Forest Landscape Assessment 

Tool (FLAT): Rapid Assessment for 

Land Management

Lisa Ciecko, David Kimmett, Jesse Saunders, Rachael Katz, Kathleen L. Wolf, 

Oliver Bazinet, Jeffrey Richardson, Weston Brinkley, and Dale J. Blahna

United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-941

September

2016
Authors

Lisa Ciecko is a plant ecologist, Seattle Parks and Recreation, 1600 S Dakota 

Street, Seattle, WA 98108; David Kimmett is a natural lands program/project 

manager, King County Parks and Recreation Division, King Street Center, 201 

S Jackson St., Room 700, Seattle, WA 98104-3855; Jesse Saunders is a resource 

information forester, American Forest Management, Inc., 11415 NE 128th Street, 

Suite 110, Kirkland, WA 98034; Rachael Katz is an environmental planner, Tetra 

Tech, 19803 North Creek Parkway, Bothell, WA 98011; Kathleen L. Wolf is a 

research social scientist and Jeffrey Richardson is a postdoctoral research associate, University of Washington, School of Environmental and Forest Sciences, Box 

352100, Seattle, WA 98195; Oliver Bazinet is an environmental analyst, Seattle 

Parks and Recreation, 309 Pontius Ave. N, Seattle, WA 98109; Weston Brinkley

is principal and owner, Street Sounds Ecology, LLC, Seattle, WA 98107; Dale 

J. Blahna is a research social scientist, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, 400 N 34th St., Seattle, WA 98103.

Cover: (left) field data collection in a King County, Washington, forest reserve area, 

photo courtesy of Forterra; (upper right) forested green spaces provide opportunities for active living, and (lower right) field training for FLAT assessment volunteers, photo courtesy of King County Department of Natural Resources and Parks.

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Forest Landscape Assessment Tool (FLAT): 

Rapid Assessment for Land Management

Lisa Ciecko, David Kimmett, Jesse Saunders, Rachael Katz, 

Kathleen L. Wolf, Oliver Bazinet, Jeffrey Richardson, Weston 

Brinkley, and Dale J. Blahna

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station 

Portland, Oregon 

General Technical Report PNW-GTR-941 

September 2016
Abstract

Ciecko, Lisa; Kimmett, David; Saunders, Jesse; Katz, Rachael; Wolf, Kathleen 

L.; Bazinet, Oliver; Richardson, Jeffrey; Brinkley, Weston; Blahna, Dale 

J. 2016. Forest Landscape Assessment Tool (FLAT): rapid assessment for land 

management. Gen. Tech. Rep. PNW-GTR-941. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 51 p.

The Forest Landscape Assessment Tool (FLAT) is a set of procedures and tools 

used to rapidly determine forest ecological conditions and potential threats. FLAT 

enables planners and managers to understand baseline conditions, determine 

and prioritize restoration needs across a landscape system, and conduct ongoing monitoring to achieve land management goals. The rapid assessment process 

presents a cost-effective opportunity for landowners that include local governments, 

private owners, and nongovernmental organizations to use ecological data to guide 

decisionmaking and improve environmental outcomes on their lands. This report 

is an introduction to FLAT, providing an overview of its purpose, methods, and 

implications for land management in diverse regions. FLAT is executed in three 

sequential phases: Phase 1—Forest Cover Type Mapping, Phase 2—Field Assessment, and Phase 3—Management Prioritization. Overall, FLAT consists of onsite 

visual estimation (aided by remote sensing) of ecological conditions by a trained 

field team to produce a forest inventory. In addition to providing baseline data and a 

framework to prioritize actions, FLAT can be used as a monitoring tool to evaluate 

changing conditions and inform adjustments in management strategies and priorities. To illustrate FLAT implementation, the King County Department of Natural 

Resources and Parks case study details a pilot project conducted on 24,700 of the 

more than 26,000 ac of county lands. King County is using the results from FLAT 

to develop and implement forest stewardship plans and target efforts of its volunteer 

restoration program. Although the tool was initially used in lowland forests in the 

Puget Sound region, in concept, FLAT could be expanded and adapted for use in a 

wide variety of ecosystem types.

Keywords: Forest, assessment, stewardship, planning, ecology, baseline, 

monitoring, land management.
Executive Summary 

The Forest Landscape Assessment Tool (FLAT) is a set of procedures and tools 

designed to provide local government agencies, nongovernmental organizations, 

land managers, and private landowners with a rapid, systematic, flexible, and inexpensive environmental evaluation. The technical information produced by FLAT 

provides a standardized baseline of ecological data. 

This data can be used to evaluate forest parcels within the context of the 

broader landscape, providing information about key forest characteristics and 

potential threats. FLAT also provides a framework to identify high-ecological 

value, high-threat areas within a single site and across multiple holdings for potential management action. The results from FLAT provide an essential starting point 

for developing either a systemwide stewardship plan or management plans for single 

parcels. In addition to providing baseline data and a framework to prioritize actions, 

FLAT could also be used as a monitoring tool to evaluate changing conditions and 

inform adjustments in management strategies and priorities.

FLAT was developed and piloted by the Green Cities Research Alliance. Key 

FLAT contributors include the USDA Forest Service Pacific Northwest Research 

Station, King County, Forterra, American Forest Management (formerly International Forestry Consultants), and the University of Washington. Work on FLAT 

began in 2009 to provide a comprehensive forest resource analysis for King County, 

an important first step toward developing a long-term, systemwide forest stewardship program on more than 26,000 ac of King County lands. 

Based on principles of restoration and landscape ecology, as well as traditional 

forestry, FLAT is conducted in three broad sequential phases:

Phase 1—Forest Type Mapping: Aerial imagery and boundary data are used in 

a lab or office to divide a project area into management units (MUs), the unit of 

observation and measurement for the assessment. Data attributes are also developed 

during Phase 1 based on local conditions and assessment purposes (e.g., species 

composition, size and age classes, invasive species, tree-canopy vigor, etc).

Phase 2—Field Assessment: A trained field team visits the project area to collect 

estimates of each attribute for each MU. Such teams may include professionals, 

technicians, and volunteer stewards. 

FLAT data provide 

the basis for forest 

stewardship or 

management plans.
Phase 3—Management Strategies and Prioritization: The data, which provide 

a snapshot of ecological conditions in the project area (within and across all MUs), 

can be used to classify or rank each MU. The assigned values can be viewed spatially to provide a mapped, visual representation of landscape conditions. These results can then be used to prioritize where on-the-ground management actions would 

most improve ecological function and health, contributing to long-term sustainability of a forest area.

Owing to the success of local conservation efforts, property acquisitions, and 

various incentives to conserve open space, King County’s resource managers have 

an extensive inventory of resource lands, but have little condition information to 

guide management efforts. Seeking to learn more about these diverse holdings and 

inform management decisions, FLAT was implemented on 24,700 ac of the more 

than 26,000-ac system. 

For the first time, King County managers now have baseline ecological information about all of their forested parks and natural areas. A key finding was the 

prominence of red alder (Alnus rubra Bong.) and bigleaf maple (Acer macrophyllum 

Pursh) on many parcels. Both are relatively short-lived species, and a high percentage of these trees are 30 to 100 years old. These forests could benefit from active 

management and restoration, as the health decline of old trees signals the need for 

tree replacement and hazard management. 

FLAT utilizes a straightforward rating scale of 1 to 9 to indicate a balance 

of species composition value (favoring larger native trees) and degree of health 

threat, particularly owing to invasive species cover. Overall, more than 5,000 

ac of King County forest lands have a rating of 2 or 3, indicating high forest 

composition value and medium-to-high forest health threat values. In the case 

of McGarvey Park Open Space, as an example, a large portion of the forest 

cover is rated as 5 or 6, indicating that there is a medium composition value and 

medium-to-high presence of forest health threats. The ratings can become the 

basis for priority setting for both locations of management work, and for on-theground actions to conserve or restore forests.

King County is using FLAT results to develop forest stewardship plans for individual sites, as well as communicate and implement management priorities across 

the entire parks and open space system. In the future, FLAT may be conducted in 

recurring intervals to monitor progress and evaluate the effectiveness of restoration efforts. As a relatively new tool used in forested lands, FLAT has potential for 

further development, testing, and refinement. 

FLAT can be used 

for both baseline and 

monitoring forest 

assessment.
Resource managers in other communities can learn from King County’s experiences and modify the FLAT methods to conduct an assessment that addresses local 

conditions and priorities. Although currently designed for use in lowland forests 

of the Puget Sound region, the tool’s basic framework and data variables could 

be adapted for a wide variety of ecosystem types. Future research could generate 

indicator and matrix tools that inform management prioritization for additional 

ecosystem types (such as pine forest or riparian systems). Practitioners may also be 

interested in applying FLAT as a practical method for general ecosystem monitoring focused on a particular ecosystem condition or outcome, such as biodiversity, 

wildlife habitat, or local effects of climate change.
Contents

1 Introduction

2 Background

2 Importance of Assessment for Decisionmaking and Adaptive Management

4 Overview of FLAT

5 Foundations of FLAT

8 Comparison to Existing Assessment Methods

12 FLAT Methodology

12 Project Considerations and Planning

14 Phase 1: Forest Cover Type Mapping

15 Phase 2: Field Assessment

16 Phase 3: Management Prioritization

19 King County Parks and Open Spaces Case Study

20 Project Process

26 Results

30 Management Implications

32 Conclusions

32 Importance in Land Management

33 FLAT Feasibility

34 Stewardship

35 Limitations

36 Next Steps

37 Acknowledgments

38 References

40 Appendix 1—How to Develop the Assessment Area and Management Units

45 Appendix 2—Necessary Data

47 Appendix 3—Equipment List

48 Appendix 4—King County Data Attributes

50 Appendix 5—Cost Analysis

51 Appendix 6—FLAT Field Manual
1

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Introduction 

The Forest Landscape Assessment Tool (FLAT) is a set of processes and tools that 

produces an ecological assessment for environmental land use planning and management. In its pilot use, FLAT has proven to be a flexible, systematic, and low-cost 

process for land managers and related decisionmakers to achieve a rapid ecological 

survey of a portfolio of diverse parcels and land holdings.

FLAT can provide landowners with straightforward ecological information 

regarding the composition and overall health of their forest lands, and to understand 

potential threats. It is a tool that can readily be used by such landowners as local 

governments, private owners, and nongovernmental organizations (NGOs) with 

minimal training and time invested to achieve useful results. The data and rating 

framework provided by FLAT better prepare land managers and planners to make 

strategic land management and restoration decisions.

Assessments can potentially be repeated over time as a practical monitoring 

program to observe forest changes and gauge the effectiveness of management 

actions. A longer view can help managers understand how and why conditions are 

changing on the ground and enable them to adjust programs accordingly.

Although the FLAT protocols presented here have been developed for use in 

lowland forests of the Puget Sound region located along an urban-to-rural landscape 

gradient, the tool could be adapted for land managers working in other ecosystems 

as well.

This report provides an overview of the FLAT approach. The following sections will demonstrate its implementation and describe its data outputs, including 

assessment options. The “Background” section first reviews the importance of 

ecological assessment for effective land management, then describes FLAT, including its origins and function relative to other assessment methods. With this understanding, the “FLAT Methodology” section provides details on important project 

considerations and how to use FLAT. Additional method details are provided in 

the appendices. Next, the “King County Parks Case Study” section presents results 

and insights from a pilot FLAT project, and is one example of how FLAT has 

been successfully applied to support stewardship goals. The report concludes with 

discussion of FLAT’s unique advantages, some key limitations, and potential next 

steps for management application, monitoring, and research.

FLAT is a flexible 

low-cost tool for 

rapid ecological 

field surveys.
2

GENERAL TECHNICAL REPORT PNW-GTR-941

Background

Importance of Assessment for Decisionmaking and 

Adaptive Management

The need for FLAT stems from the importance of having comprehensive, reliable, 

and unbiased data for decisionmaking. For instance, private firms recognize the 

importance of information to drive decisions. Private companies develop ways to 

access and purchase information to understand their clients and the markets they 

compete in, as well as their competitors. Having high-quality data is considered 

a defining element of rational strategic management and has been found to significantly influence decision effectiveness (Dean and Sharfman 1996). Access to 

good data for decision support is equally important for land use management and 

environmental planning. 

An ecological assessment is an important component of the environmental 

planning process (fig. 1) (Randolph 2011). This is particularly true in situations in 

which initial data are minimal or lacking altogether. For example, designations of 

conservation easements (by NGOs and landowners) may happen opportunistically 

and result in public land managers having little information about lands for which 

they have become responsible. For many local agencies, data collection is low on 

the priority list owing to budget constraints and other pressing needs. In many 

Social Goals

Examples: 

recreation uses, 

equity, aesthetics

Social Assessment

Examples: 

park ambassador program,

user surveys

Ecological Assessment

Examples: 

FLAT, FIA, REA

Ecological Goals

Examples: 

ecosystem services, habitat

protection, sustainable harvest

Economic Goals

Examples: 

revenue, employment

self-sufficiency

Economic Assessment

Examples: 

cost-benefit analysis, valuation

studies, market analysis

Management

Decision

Figure 1—Importance of assessment in the land-management decision process. FIA = ForestInventory andAnalysis, REA = Rapid 

Ecological Assessment.
3

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

cases, even if information exists, it may not be comparable in methodology, completeness, or quality across different parcels. This lack of consistent information 

can make it difficult, if not impossible, to strategically prioritize sites for management actions or target resource allocations across a system.

For the forested landscapes considered by FLAT, management and planning decisions often address multiple, nested landscape scales. They may start 

from large, more broadly defined areas and system goals (such as a watershed), 

then be translated to site-specific plans (Marsh 1978). Some form of assessment 

ideally takes place at each of these scales so that strategies can be developed to 

achieve goals:

• Across a landscape system, by protecting specific, strategically located 

lands with easements, conservation status, or acquisition,

• That consider action alternatives, such as restoration, development, 

or harvest,

• Applied within a parcel or holding, for instance, by prioritizing certain 

areas for restoration work, and

• To address particular needs of any distinct system having a natural boundary (such as a riparian corridor or wetland). 

FLAT is designed primarily for use on the landscape scale to provide key ecological information for each of any number of sites within an ecological system or 

parcel network. The data can be aggregated and analyzed to support a management 

approach that is successful and sustainable over time. FLAT data can also serve as 

a starting point for more detailed site-specific plans or monitoring. 

Specifically, FLAT can provide critical inputs in the adaptive management cycle 

(fig. 2). Its use can provide both upfront baseline information—assessment—and 

evidence of change over time—monitoring. Ongoing, systematic land assessments 

allow managers to recognize important environmental gaps and potential tipping 

points. Cost savings are possible because planned actions can both prevent emergency conditions and optimize possible revenue. Ongoing assessment also helps 

landowners and decisionmakers tell the story of their land and its variety of public 

and private benefits that are worthy of investment and protection. 

Of course, assessment is useful only when it is carried out successfully. As 

discussed later, FLAT fills a need for a rapid, low-cost method that can be applied 

in a range of environmental contexts, and particularly in the high-pressure interface 

of natural and urban or suburban areas.

FLAT generates 

assessment data 

across a system of 

lands for land units 

within a parcel.
4

GENERAL TECHNICAL REPORT PNW-GTR-941

Overview of FLAT 

FLAT is an assessment technique that provides land managers and planners with 

ecological information to: 

• Make decisions at the broader, systemwide scale, and 

• Prioritize different parcels or areas within parcels for specific land uses 

or actions. 

Its advantage over more traditional forestry sampling, measurement, and 

extrapolation techniques is that it allows a rapid assessment of ecological conditions based on visual on-the-ground surveys of management units delineated from 

remote-sensing data. Although FLAT may not be as precise as more researchoriented sampling techniques, it efficiently provides accurate, practical descriptions 

of ecological conditions within and across a collection of sites. This “thumb-nail 

sketch” can be used to target where indepth assessment may be necessary.

Generally, FLAT consists of visual estimates of ecological conditions by trained 

individuals to produce a forest inventory. Although field teams should follow 

guidelines to calibrate their estimates, the assessment itself is generally qualitative 

and relies on carefully prepared estimates rather than precise measurements. FLAT 

is executed in three phases: 

Phase 1: forest cover type mapping—

Aerial imagery and boundary data are used to divide each parcel within a project 

area (such as King County, as described below) into polygons, and to delineate 

Figure 2—Assessment-based adaptive management cycle.

Assess

Monitor

Define

strategy

Implement

Adjust

Evaluate
5

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

management units (MUs). This work is accomplished and recorded using geographic information system (GIS) tools. Management units are the units of observation and measurement for the onsite assessment.

Phase 2: field assessment—

Trained field teams visit each MU within the project area to collect data for predetermined attributes. As an example, collected attributes might include vegetative 

or built land covers, nonnative species in order of abundance, and tree age-class 

distribution. A condition rating is also recorded, based on a matrix and flow-chart 

analysis (described as Tree-iage). Data are collected for each MU and stored (using 

a GIS or other data management system) for each parcel within the project area. 

Field teams may also verify the boundaries of MUs in this phase.

Phase 3: management strategies and prioritization—

The field procedures of FLAT provide a ranking of landscape conditions of the 

project area, both across numerous parcels and for subunits (the MUs) within a 

large parcel. Summary data and rankings are easily compared across the land 

management area. They can be used to establish management priorities for each 

MU, or aggregated to develop priorities at larger scales, such as across the entire 

open space or parks system. 

FLAT provides a standardized baseline of ecological condition data. This 

information can be used to view each MU within the context of an entire land 

management system, as well as provide a starting point for developing a land-use 

or stewardship plan for particular parcels. Repeated over time, FLAT could serve 

as an effective monitoring tool for managers to review and then adapt management 

priorities and actions based on changing conditions.

This streamlined and systematic approach to ecological assessment applies 

principles of ecology and forestry to provide quality data that can inform land 

management priorities. 

Foundations of FLAT

Starting in 2009, FLAT was developed and piloted by the King County Natural 

Resources and Parks Department, in collaboration with the U. S. Department of 

Agriculture (USDA) Forest Service and other partners. The resulting comprehensive resource analysis was an important first step toward developing a long-term, 

systemwide forest stewardship program on King County lands. FLAT incorporated 

the Tree-iage analysis approach that was initially developed in 2005 by the Green 

Seattle Partnership to prioritize restoration sites.

As its name suggests, the Tree-iage analysis is drawn from the medical practice 

of triage. Within the medical professions, triage emerged from the demands of war 
6

GENERAL TECHNICAL REPORT PNW-GTR-941

casualties, where medical providers were faced with a scarcity of resources and 

needed to rapidly assess each patient’s condition, and, based on an established system or plan, determine the patient’s specific treatment or treatment priority (Iserson 

and Moskop 2007). 

Just as a nurse or doctor in an emergency department uses a systematic checklist of symptoms or a set of criteria to rapidly prioritize patient care, FLAT provides 

a systematic checklist of indicators that determine an actionable score for a designated forest area. A 9-point scoring matrix (fig. 3) combines observations of native 

tree and canopy composition with degree of invasive plant presence, particularly on 

the forest floor. Generally, low scores indicate lands that have good forest integrity 

and low invasive-species presence, thus merit management that will sustain their 

quality. High scores are indicative of highly altered forests combined with extensive 

invasive-species presence that require more resources to recover or restore.

Figure 3—Original Green Seattle Partnership Tree-iage matrix, including acreage per category in 

Seattle, Washington.

1

Monitoring and

stewardship

(41 ac)

4

Planting

(39 ac)

7

Evaluation and

major planting

(44 ac)

2

Invasive plant

reduction

(330 ac)

5

Invasive plant

reduction and 

planting 

(442 ac)

8

Invasive plant

reduction and

major planting

(380 ac)

3

Major invasive 

plant reduction

(95 ac)

6

Major invasive 

plant reduction 

and planting 

(608 ac)

9

Major invasive 

plant reduction and

major planting

(633 ac)

HIGH

>25% native tree

canopy cover,

>50% canopy cover

is evergreen

MEDIUM

>25% native tree

canopy cover,

<50% canopy cover

is evergreen

LOW

<25% native tree

canopy cover

LOW

<5% invasive cover

MEDIUM

5 to 50% 

invasive cover

HIGH

>50% invasive cover

Threat

Tree composition value
7

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

The original Tree-iage matrix was enhanced for use in FLAT to serve more 

diverse needs and ecological conditions. Modified flowcharts accommodate 

additional ecosystem types such as wetlands. Additional data attributes include 

more indicators of forest health, adding new insight for management strategies. 

New attributes include stocking, crown closure estimates, and forest health 

indicators. Health indicator measures include low tree-canopy vigor, root rot, 

mistletoe, bare soils resulting from erosion, and the presence or lack of regenerating 

trees (canopy species less than 20 ft tall). Further, each visited stand is deemed 

“plantable” or “not plantable” based on whether site conditions are appropriate for 

tree seedling establishment.

Tree-iage was first used to evaluate forest condition of open spaces (of up to 

100 ac) in highly urban areas in Seattle (fig. 3; Green Seattle Partnership 2006). The 

tool was exported to several other cities under the regional Green City Partnership 

model, including Everett, Kent, Tacoma, Kirkland, and Redmond. The development 

and use of FLAT in King County expanded use of the early tool for assessments of 

larger parcels (up to 2,500 ac). The FLAT team also prepared fieldwork protocols 

that are used for data-collection training of park staff and citizen volunteers.

The FLAT process borrows from a number of scientific disciplines. The axes 

and decision flowcharts of the Tree-iage matrix are based on principles of restoration ecology. Restoration ecology involves identifying prior or potential ecological 

conditions for a site that are then considered as goals or targets of the restoration 

effort and process. Such conditions (past and future) are shaped by a number of abiotic factors such as climate, elevation, moisture and precipitation cycles, nutrients, 

water bodies, fire cycles, and soil or substrate (Clewell and Aronson 2007).

FLAT has been used exclusively within western Washington thus far. The classification matrix and flowcharts therefore assume a desired condition of late-successional lowland native forests of the Puget Sound basin, characterized by mature 

conifer trees of mixed age classes and species, mixed with large deciduous trees. 

The matrix could be adjusted to acknowledge other Pacific Northwest ecosystems 

(such as shoreline, pine forest, or shrub-steppe) by specifying key ecological conditions and species indicators for the vertical and horizontal axes. To adapt FLAT for 

use in other ecosystems, goals of a desired condition or resource use would inform 

how the matrix and attributes were set up.

The FLAT process also incorporates knowledge from the field of landscape ecology, which makes explicit the importance of ecological diversity within the landscape 

(Turner 2005). When applied to land management, landscape ecology can provide 

insight on how bordering development, land use, or ecosystem types may influence 

restoration outcomes or ecosystem development within a particular management unit 

FLAT is based on 

the best practices of 

foresty, restoration 

ecology, and 

landscape ecology.
8

GENERAL TECHNICAL REPORT PNW-GTR-941

(Turner 2005). One of the main products of FLAT is MU-specific information that 

can be viewed and queried in GIS together with information on neighboring MUs 

and other relevant spatial data or imagery. This integrated format allows managers to 

consider a landscape-scale perspective when making management decisions.

FLAT also includes aspects of traditional forestry field methods that were 

modified to include less data collection concerning timber evaluation. Associated 

data attributes in the FLAT include estimates of diameter at breast height (dbh) or 

size class, stocking, age class, and a number of forest health indicators as noted 

earlier (e.g., root rot, mistletoe, regenerating trees). This provides a critical starting 

point for developing silvicultural restoration actions that can promote forest stand 

recovery and long-term resilience. If a parcel is deemed to be suitable for harvest 

management, more detailed forest mensuration can be done.

Comparison to Existing Assessment Methods

In recent years, many approaches to landscape or resource assessment have 

emerged. The following summary and table 1 provide a comparison of FLAT with 

other assessment programs. 

Forest Inventory and Analysis Forest Monitoring Program (FIA)—

Perhaps the most well-known forest inventory protocol is the one used by the Forest 

Service’s Forest Inventory and Analysis (FIA) program. The FIA consists of “a 

three-phase sample used to track status and trends in forest extent, cover, growth, 

mortality, removals, and overall health” for the entire United States. Data collection 

takes place through stratified random sampling, selecting one site for every 6,000 

ac of forest (USDA FS 2014). The FIA has produced a dataset that enables analysis 

on a scale that is appropriate and useful for national and some statewide management and decisionmaking.

Because of its plot sampling design, FIA may not provide data at the scale that 

most local or regional landowners would need to make management decisions. Further, the extensive range of detailed measurements involved may be much too costly 

and time-consuming for managers with a limited budget to consider. As an example, FLAT data variables for King County numbered just 28, while FIA’s urban 

phase 2 protocol calls for more than a hundred variables to be carefully measured 

(USDA FS 2015). The advantage of FLAT compared to FIA is its lower cost, greater 

simplicity, and ability to provide information about each parcel and the management 

units within them. Nonetheless, in recent years, FIA has been extended to areas that 

are identified as urban. Urban FIA was launched in 2014, with Baltimore and Austin 

as the pilot cities. Data-collection protocols have incorporated i-Tree tools, and are 

being adapted to more directly reflect local community needs.
9

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Table 1—Summary comparison of FLAT to other assessment methods

Key features FLAT FIA i-Tree Eco REA NRCAGeographic focus Urban/suburban/rural National Urban National National Park System

City/regional/county International

Scale Management unit (MU) Approximately one 

site per 6,000 ac across the United States

City or land-use type Variable; often largescale landscapes National park unit (e.g., Olympic 

National Park)

Methods GIS forest cover mapping Field sample 

plots established for annual data collection

Stratified field sampling 

or inventories

Remote sensing or photo 

delineation of ecological units

Synthesis and analysis 

of existing data

Field survey of MUs 100+ variables Measurements focused 

on urban forest structure

Field sampling Development of 

indicators for variety of ecosystem types

Estimates of a limited set of 

variables focused on forest composition and health characterization

Detailed 

measurements

Model estimates 

ecosystem functions and economic values based on forest structure data

Measurements include 

flora and fauna

High-level park 

management prioritization

Explicit management 

prioritization framework

Database open for 

variety of analyses

Unique analyses and 

workshops for each REA to review information and set priorities

FLAT = Forest Landscape Assessment Tool; FIA = Forest Inventory and Analysis; REA = Rapid Ecological Assessment; NRCA = Natural Resource Condition Assessment; GIS = geographic information system.
10

GENERAL TECHNICAL REPORT PNW-GTR-941

I-Tree Eco—

A widely-used inventory and assessment tool that has been used in cities across the 

world is i-Tree Eco, also developed and supported by the Forest Service. The i-Tree 

Eco tool is set up to measure and monitor urban forests in order to estimate ecosystem 

functions and economic values for any metropolitan area. These estimates include 

urban forest structure and associated ecosystem services, such as carbon storage and 

sequestration, and air pollution removal, as well as residential building energy effects, 

rainwater interception, and public health benefits (Nowak et al. 2008). When applied 

to metropolitan areas or counties, this tool uses random or stratified sampling.

The i-Tree Eco model provides an important baseline for systemwide information, especially within a city. However, much like FIA, these techniques do not 

readily describe the status of a specific site. An entire city, or the land-use types 

within, is the unit of analysis in i-Tree and results can be used to help identify 

systemwide goals and opportunities. Because FLAT’s unit of observation is a parcel 

(and even subunits within), it can be more readily used to create plans to meet 

place-based goals. Thus, although an i-Tree Eco assessment and analysis can be a 

useful complement to FLAT, its information is not tied as directly to site-specific 

management needs because of the difference in focus and scale of data collection. It 

should be noted that i-Tree Canopy and Landscape tools are applicable at the parcel 

scale, but do not address forest health and stand conditions.

The Nature Conservancy’s Rapid Ecological Assessment—

The Rapid Ecological Assessment (REA) was developed by The Nature Conservancy to identify priority areas for conservation of biodiversity. In many ways, it 

is very similar to the FLAT process. In both, orthophotos or other remote-sensing 

technologies are used to classify and divide landscapes into ecological units, then 

field-based assessments are used to characterize the biota within these units. The 

purpose of REA is also to evaluate ecological conditions of specific units to support 

decisions about management priorities (Sayre et al. 2000).

There are a few key differences between REA and FLAT, however. Methodologically, REA field assessments (like FIA and i-Tree) consist of plot-based 

samples and interpolation of those measures across a much larger area. The REA 

also includes fauna explicitly in its sampling technique while FLAT focuses exclusively on flora. Unlike FLAT, REA was developed for use in lands more remote 

from human activity. The REA also involves a unique process of technical analyses 

and workshops in which scientists and managers review the information collected 

and decide on unit prioritization (Sayre et al. 2000). FLAT, on the other hand, has 

a predefined framework of matrices and flowcharts that can be used to readily 

translate assessment data into ecological management priorities.
11

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

National Park Service Natural Resource Condition Assessments—

The National Park Service (NPS) Natural Resource Condition Assessment (NRCA) 

serves a similar purpose to FLAT. These reports synthesize preexisting scientific 

information on a particular park to support the development of management plans 

and identify what additional information is needed, as well as to develop priorities 

across the National Park System. Although some of the data may be collected in the 

field, the majority of report content is expected to come from “existing data from 

NPS and other sources” (USDI NPS 2009). The NRCA reliance on existing data is 

one important distinction between it and the FLAT method. 

Another important difference stems from the diversity of holdings within the 

National Park System. The FLAT process can be customized based on a general 

understanding of the range of ecosystems that natural area managers or planners 

might encounter, as well as the management challenges that may be present. Within 

this range, specific indicators as well as flowchart and matrix analyses are created 

to generate information that is directly helpful to prioritize MUs and management 

activity. The NRCAs, on the other hand, are a step earlier in the process. The 

NRCAs are oriented toward identifying which indicators should be used to set 

priorities going forward for each park across a much wider range of habitats and 

conditions. The NRCAs might be used to define each park’s prioritization matrix 

and flowcharts for conducting FLAT. The differences between FLAT and the 

NRCAs are thus of process timing and scale.

Ecological Integrity Assessment of the Washington Natural 

Heritage Program—

An Ecological Integrity Assessment (EIA) rates the current ecological integrity 

of an occurrence of a plant association or ecological system. NatureServe and 

the Natural Heritage Network have developed the EIA as an index of ecological 

integrity based on metrics of biotic and abiotic condition, size, and landscape 

context. Each metric is rated by comparing measured values with the expected 

values under relatively pristine conditions. The ratings are aggregated into a total 

score or a scorecard matrix. The EIA can be applied to multiple spatial scales 

(e.g., landscape-or site-scale) and with a variety of data types (e.g., GIS or fieldbased). The EIAs are developed for ecological systems using a three-level-metrics 

approach: remote sensing, rapid ground-based, intensive ground-based. In summary, the EIA framework provides a standardized currency of ecosystem integrity 

across all terrestrial ecosystem types. This information can then be used for 

setting conservation priorities, identifying restoration strategies, and monitoring 

the effectiveness of conservation actions. 
12

GENERAL TECHNICAL REPORT PNW-GTR-941

The EIA three-level-metrics approach may offer helpful concepts and 

methods for future updates of FLAT. The EIA is used to evaluate more pristine 

parcels for wildland conservation or management. FLAT is structured to enable 

land managers to integrate measures that reflect the local situations of urban and 

community forest systems. FLAT can be adjusted to reflect the “relative” value of 

natural parcels concerning desired attributes, whereas the EIA indicates “absolute” ecological values. Furthermore, although the EIA is intended to create a 

common currency of comparison between different ecosystem types, the metrics 

and methodologies employed in the field are detailed and ecosystem specific. 

FLAT, on the other hand, uses a consistent set of less detailed metrics in the field, 

without assuming prior knowledge of the ecosystem type. After FLAT is done, 

EIA could be used as a next step if more detailed assessment is needed within 

specific MUs.

Overall, FLAT fills an assessment role that traditional forestry assessments and 

the other methods discussed above do not address: 

• Uses methods that are simple and adaptable to project-specific goals.

• Provides adequate, reliable, systematic, cost-effective, and local, site-based 

information. 

• Informs decisions about where to initiate healthy forest management, stewardship programs, restoration activities, or stand management for harvest. 

• Identifies where additional, more precise data may be needed.

• May be used to monitor conditions and progress over time.

FLAT Methodology

This section provides a summary of key project considerations and the basic methods for the three phases of FLAT. 

Project Considerations and Planning

Before starting a FLAT project, managers should review and consider important 

factors for project planning, including the desired budget, staffing and training 

needs, equipment requirements, and a potential timeline. 

Costs—

The cost of executing FLAT will vary considerably depending on the size and 

nature of the assessment area. By far, the highest cost of the assessment will be 

the staff or contractor time needed to do fieldwork. Expenses will grow as the size 

of the assessment area, number of sites, and distances between sites increases. In 

addition, project planning, forest type mapping and MU delineation, as well as field 

supply purchases, contribute to the total cost. The cost per acre can be expected to 
13

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

decrease as field crews and project coordinators become more experienced. Some of 

these relationships can be illustrated by comparing King County’s costs to those of 

municipalities that implemented the same or similar programs. Appendix 5 illustrates the comparative costs in King County and the City of Everett where the same 

set of attributes data were collected. 

Staffing and training— 

There are four main project responsibilities within the FLAT protocol: forest-covertype mapping, field assessment, database design and management, and project 

management and planning. Those considering using FLAT should review what their 

in-house capabilities are for each of these activities, especially for more specialized skills such as GIS and data processing, and decide what can be accomplished 

through training or seeking additional support. Training is most important for those 

doing field data collection and will help assure consistent and high-quality data. 

Training should include a walk-through of the protocols at a field site. In addition, 

it is helpful to pair new field staff with more experienced field staff, which helps to 

calibrate how estimates are done and improves consistency across field crews. The 

FLAT Field Manual provides guidance and detailed instructions that can support 

field staff training (see app. 6).

Equipment—

The basic equipment needed to conduct a FLAT assessment include GIS, navigation 

devices, a field data entry system, plant identification resources, and measurement 

tools. The actual equipment that is needed or desired will depend on the size of 

the project and desired application of the collected data. For an equipment list and 

considerations, see appendix 3.

Timeline—

In general, the FLAT timeline starts with a pre-field season to plan the project, purchase equipment as needed, train staff, and complete Phase 1 tasks (below). This is 

a critical time for FLAT projects to ensure that all components of a project are laid 

out adequately and that the plan for moving forward is understood by decisionmakers. The pre-field season is followed by one or more field seasons, most often but not 

necessarily during summer, to complete the field assessment (Phase 2). Fieldwork is 

the most time-consuming activity, so will differ based on condition and number of 

assessment sites. Lastly, post-fieldwork data management, analysis, and reporting 

should be estimated. Overall, the timeline will vary with the size and complexity of 

the project, and time requirements will decrease even for large projects as managers 

gain experience with FLAT implementation. To date, projects have ranged from a 

few months to a multiyear effort. 
14

GENERAL TECHNICAL REPORT PNW-GTR-941

Phase 1: Forest Cover Type Mapping

Defining the project area— 

A FLAT project area is determined in several steps using a mapping system, 

management boundaries, and vegetation cover information. See appendix 1 for 

detailed procedures. Existing geospatial data are needed, including aerial imagery, 

parcel boundaries, and other management boundaries, to set up this process. For an 

explanation of necessary data, along with potential sources and considerations, see 

appendix 2.

Step 1: Determine which properties will be included in the assessment. This 

involves establishing initial site boundaries based on existing ownership and 

management boundaries. 

Step 2: Designate land cover type, such as King County designations of forested, 

other ecosystems, natural (vegetation but <25 percent canopy cover), open water, 

hardscape, and cultural landscape. 

Step 3: Delineate MUs based on forest species associations, geomorphic conditions, 

and land cover. 

The resulting polygons will be the MUs, the unit of measure for the FLAT 

process. Each MU should be assigned a unique identifier to be used throughout 

the project. This is especially important on projects having multiple sites (e.g., 

dispersed parks or land holdings) that are then delineated into MUs. Keeping good 

records of MUs identifiers and associated data is critical to ensuring that FLAT 

information is easy to use during the Phase 3 analyses (discussed below), and for 

using FLAT to monitor changes over time. 

Defining the data attributes—

Field data collection is done using predefined attributes. The attributes are related to 

vegetation species, abundance, size and age classes, and identification of restoration 

opportunities. The list of attributes used during the King County pilot are included 

as appendix 4. Agencies or organizations using FLAT will have their own management priorities, so they may add a custom set of assessment attributes. 

Defining the data attributes includes determining the level of measurement 

detail used during the field assessment. For example, forest overstory age classes 

may be divided into increments of 10 or 30 years. Selecting appropriate attributes 

and data-collection categories will require the expertise of people who are knowledgeable about the ecosystems being assessed, as well as those qualified to develop 

management priorities.
15

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

The attributes that are selected will shape the analysis that is done in Phase 3. 

Development of flowcharts and criteria that reflect potential management priorities 

should occur alongside data attribute selection. That is, managers should start the process of thinking through how their selected data attributes can be used to differentiate 

MUs and rank areas in order of urgency for management intervention. This can ensure 

that the most useful data for MUs classification are collected in the field (Phase 2). The 

actual flowcharts and criteria can then be refined and finalized as part of Phase 3.

Phase 2: Field Assessment

Assessment procedure—

The field procedure entails visual estimates of the data attributes defined during 

Phase 1. Teams of two or more people visit each MU, walk through it, and record 

an estimate for each data attribute. The following summarizes key processes and 

considerations. The FLAT Field Manual, found in appendix 6, further describes the 

field data-collection methods. 

Provide attribute estimates for the entire MU—The field team walks through each 

MU and records an estimated average attribute value for each of the variables selected in Phase 1. There may be patches of unusual species or conditions, but a best 

effort is made to estimate for the entire MU. Teams should be observant, perhaps 

even keeping notes of what they see. Judgments are made for each attribute and are 

entered into a data entry device before leaving the MU.

Use measurement tools to calibrate estimates—Some variables, such as dbh, 

crown closure, age, and height of regenerating trees (e.g., those less than 20 ft in 

height) can be measured with tools while in the field. It may be helpful to do this 

once or twice in an MU to calibrate the field team’s estimates. Determing the appropriate level of accuracy is important, as excessive measurement will slow the rapid 

assessment process. 

Alter MUs as needed—Once in the field and seeing on-the-ground conditions, the 

crew will consider whether the boundaries of the MUs should be altered, and if 

so, how. Pervasive differences in composition or age classes for large areas of the 

MU may necessitate either redrawing boundaries or splitting the MU into multiple, 

smaller MUs.

Record additional notes and variables—Field teams may find that something important is in the MU that doesn’t necessarily fit into any of the attribute categories. 

Examples include unanticipated environmental or social hazards, such as homeless 

encampments or trail damage, which could supplement management decision criteria. This information can be captured in the “notes” field in the data entry tool.
16

GENERAL TECHNICAL REPORT PNW-GTR-941

Decide when to leave the road or trail system—Each attribute determination will 

apply to the entire MU, thus it is important that enough of the MU is observed by 

the field team. Sometimes this will require leaving trails and traveling on challenging terrain. In other MUs, the view from a trail may be sufficient to make a judgment on most, if not all, of the attributes. Teams should be careful to recognize, and 

average into the MU estimate, any edge effect along trails where vegetation may 

have been influenced by trail activity or disturbance. If the area is large enough, the 

road or trail area may become a distinct MU. These decisions are left to the discretion of the field team; the desired speed of assessment must be balanced against the 

assessment’s accuracy. 

Check for completeness—Sometimes MUs will be strangely shaped so that the terrain or trails will lead teams to travel in and out of a number of MUs. Data may be 

entered for each MU in order of discovery, but should be checked for completeness 

before leaving the MU. 

Data management—

Once the fieldwork is complete with MU attributes recorded using the data entry 

tools or field forms, the data should be checked and edited if needed, then entered 

into a database for analysis. The database type can be chosen according to the 

complexity of the project. For example, a small project could be managed with 

Microsoft Excel®, while a larger project with many different sites could be set up in 

Microsoft Access®, a server-based database management system, or a geo-database 

that includes the spatial information.1

The data from each field site can be compiled into one dataset across the 

project, then queried using the categories defined for each attribute. This system 

can help with data quality control, allowing managers to query data and check for 

discrepancies or abnormal values.

Phase 3: Management Prioritization

The last phase of FLAT translates the results of the preceding mapping and field 

assessment efforts into valuable ecological information that can be readily understood by landowners, managers, and the general public. Once the summary and 

analysis processes are done, FLAT provides an advanced snapshot of conditions on 

the ground and evidence-based input toward determining land management goals 

and priorities. 

1

 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.
17

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Summary analysis—

The data can be summarized to meet the specific needs of the user at various scales. 

FLAT provides the opportunity to summarize key characteristics about the parks or 

open space system, one project parcel within the system, or a subset of MUs chosen 

for a specific analysis. 

Simple queries or formulas can be put to the database to report the number of 

MU acres representing attributes that were reported in the field data. These basic 

summaries can be reported as charts or maps (such as land-cover areas). Any 

attribute(s) can be displayed for the entire system or any subset within.

More specific analysis can be done with vegetation attributes, such as species 

composition, forest health indicators, invasive species, and so forth. Examples of 

summary analyses can range from a very basic percentage or total acreage of MUs 

containing a single vegetation attribute of choice, to a combination of attributes. 

For example, King County has produced summary statistics about the area of land 

occupied by different primary overstory species, the percentage of stocking classes 

found in young regenerating tree species, sites where a health threat indicator was 

identified, and a type categorization for each MU describing the primary species 

size and stocking.

There are numerous possible strategies for queries. One can query to show 

management subunits within the area of a larger parcel, or use one or more key 

attributes as criteria to identify individual MUs across the entire land base being 

assessed. The prioritization framework discussed below allows FLAT users to 

review their project area data and develop clear, ecological management priorities. 

Prioritization analysis—

Identifying and prioritizing areas in need of management is a key output of the 

FLAT process. Attributes assessed in the field are used to produce a qualitative 

value that combines two axes of a matrix. In King County, forest composition 

(y-axis) and forest threats (x-axis) were used (more detail is provided in the case 

study section that follows). The matrix combines the multi-attribute information 

to produce a classification value for each MU (e.g., fig. 4 is the matrix used for the 

King County project). This is the Tree-iage step of FLAT.

A flowchart can be used to determine MU values for the selected attributes. 

Generally, the resource composition flowchart and threat criteria are specific to 

each project’s conditions and priorities; the King County project is but one example 

(see fig. 8 on p. 25). 

Resource composition values and threat values are determined, then combined 

using the matrix to produce classification values for each MU. This value represents 

how important taking action may be for a particular MU in relation to other MUs 
18

GENERAL TECHNICAL REPORT PNW-GTR-941

within the site or larger system. The values suggest a ranking of priority for management actions. For example, in figure 4, values 1 through 3 represent MUs with a 

tree composition that has high ecological value, and so are important to protect and 

maintain. Values 2 and 3 also represent the presence of a forest health threat and 

could be prioritized for restoration or maintenance. On the other end of the spectrum, an MU with a value of 9 has a high threat presence and a lower quality tree 

composition, and therefore may not be as high a priority for management actions. 

A matrix value can be entered into the project database as an attribute for each MU 

and then mapped with GIS. 

Based on the flexibility of this approach, other projects could use matrix tools 

that acknowledge project-specific ecosystems, threats, and management goals. As 

discussed in Phase 1, initial development of criteria and flowcharts should occur 

alongside data attribute development to ensure that desired data are collected in 

the field assessment. Then the flowchart(s) and criteria can be refined as needed to 

accurately reflect local conditions. 

Threat

Value

Forest composition

Forest health

LOW MEDIUM HIGH

LOW MEDIUM HIGH

1

4

7

2

5

8

3

6

9

Figure 4—Management unit classification matrix (Tree-iage) example, King 

County, Washington.
19

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

How the results of the matrix analysis are used in subsequent land management 

decisions is up to each FLAT user. FLAT provides an ecological input for land 

management decisionmaking. The combination of field data collection, flowchart 

processing, and classification of MUs using the matrix can be used to prioritize 

future management actions and monitoring. Resource managers will also consider 

other social and economic factors to create a strategic and feasible management 

approach for their lands. The following section details how FLAT was developed 

and tested in its pilot application in King County, an example of one way that FLAT 

has been used. 

King County Parks and Open Spaces Case Study

The King County Natural Resources and Parks Division, Parks and Recreation 

Division, deployed FLAT for a landscape-scale assessment of county parklands 

from 2010 to 2013. The implementation of FLAT in King County is an informative case study that demonstrates the entire FLAT process from planning to final 

results. This case study presents the overall FLAT planning, implementation, and 

interpretation phases, as well as specific examples of park land assessment within 

King County. 

The heterogeneity of forest lands within King County makes it an ideal case 

study. Although the west side of King County contains shoreline and pockets of 

urban forest within the large cities of Seattle and Bellevue, the east side consists 

predominantly of rural communities, agricultural lands, working forests, and 

wilderness. A long history of logging coupled with more recent rapid urbanization 

has created the diverse landscapes of today.

In 1989 and 2007, voters in King County approved measures that provided 

a combined total of more than $201 million toward open-space acquisition and 

improvement (Trust for Public Land 2012). In addition, King County (as part of its 

growth management policy) implemented an incentive program that allows 1 ac of 

land to be reassigned from rural to urban zoning if an associated 4 ac are dedicated 

to the county as permanent open space. The resulting rapid land acquisition has led 

to a diverse portfolio of more than 26,000 ac managed by King County. Holdings 

differ in terms of the level of previous use and surrounding development and the 

type of land cover, size, ecosystem type, and biotic composition. King County’s 

portfolio includes high-use active recreation parks, former agricultural lands and 

gravel mines, river floodplains, and working forests in the foothills of the Cascade 

Range, as well as Puget Sound coastline. More than 21,000 of the 26,000 ac are 

forested. Table 2 shows the number of acres and park sites per park classification. 

FLAT was used to 

open the condition of 

nearley 25,000 acres 

of parks and open 

spaces, including 

nearly 200 parcels.
20

GENERAL TECHNICAL REPORT PNW-GTR-941

The large and rapid expansion of land holdings left King County managers 

with little information regarding forest composition, health, and diversity across the 

different park lands. As noted earlier, FLAT was developed and piloted in collaboration with the Forest Service to provide a comprehensive forest assessment for 

King County Parks. Specific goals for the county included (1) assessing the timber 

resources and management needs within working forest lands, (2) assessing forest 

health and composition in forests managed for recreation and wildlife, (3) prioritizing areas for forest stewardship and management, and (4) minimizing expenses.

Project Process 

Planning—

King County received funding from the American Recovery and Reinvestment 

Act of 2009 (in coordination with the USDA Forest Service) to support job creation 

and training. These funds were used to support the implementation of FLAT over 3 

years. The total budget for the project was about $194,000 and was used for FLAT 

development, field planning, fieldwork, database management, and data analysis. 

Planning occurred during 2009 and 2010, with fieldwork commencing in the summer of 2010.

In total, 24,722 of the 26,000 ac of King County Parks were chosen for a FLAT 

assessment. These King County sites each contain a contiguous forest canopy component suited for a FLAT assessment. A FLAT assessment was performed on all 

of the multi-use, natural areas (lands managed to conserve and enhance ecological 

value and to accommodate passive recreational use), and working resource forest 

lands, which all are primarily heavily forested sites. Some recreation sites were 

assessed if they had a significant contiguous forest canopy. For example, Marymoor 

Park is a classic urban park with a traditional urban forest canopy of hundreds 

of single trees in a highly maintained park setting. Generally, these areas would 

be classified as “landscape” in the use of FLAT. However, Marymoor also has a 

significant riparian forest component along the river and lake that is classified as 

Table 2—King County Parks distribution by designation, acreage, 

and number of sites

Park classification Area Number of parks

Acres

Working forest/resource 3,455 7

Multi-use open space 12,812 41

Natural areas 7,321 78

Active recreation parks 2,472 67

Total 26,066 190
21

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

“forest” by FLAT guidelines. Regional multi-use trail corridors, such as the Burke 

Gilman Trail, were not assessed. Although most of these trails have a linear forest 

component, these were not considered to be a contiguous forest area, and would fall 

into the “landscape” category. Table 3 shows the land-cover-type results for the total 

King County FLAT project area across the different park designations. 

Table 3—Acres of land-cover types in 

entire King County FLAT project area 

Landcover type Area

Acres

Forested 21,058

Natural 2,024

Water 639

Hardscape 48

Landscape 953

Total 24,722

Phase 1: forest cover type mapping—

Several years of orthophotos were used in developing the MUs, including 2009 

true color imagery having 0.5 ft per pixel resolution collected specifically for King 

County; 2009 and 2001 USDA Natural Aerial Imagery Program (NAIP) imagery 

with 1 m per pixel resolution; and online public access oblique angle imagery. 

Multiple datasets were used to reduce errors resulting from shadows, parallax, 

photo mosaicing, and varying light conditions. In addition, raster files developed 

from 2003 LiDAR data collected over King County were used for assessing tree 

height in GIS.

Site boundaries were provided by the King County GIS Center in Esri shapefile 

format and included, at a minimum, a unique facilities identification number and 

a site name. Sites that were smaller than the minimum mapping size, or where the 

land cover appeared to be homogenous in the imagery, were classified as a single 

MU. Sites that contained multiple MUs were first delineated based on general 

land cover type. The land cover type was classified as forested, natural (vegetation 

with <25 percent forest canopy cover), water, hardscape, or landscape. Small and 

isolated areas of landscape and hardscape were combined with surrounding cover 

types, as it was not the intent of this project to track such features. The remaining 

MUs categorized as forest and natural were then viewed with the aerial imagery 

to further delineate polygons containing similar vegetation types. Color, texture, 

tree shadows, and crown shape were used to determine MU edge breaks. Because 

these characteristics can appear differently depending on the imagery used, several 
22

GENERAL TECHNICAL REPORT PNW-GTR-941

available image sources were reviewed, as listed above. These provided views 

having different lighting, color balance, and resolution. A canopy height raster file 

generated from King County LiDAR ground and surface models was also used to 

provide guidance on stand heights.

McGarvey Park Open Space serves as an example of how Phase 1 was carried 

out in King County. The park was acquired by King County in 2000 through the 4:1 

program, and is composed of 401 ac of forest (fig. 5). Photo interpretation was used 

to classify the park into 35 MUs as seen in figure 6, guided by differences in color 

and texture. For instance, MU 8 is a much lighter shade of green than the surrounding MUs. 

Figure 5—Boundaries of McGarvey Park Open Space.
23

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Phase 2: field assessment—

Specific forest measurements and observations were selected to meet the specific 

King County objectives of assessing timber resources and assessing the integrity 

and health of forest ecosystems. Attributes such as overstory tree diameter, stocking, and age were collected to evaluate the timber resource within the MU, while 

attributes such as native tree composition, invasive species presence, and forest 

health were collected to evaluate ecological integrity. Attributes were separated 

into broad categories that could be quickly estimated in the field. A full list of data 

attributes used in the King County FLAT is in appendix 4.

The ideal crew size was found to be two people (fig. 7). The learning curve 

for the fieldwork was minor, but it was essential that all field personnel be able to 

identify native plants and common nonnative invasive plants and noxious weeds. 

Figure 6—McGarvey Park Open Space management unit delineation.
24

GENERAL TECHNICAL REPORT PNW-GTR-941

The crews carried both hardcopy orthophotos and digital versions on field global 

positioning system (GPS) units. Crews surveyed MUs that were clustered geographically in the same day. To begin surveying an MU, crews would drive to the closest 

road access point. Some of the larger sites would require using multiple access 

points, while other sites had trail networks that allowed crews to easily move from 

MU to MU. Depending on the size of the MUs and the level of forest heterogeneity, 

a crew was able to survey between 3 and 20 MUs in one day. It was also important 

for crews to maintain their “ocular” calibration by measuring tree diameters and 

ages at least once on every site. Many of the forests in King County were clearcut 

in the last 100 years, and it was relatively reliable to assign most trees to the 50- to 

99-year-old age class, but there are clusters of both younger and older trees. The 

FLAT Field Manual (app. 6) provides further information about the process. 

The field campaign took three summer seasons to complete (2010–2012). Factors 

that affected the speed of the field crews were (1) heterogeneity of parks—parks 

with many separate MUs in a small geographic area took longer to survey than 

parks with a single forest type; (2) size of the park—larger parks required less driving time to get from MU to MU; and (3) distance between parks—parks within a 

small geographic extent required less driving time. The forests of multiple MUs also 

tended to be more similar, resulting in less time spent calibrating ocular estimates.

Figure 7—King County field staff Jack Simonson and Brett Roberts consult management unit maps.

David Kimmett
25

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Phase 3: management prioritization—

Variables were selected to construct the matrix (fig. 4), which includes forest 

composition on the x-axis and forest health threats on the y-axis. The forest composition values were defined prior to the fieldwork using forest ecology principles. 

In the field, teams determined a high, medium, or low forest composition value for 

each MU using a flowchart (fig. 8). This flowchart was designed to appropriately 

rate important landscapes that may not necessarily support a full forest canopy, 

particularly wetlands.

The y-axis of the Tree-iage matrix for King County includes low, medium, and 

high threat values. In the Green Seattle Partnership analysis noted above, the extent 

of invasive species cover was used as the criterion for determining threat values 

(i.e., low = <5 percent invasive cover; medium = 5 to 50 percent invasive cover; 

high = >50 percent invasive cover). In this King County case study, managers used 

additional multiple criteria to describe forest health threats, including the presence 

of root rot, mechanical tree failure, low tree vigor, presence of mistletoe, bare soil 

(as an indicator of disturbance), and an “other” category that allowed field teams 

to record additional observations. Each health indicator was recorded as a yes/no 

Figure 8—Forest composition flowchart for King County management units.

All sites

<25% native tree 

canopy cover

>25% native tree 

canopy cover

 0% of canopy 

is conifer 

and/or

madrone

 Unable to 

support

>50%

conifer or 

madrone

cover

 Able to 

support

>50%

conifer or 

madrone

cover

 Able to 

support

1 to 50%

conifer or 

madrone

cover

 Able to 

support

>50%

conifer or 

madrone

cover

Capability

to support

canopy

Modified

tree

composition

value

Conifer/

madrone

cover

Canopy

cover

>50% of

canopy is

conifer and/or

madrone

 Unable to 

support

>50%

conifer or 

madrone

cover

 Able to 

support

>50%

conifer or 

madrone

cover

 Unable to 

support

conifer or 

madrone

cover

 Unable to 

support

conifer or 

madrone

cover

 1 to 50% of

canopy is

conifer and/or

madrone

HIGH

LOW

MEDIUM
26

GENERAL TECHNICAL REPORT PNW-GTR-941

observation. To determine if an MU had a low, medium, or high threat value, the 

number of observed health threat indicators were tallied back at the office. Low 

is interpreted as no observed health threats, medium as one observed threat, and 

high as two or more observed health threats (table 4). Other projects may develop 

a tiered approach, creating a decision tree akin to the forest composition flowchart 

to differentiate between low, medium, and high threat levels incorporating multiple 

types of indicators.

Table 4—Methodology used to select management unit 

threat value

Threat value Observed unhealthy forest values

Low 0

Medium 1

High 2 or greater

Results

An ArcGIS® geodatabase was assembled from the field data. It was used to produce 

maps, conduct database queries, and generate summary statistics.

Summary analysis of forest characteristics—

The forest variables collected using FLAT helped King County managers to 

understand baseline information about all of their forested parklands for the first 

time. Each of the forest attributes collected (app. 4) can be summarized across King 

County. For instance, the age class structure of the forest can be quickly viewed in 

figure 9. These data show that red alder (Alnus rubra bong.), bigleaf maple (Acer 

macrophyllum), and Douglas-fir (Pseudotsuga menziesii Pursh) dominate forest 

cover. Red alder and bigleaf maple are relatively short-lived species, and the large 

numbers of trees in age classes 2 (30 to 49 years old) and 3 (50 to 99 years old) 

suggest that these forests are in need of management and restoration. 

Adding a third variable, stocking density, allows for creating type calls that 

can be useful for timber harvest planning and management (fig. 10). A type call is 

an integration of the major species (table 5), its size class, and the stocking class 

of that MU. Size class contains four categories: 1 = 0 to 5 inches dbh, 2 = 6 to 10 

inches dbh, 3 = 11 to 20 inches dbh, and 4 = >21 inches dbh. Stocking also has four 

categories: 0 = <10 percent crown closure, 1 = 10 to 39 percent crown closure, 2 = 

40 to 69 percent crown closure, and 3 = >70 percent crown closure. The FLAT data 

describes a hardwood-dominated forest structure at McGarvey Park (fig. 11). 
27

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Figure 9—The predominant overstory species and age class across all management units surveyed in King County using FLAT. Acres

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Age class 1 (0–29 years)

Age class 2 (30–49 years)

Age class 3 (50–99 years)

Age class 4 (100+ years)

Dominant overstory species

Alnus rubra

Acer macrophyllum

Pseudotsuga menziesii

Tsuga heterophylla

Populus balamiphera

Thuja plicata

Abies amabilis

Picea sitchensis

Fraxinus latifolia

Betula papyrifera

Salix spp.

Arbutus menziesii

Table 5—Major overstory species

Acronym Scientific name Common name

CW Populus balsamifera L. ssp. tricocarpa (Torr. & A. Gray ex Hook.) Brayshaw Black cottonwood

RA Alnus rubra Bong. Red alder

DF Pseudotsuga menziesii (Mirb.) Franco Douglas-fir

BM Acer macrophyllum Pursh Bigleaf maple

WH Tsuga heterophylla (Raf.) Sarg. Western hemlock

WI Salix sp. Willow species

SF Abies amabalis (Douglas ex Loudon) Douglas ex Forbes Pacific silver fir

RC Thuja plicata Donn ex D. Don Western red cedar
28

GENERAL TECHNICAL REPORT PNW-GTR-941

Prioritization analysis—

Because both variables of forest composition and forest health represent important 

concerns in King County’s open space management, the matrix analysis immediately suggests management priorities (with #1 indicating the highest quality forest 

condition and #9 the lowest). The number of acres of each prioritization value is 

shown in figure 12, and these results mapped across King County are shown in 

figure 13. 

The matrix values for McGarvey Park are shown in figure 14. Note that a large 

proportion of McGarvey Park is classified as 5 and 6, indicating that there is a 

medium composition value with forest health threats present. For example, an MU 

having forest composition keyed out to medium, and having health threats of both 

root rot and low vigor (keyed to high in table 4) would be placed in the matrix of 

figure 13 as a “6.” At McGarvey Park, this translates to MUs that contain dominant 

red alder and bigleaf maple that are near the end of their productive lives, and have 

at least one area of conifers infected with root rot. The matrix value of an MU can 

inform decisions about the priority of action based on management goals.

Figure 10—Type calls identified within the King County FLAT that comprised more than 1 percent of the project by area. Note: 

The type call includes an acronym for the major species, the first digit represents the size class, and the second digit represents the 

stocking class.

0

2.0

4.0

6.0

8.0

10.0

12.0

RA33

DF43

BM33

RA23

DF33

BM42

BM32

RA32

WH33

DF23

BM43

WH43

DF42

RA22

RA13

CW43

CW33

SF23

DF32

RC43

WI21

RA21

SF33

CW42

Percentage of all management units

Type call
29

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Figure 12—Priority matrix for King County parklands including number of acres 

of each ranked category.

Figure 11—Type calls identified within McGarvey Park Open Space by acres. 

Note: The type call includes an acronym for the major species, the first digit 

represents the size class, and the second digit represents the stocking class. 

0

20

40

60

80

100

120

140

160

BM33BM32

WH33

DF33

WH22

RC32

RC41

DF42

Acres

Type call

Threat

Value

Forest composition

Forest health

LOW MEDIUM HIGH

LOW MEDIUM HIGH

1

4

7

2

5

8

3

6

9

3,795 3,784 1,384

7,107 4,853 802

1,066 209 75
30

GENERAL TECHNICAL REPORT PNW-GTR-941

Management Implications

FLAT results were used immediately. For instance, in 2012, FLAT data guided 

forest management decisions for McGarvey Park, where old, dying, and diseased 

red alder and bigleaf maples were harvested for revenue. These areas were replanted 

with mixed conifers to meet forest restoration and future timber harvest goals. As 

FLAT data are updated during future monitoring phases, a shift should be seen in 

composition from older hardwood to vigorous conifers that return greater benefits. 

Note that to complete a timber sale, a supplemental timber cruise is required 

to establish the quantity of trees to be removed and subsequent value. More 

Figure 13—Distribution of management units classified by Tree-iage matrix values for all King County parks.
31

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

information about how FLAT results were implemented can be seen in the McGarvey Park Open Space Stewardship Plan (King County 2011).

FLAT results are of value across the county’s holdings, and will inform management decisions for years to come. With many recent acquisitions of forest land, 

King County Parks is using FLAT results as baseline forest data for forest stewardship strategies. For the 3,000 ac of designated working forests in King County, 

FLAT will support the creation or update of required forest stewardship plans. 

Of the other lands in the multi-use and ecological designations that do not require 

Figure 14—Distribution of management units classified by Tree-iage tree composition and health threat values for McGarvey Park 

Open Space.
32

GENERAL TECHNICAL REPORT PNW-GTR-941

formal stewardship plans, FLAT will provide data to help King County identify 

specific MUs that need further attention because of forest health threats. Based on 

the field data collected through FLAT, King County has started to conduct additional site-specific analyses, including actions that target areas of root rot. With the 

data now in hand, King County managers can continue to assess their lands and 

designate planning resources strategically.

Conclusions

The purpose of FLAT is to provide a systematic survey of forested lands that 

supports the prioritization of future management actions in a landscape system. 

This information can be used widely to implement landscape-level planning, unitspecific management, and ecological knowledge building. 

Importance in Land Management 

King County and the other municipalities that have implemented FLAT and FLATprecursors have used the resulting data in a number of ways. Their primary use has 

been in the planning and budgeting for stewardship and restoration programs. A 

baseline understanding of the status of each MU gives natural resource managers 

the ability to estimate the costs of future action as well as determine where and 

what more detailed or site-specific assessments may need to take place. The rapid 

assessment process can also alert them to threats or land conditions they may not 

have anticipated or suspected. 

The FLAT data can also be used in conjunction with other spatial data or information for decisionmaking. As an example, figure 15 shows how the Green Redmond Partnership used assessment data along with social information to prioritize 

restoration activities (Green Redmond Partnership 2009). Using GIS, FLAT data 

can be combined with demographic, ecological, or environmental data to investigate trends and identify relationships across a landscape or public lands system.

FLAT was designed to purposefully support adaptive management. FLAT can 

be iteratively applied and combined with other assessments. Also, as repeated FLAT 

assessments are conducted and management is carried out, the repeated measures 

can provide managers with a way to observe the effectiveness of management 

actions. This application of FLAT has not yet been done in King County. In principle, by supporting longer term observations of patterns of ecosystem response, 

FLAT can provide more than just a snapshot survey. As FLAT is a practical and 

fairly low-cost tool, it may be redone on a periodic basis (perhaps every 5 years) and 

become a monitoring tool for management assessment and improvement. Because 

it is straightforward to learn and do, a variety of participants can be involved in a 

data-collection cycle, from staff to volunteers to local stewardship groups. 

Local governments 

have used FLAT 

data to plan their 

forest stewardship 

and restoration 

programs.
33

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

FLAT Feasibility

Simplicity, flexibility, and low cost make FLAT an appropriate tool for many 

users. By assessing a limited set of variables and applying methods, including field 

protocols, that are easily teachable, FLAT makes forest assessment doable across 

a wide spectrum of landowners. Where forest condition information is lacking or 

piecemeal, FLAT can fill the gap and provide consistency without requiring significant time, training, or large equipment purchases. 

In addition, the FLAT framework is inherently flexible for use on a wide range 

of land ownerships, from small and contained to large and complex. For any given 

project, there is also flexibility in how detailed to make the field assessment, matching procedures to the size of the forest and intended use of the data. 

The simplicity and flexibility of FLAT allows it to be implemented within a 

tight budget. This is a crucial factor that makes FLAT feasible for cash-strapped 

local government, private, and NGO landowners. In addition to the reduced time, 

training, and equipment that normally make assessment more expensive, costs can 

also be kept low by using the growing interest in citizen science. Where appropriate, projects can support community involvement by using volunteers to conduct 

fieldwork. Project planning should then include training costs and field checks for 

data reliability.

Current

high-value

forest

composition?

Expressed

community

importance or

critical area?

Geographic

distribution?

Priority site:

Create work 

plan; begin 

restoration and

maintenance

Yes Yes Yes

Volunteer interest or available Forest Steward?

Not a priority site at this time

No No

No

No

Yes

Figure 15—Green Redmond Partnership decision tree for prioritizing restoration sites.
34

GENERAL TECHNICAL REPORT PNW-GTR-941

Stewardship

The landscape assessment of King County’s parks and open spaces revealed that 

there are natural systems that in some instances are robust and healthy and, in 

other cases, are in decline or face substantial threats. Ongoing forest resource 

management is needed across all these conditions, both to restore compromised 

landscapes and sustain those that are healthy or moving to an improved condition. 

Forest and landscape management is an intensive activity, involving strategic 

planning, technical and scientific input, and on-the-ground actions. To carry out 

these activities, a resource management agency can use its own staff or contracted 

consultants to do land-based work. Yet fiscal constraints in local government and 

environmental resource organizations can limit their capacity to address ecosystem 

needs and recovery. 

In the face of limited and declining fiscal and technical resources for ecosystem 

management, communities and agencies must consider new solutions to restore and 

sustain natural systems, particularly in urban settings (Wolf et al. 2013). Engagement of people and social systems, from individuals to organizations, is another 

stewardship option (Wolf 2012). Environmental stewardship is an increasingly common response to the decline of natural systems in and around built environments 

(Romolini et al. 2012). Recent research in multiple U.S. cities suggests that stewardship may be an effective and viable strategy for ecosystem management (Svendsen 

and Campbell 2008), particularly in urbanized areas.

There is a great deal of variability in the organizational and administrative 

structures that support stewardship efforts. At one level, programs and projects 

may be activated by landscape-scale policies and associated regulations that are 

promulgated by community planners, agency officials, and policy decisionmakers 

(Brunckhorst 2002). Other stewardship groups are composed of citizens organized 

to address a defined ecosystem condition that has direct personal consequences, 

acting for change through place-based projects and resources. Some groups are 

formally self-organized and have 501(c)(3) status, some are informal organizations 

without legal status, and some are membership organizations facilitated by a public 

entity (Brinkley et al. 2010).

No matter who participates in stewardship, and whatever their motivations, 

better landscape health outcomes can be promoted using the information provided 

by a landscape assessment like FLAT. For instance, having full knowledge of parcel 

conditions can help a stewardship coordinator within an agency to prioritize stewardship programs across a landscape system, to ensure that the greatest work effort 

is assigned to places having the greatest need. In addition, those nonprofit organizations that host stewardship projects can use an assessment to assign volunteer 

Stewardship 

organizations and 

volunteers can assist 

with FLAT data 

collection, contributing 

to better management 

of urban forests.
35

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

work parties within their activity area, as well as communicate to the public about 

the high-health parcels that may deserve conservation status. Finally, a “friends 

of” group of citizen stewards can use assessment information to identify the most 

important project sites within a park or open space within their community, so that 

they achieve satisfying outcomes from their self-sufficient efforts. In all these situations, periodic reassessment can help the agency, organization, or group to better 

understand how their efforts contribute to better ecological health. 

Limitations 

FLAT is a useful tool for planning, budgeting, stewardship, and performance 

measurement. However, its application does have limitations that are important to 

consider. The qualitative nature of the field assessment makes the FLAT unsuitable 

as evidence of compliance with environmental standards or the creation of environmental impact statements under state or federal law. FLAT would also not be 

sufficient to develop a sustainable harvest plan, though it can serve as a first step to 

get a picture of resources on the land. 

Another limitation of FLAT is the coarse-grain scale of the data. Management 

Unit sizes may limit use of FLAT as a tool for understanding fine-scale ecological 

structures and processes. Most MUs will be larger than the level at which some 

plant interactions take place. Attempts to scale the MU down to small land areas 

will greatly increase the number of MUs, increasing cost and time of the assessment. FLAT, therefore, does not take into account small pockets or sites within the 

unit. In theory, a unit with a number of concentrated pockets of invasive species 

would appear the same as a unit within which a similar proportion of invasive 

species are found throughout a more dispersed area. Although a classification of 

heterogeneity could be added, a more detailed assessment will likely be required 

to understand site-specific details and evaluate what type of action may be necessary within an MU. The purpose of FLAT is therefore to determine where these 

detailed assessments may need to take place. 

While FLAT is limited in spatial scale, it is also limited in its scale of possible 

indicators. As more indicators are added, the speed and simplicity of the assessment—two of the primary advantages—will be reduced. For FLAT to retain these 

qualities, it may be necessary to exclude certain variables or indicators that may 

be useful in specific management situations. The current FLAT protocols and 

analysis are a reflection of both the priorities of King County as a pilot user and 

the need for FLAT to retain its rapid deployment character. Project managers will 

have to carefully weigh the tradeoffs of including more indicators based on their 

specific circumstances. 
36

GENERAL TECHNICAL REPORT PNW-GTR-941

In addition, the current FLAT protocols do not include wildlife. If managers 

have a specific species or cohort of species in mind, attributes could be added that 

indicate habitat suitability or presence. 

Next Steps

FLAT is a relatively new tool, and as such, has potential for future development, 

testing, and refinement. Formal testing would enhance the credibility of FLAT. 

Although it has already been informally tested through application to the satisfaction of practitioners, neither its reliability nor accuracy has been systematically 

evaluated. A reliability test might entail examining whether individuals with similar 

training and experience using FLAT on the same MU would produce similar 

results. An accuracy test might compare the results of a FLAT assessment for 

particular MUs with evaluations that involve random sampling and more traditional 

forest ecological data collection within those MUs.

As it currently exists, FLAT is designed for use in the lowland forests of the 

Puget Sound basin and, to a limited degree, in wetlands. Expanding its use to other 

ecosystem types in other parts of the Pacific Northwest, the Nation, or the world 

would require revising the measures variables. In principle, however, the basic 

framework should work for any number of ecosystem types. As discussed in the 

“FLAT Methodology” section, the system is oriented to a project-specific selection 

of prioritized data attributes and criteria, in order to best achieve local management 

goals. Expanding FLAT’s ecosystem range is thus a matter of degree, requiring 

additional work to develop appropriate data attributes and field test new implementations. Concerns about different land scales and vegetation associations would 

have to be addressed.

One way that this extension of FLAT might be facilitated would be the development of a publicly available clearinghouse that catalogs indicators, classification 

schemes for each indicator, and prioritization systems that have been developed for 

various ecosystem types. Practitioners planning a FLAT project could then devise a 

system built on the experiences of others, just as those in the Pacific Northwest can 

use the King County example as a starting point. 

Lastly, future research may explore applying FLAT as a practical method of 

monitoring ecosystem changes not necessarily tied to natural resource management 

goals. For example, decisionmakers interested in more general information regarding emerging local effects of climate change, or changes in habitat quantity or 

quality, or shifts in biodiversity, or any number of other topics, may adapt FLAT to 

focus on such system-level questions. 

Although used for 

forest assessment 

in this report, FLAT 

can be adapted for 

assessment of 

every ecosystem 

type in the Pacific 

Northwest region.
37

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Acknowledgments

This work was developed by the Green Cities Research Alliance (GCRA). In 2009, 

GCRA was initiated by the USDA Forest Service, Pacific Northwest Research 

Station, to build a program of research about urban ecosystems in the Puget Sound 

region. The GCRA is an integrated social-ecological research program that engages 

the social and biophysical sciences to meet the practical needs and concerns of local 

organizations and agencies. It also is an effort to coordinate science and community 

partners within the Pacific Northwest region and to link investigations to other U.S. 

urban areas. The goal of this collaboration is to increase the knowledge necessary 

to build healthy, sustainable urban environments. The GCRA pairs scientists with 

practitioners and local decisionmakers to co-design and implement research efforts 

that provide relevant and practical information. Major collaborators include the 

University of Washington, King County, Forterra, and the City of Seattle. For more 

information, visit http://www.fs.fed.us/pnw/research/gcra. 

The GCRA’s work on FLAT has been particularly informed and supported by 

the Green Seattle Partnership, with special thanks to Mark Mead for his efforts on 

developing the Tree-iage analysis tool. The Green Everett Partnership, Forterra, 

and American Forest Management have also been instrumental in sharing the 

FLAT analysis process and database. Additional thanks and acknowledgement 

goes to Ara Erickson (Forterra, now with Weyerhaeuser Company), Kim Frappier 

(Forterra, now with the City of Mercer Island), Bill Loeber (King County), and Ted 

Hitzroth (American Forest Management) for their help in developing FLAT. Project 

field implementation was provided by the King County Department of Natural 

Resources and Parks, led by Brett Roberts and Jack Simonson. 

Major funding for this project was provided by the American Recovery and 

Reinvestment Act of 2009 (ARRA) and the USDA Forest Service Pacific Northwest 

Research Station.
38

GENERAL TECHNICAL REPORT PNW-GTR-941

References

Brinkley, W.; Wolf, K.L.; Blahna, D.J. 2010. Stewardship footprints and potential 

ecosystem recovery: preliminary data for Seattle and Puget Sound. In: Laband, 

D., ed. Emerging issues along urban/rural interfaces III: linking science and 

society (Proceedings). Atlanta, GA: 24–30. http://www.naturewithin.info/

CivicEco/InterfacesIII%20Prcdngs_GCRA.Dec2010.pdf. (December 17, 2015).

Brunckhorst, D. 2002. Institutions to sustain ecological and social systems. 

Ecological Management and Restoration. 3(2): 108–116.

Clewell, A.F.; Aronson, J. 2007. Ecological restoration: principles, values, and 

structure of an emerging profession. Washington, DC: Island Press. 232 p.

Dean, J.W., Jr.; Sharfman, M.P. 1996. Does decision process matter? A study of 

strategic decision-making effectiveness. Academy of Management Journal. 

39(2): 368–396.

Green Redmond Partnership. 2009. 20-year forest management plan. Redmond, 

WA: City of Redmond. http://forterra.org/wp-content/uploads/2015/05/FINAL_

GRP_20_YP_InDesign_FORWEB.pdf. (December 17, 2015).

Green Seattle Partnership. 2006. 20-year strategic plan. Seattle, WA: Cascade 

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and types. Annals of Emergency Medicine. 49(3): 275–281. doi:10.1016/j.

annemergmed.2006.05.019. 

King County. 2011. McGarvey Park Open Space forest stewardship plan. Seattle, 

WA: King County Department of Natural Resources and Parks, Water and Land 

Resources Division. http://your.kingcounty.gov/dnrp/library/2011/kcr2230.pdf. 

(December 17, 2015).

Marsh, W.M. 1978. Environmental analysis: for land use and site planning. 

New York: McGraw-Hill, Inc. 292 p.

Nowak, D.J.; Crane, D.E.; Stevens, J.C.; Hoehn, R.E.; Walton, J.T. 2008. A 

ground-based method of assessing urban forest structure and ecosystem services. 

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Randolph, J. 2011. Environmental land use planning and management. 

Washington, DC: Island Press. 776 p.
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Romolini, M.; Brinkley, W.; Wolf, K.L. 2012. What is urban environmental 

stewardship? constructing a practitioner-derived framework. Res. Note PNWRN-566. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

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S. 2000. Nature in focus: rapid ecological assessment. Washington, DC: Island 

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Svendsen, E.S.; Campbell, L.K. 2008. Urban ecological stewardship: 

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db/bbqna2qct?a=dbpage&pageID=10. (December 17, 2015).

Turner, M.G. 2005. Landscape ecology in North America: past, present, and 

future. Ecology. 86(8): 1967–1974.

U.S. Department of Agriculture, Forest Service [USDA FS]. 2014. The forest 

inventory and analysis phase 3 indicators database 6.0: description and user 

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U.S. Department of Agriculture, Forest Service [USDA FS]. 2015. Urban field 

data collection procedures. The Forest Inventory and Analysis database: database 

description and user guide for urban data. Washington, DC. 464 p. http://www.

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Appendix 1: How to Develop the Assessment Area and 

Management Units

The forest-cover-type mapping process defines the boundaries and area for each 

management unit (MU). This process establishes boundaries based on existing 

ownership, management goals, and vegetation and land-coverage typing from aerial 

or satellite photos. This section describes the steps of this process.

STEP 1: Determine Which Properties Will Be Included 

in the Assessment

The project area should be defined in this stage based on relevant management 

programs and resource information needs (see fig. 16 for an example). It may help 

to consider if different sites will be managed by different crews or be located in 

different districts that will affect how management decisions are made, and may 

change desired assessment boundaries. Thought should also be given to regulatory 

designations such as wetland or riparian areas that may limit or specify management actions. Each site should have a unique name and number for identification 

throughout the project. For small projects, there may only be one site, having a 

single project area name and boundary.

STEP 2: Designate Land Cover Type

Once the assessment sites have been chosen and identified by property ownership 

or management blocks, the next step is to use aerial or satellite imagery to review, 

then delineate similar conditions within the predesignated site or project areas. The 

general purpose of this photo-typing is to identify zones based on the major differences in land or forest cover. 

Sites should be divided into units that are given one of a number of broad land 

cover designations. The land cover designations for the King County pilot project 

are included below in table 6. Some Forest Landscape Assessment Tool (FLAT) 

users may wish to expand these designations to include types such as wetlands or 

shoreline, given the particular characteristics of the system they are evaluating. 

The boundaries between land cover types should be based on breaks visible 

in the aerial or satellite photos. It is not necessary to precisely measure the forest canopy cover to differentiate between, for example, a forested or natural area 

designation. An estimate should be fine in these cases because the typing will be 

verified during field data collection (see the FLAT Field Manual in app. 6). The 

designations can be done largely based on viewing the photos, but some may choose 

to use software or tools like Feature Analyst. 

When in doubt about a land cover type, a convention should be established and 

applied consistently throughout the process. In some cases, it may make sense to 
41

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

create a new category that indicates the call will be made in the field assessment 

process. The conventions will likely change based on the team and their priorities, 

thus it is essential to make assumptions explicit so that users of the information 

later on will take that into consideration as they make decisions.

A threshold should be set for the minimum size of a land cover designation. In 

King County, significant and distinct nonforest areas were delineated down to 0.1 

ac. It is not the intent of FLAT to map paved paths, tennis courts, or other facilities 

within site areas. These smaller developed features can be lumped into surrounding 

landscape or hardscape MUs. 

Figure 16—Forest Landscpe Assessment Tool assessment boundary for Maury Island Marine Park, a project site in King County, Washington.
42

GENERAL TECHNICAL REPORT PNW-GTR-941

STEP 3: Delineate Management Units Based on Vegetation 

Differences.

With property information and land cover differences delineated, the next step is 

to further divide a site according to vegetation differences (fig. 17). This applies 

specifically to land cover designations that fall within the purview of FLAT (i.e., not 

open water, hardscape, or landscape).

The forested and natural cover typing of Step 2 should be reviewed using aerial 

imagery to delineate polygons within that contain similar vegetation types. Areas 

with clearly different species, structural, or age composition should be placed 

into different MUs. Color, texture, tree shadows, and crown shape can be used 

to determine MU breaks. Because these characteristics can appear differently on 

different imagery, use of several image sources is recommended to provide views 

with varied lighting, color balance, and resolution (see app. 2 for further discussion 

of necessary data). This vegetation typing requires the most skill and experience 

with photo-typing and boundary delineation. In addition, if data are available and 

project staff have the needed expertise, remote-sensing ground and surface datasets 

(e.g., LiDAR) may be used to generate a canopy height layer for use in a geographic 

information system (GIS) to assist in differentiating stand heights. 

The MU size will vary based on the amount of contiguous similar vegetation 

coverage as well as thresholds decided on by the FLAT project manager. As an 

example, King County established a minimum MU size of 5 ac, as it was determined that further subdivisions of MUs would add too significantly to the total 

number of MUs and time necessary to complete field data collection. This is a judgment call—what you may gain in precision, you may sacrifice in time. In smaller 

communities, cities, or systems of parcel holdings, it may make sense for MUs to be 

much smaller. 

Table 6—King County Parks Forest Landscape Assessment Tool land cover designations

Type Acronym Definition 

Forested FOR ≥25 percent of the area covered by forest canopy

Natural NAT Any natural vegetation that has <25 percent forest canopy cover 

Open water WAT Open water without woody vegetation

Hardscape HS Impervious surface such as parking or buildings

Landscape LS Areas that are currently landscaped or have been mechanically 

maintained within the past year
43

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Data format—

If you are using digital GIS software to delineate management units, an attribute 

field should be created within the polygon layer so that each MU polygon feature 

has a unique identifying number. This unique identifier should reference both the 

site number and the MU number within the site. Table 7 shows an example of this 

as the FID_MU where the first four digits are from the site name and the two digits 

after the dash specify the MU within the site. It is important to track MUs in this 

precise manner as fieldwork may call for merging or splitting MUs, resulting in 

new polygons within a site that require careful data management updates. The MU 

Figure 17—Delineated management units within Maury Island Marine Park, King County, Washington.
44

GENERAL TECHNICAL REPORT PNW-GTR-941

should also have attribute fields for its land cover designation and site, as well as 

for any important administrative or legal boundaries. This georeferenced attribute 

table will form the basis of data entry for fieldwork, and into future assessments. 

Table 7 is an illustration of what this attribute table might look like at the end of 

this process.

Again, for small-scale projects, there may be only one “site,” a single property, 

and, in that case, MU identifiers can be a simple sequential numbering system. 

Without GIS software, the MU identifying number should be written directly 

onto the hardcopy aerial photos used to draw the MU boundaries. The same table 

should be created in a digital form such as Microsoft Excel or Access. 

Table 7—A sample attribute table after type mapping and data collection

FLAT_MU

FID_MU SITE_NAME ACRE LAND_COV ASPECT SLOPE AGE_CLASS OVR1_SPC OVR1_SIZE

4601-01 Sugarloaf 

Mountain 

Forest

10.6 FOR 72 54 2 PSME 3

4601-02 Sugarloaf 

Mountain 

Forest

273.6 FOR 165 34 1 ALRU 1

4632-01 Mirrormont 

Park

4.9 FOR 215 4 3 ALRU 3

4632-02 Mirrormont 

Park

4.2 FOR 215 5 3 PSME 4

4632-03 Mirrormont 

Park

1.7 LS 259 7

4634-01 Fall City 

Natural Area

2 WAT 196 2

4634-02 Fall City 

Natural Area

3.9 NAT 194 0 1 ALRU 2
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Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Appendix 2: Necessary Data 

Aerial Imagery

Aerial photographs or satellite imagery are important sources of information for 

initial management unit (MU) delineation and mapping. There are several considerations that will help determine your best source of data: 

• If you are using geographic information system (GIS) software, it is 

important that the imagery used be geometrically corrected, or “orthorectified,” and geographically referenced so that boundaries drawn using the 

photos will correspond to the correct places and distances on the surface 

of the earth. 

• Resolution should be high enough to distinguish between different coverage types, and color makes this interpretation much easier. If tools such as 

Feature Analyst are being used to aid in the photo typing, there may also be 

resolution thresholds or requirements. 

• Photos should be taken during the summer, when vegetation from both 

deciduous and evergreen trees and shrubs are visible, as vegetation types 

will be used to define MU boundaries. In some cases, having multiple 

image sets at different times of the year will be helpful. 

For the King County pilot project, several sets of aerial imagery were used, in 

part to be able to compare conditions. These included: 

• King County 2009 natural color ortho photos in 0.5 to 1 ft resolution. 

These data displayed significant parallax along mosaic edges, which made 

typing larger areas more difficult. 

• Arc® Online imagery. Arc images can change without notice and 

there is no control over color balance, and the radiometric settings are 

more likely to have rectification errors. Overall, there are fewer parallax and mosaic issues. 

• U. S. Department of Agriculture National Aerial Imagery Program 

(NAIP) 1 m orthophotos. 

Parcels

Parcel data are important for delineating MUs. Aerial and satellite imagery can 

be used to type by natural conditions (such as vegetation cover, or surface water 

elements), but parcel boundaries are often the basis of management programs. 

Because the Forest Landscape Assessment Tool (FLAT) is a tool intended to support management decisions, MUs should be informed by ownership and management jurisdictions. In most cases, parcel data will be the source of legal boundary 

delineation. Some considerations:
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GENERAL TECHNICAL REPORT PNW-GTR-941

• Parcel data should include attributes that indicate ownership or management goals for each feature. At the very least, it is important to determine if 

a parcel is managed by the organization conducting the FLAT assessment.

• Note that assessor parcel GIS layers often display approximate boundaries. 

Locations do not always match imagery and other layers. This is usually 

adequate for the level of mapping used for FLAT. 

Other Management Boundaries

• Sometimes there may be other management program boundaries that 

should be taken into consideration when creating MUs.

• If there are existing stewardship plans in effect or additional information 

about a specific area, it may make sense to classify MUs to accommodate 

preexisting documents or delineations. 

• Management systems will often be divided into subdistricts or areas, each 

having their own programs or assigned management staff. This may also 

be important information for dividing MUs and should be considered along 

with the parcel and ownership data. 

• Legal designations that limit management actions or have additional regulatory requirements, such as riparian zones or wetlands, should also be indicated when delineating MUs.
47

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Appendix 3—Equipment List 

Traditional forestry tools and equipment are used in a Forest Landscape Assessment 

Tool (FLAT) assessment (table 8) to help field crews calibrate their visual estimates. 

Depending on the ecosystem type, management goals, and complexity of other 

FLAT projects, this list can be modified to incorporate additional or different tools. 

The most expensive piece of equipment is the global positioning system (GPS) unit. 

In lieu of a costly GPS unit, most smartphones have GPS capabilities and can be 

programmed with a background map. If a GPS unit or smartphone is unavailable 

or not desired, a FLAT assessment can be implemented with a hardcopy paper map 

and data sheet. 

Table 8—King County, Washington, equipment list

Item Considerations King County pilot project

Geographic information system (GIS) Having expensive GIS software such as 

ArcGIS® in which to track and query 

information is convenient for conducting 

FLAT but probably not necessary

ArcGIS 10.0

Free open source tools such as GrassGIS or 

even free “cloud-based” tools such as Google 

Earth® or Google Fusion Tables

High-resolution orthophotos, maps and 

handwritten tables could qualify as a GIS 

if necessary

Paper map Boundaries of each management unit should be 

overlaid and labeled on the map

Yes

Camera Photos of forest conditions are very helpful Yes

Compass Good for orientation Yes

Global positioning system (GPS) A smartphone could also be used in some cases 

instead of a GPS unit

Trimble Geo XT

Data entry tool Microsoft Office® is compatible on most 

smartphones as well

Trimble Geo XT®

with Microsoft 

Windows Mobile®

Paper data forms Good for backup in case electronic devices are 

not working

Yes

Plant identification reference books Very important that field crews are able to 

identify native and nonnative plants

Pojar and MacKinnon (2004)

FLAT Field Manual This provides a quick reference to the datacollection procedures

Yes (early draft)

Diameter tape For estimate calibration Yes

Clinometer For estimate calibration Yes

Increment Borer For estimate calibration Yes

Densiometer For estimate calibration No

FLAT = Forest Landscape Assessment Tool.
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GENERAL TECHNICAL REPORT PNW-GTR-941

Appendix 4: King County Data Attributes 

Table 9 displays the data attributes included in the King County Parks pilot project. 

Attributes were chosen that described forest characteristics considered most relevant for setting land management priorities and subsequent development of land 

management plans for forested parks and resource lands in the county.

Table 9—King County, Washington, data attributes (continued)

Data attribute Data field Explanation/criteria 

Site name SITE NAME GIS identifier

Management unit number HMU_NO GIS identifier

Date of data collection DATE

Assessors initials CREW

Land cover LANDCOV

 Forested FOR ≥25 percent forest canopy

 Natural Area NAT <25 percent forest canopy

 Open Water WAT No woody vegetation

 Hardscape HS Buildings, parking

 Landscaped LS Landscaped, mechanically maintained

Age class AGECLASS

 Category 1 1 0 to 29 years

 Category 2 2 30 to 49 years

 Category 3 3 50 to 99 years

 Category 4 4 100+ years

Overstory species OVR1_SPC Overstory species, most abundant dominant or 

codominant >20 ft

Overstory size OVR1_SIZE Overstory dbh size class

Category 1 1 0 to 5 inches dbh

Category 2 2 6 to 10 inches dbh

Category 3 3 11 to 20 inches dbh

Category 4 4 21+ inches dbh

Second overstory species OVR2_SPC Second overstory species, in order of abundance 

codominant >20 ft

Second overstory size OVR2_SIZE Overstory dbh size class, see size class chart above

Third overstory species OVR3_SPC Third overstory species, if present, in order of abundance 

codominant >20 ft

Third overstory size OVR3_SIZE Overstory dbh size class; see size class chart above

Stocking STOCKING Crown closure estimate, as viewed directly above

Category 0 0 Less than 10 percent crown closure 

Category 1 1 10 to 39 percent crown closure 

Category 2 2 40 to 69 percent crown closure

Category 3 3 70+ percent crown closure 

Management unit composition HMU_CMP
49

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Table 9—King County, Washington, data attributes (continued)

High composition H >50 percent conifer/madrone; or

≤50 percent conifer/madrone with no capacity for 

restoration (includes wetlands)

Medium composition M 1 to 50 percent conifer/madrone with capacity to support 

restoration to H; or

<25 percent native cover with capacity to restore up to 50 

percent conifer

Low composition L < 25 percent native cover with capacity for full restoration 

planting; or

No conifer/madrone with capacity for full restoration

Low vigor LOW VIGOR Conifer: live crown ≤40 percent? Y or N 

Hardwood decline: top dieback or snags ≥5 percent? Y 

or N 

Mechanical tree failure FAILURE Mechanical tree failure in ≥1 percent of MU, Y or N 

(e.g., windthrow, landslide)

Root rot ROOT ROT Root rot pockets present? Y or N

Mistletoe MISTLETOE Mistletoe present? Y or N

Bare soil BARE SOIL ≥1% bare soil present from recent disturbance, erosion, 

etc.? Y or N

Other OTHER Present in ≥1 percent of MU? Y or N (*note in 

comments*)

Regeneration species RGN1_SPC Regeneration species <20 ft height, in order of abundance

Second regeneration species RGN2_SPC Regeneration species <20 ft height HT, in order of 

abundance

Regeneration stocking class RGN_TPA

Category 1 1 0 to 49 TPA (>30 ft spacing)

Category 2 2 50-149 TPA (between 30 and 16 ft spacing)

Category 3 3 150+ TPA (<16 ft spacing)

Plantable space PLANTABLE Suitable growing space for restoration planting? Y or N

Native shrub and herb species GRD1_SPC Native shrubs and herbs, most abundant

Native shrub and herb species GRD2_SPC Second native shrubs and herbs in order of abundance

Invasive species INV1_SPC Nonnative species, most abundant

Invasive species INV2_SPC Second nonnative species in order of abundance

Invasive species INV3_SPC Third nonnative species in order of abundance

Invasive species INV4_SPC Fourth nonnative species in order of abundance

Invasive species INV5_SPC Fifth nonnative species in order of abundance

Total invasive cover INVCOV Total invasive cover

 High cover H > 50 percent

 Medium cover M 5 to 50 percent

 Low cover L <5 percent

Notes NOTES Unique site conditions and other dominant trees present
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GENERAL TECHNICAL REPORT PNW-GTR-941

Table 10—Comparative cost estimates of implementing Forest Landscape 

Assessment Tool (FLAT) in the city of Everett and in King County, Washington

Assessment attributes King County parks Everett parks

Total project area (acres) 24,724 605

Total time Three summer field seasons Two weeks

Cost in dollars:

 Staff 111,000 6,000 

 Consultant 72,000 15,000 

 Travel 7,400 200 

 Supplies/materials 3,600 200 

 Total cost 194,000 21,400 

Appendix 5—Cost Analysis

Table 10 compares the estimated costs associated with carrying out the FLAT 

assessment in two park systems, those of King County and the City of Everett, 

Washington. Staff costs include project management and fieldwork, while consultant costs include project development and phototyping, as well fieldwork during the 

first field season. For the King County Forest Landscape Assessment Tool (FLAT), 

the staff spent an estimated 2,880 hours implementing the project. The difference 

in supplies and materials costs can be explained by King County’s fieldwork startup 

costs, whereas the Everett project made use of equipment owned by consultants.
51

Forest Landscape Assessment Tool (FLAT): Rapid Assessment for Land Management

Appendix 6— Forest Landscape Assessment Tool 

(FLAT) Field Manual

This field manual was prepared by the project partners to provide a concise procedures manual for reference in the field, and to provide information for field crew 

training. Readers can cross reference the procedures described in this technical 

report with this appendix. In addition, a .pdf file of the manual can be downloaded 

at http://www.naturewithin.info/UF/FLAT_Field_Manual.pdf.
FLAT F i e l d M a n u a l

The Forest Landscape Assessment Tool

GREENCITIESRESEARCHALLIANCE Department of Natural Resources and Parks Parks and Recreation Division
Acknowledgements

The Green Cities Research Alliance was initiated by the USDA Forest Service Pacific Northwest Research Station in 2009 to build program of research about urban ecosystem in the Puget Sound region. GCRA pairs scientists with practitioners and local decision makers to co-design and implement research efforts that provide relevant and practical information. The Forest Landscape Assessment Tool was developed by the following partners: 

King County Parks and Natural ResourcesUSDA Forest Service PNW Research StationAmerican Forest Management

Forterra 

University of Washington

For more information, visit 

www.fs.fed.us/pnw/research/gcra 

Funding for this project was provided by the 2009 United States American Reinvestment and Recovery Act (ARRA) coordinated by the USDA Forest Service Pacific Northwest (PNW) Research Station.

In accordance with Federal law and U.S. Department of Agriculture policy, this institution is 

prohibited from discrimination on the basis of race, color, national origin, sex, age or disability. 

USDA is an equal opportunity provider and employer.

Contents

Project Background 2Summary of the FLAT Process 3Getting Started 4Quality Control Methods 6Forest Cover Type Mapping 7Understanding Management Unit Delineation 9Introduction to Field Procedures 10Data Collection Definitions and Procedures 12Appendix 24References 39Notes 40

Front cover photo credit: King County Parks and Natural Resources, Ring Hill Forest

First edition - December 2013
3

Summary of the FLAT Process

At its core, FLAT consists of visual estimates of ecological conditions by trained staff to produce a forest inventory. While field teams may make some measurements to calibrate their estimates, the assessment itself is generally qualitative and relies on carefully prepared estimates rather than precise measurements. FLAT is executed in three broad, sequential phases. These include: Phase 1 – Forest Cover Type MappingAerial imagery and boundary data of the lands under consideration are used to divide the area into polygons and delineate Management Units (MUs). This work is accomplished and recorded using Geographic Information System (GIS) tools. These MUs become the unit of observation and measurement for the on-site assessment.Phase 2 - Field AssessmentTrained field teams visit each MU within the project area to collect predetermined attributes. As an example, collected attributes might include land cover, non-native species in order of abundance, and tree age class distribution. Data is collected for MUs and stored (using GIS or other data management system) for all surveyed parcels.Phase 3 - Management PrioritizationThe field procedures of FLAT provide a ranking of parcel conditions, both across numerous parcels and for subunits within a large parcel. Summary data can be used to establish management priorities for each MU, and aggregated to develop priorities at larger scales, such as the park or park system-scale (as presented in the King County example later in this report). This prioritization approach is based on a matrix and flow-chart analysis (called Tree-iage) that classifies each MU using ratings of landscape quality that are easily compared across land management units. The information produced by the FLAT provides a standardized baseline of ecological data for a variety of landscape types. This information can be used to view each MU within the context of an entire land management system, as well as provide a starting point for developing a land-use or stewardship plan for particular parcels. Repeated over time, FLAT serves as an effective monitoring tool for managers to review and then adapt management priorities and actions based on changing conditions. This straightforward, systematic approach to ecological assessment applies principles of ecology and forestry to provide quality data and help determine land management priorities. The following discusses the origins and basis of FLAT. 

2

Project Background

The Forest Landscape Assessment Tool (FLAT) was developed by the Green Cities Research Alliance and coordinated by the USDA Forest Service Pacific Northwest Research Station, in partnership with King County, Forterra and the University of Washington. FLAT was built on the framework of the Tree-iage tool, initially developed in 2005 by Green Seattle Partnership staff. Similar to medical triage, the approach allows land managers to rapidly assess landscape conditions, then prioritize restoration activities. Using high, medium, or low values for both forest canopy composition and forest health threats, each Management Unit (MU) is assigned one of nine descriptive categories. The model assumes that without disturbance, natural areas would be dominated by mature, evergreen conifer trees, such as western hemlock and Douglas-fir, with a medium- to high-density canopy, mixed age-classes, and species diversity. These high-quality forest stands, lacking invasive species, represent a typical Pacific Northwest lowland forest - the reference for the tree-iage analysis.Based on input from King County Department of Natural Resources and Parks, American Forest Management (AFM, formerly International Forestry Consultants, Inc.), and Forterra, FLAT was developed to further support forest management needs. More attributes were added to refine outputs and new flowcharts created to accommodate alternative habitats. The tool was also modified to include a forest health value, presenting a new dimension to the tree-iage matrix analysis. The addition of type call information gives each MU a species-stocking-density code that can be used to stratify MU's for future management. This new tool captures information essential to developing management strategies for individual Management Units, as well as for drafting management plans at the park or forest scale. The FLAT tool was piloted during 2010-2012 field seasons at 149 sites composed of 1,457 MU’s, covering approximately 24,700 acres of King County parklands. FLAT was an important first step in developing a long term, system-wide forest stewardship program. Determining the condition and health of all forest lands will help guide King County in making critical forest management decisions. This manual was developed to record the FLAT process and protocols for future use at King County Parks, as well as for land managers interested in an innovative rapid assessment tool.
4

Getting Started 

ConsiderationsStaffWhen possible, field assessments should be conducted by two or more people both as a safety precaution and for quality control reasons. Since the FLAT assessment relies on ocular estimates of ecological conditions, it is often helpful to have another set of eyes and a companion to compare findings.SeasonFor the data collection component of the FLAT, field assessments should take place during the period when foliage is most visible and vigorous. This will allow field teams to positively identify plant species and make the best evaluation of site conditions. Tools and Materials Before leaving on a field assessment, a team should be properly equipped with required data collection tools as well as the ’11 essentials’ particularly when out in the field for a full day. All field teams should have: •	 Data entry tools

Hand-held electronic data recorder or field data sheets

•	 Navigation devices

GPS 

Map (including overlay of the MU boundaries)Compass

•	 Plant identification resources

•	 Camera•	 Tree and canopy measurement tools

Recommended for training and calibration of ocular estimates but not required:

Increment borerDiameter tapeDensitometer (Moosehorn)Clinometer

5

THE “11” ESSENTIALS

for comfort and safety in the field

1. Sun protection (sunglasses, lip balm, and sunscreen)2. Bug repellent3. Proper clothing and footwear to deal with harsh terrain

or inclemate weather such as rain gear, waterproof hiking/work boots, gaiters, and insulation like gloves, hats, and jackets.

4. First aid supplies5. Utility knife or muli-tools (e.g. Leatherman, Swiss army 

knife)

6. Food (plus an extra day’s supply)

7. Lots of Water! (plus an extra day’s supply)8. Headlamp or illumination source9. Fire (matches or lighter in waterproof container10. Emergency shelter (tent, tarp, bivy, or reflective blanket)11. Communication device like cell phone or two way radio

Photo credit: Lisa Ciecko
7

Forest Cover Type Mapping 

Forest Cover Type Mapping is the process of dividing a parcel of land into areas of similar landcover and vegetation/forest types. The property you are to assess has been “forest-typed” using aerial photography into Management Units (MUs). Ownership and Management BoundariesEach property or parcel is first delineated by property ownership and management boundary. Next, orthophotographs or other aerial imagery are used to group and delineate the land into five broad landcover classifications. These are forested, natural, open water, hardscape, and landscape. Vegetation FeaturesWith property information and landcover differences delineated, the next step is to refine forested and natural sites according to their vegetation features. Large areas with clearly different species, structural features, or age composition are placed into different Management Units. LIDAR data can also be processed to display canopy heights. This is useful in interpreting stand boundaries, especially when stereo aerial imagery is not available.NOTE: All MU boundaries are drawn directly into the GIS so that they do not need to be digitized post data collection.

Photo credit: King County Parks and Natural Resources 

Bass Lake Natural Area

6

Quality Control Methods 

Quality control consists of any procedures used to “calibrate” or review field assessments. Because of the qualitative nature of rapid assessment, it is expected that individuals will have slightly different interpretations of each attribute. The goal is to encourage similar or consistent interpretation and assessments. To test consistency of field data employ the following quality control procedures:Pre-assessment Training•	 Perform the FLAT on a known MU that has already been assessed and 

compare your findings.

•	 Choose one or more MUs and have them assessed by two separate 

teams under the supervision of a crew leader familiar with FLAT. If the teams’ attribute measures differ significantly, it suggests that some calibration may be necessary.

Daily - ongoing•	 Self check each day. Example – Do an ocular estimate of diameter-atbreast height. After writing down your answer, take the measurements 

using a dbh tape and compare your estimates. This can be done for other data variables using other calibration devices such as tree age using a tree corer.

Photo credit: Kim Frappier
8

LANDCOVER DESIGNATIONS 

MUs are assigned one of five broad land cover designations:

Forested (FOR) ≥ 25% of the area covered by forest canopy

Natural (NAT) natural vegetation that has < 25% forest 

canopy cover 

Open Water (WAT) open water without woody vegetation 

Hardscape (HS) impervious surface such as parking or buildings

Landscape (LS) landscaped or have been mechanically 

maintained within the last year. 

The Truth about Ground TruthingThe MUs are initially delineated without the benefit of ground truthing. Field teams are responsible for field verification and should adjust boundaries and land cover types if needed. Some issues that may be encountered include:•	 Landscaping under canopy •	 Changes that have occurred since the photo date, and •	 Areas being restored to a natural condition •	 Inaccuracies in interpretation due to deep shadows, parallax, and photo 

mosaic boundaries on aerial imagery

Transmission line corridors were assigned a “Natural” designation when typed in the GIS, but can be given a field designation of “Landscape” due to vegetation maintenance in those areas. In areas of low canopy and shrubs, typically seen in wet areas, MUs were assigned a “Forest” call over a “Natural” call. These areas must have more than 25% canopy cover. 

Field Technicians make the final call!

9

Understanding Management Unit Delineation 

BoundariesManagement Units were delineated based on forest composition so that heterogeneity within parks or parcels could be accounted for. MUs do not cross “administrative” boundaries, even if the cover type is identical on both sides. SizeThe minimum size for an MU is 5 acres, unless the type is surrounded by distinct non-forest types or property boundaries. Significant and distinct non-forest areas may be delineated down to 0.1 acre. It is not the intent of this assessment to map paved paths, tennis courts, or other facilities within landscaped areas. These smaller developed features are grouped with surrounding landscape or hardscape MUs. LabelsEach park or parcel has a unique FID_MU identifier. FID refers to the park’s Facility Identification number. The MU refers to the polygon number within that park. This identifier is specific to each polygon for ease of identification, sorting, and analysis of data. For example, the Duvall Park FID is 2598, and there are five MU’s in that park. The FID_MU identifiers are 2598-01, 2598-02, 2598-03, 2598-04, and 2598-05.

Image credit: King County Parks and Natural Resources 

Maury Island MUs
11

Check for completenessSometimes MUs will be strangely shaped so that the terrain or trails will lead teams traveling in and out of different MUs. Data may be entered for each MU in order of discovery but should be checked for completeness before leaving the MU. When to leave the road or trail systemEach attribute determination will apply to the entire MU, thus it is important that enough of the MU is seen by the field team. Sometimes this will require leaving trails and traveling on challenging terrain. In other MUs the view from a trail may be sufficient to make a judgment on most, if not all, of the attributes. Teams should be careful to recognize, and average into the MU estimate, any edge effect along trails where vegetation may have been influenced by trail activity or disturbance. A good question to ask before bushwhacking through a site may be “How much greater understanding of the MU will I gain from the time it will take me to do this?” 

Preparing the GPS unit or data forms 

for field assessments 

•	 GIS staff or project leads should pre-load background images of 

vegetation overlayed with the MU maps in accordance with the hardware and software in use. 

•	 Electronic data collection software or spreadsheet should be preloaded with the initial MU Landcover codes. If using a GPS with the 

background loaded, the image file name should correspond to the map number. 

•	 Information on the MU landcover designation and boundaries must 

be included either on the map, within the data entry system, or on field forms. 

Field teams will then be able to ground truth the landcover designation as well as know when they have entered or exited the MU.Since different GPS units have very different instructions, this process will be device specific.

10

Introduction to Field Procedures 

The field procedures entail ocular estimates of predetermined forest qualities. Field teams of one or more people visit each MU, walk through it, and record an average attribute value for each of the variables listed in this field manual or data entry system. The field manual contains definitions for each variable to be assessed. As field teams walk through the MU, they should be observant and keep a mental catalog of what they see. Things to keep in mind while conducting the assessment:Provide attribute estimates for the entire MUThere may be patches of certain species or conditions, but a best effort should be made to estimate for the entire MU. This is why many of the attribute values will be entered upon leaving the MU.Measurement tools can be used to calibrate your estimatesSome attributes such as DBH, crown closure, age, and regeneration trees (trees less than 20ft in height) can be measured with tools in the field. It may be helpful to do this once or twice on an MU to calibrate estimations. However, excessive measurement taking in the field will slow down the rapid assessment process. Do the MU boundaries need to be altered?To split or lump?The field team should ask themselves whether or not the boundaries of the MUs should be altered, and if so, how. There are often patches of different vegetation within an MU which is expected, but pervasive differences in composition or age classes for large areas of the MU may necessitate redrawing boundaries. This may involve combining or lumping smaller MUs into one larger one or splitting an MU into multiple, smaller MUs.Additional Notes and ObservationsField teams may find that there is something important of note in the MU that doesn’t necessarily fit into any of the attribute categories. For this reason it is good to include additional information in the “notes” field of the data entry tool or data collection sheet. Examples of additional notes include: any new tree or plant species, prevalence of a 4th overstory tree, unusual site or soil conditions, recent disturbances, and any recent or ongoing land management activities.
12

Data Collection Definitions and Procedures 

LANDCOVER

Landcover is initially assigned in the office by the GIS professional and needs to be field verified.There are five landcover categories used for the King County FLAT assessment: Forested, Natural, Water, Hardscape, and Landscape. Using your field maps with MU boundaries, verify and record the MUs landcover classification.

Verify and record landcover using the following 

categories

ASPECT AND SLOPE

Aspect and slope are processed in the office through available digital elevation model data in GIS for each MU. This is especially useful for larger MU’s where aspect and slope vary throughout the unit. If a project calls for field determination then proceed as follows:

•	 Use a compass to determine the predominant direction of the slope on 

the site. 

•	 Aspect is often described as the direction in which water flows off a site•	 Code as follows: N, NE, E, SE, S, SW, W, NW or flat. •	 Detailed instructions on use of a compass can be found in Appendix E.

Forested (FOR) ≥ 25% of the area covered by forest canopyNatural (NAT) natural vegetation that has < 25% forest canopy coverOpen Water (WAT) open water without woody vegetation

Hardscape (HS) impervious surface such as parking or buildingsLandscape (LS) landscaped or have been mechanically maintained 

within the last year. 

13

OVERSTORYOverstory refers to trees whose foliage forms the uppermost crown cover or canopy of a forest stand. The forest assessment team will characterize dominant or co-dominant overstory tree species in order of abundance found in the MU including the size class of each species. Age class information will only be collected for the dominant overstory species. The first, second, and third order of abundance should be determined by the two dimensional area that the species would occupy if looking at the aerial image. Field teams can use the aerial imagery to help identify the presence in the MU.

Collect the following overstory data

Overstory Species 1: The most abundant dominant or co-dominant 

overstory species greater than 20 feet in height. 

•	 Species code (Note: if the tree is not listed in the plant list in Appendix B, 

provide the 4 digit species code, common, and scientific names in the notes section)

•	 Age Class•	 Size ClassOverstory Species	2	and 3: The second and third most abundant codominant overstory species greater than 20 feet in height. 

•	 Species code (Note: if the tree is not listed in the plant list in Appendix B, 

provide the 4 digit species code, common, and scientific names in the notes 

section)

•	 Size Class for each species.

Dominant versus Co-dominant

Dominance is a relative designation of tree crowns and is also referred to as crown class. Dominant trees are those with crowns above the general level of the canopy. Co-dominance refers to trees whose crowns form the general level of the canopy. 
15

SIZE CLASS

Second, record the size class or average diameter at breast height (DBH) for each dominant or co-dominant tree listed above. DBH is the diameter of a tree at 4.5 feet above the ground on the uphill side of the tree. Calibration trees can be chosen from average defect-free trees in the MU, keeping in mind the target classes listed in the table below. For more information on measuring tree diameter, see Appendix C.

Record Size Class using the following categories

Size Class Code DBH in inches

1 0 -5”2 6 - 10”3 11 - 20”4 21”+

Photo credit: Lisa Ciecko

14

AGE CLASS

Age class is an estimate of the age range of the dominant overstory trees (see below). Age classes provide managers with an overview of which MUs have the opportunity for stand improvement operations, harvest, or preservation opportunities. It is important to calibrate estimates of tree age using an increment borer. This should be done at the beginning of the assessment process to calibrate your ocular estimates and then spot check yourself throughout the field assessment. See Appendix F for instructions on how to use an increment borer. External indicators of the associated age range can be used to estimate age class. These include tree size and growth characteristics (e.g. branch whorls on Douglas fir), bark appearance, nearby cut stumps or fallen trees. However, soil and site quality determine actual growth rates, so this should be taken into consideration when assessing the age class of the stand or management unit.

Record the average Age Class across the MU 

using the following categories

Code Age Range 

(years)

Category Description

1 0-29 Pre-merchantable

Stand of commercial species that have not yet grown large enough to be saleable

2 30-49 Submerchantable Stand just coming into maximum value, but not ready for harvest. 

3 50-99 Merchantable

Stand has grown large enough to be saleable and is ready for harvest and replanting

4 100 + Mature

Potential for future old growth characteristics. May aim to improve health and retain mature trees.

Test Yourself! 

Remember that external indicators do not always provide sufficient information to determine the age class of a tree, so age coring similar trees can be done to calibrate your estimates. Age by eye, then core to confirm! How close was your estimate?
16

STOCKING

Field teams will be using measures of canopy cover to estimate stocking in the MUs. Canopy cover is a vertical measure of the canopy (dominant and co-dominant tree crowns) as would be seen on an aerial photograph or looking up vertically from one point on the ground. Measures of cover assess the presence or absence of canopy vertically above a sample of points across a definitive area of forest and help assess forest structure (Jennings et al, 1999). To measure canopy cover, stand in one location, and assess the canopy directly overhead (see Figure 1). Field staff can also compare on the ground measurements with the orthophoto of the site to aid in estimating cover. In order to capture the variability across large MUs, field crews must take this reading at different points across the MU and then average those conditions. To calibrate ocular estimates, canopy cover can be measured using instruments such as a densitometer, sometimes called a “moose horn.” The densitometer provides a point measure of canopy cover. See Appendix D for additional information about densitometers.

Figure 1. Canopy cover measured from one point on 

the ground. Image credit: Korhonen et al, 2006

17

 Record stocking using the following categories

Stocking Code Canopy Cover Range in Percent

0 Less than 10% canopy cover 

1 10 - 39% canopy cover 2 40 - 69% canopy cover3 Greater than 70% canopy cover 

What is stocking?

•	 Stocking (related to stand density) is a measure of the 

crowding of trees in a stand or the area occupied by trees relative to an optimum or desired level of density that supports growth for timber management. 

•	 Stocking can be expressed quantitatively as the basal area 

or volume of trees per acre. 

•	 The FLAT measures stocking in relative terms using canopy 

cover estimates as an indication of how crowded tree 

crowns are within a stand. 

•	 Often stocking is described in relative terms, such as 

partially stocked, adequately stocked, or overstocked.

MU Tree Canopy Composition Each MU is assigned a value (High, Medium, or Low) for tree canopy composition, based on percent native tree canopy cover, and percent of canopy cover made up by evergreens and/or madrones. Tree Canopy Composition is one of the variables (along with invasive threat cover) used to determine the Tree-iage Category of each MU. 

 Record Tree Canopy Composition as High, Medium, or Low based on the flow chart on page 18
19

Overstory Forest Health Threat Indicators Forest health threat indicators refer to attributes indicative of poor tree health and canopy decline. These attributes negatively affect the longterm sustainability of the forest canopy and directly affect management strategies. The indicators must be present in 1% or more of the MU to trigger recording a “Yes.” If not observed in the walkthrough it will be recorded as “No.”

Record the following Overstory Forest Health Threat Indicators

Low vigorDetermine low vigor by assessing the tree’s live crown ratio. Crown ratio is a measure of the length of a tree’s live crown relative to total tree height. Record a “yes” call if conifers have a live crown of 40% or less of the total height of that tree. Hardwood decline in the form of snags or top dieback of 5% or greater also receive a “Yes” call. Failure Mechanical tree failure refers to the breakage of tree trunks and branches and the uprooting of trees caused by factors such as landslides, ice and snow damage, high winds, old age, parasites or disease. Record “Yes” if found in 1% or more of the MU. Root RotA fungal root infection that attacks the live and dead roots of some conifers. Douglas-fir is highly susceptible along with true firs such as grand fir. Western redcedar is resistant to infection and hardwood trees are immune. For more information on root rot disease go to: http://ext.nrs.wsu.edu/forestryext/foresthealth/notes/laminatedrootrot.htmRoot rot symptoms to look for:•	 Pockets of standing dead trees•	 A clearing with many trees fallen to the ground•	 Stunted root balls on fallen trees•	 A yellow and thinning tree crown

(Overstory forest health threat indicators continued on pg 20)

What is 1% of an MU?

For a 10 acre parcel, 1% 

= 1/10 of an acre = 4,356 square feet= 66 x 66 feet

18

Canopy Composition Flow Chart
20

Mistletoe Mistletoe is a parasitic plant that causes growth reduction and deformities on trees. Infected trees will produce “witches brooms” which are abnormal growths of small twigs. Symptoms of infections on branches include a spindle-shaped, swollen appearance. On trunks, infections may cause the tree to swell to twice its original diameter. The most common species that infects conifers in Puget Sound forests is Dwarf mistletoe (Arceuthobium 

sp.). For more information 

go to: http://ext.nrs.wsu.edu/forestryext/foresthealth/notes/dwarfmistletoe.Field staff do not need to identify specific species of mistletoe, but simply note its presence or absence. If mistletoe is observed, a “Yes” call is given. Also, include which tree species have been infected in the notes field of your data collection sheet or GPS unit. 

Bare Soil1% or more of the MU is devoid of vegetation and has exposed bare soil due to unstable soils and/or recent disturbance, landslide etc.Other If “Yes” is recorded, details must be provided in the “notes” field of the data sheet or GPS unit. Examples of additional forest health concerns include but are not limited to:•	 Bear damage•	 Beetle damage•	 Brown cubical butt rot•	 Large gap in center of MU – potential root rot damage

Photo credit: Jesse Saunders

21

Regeneration SpeciesRegeneration species refer to overstory canopy species under 20 feet tall. These are the trees that will become dominant and co-dominant species as the forest matures. 

Record the two most abundant overstory tree species under 20 feet tall

Regeneration Species Stocking ClassRecord the combined estimated stocking class of the two most abundant regeneration tree species under 20 feet in the MU. This is measured in trees per acre (TPA). 

Record stocking class using the following codes:

Stocking Code Trees per acre Reference

1 0-49 TPA > 30 ft. x 30 ft.2 50-149 TPA Between 30 ft. and 16 ft. spacing3 150 + TPA < 16 ft. x 16 ft.

Is that a SHRUB or REGENERATION TREE?

Refer to the FLAT plant guide in Appendix B if you have questions 

about whether a specific species is considered a tree (and therefore 

considered part of the overstory) or a shrub. 

Example:

Willow species are considered trees 

whereas Vine maple is a shrub.
23

Invasive Species Each MU is assigned a value (High, Medium, or Low) for invasive cover threat, based on percent invasive plant cover. Remember that this is a qualitative assessment to reflect percent cover across the MU. Quantitative methods using a transect line or quadrat are not required. Invasive plant cover is one of the variables (along with tree canopy cover) used to determine the Tree-iage Category of each MU. If the invasive cover threat observation 

is uncertain and you are unsure whether the MU should be coded as Medium or High, be conservative and code the MU as High. 

Assign each MU one of the following invasive cover- threat values

HIGH MUs with more than 50% invasive species cover

MEDIUM MUs with between 5% and 50% invasive species cover

LOW MUs with less than 5% invasive species 

cover

Record the species code of all invasive species present in order of abundance

What if I observe a species not included in the FLAT plant list?

Record all non-native invasive species present in the MU. These include herbaceous species as well as invasive tree species such as English holly. These must be recorded in order of abundance. See Appendix B for a list of the most common invasive species found in Puget Sound lowland forests.

Enter the 4-digit species code and record the common and scientific 

name in the notes section. This applies to overstory trees and all native 

ground and non-native invasive species.

Photo credit: Forterra

22

PlantableThis attribute provides information regarding presence of available growing space to promote tree seedling restoration to help shade out potential invasive species. Plantable areas may be observed as bare soil following current or future invasive removal, or large openings in forest cover resulting from extensive root rot or wind failures.

Record whether an MU is “plantable” using thefollowing criteria

A “yes” call indicates a priority area for planting that is exposed to full sun 

and may have bare soil where invasive species could establish or expand if the area is not planted to native species in the short term.

A “no” call indicates that the majority of growing space is already occupied 

by native species and does not warrant immediate action.

Ground SpeciesGround species are native herbs and shrubs in the understory of the forest. These include but are not limited to all understory shrubs and herbs found in the plant lists found in Appendix B. Note that ground species do not include saplings or regeneration trees of canopy species. Use the four letter species code to represent the species. Example: Polystichum munitum (POMU)

Record the two most abundant ground cover species present in the MU

Ground species must be listed in order of abundance:•	 Ground species 1 – Primary or most abundant•	 Ground species 2 – Secondary or second most abundant. 

Photo credit: Forterra
24

Appendix

Appendix A: Tree-iage Matrix Analysis Identifying and prioritizing areas in need of management is a key output of the FLAT process. Attributes assessed in the field are used to produce a qualitative value for two axes of a matrix. In King County, forest composition (y-axis) and forest threats (x-axis) were used. The matrix combines the attribute information to produce a classification value for each MU. In the figure at the right, values 1-3 represent MUs with a tree composition that has high ecological value, and so are important to protect and maintain. Values 2 and 3 also represent the presence of a forest health threat and could be prioritized for restoration or maintenance. On the other end of the spectrum, a MU with a value of 9 has a high threat presence and a lower tree composition, and therefore may not be not be as high a priority for management actions. How the results of the matrix analysis are used in subsequent land management decisions is up to each FLAT user. FLAT provides an ecological input for land management decision-making. The combination of field data collection, flow chart processing, and classification of MUs using the matrix can be used to prioritize future management actions and monitoring. 

25

Appendix B: Common Plant Species ListTREES	

 Code Species Scientific Name 

 ACMA Bigleaf maple Acer macrophyllum PREM Bitter cherry Prunus emarginata POBA Black cottonwood Populus balsamifera  CRDO Black hawthorn Crataegus douglasii  RHPU Cascara Rhamnus purshiana  PSME Douglas fir Pseudotsuga menziesii

ACGL Douglas maple Acer glabrum

 QUGA Garry oak Quercus garryana ABGR Grand fir Abies grandis SAHO Hooker’s willow Salix hookeriana

ABPR Noble fir Abies procera

 FRLA Oregon ash Fraxinus latifolia MAFU Pacific crabapple Malus fusca

CONU Pacific dogwood Cornus nuttallii

 ARME Pacific madrone Arbutus menziesii

ABAM Pacific silver fir Abies amabalis 

 TABR Pacific yew Taxus brevifolia SALU Pacific willow Salix lucida BEPA Paper birch Betula papyifera POTR Quaking aspen Populus tremuloides ALRU Red alder Alnus rubra PICO Shore pine Pinus contorta

ALCR Sitka alder Alnus crispa ssp. sinuata

 PISI Sitka spruce Picea sitchensis SASI Sitka willow Salix sitchensis ACCI Vine maple Acer circinatum TSHE Western hemlock Tsuga heterophylla THPL Western redcedar Thuja plicata

PIMO Western white pine Pinus monticolaQUAL White oak Quercus alba 
27

HERBACEOUS

Code Species Scientific Name 

 FRCH Beach strawberry Fragaria chiloensis

DIFO Bleeding heart Dicentra formosa SIID Blue-eyed grass Sisyrinchium idahoensis

 PTAQ Bracken fern Pteridium aquilinum

COUN Bunchberry Cornus unalaschkensisTRIF Clover species Trifolium sp.EQAR Common horsetail Equisetum arvenseHELA Cow parsnip Heracleum lanatumBLSP Deer fern Blechnum spicantCIDO Douglas water-hemlock Cicuta douglasiiMADI False lily-of-the valley Maianthemum dilatatumSMRA False Solomon’s seal Smilacina racemosaEPAU Fireweed Epilobium augustifolium

 TITR Foamflower Tiarella trifoliata

DIPU Foxglove Digitalis purpurea

 TEGR Fringecup Tellima grandiflora

ALAC Hooker’s onion Allium acuminatum FAHE Inside-out flower Vancouveria hexandra

 ARUV Kinnikinnick Arctostaphylos uva-ursi ATFI Lady fern Athyrium filix-femina POGL Licorice fern Polypodium glycyrrhiza ADPE Maidenhair fern Adiantum pedatum

CLPE Miners Lettuce Claytonia perfoliataGYDR Oak fern Gymnocarpium dryopteris

 IRTE Oregon iris Iris tenax

OXOR Oxalis; wood sorrel Oxalis oregana

 DIFO Pacific bleeding heart Dicentra formosa

HYTE Pacific waterleaf Hydrophyllum tenuipes

 TODI Pacific poison-oak Toxicodendron diversilobum PEFR Palmate coltsfoot Petasites palmatus 

COMA Poison hemlock Conium maculatumSAXI Saxifrage species Saxifraga sp.

 ANMA Pearly everlasting Anaphalis margaritacea TOME Piggy-back plant Tolmiea menziesii

GOOB Rattlesnake plantain Goodyera oblongifolia

 COSC Scouler’s corydalis Corydalis scouleri

LYAM Skunk cabbage Lysichiton americanumTRLA Starflower Trientalis latifoliaVIGL Stream violet Viola glabellaURDI Stinging nettle Urtica dioica

 POMU Sword fern Polystichum munitum

LIBO Twinflower Lineae borealisACTR Vanilla leaf Achlys triphylla 26

SHRUBS	

Code Species Scientific Name 

ROGY Baldhip rose Rosa gymnocarpa

 COCO Beaked hazelnut Corylus cornuta 

RULE Blackcap Rubus leucodermisRILA Black gooseberry Ribes lacustreVAME Black huckleberry Vaccinium membranaceumSACA Blue elderberry Sambucus caeruleaKAMI Bog laurel Kalmia microphyllaANPO Bog rosemary Andromeda polifolia

 OPHO Devil’s club Oplopanax horridus MANE Dull Oregon grape Mahonia nervosa VAOV Evergreen huckleberry Vaccinium ovatum MEFE False azalea Menziesia ferruginea ARDI Goat’s beard Aruncus dioicus ARCO Hairy manzanita Arctostaphylos columbiana SPDO Hardhack; spiraea Spiraea douglasii VIED Highbush cranberry Viburnum edule  OECE Indian plum Oemlaria cerasiformis

LEGR Labrador tea Ledum groenlandicum

 PHLE Mock orange Philadelphus lewisii RONU Nootka rose Rosa nutkana HODI Oceanspray Holodiscus discolor PYMY Oregon boxwood Pachistima myrsinites PHCA Pacific ninebark Physocarpus capitatus RHMA Pacific rhododendron Rhododendron macrophyllum MYCA Pacific wax myrtle Myrica californica SARA Red elderberry Sambucus racemosa VAPA Red huckleberry Vaccinium parvifolium

CESA Red stem ceonothus Ceanothus sanguineum

 COSE Red osier dogwood Cornus sericea RISA Red-flowering currant Ribes sanguineum GASH Salal Gaultheria shallon RUSP Salmonberry Rubus spectabilis AMAL Serviceberry Amelanchier alnifolia SOSI Sitka mountain ash Sorbus sitchensis SYAL Snowberry Symphoricarpos albus RIBR Stink currant Ribes bracteosum

ROPI Swamp rose Rosa pisocarpaMYGA Sweet gale Myrica gale 

 MAAQ Tall Oregon grape Mahonia aquifolium  RUPA Thimbleberry Rubus parviflorus LOIN Twinberry Lonicera involucrata

SPBE White spirea Spirea betulifolia var.lucida
28

HERBACEOUS CONT.	

Code Species Scientific Name 

AQFO Western columbine Aquilegia formosa

 TROV Western trillium Trillium ovatum

ASCA Wild ginger Asorum caudatumFRVE Wild strawberry Fragaria virginiana

 FRVE Woodland strawberry Fragaria vesca ACMI Yarrow Achillea millefolium MIGU Yellow monkey-flower Mimulus guttatusGRASS-LIKE GRASS Native grasses SCAM American bulrush Scirpus americanus TYLA Cattail Typha latifolia JUEN Dagger-leaved rush Juncus ensifolius CADE Dewey’s sedge Carex deweyana

ELMO Dunegrass Elymus mollis

 SCAC Hardstem bullrush Scirpus acutus 

FEID Idaho fescue Festuca idahoensisCALY Lyngbye’s sedge Carex lyngbyei

 CAOB Slough sedge Carex obnupta SCMI Small-fruited bullrush Scirpus microcarpus

CAPA Thick headed sedge Carex pachystachyaDECE Tufted hairgrass Deschampsia cespitosaWEFE Western fescue Festuca occidentalis

VINES

LOHI Hairy honesuckle Lonicera hispidula 

 LOCI Orange honeysuckle Lonicera ciliosa RUUR Trailing blackberry Rubus ursinus

29

NON-NATIVE		HERBS	AND	SHRUBS	Code Species Scientific Name Weed Class SODU Bittersweet nightshade Solanum dulcamara Weed of concern **POBO Bohemian knotweed Polygonum x bohemicum Non-regulated ***CIVU Bull thistle Cirsium vulgare Non-regulatedBUDA Butterfly bush Buddleia davidii Non-regulated

CIAR Canada thistle Cirsium arvense Non-regulatedCLVI Clematis; Old man’s beard Clematis vitalba Non-regulated VIMI Common periwinkle Vinca minor NoneTAVU Common tansy Tanacetum vulgare Non-regulated DIFU Common teasel Dipsacus fullonum Non-regulatedRARE Creeping buttercup Ranunculus repens Weed of concernHEHE English ivy Hedera helix Non-regulatedMYSP Eurasian watermilfoil Myriophyllum spicatum Non-regulatedRULA Evergreen blackberry Rubus laciniatus Non-regulated

LYVU Garden loosestrife Lysimachia vulgaris Class B*ALPE Garlic mustard Alliaria petiolata Class A*HEMA Giant hogweed Heracleum mantegazzianum Class AHIER Hawkweeds Hieracium sp. Class BGERO Herb Robert Geranium robertianum Non-regulatedRUAR Himalayan blackberry Rubus armeniacus Non-regulated

PLCU Japanese knotweed Polygonum cuspidatum Non-regulated CASE Morning Glory; Hedge/Field bindweed Calystegia sepium Weed of concernLEVU Oxeye daisy Leucanthemum vulgare Non-regulatedCOMA Poison hemlock Conium maculatum Non-regulatedIMGL Policeman’s helmit Impatiens glandulifera Class BLYSA Purple loosestrife Lythrum salicaria Class BPHAR Reed-canary grass Phalaris arundinacea Non-regulatedCYSC Scotch broom Cytisus scoparius Non-regulatedPOHY Smartweed; water-pepper Polygonum hydropiperoides NoneSPAL Smooth cordgrass Spartina alterniflora Class ACEST Spotted knapweed Centaurea stoebe Class BPORE Sulfur cinquefoil Potentilla recta Class BSEJA Tansy ragwort Senecio jacobaea Class BLAGA Yellow archangel Lamium galeobdolon Non-regulatedIRPS Yellow flag iris Iris pseudacorus Non-regulated* Washington State Weed Class A and B - control required in King County** Weed of concern: applies to King County only; No state classification; control recommended but not required in King County*** Non-regulated Class B and C noxious weeds; control recommended but not required in King County
31 31

Appendix C: How To Measure Tree Diameter-at-Breast HeightThe standard protocol for measuring diameter at breast height is as follows:

•	 Using a diameter tape, measure the diameter at breast height to the 

nearest inches.

•	 Breast height is considered to be 4.5 feet above the ground on the 

uphill side of the tree.

•	 For trees with swellings, bumps, depressions, and branches at DBH, 

measure diameter above the irregularity where the trunk is no longer affected.

Image credit: www.woodlandstewardship.org

Image credit: www.woodlandstewardship.org 

30

NON-NATIVE TREES Code Species Scientific Name Weed Class MALUS Apple Malus sp. NoneCRMO Common hawthorn Crataegus douglasii Weed of concernCUPP Cypress Cupressus sp. NoneROPS Black locust Robinia pseudoacacia L. None PRLA English laurel Prunus laurocerasus Weed of concernILAQ English holly Ilex aquifolium Weed of concernSOAU European mountain ash Sorbus aucuparia Weed of concernPONI Lombardy poplar Populus nigra L. NoneACRU Red maple Acer rubrum NoneAIAL Tree of heaven Ailanthus altissima Non-regulated PRUN Wild plum Prunus sp. NoneORNAM Other hardwood ornamentals None* Washington State Weed Class A and B - control required in King County** Weed of concern: applies to King County only; No state classification; control recommended but not required in King County*** Non-regulated Class B and C noxious weeds; control recommended but not required in King County
32

Appendix D: How To Use A Densitometer (Moosehorn)Densitometers are small sighting instruments with crosshairs and a bubble level that allows the observer to determine whether canopy is present directly overhead. Since the densitometer measures canopy cover (presence or absence) at a single point, multiple sample points must be measured to obtain a canopy cover estimate. Usually, sample points are spaced along a transect or arranged in a grid pattern to obtain an estimate for a specified area. Note, because FLAT is a rapid assessment and data is not collected quantitatively, field staff should only use a densitometer (moose horn) as a quality control measure or training exercise to calibrate estimates. 

For additional information go to: http://www.forestry-suppliers.com/product_

pages/View_Catalog_Page.asp?mi=65121&title=GRS+Densitometer

Image credit: 

www.forestry-suppliers.com

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Appendix E: How To Use A CompassImportant VocabularyMagnetic NorthIt is important to know that magnetic north is always moving and differs at different locations on the earth. Compass needles always point toward magnetic north.True NorthTrue north is the geographic north. It does not move, it is the fixed location on the earth where the North Pole is located.DeclinationThis refers to the difference in degrees between magnetic north and true north. Here in Seattle in 2012 the declination is about 17 degrees East. This can change over time and location. Many compasses, including those provided in this monitoring program, are adjusted for declination. If using your own compass, it is recommended to have a compass that allows you to adjust for the declination so your reading is accurate and no math is needed.Anatomy of a Compass

Image credit: www.offroad-ed.com
35

Appendix F: Use of Increment Borers to Determine Tree AgeTree cores can be used to determine the average age of a forest stand or find an exact age of a single tree. An increment borer is the least invasive method used to count tree rings. This involves taking a small (0.2 inch diameter) straw-like sample from the bark to the pith of a tree. Though the hole is small, it can still introduce disease into the trunk. 1. Assemble the increment borer by 

attaching the bit to the handle; set the extractor aside. 

2. Drill in toward the center of the tree at breast height (4 1⁄2 ft.). 3. When you have gone far enough to reach the center of the tree, insert 

the extractor and reverse the bit one full turn. 

4. Slowly remove the extractor. If the increment core does not come out the 

first time, try again. 

5. Once the core is out, set it and the extractor aside. 6. Immediately remove the increment borer from the tree, before it 

becomes stuck. 

7. Once the increment borer is back out of the tree, count the rings on the 

increment core to determine breast height age. Add several years to estimate total age.

For the detailed explanation on increment borer use, go to: 

http://forestandrange.org/Virtual%20Cruiser%20Vest/ 

lessons/lesson_06/Lesson_6_PDF.pdf

Photo credit: www.redorbit.com

Photo credit: www.forestry-suppliers.com

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How to determine cardinal directions1. Standing at plot center, hold 

your compass in your hand so that the baseplate is level and the lid is open at about 60 degrees. Hold it out in front of you with arm extended half way and the compass at eye level. You will be looking into the mirror, not directly at the dial.

2. Turn the compass dial until 

the direction you want to go is located at the top of the dial. For example, to go east, “E” would be located at the top of the dial.

3. Once you have the direction of travel determined, rotate your body until 

the red orienting arrow on the compass housing (outline of an arrow) lines up with the magnetic needle (the floating red magnetic arrow). 

4. Use the line of sight (triangular notch in top of compass lid) as your point 

of direction. Have your monitoring team member stand at the edge of the plot and guide them left/right to align with your cardinal direction reading. Once in the right position, hang flagging to mark the plot edge at the cardinal direction. Repeat steps for each of the cardinal directions.

How to take a bearing1. Hold your compass in your hand so that the baseplate is level and the lid 

is open at about 60 degrees Hold it out in front of you with arm extended half way and the compass at eye level. You will be looking into the mirror, not directly at the dial.

2. Turn your body until the desired target (e.g. a Douglas fir) is in sight. Use 

the triangular notch in the lid of the compass as your center of sight, aiming it at the center of the tree.

3. Make sure that the sighting line in the mirror runs through the middle of 

the capsule view.

4. Rotate the compass dial until the red orienting arrow on the compass 

housing (outline of an arrow) lines up with the magnetic needle (the floating red magnetic arrow). 

5. You can read the bearing in degrees. Read from the top of the compass 

(where the compass and mirrored lid meet), there is a small indicator line (direction of travel arrow). Note: do not give cardinal directions; give exact degrees (ex. 76 degrees).
36

Data Attribute Data Field Explanation 

Site Name SITE NAME GIS identifier

Management Unit Number MU_NO GIS identifier

Date of data collection DATE

Assessors initials CREWLandcover LANDCOV

Forested FOR ≥ 25% forest canopy

Natural Area NAT < 25% forest canopy

Open Water WAT No woody vegetation

Hardscape HS Buildings, parking

Lansdscaped LS Landscaped, mechanically maintained

Age Class AGECLASS

category 1 1 0-29 yearscategory 2 2 30-49 yearscategory 3 3 50-99 yearscategory 4 4 100 + years

Overstory Species OVR1_SPC

Overstory species, most abundant dominant or codominant >20ft)

Overstory Size OVR1_SIZE Overstory DBH size class

category 1 1 0 -5" DBHcategory 2 2 6 - 10" DBHcategory 3 3 11 - 20" DBHcategory 4 4 21"+ DBH

Second Overstory Species OVR2_SPC 2nd overstory species, in order of abundance codominant > 20ft

Appendix G: FLAT Definitions At-A-Glance

37

Data Attribute Data Field Explanation

Second Overstory Size OVR2_SIZE Overstory DBH size class, see size class chart above

Third Overstory Species OVR3_SPC

3rd overstory species, if present, in order of abundance codominant > 20ft

Third Overstory Size OVR3_SIZE Overstory DBH size class, see size class chart aboveStocking STOCKING Canopy cover estimate, as viewed directly above

category 0 0 Less than 10% canopy covercategory 1 1 10 - 39% canopy cover category 2 2 40 - 69% canopy covercategory 3 3 70% + canopy cover

Management Unit Composition MU_CMP

High Composition H

> 50% conifer/madrone  OR ≤50% conifer/madrone with no capacity for restoration (includes wetlands)

Medium Composition M

1-50% conifer/madrone with capacity to support restoration to H  OR<25% native cover with capacity to restore up to 50% conifer

Low Composition L

< 25% native cover with capacity for full restoration planting OR No conifer/madrone with capacity for full restoration
39

Data Attribute Data Field Explanation

Native Shrubs and Herbs Species GRD1_SPC Native shrubs and herbs, most abundant

Native Shrubs and Herbs Species GRD2_SPC Second native shrubs and herbs in order of abundance

Invasive Species INV1_SPC Non-native species, most abun- dantInvasive Species INV2_SPC Second non-native species in order of abundanceInvasive Species INV3_SPC Third non-native species in order of abundanceInvasive Species INV4_SPC Fourth non-native species in order of abundance

Invasive Species INV5_SPC Fifth non-native species in order of abundance

Total Invasive Cover INVCOV Total Invasive Cover

High cover H > 50%

Medium cover M 5% - 50%

Low cover L <5%

Notes NOTES Unique site conditions and other dominant trees present.

38

Data Attribute Data Field Explanation

Low Vigor LOW VIGOR

Conifer: Live Crown ≤40%, Y or N; Hardwood decline: Top Dieback or Snags ≥ 5%, Y or N 

Mechanical Tree Failure FAILURE

Mechanical Tree Failure in ≥ 1% of MU , Y or N) ex.: windthrow, landslide

Root Rot ROOT ROT Root Rot Pockets present, 

Y or N

Mistletoe MISTLETOE Mistletoe present, Y or N

Bare Soil BARE SOIL

≥1% Bare Soil present from recent disturbance, erosion, etc. Y or N

Other OTHER

Present in ≥ 1% of MU, Y or N *Note in comments required

Regeneration Species RGN1_SPC Regeneration species <20FT HT, in order of abundance

Second Regeneration Species RGN2_SPC Regeneration species <20FT HT, in order of abundance

Regeneration Stocking

Class RGN_TPA

category 1 1 0-49 TPA (> 30 ft spacing)category 2 2 50-149 TPA (between 30 and 16 ft spacing)category 3 3 150+ TPA (< 16 ft spacing)

Plantable Space PLANTABLE Suitable growing space for res- toration planting? Y or N
40

References

Arney, J.D., K.S. Milner, and B.L. Kleinhenz. 2008. Biometrics of Forest Inventory, Forest Growth, and Forest Planning. Technical Report No. 12. Forest Biometrics Research Institute.Jennings S.B., N.D. Brown, and D. Sheil. 1999. “Assessing forest canopies and understory illumination: canopy closure, canopy cover and other measures.” Forestry, Vol 72, No.1 pp 59-73.Korhonen, L., K.T. Korhonen, M. Rautiainen, and P. Stenberg. 2006. “Estimation of forest canopy cover: A comparison of field measurement techniques.” Silva Fennica 40(4): 577–588.Wolf, K. et al. 2013 [In Press]. Forest Landscape Assessment Tool General Technical Report. USDA Forest Service PNW Research Station. Monitoring Data Collection Methods. 2013. Developed by the Green City Partnerships for the Regional Standardized Monitoring Project. Ben Meadows. URL: www.benmeadows.comThe National Learning Center for Private Forest and Range Landowners. URL: www.forestandrange.org/Virtual%20Cruiser%20Vest/lessons/lesson_06/Lesson_6_PDF.pdfWoodland Stewardship Online Resource. 2011. University of Minnesota. URL: www.woodlandstewardship.org

Notes

Pacific Northwest Research Station

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