods and 

analysis plans.
10 GTR-PSW-266

When measuring diameter at breast height (d.b.h.), volunteers are generally 

within 1 inch (2.54 cm) of accuracy, which is acceptable for municipal 

arborists to describe size class distributions and contract for tree removal, 

but may not be acceptable for scientific studies of tree growth (Roman et al. 

2017). Data collected by researchers and professionals are not infallible, but 

can be more consistent than data collected by volunteers (Bloniarz and Ryan 

1996, Crall et al. 2011). Whether tree monitoring is conducted by researchers, 

professionals, interns, or volunteers, examining the extent and sources of 

error helps to identify best practices for training crews, conducting fieldwork, 

and managing data (Bancks et al. 2018; Hallett and Hallett 2018; Hamilton 

et al. 2018; Roman et al. 2017, 2018b; van Doorn 2014). We discuss tips for 

training and managing field crews that can promote data quality and effective 

data management in section 3.

One urban forest manager who conducts tree monitoring projects offered 

the following suggestion for other practitioners and researchers seeking to 

establish monitoring programs:

“They need to know what the purpose is for the information. If 

you’re taking the time to do it, what’s the point? This helps drive 

what data you collect. Know who is going to do the work, and make 

sure they have the time and experience to do it properly.”

(Roman et al. 2013)

1.4.1. Development of the monitoring standards 

The Resource Guide was developed by the Urban Tree Growth and 

Longevity (UTGL) working group and affiliated urban forestry researchers 

at the USDA Forest Service. The UTGL working group, founded in 2010, 

is part of the Arboriculture Research and Education Academy of the 

International Society of Arboriculture (Campbell et al. 2016, Scharenbroch 

et al. 2014). The working group’s mission is to foster communication among 

researchers and professionals; enrich scientific exchange; and enhance the 

quality, productivity, and timeliness of research on tree mortality, growth, 

and longevity through collaboration. This community of practice includes 

members (currently nearly 400) representing scientists, urban forestry 

professionals, and students. 

The need for urban tree monitoring protocols and standardized data 

collection was identified at the first UTGL symposium at The Morton 

Arboretum (Lisle, IL) in 2011. As part of that event, attendees participated 

in a roundtable discussion on research priorities. Several of the top priorities 

related to standardized, comprehensive field-based monitoring protocols 

that would enable data sharing and collaboration (Leibowitz 2012). These 

desired protocols could detect change over time and across cities, while 

providing flexibility required by diverse users. That sentiment was echoed by 

respondents to a national survey of 32 local urban forestry organizations that 

already engage in urban tree monitoring. Staff at these organizations were 

asked about the goals, challenges, methods, and uses of their monitoring 

1.4. Background 
Urban Tree Monitoring: A Resource Guide 11

programs; this information directly fed into the new protocols. Instead of 

“re-creating the wheel” and each developing their own monitoring protocol, 

respondents expressed interest in adopting a proven protocol (Roman et al. 

2013), thereby freeing up scarce resources for other purposes and enabling 

comparisons across programs. 

When asked for their recommendations for new standard protocols, survey 

participants suggested that the process should be inclusive and involve 

practitioners, and that protocols should be kept simple for users, rather 

than “complicated and academic” (Roman et al. 2013). Respondents also 

suggested that protocols should be adaptable to different organizational 

capacities and needs and noted that they would benefit from guidance from 

researchers and other organizations. As two participants expressed:

“It would be helpful if the standardized protocols are developed 

with various respondents’ program designs/capacities in mind, that 

information is supplied suggesting the relevance/appropriateness of 

suggested protocols to the diversity of programs.” 

“Our small organization does not have the capacity to do this 

research ourselves and search for and interview other programs. 

By providing information on what other programs do, suggesting 

protocols, and providing guidance, researchers could help us 

improve our work.” (Roman 2013)

These research findings document the critical need and strong support for a 

monitoring standard. The Field Guide and Resource Guide respond to this 

need by providing guidance and field methods concerning long-term data 

collection. 

From 2013 to 2014, an advisory group of several dozen urban forestry 

professionals, researchers, and students had regular conference calls and 

webinars to refine the protocols, facilitated by the UTGL working group. 

This approach was fundamentally bottom-up, with protocols responding to 

needs identified by local urban foresters, as group members included urban 

forest managers throughout the process. UTGL leadership emphasized an 

approach focused on co-learning across research and practice (Campbell 

et al. 2016).

Suggestions from survey respondents (Roman et al. 2013) formed a set of 

guiding principles for the development of protocols that meet the needs of 

most users:

• Keep it simple. Protocols should be straightforward, accessible to 

practitioners and managers, and relevant to organizations that rely on 

interns and volunteers for data collection.

• Make it flexible and easily applied by a diversity of users. Urban 

forestry practitioners collect monitoring data for a variety of purposes, so 

the protocols must be adaptable to different management needs.
12 GTR-PSW-266

• Seek input from practitioners. To ensure that the protocols will be 

relevant to local arborists, municipalities, nonprofit organizations, and 

other users, the protocol development process should seek frequent input 

from practitioners.

• Answer key research questions. There must be clear examples of how 

the data generated from this monitoring network can answer key research 

questions about urban tree mortality, growth, vigor, and longevity.

• Promote management objectives. Data collected should be useful for 

local practitioners to manage their urban forest resources.

With input from the many contributors to the UTGL protocols development 

team (see section 11) and the many authors of the Field Guide and Resource 

Guide, we drew on the knowledge of researchers and professionals who have 

years of experience designing and conducting longitudinal studies for both 

urban and rural trees (see Foreword). This addressed the guiding principle 

to be inclusive and involve practitioners. We kept the protocols simple yet 

flexible by establishing a Minimum Data Set (see Table 1 and section 6)

—the smallest set of variables deemed necessary for longitudinal urban 

tree data—and four Supplemental Data Sets (see sections 7, 8, 9, 10). The 

Supplemental Data Sets are lists of variables that could be relevant for more 

time-consuming and skill-intensive data collection. This Resource Guide 

addresses the goal to answer research questions and address management 

objectives, including a discussion about goals that can be answered with field 

monitoring (section 1.1) and examples of how those goals are connected to 

data collection plans (section 1.4.3).

1.4.2. Connections to other urban tree inventory protocols 

The field methods described here and in the Field Guide draw on existing 

methods, including i-Tree Eco, i-Tree Streets, and Urban Forest Inventory 

and Analysis (UFIA) from the USDA Forest Service. The i-Tree software 

suite is a set of free tools that assess the benefits provided by trees and 

forests in urban settings. Within the i-Tree suite, the i-Tree Streets and i-Tree 

Eco tools involve field data collection on individual trees. i-Tree Streets is 

intended to evaluate a complete or sample inventory of street trees. i-Tree 

Eco can be used to evaluate complete or plot-based sample inventories 

across the entire urban landscape including all land uses and site types. Both 

programs quantify the structure of tree populations and model the ecosystem 

services that trees provide (i-Tree 2017a, McPherson et al. 2005, Nowak et 

al. 2008). 

UFIA is the urban expansion (Cumming et al. 2007, Nowak et al. 2016a, 

USDA FS 2020) of the USDA Forest Service’s congressionally mandated 

ongoing forest inventory and monitoring across the United States through a 

national plot system, called Forest Inventory and Analysis (FIA). Traditional 

FIA did not include most urban landscapes, with the exception of areas 

within cities, towns, and suburbs that met the FIA definition of forest land—

The Minimum Data Set is 

the smallest set of variables 

deemed necessary for 

longitudinal urban tree data.
Urban Tree Monitoring: A Resource Guide 13

Table 1.—Summary of variables included in the Minimum Data Set for urban tree monitoringa

Variable Description

Field crew identification Information about the individual(s) who collected field data on this tree

Field crew experience level Experience level of the most experienced individual on the field crew

Date of observation Year, month, and day of field data collection

Tree record identifier Unique identifier that remains connected to the tree during future monitoring

Location Information about the tree’s geographic position in the landscape; several protocols available

Tree photo A photograph taken to include the entire tree in the context of its immediate location and 

showing nearby built infrastructure objects

Site type A description of the tree’s immediate location

Land use A description of the way the property around or adjacent to the tree is used by humans

Species The species of the tree being monitored

Mortality status A record of whether the tree is alive, standing dead, removed, or in some other state

Basal sprouts Growths from the base of the trunk or in the roots (record only for standing dead trees and 

stumps)

Crown vigor A holistic assessment of overall crown health which reflects the proportion of the crown with 

foliage problems and major branch loss

Trunk diameter Diameter of the tree’s trunk recorded at either 4.5 ft (1.37 m) or 1 ft (30.5 cm) depending on 

tree form, with many special rules

Height of trunk diameter The exact height at which trunk diameter was recorded

Notes for supervisory review Issues that cannot be resolved in the field; entering a note flags the tree for review by the 

project supervisor

a For the original table, see Table 1 in the Field Guide.

namely, areas at least 1 ac (0.4 ha), 120 ft (36.6 m) wide, 10 percent stocked 

with trees, and with undisturbed understories (Cumming et al. 2007, 2008; 

Oswalt et al. 2014). This means that some urban parks and forest fragments 

with dense tree cover are included in the traditional FIA plot system, but 

conventional FIA does not include isolated trees along streets, in parking 

lots, or in lawns. UFIA fuses methods from traditional FIA and i-Tree Eco. 

Austin, Texas, was the first city to have complete UFIA data (Nowak et al. 

2016a), and several other states had served as precursors of UFIA (Cumming 

et al. 2008). The UFIA program is expanding to 100 cities across the United 

States (USDA FS 2020). Notably, FIA and UFIA plots are typically observed 

on a rotating panel system (e.g., all plots are visited over a 7-year period, 

at a rate of one-seventh each year, then repeating again), whereas all i-Tree 

Eco plots for a given city are typically collected over 1 or 2 years (although 

particular cities and states may follow different strategies for both i-Tree Eco 

and UFIA). 

Of these three inventory protocols (i-Tree Streets, i-Tree Eco, UFIA), only 

UFIA is specifically designed for remeasurements of the exact same plots 

and longitudinal data collection to track individual trees. i-Tree Streets does 
14 GTR-PSW-266

Plots which have not been 

permanently referenced 

cannot be reliably found 

again.

not provide protocols for longitudinal data collection. Plots collected with 

i-Tree Eco have sometimes been remeasured (e.g., Lawrence et al. 2012, 

Nowak et al. 2004), but Eco was designed to produce aggregate population 

and ecosystem services estimates at a single point in time, not to monitor 

tree demographic changes over time. i-Tree Eco plots which have not been 

permanently referenced (i.e., carefully recording information about plot 

center) cannot be reliably found again by future field crews. For example, 

plots collected in Philadelphia, Pennsylvania, in 1996 were not permanently 

referenced, and when i-Tree Eco was repeated for this city in 2012, new 

plots had to be laid out (Nowak et al. 2016b). Different size cutoffs that 

defined what trees to include in each inventory, in addition to the different 

sampling locations for the 1996 and 2012 data, precluded rigorous analysis 

of individual tree-level change over time. Other analyses of permanently 

referenced i-Tree Eco plots recorded in two or more different years have been 

able to report change metrics such as rates of mortality and growth, as well 

as species composition changes (Lawrence et al. 2012; Nowak et al. 2004, 

2013b). See appendix 3 for descriptions of other inventory and monitoring 

protocols for urban trees.

As mentioned previously, longitudinal data are needed for analysis of 

urban tree demography, such as change over time in mortality, growth, and 

health. While UFIA will fill this gap in long-term data in the coming years, 

with standard data collected across urban areas in the United States, it is a 

very intensive protocol that requires field crews to have certification in the 

methods, with exhaustive tree and plot data (i-Tree 2017a). Additionally, 

results from UFIA will also be most relevant at regional and state scales, 

while urban foresters often need data tied to specific planting initiatives or 

management programs (e.g., data for trees planted by a nonprofit or city 

planting campaign; data for neighborhood parks and recreation centers 

managed by a municipality). Urban forestry researchers and professionals 

may also be interested in focusing on a particular component of a city’s 

urban forest, such as street, yard, or parking lot trees, owing to their 

distinctive governance structures, programmatic operations, and biophysical 

characteristics (Celestian and Martin 2005, Mincey et al. 2013, Nguyen 

et al. 2017). In these cases, randomly located plots across an entire city 

or metropolitan region, such as those in UFIA or i-Tree Eco, are unlikely 

to adequately represent the population of interest (e.g., street trees, yard 

trees) because the sample could be dominated by trees in natural areas. 

This is because trees in natural areas can make up a substantial portion of 

the overall urban forest. For example, for i-Tree Eco plots in Philadelphia, 

Pennsylvania, trees in parklands constituted over one-third of the trees 

inventoried even though parkland only covered about 9 percent of the city 

area (Nowak et al. 2016b). While parklands and natural areas are crucial 

components of the overall urban forest (Pregitzer et al. 2018), if managers 

seek data reflecting their other program or mission areas, different sampling 

strategies are appropriate. Standard methods and practical guidance are 

needed for managers and researchers seeking to embark on their own urban 

tree monitoring projects.

Longitudinal data are needed 

for analysis of urban tree 

demography.
Urban Tree Monitoring: A Resource Guide 15

With regard to urban tree data already collected by local practitioners, 

municipal foresters and nonprofit staff manage their inventories and 

monitoring programs for different purposes, such as asset and tree risk 

management, assessing planting program performance, evaluating species 

diversity and size class distribution, and engaging the public (Bond 2013, 

Harris et al. 2004, McPherson and Kotow 2013, Roman et al. 2013, Sjöman 

et al. 2012). Such management-oriented inventory data are not necessarily 

collected in a manner conducive to analysis of demographic rates of change. 

For example, if the trunk diameter is recorded to the nearest 1 inch (2.54 

cm) and the exact height at which the diameter is taken is not noted—a 

common situation in many municipal tree inventories—then the data are not 

precise enough to support analyzing tree d.b.h. growth (i.e., radial growth 

of the trunk), even though the records are certainly useful for municipal 

arborists to manage tree inspection cycles and removal work orders. 

Tracking tree location is also challenging. In conventional municipal tree 

inventories, location has often been recorded in ways that prevent field crews 

from finding all trees based on data from several years prior. For instance, 

municipal tree inventories or planting records may use only an address to 

denote location, yet there can be more than one tree per address. Field crews 

may not know whether a tree that seems to be “missing” was removed 

since the previous inventory, or was merely not found owing to insufficient 

location information. Some approaches to recording street tree inventories, 

such as the address and site code method (see Field Guide section 2.4.1), can 

be cumbersome to update with each new year of data collection, particularly 

as trees get added and removed, which disrupts the site code ordering (see 

section 2.3 for a discussion of pros and cons of various location methods 

for monitoring). Furthermore, data precision and data quality issues are 

different in longitudinal data compared to static inventories. We explain more 

about the rationale for recording particular variables in specific ways for 

monitoring projects in section 6. Urban forest practitioners wishing to assess 

tree mortality, growth, and health may need to modify their established data 

collection protocols to analyze change over time. 

Our Field Guide and Resource Guide lay the foundation for practitionerdriven long-term studies of tree mortality, growth, and health. In the Field 

Guide, we emphasize simple user-friendly explanations for volunteers and 

interns, precise location information for varied circumstances, and consistent 

measurements of tree size to facilitate growth analyses. Additionally, because 

we recognize that there is a wide range of user needs and resources for 

monitoring, and that long-term monitoring is not necessarily embedded in the 

design of most city tree inventories, this Resource Guide provides tips and 

strategies for planning and managing long-term data collection. Studies about 

urban forest change over time using field data are relatively recent in the 

urban forestry literature, meaning that procedural issues of longitudinal data 

collection and management are only now getting attention.

The Field Guide and 

Resource Guide lay the 

foundation for practitionerdriven long-term studies of 

tree mortality, growth, and 

health.
16 GTR-PSW-266

1.4.3. Examples of the Field Guide in action

The field methods described in the Field Guide have already been used and 

adapted by urban forestry researchers and professionals. We share examples 

here to provide tangible guidance as to how these standards work in action.

• Residential yard tree monitoring. Researchers at Clark University, in 

collaboration with the Massachusetts Department of Conservation and 

Recreation, supervised undergraduate interns monitoring trees planted by 

the state in residential yards as a reforestation project following removal 

of trees because of Asian longhorned beetle (Anoplophora glabripennis

[ALB]) quarantine (Bird 2014, Elmes et al. 2018, Hostetler et al. 2013). 

This was a planting cohort monitoring project with a goal of assessing 

what factors are related to young tree mortality outcomes, with particular 

interests in socioeconomic and built environmental factors. The project 

used the Minimum Data Set and linked tree locations to neighborhood 

socioeconomic variables, in addition to tree and site characteristics, to 

analyze mortality outcomes. Student researchers conducted interviews 

with study participants to assess residential motivations for tree planting, 

perceptions of tree loss from ALB, and gains from the new planting 

initiative (Goldman 2017).

• Shade tree survival audits. The Energy-Saving Trees program of the 

Arbor Day Foundation distributes free shade trees to private residences 

across the United States in partnerships with utility districts (Arbor 

Day Foundation 2018). To assess survival, Arbor Day Foundation staff 

collaborated with the Texas Forest Service to audit trees given away in 

the Houston area; fieldwork was carried out by professional foresters. 

This was a planting cohort monitoring project with a goal of providing 

summary information about tree mortality and growth, as well as 

determining the extent to which participants were maintaining their yard 

trees. In addition to the Minimum Data Set, field crews collected data 

about residential maintenance practices, borrowing methods from the 

Planted Tree Re-Inventory Protocols developed by researchers at Indiana 

University–Bloomington (Vogt and Fischer 2014).

• Citizen science street tree monitoring. The Tree Checkers program 

at Pennsylvania Horticultural Society used volunteers to monitor 

recently planted street trees in Philadelphia (Roman et al. 2018b). These 

volunteers were already engaged in tree stewardship through the Tree 

Tenders planting program. The monitoring goals were to produce planted 

tree mortality rates as a metric of program performance, while also 

engaging local communities in ongoing stewardship. TreeCheckers staff 

updated their methods based on standards in the Field Guide. A similar 

citizen science monitoring project was developed by the Pennsylvania 

Department of Conservation and Natural Resources to track young 

street tree survival in tree planting projects they fund across the state; 

a pilot test was conducted in Harrisburg, PA (Pennsylvania Urban and 

Community Forestry Council 2018). In addition to the Minimum Data 
Urban Tree Monitoring: A Resource Guide 17

Set, both of these projects recorded information about maintenance 

practices, based on recommended stewardship actions from those 

planting programs. Similar to the Arbor Day Foundation example, the 

planting program staff in Pennsylvania wanted to check how closely 

program participants followed tree care instructions. Tree Checkers also 

paired the data collection with carrying out routine maintenance actions 

as well as “report cards” to residents with reminders about stewardship. 

These tasks were all carried out by volunteers.

• Pilot test about volunteer data quality. To evaluate the draft Field 

Guide, and, in particular, its potential application to minimally trained 

field crews, a study was conducted about volunteer data quality in urban 

tree inventories (Roman et al. 2017). This project involved citizen 

scientists in four cities (Lombard, IL; Grand Rapids, MI; Philadelphia, 

PA; Malmö, Sweden). Results showed that volunteer data quality would 

be appropriate for some uses of tree inventories and monitoring, but not 

others. The findings also indicated which variables had particularly low 

data quality (e.g., crown transparency and wood condition) and should 

be dropped from the protocols, as well as particular challenges for other 

variables like d.b.h. for multi-stem trees.

• Plots for street tree health monitoring. For long-term monitoring of 

urban tree health, street tree plots were established in New York City 

and Philadelphia, with each plot consisting of several adjacent block 

segments. The project goals were to understand long-term changes in 

street tree populations, composition, and health, including crown vigor. 

This project was a collaboration between the USDA Forest Service 

Northern Research Station and The Nature Conservancy’s Healthy Trees, 

Healthy Cities initiative. The project was set up to allow for re-inventory 

of sampled plots; thus far, only the baseline data have been collected. In 

addition to the Minimum Data Set, interns collected data about foliage 

health. Forest Service scientists and program staff from The Nature 

Conservancy closely supervised the interns.

• Public tree inventory and monitoring in Hawaii. The Citizen 

Forester Program was created by the State of Hawaii Division of 

Forestry and Wildlife as an effort to engage communities in their urban 

forest and fulfill a key strategic goal in Hawaii’s Forest Action Plan, 

the development of an urban tree inventory. The purpose of the urban 

tree inventory is to facilitate management including maintaining and 

replacing trees and planning for disasters. Community volunteers are 

trained to collect tree data and work with a team leader, while a certified 

arborist ensures quality control of the data. Mapped trees along streets 

and in public parks are displayed through an online map tool 

(https://pg-cloud.com/hawaii/) displaying species and d.b.h. Developers 

of the program took advantage of the Field Guide’s modular design by 

collecting some of the Minimum Data Set but also including maintenance 

and condition variables to meet their goals.
18 GTR-PSW-266

• Climate-ready trees project. The goal of the climate-ready trees study 

is to evaluate the tolerance of underutilized tree species to stressors 

associated with climate change, especially drought, by tracking their 

mortality, growth, and health (McPherson et al. 2018). The study is being 

conducted in three climate regions of California (Inland Valleys, Inland 

Empire, and Southern California Coast) and is expected to run for 20 

years. Trees were selected for testing because of their apparent resilience 

to stressors such as heat, drought, high winds, salinity, and pests, as 

well as suitability in urban environments with respect to potential 

hazard to people and infrastructure. As part of this planting cohort 

monitoring project, tree species replicates were planted in reference sites 

on university experimental stations as well as urban parks, which will 

allow for direct comparisons of growth and survival under a range of site 

conditions. In addition to the Minimum Data Set, aspects of Tree, Site, 

and Young Tree Management Data Sets have been incorporated into the 

monitoring protocol. Trees are monitored yearly for mortality status, 

growth (d.b.h., total height, crown width), and suitability as urban trees 

(e.g., arboricultural/structural characteristics, health issues, presence of 

pest and disease, maintenance needs). 

• Undergraduate urban forestry course. For an urban forestry course 

at the University of California, Davis, instructors use components of 

the Field Guide to teach inventory and monitoring methods. Learning 

objectives include how to use standard equipment, how to ask questions 

and answer them with data, and how to get accurate and repeatable 

data to assess change through time. Material from the Field Guide was 

adapted for a student group assignment to conduct a tree inventory and 

site assessment, with students using those data to write a scientific-style 

report (M.L. Cadenasso, pers. comm.).
19

2. Getting Started

Establishing and continuing urban tree monitoring projects requires advance 

planning. Having clear project goals, data collection and management 

protocols, and intended analyses planned in advance are critical to any 

ecological monitoring endeavor (Lindenmayer and Likens 2010a). Below, we 

list 18 key steps that anyone running an urban tree monitoring project should 

undertake before training and data collection begin. 

2.1.1. Articulate clear and achievable goals for your urban tree 

 monitoring project 

These goals will determine what variables you collect and help to decide 

whether your project should expand beyond the Minimum Data Set. See 

section 1.2 for potential goals of urban tree monitoring and section 1.3 for 

examples of connecting goals to methods. 

2.1.2. Finalize the suite of variables to be collected in the field

This is determined based on project goals, field crew skill level, and available 

resources (e.g., funds for specialized equipment and professional field crews). 

Projects using citizen scientists may need to adjust their goals to focus on 

data that volunteers with minimal prior experience can collect properly 

(Bancks et al. 2018; Hallett and Hallett 2018; Hamilton et al. 2018; Roman 

et al. 2017, 2018b). Projects using arborists certified by the International 

Society of Arboriculture or specialized researchers can potentially record 

more variables, although it should not be assumed that such expert data are 

infallible or always consistent across expert observers (Bloniarz and Ryan 

1996, Koeser and Smiley 2017, Roman et al. 2017, Westfall and Woodall 

2007), or that every expert is well-versed in all necessary field methods. 

Every variable collected should have a clear purpose related to the project 

goals and data analysis plans (section 1.3). Collecting an exhaustive set of 

variables—the “laundry list”—can be counterproductive in that such an 

approach takes focus (and resources) away from the variables that matter 

most for intended analyses (Lindenmayer and Likens 2010a).

2.1.3. Determine the best method for recording tree location 

 in your study 

In this Resource Guide, we offer guidance about location methods for trees 

in various site types, including pros and cons of each method (section 2.3), 

and we give more detailed protocols for a few of those location methods in 

the Field Guide (section 2.4). The best location methods for your project 

will depend on what site types may be encountered, field crew expertise, 

2.1. Planning Ahead 

for Data Collection, 

Management, 

and Analysis

Establishing and continuing 

urban tree monitoring 

projects requires 

advance planning.

Collecting an exhaustive 

set of variables—the 

“laundry list”—can be 

counterproductive.
20 GTR-PSW-266

and available data collection equipment (especially paper versus mobile data 

collection). We recommend the use of several complimentary methods of 

recording location, including photos of every tree when feasible, to ensure 

that individual trees can be reliably found in future years. 

2.1.4. Make a plan for sampling your trees 

While some projects might sample all trees from a planting cohort, or 

conduct a complete census of all trees in a geographic area, this may not 

be logistically feasible, so many monitoring projects will need to select a 

sample. For example, the monitoring project could use a random or stratified 

subsample of all trees planted through a particular initiative over the span 

of several years. Alternatively, the monitoring could be associated with a 

total street tree inventory or with a plan to re-inventory the trees in future 

years, perhaps associated with maintenance/inspection cycles, or randomly 

selected street segments. Depending on the project goals, sampling design 

may also have to take into consideration target species, species functional 

types, geographic areas (e.g., ZIP codes, planning districts) or neighborhood 

characteristics. The sample design and sample size also affect statistical 

analysis options for the project. For municipal arborists and nonprofit 

program managers, we recommend contacting researchers or graduate 

students at universities and the Forest Service for advice regarding statistical 

sampling strategies for your specific project. 

2.1.5. Estimate the time required for data collection 

 and transportation

Based on the pilot test of the Minimum Data Set, recording that small 

suite of variables requires approximately 3 minutes per tree (Roman et 

al. 2017). Recording additional information about site characteristics 

requires approximately 5 minutes (Scharenbroch et al. 2017). Neither of 

those estimates included transportation time, but the geographic spread 

of tree locations can drastically influence the transportation time required 

for fieldwork. For example, monitoring hundreds of street trees within a 

single neighborhood will require little transportation time and could be 

achieved in a matter of weeks, but monitoring hundreds of private yard trees 

or plots spread across a large city or metropolitan region will have longer 

transportation times and likely take several months. For studies involving 

residential yard trees and other private properties, communicating with 

residents or property managers to gain access to their land can drastically 

lengthen fieldwork time required. Some cities may also have challenges 

with excessive rush hour traffic and difficulty finding parking, so the project 

supervisor needs to select the most productive strategies for fieldwork timing 

(e.g., begin before rush hour) and mode of transportation (e.g., car, bicycle, 

public transit). 
Urban Tree Monitoring: A Resource Guide 21

2.1.6. Decide how the term “tree” will be defined 

 for your monitoring project 

There is no broadly accepted definition for “tree” in botany or forestry 

(Gschwanter et al. 2009, Lund 1999). See section 2.2 for possible definitions 

of “tree” in urban tree monitoring. Select the definition of tree for the 

particular monitoring project and explain it to your field crews. Confusion 

over this issue can introduce substantial errors in the monitoring, as crews 

may be unclear which plants are “in” or “out” of the study (Roman et al. 

2014b, 2017). This can lead to problems quantifying mortality rates and net 

changes to total tree counts (i.e., population growth rates). Similarly, urban 

foresters may be interested specifically in “street trees” or “public trees,” 

but the use of these terms can vary across and even within municipalities, so 

project supervisors need to clearly and simply define them (see section 2.2).

2.1.7. Select the data collection system 

Optimal data collection systems will differ by the purpose and needs of each 

project. Although many urban foresters have moved to mobile data collection 

systems, paper might be preferable in some cases—for instance, when doing 

a citizen science inventory with volunteers who do not have smartphones or 

tablets. Furthermore, while there are many software packages for urban tree 

inventories, they are generally not designed for longitudinal data collection 

(Boyer et al. 2016). For more information about the pros and cons of paper 

versus mobile data collection and further discussion of software packages, 

see section 2.6. 

2.1.8. Determine the type of monitoring project

As previously discussed, there are two general types of monitoring projects 

addressed in the Field Guide and the Resource Guide (see section 1.2): 

planting cohort monitoring and multi-age inventory monitoring. The study 

type chosen should be kept in mind when comparing to other monitoring 

studies. For instance, mortality and growth rates in a street tree planting 

cohort monitoring project should be evaluated in relation to other street 

tree planting cohort studies, preferably with a similar time period postplanting. Likewise, a repeated inventory of sampled i-Tree Eco plots is most 

appropriately compared to other plot-based repeated inventory studies.

2.1.9. Ensure that baseline records are complete 

 and have high data quality 

Baseline records are the first set of tree data relevant to the monitoring 

project—the starting point from which comparisons will be made going 

forward—and good baseline records are essential for continued data 

collection. For planting cohort monitoring, the baseline records are atplanting data (Vogt et al. 2015b). The “at-planting” records must include the 

planting date, preferably an actual date rather than a year or season (e.g., 

spring 2017). For multi-age inventory monitoring, the baseline records 

are the first inventory. Planting date is not strictly necessary for multi-age 

Baseline records are the first 

set of tree data relevant to 

the monitoring project.
22 GTR-PSW-266

inventory monitoring. Nonetheless, planting date, or more broadly tree 

age, would be helpful in that situation as well, if available. Tree age can be 

obtained from municipal tree records, interviews with urban foresters and 

homeowners, and tree core increments. Programs interested in tracking 

population cycles may also want to record tree removal date. For any 

monitoring project, if the baseline records were not originally collected 

with monitoring in mind, considerable time should be spent inspecting 

those records to ensure that they can be used for monitoring. For instance, 

if location data involved only addresses, then crews may not be able to 

locate individual trees in the field (see section 2.3 for a discussion of pros 

and cons of various location methods). Organizations may need to adjust 

their approach to baseline records to enable monitoring. If baseline records 

for tree locations, species, or other variables are incomplete or inaccurate, 

then project supervisors and field crews will need to clean up the data, and 

excessively messy records may be ultimately unsuitable for monitoring. It 

is important to understand the data quality of each variable in the baseline 

records and ensure that the observations regarding data quality match the 

specifications for the monitoring project, including acceptability thresholds 

for each variable (see section 2.1.13). 

2.1.10. Decide whether field crews will measure in metric or 

 U.S. customary units and level of measurement precision 

Ensure consistency and always note on the data sheet or in the mobile 

data collection system which units are being used. The same units should 

be used by all crews and across all equipment and throughout different 

monitoring years. Although metric units (e.g., meters and centimeters) are 

the norm in scientific research, urban forest managers in the United States 

typically use U.S. customary units (e.g., feet and inches), and a particular 

monitoring project might be bound to using certain units owing to available 

equipment. Similarly, decide whether trunk circumference versus d.b.h. 

will be measured. While recording trunk diameters is the norm in forest 

ecology research, some municipal foresters and nonprofit staff may prefer 

to record circumference at breast height owing to available equipment. 

While we strongly recommend recording trunk diameter, if circumference 

is recorded, this must be noted to enable conversions later. Additionally, 

project supervisors will need to decide what level of measurement precision 

is needed. When recording d.b.h., we recommend recording to the nearest 

millimeter or tenth-inch. However, some urban forestry professionals only 

have equipment to measure to the nearest half-inch or quarter-inch. Such 

equipment is not conducive to analyzing individual tree growth rates but can 

still be used to group trees into size class categories. If using U.S. customary 

units, we recommend using equipment with tenth-unit graduations, as this is 

easier to record than other gradations (e.g., tenth-inches instead of quarteror eighth-inches for measuring tapes; tenth-feet instead of feet and inches 

for height poles). We also specify in the Field Guide that height to the d.b.h. 

measurement point should always be recorded for every tree. This is because 

Note which units of 

measurement are being 

used.

Height to the d.b.h. 

measurement point should 

always be recorded 

for every tree.
Urban Tree Monitoring: A Resource Guide 23

(a) while 4.5 ft (1.37 m) is the norm in the United States, different default 

heights can be used both within the United States and around the world 

(Brokaw and Thompson 2000), and (b) d.b.h. can be recorded at different 

heights owing to tree form, such as low-forking multi-stemmed trees (see 

section 6.6 for more information about challenges of measuring d.b.h. in 

urban settings). Whatever units are chosen, the decision should be noted in a 

data dictionary or metadata accompanying the raw field data.

2.1.11. Decide on level of detail for species identification 

 and species codes 

For multi-age inventory monitoring projects, crews will need to identify tree 

species in the field, whereas for planting cohort monitoring projects, crews 

will need to confirm species based on the at-planting records. Some trees are 

difficult to identify to the species level, even for the keenest eye. Common 

urban genera that are difficult to identify to the species level include 

crabapples (Malus spp., code MALUS), ornamental cherries and plums 

(Prunus spp., code PRUNU), and hawthorns (Crataegus spp., code CRATA). 

These should be entered as genera only unless there are clear records of 

which species, hybrid, or cultivar was planted. Note that for mobile interfaces 

with a drop-down menu of tree species, this means that there should be an 

option for genus-only identification (e.g., MALUS, PRUNU, and CRATA). 

In addition, less experienced field crews might have trouble with other 

genera, so supporting a genus-level identification may be necessary unless 

the objectives of the study do not allow for that level of coarseness. For 

instance, if crews are confident that a tree is a maple, but unsure which 

species, they could record only the genus (Acer spp., code ACER). Having 

an option for “unknown” for genus and species identification is also wise 

to enable crews to note where they are unsure. Another option is to add 

categories such as “unknown broadleaf” and “unknown conifer.” Species or 

genus identification, coupled with information about the size of the tree, can 

be suitable for making inferences about tree biomass and ecosystem services 

(McPherson et al. 2016). For planting cohort monitoring, the at-planting 

data may include species and even cultivar, so that level of detail could be 

included in analysis. Additionally, projects will need to use species or genus 

names consistently (and cultivar names, if used). When using species codes, 

we recommend the codes from the UFIA program (i-Tree 2017a), see Field 

Guide section 2.8.

2.1.12. Decide how crew will investigate and resolve 

 unknown species

Species identification of urban trees can be a challenge, as there are many 

native and exotic species. Strategies, guides, or tools for identifying unknown 

species will depend on the protocol set by the supervisor regarding which 

information to record and what documentation to note for the supervisor. 

Options for resolving unknown species include field guides to be used 

onsite, taking leaf samples to examine further in the office, taking detailed 

photos to send to experts, and mobile leaf apps for species identification 
24 GTR-PSW-266

(see appendix 2 for a list of species identification resources). Because the 

technical expertise required for using species identification field guides 

differs, crews may need training in using the guides. Supervisors may wish 

to prepare a customized guide for their project with common tree species for 

their city or region. If pictures are taken for identification purposes, include 

images of leaves; fruit, nut or flower; bark; and the whole-tree profile. These 

photos should be labeled with the tree record identifier; if using paper data 

sheets, a blank sheet of paper or mini white board is handy for using a 

backdrop to display the tree record identifier. Additionally, information about 

the environmental conditions in which the tree is growing (e.g., wet or dry), 

where it is located in the landscape, and whether the tree appears to have 

been planted or naturally occurring can help inform species identification. If 

enough of the specimen tree is present, and it is permitted, field crews may 

want to collect physical leaf samples, in which case they will need paper bags 

and a permanent marker for recording the tree record identifier. 

2.1.13. Set acceptability thresholds for each variable 

Different projects will have different needs for the accuracy and precision of 

each variable, as alluded to in the earlier discussion of species identification 

(section 2.1.11). As expressed by Loshin (2011), writing about data quality 

for businesses, “[the] acceptability threshold is the point at which measured 

noncompliance with user expectations may lead to material business impact.” 

In the urban forestry context, acceptability thresholds relate to intended 

uses of the data in management or research. Setting acceptable levels of 

inconsistency or error for each variable provides guidance when evaluating 

field crews during training or quality control checks (see sections 3.1.2 and 

3.2). For instance, Roman et al. (2017) suggested that d.b.h. measurements 

by volunteers that are within ± 1 in (± 2.54 cm) of expert measurements are 

acceptable for most management applications, whereas a d.b.h. tolerance 

threshold of ± 0.1 in (± 0.254 cm) is appropriate for most scientific research. 

Notably, UFIA sets data quality standards for genus, species, d.b.h., and 

many other variables (USDA FS 2016). The FIA and UFIA programs refer 

to measurement repeatability, which takes into account the acceptable range 

of differences between measurements (i.e., tolerance) and the acceptable 

proportion of differences within that tolerance (i.e., measurement quality 

objectives [MQO]). The tolerance threshold must be met for a proportion of 

the observations for the data quality goal to be achieved. An evaluation of 

adherence to MQO for coarse and fine woody debris (which serve as fuel for 

forest fires) was described in Westfall and Woodall (2007); many variables 

did not meet the desired repeatability levels. Kitahara et al. (2009) described 

consistency between field teams and control teams for the Japanese National 

Forest Inventory, and again many variables did not meet the MQO thresholds 

set for FIA data. Therefore, it is important to note that even professionally 

certified crews do not produce flawless data. The acceptability thresholds for 

a given project should consider both the data quality needs of the intended 

analyses as well as the practicality of reproducibility among crews.

Acceptability thresholds 

should consider both 

data quality needs and 

reproducibility among crews.
Urban Tree Monitoring: A Resource Guide 25

2.1.14. Decide whether to track planting sites, trees, or both 

Some monitoring projects will track the mortality, growth, and health of 

a group of trees, without concern for replacement plantings when trees 

are removed. Other projects may focus on planting sites, such as sidewalk 

cut-outs (also known in some regions as “tree pits”), which may have a 

series of trees through the years, but the geographic location of the planting 

site remains constant, and the maximum number of trees at the site is one. 

That site is tracked through time and monitored even when the tree pit is 

empty. Tracking street tree sites is relevant to planning and management 

(e.g., stocking levels, planting needs) and could be used for multi-age 

inventory monitoring projects, particularly for monitoring replacements (see 

section 2.1.15). Additional information (e.g., overhead utility conflict, soil 

characteristics) may be collected about the site regardless of the presence/

absence of a tree to help inform management activities. Decisions about 

whether to track sites, trees, or both affect the structure of the longitudinal 

database for a monitoring project and the assignment of primary and unique 

keys (see section 2.5.1).

2.1.15. Decide how to deal with replacement trees 

Replacement trees—defined here as trees planted in the exact same location 

as removed trees—can be of interest for street tree management, where 

the possible locations of trees are relatively fixed. Municipal street tree 

planting contracts may include a replacement requirement, with contractors 

responsible for replacing dead trees within a few years after planting. 

Likewise, nonprofit programs tracking the street trees that they plant can 

use monitoring data to generate replacement requests. Noting that a tree is 

a replacement planting can be part of planting cohort monitoring studies (if 

there is interest in tracking the replacement trees, not simply the original 

planting cohort). Tracking replacements for either planting cohort monitoring 

or multi-age inventory monitoring projects can also help managers recognize 

that certain sites are continually losing trees and therefore could be declared 

unsuitable for planting. Researchers also may be interested in tracking 

replacement rates, as these can reveal trends in stocking levels and species 

selection preferences as well as inform tree population projections (van 

Doorn and McPherson 2018). 

2.1.16. Decide how to deal with “zombie trees” 

By the criteria presented in the Field Guide for mortality status (see Field 

Guide section 2.9), for a tree to be classified as standing dead, it must have 

no green leaves, no live buds, and no green tissue under the bark. In practice, 

it is fairly easy to confirm the first two criteria for smaller trees, but much 

more difficult to confirm absence of green tissue, particularly on larger trees. 

Given the possibility that recently defoliated trees may have remaining 

resources to releaf and survive, inconsistencies in mortality status cannot be 

completely eliminated. Thus arises the issue of “zombie trees”—which we 

define as trees recorded as standing dead in one field visit (e.g., time 0) but 

alive in the next field visit (e.g., time 1). If any “zombie trees” are observed 

Replacement trees are trees 

planted in the exact same 

location as removed trees.
26 GTR-PSW-266

at time 1, a possible database solution is to create a corrected mortality status 

column for time 0 and change the designation to alive. In other words, a tree 

that was previously thought to be standing dead at time 0 would be corrected 

to alive to reflect information learned at time 1. A similar but separate issue 

arises when trees are planted (or naturally regenerate) but then get removed 

before they can be added to the monitoring project—in other words, a tree 

that was planted but then removed so quickly that the monitoring field crews 

did not get a chance to observe it. These are referred to as “ghost mortalities” 

(Sheil 1995, van Mantgem and Stephenson 2005). Some studies specify 

the ghost mortality rate and report it as part of the error rate (van Doorn 

et al. 2011). Occurrences of “zombie trees” and “ghost mortalities” are 

rare overall, but because they can affect mortality rate calculations, project 

supervisors should think about how to deal with these situations ahead of 

time. 

2.1.17. Decide the intended use of the basal sprout variable 

The intended use of basal sprout variable (see Field Guide section 2.10) may 

vary depending on project goals. Monitoring basal sprouts, for example, 

may change the way managers choose to code mortality status (see Field 

Guide section 2.10). For management purposes, trees that are dead in the 

main stem should be removed in most situations, especially large dead trees 

that could cause damage to people or infrastructure, regardless of whether 

basal sprouts are present. Our definition of mortality status categories in the 

Field Guide reflects this real-world understanding of a “dead” tree, with trees 

that are dead in the main stem, but have living basal sprouts, categorized as 

standing dead. Yet, technically, if basal sprouts are present on an individual 

tree, that organism is not truly dead. A “new” tree can even emerge from 

the sprouts. For instance, we have seen cases of recently planted trees that 

are dead in the main stem, but have basal sprouts, get left alone (i.e., not 

removed) in residential yards, neglected streets, or experimental plots, 

and then grow back into full trees from a sprout. Such trees can have large 

canopies, but they might not necessarily grow into attractive forms. Figure 

1 shows an example of a sprout growing into a full tree from a monitoring 

study of experimental plantings for climate-ready trees (McPherson et al. 

2018). Although the tree is still the same living organism, according to our 

way of recording mortality status (see Field Guide section 2.9), the stem 

emerging from the basal sprout would get recorded as a new or replacement 

tree. Considering studies focused on d.b.h. growth, recording the emergent 

basal sprout as a new tree would be helpful because it does not make sense to 

directly compare the diameters of the old dead main stem and the new stem 

growing from a sprout. However, if a researcher wanted to record mortality 

in the strictest sense—with trees needing to be “all the way dead” to count 

as mortality—then a tree with a dead main stem but living basal sprouts 

would be considered alive. In this case, an analyst would need to re-code 

Mortality Status to reflect the different use of the basal sprout variable. 
Urban Tree Monitoring: A Resource Guide 27

Figure 1.—Parkinsonia x “Desert Museum” planted at University of California, Davis, is an example of a basal sprout that has 

regrown into a new main stem. In 2015, the Parkinsonia x “Desert Museum” (A, tree in the forefront) was alive and the main 

stem was intact. The tree was recorded as mortality status alive. By 2016, the main stem had broken off (B) and a small sprout 

was growing. The tree was recorded as a stump, with basal sprout present. In 2018, the sprout increased in size (C) and had 

many leaves and flowers, resulting in it being the new main stem. The sprout received a new tree record identifier and was 

recorded as alive in 2018 with a note about its sprout origin and the tree record identifier of the original stem. Making note of 

these details allows for this situation to be more easily filtered out depending on the analysis. The notes for supervisory review 

variable in the Minimum Data Set was designed to capture this sort of unusual situation (see Field Guide section 2.13).

C

A B

Challenges from basal sprouts in interpreting mortality results are relatively 

infrequent in urban tree monitoring, yet these rare challenges can vex field 

crews and analysts, so we raise this topic to encourage project supervisors to 

carefully consider how basal sprouts on otherwise dead trees will be handled. 

Being able to identify the strange but rare cases allows for more informed 

decisionmaking in monitoring studies.

2.1.18. Design a spreadsheet or database to organize 

 the monitoring data

 A well-designed spreadsheet or database is crucial for organizing the 

collected data and enabling analysis, and it is important to plan how to 

organize the data before data collection begins. Broman and Woo (2018) 

Being able to identify the 

strange but rare cases allows 

for more informed decisionmaking in monitoring studies.
28 GTR-PSW-266

suggested general guidelines for data organization in spreadsheets, and 

many of their recommendations will probably be familiar to urban foresters 

that have dealt with tree inventory data. Their guidelines include the 

following: be consistent (particularly with variable naming conventions, 

codes for categorical variables, and dates), use a code for missing values 

(rather than leaving cells blank), and create a data dictionary (also called 

metadata). Designing a spreadsheet or database also involves setting up 

primary and unique keys (see section 2.5.1). There are further considerations 

for monitoring projects that produce longitudinal data. In particular, it is 

important to decide on tabular data presentation: long versus wide format 

(see section 2.5.2). For particularly large or complex data sets, it is advisable 

to create a master data set stored in a repository maintained by one or a 

few select people so as to minimize the possibility of contamination. To 

conduct analyses, the desired data may be extracted from the repository into 

a working file. Any errors detected in the working file should be brought to 

the attention of the person(s) responsible for maintaining the master copy to 

resolve the issues. 

The word “tree” does not have a standard botanical definition (Gschwantner 

et al. 2009, Lund 1999). Although “tree” is generally taken to mean woody 

plants with secondary growth (i.e., the trunk thickens each year by growing 

outwards) and a distinct crown, there are various ways to define “tree” or 

to delineate which “trees” to monitor for a study. For example, palms are 

not considered to be “trees” by typical forestry definitions, yet palms are 

managed as trees in many urban forests. Note that including palms would 

also necessitate modifications in recording growth and vigor, as methods 

suitable for deciduous trees and conifers do not translate to palms (Blair et 

al. 2019). For instance, monitoring studies for palms seeking to characterize 

growth should consider growth in total height, as d.b.h. growth and canopy 

width growth are not biologically relevant. 

Other “tree” definitions relate to the number of stems, with trees typically 

considered to be plants that have one main stem, and mature height of the 

plant, with trees being taller than shrubs. Shrubs are often distinguished 

from trees by their multiple trunks and shorter height, but there is not 

necessarily a strict height cutoff. In urban forestry, some vegetation that 

would be considered a “shrub” owing to growth habit (i.e., multiple “trunks”) 

may be pruned into a “tree,” adding to the confusion. Examples include 

serviceberry (Amelanchier spp.), crape myrtle (Laegerstroemia spp.), and 

Spanish dagger (Yucca spp.). Differences in which plants are included in 

urban forest monitoring projects could make comparisons across cities and 

programs difficult, and neglecting to define “tree” for a project can lead to 

inconsistencies in field data. 

One way to define “tree” for purposes of an inventory or monitoring project 

is to set minimum sizes for height and d.b.h. that exclude both shrubs and 

tree saplings, as is common in rural forestry research. Plot-based inventories 

2.2. What Is a Tree?

Neglecting to define “tree” 

for a project can lead to 

inconsistencies in field data.
Urban Tree Monitoring: A Resource Guide 29

and repeated census studies in urban forestry have followed that precedent. 

For example, FIA in rural forests has traditionally recorded trees with d.b.h. 

at least 5 inches (12.7 cm), with recent incorporation of saplings down to 1 

inch (2.54 cm) for microplots (McRoberts et al. 2005). A similar approach 

has been used for the i-Tree Eco protocol for urban forestry plots, which calls 

for recording all woody stems at least 1-inch (2.54 cm) d.b.h. (i-Tree 2017a). 

Yet the approach using size cutoffs to define what “counts” as a tree can also 

become problematic, leading to species commonly considered to be “shrubs” 

to be declared the most common “tree” in urban forests. According to an 

i-Tree Eco study in Philadelphia, PA, spicebush (Lindera benzoin), generally 

considered a shrub, is the most common tree (Nowak et al. 2016b). A similar 

finding occurred in Chicago, IL, where European buckthorn (Rhamnus 

cathartica) was the most common tree (Nowak et al. 2013a). Additionally, 

using a 1-inch (2.54-cm) cutoff in urban forests excluded some small, 

recently planted trees that are highly important to managers and researchers 

studying the establishment phase. Conversely, for some managers and 

researchers, these small trees may be less relevant, as they are not yet large 

enough to provide substantial environmental benefits. An advantage of the 

1-inch (2.54-cm) cutoff is that this approach can be relatively straightforward 

for field crews to understand and consistently implement. Remeasurements 

of i-Tree Eco plots have successfully used the 1-inch (2.54-cm) cutoff to 

assess change over time when the plots were permanently referenced (e.g., 

Lawrence et al. 2012, Nowak et al. 2004). 

A strategy to prevent plants that are viewed as “shrubs” from being recorded 

as “trees” is to limit data collection to a predetermined species list, composed 

of species generally considered by researchers and managers to be “trees.” 

With this approach, field crews would receive a list of species that are 

eligible for inclusion in the study. This strategy can also align with training 

and productivity for field crews, as their identification tasks are limited to a 

predetermined group of species. The UFIA program employs this strategy 

in addition to a d.b.h. cutoff. UFIA species lists for different regions of 

the United States are available in the UFIA Field Guide (i-Tree 2017a). 

Alternatively, crews could receive a list of species to be excluded that are not 

considered “trees” for purposes of the study at hand. For example, instruct 

crews to record trees with at least 1-inch (2.54 cm) d.b.h., but exclude certain 

shrubby species (e.g., exclude spicebush in Philadelphia, PA, and European 

buckthorn in Chicago, IL).

Another potential source of confusion occurs in monitoring studies that are 

focused on street trees. What managers define as a “street tree” may vary 

across cities and even within cities. For instance, street trees could be limited 

to trees in sidewalk spaces that have planting cutouts or planting strips; 

such spaces may be the dominant kind of street tree in road networks that 

feature sidewalks. Yet other streets and roads that lack sidewalks may have 

trees planted in front yards that are considered street trees owing to their 

placement within the public right-of-way. Even when there is a sidewalk, 
30 GTR-PSW-266

a tree located on the building side of the sidewalk might be considered a 

private property tree, or a public municipal street tree, depending on local 

context. A street tree repeated census study in Oakland, CA, addressed this 

challenge by using a very restrictive definition of street trees: only trees 

located between the sidewalk and the curb were included (Roman et al. 

2014b). Yet this very restrictive street tree definition is only an option when 

all streets have sidewalks.

For a street tree multi-age inventory monitoring study, project managers will 

need to decide (1) whether “tree” has a minimum d.b.h. cutoff, predetermined 

species list, or both, and (2) whether “street tree” is defined by right-of-way 

versus relationship to sidewalks and medians. For planting cohort monitoring 

of street trees, “tree” can be defined much more simply as all the individuals 

planted through a particular program. The eligible species would be 

determined based on what species were in the planting records. Such projects 

would not need to have a minimum size threshold for trees to be included in 

the study because the trees under consideration are all of the individuals from 

the planting program, regardless of current size.

Whichever choice is made, it is important to pick a definition of “tree” 

and remain consistent throughout the monitoring study. Lack of a clear 

and consistent definition of “tree” can lead to a confused interpretation of 

change over time owing to discrepancies over which plants are included and 

excluded from the study. This can lead to crews having different population 

counts (Roman et al. 2017), or analysts may need to amend baseline records 

to make the meaning of “tree” more consistent (Roman et al. 2014b), making 

it challenging to interpret findings about changes in total population size 

and potentially introducing error into calculations of mortality, growth, and 

health. In Table 2, we offer potential definitions of “tree” for different types 

of monitoring projects.

Location information is used to reliably locate the same trees across time 

and to connect tree field data to other geospatial data sets. Geographic 

location is one of the most essential pieces of data for monitoring; without an 

accurate location, a tree cannot be found for remeasurement, and a tree’s data 

cannot be connected to future and past observations. Below are guidelines 

on appropriate location methods for various site types. We recommend 

selecting at least two complementary location methods. If there is a problem 

with one method, the other method is available as a backup to help future 

crews find the tree. For example, a crew might locate a street tree using 

address and site code and also note highly accurate latitude and longitude 

coordinates with a global positioning system (GPS) receiver. Whenever 

possible based on program capacity, we recommend identification tags, toscale site maps, or highly accurate GPS units (i.e., submeter accuracy). We 

also strongly encourage photos of every individual tree, preferably showing 

semipermanent built environment features in the background (e.g., buildings, 

traffic signs, other built infrastructure), as a visual clue for future field crews 

to find the tree (see Field Guide section 2.5). Note that, in the Field Guide, 

2.3. Choosing the 

Appropriate Location 

Method 

The use of several 

complimentary methods 

for recording location will 

ensure that individual trees 

can be reliably found.
Urban Tree Monitoring: A Resource Guide 31

Table 2.—Tree definitions for various monitoring project typesa

Monitoring project type Tree definition short name Description

Planting cohort monitoring Program-specific planted 

tree

Tree is defined by inclusion in a program’s planting list. Only 

trees planted through that program are monitored, and all are 

considered trees for inclusion in the study, regardless of size or 

growth habit (e.g., even species often considered shrubs like 

crape myrtles [Laegerstroemia spp.] are included, if on program 

list). 

Multi-age inventory 

(general) 

d.b.h. minimum 1-inch 

(2.54 cm)

Trees are defined as woody plants with a minimum 1-inch 

(2.54 cm) diameter at breast height. This method is based on 

i-Tree Eco and Urban Forest Inventory and Analysis from the 

USDA Forest Service and has the benefit of being easy for data 

collectors to reliably determine in the field, but it may exclude 

some small recently planted trees while including species that 

are typically considered shrubs.

Set list of species Trees are defined by a set list of species to be included or 

excluded. Species that are not of interest are explicitly not 

considered “trees” for the study at hand. For example, a 

repeated inventory could exclude plants with shrubby growth 

habit (e.g., bamboo clusters, spicebush, buckthorn, privet). 

Multi-age inventory (street 

trees)

Trees between sidewalk 

and street, and medians 

Monitoring is limited to sidewalk cutouts and planting strips 

site types only if they are located between the sidewalk and 

the street, and trees planted in street medians. This is a very 

restrictive definition of street tree (as it excludes sidewalk trees 

located immediately next to buildings, but includes sidewalk 

trees immediately next to the curb). This definition may be more 

consistently applied across field crews over many years of 

monitoring. 

Trees in sidewalks and 

medians 

Monitoring is limited to the following site types (section 2.4): 

sidewalk cutout, sidewalk planting strip, and median. (Trees on 

lawns are not included, even if they are within the right-of-way.) 

This definition is most relevant in cities where street trees are 

primarily managed as the trees in sidewalk space. There may 

be confusion among crews as to whether to record trees located 

in the sidewalk space, but adjacent to buildings (not the street). 

By this definition, such trees would be recorded, whereas by 

the definition in the preceding row, such trees would not be 

recorded. 

Trees within right-of-way Monitoring is based on a fixed distance from road center, curb, 

or edge of road bed (i.e., the right-of-way) as defined by the 

right-of-way for the local municipality. Any trees within that space 

are included for monitoring, regardless of whether land use is 

private versus public land, or site type is front yard versus in 

sidewalk. This may be the definition most relevant to municipal 

foresters, but it can be difficult to apply in the field as crews may 

not know how far the right-of-way extends from the road center 

for every street. This definition is also most relevant for towns 

and suburbs that do not have sidewalks. Such towns often 

consider yard trees near the road to be street trees.

a Project supervisors may need to combine definitions, e.g., use both a species list and size cutoff, or use both a street tree definition and 

size cutoff.
32 GTR-PSW-266

we cover three location protocols in depth. Additional methods and details 

about pros and cons of all the methods are covered here in the Resource 

Guide (Table 4 and the remainder of section 2.3).

Although generating latitude and longitude coordinates for each tree might 

seem like the ideal information for mapping urban trees, do not rely on 

these coordinates alone. When coordinates are generated by geocoding 

street addresses, those coordinates typically fall into the center of a parcel 

(i.e., not where the tree is really located). When using GPS equipment 

in the field, readings can be off by several meters if using conventional 

equipment, although some devices can deliver submeter accuracy with 

post-processing. Additional issues for generating map coordinates with GPS 

units are discussed below. Each location method has its own strengths and 

weaknesses, and the project supervisor must determine which two (or more) 

methods are best suited for the project.

Note that we do not give detailed guidelines for recording locations with 

plot-based inventories here. Plots are a special case and randomly generated 

plot centers can often be difficult to locate. To reliably relocate plot centers, 

a variety of methods can be employed: reference objects, distance and 

direction to all trees from plot center, written navigation descriptions to plot 

center, photos showing a person standing in plot center, and photos taken 

from plot center facing in each cardinal direction (see Table 3 and section 

2.3.9 on reference objects). Permanently marked plot centers and boundaries 

are ideal (e.g., using flagging and staking) but may not be possible in urban 

landscapes with numerous private landowners and the potential for public 

interference with markers. For more information about plot-based location 

methods, see van Doorn (2014) and i-Tree (2017a).

2.3.1. Address and site code method

Property addresses and site codes are generally used for street trees (see Field 

Guide section 2.4.1) and it is important for them to be used together as the 

site code is in relation to the address. For example, address 100 Main Street 

could have trees at Site Codes 1F and 2F (to indicate two trees in the front 

of the property), and then 102 Main Street would have site code numbering 

begin anew with 1F. When there are multiple trees attributed to one address, 

the use of only an address can result in difficulty distinguishing individual 

trees from each other. Recording a site code can prevent the ambiguity by 

identifying the relative position of the tree on the property. Pros and cons of 

this method are discussed in Table 4.

Depending on project goals, recording address only (without site code) could 

still be useful in ways unrelated to finding the tree in the field. Addresses can 

link the tree information to parcel data or U.S. Census Bureau socioeconomic 

data or support sending mail to the resident to learn about the tree in question 

through qualitative surveys. In this case, address should not be relied on as 

a location method to find the tree, but can be used as an additional piece of 

information for research or management.

Locating trees with GPS 

coordinates in the field has 

shortcomings, particularly 

when using conventional 

equipment.
Urban Tree Monitoring: A Resource Guide 33

Table 3.—Location methods for urban trees and relevant site types for each method

Location method Definition Relevant site types

Address and site 

code

Address is the property number and street name for the property on which 

the tree is located, or the property adjacent to the tree. Site code is a location 

identification code for the address. This method is most commonly used for 

street trees. For example, F for front of the address, S for side of the building, 

M for median. See Field Guide section 2.4.1 for additional details on address 

and site code protocols for street trees.

Sidewalk cutout, 

sidewalk planting 

strip, median, front 

yard, back yard

Block edge distance For street trees, this method involves measuring the distance in a straight 

line from one intersection curb edge to the next intersection curb edge with a 

range finder, landscaper’s tape, surveyor tape, or measuring wheel. See Field 

Guide section 2.4.2 for protocols for this method.

Sidewalk cutout, 

sidewalk planting 

strip, median

Digitizing locations 

on satellite imagery

A printed or digital satellite image of the study area is marked in the field to 

show location of the study tree. The location is converted to digital format 

using a computer program. See Field Guide section 2.4.3 for protocols for this 

method.

All site types

Identification tag Identification tag with a code or number, typically affixed to young trees with 

plastic ties or nailed into large trees. 

All site types but 

particularly useful in 

natural areas

Global positioning 

system (GPS) 

coordinates

Tree’s location coordinates (e.g., latitude and longitude). Be sure to note 

the average accuracy reported by GPS unit (e.g., ± 3 m, ± 10 m, etc.), units, 

coordinate system, and datum.

All site types

Landscape site map A site map drawn to scale illustrating trees and other useful landmarks in the 

landscape. Landscape site maps are typically drawn by a professional arborist 

or landscape architect, but there are online tools and books to help less 

experienced participants make landscape maps (Ellis 2015).

All site types, but 

especially useful 

for yards and 

maintained parks

On, from, and to 

streets 

A system for noting the specific block segment on which a tree is located, best 

suited for street tree inventories on a gridded street system. “On” is the street 

the tree is actually on, “from” is the street in descending address direction, “to” 

is the next street in ascending address direction. The side of street could also 

be recorded (i.e., odd versus even address numbers, north versus south side 

of the block). See Field Guide section 2.4.1 for details and example.

Sidewalk cutout, 

sidewalk planting 

strip, median, front 

yards

Photos This method involves taking a photo of the entire tree with enough of a 

background that will provide context of location, such as an identifiable 

piece of infrastructure (i.e., house, lamp post, fire hydrant, street corner). 

New photos should be taken with each field visit to capture changes in the 

landscape to help future crews find the tree.

All site types

Reference objects Distance and orientation to objects in the landscape that are unlikely to move 

for several decades. For example, a reference object for a yard tree could 

be the northwest corner of the house exterior. The field crews would then 

measure distance and compass orientation from the tree to that house corner. 

This method is based on protocols developed for plot-based inventories such 

as i-Tree Eco. For detailed protocols, see i-Tree (2017a). 

Yards; maintained 

parks
34 GTR-PSW-266

Table 4.—Pros and cons of the address and site code method

Pros Cons

Easy to collect in the field for baseline data. Provides low-accuracy location information for future monitoring. 

Does not require extra equipment. There can be ambiguity if trees are removed or added over time.

Aligns with street tree inventory methods in many cities. In cases of building demolition or redevelopment, addresses and 

site codes may become irrelevant.

Property boundaries and address numbers can occasionally be 

difficult to discern in the field.

2.3.2. Block edge distance method

The block edge distance method is used for street trees and works best 

on gridded street systems with straight roads. In this method, distance 

is measured along the curb edge of a street to each tree (see Field Guide 

section 2.4.2). In addition to the distance measurements, field crews must 

record street intersections of start and end points and which side of the street 

they are measuring in relation to the road’s centerline (right or left); this 

information is similar to the on, from, and to streets method (Table 4). The 

block edge distance method is most efficient when using a measuring wheel 

but can be done with a long measuring tape. Digital range finders could also 

be used if the streetscape environment does not have many obstructions (e.g., 

pedestrian traffic). An efficient way to handle workflow would be to record 

all the tree distances first and then go back and record other information such 

as species and d.b.h. 

The block edge distance method was developed by TreeKIT (Silva et al. 

2013) and has been extensively used in New York City, where tree distances 

can be translated into map positions using the custom software system 

specially developed for the city’s citizen science street tree census (Crown 

et al. 2018). The method was also adapted there to enable measurements on 

curved streets, cul-de-sacs, and other non-gridded streets.

The block edge distance method takes more time to set up compared to the 

address and site code method, but it provides detailed location information 

to help future field crews find the same tree without the ambiguities of the 

address and site code method (Table 5). 

In the block edge distance method, all tree measurements are taken in 

relation to an imaginary starting point that is the projected curb edge. The 

distance measurement for every tree on that block face is taken in relation 

to this starting point (e.g., tree at 20 ft [6.1 m] from projected edge, second 

tree at 40 ft [12.2 m]). However, Silva et al. (2013) originally set up the 

method to measure the intervals between trees. In their original method, 
Urban Tree Monitoring: A Resource Guide 35

Table 5.—Pros and cons of the block edge distance method 

Pros Cons

Provides highly accurate location information 

for future monitoring.

Takes time to set up, particularly for the first year (baseline data). 

If using a measuring wheel or range finder, it 

is very easy to measure long distances. 

If using a transect tape, inefficient on long blocks because the transect 

tape may need to be rolled up and laid out again. If using a range finder, 

pedestrians or other obstructions could interfere with measurements.

Easy to add new trees because 

measurements to trees are independent of 

one another.

If there is an error in laying out the transect tape in the beginning, that error 

will affect all measurements. 

Custom software or time-consuming manual efforts are needed to translate 

distance measurements into map coordinates.

Can be difficult to implement for non-gridded street systems (e.g., curving 

roads, cul-de-sacs) or for grid systems with extremely long block faces.

Requires equipment that some organizations may not have readily 

available.

the first measurement is made by recording the distance between the 

starting point and the center point of the first tree. The measurement on the 

measuring wheel is then “zeroed out,” and the second measurement is taken 

by measuring from the center point of the first tree to the center point of the 

second tree. This procedure is repeated for subsequent trees on the block. 

The last measurement is taken between the center point of the last tree and 

the end point, (i.e., projected curb edge of perpendicular streets). An issue 

with this approach for future remeasurements is that adding new trees into 

the inventory will change the previous distances between trees. Hence, we 

recommend the use of the block edge distance method in which distances 

are recorded to each tree’s center point without resetting the measurement. 

However, as long as the distance measurements are taken carefully, it should 

be easy to convert from one approach to the other.

2.3.3. Digitizing locations on satellite imagery method

The digitizing locations on satellite imagery method involves referencing 

a printed satellite image of the study area in the field. These images show 

overhead outlines of tree canopies, streets, and neighboring buildings. Using 

these objects as references, the location of the study tree is marked on the 

image with a pen or marker. Later, the location is converted to digital format 

on a computer using software such as ArcGIS, Google Maps, or Google 

Earth. A more direct “high tech” method is to bring a mobile device into the 

field loaded with a spatial app (e.g., ESRI Collector or Google’s My Maps) 

that allows dropping points on the satellite image while standing in the field. 

This cuts down on data processing time in the office. 
36 GTR-PSW-266

Using satellite imagery works well in many street tree and lawn situations 

but may be challenging in wooded natural areas and on densely planted 

lawns and sidewalks where canopies overlap. When the original location 

information was based on address and site code, it was possible to prepare 

a rough estimate of where the tree might be located in order to complete 

subsequent remeasurements. Online tools such as address to latitude/

longitude converters make it possible to plot the approximate property 

location on a satellite image. In the field, the crew could refine where the tree 

is located based on the actual canopy location. Note that it is also best to use 

relatively recent satellite imagery (within the past 1 to 2 years if possible), 

and that construction, storms, and other events can drastically change the 

appearance of the landscape image.

For the high-tech method of bringing a mobile device in the field equipped 

with a GPS receiver, refer to the section on “GPS Coordinates” below 

(section 2.3.5) for pitfalls related to using GPS receivers. Pros and cons of 

the digitizing locations on satellite imagery method are discussed in Table 6.

2.3.4. Identification tag method

In rural forest plots, trees are often tagged with unique identification numbers 

or codes that enable field crews to verify the identity of individual trees (van 

Doorn 2014). Such tags are often nailed into the trunks of mature trees. The 

tag can display the tree record identifier. While identification tags are used 

much more rarely in urban forestry, they have been integrated into a few 

projects, including a study of programs with high survival in the first several 

years after planting (Roman et al. 2015). Those tags were loosely tied around 

a tree branch. Maintenance of identification tags in repeated surveys includes 

Table 6.—Pros and cons of digitizing locations on satellite imagery method

Pros Cons

There are open-source satellite images available that make 

this method potentially inexpensive. 

For the low-tech method with paper, it is time consuming to 

prepare satellite image copies to take into the field and to 

process data afterwards. 

Global positioning system (GPS) accuracy on mobile devices 

varies, but if crews use high-end devices, this method 

can provide highly accurate position information for future 

monitoring and cuts down on processing time. 

The high-tech method could require acquisition of potentially 

cost-prohibitive mobile devices and proprietary software. 

Allows for the creation of a map of tree positions useful for 

analysis, display, and remeasurement.

Requires technical knowledge for processing data after 

marking trees in the field on satellite images. 

Use of mobile devices with GPS has limitations that lead to 

low position accuracy, especially in “urban canyons” between 

buildings and under thick, dense canopy. 

Requires relatively recent (past 1-2 years) satellite imagery; 

and imagery can get outdated if the landscape has drastically 

changed owing to construction or storms.
Urban Tree Monitoring: A Resource Guide 37

ensuring the tree is not growing over the tag, that the tag is not girdling the 

stem, and that the identifier is still readable (e.g., not sunbleached). Crews 

should be prepared to find a given tree using alternate location methods even 

if the tag has been removed or damaged. Pros and cons of this method are 

discussed in Table 7.

2.3.5. GPS coordinates method

GPS receiver location accuracy can range from being too coarse to 

differentiate between two trees, to fine enough that trees planted next 

to each other are individually identified. Differential global positioning 

systems (DGPS) provide improved accuracy, from around 15 m (49.2 ft) 

of ordinary GPS to about 10 cm (3.9 inches) in the best-case scenario. Not 

all devices are capable of utilizing DGPS, and those that do are generally 

more expensive and often cost prohibitive for programs with small budgets. 

Supervisors should weigh tradeoffs between accuracy and cost to reach their 

program goals. Regardless of which type of device is used, accuracy of 

the readings (displayed as ± meters or feet) should be recorded along with 

any GPS coordinates. Devices depend on clear line of sight with satellites 

and therefore encounter “urban canyon” problems characterized by having 

trouble locating satellites under large dense canopies or in downtown areas 

where signals may reflect off tall buildings. Downtown areas with tall 

buildings often have accuracy limitations, such as Manhattan in New York 

City (Silva et al. 2013). However, for urban areas with shorter buildings, the 

“urban canyon” issue is not a problem. Pros and cons of the GPS coordinates 

method are discussed in Table 8.

2.3.6. Landscape site map method

Site maps are particularly relevant for neighborhood parks, gardens, and 

residential yards. In such landscapes, distinguishing between trees in large 

areas of lawn can be challenging, and planting or inventory records that only 

report location in general terms (i.e., “10 trees on south side of building”) do 

Table 7.—Pros and cons of the identification tag method

Pros Cons

Eliminates ambiguity regarding which tree is in the 

record. 

Requires extra equipment.

Works well for trees in natural areas where interaction 

with the public (and risk of vandalism) is low.

Tags can be vandalized or otherwise damaged, removed, or lost. 

Can work well for trees in parks and along streets as 

long as tags are periodically adjusted as trees grow.

Requires periodic maintenance action to prevent tree from 

overgrowing the tag and to prevent the tag from girdling the tree. 

Tags nailed into trunks is generally not a viable option for large 

urban trees that may need to be removed and chipped one day 

(aluminum tags are safe for chainsaws). But tags around branches 

can work well for small recently planted trees.
38 GTR-PSW-266

Table 8.—Pros and cons of the global positioning system (GPS) coordinates method

Pros Cons 

High-resolution devices provide submeter 

location accuracy. 

Requires extra equipment, and particularly high-cost equipment for 

submeter accuracy.

Can be used to produce maps for geospatial 

analysis and visualization of sites.

Not all devices provide the needed level of accuracy. 

Receiving a signal in an urban area can be challenging, especially in “urban 

canyons” and under thick, dense canopy, leading to low position accuracy.

not enable future field crews to locate individual trees. Landscape site maps 

can be made into two different styles: to-scale and not-to-scale. To-scale site 

maps have been used in case studies that have shown high survival (Roman 

et al. 2016). These maps, along with tree tags, allowed for researchers to 

easily find individual trees over many years of fieldwork. However, these 

maps are time consuming to construct and thus are rare. For high-profile 

park, campus, or street projects, there may be opportunities for cities or 

nonprofits to contract with landscape architects or arborists to develop toscale site maps used for monitoring or maintenance/inspection cycles. If 

to-scale maps are unavailable, not-to-scale maps can still be very useful. For 

example, not-to-scale site maps showing where trees were planted relative to 

other built features enabled field crews to find the yard trees planted through 

a shade tree program in Sacramento, CA (Roman et al. 2014a). However, in 

that study, only one to three program trees were at most houses, and visual 

clues (e.g., stakes and nursery ties that were distinctive to the program) 

helped confirm that the crews were standing at the correct tree. To-scale site 

maps would have prevented some instances of confusion, but the not-toscale maps were sufficient for most properties. Pros and cons of the site map 

method are discussed in Table 9. 

2.3.7. On, from, and to street method

The on, from, and to streets information allows tree records to be grouped 

at the level of the city block, which can be helpful for both management and 

field crew logistics. The “on” street refers to the street that the tree is actually 

Table 9.—Pros and cons of the site map method

Pros Cons 

Site maps provide highly accurate location information for 

future monitoring. 

To-scale site maps are laborious and require specific 

knowledge and skills.

Limit ambiguity if trees are removed or added over time. To-scale site maps are time consuming and costly. 

May require extra field equipment and software for to-scale 

maps.

Not-to-scale maps may miss important features if done 

poorly. 
Urban Tree Monitoring: A Resource Guide 39

on (which can be different than the parcel address). The “from” street is 

the street closest to the lowest address, while the “to” street is the next 

encountered street (going in the direction of ascending addresses). Knowing 

the on, from, and to streets can help crews return to the location more quickly 

and allows supervisors to efficiently plan the sampling route for that day. 

This method can complement the address and site code method (see Field 

Guide section 2.4.1) and is a required component of the block edge distance 

method. Pros and cons of this method are discussed in Table 10.

2.3.8. Photos as a location method

We strongly encourage that all monitoring projects include a photo of each 

tree whenever possible, preferably showing the full tree with recognizable 

built environment or other permanent reference features in the background 

such as a house or utility pole (see Field Guide section 2.5). This allows field 

crews to use visual clues in the landscape to ensure that they are standing at 

the same tree from previous field surveys. New photos should be taken each 

time the tree is visited to capture changes in the tree and built environment. 

A photo can serve as a critical backup for other location methods previously 

described and can help to resolve ambiguities in other records. While we 

have found photos to be extremely useful for tree location in the most built 

up parts of the urban forest, such as along streets and in residential yards, 

individual tree photos may not be relevant in natural areas (however, photos 

of plot center might be useful). Besides being used for relocation of a tree 

in subsequent surveys, photos of every tree can assist in catching egregious 

observation errors when the quality of the data is in question yet there is no 

possibility of returning to the field (e.g., species or d.b.h. seems impossible 

for that study area). 

Photos for every tree are generally easiest to manage on a large scale when 

smartphones or other mobile devices are used for fieldwork, so that each 

photo can be matched to the appropriate tree’s record in the database. When 

it is not feasible to use mobile data collection, photos can still be taken, but 

need to be organized to ensure that each photo is linked to a specific tree. 

For example, a photo could be taken with a field crew member standing 

Table 10.—Pros and cons of on, from, and to streets method

Pros Cons 

Easy to collect in the field. Not a stand-alone method; this method should be used in 

conjunction with either site codes (see section 2.3.1) or block 

edge distance measurements (see section 2.3.2).

Does not require extra equipment.

Aligns with street tree inventory method in many cities.

Allows for efficient planning of daily data collection routes. 
40 GTR-PSW-266

next to the tree, holding a white board with the tree record identifier, or the 

photo number automatically generated by the camera can be recorded on 

the data collection sheet. Alternatively, a photo could be taken, first, of a 

data collection sheet to show the tree record, and then second, of the tree 

itself. Photos could then be labeled and organized using photo management 

tools (e.g., Google Photos, Flickr, Tropy). Without that kind of careful 

organization, finding a photo when it is needed in the field is a difficult 

task. See Table 11 for a discussion of pros and cons of photos as a location 

method.

Even if a project supervisor chooses not to take photos of every individual 

tree, we recommend using photos for the narrower purpose of tree species 

identification: have field crews take photos of unknown species (see Field 

Guide section 2.8). 

2.3.9. Reference objects method

Field crews record the distance and direction from the reference object (an 

object that is likely to remain in the same location for several decades) to the 

tree. Examples of reference objects include fire hydrants, overhead utility 

wire poles, survey markers from the National Geodetic Survey (NGS), and 

manholes. In i-Tree Eco plots, the reference object information is recorded in 

relation to plot center, and then tree positions are recorded in relation to plot 

center as well. Reference objects are often more difficult to find in natural 

areas as built infrastructure is less likely to exist nearby. For natural area 

plots, reference objects can be the largest trees on site or the most unusual 

species. Another approach for natural areas is to install markers such as 

rebar or whisker strakes at plot center. For more detail about protocols for 

recording reference objects with i-Tree Eco plots, see i-Tree (2017a).

Poor reference objects include items that are easily moveable, occur 

frequently and in proximity to each other, or are not easily identifiable owing 

Table 11.—Pros and cons of using photos as a location method

Pros Cons 

Provides visual clues and backup for future monitoring so 

that crews can be certain they are recording the correct tree.

Photo management is simplest when using a mobile 

device with data collection integrated with photos, but such 

technology may be cost prohibitive. 

Is an effective complement to all other location methods 

described in the Resource Guide.

When taking photos outside of a mobile data collection 

system, labeling and organizing photos can be very time 

consuming.

Generally quick and easy to take photos during fieldwork.

Visual record of changes in tree growth, health, and 

environmental context, which can be used in presentations 

or field crew training.
Urban Tree Monitoring: A Resource Guide 41

to size or location. However, even for relatively common infrastructure 

objects such as lamp posts, there are ways to make it easier for future crews 

to locate the object. For example, in a survey of i-Tree plots in Philadelphia, 

a satellite image from Google Maps was printed and brought into the field. 

Crews used a gold marker to note their three reference objects along with 

distance and orientation to each object. In addition, the crew took four 

pictures while standing at plot center facing north, south, east, and west, and 

a fifth picture of a crew member standing in plot center. When researchers 

returned to plots to check on data quality, they were able to easily determine 

plot center. For more discussion of data quality issues in permanent forest 

plots, including strategies to prevent and respond to those challenges, see 

van Doorn (2014).

Risks to the stability of reference objects include redevelopment of an urban 

area or development of a natural area. Such redeveloped or developed areas 

may have had all trees removed during construction, but sometimes trees 

are preserved (Ames and Dewald 2003, Briber et al. 2015), and it can be 

challenging to link these to the original plot and tree data. Table 12 discusses 

pros and cons of the reference objects method.

In the Field Guide, we define site type as a description of the area 

immediately surrounding a tree’s location, and land use as a description of 

the way the property around or adjacent to the tree is used by humans (see 

Field Guide sections 2.6 and 2.7). Land use has been more broadly defined in 

urban planning as “the occupation or use of land or water area for any human 

activity or any purpose” (The Institute for Local Government 2010; see also 

Anderson et al. 1976 and Lambin et al. 2006 for additional information about 

land use definitions and concepts). 

Site type categories include sidewalk cutout, sidewalk planting strip, median, 

and front yard. Land use categories include single family residential 

(attached and detached), commercial, institutional, and cemetery. The site 

type and land use variables are related but distinct. For instance, a tree 

located in a sidewalk cutout (site type) could be adjacent to a property that is 

Table 12.—Pros and cons of the reference objects method

Pros Cons 

Not dependent on current location of trees. Finding unique reference objects that seem likely to persist over time 

may be difficult, particularly in natural areas. 

Builds on plot methods for natural areas in 

forestry ecology. 

Redevelopment puts reference objects at risk from being removed or 

moved causing ambiguity.

Reference objects far from target tree or plot center are difficult to locate. 

Requires extra field equipment (e.g., transect tapes or range finders to 

get distances, and compasses to get orientation). 

2.4. Site Type 

and Land Use
42 GTR-PSW-266

residential, commercial, or institutional (land use). Similarly, a tree located 

in a maintained park-like environment (site type) could be within a public 

park, a college campus, or a cemetery (land use). For additional site type and 

land use categories for real-world examples, see the Field Guide Table 8, and 

appendix 1 in this Resource Guide. 

There are two main reasons to record site type: (1) each site type has 

recommended methods of recording tree location, and (2) mortality, growth, 

and health may vary across site type categories. The site type categories 

indicate broad information about the area surrounding the planting space 

(hardscape versus non-hardscape) and controls on tree ingrowth and 

removals (human-dominated planting versus natural regeneration). We 

use the term hardscape environment to describe urban trees surrounded by 

human-constructed hard surfaces, often impervious (e.g., asphalt, concrete) 

but can also include modern pervious materials (e.g., permeable pavers). 

Each site type category includes recommended methods for recording 

location (Table 13). This information is intended to assist project supervisors, 

who must decide which location method is best for each project. In Table 13, 

site type is separated according to trees in a hardscape, a maintained 

landscape (non-hardscape), or a natural area. 

While we have not listed the GPS coordinates, photos, and identification 

tag methods for the site type categories in Table 13, this is because those 

three methods are appropriate for all site types. However, any of those three 

methods should be used in conjunction with another complementary method, 

as described in section 2.3.

Category (code) Description

Recommended methods for 

recording location

Trees in hardscape environments

Tree plantings and removals for these site types are human dominated.

Sidewalk cutout 

(SC)

Tree is located in a soil pit in the sidewalk. The cutout can be 

anywhere in the sidewalk space (e.g., adjacent to the curb, adjacent 

to a building). This kind of site type is usually intended to fit just one 

tree. 

Address and site code; block 

edge distance method; satellite 

image with global positioning 

system (GPS)

Sidewalk planting 

strip (SP) 

Tree is located in a planting strip next to the sidewalk. This planting 

strip can be anywhere in the sidewalk space (e.g., between the 

sidewalk and curb, between the sidewalk and building). Planting 

strips can fit multiple trees planted in a row (even if only one tree is 

present). The length of a planting strip is generally at least 10 ft (3.05 

m), although there could be exceptions or different sizing descriptions 

in some cities.

Address and site code; block 

edge distance method; satellite 

image with GPS

Table 13.—Site type categories with management notes and recommended methods for recording 

locations. Taking GPS coordinates, using photos, and identification tags apply to all site types.
Urban Tree Monitoring: A Resource Guide 43

Category (code) Description

Recommended methods for 

recording location

Median (M) Tree is located in a planting space in the center of the road between 

traffic lanes. Includes center medians, traffic circles, and triangular 

cut-outs.

Address and site code; block 

edge distance method; satellite 

image with GPS; landscape 

site map

Planter box (PB) Tree is located in a raised planter box. Address and site code; block 

edge distance method; satellite 

image with GPS

Other hardscapes 

(OH) 

Tree is located in a hardscape other than a sidewalk or median, such 

as cutouts in a park plaza or parking lot.

Satellite image with GPS; 

distance and orientation to 

reference objects; landscape 

site maps

Trees in maintained landscapes, non-hardscape 

Tree plantings and removals for these site types are human dominated.

Front yard (FY) Tree is located in the yard in front of a building (on the street side of 

the building). This includes side yards (although some studies could 

record side yards separately). Front yards are typically associated 

with residential properties but may also be associated with other land 

use types. 

Distance and orientation to 

reference objects; landscape 

site maps; satellite image with 

GPS

Backyard (BY) Tree is located behind a building. Backyards are typically associated 

with residential properties but may also be in back of other land use 

types. 

Distance and orientation to 

reference objects; landscape 

site maps; satellite image with 

GPS

Maintained park 

(MP) 

Tree is located in a maintained park or park-like setting, such as a 

city park, school campus, or cemetery. This category is specifically 

for trees in lawns and other landscaped areas; park trees located in 

hardscapes belong in the “other maintained hardscapes” category. 

Note: maintained park is both a site type and land use. 

Distance and orientation to 

reference objects; landscape 

site maps

Other maintained 

landscaped area 

(OM) 

Tree is located in a landscaped area not described by the yard and 

maintained park categories. Use this category sparingly.

Distance and orientation to 

reference objects; landscape 

site maps

Trees in natural areas 

Tree additions and removals for this site type are generally natural (e.g., natural regeneration and death in place). 

Natural area (NAT) Tree is located in a natural park, open space area, or vegetated 

vacant lot that has minimal human intervention. This includes 

remnant forest patches and other natural or unmaintained areas, 

regardless of property type (e.g., forest patches on a residential 

property or institutional property are included here). Natural areas 

include forests, prairies, woodlands, and other natural or minimally 

managed habitats. Some project supervisors may choose to separate 

out the various kinds of locally relevant natural habitats (e.g., forest 

versus prairie). Note: natural area is both a land use and site type. 

Distance and orientation to 

reference objects

Table 13 (continued).—Site type categories with management notes and recommended methods for 

recording locations. Taking GPS coordinates, using photos, and identification tags apply to all site types.
44 GTR-PSW-266

Note that “natural area” and “maintained park” are both site types and land 

uses. A tree located on one of these site types will not automatically have 

the same land use, and vice versa. Taking examples from the Field Guide 

(Table 8), consider a tree in a landscaped lawn of a municipal recreation 

center. That tree would have “maintained park” for both site type and 

land use. Now consider a tree located in the landscaped lawn of a hospital 

complex. That tree would have site type “maintained park” and land use 

“institutional.” While some urban tree inventory protocols combine the site 

and property characteristics into one “land use” variable (e.g., i-Tree 2017a), 

we recommend keeping site type and land use separate because they describe 

different characteristics—namely, the tree’s immediate growing environment 

versus how humans use the property.

While there are general guidelines for organizing spreadsheets and 

databases (see section 2.1.18), we discuss here some considerations specific 

to longitudinal data of urban trees: selecting an appropriate tree record 

identifier, primary and unique keys, choosing long versus wide data structure, 

and tracking planting sites and replacement trees.

For more guidance about keys and database structure, see Boyer et al. (2016), 

which includes a proposed data model for urban tree monitoring, or see more 

general references about designing relational databases and spreadsheets 

(e.g., Broman and Woo 2018, Harrington 2009, Hernandez 2013). Project 

supervisors may also wish to consult with a database administrator or 

database engineer to ensure that the database design is conducive to 

monitoring goals, as we barely scratched the surface here regarding database 

design and administration.

2.5.1. Selecting primary and unique keys

In database design, keys allow users to identify, sort, and access information. 

We discuss two kinds of keys here: unique keys and primary keys.

A unique key (uk), also called a unique constraint, defines uniqueness for the 

entity that is being stored on the record. For example, in databases of human 

information, each individual should have a unique identifier such as driver’s 

license number or employee number. Thus, with urban tree databases, each 

tree record should have a unique record identifier that remains with that tree 

(and only that tree) during future monitoring. In the Minimum Data Set, the 

tree record identifier is an example of a unique key (see Field Guide section 

2.3). See Figure 2 for examples of tree record identifiers. A unique key can 

be derived from a tree tag (when present) or planting records. For planting 

cohort monitoring projects, the key used to define the uniqueness of a given 

tree may link with planting records. There may be multiple unique keys 

defined in a table. For example, a street tree monitored as part of a municipal 

inventory may have, in addition to a tree record identifier designated by the 

city arborist, one designated by an outside organization that maintains the 

trees (e.g., a contractor). It may be valuable to keep track of both tree record 

2.5. Longitudinal 

Database 

Considerations

A database administrator or 

database engineer can help 

with ensuring that database 

design is conducive to 

monitoring goals.

In database design, keys 

allow users to identify, sort, 

and access information.
Urban Tree Monitoring: A Resource Guide 45

Figure 2.—(A) Tree tag at the University of Pennsylvania in Philadelphia, PA. The lower right corner of the tag has a tree 

record identifier that reflects a grid cell that corresponds to a campus map and tree number within the grid cell. Photo by 

J.P. Fristensky, used with permission. (B) Street tree with affixed tag, planted by University City Green, Philadelphia, PA. 

The tree record identifier here is the identification number used in the nursery. Photo by L.A. Roman, USDA Forest Service. 

(C) Inventory site map for Casey Trees in Washington, D.C. The tree record identifier is a numerical sequence of trees in this 

specific area. Image by J.R. Sanders, used with permission.

A B

C
46 GTR-PSW-266

identifiers to allow for cross-over in databases from both organizations. 

Another thing to consider is that one or more columns or fields can make up 

a unique key. A unique key can be constructed in a compound fashion from 

two or more fields (e.g., the tree number, site number, or year of observation), 

as long as the combination is unique. As a result, a unique key may span 

multiple columns. Some users may choose to combine the fields that make 

up a unique key into one new column and designate that as the unique key. 

In the examples that follow, we designate unique key spanning multiple 

columns by highlighting the names in dark grey.

We recommend that the tree record identifier, as the unique key for an 

individual tree, should only be used for that specific tree, and when a tree is 

dead or removed, that identifier should be retired to avoid future confusion 

with a new tree in the same location. 

A primary key is one of the most crucial pieces of information for tracking 

individual trees through time and is essential for managing longitudinal data. 

The primary key is a unique value for each record in the database, regardless 

of what the record represents (Harrington 2009, Hernandez 2013). No two 

tree records can have the same primary key, and there can be one (and only 

one) primary key used in a table. Primary keys should not have null values, 

that is, every record should have a primary key. There can only be one 

primary key column for a given table, while there can be many unique key 

columns defined in a table. In practice, a primary key identifies the row of a 

database and thus should not be repeated within the database or changed. The 

primary key also serves to join tables within a relational database structure in 

the easiest, most robust way possible. Relational databases contain multiple 

tables, and the primary key enables these tables to link together. Primary keys 

should be something that never (or very rarely) change (Silberschatz et al. 

2011). In the examples that follow, primary keys are highlighted in light grey.

A foreign key is a column or group of columns in a table that provide a link 

between data in the same table (in this case a recursive foreign key) or two 

different tables. A foreign key references the primary key of another table, 

thereby providing the link between tables. This is particularly useful in long 

format tables to link to past visits and replacement trees. 

While primary and unique keys may sound quite similar, they are distinct. 

In practice, unique keys are often (although not always) produced using 

attributes that have some natural meaning in the system, reflecting values or 

sequences of numbers that are somehow relevant. For example, in databases 

of human information, driver’s license numbers can be unique keys, or in 

databases of urban tree monitoring to track planting cohorts, the numerical 

sequence from a nursery tag could be the unique key (and thus the tree 

record identifier). In contrast, primary keys are often (although not always) 

produced within the database and have no deeper relevance or meaning 

outside the database (Larsen 2011). For more reading about database design 

and keys, see Harrington (2009), Hernandez (2013), Larsen (2011), and 

Silberschatz et al. (2011).

Unique keys often reflect 

attributes that are meaningful 

or natural in the system, 

while primary keys are 

generally record numbers 

that have no deeper meaning 

outside of the database.

A primary key is one of 

the most critical pieces of 

information for managing 

longitudinal data.
Urban Tree Monitoring: A Resource Guide 47

2.5.2. Long versus wide data structure

Longitudinal data can be presented in wide or long format (Long 2012). 

Long format data, also known as stacked data, have the same tree 

represented across multiple rows, with every observation getting its own 

row (Table 14). In Table 14, Rec_id is a number generated by a sequence 

and represents the primary key. The unique key is composed of Tree_id and 

Year. The combination of these two must be unique. In long format, although 

there is only one tree labeled as Tree_id #23, the same tree may be repeated 

in multiple records because more than one measurement was recorded 

(e.g., Tree_id #23 was measured in 2010 and then in 2015). Constraints 

or data validation logic could be used to enforce the unique keys and 

minimize errors. For example, by instruction from the manager, the database 

could reject values of Year less than 2010 if there were no data-collecting 

campaigns prior to 2010. 

In contrast, for the wide format data, also known as unstacked data, Year is 

combined with the collected variables (e.g., d.b.h) to generate columns names 

such as Dbh.cm.2010 and Dbh.cm.2015 (Table 15). Each tree is represented 

by a single row, with observations across time spanning multiple columns. As 

in the long data format, the primary key is a number generated by a sequence 

(Rec_id), but the unique key is composed of the tree identification number 

(Tree_id) and Year in which the observations were made. In this example, 

Tree_id happens to uniquely identify the database record (i.e., table row) and 

could theoretically be designated as the primary key, but we still recommend 

having separate primary and unique keys. As mentioned in section 2.5.1, 

generally speaking, primary keys are usually generated within the database 

to have no real-world relevance or deeper meaning. By creating a primary 

key whose value has no deeper meaning, if an error were to occur such as an 

incorrect tree record identifier, the primary key would not be corrupted.

Table 14.—Example of data presented in long formata

pk uk

Rec_id Tree_id Year Dbh.cm

1 23 2010 2.5

2 24 2010 31.2

3 25 2010 3.2

4 23 2015 6.7

5 24 2015 31.5

6 25 2015 3.9

a The primary key (pk) is the record id (Rec_id) and the unique key (uk) is based on a 

combination of the Tree_id and Year and therefore spans multiple fields. Separately, Tree_id 

and Year are not unique, but their combination is unique. Database managers may choose to 

combine them into one column.

Long and short format data 

are two options for organizing 

the structure of a database 

or spreadsheet that contains 

longitudinal data.
48 GTR-PSW-266

Table 15.—Example of data presented in wide formata

pk uk

Rec_id Tree_id Dbh.cm.2010 Dbh.cm.2015

1 23 2.5 3.5

2 24 31.2 31.5

3 25 3.2 3.9

a The primary key (pk) is the record identification number (Rec_id) and the unique key (uk) is the 

tree identification number. Trunk diameter measurements were collected in 2010 and 2015.

Among database managers, long format is preferred to wide format for a 

variety of reasons. Long format is often required for advanced analyses 

and graphing and is well suited for vectorized programming languages like 

R (R Core Team 2017). If there are many variables or many years of data, 

tabular data storage can become unwieldly, especially in wide format. When 

a large amount of data is collected and recorded, it can be advantageous to 

move from simple tabular software, like Microsoft® Excel, to a more robust 

relational database system. A long format is also more robust to the issue of 

adding or removing variables in the data collection protocol, as sometimes 

happens in the evolution of a long-term monitoring project. Note that it is 

possible to convert from wide to long format and vice versa after the data 

have been collected, as long as the primary key has been retained for all 

observations, highlighting the importance of the primary key being kept 

separate from the unique key. One can use a long format for the master 

data set and convert to wide format for data collection or analysis if that is 

needed.

As emphasized in the beginning of section 2.5, urban forestry professionals 

who are unfamiliar with these kinds of database issues may want to consult a 

database expert to ensure that their monitoring data sets are structured in the 

most useful manner for the intended analyses. 

2.5.3. Tracking planting sites and replacement trees

Some projects may choose to keep track of both trees and planting sites, 

especially for street tree monitoring or other hardscapes for which planting 

sites remain relatively fixed in space. Planting sites with trees that have been 

removed either remain vacant or are replaced with a new tree. A planting 

site can be occupied by different trees over the years. Planting site vacancies 

and replacement rates may be of interest to managers and researchers as they 

have implications for stocking levels and population trajectories. 

Tables 16 and 17 illustrate how to track planting sites and replacement trees 

in wide and long format data. In both examples, Site_id refers to a specific 

planting site, whereas Tree_id refers to a specific tree. 
Urban Tree Monitoring: A Resource Guide 49

Table 16.—A table of data in wide format shows how to track planting sites and replacement treesa

pk uk

Rec_id Site_id Tree_id.2010 Dbh.cm.2010 Tree_id.2015 Dbh.cm.2015 New_tree.2015 Replaced.2015

1 1 23 2.5 59 2.2 Yes Yes

2 2 24 31.2 24 31.5 No No

3 3 25 3.2 25 3.9 No No

4 4 NA NA 60 2.6 Yes No

a Rec_id represents the primary key while the unique key spans multiple columns and is composed of Site_id and Tree_id.2010. Variable 

names for observations and measurements (Tree_id, Species, and Dbh.cm) are combined with Year to reflect when the observations were 

made.

Table 17.—A table of data in long format show how to track planting sites and replacement treesa

pk uk

Rec_id Site_id Tree_id Year Dbh.cm New_tree Replaced

1 1 23 2010 2.5 No No

2 2 24 2010 31.2 No No

3 3 25 2010 3.2 No No

4 4 NA 2010 NA NA NA

5 1 59 2015 2.2 Yes Yes

6 2 24 2015 31.5 No No

7 3 25 2015 3.9 No No

8 4 60 2015 2.6 Yes No

a Rec_id represents the primary key while the unique key spans multiple columns and is composed of Site_id, Tree_id, and Year.

In the wide data format (Table 16), Tree_id is recorded in separate columns 

for each time it was observed (e.g., Tree_id.2010 refers to trees recorded in 

2010). If a specific tree is new to the site, we designate “yes” in the column 

New_tree.2015, as with Rec_id #1 and Rec_id #4. Trees can be designated 

as new whether they occupy a previously filled or previously empty site. 

If the distinction between site occupancy is needed, a new column such as 

Replaced.2015 (Table 16) can provide additional specificity, as in the next 

example. Similarly, if plantings in previously vacant planting sites are of 

interest, a new column can be added to collect that information. 
50 GTR-PSW-266

The first record (Rec_id #1) in Table 16 is an example of a site in which a 

tree was replaced after the first visit. It was originally Tree_id #23 in 2010 

with a 2.5 cm d.b.h. tree but was replaced by a 2.2 cm d.b.h. tree with 

Tree_id #59. Because the tree was new to the site, it was added to the 

New_tree.2015 column. For clarity, we added another column 

(Replaced.2015) to designate that Tree_id #59 replaced a tree that had 

occupied the site in the previous measurement; this allows for easy querying 

to find all the replacement trees if calculating the replacement rate is of 

interest.

Table 16 also illustrates the differentiation between sites and trees. The 

fourth record (Rec_id #4) represents a site that did not have a tree in 2010 

(i.e., vacant site). Note that in this context, vacant site refers specifically 

to a planting site that is unoccupied, i.e., does not have a tree. In the street 

tree context, a vacant site is sometimes called an empty tree pit. Because 

Site_id was a variable in the table, the crew could record that Site_id #4 

was visited but there was no tree present. Following the best practices laid 

out by Broman and Woo (2018), rather than leaving these cells blank, we 

use “NA” to show the cells were not left blank by accident. When the crew 

returned in 2015, a tree was observed at that site (Tree_id.2015 #60) and 

measured (2.6 cm d.b.h.). Again, for clarity and easy querying, we added a 

column (New_tree.2015) to designate that a tree was newly observed as of 

2015. Without New_tree.2015, it would be unclear from Rec_id #4 whether 

the site was visited in 2010 and no tree was found or whether the site had 

never been visited. One could also add a column to designate empty sites in 

any given year (e.g., Vacant_site.2010, Vacant_site.2015), if calculating the 

percentage of occupied sites (or stocking level) is of interest. A drawback of 

the wide data format is that this procedure of keeping track of replacement or 

new trees is prone to errors as the database grows with successive monitoring 

years. 

The same data set in long format will look different, but the distinction 

between site and tree remain the same (Table 17). Instead of columns being 

made up of variables collected in a particular year (e.g., Dbh.cm.2010, Dbh.

cm.2015), columns containing the variable (e.g., Dbh.cm), and the years in 

which the variable was collected make up different rows.

Data may also be organized in a relational database. Splitting data into a 

number of related tables provides some advantages. Data are only stored 

once, so changes do not require multiple record changes. Relational 

databases provide additional flexibility for future additions, even if no other 

records are using the additional records. For example, a table with planting 

site information can include all possible planting sites even if there are 

no corresponding trees in a tree table. In Table 18, the data presented in 

long format consists of two subtables in a relational database: the parent 

table (Site table) describes planting site information, and the child table 

(Tree table) describes tree information. Given that planting site and tree 

information are in separate tables, different trees can occupy the site over the 

years. Rec_id is the primary key in each sub-table. In Site table, the unique 
Urban Tree Monitoring: A Resource Guide 51

key is composed of Site_id and Visit_number. Site_id and Visit_number 

are part of what makes each record unique. Site table and Tree table are 

linked by a foreign key (Site_table_rec_id in Tree table). Site_table_rec_id 

references the primary key in Site table (Rec_id, not Site_id). In the Tree 

table, the unique key is composed of Site_table_rec_id and Tree_id.

Record #12 in Site table (Table 18) shows that site #3 was sampled in 2010. 

Because Site_table_rec_id provides the link from Tree table to Site table, we 

can look for Site_table_rec_id #12 to see if any trees were recorded at site 

#3. Indeed, record #22 shows that tree #25 was measured. To look up the soil 

depth for tree #25, we could follow the Site_table_rec_id link back to record 

#12 in Site table to see that the soil depth was 100 cm. Record #15 in Site 

table shows that in 2015, site #3 was once again sampled. Tree #25 was still 

present so it was measured (Rec_id #25). 

Foreign keys provide the link between tables. In Tree table, Site_table_rec_id 

is a foreign key to the parent Site table record. It points to the primary key in 

Site table. For example, Rec_id #20 refers to Site_id #1 because the foreign 

key (Site_table_rec_id) points to Site table’s Rec_id #10. 

Table 18.—Tables of data in long format show how to track planting sites and replacement trees in a 

relational databasea

Site table

pk uk

Rec_id Site_id Visit_number Year Soil_depth.cm

10 1 1 2010 50

11 2 1 2010 70

12 3 1 2010 100

13 1 2 2015 45

14 2 2 2015 30

15 3 2 2015 100

16 4 1 2015 40

Tree table

pk uk fk

Rec_id Site_table_rec_id a Tree_id New_tree Replaced_tree_rec_id Dbh.cm

20 10 23 No NA 2.5

21 11 24 No NA 31.2

22 12 25 No NA 3.2

23 13 59 Yes 20 2.2

24 14 24 No NA 33.0

25 15 25 No NA 3.9

26 16 60 Yes NA 2.6

a Site_table_rec_id is also a foreign key (fk). In both Site table and Tree table, the primary key (pk) is the record id (Rec_id). In Site table, 

the unique key (uk) is the combination of Site_id and Visit_number. Similarly, in Tree table, the uk is the combination of Site_table_rec_id 

and Tree_id. The Tree table is linked to the Site table by way of the foreign key (Site_table_rec_id). Site_table_rec_id references the primary 

key in Site table (Rec_id, not Site_id). 
52 GTR-PSW-266

A foreign key also presents a way to track replacement trees. For example, 

Tree table’s Rec_id #23 shows an entry for Replaced_tree_rec_id (#20). 

Replaced_tree_rec_id is a foreign key that links record #23 (which holds 

information for tree #59) to record #20 (which holds information for tree 

#23). In other words, in 2010, the crew sampled site #1 and measured a tree 

labelled Tree_id #23, but in 2015 they encountered a different tree in its 

place, determined it was a replacement tree, and labelled it Tree_id #59. The 

field Replaced_tree_rec_id also helps differentiate between trees planted in 

vacant sites and trees replacing other trees (i.e., replacements). For example, 

Tree table’s record #26 shows that Tree_id #60 is a newly planted tree in a 

new location (i.e., not a replacement tree) as it only has information for year 

2015, it is listed in the New_tree column, and column Replaced_tree_id 

remains empty. 

2.6. Data Collection System 

Field-based urban tree monitoring data can be collected with either mobile 

devices or paper. There are pros and cons of each approach. Despite 

technological advances in conducting tree inventories with mobile devices, 

many monitoring projects may continue to use paper, owing to resource 

limitations in acquiring hardware and software, or for citizen science 

projects, paper data collection may make participation more widely 

accessible to all residents. However, drawbacks to paper data collection 

include the possibility for transcription errors during data collection and the 

need to regularly copy or scan original data sheets as backup (Table 19). A 

template data collection sheet for the Minimum Data Set is provided in the 

Field Guide appendix 2. 

Table 19.—Pros and cons of using paper data collection

Pros Cons

Low cost, low-tech method. Backing up data is more time consuming 

(e.g., copying or scanning data sheets versus 

downloading an electronic file).

If pursuing a citizen science approach, paper data sheets may be more 

comfortable for some volunteers (e.g., older residents or those without 

smart phones).

Data entry into a computer is time consuming and 

introduces a possibility of transcription errors. 

Data entry into a computer could provide an opportunity for a transcriber 

to review all the data and flag unusual entries.

No way to restrict variables to prevent out-ofbounds errors.

Provides a hard copy backup of data when computers fail.

Flexibility with work flow for field crews (i.e., crews using mobile 

systems often have to enter one tree at a time, but with paper, crews 

could measure and record all location information, then all d.b.h. 

measurements, or other options to maximize flow).
Urban Tree Monitoring: A Resource Guide 53

A comprehensive assessment of software packages that might be useful for 

urban tree monitoring, including many mobile data collection packages, 

is given in Boyer et al. (2016). However, none of the software systems 

evaluated in that report were perfectly suited out of the box to longitudinal 

data collection and repeated monitoring. Since release of that report, two 

software systems (OpenTreeMap by Azavea and the Urban Forest Cloud 

by Plan-It Geo) have begun offering options for monitoring planting 

cohorts. When selecting what software to use for a monitoring project, we 

recommend carefully evaluating software requirements and intended users 

(with Boyer et al. 2016 as a guide for both those issues). Existing software 

options evaluated in that report include proprietary urban forestry software, 

proprietary nonforestry specific software, and free and open source software. 

Some proprietary urban forestry software can be customized for monitoring, 

but with greater customization comes greater financial cost.

Among the various requirements of a monitoring software system, we 

highlight two issues: location data and longitudinal data management. 

Existing software packages may not be structured to record location data 

using the methods described in section 2.3 or may not be structured to 

enable recording of at least two forms of location, which we recommended 

as a way to have backup information. We also strongly encourage photos of 

individual trees for most urban forest monitoring studies. However, having 

photos linked to individual tree records and the ability to mass export photos 

is not a feature in all the software packages evaluated in Boyer et al. (2016). 

Additionally, available software packages for urban tree inventories may 

not be conducive to longitudinal data analysis. When proprietary urban 

forestry software is designed to manage tasks for maintenance crews, new 

data about a tree may literally replace old data for the user’s view of the 

information, without retaining the longitudinal records in a manner that 

allows calculation of mortality and growth or tracking of replacement and 

new plantings. Such data may sometimes still exist in the software database, 

but it may be cumbersome for managers to access and connect records across 

time. Nevertheless, mobile data collection does have advantages, including 

eliminating the step of transcribing data sheets and potentially enabling 

photos to be linked to tree records. We summarize pros and cons of using 

mobile data collection software in Table 20.

2.7. Research-Practice Partnerships

To carry out productive urban tree monitoring studies, it helps to have 

strong partnerships between researchers and practitioners. Both parties bring 

essential skills and experience to the table. Researchers know how to design 

study samples, analyze data with rigorous statistics, and match those issues 

with clear research objectives, while urban forestry professionals have indepth experience managing their trees and can generate innovative questions 

and insights to interpret results. Forging partnerships between researchers 

and practitioners can also potentially lead to outputs that are actionable for 

urban forest managers, such as internal program reports or presentations 

Collaborative researchpractice partnerships can 

strengthen urban tree 

monitoring studies.
54 GTR-PSW-266

to local professionals, in addition to peer-reviewed journal articles. For a 

discussion of various forms of knowledge co-production at the researchpractice interface in urban forestry, see Campbell et al. (2016). Examples of 

research-practice collaborations for urban forest monitoring include analysis 

of survival and growth in planting initiatives from San Francisco, CA (Martin 

et al. 2016), towns across Florida (Koeser et al. 2014), Indianapolis, IN (Vogt 

et al. 2015a), Sacramento, CA (Ko et al. 2015a, Roman et al. 2014a,), and 

East Palo Alto, CA, and Philadelphia, PA (Roman et al. 2015). These studies 

have advanced the basic science of urban tree survival and growth while also 

producing useful data for local managers to apply to their programs.

Table 20.—Pros and cons using mobile data collection software

Pros Cons

May allow field crews to view tree locations on a map or 

satellite image, if the device has a data plan or ability to 

download maps and tree location data exists.

Software, mobile devices, and data plan access may be cost 

prohibitive.

Data entry options can be restricted for each variable to 

prevent out-of-bounds errors for continuous variables and 

provide a limited set of options for categorical variables (e.g., 

d.b.h. cannot exceed 100, species and mortality status are 

selected from drop-down menus).

If pursuing a citizen science approach, paper data sheets 

may be more comfortable for some volunteers. Glare on the 

screen of a mobile device can also be challenging in some 

weather conditions. 

May enable photos to be associated with individual tree 

records in the database.

Mobile device may need to be charged nightly and a spare 

source of power should be taken into the field. In addition, 

data should be downloaded frequently to prevent data 

losses. 

Can require expensive data plans for every device to record 

map coordinates and upload data.

Mass export options of data and files (e.g., photos) may not 

be available.
55

3. Managing Fieldwork

Once the organizers of a monitoring project have solidified project goals, 

decided upon sampling and data collection strategies, and hired field crews, 

it is time to train the crews. Based on our collective experience managing 

hundreds of field crews, the more time spent on training and technical 

assistance during the field season, the more consistent the data will likely 

be, and with fewer data quality issues. Designing and following a training 

protocol will help guide these efforts. If training time is limited (i.e., a day or 

less is available), focus on the more challenging parts such as trunk diameter 

and species. See appendix 4 for sample training agendas and activities. 

3.1.1. Indoor training

Part of the training can occur indoors. We recommend emphasizing the 

following items with indoor training: 

• Present an overview of the Field Guide with a focus on the more 

difficult sections. During training, supervisors can present an overview 

of the guide and go more in depth on the more difficult sections. Do not 

assume that all crew members have read the entire Field Guide ahead of 

time. Allocating time to go over a few subsections (perhaps even reading 

some aloud together) can ensure that all crew members have reviewed 

key information.

• Introduce all equipment. Trainees should become familiar with all the 

equipment they will be using in the field (i.e., what each item is used for, 

what it is called, what units to use, what are possible pitfalls) and any 

written material they will need to take with them into the field (e.g., field 

guides or cheat sheets). The crews should practice using this equipment 

outdoors but should first be introduced to their tools indoors. For 

example, crews could practice measuring diameters of each other’s wrists 

in the classroom before practicing d.b.h. measurements on trees outside. 

The supervisor can ensure that everybody is reading the diameter tape 

(generally referred to as d-tape) correctly (see sections 3.3 and appendix 

4 activity “D-tapes and Wrists”). This specialized forester’s measuring 

tape is the best piece of equipment for measuring trunks 1 inch (2.54 cm) 

or larger. For electronic equipment, trainees should also learn how to 

calibrate electronic equipment and follow a protocol of how frequently 

to calibrate. For example, if measuring tree height (see the Tree Data Set, 

section 7), hypsometers should be calibrated daily because they depend 

on ambient temperature. 

3.1. Tips for Training 

Field Crews 

Training and actively 

supervising field crews 

promotes data quality.
56 GTR-PSW-266

• Practice activities to promote active learning. Such activities are 

meant to preview the work that crews will do in the field. For example, 

trainees can do practice worksheets to learn how to record location with 

address and site code, practice using measuring wheels to record the 

block edge distance method, or practice measuring d.b.h. using each 

other’s wrists. The supervisor can also train on species identification 

indoors using images of trees paired with leaf samples, to be 

complemented by practicing species identification outside later. Example 

activities (“D-tapes and Wrists” and “Address and Site Code Map Quiz”) 

are provided in appendix 4. 

• Discuss safety. The indoor session is also a good time to discuss safety 

concerns and safety-related protocols. These protocols may differ by 

organization and city but should generally include safe practices for 

drivers and pedestrians, any locally specific threats (e.g., tick-borne 

illnesses, poisonous plants that could cause rashes), and how to report 

an incident. Have the trainees sign appropriate liability waivers or 

insurance coverage forms. Safety equipment should be customized for 

the area in which the crew is working. Brightly colored safety vests 

provide visibility for traffic and can make crews look more official for 

pedestrians and residents. Hard hats are recommended if there is a risk of 

falling materials. First aid kits can be customized to include remedies for 

situations such as bee stings and poison ivy. Project supervisors should 

continue to discuss safety with field crews throughout the field season. 

Supervisors should also stress that sexual harassment and bullying 

are not tolerated. Many resources with safety guidelines are available 

online, and include the following topics: heat illness, stress, and safety 

(CDCP NIOSH, n.d. a; NWCG, n.d.; NWS, n.d.), driving hazards (CDCP 

NIOSH, n.d. b), and personal protective equipment (USDL OSHA, n.d.).

• Discuss how to interact with the public. Urban forestry fieldwork 

often involves interactions with pedestrians and residents. In the case 

of planting cohort monitoring, fieldwork may also involve interactions 

with residents who received trees. Supervisors should coach field 

crews regarding what to say during these interactions, such as short 

explanations of the study goals and who the public can contact for 

more information. Such training for field crews is not only important 

to maintain good public relations for the organization(s) that the crews 

represent, but can also help to promote data quality. The workflow of 

tree measurements can be disrupted by conversations with residents and 

pedestrians so it is important to have a strategy ahead of time to address 

these interactions politely and efficiently. A script for field crews to 

follow during these conversations, or a handout for them to distribute, 

could be helpful. Some residents may want advice about tree planting, 

maintenance, and removal, so crews should come prepared with contact 

information or brochures for relevant local programs. Dyson et al. (2019) 

provide additional tips on conducting research on private property. 

Safety discussions are an 

important part of field crew 

training.
Urban Tree Monitoring: A Resource Guide 57

• Discuss sources of error. We recommend having an explicit discussion 

with the trainees about potential sources of error. The purpose of this 

discussion is to encourage field crews to imagine the ways in which they 

might make mistakes, explain how to reduce those mistakes, and discuss 

how to respond to errors if they are discovered after the fact. An example 

activity on this topic is provided in appendix 4 (“Sources of Error”).

3.1.2. Outdoor training

An outdoor training session would include putting into practice what has 

been learned. This is a good time to make sure that crews are comfortable 

with the equipment and are using it properly. We recommend including the 

following items in the outdoor training session: 

• Everyone should practice making observations, taking 

measurements, and recording data. All members should practice 

measuring the same set of trees so that results can be compared and 

differences can be discussed. Often times a trainee does not realize the 

assumptions that are leading to systematic errors, so crew members may 

not anticipate where they need assistance. Crew members should rotate 

roles of making observations, taking measurements, and recording data. 

Make sure there is consistency in observations across individuals and 

teams. Data do not necessarily need to be identical across every trainee 

but should be within reasonable bounds for the study goals (see 2.1.13). 

For example, Roman et al. (2017) proposed that d.b.h. measurements 

within ±1 inch (2.54 cm) of expert measurements are appropriate for 

most urban forest management applications, whereas a tolerance of 

±0.1 inch (0.254 cm) is appropriate for most research applications. In 

that study, citizen scientists recorded d.b.h. values within ±1 inch (2.54 

cm) of experts for 93.3 percent of single-stem trees, and within ±0.1 

inch (0.254 cm) of experts for 54.4 percent. With brief training and a 

little outdoor practice, most intern or volunteer crews should be at least 

that consistent, if not better. If the data from this training session are 

recorded and given to the supervisor, then they could be used to make 

quick graphs illustrating the level of agreement across crews (e.g., for 

trunk diameter and crown vigor). Such an exercise can demonstrate areas 

where trainees need more help as well as serve as a learning experience 

about data quality.

• Practice data collection call-and-response. We recommend that data 

recorders should get into the habit of reading back what the measurer 

calls out before writing it down (or entering into a mobile device). This 

is especially important when there are loud city sounds or distracting 

pedestrians. For example, a person measuring a tree would call out “d.b.h 

is 3.5 inches at 4.3 feet” and the recorder would respond “I heard 3.5 

inches at 4.3 feet.” This gives the measurer the opportunity to correct the 

recorder if needed. This process also forces the measurer to slow down 

and keep the same pace as the recorder (see description of potential 

division of labor in section 3.2). 

Field crew training should 

include explicit discussions 

about sources of error and 

how to respond to any errors 

that are discovered.

To maintain data integrity, 

data recorders should read 

back what the measurer calls 

out before writing it down. 
58 GTR-PSW-266

• Species identification practice. Beyond practicing the entire data 

collection protocol on a few trees, additional time could be spent 

practicing species identification. The supervisor could go for a walk 

with the crew in a neighborhood or park that has many species they are 

likely to encounter during the field season. When coming upon a species 

that has been reviewed, ask crew to identify it. How do they know? 

What characteristics help them identify it? Reinforce features of the tree 

species’ leaves, bark, or fruit that make it stand out or that distinguish it 

from other similar-looking species. Bring any portable tree identification 

resources and practice using them in the field (see appendix 2 for a list 

of urban tree species identification resources). Make sure that crews 

know what to do when they encounter unknown species, including 

consulting with online or print resources, asking the supervisor for help, 

or recording the tree as “unknown species” (see Field Guide section 2.8). 

The time needed on species identification will depend upon the crews’

level of prior training as well as how important highly accurate species 

data are for the project. If genus-level identification is sufficient, then less 

time needs to be spent with this training. If species-level identification 

is paramount, then more time needs to be spent with this training and 

the supervisor should consider hiring expert crews or using photos for 

species validation (see Field Guide section 2.8 and Roman et al. 2017). 

For planting cohort monitoring studies, crews do not need to know all 

possible species in an area, but rather, only the set of species planted for 

that cohort; such crews are using prepopulated species data from planting 

records to confirm that they are at the right tree, rather than identifying a 

given species “from scratch.”

• Practice with the intended data collection platform. Whether using 

paper data sheets or a mobile device, have crews practice their data 

collection with the actual system they will be using for the entire field 

season. Check their entries to make sure that they are recording data 

appropriately. Learn about the crew members’ strengths with data entry, 

so that if one person has neater handwriting or is more adept at using 

a mobile app, that person may primarily assume the relevant role (see 

section 3.2 about field crew roles).

• Allow ample time for questions. Include sufficient time for crews to ask 

questions while practicing data collection in the field. Do not rush their 

practice time. Ask the trainees which variables they found most difficult 

to collect and which they found easiest; then practice or demonstrate 

again as needed.

• Provide technical assistance for the first data collection day. 

Supervisors, crew leaders, or trainers should accompany crews on their 

first outing. Such supervisory staff should be present to quickly answer 

technical questions and give guidance, not to collect data for the group. 

This supervised data collection is especially important for inexperienced 

field crews (e.g., citizen scientists, interns with little prior fieldwork), 

Strategies for species 

identification training will 

depend on data quality needs 

as well as prior expertise of 

field crews. 
Urban Tree Monitoring: A Resource Guide 59

but also matters for more seasoned field crews to ensure that they are 

following the specific protocols designed for a particular monitoring 

project. For monitoring projects that are particularly focused on high data 

quality, field crews could be accompanied by supervisors or trainers for 

their first full week of data collection during a season of fieldwork.

Effective management of field crews is a key role of the project supervisor. 

Investing time and resources into higher level planning can make crews more 

efficient with their time in the field, and, we suspect, also leads to higher data 

quality. We recommend considering the following items:

• Seasonality of data collection. Consider deciduous trees and the timing 

of spring leaf out and fall leaf changes, which can affect determination 

of mortality status and crown vigor. If data are collected too early in the 

year, the leaves might not have appeared in full yet, whereas collecting 

data too late may result in leaves that are already browning. In such 

circumstances, crews may not be able to distinguish seasonality versus 

loss of vigor. If the study intends to measure growth and health changes 

between years, then repeated measures should be conducted at roughly 

the same time of year, and typically after trees have stopped growing for 

the year. Conservative estimates of how long field surveys will take are 

preferable as overflow into another season might not be appropriate. Plan 

ahead by estimating the length of fieldwork based on time per tree and 

transportation to ensure that field surveys can be accomplished within the 

desired season (see section 2.1.5). 

• Plan the day-to-day route. Carefully plan transportation routes each 

day for fieldwork that is scattered across a city. This will make the days 

much more efficient and allow crews to count more trees per day (see 

section 2.1.5). Different routes will be required depending on the mode 

of transportation (e.g., car, public transit, bicycles). Transportation 

routes should include the optimal sequence of trees or plots to visit as 

well as directions between sites. Field crews or the project supervisor 

should allocate office time to plan their transportation routes (e.g., 

using Google Maps). For cities with particularly bad traffic problems, 

consider scheduling fieldwork to begin before morning rush hour, or 

using bicycles or public transit rather than cars. For residential yard trees, 

consider evening or weekend fieldwork when residents are more likely to 

be home and allow access (and adjust expected travel times accordingly).

• Include frequency of data transfer and backups in the field crew 

management protocol. If recording data on paper in the field, enter data 

into computer daily (ideally) or at a minimum, weekly. With frequent 

data entry, any discrepancy or confusion can be addressed while the site 

is clear in the crew’s memory, or revisiting the site may be possible. 

When sheets are complete, leave them in a safe place in the office, 

instead of having the crew continue to take them out into the field. 

3.2. Tips for Managing 

and Supporting 

Field Crews 

Careful planning for field 

crew transportation routes 

can lead to more effective 

data collection. 
60 GTR-PSW-266

Make scans as a backup. Daily backups are preferred for electronic data 

recording. Rename the file to the day it was downloaded in case future 

files are corrupted and there is a need to revert to the last working data 

file. If the recorded data are automatically uploaded into a cloud system, 

the supervisor should ensure that the entered data are complete.

• Have data recorder check against previous measurements. If trees 

are being remeasured, it is helpful for the recorder to have the previous 

measurements to check when there is confusion about a tree and to 

identify “blunders” (van Doorn 2014) while still in the field where they 

can be fixed (see “Create a quality control protocol” below). This will 

require having prior measurements available as a reference either on 

paper or electronically. For example, imagine a scenario where a crew 

is measuring a tree that was measured 5 years previously. The recorder 

should know the location and species information and should also have 

the tree photo (if available) to help find the same individual tree. The 

person measuring the tree should not know all of the previous data, 

such as trunk diameter and crown vigor, so that there is no bias in the 

current measurement. However, to ensure that d.b.h. growth is properly 

measured at the exact same spot on the trunk, the recorder will need to 

tell the measurer what height was used with the previous data. 

• Have regular team meetings with the field crews. The supervisor 

and field crew members should meet regularly to discuss progress 

and address any challenges that have arisen. Topics to discuss include 

difficulties with equipment, resolving any unknown species, sharing 

stories of interactions with the public, and reiterating safety protocols. 

Ideally, these team meetings should occur weekly and could be paired 

with other office work for the crews, such as scanning data sheets and 

planning transportation routes for the coming week.

• Optimize crew sizes based on available people and data needs. 

Although it might seem that having more crew members is better, at 

some point a large crew size is inefficient. The main limiting factor is 

the speed of the recorder, as too many people taking measurements and 

relaying data can result in mistakes or miscommunication.

• A two-person crew. Typically, the minimum comfortable crew 

unit is one recorder and one measurer. In addition to the efficiency 

of two pairs of hands for carrying both the recording device and 

measurement tools, this crew size provides a chance for unbiased 

quality control checking. A two-person crew is also beneficial for 

safety reasons in case of an emergency or injury. In addition, crown 

assessments (e.g., crown vigor) benefit from having two pairs of 

eyes inspect and average the results, and trunk diameter benefits 

from careful measurements of both trunk diameter and height 

to the measurement point, requiring two sets of hands. Having 

a small crew may mean that the recorder waits for data to enter, 

Regular meetings with the 

field crews and supervisor 

are important to manage 

logistics, discuss challenges, 

resolve data issues, and 

reiterate safety procedures.
Urban Tree Monitoring: A Resource Guide 61

although it is possible for the recorder to take on other tasks, such as 

photographing the tree. The recorder should also implement quality 

control protocols in the field, asking for remeasurements when 

needed (see bullet point below in this section for tips on establishing 

a quality control protocol). 

• A three-person crew. The advantage of having an additional crew 

member is that the recorder is kept busy with the flow of data. This 

also means that the recorder has more data to consider rapidly and 

should be very organized and prepared to set the timing of data entry 

at a productive yet unrushed pace. It helps to have the data collectors 

try on the role of recording so that they know at which pace to call 

out data and in which order (e.g., mortality status first, then crown 

vigor, then trunk diameter, or whatever flow seems to work best for 

that project). If possible, data collectors should queue up all the data 

for one tree and call out the information while having undivided 

attention from the recorder. Then the recorder can direct his/her 

attention on the next tree. Alternatively, the third person can take 

on tasks that require minimal interaction with the recorder, such as 

photographing the tree, and providing an extra hand with d.b.h. The 

third person could also be primarily in charge of talking to residents, 

allowing the other two individuals to remain focused on the data. 

A third person can also be particularly helpful if variables from the 

supplemental data sets are collected (sections 7, 8, 9, 10).

• A four (or more)-person crew. There is such a thing as too big of a 

crew. In some cases, it is better to split up into multiple groups and 

inventory plots/trees in parallel. However, depending on individual 

abilities and interests, it could still be beneficial to have a four-person 

crew. Four crew members could drive together to a neighborhood 

and have pre-assigned plots or street segments near each other, so 

that they are operating in parallel but in proximity. Four people 

may also be appropriate if monitoring is paired with other tree 

maintenance work in the field. With planting cohort monitoring, the 

local planting organization may wish to have field crews discuss tree 

stewardship with residents or do basic tree care in conjunction with 

the data collection (Roman et al. 2018b). In a four-person crew, two 

individuals could complete data collection and recording while two 

others could focus on tree care and resident interactions.

• Create a quality control protocol. Quality control is a system of 

maintaining a standard of data quality by testing a sample of the 

data collected against the specifications. The purpose of a quality 

control protocol is to provide a set of tangible steps that help reduce 

nonrandom errors. There are different time increments throughout the 

data collection process that can serve as data quality checks. Many of 

the points discussed below are based on the experience of scientists at 

Hubbard Brook Experimental Forest (in the White Mountains of New 
62 GTR-PSW-266

Hampshire) who have collected and managed tree data for decades. 

While these suggestions come from monitoring projects conducted by 

research scientists and graduate students, with data intended for scholarly 

analysis and scientific publications, urban foresters at municipalities and 

nonprofits may also find these suggestions valuable. 

• Daily: Before leaving a street, plot, or other sampling area, a crew 

member should check that all fields on the data sheet have been 

filled in and that all trees have been recorded. For example, when 

recording all street trees on a city block, crews could go back and 

count how many trees were on that block, as a way of doublechecking if they missed or double-counted any trees (Roman et al. 

2017). A similar strategy can be applied to plots. 

• Weekly: The supervisor should allocate time for addressing notes 

for supervisory review (see Field Guide section 2.13) in the collected 

data, either daily, weekly, or biweekly. Do not leave this task to the 

end of the field season. It is more efficient to review issues when the 

data are fresh in the crew’s minds. 

• Seasonally: The supervisor should conduct checks at the beginning 

of the field season, preferably during the first week to catch any 

major errors. For example, crews might have specific species 

misidentification problems or make repeated mistakes with the 

equipment (e.g., reading d-tape backwards; see Field Guide section 

2.12.2). Catching these mistakes early allows crews to improve their 

work and may provide time to re-do trees or plots with inaccurate 

data. Check on crews periodically throughout the field season to 

ensure that they are continuing to collect high-quality data. FIA and 

UFIA programs provide protocols (USDA FS 2016, 2017) on data 

quality checks, with expert and highly experienced crews checking 

on data collected by seasonal professional foresters. However, it is 

important to keep in mind that these are professional field crews with 

levels of resources that may be beyond what urban forestry programs 

with limited resources are able to provide. 

• Data value thresholds: The supervisor should set, before the 

project begins, acceptability thresholds for what is an acceptable 

level of change from previous measurements that would distinguish 

a biologically driven change versus a “blunder” (van Doorn 2014). 

For quantitative measures such as d.b.h., this can be a percentage or 

amount above or below d.b.h. (see section 2.1.13). If a measurement 

is read outside of that threshold, the recorder would ask for a 

repeated measure, without revealing the issue. The next measurement 

would be recorded, regardless of whether it is higher or lower than 

the first measurement, to not introduce bias. The same strategy 

can be applied to qualitative evaluations such as crown vigor. The 

supervisor can set thresholds such as a two-class vigor change from 

Quality control is a system 

of maintaining a standard 

of data quality by testing a 

sample of the data collected 

against the specifications.
Urban Tree Monitoring: A Resource Guide 63

previous data. For example, at Hubbard Brook Experimental Forest, 

the protocol calls for the recorder to look at the vigor rating recorded 

in the previous survey to make sure that current tree vigor is not 

mistakenly recorded as alive when previously recorded as dead (see 

discussion of “zombie trees,” section 2.1.16). In addition, any twostep crown vigor class change, in any direction, requires a repeat 

evaluation as sometimes the wrong tree crown was inspected.

• Allocate time for active management of the project. Supervising field 

crews is not a side job! Program managers taking on crew supervision 

responsibilities should be aware that work begins 2 to 4 weeks before 

the crews start and ends 2 to 4 weeks after the crews finish (if not more!) 

owing to the time it takes to prepare and organize the field season. 

To ensure that field crews can make the most productive use of their 

time and collect ample data, the project supervisor needs to have a 

clear plan for the entire season. This includes preparations for training 

activities. During the field season, the supervisor will need to spend 

considerable time communicating with the crew and responding to 

questions, addressing notes for supervisory review, and having regular 

check-in meetings with the crew. Hiring a temporary summer field 

team supervisor can allow the primary program staff to continue with 

their regular work. For example, graduate students with years of field 

experience could be the temporary supervisor while undergraduates are 

the field crew interns. To balance the financial costs, it may be necessary 

to hire fewer field crew interns so there can be a temporary supervisor. 

Programs with limited resources should be aware that if a regular 

permanent staff member takes on the task of supervising field crews, that 

person will need to have reduced workloads in other areas.

• Additional considerations for citizen science projects. Urban tree 

monitoring projects initiated by urban forestry professionals sometimes 

rely on citizen scientists for fieldwork (Roman et al. 2013). There are 

additional considerations for effectively managing these volunteer field 

crews. As unpaid crews, it is critical to ensure that their time is valued 

and that the data collection experience is meaningful, so that they are 

encouraged to volunteer again. With urban tree monitoring, fieldwork 

days could be organized as 1-day “mapping parties” in which volunteers 

get a quick morning training followed by data collection for preassigned 

plots. This approach has been used in New York City, NY (Silva et 

al. 2013). In another example, mapping events in Portland, Oregon, 

include arborists circulating by bicycle to offer assistance with species 

identification and other troubleshooting (di Salvo 2016). For planting 

cohort monitoring, the data collection may be done in tandem with 

tree maintenance or conversations with residents about stewardship. 

If that is the case, then volunteers will need additional training about 

how to approach those resident conversations and how to carry out 

appropriate maintenance. For example, volunteers with the Pennsylvania 

Supervising field crews

 is not a side job.
64 GTR-PSW-266

Horticultural Society in Philadelphia, PA, knock on doors when they 

do urban tree monitoring to discuss proper watering, mulching, and 

staking techniques with residents who requested trees (Roman et al. 

2018b). Volunteers then leave “report cards” for residents to note 

whether their maintenance techniques are done properly. When working 

with citizen scientists, it is important to acknowledge volunteers that 

devote significant time to the monitoring project by thanking them in 

newsletters or with award ceremonies. If using a mobile app, the citizen 

science project could take a gamification approach, whereby volunteers 

earn badges or other fun rewards for their service (Bowser et al. 2014, 

Crown et al. 2018). Additional resources for designing and implementing 

successful citizen science projects are available at the Federal Citizen 

Science and Crowdsourcing Toolkit website (citizenscience.gov). 

Published examples of citizen science projects in urban forestry 

(including data quality issues) include Roman et al. (2017), Bancks et al. 

(2018), Crown et al. (2018), Hallett and Hallett (2018), Hamilton et al. 

(2018), and Roman et al. (2018b).

An equipment list for collecting the Minimum Data Set is included in the 

Field Guide (appendix 3). Specific equipment decisions will differ by project 

needs based on the size of the trees being measured, the social context of the 

study area (i.e., private versus public trees), and the financial resources of the 

study. Speaking with other project leaders is recommended to gather ideas 

about which equipment to use based on monitoring goals and budget. We list 

below considerations for measuring d.b.h. (from the Minimum Data Set) as 

well as total tree height and canopy width (from the Tree Data Set). 

• On measuring trunk diameter. As previously mentioned, diameter tape 

(d-tape) is the most commonly used tool for measuring trunk diameter 

in forest ecology. This specialized forestry tool has regular units on 

one side—which can be used to measure circumference—and diameter 

units on the other side. This uses the classic formula from geometry: 

circumference = diameter * pi. However, not all urban forest inventory 

and monitoring projects use d-tape. When the trees being measured are 

quite small—less than 1-inch (2.54 cm) d.b.h.—engineering caliper 

tools are more suitable (particularly digital calipers). Wrapping d-tape 

around a very small tree is quite awkward and requires holding the tape 

angled to read the value. Caliper tools can also be suitable for trunks that 

have obstructions (i.e., staking ties, vines) that would make it difficult 

to wrap a d-tape. Additionally, there are many kinds of d-tape available 

for purchase, of varying materials (e.g., fabric, stainless steel) and tape 

widths. For larger diameter trees, there are d-tapes with claw hooks on 

the end, so that hooks can be situated in the bark while a person wraps 

the tape around the tree, allowing one person to measure a large tree 

without assistance (e.g., fabric or steel 20-ft long tapes, 50+ ft heavyduty logger tapes). Fabric d-tape (rather than stainless steel) can be 

easier to wrap around small to mid-size trees. However, when crews 

3.3. Field Equipment 

Suggestions

Diameter tape (d-tape) is the 

most commonly used tool

 for measuring trunk diameter 

in forest ecology.
Urban Tree Monitoring: A Resource Guide 65

use fabric d-tapes, they will need to be careful to avoid stretching the 

fabric; d-tape should be pulled snug, but not too tight, around the trunk. 

Project supervisors should ensure that previously used fabric d-tapes 

have not stretched so much as to become inaccurate. For smaller trees 

(but greater than 1-inch d.b.h.), thinner “pocket” d-tapes are available 

(e.g., 6-ft thin-line tapes). Notably, Biltmore sticks—a forester’s tool to 

roughly measure d.b.h.—are not conducive to precise remeasurement of 

d.b.h. Likewise, d-tapes or caliper tools with coarse resolution (e.g., units 

displayed include only 0.5 inch) are inappropriate for projects that seek 

to measure d.b.h. growth over time; the resolution of such measurement 

devices is not fine enough to reflect the relatively slow growth of tree 

trunks. For projects using U.S. customary units, we recommend d-tapes 

that have inches and 1/10-inch units. For projects using metric units, 

we recommend d-tapes that have centimeters and millimeter units (see 

section 2.1.10).

In addition to d.b.h., our protocols in the Field Guide call for recording 

the height of diameter measurement (see Field Guide section 2.12.1). 

Although that height could be taken with the regular units side of the 

d-tape, it is generally easier to have a different piece of equipment solely 

for measuring that height. We recommend either a contractor-grade 

stiff measuring tape (similar to the measuring tapes that many people 

have for home use) or a custom-cut 4.5-ft (1.37-m) height pole (from 

a material like PVC). However, if using a custom-cut height pole, it 

would be necessary to have markings for other heights, for situations in 

which diameter is measured lower or higher than 4.5 ft (1.37 m). If using 

U.S. customary units, the markings could be at every 10th foot (and not 

foot with inch gradations). A manual height pole (sometimes called a 

telescoping survey rod) could also be used to measure height to the d.b.h. 

point. Whichever measuring tool is used to determine the height to the 

d.b.h. point, always note measurement units in the final database. 

Crews should not use “breast height” on their bodies as a height 

reference, even though some crews may be familiar with this approach 

from prior coursework and field experiences. That approach may be 

acceptable when coarse d.b.h. measurements are needed (e.g., recording 

d.b.h. as being within a size class range of several inches), but it is not 

appropriate for d.b.h when repeated growth measurements are intended. 

• On measuring tree height. Tool choices for measuring total tree height 

include a manual height pole (also called telescoping measuring pole or 

survey rod), clinometer used in conjunction with measuring tape, and 

digital laser hypsometer. Equipment selection will depend on the general 

height of the trees. For short trees (<25 ft or 7.6 m), a height pole is 

recommended, as the crews can clearly see where the pole reaches the 

top of the tree. Clinometers or digital hypsometers are more appropriate 

for mid-size to tall trees, and such equipment has varying options for 

expense and accuracy. 
66 GTR-PSW-266

• On measuring crown width. Options for measuring crown width 

include contractor-grade or household tape measuring tape and digital 

laser hypsometers. For small trees, a measuring tape is easy to use 

and quite adequate. When the tree crown width is large enough that a 

measuring tape is unwieldy, electronic devices such as hypsometers 

become preferable. See the Tree Data Set (section 7) for more 

information about options for recording crown width.

• Use the same measuring tools across all crews and over time. For 

consistency, it is important for all crews to use the same equipment and 

same units of measure. Projects initiated in U.S. customary units should 

stay in those units over successive monitoring years, and likewise for 

metric units. Remeasurements for growth particularly require using the 

same equipment as the previous data collection. If mixing equipment is 

unavoidable (e.g., borrowing equipment from different partners owing to 

limited costs, measuring some very short trees with manual height poles 

and some very tall trees with hypsometers), make sure to calibrate. In 

other words, measure the same trees with both pieces of equipment to 

ensure that differences between them are within a reasonable margin of 

error.

It is important for all crews to 

use the same equipment and 

same units of measure.
67

4. Conclusion

In this Resource Guide, we have described designing and implementing 

techniques for field-based urban tree monitoring projects. The main point 

is that advance planning is needed to ensure that monitoring projects run 

smoothly with well-organized data collection, and to produce findings 

that are ultimately useful for the project goals. The remaining sections 

of this Resource Guide address the background on variable selection for 

the Minimum Data Set, variables to consider for projects going beyond 

the Minimum Data Set, as well as various supporting documentation 

related to urban tree monitoring (e.g., species identification resources, 

glossary, activities for training). Collectively, these resources should enable 

customization for projects with varying goals and personnel experience 

levels. Although long-term data collection for urban trees is a relatively 

recent area of scholarship and resource management, we can build on the 

long traditions of tree monitoring in rural ecosystems, and learn from the 

experiences of researchers and practitioners who have been pioneering tree 

monitoring in urban areas. 

69

Part II: Data Sets for Urban Tree Monitoring
70

5. Data Sets Framework

We have organized the monitoring protocols into a Minimum Data Set and 

four supplemental data sets. This structure allows urban forestry practitioners 

and researchers to adapt these protocols to their own needs based on 

monitoring project goals and organizational capacity. See section 1.4.3 for 

examples of the protocols in action with adaptation to local monitoring goals.

The data sets are organized as follows:

• Minimum Data Set. The core variables necessary for any urban tree 

monitoring project, including field crew identification, field crew 

experience level, species, location, site type, land use, mortality status, 

crown vigor, and trunk diameter (section 6).

• Tree Data Set. Tree size, growth, and health variables, including total 

height, crown width, presence of pests and diseases, and maintenance 

tasks. Includes variables related to mature tree management (section 7).

• Site Data Set. The site characteristics of the urban landscape surrounding 

the tree, including the planting site, built environment, and soils (Urban 

2008) (section 8).

• Young Tree Management Data Set. Recommended tree care practices 

by local organizations, and stewardship actions observed on the ground, 

and information about the programs and institutions that plant and care 

for trees (section 9).

• Community Data Set. Socioeconomic information about the human 

community surrounding the tree, pulled from existing databases 

(e.g., U.S. Census Bureau) for variables including median income, 

housing value, and population density. The Community Data Set does 

not require additional fieldwork but does require staff with GIS expertise 

(section 10).

The Field Guide provides detailed field protocols for only the Minimum Data 

Set. The Resource Guide provides lists of variables for each of the five data 

sets, with general descriptions, but without detailed field protocols; instead, 

we provide citations to other resources for readers interested in incorporating 

those variables into their project. We also provide explanations as to why 

some of the methods used in the Minimum Data Set were selected.

For most urban forest managers and even many research studies, the 

Minimum Data Set should be sufficient to meet monitoring program goals, 

such as evaluating performance in terms of mortality and growth or tracking 
Urban Tree Monitoring: A Resource Guide 71

changes in total tree population counts. As stressed in section 1.3, we 

urge those designing monitoring studies to be cautious when adding other 

variables. Be certain that there is an intended use for every variable collected, 

that the methods match the objectives of the monitoring project, and that the 

field crews have the appropriate training and skills to accomplish the datacollection tasks.

Furthermore, the variables listed in each data set are not exhaustive of all 

possible variables that could be collected to monitor urban trees. For those 

interested in establishing monitoring studies with intensive fieldwork, 

particularly for tree and site variables, we recommend exploring the other 

tree inventory and monitoring protocols listed in appendix 3.

For each of the data sets, we list potential goals for monitoring studies that 

could be met by using that data set (or portions of it). We have also coded the 

data sets by tiers to represent different levels of difficulty. These tiers allow 

customization of the protocols based on field crew skill levels and available 

resources, in terms of field crew time, equipment, and post-fieldwork 

processing.

• Tier 0: Basic. This is the easiest data to collect and can be gathered 

in the field by crews with minimal training. For projects relying on 

volunteers and minimally trained interns, we recommend using only the 

Minimum Data Set.

• Tier 1: Moderate. This is more extensive field data, with a suite of 

variables from the Tree, Site, and Young Tree Management Data Sets, 

in addition to the Minimum Data Set. Interns and volunteers could still 

potentially collect these data, but more in-depth field crew training is 

required, and more time is needed per tree.

• Tier 2: Difficult. This is advanced field data collection that requires 

individuals with prior experience and specialized equipment. These 

options involve more background knowledge than the basic and 

moderate options, but do not necessarily take more time in the field.

• Tier 3: Expert. This data collection requires expert-level training and 

analysis in specialty areas, such as tree risk management, soil testing, 

and social sciences. Data processing and statistical analysis outside of 

fieldwork is also required for some components. We recommend that this 

level of data collection be undertaken in collaboration with experts, such 

as research scientists or master arborists.

Because the Community Data Set does not involve fieldwork, we do not have 

tiers for that data set. Instead, the Community Data Set involves accessing 

socioeconomic data from existing databases and is therefore organized 

according to data sources (e.g., U.S. Census Bureau, local crime data). 
72

6. Minimum Data Set

The Minimum Data Set consists of information that allows practitioners 

and researchers to assess urban tree mortality, growth, and health. More 

specifically, the goals of the Minimum Data Set are to provide information to 

enable users to:

• Reliably locate trees for future fieldwork with different field crews.

• Identify and investigate errors associated with field data collection and 

conduct quality control/quality assurance.

• Report survival and mortality rates, and construct mortality curves by 

time since planting (planting cohort monitoring studies) or by d.b.h. size 

class (repeated census studies).

• Report d.b.h. growth rates.

• Report changes in foliage health over time.

• Analyze potential risk factors for growth and mortality that are included 

in the Minimum Data Set:

• Age (time since planting) or size class (d.b.h.)

• Species

• Crown vigor

• Site type

• Land use

Studies using the Minimum Data Set can also connect to other information 

that does not require additional fieldwork but may be used for statistical 

analysis of mortality, growth, and health outcomes:

• Species groups (e.g., levels of drought or flood tolerance, natives versus 

nonnatives, mature tree size, as determined by textbooks and fact sheets 

on urban trees).

• Neighborhood socioeconomic characteristics (e.g., U.S. Census data, 

from the Community Data Set, see section 10).

Detailed protocols for the Minimum Data Set are provided in the Field 

Guide. The variables included in the Minimum Data Set are shown in 

Figure 3. We provide justification as to why each variable is included in the 

Minimum Data Set in the Field Guide (section 2). Below we provide brief 

descriptions of each variable plus some additional explanation as to how we 

decided upon specific protocols for mortality status, crown vigor, and trunk 

diameter.
Urban Tree Monitoring: A Resource Guide 73

Basic record information:

Field crew identification0

Field crew experience level0

Date of observation0

Tree record identifier0

Location and site: 

Location0

Tree photo0

Site type0

Land use0

Tree data:

Species0,1,2

Mortality status0

Basal sprouts0

Crown vigor0

Trunk diameter0

Note for supervisory review0

Minimum 

Data Set

Figure 3.—Variables in the Minimum Data Set. Superscripts represent tiers (see 

section 5), with more than one tier indicating that crews with more skills or equipment 

could obtain enhanced data.

6.1.1. Field crew identification

Description: Field crew identification is information about the individual(s) 

who collected field data on this tree. Crew names, initials, or team numbers 

may be used but should be consistent within a given project.

6.1.2. Field crew experience level

Description: The experience level of the most experienced individual on the 

field crew team that collected data on this tree.

6.1.3. Date of observation

Description: Date (year, month, day) of the field data collection.

6.1.4. Tree record identifier

Description: Each tree record should have a unique identification code that 

remains connected to the tree during future monitoring. For projects that 

track recently planted trees, the unique key could link with planting records.

6.1. Basic Record 

Information 
74 GTR-PSW-266

6.2.1. Location

Description: Information about the geographic position of the tree in the 

landscape, used to relocate the tree for future monitoring and to link this tree 

to other geospatial data sets. Different options for tree location methods are 

discussed in the Field Guide (section 2.4) and Resource Guide (section 2.3). 

6.2.2. Tree photo

Description: A photograph taken to show the entire tree in the context of its 

immediate location and built infrastructure objects in the landscape, with a 

view that would help future field crews reliably find the same tree.

6.2.3. Site type

Description: Site type is a description of the tree’s immediate location. The 

site type categories indicate broad information about the immediate area 

surrounding the tree and controls on tree inputs and removals (humandominated versus natural).

6.2.4. Land Use

Description: Land use is a description of the way the property around (or 

adjacent to) the tree is used by humans. Land use is distinct from site type, 

although the two variables are related and there is some overlap in their 

definitions, particularly with parks and natural areas. We use land use to refer 

to land use at the property level, not at the tree site.

6.3.1. Species

Description: Record species using standard botanical names, with both genus 

and species, or species codes. When using species codes, we recommend the 

codes from the Urban Forest Inventory and Analysis program of the Forest 

Service (USDA FS 2017). These codes consist of the first two letters of 

the genus and species. For example, red maple should be recorded as Acer 

rubrum or species code ACRU. Crews with less species-identification skill 

could potentially record only genus information or only record the most 

common species (Roman et al. 2017).

6.3.2. Mortality status

Description: Mortality status is a record of whether the tree is alive, standing 

dead, removed, or in some other state.

6.3.3. Basal sprouts

Description: Basal sprouts, sometimes called suckers or water sprouts, grow 

from buds at the base of the stem or in the roots of a tree. Basal sprouts can 

indicate that the root system is still alive in a stump or standing dead tree. 

Record “present” if basal sprouts are present or “absent” if basal sprouts 

are not present. See section 2.1.17 for a discussion about deciding on the 

intended use of the basal sprout variable.

6.2. Location and Site

6.3. Tree Data
Urban Tree Monitoring: A Resource Guide 75

6.3.4. Crown vigor

Description: Crown vigor consists of five classes based on visual 

examination of crown health. It is a holistic assessment of overall crown 

health and reflects the proportion of the crown with foliage problems and 

major branch loss. Note that crown vigor does not involve evaluation of 

trunk condition or structural stability. 

6.3.5. Trunk diameter

Description: Trunk diameter is recorded either as diameter at breast height 

(d.b.h.) or diameter at caliper height (d.c.h.), depending on the tree’s 

characteristics: d.b.h. is the measure of diameter at 4.5 ft (1.37 m) from the 

ground, and d.c.h. is the diameter at 1 ft (30.5 cm) from the ground (using the 

meaning of caliper from the nursery trade, which is different from a caliper 

measuring tool). While d.b.h. is the standard way to measure trunk size for 

forest ecologists and most urban foresters, d.c.h. is the common way to report 

sizes of nursery stock. Measurements for trunk diameter include the diameter 

itself and the height at which the diameter is taken. The protocols in the Field 

Guide also have specific instructions for measuring multi-stemmed trees 

(see Field Guide section 2.12.4, and section 6.5 below, as well as Magarik et 

al. 2020). Note that while we recommend recording diameter, some project 

may prefer circumference, and this must be noted with data collection so that 

conversions can occur during data processing.

6.3.6. Notes for supervisory review 

Description: This is a place to note difficulties with species, mortality status, 

trunk diameter measurements, or other variables. Entering a note here flags 

this tree for review by the project supervisor.

While mortality may seem like a fairly obvious variable to record, it has 

caused some confusion in urban forestry research and been defined in 

different ways. As noted in section 1.1.1, we define mortality as including 

trees that are observed standing dead and those that have been removed. This 

is consistent with dozens of prior studies on urban tree mortality (Hilbert et 

al. 2019). Yet even with mortality reflecting a combination of trees that die 

standing in place, and those that are removed, it is still helpful to differentiate 

removals from standing dead outcomes. The categories that we specified for 

mortality status are: alive, standing dead, stump, removed, never planted, and 

unknown (see Field Guide section 2.9).

Our reasoning behind these categories reflect both practical management 

considerations as well as research applications. For management purposes, 

standing dead trees require followup to remove them, and large standing 

dead trees can create risks for human safety and property damage. Using a 

6.4. Background 

on Mortality Status
76 GTR-PSW-266

similar rationale, we include stump as a category within mortality status; 

arborists may want to know where stumps are located so that workers can 

subsequently grind up the stumps. Recording standing dead trees as their 

own category, separately from removed trees, also has value for research 

studies. For example, in a multi-age inventory street tree monitoring study in 

Oakland, California, trees were monitored for mortality annually for 5 years 

(Roman et al. 2014b). Transitions in the street tree population were tracked, 

in terms of new trees, dying trees, and removals. The average proportion 

of trees observed standing dead was 1.7 percent. Yet many of these trees 

persisted in the landscape over time, remaining dead rather than being 

removed: of the standing dead trees observed in a given year, on average, 

56.7 percent were removed by the following year. These findings point to the 

lack of followup care to handle dead tree removals in the study area.

The category of “never planted” within mortality status was created for 

planting cohort studies, in recognition of urban forestry programs that 

operate as giveaways (Nguyen et al. 2017). Such programs distribute trees, 

typically to residents, who are then responsible for planting. However, 

not every tree distributed will actually get planted by the residents in their 

yards. For example, in a yard tree giveaway program in Sacramento, CA, 

15.1 percent of trees distributed were not planted (Roman et al. 2014a). 

While failure to plant and post-planting mortality are both problems for the 

program, they are distinct phenomena with potentially different causes. The 

never planted category in mortality status enables field crews to track that 

outcome. Notably, for giveaway programs, using field data alone, it can be 

challenging to distinguish between trees that residents never planted versus 

trees that were planted and subsequently removed. Discussions with residents 

may be necessary to determine what happened to any “missing” trees.

While the never planted category is certainly necessary for cohort monitoring 

of yard tree giveaway programs, it can also be relevant to tracking street tree 

planting programs. The at-planting records for street tree programs might 

erroneously contain trees that did not get planted for one reason or another 

(Roman et al. 2018b), such as last-minute refusal by an adjacent property 

owner, or unexpected utilities underground.

Finally, we included the unknown category within mortality status because 

of the unusual cases that can arise in urban tree monitoring. This includes 

situations of “zombie trees” discussed in section 2.1.16, as well as any 

instances in which there is locational confusion finding the tree from baseline 

records. Although field crews will rarely assign a tree to unknown mortality, 

we have it as an option to capture those unique situations, so that project 

supervisors can resolve them later.
Urban Tree Monitoring: A Resource Guide 77

There are many options for recording urban tree health and condition, many 

of which are reviewed in Bond (2010). Other protocols for evaluating tree 

health that have been applied to urban areas include Bond (2012), Östberg 

et al. (2013), Pontius and Hallett (2014), and Vogt and Fischer (2014). Many 

methods for visually evaluating tree health, condition, vigor or vitality 

require prior knowledge about the typical performance of various tree 

species in the region of study. This approach is challenging to implement for 

many intern and volunteer field crews, so we wanted to pursue a means of 

evaluating the tree that required less prior expertise. 

For the final Minimum Data Set, we used the crown vigor variable based 

on Pontius and Hallett (2014), which in turn is based on protocols used for 

the North American Maple Project (NAMP) (Millers et al. 1991, Steinman 

1998,) and used to study regional sugar maple decline disease (Hallett et al. 

2006). While this variable was not tested in the Field Guide pilot study, it 

was separately evaluated in a study of urban tree inventory data quality from 

high school students (Hallett and Hallett 2018), which showed that student 

crews were within one vigor class of expert crews for 56 percent of trees, and 

within two vigor classes for 92 percent of trees. In a study of monitoring data 

from recently planted trees, volunteer vigor was within one class of intern 

vigor for 90 percent of trees (Roman et al. 2018). Notably, the Hallett and 

Hallett (2018) study also involved recording discoloration and defoliation 

separately, which may be of interest to some tree monitoring studies that 

seek more in-depth data about tree health trends to detect stress response and 

decline over time. That approach has been used to study the stress response 

of street trees that were flooded by salt water during Hurricane Sandy in New 

York City, NY (Hallett et al. 2018).

Readers of this report may prefer one of the other protocols for visually 

evaluating urban tree health cited in the first paragraph of this section, 

rather than crown vigor as defined in the Field Guide. Whichever method is 

chosen, we strongly encourage using variables for health, condition, dieback, 

vigor, or vitality with clear definitions and rubrics that distinguish among 

classes. We also strongly advise limiting evaluations of wood condition, 

structural stability, and maintenance or pruning needs to certified arborists or 

comparable experts, based on findings of Bloniarz and Ryan (1996), Cozad 

(2005), and Roman et al. (2017). For further guidance regarding variables 

specific to tree health and wood condition, see the Tree Data Set (section 7).

It is important to note that all of the methods for evaluating tree health, 

condition, dieback, vigor, or vitality are designed for deciduous or 

evergreen trees, generally in temperate climates. However, cities in tropical, 

subtropical, or Mediterranean climates also often have palms as part of their 

urban forests. A recent publication provides guidance about a visual health 

assessment method for palms (Blair et al. 2019).

6.5. Background 

on Crown Vigor
78 GTR-PSW-266

While d.b.h. is a core component of nearly all urban tree inventory and 

monitoring methods (Bond 2013, Östberg et al. 2013, Roman et al. 2013), we 

have found that many urban forestry professionals record d.b.h. in a manner 

that is not conducive to assessing tree growth over time. Specifically, d.b.h. 

is often recorded to the nearest 1 inch (2.54 cm), and height to diameter 

measurement point is not noted. That approach is appropriate for inventories 

that serve management purposes, such as using d.b.h. to place work orders 

for tree removal, or constructing basic graphs of tree size class distribution. 

However, such coarse d.b.h. data precludes analysis of trunk growth, and 

more precise d.b.h. data are needed for analyses of ecosystem services such 

as carbon sequestration. It is worth re-emphasizing that although the default 

height for d.b.h. is 4.5 ft (1.37 m) in the United States, and this is the height 

specified in our Field Guide, the actual measurement height used for each 

individual tree may differ owing to tree form and trunk irregularities (see 

Field Guide “Special Considerations” for d.b.h., section 2.12.3).

The variation in actual height used for d.b.h. leads researchers in forest 

ecology to employ strategies for ensuring that future remeasurements on 

individual trees are done at the same height point. One strategy is to use a 

custom-cut 4.5-ft (1.37-m) pole (van Doorn 2014) so that future crews can 

wrap d-tape at the exact same spot on the tree. Permanent plots in forest 

ecology research also sometimes mark the point of d.b.h. measurement 

with paint, permanent markers, aluminum nails, or crayons (Condit 1998, 

USDA FS 2017a). However, urban forest managers may not wish to have 

trees nailed or painted, as these techniques might be considered unappealing 

aesthetically, or upsetting to residents. Temporary solutions such as chalk 

may aid in the process of recording height to d.b.h measurement point 

(Magarik et al. 2020) while longer lasting solutions such as permanent 

marker can carry over from season to season. Yet even without marking the 

tree, if crews carefully measure the height to the initial d.b.h. measurement 

point, it is possible to remeasure d.b.h. at the same spot in future years, 

ultimately enabling assessment of growth.

Trees are also generally slow-growing organisms, so measuring trunk growth 

with precision requires recording d.b.h. to the nearest 10th inch (or if using 

metric, nearest millimeter), which is standard in forest ecology plots. This 

same approach can be applied to urban trees—which is why the protocols in 

the Field Guide call for measuring to the nearest 10th-inch (or millimeter). 

For example, the combination of nearest 10th-inch measurements and records 

of the exact d.b.h. height used enabled Roman et al. (2015) to report annual 

growth rates for recently planted trees in Philadelphia, PA: Quercus palustris

grew at 1.9 cm (0.75 inches) per year, whereas Acer rubrum grew at 1.1 

cm (0.43 inches) per year. Initial measurements were taken 1 month after 

planting, and trees were remeasured 6 years later. 

6.6. Background 

on Trunk Diameter
Urban Tree Monitoring: A Resource Guide 79

Trees that fork below 4.5 ft (1.37 m)— often referred to as multi-stemmed 

trees—create distinct challenges for measuring d.b.h. The treatment of multistemmed trees in our Field Guide is based on lessons learned in the pilot test 

(Roman et al. 2017), in which citizen scientist crews sometimes disagreed 

with each other regarding how many stems a particular tree had, even though 

they were given guidance for how to decide which stems to measure. Some 

crews in that study also commented that multi-stem d.b.h. measurements 

were difficult. In our other experiences using conventional multi-stemmed 

protocols, such as the directions to measure up to six stems in i-Tree Eco 

(i-Tree 2017b), paid intern crews and even experts have similarly expressed 

challenges. Ultimately, the coauthors of that pilot study (Roman et al. 2017) 

concluded that trunk measurements at 4.5 ft (1.37 m) are not well-suited to 

small-statured, ornamental, multi-stemmed tree species that are common 

in urban forests. Furthermore, recording multiple stems does not seem 

conducive to careful remeasurement of trunk growth, as future field crews 

cannot be guaranteed to remeasure the exact same stems, especially with the 

possibility for pruning. This led to our guidelines to measure d.b.h. below 

the fork, where there is a single stem, whenever possible. This technique 

also draws inspiration from practical methods for d.b.h. geared toward 

professional arborists (Swiecki and Bernhardt 2001). We posit that measuring 

below the fork should produce more consistent results across field crews and 

over time, thus enabling analysis of tree growth. 

However, besides diameter below fork, there is another approach to 

measuring multi-stemmed trees that may be well-suited to consistent 

remeasurements over many years: diameter at 1 ft (30.5 cm) from the 

ground. A field evaluation study of Malus spp., Prunus spp., and Zelkova

spp. street trees in Philadelphia, PA, showed that this approach had the 

most consistent heights to measurement point, unlike diameter below fork, 

where variation in height is inherent in the method (Magarik et al. 2020). In 

that study, there was no decisive advantage for any diameter measurement 

method in terms of predictive power for correlations with total tree height 

and crown width, making issues of consistency and repeatability paramount 

in method selection. Indeed, diameter at roughly caliper height is already 

used for nursery stock (American Nursery & Landscape Association 2004) as 

well as by various researchers for multi-stemmed trees in other ecosystems 

(MacDicken et al. 1991, Snowdon et al. 2002, Stewart and Dunsdon 1994, 

Stewart and Salazar 1992). Measuring diameter at 1 ft (30.5 cm) may be 

most appropriate for researchers concerned with consistency across space 

and time. However, as this study was limited to three genera in one city, 

further research is needed to evaluate pros and cons of various options for 

measuring multi-stemmed urban trees for different taxonomic groups and 

growing conditions. Different approaches to measuring multi-stemmed trees 

can have implications for analyses of tree growth, allometry, and ecosystem 

services.
80 GTR-PSW-266

Notably, the study about multi-stemmed trees in Philadelphia (Magarik et 

al. 2020) also employed notation for reporting diameter height that makes it 

easier to communicate the default, standard height: the letter “D” followed 

by a subscript with the height from the ground in centimeters (e.g., D30.5 for 

diameter at 30.5 cm, D137 for diameter at 137 cm, following Brokaw and 

Thompson 2000). This notation may be particularly helpful in comparing 

d.b.h. methods and findings across countries, as D137 is standard in the United 

States, but D130 is the norm in Europe and the most common standard in 

forest ecology studies, although many publications do not even report the 

diameter height used (Brokaw and Thompson 2000).
81

7. Tree Data Set

The Tree Data Set consists of information that allows practitioners and 

researchers to assess tree growth as well as health, presence of pests and 

diseases, maintenance requirements, and risk. The variables included in the 

Tree Data Set are shown in Figure 4. For those who are thinking of including 

variables in addition to the Minimum Data Set, we encourage focusing on the 

variables that are marked with “1” and “2” (meaning the attainment effort is 

lower; see Figure 4). 

Tree size measurements:

Height1,2

Height to top of live crown1,2

Height to base of live crown1,2

Bole height1,2

Crown diameter1

Crown shape1

Crown light exposure1

Tree condition and maintenance: 

Fine twig dieback1,2

Discoloration

Defoliation

Live crown ratio1

Shoot extension2

Condition root crown and roots2

Pests and disease2,3

Maintenance priority and task2,3

Images:

Calibrated tree photos2,3

Crown transparency2,3

Tree 

Data Set

Figure 4.—Variables in the Tree Data Set. Superscripts represent tiers (see section 

5), with more than one tier indicating that crews with more skills or equipment could 

obtain enhanced data. 
82 GTR-PSW-266

Note that maintenance variables in this data set are most relevant to mature 

urban trees, whereas maintenance and stewardship variables related to young 

trees during establishment are in the Young Tree Management Data Set. 

More specifically, the goals of the Tree Data Set are to provide information 

that will provide users the ability to: 

• Monitor tree growth with regards to height and crown size and develop 

allometric equations relating d.b.h. to these other tree size measurements.

• Assess and monitor tree health to detect stress response.

• Assess management considerations, specifically pruning, tree risk, and 

potential for infrastructure conflict owing to tree size or growth habit. 

• Provide sufficient information to calculate ecosystem services for 

other applications such as i-Tree Eco (USDA FS 2017b) and EcoSmart 

Landscapes (McPherson et al. 2014), and carbon credits with the Climate 

Action Reserve (Climate Action Reserve 2018).

• Provide baseline data for evaluation of site and soil impacts on tree 

growth and condition (in conjunction with Site Data Set).

In the tree health variables below, fine twig dieback, discoloration, and 

defoliation are based on assessment methods described in Pontius and Hallett 

(2014), Hallett and Hallett (2018), and Hallett et al. (2018).

Variables previously described in i-Tree Eco (2017a) and UFIA protocols 

(i-Tree 2017b) are cited in parentheses with matching or similar terms and 

page numbers. 

7.1.1. Height (total)

Description: Total tree height from the ground to the top of the crown 

measured vertically (i-Tree 2017a, p. 28).

7.1.2. Height to top of live crown

Description: Height from the ground level to the top of the live (green) crown 

vertically (i-Tree 2017a, p. 28).

7.1.3. Height to base of live crown

Description: Distance between the ground and the bottom of live foliage of 

the crown (i-Tree 2017a, p. 29).

7.1.4. Bole height

Description: Measurement of the length of the bole from the ground level 

to the first crown-forming branch. This is sometimes called trunk height or 

clear bole height. On palm trees, measure stem from the ground to leaf base 

(apical meristem; base of the heart leaf; also known as trunk height in palm 

standards).

7.1. Tree Size 

Measurements
Urban Tree Monitoring: A Resource Guide 83

7.1.5. Crown diameter

Description: Two horizontal perpendicular widths from leaf or twig tip 

(dripline), passing through the trunk. There are three options for recording 

those crown widths: (1) record the widest diameter of the crown first, then 

the diameter perpendicular to that, following UFIA (USDA FS 2017, p. 

142); (2) record width in the north-south and east-west cardinal directions, 

following i-Tree Eco (2017a, p. 30); and (3) for street trees, record diameter 

parallel and perpendicular to the street, following McPherson et al. (2016). 

The street tree method is often equivalent to UFIA because of typical pruning 

practices along roads. We are not aware of studies that compare these options 

for the same trees, so we recommend that monitoring project leaders pick one 

of these options and use it consistently for a given study.

7.1.6. Crown shape

Description: “Crown shape is the silhouette of a tree, drawn from branch 

tip to branch tip, which contains all of a tree’s foliage. Normally, silhouettes 

are derived from vigorous, open grown trees and tend to be species specific. 

Crown shape is used as an outline for the sides of the tree” (USDA FS 2017, 

p. 156). 

7.1.7. Crown light exposure

Description: Tree crowns are divided vertically into four equal sides (or 

quarters) plus the top for a possible total of five faces. Count how many of 

the four sides would receive direct light if the sun were directly above the 

tree. Try to divide the crown in such a way that as many quarters as possible 

receive full light. Add one if the tree receives direct light from the top 

(USDA FS 2017a, pp. 161-162, 173; USDA 2017b, p. 35).

7.2.1. Fine twig dieback

Description: Fine twig dieback indicates the death of tissues responsible 

for producing and supporting most of a tree’s leaf surface area and branch 

elongation. The amount of fine twig dieback is a reflection of the severity of 

recent stresses on the tree. Fine twig dieback reflects the percentage of crown 

area affected, focusing specifically on the upper and outer sections of the 

crown. The fine twig dieback variable is focused on recent stress response 

in the fine twigs rather than natural branch dieback (self-pruning owing 

to crown competition or shading in the lower portion of the crown), older 

branch mortality, or pruned sections of the crown.

Fine twig dieback reflects the percentage of crown volume affected (Box 1). 

Therefore, a tree with a single branch with dieback can have a large 

percentage of dieback if the overall crown is small, while a tree with a large 

crown and a comparable branch with dieback will have a much smaller 

percentage of dieback. See pictures in Box 1 for examples. Walk around the 

tree to observe fine twig dieback from several angles and record the average. 

If completing the survey as a field crew team, attempt to reach consensus for 

the dieback class.

The fine twig dieback description here is based on i-Tree (2017a, 2017b). 

7.2. Tree Condition 

and Maintenance 
84 GTR-PSW-266

Dieback class

Percentage of crown 

showing dieback

1 0-1 (none/trace)

5 2-5

10 6-10

15 11-15

20 16-20

25 21-25

30 26-30

35 31-35

40 36-40

45 41-45

50 46-50

55 51-55

60 56-60

65 61-65

70 66-70

75 71-75

80 76-80

85 81-85

90 86-90

95 91-95

99 96-99

A tree with fine twig dieback. Photo by 

J.P. Fristensky, used with permission.

Class 5: 2 to 5 percent fine twig dieback. 

The difference between the red dashed 

outline and the outer black outline 

represents the percentage of fine twig 

dieback. Overlay by J.P. Fristensky, used 

with permission.

Box 1. Fine twig dieback classes
Urban Tree Monitoring: A Resource Guide 85

7.2.2. Discoloration

Description: Foliar discoloration is the proportion of the tree canopy 

with different coloration than a healthy tree of the same species. Possible 

symptoms include dark spots on leaves or leaves that are yellow, brown, or 

even lighter in color than a healthy tree of that species. For instance, there are 

some horticultural varieties of trees where the normal healthy leaf color is 

bronze or dark red or yellow. The rating applies to the total proportion of the 

canopy that is affected.

7.2.3. Defoliation

Description: Defoliation applies to the proportion of the tree canopy with 

defoliation (holes in leaves or missing portions of leaves). The rating applies 

to the total proportion of the canopy that is affected. If 100 percent of the 

leaves have defoliation, the defoliation rating is not 100 percent (i-Tree 

2017a, pp. 144-151, pp. 30-31).

7.2.4. Live crown ratio

Description: The percentage of the total height of the tree that is occupied by 

the live green crown. When the top of the tree and the top of the live crown 

are in the same location, tree height and height to base of live crown are the 

only measurements required. If the top of the tree is dead, then height to the 

top of the live crown is also required (USDA FS 2017a, pp. 139-141).

7.2.5. Shoot extension

Description: A measure of tree health understood as shoot extension. 

Determined from canopy parameters (Levinsson et al. 2017). 

7.2.6. Condition root crown and roots

Description: An assessment of planting practices; measure of circling, 

girdling, or compressing roots currently present on the root crown and 

potential circling, girdling, or compressing roots (USDA FS 2017a, 

pp. 178-179, 187-188, 189-190).

7.2.7. Pests and diseases

Description: Information about pests and diseases can be recorded in two 

primary ways: (1) presence/absence of specific pests and diseases, or (2) 

signs and symptoms of specific pests and diseases. For protocol examples, 

see USDA FS (2017) and i-Tree (2017b).

7.2.8. Maintenance priority and task 

Description: Arborists and trained workers (tier 2) add mutually exclusive 

maintenance details that pose a possible hazard and should be inspected by 

more qualified personnel. Arborists who have passed the Tree Risk Assessor 

Course and Exam (TRACE) or taken the Tree Risk Assessment Qualification 

(TRAQ) course (tier 3) can collect data pertaining to risk and hazard, 

including the likelihood of failure, size of part which may fail, and target 

rating. The collection criteria and attributes are fully explained in the TRAQ 

course.
86 GTR-PSW-266

7.3.1. Calibrated tree photos

Description: Profile views of tree taken perpendicular to one another (i.e., if 

one is taken facing north/south, the other faces east/west; alternatively, if one 

is facing the tree parallel to the street, the other is facing perpendicular) with 

distance between camera and tree bole recorded for each. When images are to 

be used for measurement of tree components like height and height to crown 

base, a scale must be placed on the bole of each photographed tree or another 

scaling method developed.

7.3.2. Photos to estimate crown transparency 

Description: Four photographs taken as vertical images, each with the camera 

lens pointing up through one-quarter of the tree crown. Digital photographs 

can then be used to quantify the percentage of open versus dark pixels 

(Pontius and Hallett 2014). Digital images are automatically processed 

using a script written for CellProfiler (Lamprecht et al. 2007), which reports 

percentage transparency for each image. Transparency values from up to four 

photographs per tree can then be averaged to represent overall percentage of 

crown transparency. Careful records must be kept to ensure that each photo 

is associated with the correct tree, and the photographs are organized and 

curated for processing. 

For the pilot test of the Field Guide, the protocol development team devised 

condition ratings for the tree’s crown and wood, with a clearly defined fourpart scale for each (Roman et al. 2017). The crown component was further 

divided into dieback and transparency, which were deemed to be variables 

that crews could collect without consideration of species-level patterns. We 

used 25 percent bin sizes for both of those variables (i.e., 0 to 25 percent 

dieback, 26 to 50 percent, etc.). The intent was to provide a system that 

mirrored the four-part scale of tree condition in i-Tree Streets (i.e., good, fair, 

poor, dying), but with more clearly defined categories. However, in the pilot 

test, we found that volunteer field crews were not consistent with experts for 

transparency and wood condition (Roman et al. 2017), and, therefore, those 

variables were dropped from the Field Guide. We kept fine twig dieback in 

the Tree Data Set and opted to use the more holistic crown vigor variable in 

the Minimum Data Set. With the fine twig dieback description in the Tree 

Data Set, we used the 5 percent bin sizes used by the protocols for i-Tree 

Eco, FIA, and UFIA (i-Tree 2017a; USDA FS 2016, 2017). Most trees 

recorded in the pilot study had <25 percent dieback; therefore, the 5 percent 

bins could potentially reveal important differences in dieback in the range 

of 0 to 25 percent. However, we recognize that there is ongoing debate as to 

how fine twig dieback should be recorded, with other studies opting for 10 

percent bins (Vogt and Fischer 2014). Further research is needed to determine 

levels of agreement among field crews for dieback depending upon the bin 

sizes, as well as optimal ways to train field crews to visually discern dieback. 

We also suspect that crews may sometimes include large branch mortality as 

part of fine twig dieback; this is not correct protocol, as fine twig dieback is 

7.3. Images

7.4. Background 

on Fine Twig Dieback
Urban Tree Monitoring: A Resource Guide 87

intended to focus on recent growth in the outer portions of the crown (Roman 

et al. 2017). Training for this variable should include photos of varying levels 

of fine twig dieback, as it can be difficult to encounter a range of situations 

during outdoor training.
88

8. Site Data Set

The Site Data Set consists of information that allows practitioners and 

researchers to assess site quality of urban landscapes for trees and forests. 

The variables included in the Site Data Set are shown in Figure 5. The goals 

of the Site Data Set are to provide information to address the following 

issues:

• Differentiate site conditions for their potential effects on urban trees and 

forests.

• Assess and monitor urban infrastructure (roads, buildings, sidewalks, and 

utilities) for their potential impact on tree mortality, growth, and health.

• Assess and monitor urban infrastructure for impacts on trees and 

potential maintenance concerns.

• Assess and monitor growing space for urban trees and forests.

• Assess and monitor management approaches to remediate urban soil 

quality.

Most of the Site Data Set is adapted from the Rapid Urban Site Index 

(Scharenbroch et al. 2017). Note that there are other resources available for 

characterizing and managing urban soils (Lindsey and Bassuk 1991, 1992; 

Scharenbroch et al. 2005, 2018; Scharenbroch and Catania 2012; Urban 

1992, 2008). Research on nutrients and contaminants in the soil should be 

done in partnership with a soil science specialist. 

8.1.1. Type

Description: The type of the nearest road, classified by functions such as 

arterial, collector, and local (USDT FHA, n.d.). These data may be available 

from local planning agencies or could be assessed in the field.

8.1.2. Distance

Description: The distance from the center of the tree trunk to the nearest 

road.

8.1.3. Azimuth

Description: The cardinal direction or angular distance expressed as the 

direction one is facing when standing at the tree to the nearest point of the 

road. 

8.1. Roads
Urban Tree Monitoring: A Resource Guide 89

Roads:

Type1,2

Distance1,2

Azimuth1,2

Speed limit1

Number of lanes1

Parking1

Soil: 

Ground cover1,2

Soil area1,2

Texture2,3

Structure2

Density3

Aggregate stability2,3

pH2,3

Electrical conductivity2,3

Organic matter2,3

Buildings:

Distance1,2

Azimuth1,2

Height1,2

Hardscapes:

Type1

Distance1

Condition1

Overhead Conflicts:

Type2

Height2 

Site 

Data Set

Figure 5.—Variables in the Site Data Set. Superscripts represent tiers (see section 

5), with more than one tier indicating that crews with more skills or equipment could 

obtain enhanced data. 
90 GTR-PSW-266

8.1.4. Speed limit

Description: The posted speed limit on the road.

8.1.5. Number of lanes

Description: The number of lanes on the road, excluding lanes of parking, but 

including turn lanes that run along the entire road segment.

8.1.6. Parking

Description: Parking available on the side of the road on which the tree is 

located.

8.2.1. Distance

Description: The distance to the nearest building.

8.2.2. Azimuth

Description: The cardinal direction or angular distance expressed as the 

direction one is facing when standing at the tree to the nearest point of the 

building. 

8.2.3. Height

Description: The number of stories or height of the nearest building. 

8.3.1. Type

Description: The type of the hardscape nearest to the tree. 

8.3.2. Distance

Description: The distance to the nearest hardscape.

8.3.3. Condition

Description: The condition of hardscape. These data may include type, 

amount, or severity of distress to pavement (McPherson and Muchnick 2005, 

MTC 1986). 

8.4.1. Type

Description: The type of conflict present on the site, such as overhead wires, 

classified by functionality (e.g., primary lines carry electricity to substations; 

secondary lines carry electricity from the utility pole lines to a building; cable 

television and broadband lines provide cable and Internet services; telephone 

lines deliver landline telephone service).

8.4.2. Height 

Description: The height from the ground to the conflict closest to the tree 

(e.g., height from the ground to the primary electrical line).

8.2. Buildings

8.3. Hardscapes

8.4. Overhead Conflicts
Urban Tree Monitoring: A Resource Guide 91

8.5.1. Ground cover 

Description: The type of ground cover surrounding the tree.

8.5.2. Soil area

Description: An estimate of the soil area available for root growth that is 

directly accessible to the tree (also called “apparent available soil”) (Sanders 

and Grabosky 2014).

8.5.3. Texture

Description: Soil texture is a qualitative classification to distinguish the 

relative contributions of sand, silt, and clay in the soil. 

8.5.4. Structure

Description: Soil structure is the physical arrangement of solid parts of the 

soil and the pores between them. Medium and fine-size aggregates produce 

numerous pore spaces which allow for root penetration, water storage, and 

movement of organisms, nutrients, air, and water through the soil. 

8.5.5. Density

Description: Density can be assessed as soil bulk density or penetration 

resistance. Soil bulk density is the ratio of oven-dried soil mass to its bulk 

volume, inclusive of particles and pore spaces. Penetration resistance is 

a measure of the ease with which an object can be pushed into the soil. It 

gives an indication of root-impeding layers in the soil and can be used in 

comparing relative strengths among similar soil types.

8.5.6. Aggregate stability

Description: Aggregate stability is a measure of the vulnerability of soil 

aggregates to external destructive forces. 

8.5.7. pH

Description: Soil pH is a measure of the acidity or alkalinity of a soil. 

8.5.8. Electrical conductivity

Description: Soil electrical conductivity refers to the amount of salts (cations 

or anions) in the soil.

8.5.9. Organic matter

Description: Soil organic matter consists of plant and animal residues at 

various stages of decomposition, cells and tissues of soil organisms, and 

substances synthesized by soil organisms. 

8.5. Soil
92

9. Young Tree Management Data Set

The Young Tree Management Data Set captures “rules in use” related to 

young tree care and management. The variables included in the Young Tree 

Management Data Set are shown in Figure 6. These rules or strategies are 

likely facilitated through a program of some sort—thus, data are collected 

about the program and its policies related to tree maintenance or stewardship 

(i.e., the actions that are supposed to happen to ensure proper tree care). 

Such information can be collected from the office. Corresponding field data 

records maintenance or stewardship actions happening on the ground are 

the “rules in use.” The goals of the Young Tree Management Data Set are to 

provide information to address the following issues:

Program information:

Name0

Contact0

Type1,2

Purpose1,2

Tree steward information: 

Contact0

Type1,2

Training level1

Monitored1

Stewardship actions:

Type1,2

Frequency1,2

Required or suggested1,2

Observed1,2

Young Tree 

Management 

Data Set

Figure 6.—Variables in the Young Tree Management Data Set. Superscripts 

represent tiers (see section 5), with more than one tier indicating that crews with more 

skills or equipment could obtain enhanced data.
Urban Tree Monitoring: A Resource Guide 93

1. Differentiate tree growth, mortality, and health outcomes across 

programs, program types, and steward characteristics.

2. Assess level of adherence to program-based maintenance rules or 

recommendations.

3. Associate observed maintenance with tree growth, mortality, and health. 

9.1.1. Program name 

Description: If the tree was planted as part of a program, note that program’s 

name. A program might include a municipal, regional, or nonprofit planting 

initiative (e.g., NeighborWoods, Plant-a-Million). If there are multiple 

programs relating to a single tree, record all programs. The other programspecific variables below relate to each program. 

9.1.2. Program contact information

Description: Name and contact information for the leader or representative of 

the program.

9.1.3. Program type

Description: Categories of program type include municipal, county, state, 

other governmental, nonprofit, community/civic, utility, and business. 

Government programs could be subdivided by departments (e.g., parks and 

recreation, transportation). Multiple answers are allowed for one program, 

indicating partnerships.

9.1.4. Program purpose

Description: Categories of program purpose based on coded program mission 

statements, including generic planting/greening, targeted ecosystem service 

(e.g., stormwater runoff reduction, energy savings through tree shade), public 

engagement, equity, residential giveaway, canopy cover goal, and plant-amillion goal.

The following information could be collected if there is a structured 

stewardship or a maintenance component to the tree planting program.

9.2.1. Steward contact 

Description: Name and contact information for the leader or representative of 

the stewardship activities.

9.2.2. Steward type

Description: A categorization of the steward, including program staff, 

adjacent resident, adjacent business (or organization), neighborhood 

volunteer, organized stewardship group, other volunteer, and contracted 

entity (e.g., landscapers).

9.1. Program 

Information

9.2 Tree Steward 

Information 
94 GTR-PSW-266

9.2.3. Steward training level

Description: A measure of the expertise of the steward. Options could be 

similar to the field crew experience level in the Minimum Data Set (see Field 

Guide section 2.1.2), with stewardship training levels ranging from novice to 

intermediate to expert. 

9.2.4. Steward monitored

Description: Binary (yes/no) indicator of whether the steward is required 

to report his/her activities to another party or otherwise monitored by the 

program to check on the steward’s actions. 

9.3.1. Stewardship action type

Description: Categories of types of stewardship activities that are relevant 

to tree growth, mortality, and health. Categories could include watering, 

pruning, mulching, staking, fertilizing, soil aeration, tree guard management, 

and pest monitoring/treatment. The other variables pertaining to stewardship 

actions below are recorded separately for each action type.

9.3.2. Stewardship action frequency

Description: Categories describing how often each stewardship task is 

completed. 

9.3.3. Stewardship action required or suggested 

Description: Whether or not the program requires or suggests this tree 

stewardship action. 

9.3.4. Stewardship action observed

Description: For each stewardship action, record whether that action was 

observed to have occurred in the field. Less experienced field crews could 

record presence/absence of the action, while more experienced crews could 

observe varying degrees of correct/incorrect maintenance (e.g., different 

kinds of mulching problems). 

9.3. Stewardship 

Actions 
95

10. Community Data Set

The Community Data Set consists of demographic, socioeconomic, property, 

and household information related to the geographic area near a tree. The 

variables included in the Community Data Set are shown in Figure 7. Such 

data can assist practitioners and researchers in analyzing the impact these 

factors may have on urban tree growth and mortality. Specifically, the goals 

of the Community Data Set are to:

1. Identify key variables such as population density, property values, 

education rates, and other demographic and socioeconomic factors, 

including household or consumer-specific information that may relate to 

tree growth, mortality, and health.

2. Specify a level of geographic extent (e.g., census tract, ZIP code, 

neighborhood) for use in analyzing data.

High-priority national data:

Population density

Housing tenure

Housing occupancy

Property values

Household income

Educational attainment

Local data: 

Crime data

Health data

Local demographic data

Community surveys 

 and interviews

Community 

Data Set

Figure 7.—Variables in the Community Data Set.
96 GTR-PSW-266

Note that the Community Data Set is grouped by types of data sources, rather 

than tiers representing field crew skill levels and available resources. This 

is because the Community Data Set is not based on field data collection on 

individual trees. Some of the variables relate to existing data sets available 

through national public resources, such as the U.S. Census Bureau. Other 

information related to the human community surrounding urban trees could 

require extensive mail or Internet surveys, or in-person interviews with 

residents. We have not provided detailed guidance regarding social science 

methods to collect such data. We recommend collaborating with social 

science researchers or geospatial analysts to collect and interpret these data in 

relation to tree monitoring.

Within the United States, these variables can generally be obtained from the 

U.S. Census Bureau or American Community Survey and the data associated 

with trees at the census tract level. Other countries may have comparable 

national databases. 

10.1.1. Population density

Description: Total population per land area where the tree is located.

10.1.2. Housing tenure (renter or owner occupied)

Description: Percentages of owner-occupied and renter-occupied properties 

where the tree is located.

10.1.3. Housing occupancy (occupied or vacant housing units)

Description: Percentage of occupied and vacant housing units where the tree 

is located.

10.1.4. Property values (median household value of owneroccupied units)

Description: Median property value where the tree is located.

10.1.5. Household income (median income in dollars)

Description: Median household income where the tree is located.

10.1.6. Educational attainment 

Description: Percentages of population that has attained a particular level 

of education (e.g., high school diploma, college degree) where the tree is 

located.

10.2.1. Crime data

Description: Crime rates for geographic area near the tree (e.g., assaults, drug 

possession). See, for example, Kondo et al. (2017).

10.2.2. Health data

Description: Rates of health issues for the geographic area near the tree (e.g., 

asthma, heart disease).

10.1. High-Priority 

National Data

10.2. Local Data
Urban Tree Monitoring: A Resource Guide 97

10.2.3. Local demographic data

Description: Other demographic data specific to the community that may not 

be available at the national level.

Surveys and interviews of those who receive trees through planting 

initiatives, as well as others who plant, maintain, and manage trees, 

can help in understanding how urban forests change over time. As with 

other components of the Community Data Set, we strongly recommend 

collaborating with researchers to collect these data. See, for example, surveys 

of residents and urban greening programs in Moskell and Allred (2013), 

Locke et al. (2015), Conway (2016), Svendsen et al. (2016), and Breger et 

al. (2019). Community surveys and interviews could collect, for example, 

household-level sociodemographic or consumer lifestyle data, resident 

perceptions of trees, and neighborhood histories.

10.3. Community 

Surveys and Interviews 
98

11. Acknowledgments

Many individuals contributed to this Resource Guide. Current and former 

colleagues from the UTGL working group helped to refine the protocols and 

suggested material to include in this Resource Guide: Julia Bartens, David 

Bienemann, Michele Bigger, Cindy Blain, Jerry Bond, Chad Clink, Dana 

Dentice, Anne Fenkner, Burney Fischer, Jason Grabosky, Jennifer Karps, 

Emily King, Jackie Lu, Jessica Sanders, Phil Silva, Nancy Sonti, Emily 

Spillett, Matthew Stephens, Erika Teach, Doug Wildman, and Ruth Williams. 

We are indebted to their contributions to this project. We are also grateful 

to the attendees of the first UTGL symposium at The Morton Arboretum in 

2011 and to the respondents to the survey of practitioner-driven monitoring 

projects (Roman et al. 2013) for inspiring us to produce these protocols 

and associated resources, and to the citizen scientists who pilot tested the 

Field Guide (Roman et al. 2017). At The Nature Conservancy, we thank 

Bill Toomey and Rachel Holmes for collaborating with training field interns 

carrying out these protocols through the Healthy Trees, Healthy Cities 

initiative; that experience helped us to further refine the field methods and 

resources. We also thank Lindsay Shafer for her contributions to translating 

the Minimum Data Set into a user-friendly field guide, as part of her master’s 

degree at the University of Pennsylvania. We appreciate comments and 

critiques from colleagues who reviewed an earlier draft of this document: 

Mark Hatfield, Yasha Magarik, Eric North, Marin Palmer, and Sjana 

Schanning. Funding for producing the Resource Guide was provided by the 

USDA Forest Service State and Private Forestry, Urban and Community 

Forestry program. Funding for the pilot test of the protocols was provided 

by the Forest Service, Northern Research Station, Philadelphia Field Station, 

and The Morton Arboretum.
99

Part III: Supporting Documentation
100

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116

Appendix 1: Site Type and Land Use Examples

Site type and land use Figure 8 photo series illustrates examples of how 

to classify urban trees for site type and land use with the protocols. Please 

see Field Guide section 2.6 Site Type and section 2.7 Land Use for more 

information and examples of the categories. The site type and land use 

categories are listed below for convenience.

Site type categories 

• Sidewalk cutout

• Sidewalk planting strip

• Median

• Planter box

• Other hardscape

• Front yard

• Side yard

• Back yard

• Maintained park

• Other maintained landscaped area 

• Natural area 

Land use categories 

• Single-family residential 

• Attached 

• Detached

• Multi-family residential 

• Mixed use

• Commercial

• Industrial

• Institutional

• Maintained park

• Natural area

• Cemetery

• Golf course

• Agricultural

• Utility

• Water/wetland

• Transportation

• Vacant lot

• Other 
Urban Tree Monitoring: A Resource Guide 117

Figure 8.—(A) Site type: sidewalk cutout; Land use: commercial. Photo by B.C. Sharenbroch, used with permission. (B) Site 

type: sidewalk planting strip; Land use: single-family residential—attached. Photo by L.B. Shafer, used with permission. 

(C) Site type: other hardscape; Land use: institutional. Photo by L.A. Roman, USDA Forest Service. (D) Site type: sidewalk 

cutout; Land use: institutional. Photo by B.C. Sharenbroch, used with permission. (E) Site type: sidewalk cutout; Land use: 

single-family residential—attached. Photo by B.C. Sharenbroch, used with permission. (F) Site type: other hardscape; Land 

use: commercial. Photo by L.A. Roman, USDA Forest Service.

D

A B C

E F
118 GTR-PSW-266

Figure 8 (continued).—(G) Site type: planting strip (foreground tree); Land use: single-family residential—detached. Photo 

by B.C. Sharenbroch, used with permission. (H) Site type: maintained park; Land use: maintained park. Photo by L.A. Roman, 

USDA Forest Service. (I) Site type: sidewalk cutout; Land use: vacant lot. Photo by J.P. Fristensky, used with permission. 

(J) Site type: planting strip; Land use: multi-family residential. Photo by N.S. van Doorn, USDA Forest Service. (K) Site type: 

maintained park; Land use: institutional. Photo by L.A. Roman, USDA Forest Service. (L) Site type: other hardscape, trees in a 

courtyard behind row homes; Land use: single-family residential—attached. Photo by J.P. Fristensky, used with permission.

I

G H

K L

J
119

Appendix 2: Species Identification Resources

Below is a small sampling of available resources to assist with tree genus 

and species identification for urban trees in the United States and Canada. 

No single species identification resource covers all urban trees in this broad 

geographic area, and the resources listed below differ in their accessibility to 

field crews with different levels of botanical knowledge. Project supervisors 

may wish to create species identification guides specific to their cities, 

drawing on the information presented in these resources.

Arbor Day Foundation’s What Tree Is That? 

www.arborday.org/trees/whattree/mobile.cfm 

A free step-by-step app that guides the user through tree species and genus 

identification using dichotomous key characteristics 

Audubon Field Guide to North American Trees

http://www.audubonguides.com/index.html

A low-cost app with images and search function by leaf shape, region, bark, 

and other advanced options to assist in North American tree species and 

genus identification

Leafsnap

www.leafsnap.com

A free electronic app whereby users can input leaf photographs and see a list 

of potential tree species, from Columbia University, University of Maryland, 

and The Smithsonian Institution (Kumar et al. 2012)

Virginia Tech Dendrology Fact Sheets

http://dendro.cnre.vt.edu/dendrology/factsheets.cfm

Fact sheets from Virginia Tech describing trees common in North America, 

includes a slideshow about basic tree identification tips and photos for many 

species featuring leaves, reproductive parts, and bark

Web Sites and Apps
120 GTR-PSW-266

Audubon Society Field Guide to North American Trees: 

 Eastern Region

Field guide referencing nearly 700 species of trees found east of the Rocky 

Mountains, focusing mostly on natives but includes some exotic ornamental 

trees (Little 1980a)

Audubon Society Field Guide to North American Trees: 

 Western Region

Field guide referencing nearly 700 species of trees found west of the Rocky 

Mountains, focusing mostly on natives but includes some exotic ornamental 

trees (Little 1980b)

A Californian’s Guide to the Trees Among Us

Reference guide to over 150 of the most common urban trees in California 

(Ritter 2011) 

City of Trees: The Complete Field Guide to the Trees 

 of Washington, D.C.

Guided tour of Washington D.C.’s trees and botanical keys to assist with 

species identification (Choukas-Bradley 2008) 

Field Guide to the Street Trees of New York City

Drawings and photographs accompany descriptions of 50 species commonly 

found in New York City (Day and Smoke 2011)

New York City Trees

Park Department’s field guide to more than 125 metro-area species of New 

York City and the metro region (Barnard 2002) 

Philadelphia Trees: A Field Guide to the City 

 and the Surrounding Delaware Valley

Field guide to 118 species commonly encountered in and around 

Philadelphia, Pennsylvania (Barnard et al. 2017) 

Stikky Trees—Learn to recognize at a glance 

 the 15 most common trees in the United States – 

 in just one hour, guaranteed

Reference guide with images of 15 common trees within United Stated to 

assist in species identification (Holt 2010)

The Easy Tree Guide: Common Native and Cultivated Trees 

 of the United States and Canada

Portable guide useful for species identification in the field (Rushforth and 

Tomblin 2004)

Books
Urban Tree Monitoring: A Resource Guide 121

The Urban Tree Book: an Uncommon Field Guide 

 for City and Town

Identification guide for more than 200 species across North America. 

Includes illustrations and stories (Plotnik 2000)

Trees of the California Landscape: 

 a Photographic Manual of Native and Ornamental Trees

Illustrated guide to California’s trees including 107 native and 311 

ornamental species (Hatch and Faber 2007)

Trees of Vancouver

Reference guide to over 470 kinds of trees native to Canada and cultivated 

trees (Straley 1992)

Vancouver Tree Book: a Living City Field Guide

Pocket field guide profiling more than 110 of Vancouver’s important species 

(Tracey 2016)
122

Appendix 3: Other Protocols

Urban Tree Monitoring: A Field Guide (Roman et al. 2020) and Urban 

Tree Monitoring: A Resource Guide build on past work from a variety of 

researchers and professionals. The resources below offer related protocols for 

urban tree inventories and monitoring.

i-Tree Eco

Methods citations: Nowak et al. 2008, i-Tree 2017a

Overview: For those interested in monitoring change across the entire 

urban forest—to represent citywide characteristics— random plots using the 

i-Tree Eco method may be most appropriate. This well-established protocol 

produces a summary of urban forest structure, functions, and services 

based on a single one-time inventory, but it can also be used for ongoing 

monitoring as long as plots are permanently referenced (see section 2.6). 

Compared to the Minimum Data Set, a larger set of variables about tree size, 

crown, and site conditions are included. Examples of monitoring studies 

using this approach to quantify urban tree mortality, growth, population 

change, and impacts of storms include Nowak et al. (2004), Staudhammer 

et al. (2011), Lawrence et al. (2012), and Lima et al. (2013).

Urban Forest Inventory and Analysis

Methods citations: USDA FS (2017)

Overview: The USDA Forest Service’s Forest Inventory and Analysis 

(FIA) program is a long-term permanent plot network encompassing all of 

the nation’s closed-canopy forest. The goal of the FIA program is to create 

an accurate and timely inventory of the nation’s forests, to monitor current 

forest conditions, and to facilitate sustainable management. Recently, the 

FIA program has added methods for assessing urban forests that incorporate 

the statistical and scientific rigor of traditional FIA plots with methodology 

appropriate for urban settings. When fully implemented, Urban FIA (UFIA) 

will provide a nationwide network of urban forest monitoring plots spanning 

a diverse array of cities and land uses. Compared to the Minimum Data Set, a 

larger set of variables about tree size, crown, and site conditions are included. 

FIA and UFIA are conducted with professional field crews who must pass 

special certifications in these methods.
Urban Tree Monitoring: A Resource Guide 123

Planted Tree Re-Inventory Protocol 

Methods citations: Vogt and Fischer (2014)

Overview: This field protocol was developed by the Bloomington Urban 

Forest Research Group at the Center for the Study of Institutions, Populations 

and Environmental Change. The methods are specifically designed for 

neighborhood-based street tree planting projects, and include data on 

the planting site, tree growth, and observed maintenance. Examples of 

monitoring studies using this method include Vogt et al. (2015a) and Widney 

et al. (2016).

TreeKIT Collaborative Mapping Method

Methods citations: Silva et al. (2013)

Overview: This field protocol was developed to enable community 

volunteers to measure and map street trees in New York City, New York. 

The block edge distance method for recording street tree location (see Field 

Guide section 2.4.2) is based on these protocols. That location method was 

also used for TreesCount! 2015, a citizen science street tree inventory project 

in New York City.

Standard for Tree Inventory in Urban Environments—Sweden

Methods citations: Östberg et al. (2012, 2013) 

Overview: Urban forestry researchers in Sweden realized that the 

different municipalities there had widely varying tree inventory methods. 

Municipalities were surveyed to understand the different parameters that 

each used, and a common core of parameters was used by most cities. The 

standard inventory has been well-received by the urban forestry industry 

in Sweden. Benefits of the national urban tree inventory standards include 

making national recommendations on how to create a tree management plan 

as well as how to work with hazard trees and helping the industry to get more 

coherent language and terminology.
124

Appendix 4: Field Crew Training Agendas and Activities

Below are examples of field crew training agendas for the Minimum Data Set 

as well as activities that could be used during training.

This is the training schedule that was used for citizen scientist training for the 

pilot test of the Field Guide (Roman et al. 2017) as well as seasonal interns. 

This training agenda could also be adapted to take place over two evening 

sessions to enable volunteers to participate after the work day. If more time 

can be used to train interns or volunteers, the training could be lengthened to 

2 days to allow for longer lessons (e.g., more time with species identification) 

as well as a full day of field practice.

9:00-9:30 Introductions, overview of project, goals 

of the monitoring, phone contacts

9:30-10:00 Field crew information, date, site type, 

 land use, location

10:00-11:30 Species including identification practice

11:30-12:15 Lunch

12:15-1:00 Mortality status, basal sprouts, crown vigor

1:00-2:00 Trunk diameter

2:00-3:30 Outdoor practice

• Reinforce how to use standard equipment, 

 species identification 

• Have several crews record the same trees 

 independently and compare their observations

3:30-3:45 Discussion about field measurements, 

sources of error activity

3:30-4:00 Safety training and pedestrian interactions

Agenda No. 1: 

Full-Day (7 Hours) 

Field Crew Training 

Agenda for Interns 

or Citizen Scientists
Urban Tree Monitoring: A Resource Guide 125

Note that this training is for planted tree cohort monitoring and assumes 

that in-depth species identification skills are not needed because species 

information is available from planting records.

9:00-9:20 Introductions, overview of project, 

goals of the monitoring, phone contacts

9:20-9:40 Location and species confirmation

9:40-10:00 Crown vigor

10:00-10:20 Trunk diameter

10:20-10:40 Discussion, sources of error activity, 

 team assignments

10:40-11:30 Outdoor practice

Activity No. 1: D-tapes and Wrists 

Materials: d-tape, paper, pens, tape measurers two to three urban tree 

examples

Objective: To learn how to use a d-tape and to understand causes of variation 

in measurements

Time required: 5 to 8 minutes

Have the trainees pair up. Each trainee should record diameter of partner’s 

wrist. Change partners and repeat, keeping track of whose wrist corresponds 

with each measurement. 

Meanwhile, the supervisor or trainer walks around and checks to make 

sure that best practices are being followed (e.g., d-tapes are not being read 

backwards, d-tapes are wrapped snug). 

After a few measurements are recorded, reorganize into a group again. 

Explain that the wrist is not a perfect circle, as many trees will not be and that 

variations in where the d-tape is placed on the wrist will make a difference in 

the data. Discuss lessons learned. 

Activity No. 2: Sources of Error 

Materials: Post-its®, pens 

Objective: Field crew will be able to identify examples of measurement 

error (informally termed “human error” for the purpose of this exercise) and 

natural variability in tree dimensions (informally termed “tree error”). Field 

crew will brainstorm ways to correct for measurement error. 

Time required: 10 to 15 minutes

This activity should happen at the end of a training session, after the 

supervisor has explained the objectives for the urban tree monitoring project 

and protocols for recording each variable.

Each trainee gets a small stack of post-its. Trainees write down as many 

sources of error or discrepancies they can imagine while collecting data.

Agenda No. 2: 

Short (2.5 hours) 

Training Agenda 

for Interns or 

Citizen Scientists
126 GTR-PSW-266

Use post-its to create two categories: “tree and site issues” (i.e., something is 

particularly challenging about certain trees or sites) and “human issues” (i.e., 

a person made a mistake). Have crew members individually sort their post-its 

into these two categories. 

Review and discuss. Brainstorm solutions to deal with each “tree and site 

issues.” Brainstorm solutions to deal with each “human issues.”

Activity No. 3: Address and Site Code Map Quiz

Materials: Pens, printed site code table and diagram (below).

Objective: To understand the address and site code location method.

Have trainees practice recording the information in Table 21 using the map in 

Figure 9A. This is the same example used in the Field Guide (Table 4, 

Figure 2A), so have trainees close their Field Guides while they do this 

activity. Upon completion of the exercise, trainees may use the answer key 

in Figure 9B to check their work.

Table 21.—A datasheet used for practicing the address and site code method

Tree number Site code

Address # and 

street name

Block Information

On street From street To street Side of street

11 1F 200 Apple St. Apple St. Maple St. Juniper St. W

2 1F 202 Apple St. Apple St. Maple St. Juniper St. W

15 1A 204 Apple St. Apple St. Maple St. Juniper St. W

12 2A 204 Apple St. Apple St. Maple St. Juniper St. W

5 3A 204 Apple St. Apple St. Maple St. Juniper St. W

6 1F 201 Apple St. Apple St. Maple St. Juniper St. E

7 2F 201 Apple St. Apple St. Maple St. Juniper St. E

30 1F 205 Apple St. Apple St. Maple St. Juniper St. E

9 1R 208 Pear St. Apple St. Maple St. Juniper St. E

10 2R 208 Pear St. Apple St. Maple St. Juniper St. E

46 2S 208 Pear St. Juniper St. Pear St. Apple St. S

4 1S 208 Pear St. Juniper St. Pear St. Apple St. S

13

14

26

16

17

18

88

20

1

22

23

24

25

49

27

28

29

3
Urban Tree Monitoring: A Resource Guide 127

Figure 9A.—A map used for practicing site code identification. 
128 GTR-PSW-266

Figure 9B.—Address and site code map of an imaginary block which corresponds to Table 21. Arrows with the street names 

indicate ascending order of address numbers on that street. Whereas Figure 9A has tree numbers to designate each tree, this 

figure shows the site codes for each tree.
129

Glossary

allometry—The sizing relationships of trees (e.g., equations to estimate 

height, crown dimensions, and biomass from trunk size).

annual mortality rate—The proportion of individual trees dying (or getting 

removed) over a year (Roman et al. 2016).

annual survival rate—The proportion of individuals surviving over a year 

(Roman et al. 2016).

basal sprouts—Sprouts that grow from buds at the base of the stem or in the 

roots of a tree; are also sometimes called suckers or water sprouts.

baseline records—The first set of tree data relevant to the monitoring 

project; these records are the starting point from which comparisons will be 

made going forward.

caliper tool—A measurement instrument used to measure the distance 

between two sides of an object; in urban forestry, caliper tools may be used 

to measure small trees or trunks that have obstructions.

citizen science—An approach to engaging the public in ecological research 

and natural resource management (Dickinson et al. 2010, 2012; Tulloch et 

al. 2013), and a citizen scientist is generally considered “a volunteer who 

collects or processes data as part of a scientific enquiry” (Silvertown 2009).

crowdsourcing—An approach to data collection in which a large number of 

volunteers submit data when and where they wish; crowdsourcing is a citizen 

science approach that can be used for ecological field data.

diameter at breast height (d.b.h.)—Diameter of a tree’s trunk at breast 

height (standard height in the United States is 4.5 ft or 1.37 m from the 

ground).

diameter at caliper height (d.c.h.)—Diameter of a tree’s trunk at 1 ft (30.5 

cm) from the ground.

d-tape—Also known as diameter tape, this is specialized forester’s 

measuring tape that is the best piece of equipment for measuring a trunk 

diameter that is ≥ 1 in (2.5 cm).

establishment phase—The first few years after planting (Hilbert et al. 2019), 

although establishment of planted urban trees has been variously defined 

(Leers and Moore 2018, Levinsson et al. 2017).
130 GTR-PSW-266

fine twig dieback—Recent mortality in the upper and outer portion of the 

crown, and reflects the severity of recent stresses on a tree (USDA FS 2016).

foreign key—In a relational database, the foreign key is a field or collection 

of fields that references the primary key or unique key of another table, 

thereby establishing a link between them.

forest land—A definition of forest used in the FIA program, areas at least 

1 ac (0.4 ha), 120 ft (36.6 m) wide, 10 percent stocked with trees, and with 

undisturbed understories (Cumming et al. 2007, 2008; Oswalt et al. 2014).

i-Tree—A free software suite for urban forest inventories, tree cover 

evaluations, and ecosystem services estimation from the USDA Forest 

Service and Davey Tree Expert Co.

i-Tree Eco—The flagship software in the i-Tree suite, applicable to plotbased samples and complete censuses across any population of interest where 

ecosystem services estimation and valuation are of interest, formerly known 

as UFORE.

i-Tree Streets—A component of the i-Tree suite, for street tree inventories 

and ecosystem services estimation, formerly known as STRATUM.

ingrowth—For a multi-age inventory remeasurement, ingrowth reflects new 

trees in the system owing to planting or natural regeneration.

land use—Description of the way the property around (or adjacent to) the 

tree is used by humans, see also Anderson et al. (1976), Lambin et al. (2006), 

and The Institute for Local Government (2010) for more about land use 

definitions.

long-term field monitoring—“Repeated field-based empirical 

measurements” (Lindenmayer and Likens 2010a), and in the ecological 

context, long-term is taken to mean at time scales of a decade or more 

(Lindenmayer and Likens 2010b), although shorter monitoring time scales 

can be highly relevant in urban forests (e.g., mortality, growth, and health of 

trees one year after planting).

longitudinal data—Repeated observations on the same individual trees over 

time.

long format data—Also known as stacked data; a format in which there is 

at least one column listing categories (e.g., Treeid) and one column listing 

values associated with each of those categories (e.g., d.b.h. centimeters); 

categories are repeated for as many observations as were recorded.

metadata—A set of data that describes other data, and specifically a 

metadata repository (also known as a data dictionary) generally stores 

information about the fields in a database, such as definitions of each field 

and allowable values.
Urban Tree Monitoring: A Resource Guide 131

monitoring—Systematic assessment or tracking over a period of time; 

this Urban Tree Monitoring resource guide is concerned with field-based 

monitoring of urban trees, and other forms of urban forest monitoring are 

discussed in Leff (2016).

mortality—For urban trees, mortality is generally defined as a combination 

of trees that died in place and trees removed.

multi-age inventory monitoring—Monitoring trees within a given 

geographic area (e.g., plots, neighborhoods), regardless of who planted or 

when, such as repeated street tree inventories or plot-based inventories.

planting cohort—Refers to the collection of trees planted around the same 

time (e.g., same planting season or same calendar year).

planting cohort monitoring—Approach that refers to tracking trees planted 

or distributed through a specific program and planted around the same time. 

population growth rate—The rate of increase or decrease in total 

population size.

primary key—In a relational database, the primary key uniquely identifies 

every record. 

quality control—A system of maintaining a standard of data quality by 

testing a sample of the data collected against the specifications.

replacement tree—Tree planted in the exact same location as removed tree, 

often most relevant for street tree sidewalk cut-outs.

right-of-way—A strip of land occupied by certain public or transportation 

uses (The Institute for Local Government 2010); often refers in the urban 

forestry context to the public right-of-way along roads which encompasses 

street trees, but may also refer to right-of-way areas along railroad or utility 

corridors.

site type—Description of the tree’s immediate location or planting site.

stocking—The amount of trees in a particular area, generally given in 

relation to optimum levels (Powell 1999, Richards 1992).

survivorship—The proportion of a planting cohort surviving to a particular 

time (i.e., cumulative survival, Roman et al. 2016).

tree demography—The study of population dynamics of trees, including 

analysis of change over time in mortality and growth.

tree growth—Growth in tree size, can include d.b.h. (also known as radial 

growth of the main stem), height, and canopy width growth; often expressed 

as an annual rate of growth.

unique key—In a relational database, a unique key is what defines 

uniqueness for the entity that is being stored on the record.
132 GTR-PSW-266

urban forest—While various definitions of the term urban forest exist 

(Konijnendijk et al. 2006, Piana and Troxel 2014), a common definition used 

in the United States is “all publicly and privately owned trees within an urban 

area—including individual trees along streets and in backyards, as well as 

stands of remnant forest” (Nowak et al. 2010).

Urban Forest Inventory and Analysis (UFIA)—the urban expansion of the 

USDA Forest Service’s congressionally mandated ongoing forest inventory 

and monitoring across the United States through a national plot system.

wide format data—Also known as unstacked data; a format in which each 

category of variables is represented by a single row (e.g., all observations 

for a tree are in one row); each different data variable (e.g., d.b.h.cm.2010, 

d.b.h.cm.2015) is in a separate column so observations across time span 

multiple columns.


van Doorn, Natalie S.; Roman, Lara A.; McPherson, E. Gregory; Scharenbroch, Bryant C.; 

Henning, Jason G.; Östberg, Johan P.A.; Mueller, Lee S.; Koeser, Andrew K.; Mills, John 

R.; Hallet, Richard A.; Sanders, John E.; Battles, John J; Boyer, Debra J.; Fristensky, Jason 

P.; Mincey, Sarah K.; Peper, Paula J.; Vogt, Jessica M. 2020. Urban tree monitoring: 

a resource guide. Gen. Tech. Rep. PSW-GTR-266. Albany, CA: U.S. Department of 

Agriculture, Forest Service, Pacific Southwest Research Station. 132 p. 

https://doi.org/10.2737/PSW-GTR-266.

The guidelines proposed in Urban Tree Monitoring: A Resource Guide (hereafter referred to 

as the Resource Guide) were developed and refined over many years to address the need 

for standardized urban tree monitoring protocols. The Resource Guide provides in-depth 

guidance for urban forest managers and researchers who want to design and implement a 

tree monitoring project. This Resource Guide is a companion to Urban Tree Monitoring: A 

Field Guide; however, the Resource Guide can also be used on its own. The Resource Guide 

is divided into three parts. In Part I, we discuss (1) the varied goals of monitoring projects 

and how to match data collection to those goals, (2) the development of these urban tree 

monitoring standards, (3) types of monitoring projects, and (4) connections to other protocols 

for urban tree data collection. We offer guidance on methods for recording tree location, 

developing tree record identifiers, organizing spreadsheets and databases, choosing data 

collection systems, fostering research-practice partnerships, training crews, and managing 

fieldwork. In Part II, we present five monitoring data sets: Minimum Data Set, Tree Data Set, 

Site Data Set, Young Tree Management Data Set, and Community Data Set. We list study 

goals that could be addressed with each data set and descriptions of relevant variables. We 

also provide guidance regarding which variables are best suited for beginner and advanced 

crews. Lastly, in Part III we include appendices with additional resources for designing and 

implementing tree monitoring projects.

KEY WORDS: Urban forest, tree monitoring, citizen science, tree survival, tree demography,

 longitudinal data.
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