Crown-Condition 

Classification: A Guide 

to Data Collection and 

Analysis

Michael E. Schomaker,

Stanley J. Zarnoch, William A. Bechtold,

David J. Latelle, William G. Burkman,

and Susan M. Cox

United States

Department of

Agriculture

Forest Service

Southern 

Research Station

General Technical

Report SRS–102
Michael E. Schomaker (now retired), Pathologist, Colorado State Forest Service,

Fort Collins, CO 80523; Stanley J. Zarnoch, Mathematical Statistician, 

U.S. Department of Agriculture Forest Service, Southern Research Station,

Asheville, NC 28804; William A. Bechtold, Research Forester, U.S.

Department of Agriculture Forest Service, Southern Research Station, Asheville,

NC 28804; David J. Latelle, Assistant Staff Forester, Colorado State Forest

Service, Fort Collins, CO 80523; William G. Burkman, Program Manager,

U.S. Department of Agriculture Forest Service, Southern Research Station,

Forest Inventory and Analysis, Knoxville, TN 37919; and Susan M. Cox,

Conservation Education Coordinator, U.S. Department of Agriculture

Forest Service, Durham Field Office, Durham, NH 03824.

The Authors

May 2007

Southern Research Station

200 W.T. Weaver Blvd.

Asheville, NC 28804
i

Crown-Condition Classification:

A Guide to Data Collection and Analysis

Michael E. Schomaker, Stanley J. Zarnoch,

William A. Bechtold, David J. Latelle,

William G. Burkman, and Susan M. Cox
ii
iii

Table of Contents

Abstract .............................................................................................. vi

Acknowledgments ............................................................................. vi

List of Figures ................................................................................... vii

List of Tables .................................................................................... viii

1 Introduction .................................................................................. 1

1.1 Justification of Crown Indicators Used to

Assess Forest Health ................................................................ 2

2 Field Procedures and Methods ................................................... 5

2.1 General Crown Evaluation Procedures .................................... 5

2.2 Crown Rating Precautions ....................................................... 6

2.3 Descriptions of Crown Indicators ............................................ 8

2.3.1 Vigor Class ......................................................................... 8

 2.3.1.1 Definition .................................................................... 8

 2.3.1.2 Measurement ............................................................... 8

 2.3.1.3 Causes of Change ......................................................... 9

 2.3.1.4 Intended Use ............................................................... 10

 2.3.1.5 Attributes Shared with Other Indicators ..................... 10

2.3.2 Uncompacted Live Crown Ratio ........................................ 10

 2.3.2.1 Definition .................................................................... 10

 2.3.2.2 Measurement ............................................................... 10

 2.3.2.3 Causes of Change ........................................................ 12

 2.3.2.4 Intended Use ............................................................... 13

 2.3.2.5 Attributes Shared with Other Indicators ..................... 13

2.3.3 Crown Light Exposure ...................................................... 14

 2.3.3.1 Definition .................................................................... 14

 2.3.3.2 Measurement ............................................................... 15

 2.3.3.3 Causes of Change ........................................................ 16

 2.3.3.4 Intended Use ............................................................... 16

 2.3.3.5 Attributes Shared with Other Indicators ..................... 16

2.3.4 Crown Position ................................................................... 16

 2.3.4.1 Definition .................................................................... 16

 2.3.4.2 Measurement ............................................................... 16

 2.3.4.3 Causes of Change ........................................................ 18

 2.3.4.4 Intended Use ............................................................... 18

 2.3.4.5 Attributes Shared with Other Indicators ..................... 18
iv

 2.3.5 Crown Density ................................................................... 18

 2.3.5.1 Definition .................................................................... 18

 2.3.5.2 Measurement ............................................................... 19

 2.3.5.3 Causes of Change ........................................................ 22

 2.3.5.4 Intended Use ............................................................... 22

 2.3.5.5 Attributes Shared with Other Indicators ..................... 22

 2.3.6 Crown Dieback ................................................................... 22

 2.3.6.1 Definition .................................................................... 22

 2.3.6.2 Measurement ............................................................... 23

 2.3.6.3 Causes of Change ........................................................ 23

 2.3.6.4 Intended Use ............................................................... 24

 2.3.6.5 Attributes Shared with Other Indicators ..................... 25

 2.3.7 Foliage Transparency ......................................................... 25

 2.3.7.1 Definition .................................................................... 25

 2.3.7.2 Measurement ............................................................... 26

 2.3.7.3 Causes of Change ........................................................ 28

 2.3.7.4 Intended Use ............................................................... 29

 2.3.7.5 Attributes Shared with Other Indicators ..................... 29

 2.3.8 Crown Diameter ................................................................. 29

 2.3.8.1 Definition .................................................................... 29

 2.3.8.2 Measurement ............................................................... 29

 2.3.8.3 Causes of Change ........................................................ 31

 2.3.8.4 Intended Use ............................................................... 32

 2.3.8.5 Attributes Shared with Other Indicators ..................... 32

 2.4 Limitations and Assumptions Associated with Crown

 Indicators ................................................................................. 32

3 Training ........................................................................................ 35

 3.1 Prerequisites for Trainers and Trainees ................................... 36

 3.2 Training Site Requirements and Materials .............................. 37

4 Data Quality Control ................................................................... 39

 4.1 Prevention of Data Quality Problems ...................................... 39

 4.2 Assessment and Appraisal ....................................................... 40

 4.3 Correction ................................................................................ 41

5 Data Analysis ................................................................................ 43

 5.1 Analytical Objectives .............................................................. 43

 5.2 Variables of Interest ................................................................. 44

 5.2.1 Absolute and Composite Crown Indicators ........................ 44

 5.2.2 Indicator Thresholds ........................................................... 47
v

 5.2.2.1 Biological Thresholds ................................................. 47

 5.2.2.2 Subjective and Empirical Thresholds .......................... 48

 5.2.2.3 Statistical Thresholds .................................................. 49

5.2.3 Residualization and Standardization .................................. 49

5.2.4 Stratification Variables ........................................................ 51

5.2.5 Data Compilation ............................................................... 51

5.3 Structuring Analyses to Accommodate the Data and

Objectives ................................................................................ 52

5.3.1 Descriptive Statistics .......................................................... 52

5.3.2 Inferential Statistics ............................................................ 54

5.3.3 Modeling ............................................................................ 55

5.3.4 Spatial Analyses ................................................................. 56

6 Literature Cited ........................................................................... 59

7 Reference Literature ................................................................... 63

8 Appendices .................................................................................... 73

8.1 Appendix A: Field Data Codes ................................................ 73

8.2 Appendix B: Glossary of Terms .............................................. 75
vi

Abstract

The Forest Inventory and Analysis (FIA) Program of the Forest Service, 

U.S. Department of Agriculture, conducts a national inventory of forests 

across the United States. A systematic subset of permanent inventory 

plots in 45 States is currently sampled every year for numerous forest 

health indicators. One of these indicators, crown-condition classification, 

is designed to estimate tree crown dimensions and assess the impact of 

crown stressors. The indicator features eight tree-level field measurements 

in addition to variables traditionally measured in conjunction with 

FIA inventories: vigor class, uncompacted live crown ratio, crown 

light exposure, crown position, crown density, crown dieback, foliage 

transparency, and crown diameter. Indicators of crown health derived from 

the crown data are intended for analyses at the State, regional, and national 

levels, and contribute to the core tabular output in standard FIA reports. 

Crown-condition measurements were originally implemented as part of 

the Forest Health Monitoring (FHM) Program in 1990. Except for crown 

diameter, these measurements were continued when the FIA Program 

assumed responsibility for FHM plot-based detection monitoring in 2000. 

This report describes in detail the data collection and analytical techniques 

recommended for crown-condition classification.

Keywords: Forest health indicators, tree crown condition, tree crown 

health, tree crown measurement, tree health indicators.

Acknowledgments

We are pleased to thank the following individuals for their important roles 

in the development and implementation of the crown-condition indicator: 

Robert Anderson, Barbara Conkling, William Hoffard, Jason Lackey, Jay 

Lackey, Robert Mangold, Manfred Mielke, Imants Millers, Greg Reams, 

and Dale Starkey.
vii

List of Figures

Figure 2.1—Optimal viewpoints for rating tree crowns. .......................... 7

Figure 2.2—Sapling vigor rating criteria. ................................................. 9

Figure 2.3—Uncompacted live crown ratio is expressed as a

percentage of actual tree length. ................................................................ 11

Figure 2.4—Identifying the obvious live-crown base with the

5-foot rule. .................................................................................................. 12

Figure 2.5—The scales on the back of the density-transparency

card may be used to determine live crown ratio and to adjust

density ratings when part of the crown is missing. .................................... 13

Figure 2.6—Dividing the crown into the five sections necessary

to rate crown exposure. .............................................................................. 14

Figure 2.7—When rating crown exposure, count only those

sides that receive full light. ........................................................................ 15

Figure 2.8—Crown position ratings are determined relative to

the prevailing overstory canopy zone of the stand. .................................... 17

Figure 2.9—Missing portions of a tree crown are reconstructed to

determine the crown density outline. ......................................................... 19

Figure 2.10—Establishing a crown outline for the crown density

rating. ......................................................................................................... 20

Figure 2.11—The front side of the density-transparency card can be

used to assist with crown density and foliage transparency ratings. .......... 21

Figure 2.12—The dotted line represents the crown silhouette used to

rate crown dieback. .................................................................................... 24

Figure 2.13—The dotted foliage transparency outline excludes dieback,

snag branches, and areas where foliage is not expected to occur. ..............25

Figure 2.14—The transparency rating of trees with clumped foliage

is accomplished by averaging (and weighting by relative area)

the transparencies of the individual clumps. .............................................. 26

Figure 2.15—The dotted line shows the crown outline used when

rating the transparency of a uniformly foliated tree. .................................. 27

Figure 2.16—Establishing the dripline of a tree crown. ........................... 30

Figure 2.17—The first crown diameter measurement is taken at

the widest diameter, the second is 90° from the first. ................................ 31
viii

List of Tables

Table 3.1—Measurement quality objectives for the crown rating

system ........................................................................................................ 36

Table 5.1—Summary of crown indicator levels and their associated 

indicators .................................................................................................... 45
1

1 Introduction

Tree crowns convert solar radiation into the photosynthate required for tree

growth, repair, and maintenance. It logically follows that these functions

are correlated with tree crown dimensions. The crown-condition classification procedures described herein have been developed to facilitate

monitoring of spatial and temporal trends associated with tree crown health

at the ecosystem, State, and regional levels. The forest health related indicators associated with crown-condition classification range from measures

of single crown dimensions to composite estimates of crown volume and

surface area. They also include descriptive information about light interception and competitive position. The intent of this report is to provide

a reference that documents all aspects of crown-condition classification

as implemented by the Forest Inventory and Analysis (FIA) Program of

the Forest Service, U.S. Department of Agriculture. Specifically, it covers

topics associated with crown-indicator definitions, measurement units,

field protocols, training requirements, data quality assurance, estimation

methods, and analytical methods. Although tailored to FIA applications,

this information may also be of use to other researchers interested in

adopting these or similar protocols.

The indicators of crown health listed below were originally developed and

implemented by the U.S. Forest Service, Forest Health Monitoring (FHM)

Program between 1990 and 1999. The first of these (vigor class) was

designed for saplings 1.0 to 4.9 inches in diameter, and the latter seven for

live trees at least 5.0 inches in diameter.

1. Vigor class is a visual assessment of the crown vigor of saplings. The

purpose of this classification is to distinguish between excellent saplings

with superior crowns and stressed individuals with poor crowns.

2. Uncompacted live crown ratio is the ratio of live crown length to

aboveground tree length.

1 The term “uncompacted” means that crown

length is not reduced to compensate for gaps between the base of the

live crown and the live top of the tree. Crown length is one of the

dimensions needed to compute crown volume and biomass.

3. Crown light exposure estimates the amount of direct sunlight that

1 An estimate of tree length is required to compute crown length from uncompacted live

crown ratio. Tree length is not listed as a crown indicator because it is considered a standard

mensuration variable, i.e., one not measured specifically for the purpose of crown-condition

classification. The tree length used in the denominator of this ratio is “actual tree length” as

defined by FIA (see footnote 1, page 5).

reaches the live crown.
2

4. Crown position establishes the location of an individual live crown in

relation to the surrounding overstory canopy.

5. Crown density estimates the proportion of crown volume that contains

biomass, which includes foliage, branches, and reproductive structures.

6. Crown dieback measures the proportion of the crown that has experienced recent dieback, primarily in the upper and outer edges. Crown

dieback is often an early indication of stress.

7. Foliage transparency estimates the absence of foliage where foliage

normally occurs. Foliage transparency is negatively correlated with tree

health.

8. Crown diameter is one of the dimensions needed to compute crown

volume and biomass. It can also be used to measure canopy closure and

competition among trees.

The FHM plot network was merged with the FIA Program in 2000,

resulting in the “phase 3” subset of the FIA plot system, which is dedicated

to monitoring forest health (Stolte 2001). With the exception of crown

diameter, FIA adopted the entire set of crown-condition measurements

listed above, adding them to the standard mensuration data recorded for

trees encountered on FIA phase 3 plots. The national sampling design and

estimation procedures for phases 1 and 2 of the FIA Program, including

information about how phases 1 and 2 relate to phase 3, are documented by

Bechtold and Patterson (2005).

1.1 Justification of Crown Indicators Used to

 Assess Forest Health

A multitude of abiotic and biotic factors determine the relative vigor of

forest trees. These include both physiological and external factors such as

age, stand density, genetics, pest problems, climatic trends, light, water,

nutrient availability, and management practices. The effects of these factors

often manifest themselves in the physical appearance of tree crowns. When

natural or anthropogenic stresses impact a forest, the first signs of deterioration are often observed in the tree crowns. Because tree crowns form

a part of the basic structural architecture of a forest ecosystem, they also

influence the composition, processes, and vigor of understory flora and

fauna.

Tree crown dimensions are a major determinant of net primary production.

Trees with high ratings for crown diameter, uncompacted live crown
3

ratio, and crown density together with low crown dieback and foliage

transparency ratings have increased potential for carbon fixation, nutrient

storage, growth, and survival. Large, densely foliated crowns are associated with vigorous growth rates, while trees with small, sparsely foliated

crowns may be in a state of decline, growing little or not at all. The condition of a tree crown affects tree survival (Kramer 1966) and volume increment (Hamilton 1969). Maximum radial growth occurs in close proximity

to the live crown base (Duff and Nolan 1953, Labyak and Schumacher

1954, Shreve 1924, Young and Kramer 1952). Several studies of southern

species have reinforced these concepts by relating crown diameter to the

size of bottomland hardwood trees (Francis 1986) and Fraser fir (Jett and

others 1993) and crown density to the growth of loblolly pines (Anderson

and Belanger 1987, Anderson and others 1992, Belanger and Anderson

1992, Grano 1957). Uncompacted live crown ratio and crown density are

related to the growth and survival of western conifers (Dolph 1988). Crown

density, dieback, and transparency can be related to insect defoliation and

subsequent growth and survival (Kulman 1971). Allen and others (1992)

related sugar maple health to annual changes in crown dieback and transparency. Kolb and others (1992) showed that sugar maples affected by pear

thrips had thinner crowns. Nash and others (1992) developed a computerbased diagnostic system for assessing crown health of northern hardwoods.

Interpreting the relationship between tree crown dimensions and vigor is

complicated by the influence of forest dynamics and stand structure. For

example, live crown ratio of red oak (Ward 1964) and crown width of

lodgepole pine (Bonner 1964) are related to stand density. Thinning slows

the recession of crown length in loblolly pine (Kramer 1966), and affects

crown structure (Siemon and others 1976) and crown position (Lamson and

others 1990). Models have been developed that relate crown dynamics to

stand attributes for unthinned loblolly pine plantations (Dyer and Burkhart

1987, Feduccia and others 1979, Short and Burkhart 1992, Sprinz and

Burkhart 1987), open-grown loblolly and shortleaf pine (Smith and others

1992), unthinned slash pine plantations (Dell and others 1979), Douglasfir stands (Maguire and Hann 1990), and Lake States forest species

(Holdaway 1986). The effects of thinning have also been incorporated into

loblolly pine crown-height increment models (Dyer and Burkhart 1987,

Short and Burkhart 1992) and Scots pine crown-ratio models (Hynynen

1995).

Recent research has been directed at the effects of tree diseases and

global climate change stresses on tree crowns. Studies have demonstrated

the association of crown condition with basal-area growth of loblolly
4

pine (Jacobi and others 1988, Oak and Tainter 1988) and shortleaf pine

(Zarnoch and others 1994) growing on littleleaf disease sites. Young

loblolly pine needle production, which directly affects crown condition, is

related to stand basal area. It has been observed that needle retention can

be reduced by as much as 2 months in abnormally dry years (Dougherty

and others 1990, 1995; Hennessey and others 1992). Similarly, peak needle

fall of Monterey pine in Australia occurred 3 to 6 months sooner under

summer drought conditions (Raison and others 1992). Another study found

that ozone has a significant effect on the timing and rate of foliage abscission (Stow and others 1992).

The effect of stress on crown condition is expressed by foliage mass and

distribution. Using the Weibull distribution to quantify the crown profile,

Schreuder and Swank (1974) found that vertical distribution of foliage

within the crown is related to stand and site conditions. Models have been

developed for vertical crown development in lodgepole pine (Cole and

Jensen 1982). Crown profile models were fitted to plantation loblolly pines

growing under various nitrogen and phosphorus fertilizer regimes (Vose

1988). It is conceivable that alterations in crown profiles can affect canopy

architecture and consequently influence individual tree growth, stand 

growth, survival, and community dynamics. Kuuluvainen (1988) discussed

the relationship of crown architecture to stemwood production in Norway

spruce.
5

2 Field Procedures and Methods

To the extent possible, the various crown measurements must be based on

objective standardized definitions. This section defines the tree crown and

its various parts and highlights critical points for crown-condition measurements. Each crown measurement contributes to the comprehensive rating

for each tree. Crown measurements should be considered individually and

collectively when evaluating a tree. All measurements are based on appearance at the time of measurement.

The complete crown classification field manual

1

 can be downloaded from

the Web site http://socrates.lv-hrc.nevada.edu/fia/dab/databandindex.

html#4.%20%20Current%20National%20Core%20Field.

2.1 General Crown Evaluation Procedures

All crown rating procedures except crown diameter are based on twodimensional sideviews of each crown, usually from two different vantage

points. Two people are needed to view and evaluate each crown. The most

accurate measurements are achieved when crew members occupy the

recommended vantage points, which may not always be possible.

Variation between crew members is reduced by requiring them to record

the average of their individual estimates. Discussion and negotiation

between crew members are likely to reduce errors and measurement

extremes. Consistency and quality are improved by changing observation

points and repeating estimates. This reduces the likelihood of rating an

adjoining tree, using incorrect crown outlines, or overlooking damage or

dieback.

All crown variables, with the exception of vigor class, are measured for

all live trees at least 5.0 inches in diameter at breast height (d.b.h.). For

shrublike species designated by FIA as woodland species, the minimum

is 5.0 inches diameter at root collar (d.r.c.). For saplings, the variables

measured are vigor class, uncompacted live crown ratio, crown light exposure, and crown position. The recommended measurement window begins

when leaves of deciduous trees are expanded to full size, and ends before

1 U.S. Department of Agriculture Forest Service. 2005. Crowns: measurements and sampling. 

In: Forest inventory and analysis national core field guide: field data collection procedures for 

phase 3 plots. Version 3.0. Section 12, 1–21. Internal report. On file with: U.S. Department of 

Agriculture Forest Service, Forest Inventory and Analysis, Rosslyn Plaza, 1620 North Kent 

Street, Arlington, VA 22209.
6

fall leaf drop starts. All crown variables are recorded for each candidate

tree during initial plot establishment and in subsequent inventories when

these variables are remeasured. Measurements are recorded on field tally

forms or on portable data recorders (PDRs). PDRs are preferred because

they can be programmed with realtime range and logic checks. Coding

summaries are provided in appendix A.

The suggested order of data collection for each tree is:

1. Vigor class

2. Uncompacted live crown ratio

3. Crown light exposure

4. Crown position

5. Crown density

6. Crown dieback

7. Foliage transparency

8. Crown diameter

2.2 Crown Rating Precautions

The following circumstances should be considered when rating tree crowns

in the field:

• Distance from the tree—visibility of the tree top usually improves with

distance from the tree, but competing vegetation can obscure visibility

as distance increases. Crews should attempt to stay at least one-half tree

length from the tree being evaluated.

• Crew position—crew members should attempt to evaluate trees while

standing at an angle of 90 degrees from each other (using the tree as the

angle vertex). They should not stand directly opposite or beside each

other unless this is unavoidable. Crew members should also try to position themselves on approximately the same slope contour. Fulfilling the

distance and grade requirements is not possible in all situations, so adjustment may be required to obtain the best view of the tree. Trees should not

be evaluated from the downhill side unless absolutely necessary (fig. 2.1).

Overlapping branches, background trees, and lack of a good viewing area

can cause problems, but the view can often be improved with small lateral

movements once the prescribed viewing positions are attained.

• Weather conditions—cloudy or overcast skies, fog, rain, wind, and

poor sun angles may affect estimates. Uncompacted live crown ratio
7

and crown diameter measurements are less affected by adverse weather 

conditions than other crown indicators. Crown density tends to be overestimated when light is blocked by background foliage, but in open situations the light may be too bright for a good estimate. Crown dieback may 

be underestimated because it is difficult to see dead twigs or to differentiate defoliated twigs from dead twigs, or both. Foliage transparency 

estimates could be affected in either direction when it is hard to discern 

foliage from branches. Crews need to be especially careful during poor 

lighting conditions and may need to move around more than usual to get 

the best view, even when a view appears adequate at a specific location.

• Heavy defoliation—during heavy defoliation, crown dieback may be 

overestimated and foliage transparency may be underestimated because 

Figure 2.1—Optimal viewpoints for rating tree crowns.

Grade level or upslope

Crew members 90 o to each other

Viewing the crown

90o
8

of difficulty distinguishing dead twigs from defoliated ones. The use of 

binoculars will help in separating dead twigs from defoliated twigs.

• Epicormic branches—trees and saplings that do not have any live foliage 

except epicormic branches are rated as follows: uncompacted live crown 

ratio = 0, crown light exposure = 0, crown position = 3, crown density = 

0, crown dieback = 100 percent, foliage transparency = 100 percent, and 

crown diameter = 0.

2.3 Descriptions of Crown Indicators

2.3.1 Vigor Class

2.3.1.1 Definition

Vigor class is a measure of sapling crown vigor. Saplings are classified as 

vigorous, moderate vigor, or low vigor. The moderate vigor class has been 

made large because the objective is to distinguish between extremely good 

and poor crowns, while placing average saplings in the middle.

2.3.1.2 Measurement

• Units—vigor class is recorded in three classes: 1 = vigorous, 2 = 

moderate vigor, and 3 = poor vigor.

• Method of determination—the easiest way to classify vigor is to see if a 

sapling meets the criteria for vigor class 1 or 3. Saplings that qualify for 

neither of these classes are assigned to class 2 (fig. 2.2).

Vigor class 1 saplings must have an uncompacted live crown ratio of at 

least 35 percent and < 5 percent dieback. (Note that damage caused by 

browsing mammals is classified as missing foliage, and not as dieback, and 

that twigs and branches that have died as a result of normal shading are not 

included in dieback.) Also, 80 percent or more of the leaves present must 

be undamaged. Damaged foliage is defined as leaves with more than 50 

percent of their original surface area chewed, discolored, missing, or otherwise damaged.

Vigor class 2 saplings do not meet class 1 or 3 criteria. They may have any 

uncompacted live crown ratio, may or may not have dieback, and 21 to 100 

percent of their foliage is classified as normal.

Vigor class 3 saplings may have any uncompacted live crown ratio. Less 

than 20 percent of their leaves are undamaged. Leaves of twigs and 

branches that have died as a result of normal shading are not considered 

missing or damaged.
9

• Limitations—dieback may be difficult to distinguish from recent defoliation. Estimating normal foliage when the percent damaged or missing is

very close to 50 percent may cause errors in ratings.

2.3.1.3 Causes of Change

• Increase—species shade tolerance may cause positive reactions to

changes in canopy structure. Many species will respond favorably to

release. Discolored foliage may also recover over time, and dieback may

be shed or obscured.

• Decrease—species shade tolerance may cause negative reactions to

changes in canopy structure. The base of the crown may move upward

or portions of the top may be lost, thus decreasing the live crown length.

Common causes are: 

– Shading by neighboring trees due to crown closure

– Mechanical damage caused by animal browsing

– Defoliation with associated dieback or death of lower branches or

tree tops

≥ 35% live

 crown

≥ 35% live

 crown

< 35% live

 crown

Lower

dieback

excluded

Vigor

class

1

Vigor

class

2

Vigor

class

3

40%

normal

foliage

< 20%

normal

foliage

Severe

defoliation

Every needle

> 50% chewed

Figure 2.2—Sapling vigor rating criteria.
10

– Loss of tree tops or lower branches resulting from breakage or 

mortality

– Competition with other vegetation for water and nutrients

• Persistence—vigor class can change slowly or rapidy depending on 

events that change stand structure. Mechanical injury, other damage, or 

dieback in the stand may expedite this change.

2.3.1.4 Intended Use

Vigor class ratings may be used to determine growth potential and future 

stand stocking levels. The ability to determine this potential depends upon 

an analyst’s knowledge of individual species within a forest type or stand. 

Generally, greater numbers of vigorous saplings are indicative of healthier, 

faster growing stands while more saplings of low vigor may indicate stand 

decline.

2.3.1.5 Attributes Shared with Other Indicators

Vigor classes are determined partly on the basis of uncompacted live crown 

ratio. Crown light exposure and position affect vigor class ratings.

2.3.2 Uncompacted Live Crown Ratio

2.3.2.1 Definition

Uncompacted live crown ratio is the length of a tree that supports live 

foliage relative to “actual tree length” as defined by FIA.2

 Dead lower 

branches are not included as part of the live crown. The ratio is determined 

by dividing the uncompacted live crown length by the actual tree length, 

then multiplying by 100 to express the ratio as a percentage (fig. 2.3).

2.3.2.2 Measurement

• Units—uncompacted live crown ratio is recorded in 5-percent classes 

and coded as 0, 05, 10, 15, . . . , 100, where the code represents the upper 

limit of the class, e.g., 1 to 5 percent is code 05.

• Method of determination—determine uncompacted live crown ratio by 

first establishing the live crown top, which is the live foliage nearest the 

top of the tree. Exclude dieback and dead branches. Next, determine 

the base of the live crown. This is located at the point of the lowest live 

foliage of the “obvious live crown.” Typically, this location will be easily 

2 U.S. Department of Agriculture Forest Service. 2005. Forest inventory and analysis national 

core field guide: field data collection procedures for phase 2 plots. Version 3.0. Vol. 1. Internal 

report. On file with: U.S. Department of Agriculture Forest Service, Forest Inventory and 

Analysis, Rosslyn Plaza, 1620 North Kent Street, Arlington, VA 22209. 
11

recognizable. However, there will be occasions where branches extend 

beyond what would normally be considered the obvious live crown. 

When this occurs, apply the 5-foot rule (see appendix B) to establish the 

base (fig. 2.4).

An individual can use the scale on the back of the crown density-foliage 

transparency card to help estimate ratios (fig. 2.5). To use the scale, 

hold the card in one hand parallel to the tree and move the card closer 

or farther from your eye until the zero is at the live top of the tree and 

the 99 is at the base of the tree (ground). Then place your finger on the 

card at the base of the live crown. The number on the scale is the uncompacted live crown ratio. Interpolate to the nearest 5 percent if the point 

is between two values on the scale. A clinometer can also be used to 

measure uncompacted live crown ratio.

When individuals disagree by more than 10 percent (two classes)

on a rating, they should discuss the reasons for their rating. If neither 

Crown ratio = — x 100 x

y

Figure 2.3—Uncompacted live crown ratio is expressed as a percentage of actual tree length.
12

individual is willing to change an estimate, then the two ratings are averaged. Disagreement may result from differences in opinion or viewing 

angle.

• Limitations—views of tree tops may be obstructed. Tree tops may not be 

clearly identifiable for species such as oaks that have rounded crowns. 

Branch structure of some trees is such that an obvious live crown base is 

difficult to establish. Defoliation of tree tops by insects may be mistakenly interpreted as dieback. Binoculars are used to confirm dieback.

2.3.2.3 Causes of Change

• Increase—epicormic branches below the live crown base may grow and 

mature, lowering the base of the live crown. Annual terminal growth 

increases live crown length. Insect defoliated tops may recover and not 

be counted as dieback in subsequent measurements. Release from thinning may increase vigor and consequently live crown growth.

4 feet

Determining crown base and use of 5 foot rule

Base of “obvious live crown”

Point of branch attachment

“New” crown base

Figure 2.4—Identifying the obvious live-crown base with the 5-foot rule.
13

• Decrease—the base of the crown may move upward or portions of the 

top may be lost, decreasing the live crown length. Common causes are:

– Self pruning with increasing age

– Shading by neighboring trees as a result of crown closure

– Mechanical damage done by branches of neighboring trees

– Defoliation with associated dieback or death of lower branches or 

tree tops

– Loss of tree tops or lower branches through breakage or mortality

– Competition with other vegetation for water and nutrients

• Persistence—uncompacted live crown ratio normally changes slowly and 

usually declines after trees reach maturity. Mechanical injury, damage, or 

dieback may expedite change in uncompacted live crown ratio.

2.3.2.4 Intended Use

Uncompacted live crown ratio is used to calculate crown volume and 

biomass. Generally, larger ratios are indicative of healthier, faster growing 

trees. As the base of the live crown progresses toward the top of a tree, i.e., 

as the uncompacted live crown ratio declines, stem growth tends to decline. 

Uncompacted live crown ratio, in combination with tree length, crown 

diameter, and crown-density measurements, can be used as a basis for estimates of crown biomass.

2.3.2.5 Attributes Shared with Other Indicators

Uncompacted live crown ratio is used to obtain crown light exposure 

ratings. The live crown base for uncompacted live crown ratio is the same 

Figure 2.5—The scales on the back of the density-transparency card may be used to determine 

live crown ratio and to adjust density ratings when part of the crown is missing.

 Density of tree present

Percent of tree missing

Scale to estimate live crown ratio
14

base used for crown density, crown dieback, and foliage transparency 

measurements.

2.3.3 Crown Light Exposure

2.3.3.1 Definition

Crown light exposure measures the amount of direct sunlight a tree 

receives when the sun is directly overhead. Determining crown light exposure involves dividing the tree crown into five sections: four equal vertical 

quarters, i.e., faces, and the top (fig. 2.6).

Figure 2.6—Dividing the crown into the five sections necessary to rate crown exposure.

4 3

5

5

4 3

1 2

1 2

5
15

2.3.3.2 Measurement

• Units—crowns are rated from zero to five, depending on the number of 

crown sections exposed to direct sunlight.

• Method of determination—mentally divide the crown into four equal 

vertical quarters (faces) and the top (five sections total). Then observe 

the uncompacted live crown ratio of each of the four vertical quarters. 

Any quarter with an uncompacted live crown ratio rating of 35 or greater 

has the potential to be counted as receiving full light. Trees having an 

overall uncompacted live crown ratio of < 35 may have a maximum 

exposure rating of 1 (if their tops have sun exposure).

For a vertical quarter to count as receiving full light, that section of the 

tree must be at least 35-percent foliated with leaves exposed to direct 

sunlight. The top section qualifies if any portion of the top of the tree 

receives light. A top does not need to receive light on its entirety as 

do the quarter sections (fig. 2.7). Add up the total number of sections 

exposed to full light, including the top if receiving any light, and record 

the appropriate number.

• Limitations—visibility and viewing angle of the crown can cause evaluation problems. In dense stands it can be difficult to divide the crown 

accurately into quarters or see the crown top. Trying to establish uncompacted live crown ratios for each quarter may be a challenge and make it 

hard to determine whether each entire quarter is receiving light.

Figure 2.7—When rating crown exposure, count only those sides that receive full light.

No light

is not counted

Partial light

is not counted

Any part of the

top receiving light

is counted

Full light

is counted

Not counted

(no light)

Not counted

(partial light)

Counted

(full light)

Any part of the 

top receiving

light is counted
16

2.3.3.3 Causes of Change

• Increase—crown light exposure, or the number of sections receiving 

light, may increase due to:

– Removal or mortality of adjacent trees

– Increase in uncompacted live crown ratio

• Decrease—the number of crown sections receiving light may be reduced 

because of:

– Decrease in uncompacted live crown ratio

– Competition from adjacent trees or other vegetation

– Dieback or loss of branches

• Persistence—changes in crown light exposure commonly occur gradually over long periods. Significant or rapid change should only result 

from a disturbance that immediately alters canopy structure.

2.3.3.4 Intended Use

Crown light exposure estimates the amount of direct sunlight to which a 

tree is exposed. It provides information about stand structure and competition, tree and stand vigor, and growth potential. A tree receiving more 

light typically has less direct competition, thereby increasing its vigor and 

growth potential.

2.3.3.5 Attributes Shared with Other Indicators

Crown light exposure rating is designed to be used with crown position 

rating. Ratings for these two indicators can be used to compute Kraft crown 

classes (Bechtold 2003b). Changes in crown light exposure should not have 

a direct effect on other crown indicators. However, changes in other crown 

indicators may affect the crown light exposure rating.

2.3.4 Crown Position

2.3.4.1 Definition

Crown position refers to the relative position of an individual crown in 

relation to the overstory canopy zone. This crown indicator provides information regarding stand structure and competition.

2.3.4.2 Measurement

• Units—a crown position code is assigned to each tree individually, 

except in open-grown stands. Codes 1 to 4 represent the superstory, overstory, understory, and open-grown crown positions, respectively.
17

• Method of determination—to make a crown position assessment, first

establish the average live crown height for the entire stand. This defines

the “overstory canopy zone.” Next, separate the overstory canopy zone

horizontally into halves with an imaginary line through the middle.

Finally, compare each individual treeʼs live crown top to this midpoint

line. The location of the live crown top relative to this line determines the

treeʼs crown position code (fig. 2.8).

-

Figure 2.8—Crown position ratings are determined relative to the prevailing overstory canopy 

zone of the stand.

Overstory

zone

Position: 2 2 3 2 3 3 1 3 2 2 3 2 2 2

Postion: 4 4 4 4 4 4 4

Open

canopy

For a tree to qualify as a superstory tree (code 1), the live crown top must

be twice the height of the top of the overstory canopy zone. To be coded

as an overstory tree (code 2), the live crown top needs to extend past the

midline of the overstory canopy zone. An understory crown (code 3) will

have its top at or below the midline of the overstory canopy zone. The

open canopy code (code 4) is used for every tree in the stand when an

overstory zone is not discernable due to lack of canopy cover and competition.

This code is used if the tree is located in an opening (at least 1 acre in size)

that has < 50-percent crown cover, or when the overall forest condition

consists of a patchwork of clumps and open spaces that are < 1 acre in size.
18

• Limitations—competing foliage or lower portions of a crown that is

being evaluated can obstruct the view of tree tops. In dense stands this

makes it difficult to establish not only the overstory canopy zone, but

also where individual crowns begin or end. A steep slope can make

it hard to define the overstory canopy zone because it can make the

crown heights of identical trees seem unequal. To avoid these problems,

perform crown evaluations from a position on the same slope contour as

the tree base whenever possible.

2.3.4.3 Causes of Change

• Increase or decrease can be caused by:

– Removal of overstory trees

– Removal of understory trees

– Environmental stress such as drought, disease, fire, ice, and wind

– Tree-to-tree differences in growth rates

– Stand succession

• Persistence—crown position is a relatively stable and slowly evolving

measurement. There should be no drastic changes in crown positions of a

stand unless there is some manmade or natural catastrophic disturbance.

2.3.4.4 Intended Use

Crown position represents the location of each individual crown in relation to the overstory canopy zone. When considered on a large scale rather

than on an individual tree basis, crown position provides information about

stand structure and forest health. Trees with favorable crown position may

have higher vigor and survivability as well as reproductive and growth

potential. Trees with unfavorable crown positions are more likely to exhibit

less vigor, survivability, reproductive potential, and growth potential.

2.3.4.5 Attributes Shared with Other Indicators

Crown position in conjunction with crown light exposure can be used to

compute Kraft crown classes (Bechtold 2003b). Changes in crown position can directly affect crown light exposure. While this measurement

does not have a direct effect on the other crown indicators, changes in the

other crown indicators may affect crown position. For example, a change

in uncompacted live crown ratio or dieback measurements could cause

changes in crown position.

2.3.5 Crown Density

2.3.5.1 Definition

Crown density is the amount of crown stem, branches, twigs, shoots, buds,

foliage, and reproductive structures that block light penetration through the

crown. Dead branches and dead tops are part of the crown. Live and dead
19

branches below the live crown base are excluded. Broken or missing tops are 

visually reconstructed when forming this crown outline by comparing tops 

of adjacent healthy trees of the same species and stem diameter (fig. 2.9).

2.3.5.2 Measurement

• Units—crown density is recorded in 5-percent classes and coded as 0, 

05, 10, 15, . . . , 100, where the code represents the upper limit of the 

class, e.g., 1 to 5 percent is code 05.

Figure 2.9—Missing portions of a tree crown are reconstructed to determine the crown density 

outline.
20

• Method of determination—establish the crown outline by selecting the 

point on the stem used as the base for uncompacted live crown ratio and 

visualize a normal, expected crown that encompasses the branch tips for 

that tree. Exclude any foliage below the crown base (fig. 2.10).

If there is a broken or missing top, reconstruct the missing portion

by using other trees of the same size, species, and growing condition as 

Figure 2.10—Establishing a crown outline for the crown density rating.
21

a guide. After determining the outline, each crew member should hold 

the crown density-foliage transparency card (fig. 2.11) along the line 

of sight and estimate what percentage of the outlined area is blocking 

sunlight.

Crowns with portions missing and half-sided trees are rated as if

they have full symmetrical crowns. Include crown dieback and open 

areas in the crown density outline. In these situations it may be easier

to determine the percent of the tree missing and the crown density 

of the tree's remaining portion. Then use the table on the back of the 

crown density-foliage transparency card to arrive at the final crown 

density for that tree. The reverse side of this card provides a table of 

percentages for various combinations of density and percent missing 

ratings (fig. 2.5).

When individuals disagree by more than 10 percent (two classes) on a 

rating, they should discuss the reasons for their ratings. If neither individual is willing to change an estimate, then the two ratings are averaged. 

Disagreement may result from differences in opinion, or viewing angle, 

or both.

• Limitations—identifying the crown outline of the tree correctly is the 

key to determining crown density. Sources of variation include identification of the crown outline, and estimating the missing crown area 

within that outline. Tops or sides of trees may not be clearly visible at the 

same time and errors may result from “piecing the crown together” from 

various viewing points. Branch structure of some trees may make determining the base of the crown difficult. If the selected live crown base is 

wrong, this may affect crown density greatly.

5 15 25 35 45 55 65 75 85 95

95 85 75 65 55 45 35 25 15 5

Foliage transparency scale

Crown density scale

Figure 2.11—The front side of the density-transparency card can be used to assist with crown 

density and foliage transparency ratings.
22

2.3.5.3 Causes of Change

• Increase—crown density may increase for reasons such as:

– Increased growing space as a result of death or removal of neighboring trees

– Rebuilding crown structure after loss of major branches

– Recovery after defoliation by insects, diseases, or environmental

stress

– Loss of branches causing a change in the crown outline

– Recovery or regrowth of a broken or missing top or side

• Decrease—normally, crown density declines over time as a result of

competition and aging. Crown density also declines because of:

– Stress that causes loss of branches and creates gaps in the live crown

– Stress that increases transparency as a result of poor foliage production or defoliation

– Stress that stunts foliage

– Crown form changes as a result of increased stand density

– Broken or missing tops or sides

• Persistence—crown density usually changes slowly as stand density

changes. Defoliation may change crown density within a growing

season, but repeated defoliation followed by branch dieback is needed

for permanent change. Branch mortality, such as that caused by wind and

ice damage, creates rapid change in crown density.

2.3.5.4 Intended Use

Crown density is used to estimate the percentage of crown volume that

contains biomass. Trees with below average crown densities are expected

to have reduced growth compared to trees with full, symmetrical crowns

(Anderson and Belanger 1987).

2.3.5.5 Attributes Shared with Other Indicators

Crown density includes other crown characteristics that are measured

separately. These include:

• Foliage transparency as influenced by changes in foliage abundance

• Crown dieback as reflected by complete absence of foliage

• Crown diameter as influenced by competition from adjacent trees

2.3.6 Crown Dieback

2.3.6.1 Definition

Crown dieback is recent mortality of branches with fine twigs that begins at

the terminal portion of a branch and proceeds toward the trunk. Dieback is
23

considered only when it occurs in the upper and outer portions of the tree. 

When whole branches are dead in the upper crown, without obvious signs 

of damage such as breaks or animal injury, assume that the branches died 

from the terminal portion of the branch. Dead branches in the lower live 

crown are not considered as part of crown dieback unless there is continuous dieback from the upper and outer crown down to those branches.

2.3.6.2 Measurement

• Units—crown dieback is recorded in 5-percent classes and coded as 0, 

05, 10, 15, . . . , 100, where the code represents the upper limit of the 

class, e.g., 1 to 5 percent is code 05.

• Method of determination—live crown base for crown dieback determination is the same as that for the uncompacted live crown ratio estimate. 

Assume that the perimeter of the crown is a two-dimensional outline 

from branch tip to branch tip. Missing tops, sides, or portions of the tree 

are not included in this estimate.

First, each crew member should mentally draw a two-dimensional crown 

outline. Second, block in the affected area (fig. 2.12). Third, the proportion of the affected area is estimated in 5-percent classes and recorded as 

a percentage of the total live crown area outline including the estimated 

dieback area. When individuals disagree by > 10 percent (or two classes) 

on a rating, they should discuss the reasons for their ratings. If neither 

individual is willing to change an estimate, then the two ratings are averaged. Disagreement may result from differences in opinion or viewing 

angle.

• Limitations—tree tops may not be clearly visible due to an obstruction 

or may not be clearly identifiable for species that have rounded crowns, 

e.g., oaks. The branch structure of some trees makes it difficult to establish an obvious live crown base. Heavy defoliation or transparency > 50 

percent makes it difficult to separate recently dead twigs and branches 

from living, defoliated branches.

2.3.6.3 Causes of Change

• Increase—crown dieback is an indication of severe stress and increases 

with damage to the roots, stem damage that interferes with moisture and 

nutrient transport to the crown, direct injury in the crown, severe defoliation, excessive seed production, or scorch due to sudden release from 

competition.

• Decrease—crown dieback decreases through loss of dead twigs and 

branches. Also, trees frequently rebuild their crowns after the stressing 

agent is removed.
24

• Persistence—small dead twigs do not persist very long and may drop 

annually. Larger dead branches persist longer. Therefore, annual fluctuations of dieback are expected.

2.3.6.4 Intended Use

Crown dieback estimates the severity of recent stresses on trees. Increased 

dieback reduces growth. The measurement serves as an early indicator 

of loss of vigor and growth potential in response to stresses or damage. 

High crown dieback indicates defoliating agents or other stressors such as 

drought, root rots, canker diseases, and root mortality due to deep frosts. 

However, some crown dieback results from normal tree processes, such as 

excessive seed production. Thus minor crown dieback probably does not 

indicate an abnormal condition. Some species of trees, particularly conifers, do not exhibit crown dieback unless something is seriously wrong 

with the root system.

Exclude snag branches

Include recent dieback

Exclude

large holes

Figure 2.12—The dotted line represents the crown silhouette used to rate crown dieback.
25

2.3.6.5 Attributes Shared with Other Indicators

Crown dieback reduces the crown density measurement. Crown dieback 

may affect the uncompacted live crown ratio and crown diameters by 

killing a portion of the tree top or sides.

2.3.7 Foliage Transparency

2.3.7.1 Definition

Foliage transparency is the amount of skylight visible through microholes 

in the live portion of the crown, i.e., where foliage or remnants of foliage 

occur. Recently defoliated branches are included in foliage transparency 

measurements. Macroholes are excluded unless they are the result of 

recent defoliation. Dieback and dead branches are always excluded from 

the estimate. Foliage transparency is different from crown density because 

it emphasizes foliage and ignores stems, branches, fruits, and holes in the 

crown (fig. 2.13).

Exclude recent dieback

Exclude

large holes

Figure 2.13—The dotted foliage transparency outline excludes dieback, snag branches, and 

areas where foliage is not expected to occur.
26

2.3.7.2 Measurement

• Units—foliage transparency is recorded in 5-percent classes and coded 

as 0, 05, 10, 15, . . . , 100, where the code represents the upper limit of 

the class, e.g., 1 to 5 percent is code 05.

• Method of determination—when defoliation is severe, branches alone 

will screen the light, but crews should exclude the branches from foliage 

as best they can and rate the area as if the light were penetrating the 

branches. For example, an almost completely defoliated dense spruce 

may have < 20-percent skylight coming through the crown, but it will be 

rated as highly transparent because of the missing foliage.

Old trees and some hardwood species have crowns with densely foliated 

branches that are spaced far apart. The spaces between clumps of foliage 

should not be included in the foliage transparency rating (fig. 2.14). 

Figure 2.14—The transparency rating of trees with clumped foliage is accomplished by 

averaging (and weighting by relative area) the transparencies of the individual clumps.

Defoliation

Transparency outline
27

When foliage transparency in one section of the crown differs from that 

in other sections, the estimate is made by averaging the weighted (by 

area) transparencies of each section.

Foliage transparency is rated by two people using the crown densityfoliage transparency card (fig. 2.11). First, each individual mentally 

draws a two-dimensional crown outline. Second, the foliated area is 

blocked into the crown outline (fig. 2.15). Third, each individual estimates the amount of skylight that penetrates the foliated crown as a 

percentage of the total area of the foliated crown outline. The measurement is then recorded as a 5-percent class.

Figure 2.15—The dotted line shows the crown outline used when rating the transparency of a 

uniformly foliated tree.
28

When individuals disagree by more than 10 percent (two classes) on 

a rating, they should discuss the reasons for their ratings. Either an 

individual changes his or her estimate or the ratings are averaged. 

Disagreement may be due to differences in viewing angle, differences in 

judgment, or both.

• Limitations—estimates of foliage transparency for individual trees are 

quite variable because portions of the crown may not be clearly visible. 

As defoliation increases, branches and reproductive structures may 

interfere with foliage estimates. Personal judgment about including or 

excluding portions of the live crown can affect estimates. Distinguishing 

defoliated live branches from dead branches becomes difficult with 

increasing amounts of defoliation.

2.3.7.3 Causes of Change

• Increase—foliage transparency increases when foliage is reduced by:

– Insect defoliation

– Diseases

– Environmental stresses

– Increased seed production that may reduce the amount of foliage 

production

– Declining tree vigor

– Decreased needle retention of conifers caused by defoliating events 

like insect outbreaks or drought

– Decreased size of leaves or needles

– Loss of branches that decreases foliage overlap

– Loss of adjacent trees that may have been unintentionally included 

in previous estimates

• Decrease—foliage transparency decreases when foliage is added by:

– Control or collapse of insect or disease defoliation events

– Recovery from environmental stresses

– Improved tree vigor

– Increased needle retention of conifers caused by improved growing 

conditions

– Increased size of leaves or needles

– Addition of branches, which increases foliage overlap

– Growth of adjacent trees that may affect the estimate

• Persistence—foliage transparency is the most rapidly changing variable measured in tree crowns. Wind, frost, and hail can change foliage 

transparency in a short time. Changes caused by insect defoliation may 
29

be measurable days apart. Diseases may contribute to steady decline 

of foliage during a growing season. Defoliation of evergreens may 

contribute to foliage transparency changes for more than a year. Poor site 

conditions that cause higher foliage transparency may persist for the life 

of the tree.

2.3.7.4 Intended Use

Foliage transparency is used as a measurement of defoliation or stress. The 

amount of foliage in a crown is related to tree growth. A tree with more 

foliage is expected to have higher growth potential, vigor, survivability, 

and reproductive potential than a tree of the same species with less foliage. 

High foliage transparency indicates a loss of vigor and growth potential. 

Usually, serious effects are not expected until a tree loses half of its foliage 

or unless significant defoliation persists over multiple seasons. The average 

foliage transparency of healthy trees tends to be species-specific.

2.3.7.5 Attributes Shared with Other Indicators

Foliage is included in all crown measurements. It is used to determine 

crown diameter, to identify the uncompacted live crown, and as part of the 

denominator in determining crown dieback. Since foliage blocks transmission of light through the crown, the attribute measured in crown density, it 

contributes heavily to the density measurement.

2.3.8 Crown Diameter

2.3.8.1 Definition

Crown diameter is the average of two diameter measurements: (1) widest 

distance anywhere in the crown between the driplines (fig. 2.16) of two live 

branches, and (2) the distance perpendicular to the widest measurement. 

Abnormally long branches, on one side of a tree, that extend beyond the 

edge of the crown outline are excluded from the measurement on that side 

(fig. 2.17).

2.3.8.2 Measurement

• Units—crown diameter is measured to the nearest foot.

• Method of determination—a logger’s tape is used to measure crown 

width at the widest point. A second measurement of crown width is 

then taken at 90 degrees from the widest point. Both measurements are 

recorded.

Each crew member stands directly beneath the dripline of the crown and 

estimates where the dripline intersects a logger’s tape. The dripline is 
30

Drip lines dripline

established by projecting a vertical line upward from the ground to the 

branch tip above. Branches infrequently protrude well beyond the normal 

outline of a tree. These abnormal branches are excluded from crown 

diameter measurements. Occasionally it is helpful to use a device such as 

a clinometer to ensure that a crew member is directly beneath the crown 

edge. Such devices are used for training and data quality control checks. 

If the branch tips cannot be seen from beneath the tree, then both crew 

members should move outward an equal distance away from the tree 

until they both can see well enough to make the measurement.

• Limitations—the most common causes of error are improper location 

of the crown edge (dripline) and improper selection of the widest crown 

axis. Branches may be intermingled with neighboring trees or shielded 

by the lower canopy, making it difficult to see the branch tips.

Figure 2.16—Establishing the dripline of a tree crown.
31

2.3.8.3 Causes of Change

• Increase—crown diameters are likely to increase as a tree assumes

higher canopy positions. Decreasing stand density will make more space

available for lateral branch growth and larger diameters.

• Decrease—crown diameters decline with increased competition from

neighboring trees. Mortality of lower branches as a result of competition,

snow and ice damage, or insect or disease damage reduces crown diameter. Shock response after logging or similar disturbance may result in

loss of large branches and decreased crown widths.

Figure 2.17—The first crown diameter measurement is taken at the widest diameter, and the

second is taken 90° from the first.

Long, straggling branches extending

well beyond the crown outline should

be ignored.

1st measure

90o

2nd measure

To make the second (90o) measurement at

the widest point on an irregular crown, one

or both crew members may not be able to 

stand under the drip line.

Key points of crown diameter measurement

One crew member, or both crew members, 

may not be able to stand under the dripline 

when making the second (90°) measurement 

at the widest point on an irregular crown.
32

• Persistence—crown diameters are not likely to change from one year 

to the next unless some major injury has occurred. Significant changes 

may not be detected before 5 to 10 years of measurements have been 

recorded.

2.3.8.4 Intended Use

Crown diameters are associated with vigor, stand stocking, and the stem 

diameters of most species. Trees with large crown diameters are expected 

to have greater growth than trees of the same species with small crown 

diameters. Trees with large crown diameters have more foliage and surface 

area for photosynthesis and have a greater potential for carbon fixation. 

This may be offset by higher respiration costs for large mature crowns. 

Mature and overmature trees may have slower growth, wider crowns, and 

lower uncompacted live crown ratios than younger trees. Because crown 

diameters of open-grown trees tend to differ from species to species, standlevel growth predictions for multispecies stands should not be based on 

crown diameter alone.

2.3.8.5 Attributes Shared with Other Indicators

Crown diameter measurements are sometimes reflected in crown density 

measurements. When two trees are close together, their crowns are often 

reduced or missing on the side where they are in closest proximity. A 

similar reduction is recorded in crown density for the missing portion of 

the crown. Lateral dieback of branches can cause a reduction in crown 

diameter. 

2.4 Limitations and Assumptions Associated with

 Crown Indicators

Remeasurements of crown variables by quality control experts demonstrate that observer-to-observer variation in measurements can result from 

differences in regional training and interpretation of terminology. Annual 

training and certification sessions for both regional trainers and crews are 

critical to reducing such variation.

Combining crown indicators across species must be done cautiously 

because most tree stresses are species-specific and the effect of a particular 

stress may be obscured. For instance, averaging crown data across both 

loblolly pines and oaks may mask an important forest health problem that 

affects only the loblolly pine, e.g., the southern pine beetle. Unless properly adjusted for species differences, subsequent analyses of crown condition are highly influenced by species distribution. Crown indicators must 
33

either be adjusted with models or otherwise stratified by species to overcome this problem.

The composite crown indicators described later in this report are defined 

as functions of live crown length, crown diameter, and crown density. 

Calculation of crown length requires a measure of tree length, which was 

not recorded on FHM plots between 1990 and 1999. Tree length is a standard mensuration variable recorded by FIA, and is, therefore, available in 

subsequent datasets. At this writing FIA has concluded that crown-diameter measurements are prohibitively expensive, so field measurements of 

crown diameter are not available in most post-1999 FIA datasets (although 

they are available in the previous FHM datasets). Models will have to be 

substituted for field measurements if tree length or crown diameters are not 

available in a given dataset.
34
35

3 Training

The primary goal of training is to ensure that all field crews assess crown 

conditions in a consistent and standard way. Training for crown measurements is done in two sessions. The first, an indoor session, covers the 

methods, terms, and requirements for each measurement. The second 

provides hands-on outdoor demonstration and practice for students. The 

training then concludes with a written exam and field test that qualifies 

crews for field data collection. Successful completion of the written exam 

and field exam is required before a crew member or student can be certified as qualified to collect crown classification data for the current year. 

Certification must be renewed on an annual basis.

To promote consistency from one training session to the next, a detailed 

training and certification plan has been prepared to provide trainers with 

step-by-step guidance. The training package and training visual aids will 

eventually be available from the Internet; until then a training package and 

CD may be obtained from:

Forest Inventory and Analysis National Office

Forest Service, U.S. Department of Agriculture, SPPII

4th Floor, RP–C

1601 North Kent Street

Arlington, VA 22209

(703) 605–4189

Crown-condition classification training is structured to proceed in the 

following order:

1. Classroom and field instruction

2. Practice

3. Evaluation and certification

4. Retraining, evaluation, and certification if necessary

Each training session must be designed to accommodate the experience 

level of all trainees. New trainees are provided with complete information 

about methods and objectives. Experienced observers may only require a 

refresher course with updates on changes to field methods. The training 

and certification process is repeated every year, and trainees must be tested, 

regardless of experience. Those who fail to meet the measurement quality 

objectives (table 3.1) are retrained and retested prior to certification.
36

3.1 Prerequisites for Trainers and Trainees

Crown classification trainers should have the following background and 

skills:

• Previous crown-condition classification training and certification (FIA 

curriculum or equivalent)

• Field experience in crown-condition classification

• Ability to work and communicate effectively with a broad range of 

students in both indoor and outdoor settings

Trainees are assumed to have the following skills. If not, these skills must 

be acquired through additional training:

• Ability to use basic forestry field equipment

• Ability to interpret aerial photographs

• Ability to identify tree species common in the region

• Ability to work effectively under arduous field conditions with a crew 

partner

• Good or corrected vision within the normal color range

Table 3.1—Measurement quality objectives for the crown rating system

Crown indicator Reporting units Measurement quality objectivesa

Vigor class 3 classes 90 percent agreement

Uncompacted live 21 classes 90 percent agreement @ + 10 percent

 crown ratio (two classes)

Crown light 5 classes 85 percent agreement within one class

 exposure if > 0

Crown position 4 classes 85 percent agreement

Crown density 21 classes 90 percent agreement @ + 10 percent

 (two classes)

Crown dieback 21 classes 90 percent agreement @ + 10 percent

 (two classes)

Foliage 21 classes 90 percent agreement @ + 10 percent

 transparency (two classes)

Crown diameter 1 foot 90 percent @ + 5 feet of the meanb

 or 90 percent @ + 10 percent of the

 meanb c

a Percentage of field crew observations that are in agreement with field auditors.

b The arithmetic mean of the two crown diameter measurements for each tree.

c Plus or minus 10 percent is used for crowns with mean diameters larger than 50 feet.
37

3.2 Training Site Requirements and Materials

Training involves a mixture of indoor and outdoor activities. The indoor

training site must include a room that is large enough to accommodate all

trainees and suitable for viewing overheads, slides, and portable computer

presentations. Indoor training equipment should include:

• Slide and overhead projectors or LCD projector

• Projection screen or suitable wall surface

The outdoor site should be a forested area with trails or roads for mobility

and with tree species representative of the region where crews will be

working. The following materials are needed to set up the outdoor training

course:

• Index cards or paper, metal, or fabric tags for marking trees

• Permanent markers

• Plastic flagging in at least three colors (not green, stripes, or polka dots)

• Staple gun, staple hammer, or hammer and nails

In addition to the standard equipment used for forest inventory, all trainees

must be equipped with the following:

• Clinometer

• Loggerʼs tape or other 50- to 100-foot tape (at least one per training

group)

• Binoculars

• Density-and-transparency cards

• Data sheets for practice and testing

• Maps of the practice and test courses

• Clipboards

• Pencils

• Field guides (see footnote 1, page 5)
38
39

4 Data Quality Control

Quality assurance (QA) encompasses all activities performed to ensure

that the field data achieve the desired quality. The QA program focuses on

identification and documentation of operations and procedures that affect

data quality. Quality control (QC) describes the specific actions required to

maintain data quality within an acceptable range.

The goal of any QA program is to ensure that data attain the minimum

specified standards. Data collected for QA purposes can also provide the

feedback that is necessary to develop realistic measurement quality objectives (MQOs), revise methods to reduce errors, improve training, and aid in 

the interpretation of results. The main criteria used to interpret the level of

data quality in the FIA Program are:

1. Precision—the ability of a method to reproduce the same value

2. Accuracy—the ability of a method to yield the “true” value

3. Completeness—the amount of valid, usable data produced by a method

4. Comparability—the ability to combine data collected by different

methods, in different locations, and by different data collectors

An effective QA program provides for prevention of data quality problems,

assessment and appraisal, and correction.

4.1 Prevention of Data Quality Problems

Activities designed to prevent data quality problems:

• Development and documentation of standardized definitions and field

methods

• Sufficient training and calibration of field crews

• Establishment of realistic MQOs

For crown-condition classification, the first of these has been addressed by

production of the field manual. The second includes not only the successful

execution of training, but also a feedback loop designed to query trainees

about the effectiveness of each training session. This can be accomplished by

a training evaluation survey, debriefing trainees, or both. Evaluation topics

should include the facilities, instructors, general organization, and certification procedures. Maintaining records on the following aspects of training

will improve future training, as well as the interpretation of QC data:
40

• Time and place of training

• A list of trainers

• A list of individuals trained and certified

• A list of problems noted and proposed resolutions

• Certification results

• Trainee feedback

Field audits, which are conducted as an extension of training, are another 

important source of feedback. These audits are conducted by trainers who 

visit and interact with field crews as they gather field data. Audits should 

be conducted soon after training is concluded so that potential problems 

are corrected early. These interactions also provide an additional opportunity for crew feedback about the methodology and training, as well as any 

logistical problems that may have been encountered. A short report should 

be completed for future reference; it should list the personnel involved, 

location of the audit, problems and questions encountered with resolutions 

and answers, and followup action items. This information is filed with 

other program documentation.

The measurement quality objectives listed for crown-condition classification in table 3.1 represent the precision with which field crews are expected 

to measure each crown indicator. These MQOs have been determined from 

more than a decade of field experience. Trainee certification, as well as 

assessments and appraisals of field crew performance and data quality, are 

based on comparisons with these measurement quality objectives.

4.2 Assessment and Appraisal

QA assessment and appraisal activities include the remeasurement of field 

data by “audit crews,” data validation, and a debriefing of field crews at the 

end of the field season. Topics covered at the debriefing should include the 

training session, methodology, logistics, and data collection techniques.

“Blind checks” are the preferred method for acquiring remeasured data for 

QA purposes. Audit crews are sent to sites originally sampled by production crews, where they independently remeasure the trees on those sites. 

The checks are blind in that the audit crew does not have access to the original data, and production crews do not know which sites will be checked. 

The two datasets are then compared to develop estimates of precision 

and to check compliance with measurement quality objectives. The target 

remeasurement intensity is approximately 5 percent of all trees sampled, 
41

but the percentage within a given region may vary depending on the availability of resources. Note that data collected by the audit crew should not 

be used to replace the original data from the production crew because this 

would disrupt the error structure of the production data.

Data validation ensures that the recorded data do not violate any range or 

logic checks. A range check is a comparison of recorded values with a list 

of valid codes; a logic check is a comparison of entries in two or more data 

fields. For example, if the crown position is superstory, then crown light 

exposure should be greater than zero. Ideally, all range and logic checks 

are accomplished during the data collection process by using PDRs with 

software equipped to perform realtime checks.

4.3 Correction

Correction is modification of training, field protocols, and or the QA 

program to improve data quality. The need for correction and the effect 

of previous corrections are most objectively evaluated through analysis 

of blind check data. This is usually done at the end of the field season, 

although field audits and training feedback make it possible to implement 

corrective actions sooner.

Any modification to the methodology is done with extreme caution, 

because of its potential effect on trend analysis. The need for corrective 

action decreases over time as the numbers of veteran trainers and experienced crew personnel increase. The level of experience with crown classification in FIA is now at the point where changes to field methodology 

are rare. Most recent changes have focused on opportunities to improve 

training.
42
43

5 Data Analysis

Many analytical techniques may be used to interpret the crown indicator

data. Choosing the best approach requires careful attention to the following

before the analysis is initiated:

1. Specify the objectives.

2. Identify the variables of interest.

3. Structure the analysis to accommodate the data and objectives.

Here we present information related to these topics that can facilitate and

expedite the analysis of crown condition and guide future research. We

discuss methods that have already been applied and others that have yet to

be attempted. Although several potential avenues of analysis are discussed,

it should be recognized that this does not represent the full range of possibilities.

5.1 Analytical Objectives

The success of any analysis depends on proper specification of the objectives. Analyses of crown condition usually proceed along four major

analytical themes: description, detection, evaluation, and intensive site

monitoring. To date, some experience has been gained with all but the last

of these.

The crown-condition indicators have recently become part of the FIA

phase 3 plot network (Stolte 2001). Thus, analyses of crown data are being

incorporated into regular FIA reports, which have traditionally favored a

descriptive approach to analysis. This descriptive approach usually involves

the presentation and discussion of tabular and graphic summaries of population statistics. These data summaries are useful for establishing baselines

and pointing to gross differences among sets of observations, but are not

designed to identify forest health problems. Auxiliary analyses based on

statistical inference are necessary for that.

The FHM Program has promoted a three-tiered approach to analysis, which

includes detection monitoring, evaluation monitoring, and intensive site

monitoring (Riitters and Tkacz 2004). Detection monitoring is designed

to scan the available data for unusual or suspicious trends. If a potential

problem surfaces, then an evaluation monitoring project is initiated to

further investigate the available data in an attempt to explain the situation.

If evaluation monitoring fails to yield a satisfactory explanation and the
44

potential problem is not a false signal, intensive site monitoring may then 

be implemented to further study the problem. Successful implementation 

of each tier requires the judicious use of rigorous statistical methods. While 

evaluation and intensive site monitoring are beyond the scope of regular 

FIA reports, it would be prudent to incorporate some of the techniques 

developed for detection monitoring into the FIA processing system.

5.2 Variables of Interest

5.2.1 Absolute and Composite Crown Indicators

This section defines two levels of crown indicators. Absolute indicators 

are simply the individual crown variables recorded in the field (table 5.1). 

Although absolute indicators have the advantage of simplicity, they have 

the disadvantage that each represents only a single aspect of crown structure. Composite indicators formulated using multiple crown dimensions 

better represent a tree’s potential to capture and utilize solar energy. For 

this reason, composite indicators are generally favored for detection monitoring. The absolute indicators are better suited for further evaluation of 

any unusual trends that may be identified.

Measures of uncompacted crown ratio, crown density, crown diameter, and 

tree length are used to compute a variety of composite crown indicators. 

Composite crown volume (CCV) can be calculated for each sampled tree as 

follows:

(5.1)

where

R = CDIA / 2

CL = TL (ULCR/100) = crown length (feet)

TL = total tree length (feet), and CDIA, ULCR, and CDEN are as defined 

in table 5.1

Note that the first portion of this equation approximates the 

three-dimensional volume of a parabola, and the second part (CDEN/100) 

estimates the portion of the parabola filled with crown biomass. The estimation of crown volume can be fine tuned to accommodate other crown 

shapes. For example, the parabola specified by equation 5.1 can be modified to a cone by changing the 0.5 to 0.33. The latter would be more precise 

for species such as spruce and fir. Highly tuned estimates of individual 

crown volumes are not possible in the absence of field classification of 

crown shape, but crude species-level adjustments are possible if desired. 
45

Table 5.1—Summary of crown indicator levels and their associated 

indicators

Indicator 

level Indicator name Acronym Bounds Scale

Absolute Vigor class CVIG 1 < CVIG < 3 Ordinal

Uncompacted live

crown ratio ULCR 0 < ULCR < 100 Ratio

Crown light

exposure CEXP 0 < CEXP < 5 Ordinal

Crown position CPOS 1 < CPOS < 4 Nominal

b

Crown diametera CDIA CDIA > 0 Ratio

Crown density CDEN 0 < CDEN < 100 Ratio

Crown dieback CDBK 0 < CDBK < 100 Ratio

Foliage

transparency FTRAN 0 < FTRAN < 100 Ratio

Composite Composite crown

volume CCV CCV > 0 Ratio

Composite crown

surface area CCSA CCSA > 0 Ratio

Crown shape ratio CSHAPE CSHAPE > 0 Ratio

Crown production

efficiency CEFF CEFF > 0 Ratio

a Crown diameter measurements may not be available for some Forest Inventory and Analysis 

Program datasets.

b Crown position can be converted to an ordinal variable by combining the open-grown and 

overstory categories.

Additional equations for the volumes and surface areas of geometric forms

common in tree crowns can be found in Husch and others (1982). Again,

the analyst has the option of further adjusting alternative volume equations

with the CDEN term as was done in equation 5.1.

Tree length (TL) is not listed among the crown indicators because tree 

length is a standard mensuration variable measured on all FIA plots. In

fact, for trees with broken tops, FIA crews measure both actual tree length 

and total tree length (which includes the broken top). Note that some 

adjustment for the calculation of crown length (CL) in equation 5.1 may 

be necessary for trees with broken tops because ULCR and CDEN are 

measured differently. When CDEN for a broken tree is estimated in the 

field, the crown outline upon which CDEN is based includes the broken 

top, i.e., the CDEN estimate is reduced to account for the missing biomass. 

ULCR is based on the ratio of live crown to actual tree length, i.e., not the 
46

total tree length that includes the broken top. If the analyst wants to utilize 

the CDEN specified in the second part of equation 5.1, CL must be adjusted 

for trees with broken tops to retain the proper relationship between CDEN

and CL. Since the missing top is included in the CDEN field estimate, the 

missing top should also be included in the estimate of crown length:

(5.2)

where

 AL = actual tree length (feet)

If the term for CDEN is to be omitted from equation 5.1, then the crown 

lengths of trees with broken tops would be calculated as follows:

(5.3)

At this writing FIA has concluded that crown diameter measurements are 

prohibitively expensive, so field measurements of CD are not available in 

most FIA datasets. Bechtold (2003b, 2004) has developed crown diameter 

prediction models for numerous species in the Eastern and Western United 

States, which may be used when CD field measurements are not available. 

Analyses of indicators involving model-based estimates of crown diameter 

must be interpreted cautiously since any error in the predicted values will 

be propagated through the indicators. If species are encountered for which 

no crown diameter models are available then models for similar species 

will have to be substituted or else these species must be deleted from the 

analysis. An alternative to using the CCV composite indicator calculated 

with predicted CDs would be to remove the portion of equation 

(5.1) and create an indicator that is a function of only CL and CDEN.

A second composite indicator is crown surface area (CSA). CSA requires 

the same adjustments for CL described above for CCV. CSA for the parabolic crown form is

(5.4)

In addition to crown volume and surface area, crown shape ratio (CSHAPE) 

and crown production efficiency (CEFF) have also been related to a tree’s 

potential to capture and utilize solar energy (Ford 1985; Larocque and 

Marshall 1993, 1994a, 1994b; Perry 1985). These are defined as
47

(5.5)

and

(5.6)

Finally, note that all discussion of composite crown indicators in this report 

focuses on a univariate approach to analysis. Composite tree- or stand-level 

crown indicators may disregard possible interactions among the individual 

crown indicators from which they are derived, which could be inappropriate for some analyses. One alternative that circumvents this problem 

is a multivariate approach (Morrison 1976), which formulates a vector of 

responses from all variables in the analysis.

5.2.2 Indicator Thresholds

Thresholds are important to assessing forest health because they can be 

used to separate the sampled population into categories of good, average, 

and poor. However, before pursuing an analysis based on thresholds the 

analyst should consider that information may be lost by reducing the data 

to a few discrete groups. Extra caution should be used if the thresholds 

have not been rigorously derived, or if alternative methods are available to 

analyze the data in their original scale.

5.2.2.1 Biological Thresholds

Ideally, thresholds should be developed on a biological basis. 

Unfortunately, with respect to the crown indicators, the point at which a 

tree begins to decline is difficult to pinpoint. This will require the establishment of correlations between crown indicators and losses resulting 

from future growth reduction and mortality. The task is further complicated because the thresholds are likely to vary by species, and the effects 

of normal stand dynamics and attrition must be considered. Although 

biological thresholds for the crown indicators have yet to be developed, 

some consideration has been given to how such research might proceed. 

Logistic regression is one approach with the potential to yield rigorous 

biological thresholds. Because the logistic model is bounded by zero and 

one, it is particularly useful for modeling mortality and survival. Stepwise 

logistic regression where crown indicators would be employed as independent variables could be used to identify the most significant variables, 

and plots of the survival probability (zero to one) over values of the independent variables could be produced. Additional tree and stand variables 

designed to quantify the effect of natural stand dynamics might also be 

included. If risk of mortality were correlated with crown condition in this 
48

way, and if survival models were developed over multiple regions or years, 

various hypotheses about changing survival rates could be developed and 

tested.

Discriminant analysis is an alternative technique that might be used to 

objectively divide an indicator into categories. This technique produces a 

discriminant function useful for predicting to which group an observation 

from a new dataset is most likely to belong. The discriminant function is 

formed from data that has one group classification variable and several 

quantitative variables that are used by the function to maximize the probability of correct group membership. For instance, survival (dead or alive) 

may be the group classification variable and the quantitative variables 

may be some combination of crown indicators. Again, other tree and stand 

variables could be included to account for the effect of stand dynamics. 

The resulting discriminant function would then be used to group recently 

measured trees into survival categories based on the dependent variables. 

Discriminant analysis is typically based on the assumption that the data 

follow a multivariate normal distribution, but if this is not the case then 

nonparametric discriminant methods can be used.

5.2.2.2 Subjective and Empirical Thresholds

The most serious impediment to the development of biological thresholds is lack of data. Mortality is relatively rare, and individual trees 

have not yet been tracked long enough on the FIA phase 3 plot network 

to yield an adequate dataset. In the meantime, there are alternatives to 

biological thresholds. The simplest are subjective thresholds. When these 

are employed, analysts use their own experience and judgment to assign 

trees to categories. Subjective thresholds may be adequate for descriptive purposes and gross comparisons, but they are insufficient for all but 

the most elementary analyses. For some purposes, improvement over the 

subjective approach can be achieved through empirical methods that more 

objectively achieve such groupings. The field of cluster analysis provides 

numerous analytical methods to cluster observations into groups that tend 

to be similar with respect to a specified set of variables. These methods 

include average linkage, centroid, complete linkage, maximum likelihood, 

flexible beta, and Ward’s minimum-variance method (SAS Institute Inc. 

1989). It must be emphasized that these techniques have the disadvantage 

that the thresholds are relative, i.e., they can change from one set of observations to the next. In addition, the traditional analysis of variance is not 

valid for testing differences between cluster means because the clusters 

were formed by methods that are intended to maximize the difference 

between the clusters in the first place.
49

5.2.2.3 Statistical Thresholds

Statistical thresholds offer a reasonable alternative to biological thresholds. 

They are easy to establish, available for immediate use, and in some cases 

may be preferred even when biological thresholds are available. The main 

disadvantage with statistical thresholds is that they always result in a set of 

observations designated as poor, even in the absence of a problem, so they 

must be used cautiously.

Statistical thresholds can be identified by isolating observations at the 

tails of statistical distributions, or they may involve more elaborate techniques such as the one discussed in the next paragraph. When employed 

in combination with observations that have been adjusted for the natural 

influence of tree and stand characteristics, they can be quite useful for 

detecting spatial patterns and measuring change over time. The residualization and standardization techniques discussed in the next section can 

accomplish these types of adjustments, and are ideal for use with statistical thresholds.

A more elaborate technique involving statistical thresholds focuses on the 

relationship between means and variances. For example, a plot-level mean 

crown density of 0.5 may indicate that all trees have crown densities of 50 

percent or that half the trees are totally defoliated while the other half are 

100-percent foliated. An indicator based on both the mean and variance 

could distinguish between these drastically different situations. For indicators that have bounds of zero and one, the beta distribution could be fitted 

for individual tree crown indicators on a field plot (Zarnoch and others 

1995) and plotted on a two-dimensional axis for a sample of plots. Ninetyfive percent-confidence ellipsoids, i.e., thresholds, could then be developed, 

where plots outside the ellipsoids indicate atypical situations for either the 

mean or variability of the crown indicator.

5.2.3 Residualization and Standardization

Previous discussions have mentioned the need to adjust values of crown 

indicators for natural factors known to influence crown dimensions. This 

can be accomplished through residualization, whereby an indicator is 

modeled as a function of tree and stand conditions in an attempt to remove 

the effect of natural stresses and stand dynamics, thus increasing the 

signal-to-noise ratio. Any model form can be used, so long as the parameters are biologically reasonable and unbiased. The resulting residuals are 

of primary interest, particularly those in the tails of the statistical distributions. Resulting patterns (involving space, time, or specified groups 

of trees) associated with residuals may signify a potential problem. At a 
50

minimum, such adjustments should be performed by species, since reactions to natural factors differ by species, e.g., shade tolerance.

Residualization proceeds as follows. Specifically, let

 = a specific crown indicator for tree t of species s

 = the predicted value of the indicator for tree t of species s based on

an appropriate adjustment model

Then

(5.7)

where

 = the residualized indicator for tree t of species s

At this point, the residualized indicator is still scaled differently by species,

but an additional adjustment can be performed to standardize residuals

across species. To do this, one rescales the residualized indicators

from equation 5.7 to a standard deviation of one by dividing the model

residuals by their standard deviation:

(5.8)

where

 = the standardized-residual indicator for tree t of species s

 = the standard deviation of the model residuals for species s

We now have a tree-level indicator that has been adjusted for the

parameters in the model and standardized (by species) to a mean of zero,

i.e., the mean of the model residuals is zero, and a standard deviation of

one. Standardization in this manner allows indicator values for trees to be

combined across species for analysis. Values for trees can then be averaged or otherwise grouped for direct comparison by tree-level attributes,

e.g. overstory vs. understory trees, condition-level attributes, e.g., public

vs. private ownership, or plot-level attributes (Piedmont vs. Coastal Plain).

More details on residualization and standardization techniques are provided

by Zarnoch and others (2004).

Note that standardization is not absolutely contingent on modeling. Had

residualization via modeling not been desired or possible, a standardized

indicator could still have been produced by replacing the predicted

in equation 5.7 with the mean from the data. Although adjustment

by species is usually desirable, this too is not required. The residualization
51

and standardization described above could have been performed across all 

trees of interest, rather than by species.

5.2.4 Stratification Variables

Most analyses can be improved by stratification or adjustment of the crown 

indicators with auxiliary variables known to influence crown condition. 

Descriptive analyses are more informative when an indicator is stratified 

into meaningful groups of class variables. Hypothesis testing with analysis 

of variance requires the grouping of data into discrete classes. For hypothesis testing, the biological aspects of the data must be taken into account so 

that the groups have a meaningful level of within-group homogeneity and 

assumptions of the applied statistical test are not violated. It is also important to consider the degree of balance required by the statistical procedure 

as well as the number of observations available. Overstratification can 

dilute the analysis; if there are too few observations in some strata, an 

important signal can be weakened. On the other hand, the signal-to-noise 

ratio may be too low if the data are reduced to too few groups. Achieving 

the proper balance for a given analysis may require several iterations.

At the tree level, species, stem size (d.b.h. or d.r.c.), crown light exposure 

(CEXP), and canopy position (CPOS), are all candidates for meaningful 

stratification of tree crown dimensions. Note that the latter three are listed 

as absolute crown indicators in table 5.1, but their primary value is that 

they can be used to stratify or adjust indicators that provide measures of 

crown dimension. At the plot and condition class, i.e., stand, levels, it 

may be useful to partition the crown indicators by a variety of additional 

variables recorded by FIA such as latitude, elevation, slope, aspect, stand 

density (e.g., basal area per acre), forest type, physiographic class, stand 

origin (planted vs. natural), ownership, and disturbance history. Analysts 

should also consider correlating the crown indicators with overlays from 

other datasets such as weather or pollution records.

5.2.5 Data Compilation

There are a number of issues to consider when compiling crown data for 

analysis. Analyses of crown indicators usually proceed at the tree, plot, or 

population levels. Most descriptive and inferential analyses will involve 

population values such as the mean indicator value for a population or 

domain of interest, or the proportions of trees classified into categories of 

good or poor via thresholds. Less commonly, tree-level or mean plot-level 

crown indicators may be the focus of an analysis. Bechtold and Coulston 

(2005) utilized tree-level and plot-level CCVs to check for spatial clustering of trees with small crown volumes. When computing population 

means and totals from the crown indicators observed on sampled trees, 
52

calculations based on simple random sampling are recommended. Another 

alternative that might yield improved variance estimates would be to use 

the FIA data processing system, which is based on double sampling for 

stratification (Bechtold and Patterson 2005).

The level to which the crown data are compiled should also be considered 

in the interpretation of results. For example, population ecologists often 

emphasize the importance of population-level data, rather than individuallevel data, in predicting the future success of a community. However, from 

an epidemiological perspective, the distribution of a disease throughout 

a population is driven by individuals. At the initiation of an epidemic, 

a population or plot-level subset of individuals may actually exhibit 

improved crown condition as survivor trees benefit from reduced competition. Plot-level or species-level means may also increase simply due to 

increased mortality of weaker trees with smaller crowns. In such situations, 

population-level diagnostics may not detect a disease until much later, 

when many more trees have been affected.

5.3 Structuring Analyses to Accommodate the Data and

 Objectives

Whether the objective of analysis involves descriptive statistics, inferential statistics, or modeling, the nature of the variables under study must 

be understood before analysis can proceed. There are three commonly 

recognized measurement scales associated with the crown indicators, each 

of which may require different statistical treatment. In order of increasing 

scale from discrete to continuous these are the nominal, ordinal, and ratio 

scales (table 5.1). The ratio scale is required for typical parametric statistical methods that assume the normal or some other well known distribution. Nonparametric statistical methods are appropriate to the nominal 

and ordinal scales, and to ratio scales where the underlying distribution is 

nonnormal. Nonparametric procedures can also be applied to the ratio scale 

with little loss of statistical power in many cases. Tests of normality (or 

other distributions) may be performed with standard goodness-of-fit tests 

such as the Chi-square and Kolmogorov-Smirnov tests (Conover 1980).

5.3.1 Descriptive Statistics

The most fundamental descriptors of any ratio variable are measures of 

central tendency, and the statistic most commonly used for this purpose 

is the mean. For a mean to be valid, the sample must be randomly drawn 

from the population of interest. The FIA sampling framework is partially 

systematic, but this is a reasonably good approximation of a random 

sample for most practical situations (Milne 1959). The median may be 
53

more appropriate than the mean for highly skewed nonnormal data distributions, particularly those with large outliers that substantially influence 

the sample mean. The median represents the midpoint or 50th percentile; 

half of the observations in the sample fall below it and the other half above 

it.

Measures of central tendency are usually accompanied by measures of 

dispersion, such as the sample variance. The utility and interpretation of 

the variance is often disregarded, but it is as important as the mean. This is 

particularly true in forest health problems that focus on epidemiology. For 

example, if the mean plot-level incidence of diseased trees is 50 percent 

in each of two populations, then the sample means are equal. However, if 

population 1 has low variance while population 2 has high variance, it may 

be inferred that most plots in population 1 are infected while only a portion 

are infected in population 2. This may indicate that population 2 is exhibiting some kind of biological resistance to the pathogen or may be in an 

earlier stage of spread of the disease.

There are additional statistics directly related to the variance that have 

slightly different interpretations and usages. The standard deviation, which 

is the square root of the variance, expresses the variance in the same units 

as the mean. It is commonly assumed that the mean plus or minus two standard deviations contains approximately 95 percent of the population, but 

this assumption is contingent on a normal distribution. Care must be exercised when applying this rule to the crown variables because many of them 

may have nonnormal distributions. Another measure of dispersion is the 

standard error of the mean. Unlike the mean, variance, and standard deviation, the standard error is not actually an estimate of any population characteristic since it is a decreasing function of the sample size. However, it 

is extremely useful for measuring the variability of the mean, establishing 

confidence intervals, and testing hypotheses.

Beyond the presentation of central tendency and dispersion statistics, it 

is often useful to characterize the entire frequency distribution associated 

with an indicator. The upper and lower percentiles (tails) of statistical 

distribution may be more sensitive than measures of central tendency in 

connection with forest health issues, so they should definitely not be overlooked. Statistical distributions are commonly displayed with histograms, 

which plot the distribution of observations over discrete points, intervals, 

or arbitrary groupings of the variable of interest. Histograms are appropriate for both discrete and continuous variables. When used with continuous data, histograms often include the locations of the mean, median, and 

various percentiles.
54

An equivalent (but less common) method to display frequency distributions 

is the cumulative distribution function (CDF). This is a sigmoid (s-shaped) 

step function defined as F(x), where F(x) is the proportion of observations 

less than or equal to x, and x ranges over all values of the crown indicator. 

The advantage of the CDF is the simplicity with which population percentiles may be calculated and compared. For instance, the lower 10th percentile may be obtained as x such that . Another example is ease 

of computing the proportion of observations between some range of values 

x1 and x2; this is simply . 

5.3.2 Inferential Statistics

The hypothesis testing required for detection and evaluation monitoring 

leads to the field of inferential statistics. There are three types of change 

detection of primary interest to the crown indicator:

1. Detection of change over space at a point in time

2. Detection of differences among groups of observations at a point in 

time, e.g., forest types

3. Detection of change over time at a specified geographical location

Statistical tests designed for hypothesis testing must be properly matched 

to the experimental design. The two simplest experimental designs that 

might be used in conjunction with FIA crown classification data are the 

completely random design and the randomized block design. To detect 

differences among geographic locations or among condition classes at 

a point in time, a completely random design is generally appropriate if 

the observations are selected at random. When analyzing change over 

time for a given geographic location, two situations may be encountered. 

If the observations made during one period are independent of those 

made during the other period, a completely random design can be used. 

However, if the observations are dependent (which may be the case when 

the same plot is measured at both times), a randomized block design is 

appropriate. For more complex situations, a repeated-measures analysis 

may be a better choice. More information about experimental designs and 

inferential statistics can be found in Kempthorne (1952), Cochran and 

Cox (1957), or John (1971). Also, when one uses tree-level observations 

to test hypotheses, one should be aware that the number of trees usually 

varies from plot to plot, resulting in an unbalanced design. An approximate analysis is possible if one simply uses plot-level means but this does 

not take into account the unbalanced nature of the design. Therefore, it is 

recommended that trees be nested within plots and that a two-stage nested 

design be employed.
55

Given a random sample from two sets of observations under a completely 

randomized design, a two-sample t-test can be performed to test whether 

the means are equivalent:

where

 H = hypothesis

 u = population mean

If three or more locations are tested, this will generalize to the well-known 

analysis of variance.

5.3.3 Modeling

Modeling has many potential applications to the crown indicators. For 

example, if the observations of interest in the hypothesis tests described in 

the previous section occur on a gradient across the landscape, regression 

methods could be utilized to test for significant gradient effects. Modeling 

is particularly useful for residualization and other techniques designed to 

adjust the crown indicators for stand dynamics and other natural factors. 

Insights into forest health might also be gained by using models to quantify 

the relationships between two or more crown variables. Those relationships can also be evaluated for change. Here we briefly discuss some of the 

various model forms that might be considered.

Simple linear regression is the method most commonly used to obtain relationships between a dependent variable y and an independent variable x. 

The regression equation is 

where

 the parameters and are estimated from the data

Since numerous assumptions and modifications are required or possible 

when using regression methods, a good statistical regression text such as 

Draper and Smith (1981) should be consulted for more detail. Multiple 

linear regression is an extension of simple linear regression; in multiple 

linear regression several independent variables are used to model the 

dependent variable. Computation of the parameters becomes more involved 

with each additional independent variable, so it is almost mandatory to use 

a statistical software package to do this work. A further extension is the use 

of nonlinear regression methods for fitting complex models that may be 

more biologically realistic.
56

The logistic model is quite useful for modeling a dichotomous (0, 1) variable as a function of continuous or discrete independent variables. It can 

be especially useful in the development of risk assessment or mortality 

models. The logistic model is defined as

where

 = the predicted probability of a defined event (mortality, survival, etc.)

 = the ith independent variable

 are estimated from the data

Categorical data modeling may be quite useful for analyzing relationships 

when an indicator can have only a limited number of distinct values (as 

in the case of crown light exposure, for example). This is often the situation in monitoring forest health, where typical data could be represented 

by r-way contingency tables that are formed by cross tabulation of r 

different nominal and ordinal variables. In the usual regression modeling 

approach, the dependent variable is usually continuous and has an approximately normal distribution. In logistic regression the dependent variable 

is dichotomous. Hence, categorical modeling is a generalization of these 

methods in which the dependent and independent variables may take on a 

limited number of values (two or more). It fits linear models to functions 

of response frequencies and can be used for linear modeling, log-linear 

modeling, logistic regression, and repeated measures analysis.

In contrast to modeling, where one variable is predicted as a function of 

one or many variables, correlation analysis measures the strength of the 

linear relationship between two variables. Generally the Pearson correlation coefficient is calculated; a zero indicates that there is no relationship, 

and 1 and -1 represent strong positive and negative linear associations, 

respectively. If the variables under consideration are nonnormal, then 

nonparametric measures of association such as Kendall’s or Spearman’s 

should be employed.

5.3.4 Spatial Analyses

The combination of spatial scan statistics with residualization, standardization, and statistical thresholds is a powerful tool for detection monitoring. 

Bechtold and Coulston (2005) implemented this approach to check for 

unusual spatial patterns associated with crown condition in South Carolina, 

where the spatial scan statistic developed by Kulldorff (1997) was used 

to search for potential clusters of plots with below average CCVs. The 

Kulldorff statistic was originally developed to test for randomness of 
57

disease occurrence in the spatial and spatio-temporal domains and has been 

applied to other indicators of forest health by Coulston and Riitters (2003). 

The scanning proceeds by visiting every location, i.e. plot, in the study 

area. A series of circular windows of increasing size (up to 50 percent of 

the study area) is then superimposed over each location. The test statistic,

 , is then calculated using the total number of “events” inside and outside 

each window. is the likelihood ratio, based on the Bernoulli distribution, that the occurrence of events is the same everywhere after adjusting 

for differences in the total number of observations (events and nonevents) 

inside and outside the window:

(5.9)

where

 Ec = the number of events within the window

 Nc = the number of nonevents within the window

 = the number of events outside the window

 = the number of nonevents outside the window

 I = 1 if , or 0 otherwise

The indicator function I in equation 5.9 sets up a one-sided test of the

null hypothesis against the alternative that the

rate of events is higher inside the window.

The distribution of across the study area and p-values associated with

 are obtained by Monte Carlo simulation based on numerous random 

replications of the full dataset under the null hypothesis of complete spatial 

randomness. The significance test for the cluster of observations within

the window compares for the window to the distribution of from

the Monte Carlo simulation. If the value of exceeds 95 percent of the

values from the Monte Carlo simulation, then the cluster associated with

a given window is considered significant at the 5-percent level. An event

E is defined as a tree with a residualized indicator value below some

threshold percentile of the frequency distribution of all trees in the study 

area, and nonevents N as trees with values above the specified threshold:
58

(5.10)

where

 Ep = the sum of events on plot p

 Etp = 1 if tree t is on plot p and or 0 otherwise

 Ts = the specified percentile of the distribution of the residuals for

 species s across the study area

When checking for spatial trends, the statistical threshold E might initially 

be set relatively high, e.g., 25th percentile, and then lowered to verify any 

spatial trends that result. This technique works with indicators formulated 

at either the tree level or plot level. The larger sample size attained by 

using tree-level values increases the power of the test.
59

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73

8 Appendices

8.1 Appendix A: Field Data Codes

Portable data recorder (PDR) prompt codes for crown

indicator variables

Crown vigor

1 CVIG

Crown light exposure CEXP

Crown position CPOS

Crown diameter CDIA

Uncompacted live crown ratio ULCR

Crown density CDEN

Crown dieback CDBK

Foliage transparency FTRAN

1

Crown vigor is recorded for trees < 5.0 inches d.b.h. (or d.r.c.

[diameter at root collar]). All other variables are recorded for trees

5.0 inches d.b.h. (or d.r.c.) and larger.

Crown vigor codes

1 Live crown ratio at least 35 percent, at least 80 percent normal foliage

(50 percent of each leaf undamaged), and < 6 percent dieback

2 Does not meet class 1 or 3 and has at least 21 percent normal foliage

3 Any live crown ratio, 1 to 20 percent normal foliage, and any amount

of dieback

Crown light exposure codes

0 The tree receives no direct light because it is heavily shaded by trees,

vines, or other vegetation

1 The tree receives direct light from the top or one side

2 The tree receives direct light from the top and one side (or two sides

without the top)

3 The tree receives direct light from the top and two sides

4 The tree receives direct light from the top and three sides

5 The tree receives direct light from the top and four sides
74

Crown position codes

1 Superstory1—the live crown top is at least twice the height of the top 

of the overstory canopy zone 

2 Overstory—the live crown top is above the middle of the overstory 

canopy zone

3 Understory—the live crown top is at or below the middle of the overstory canopy zone

4 Open canopy—an overstory canopy zone is not evident because the 

tree crowns in this condition are not fully closed (< 50 percent crown 

cover)

1

Code 1 is valid only for trees at least 5.0 inches d.b.h. (or d.r.c. [diameter at root collar]).

Crown diameter codes

00 Epicormic branches only 000 Epicormic branches only

01 < 1.5 feet 001 < 0.15 m

02 1.6 to 2.5 feet 002 0.16 to 0.25 m

03 2.6 to 3.5 feet 003 0.26 to 0.35 m . . . . . .

99 98.6 to 100 feet 995 98.6 to 100 m

Uncompacted live crown ratio, crown density, crown dieback, and 

foliage transparency codes1

 percent percent percent

00 0 35 31 – 35 70 66 – 70

05 1 – 5 40 36 – 40 75 71 – 75

10 6 – 10 45 41 – 45 80 76 – 80

15 11 – 15 50 46 – 50 85 81 – 85

20 16 – 20 55 51 – 55 90 86 – 90

25 21 – 25 60 56 – 60 95 91 – 95

30 26 – 30 65 61 – 65 99 96 – 100

1

For uncompacted live crown ratio and crown density, the code 00 is used for trees with 

epicormic branches only.
75

8.2 Appendix B: Glossary of Terms

crown: the crown stem, branches, twigs, buds, foliage, and seeds or cones 

of a tree located above the obvious live crown base. Dead lower branches 

and straggler live branches below the obvious live crown base are normally 

excluded.

crown cover: the percentage of the ground surface covered by a vertical 

projection of crowns from above. 

crown outline: the two-dimensional side view of a tree crown. Crown 

outlines applied to sample trees are based on the expected values for a 

vigorous tree of the same species and stem size. 

crown stem: the main axis of a tree stem between the live crown base and 

the crown top.

crown top: the top of a tree. For uncompacted live crown ratio, the highest 

live foliage is considered the crown top. The crown outline used for crown 

density estimates includes dead tops.

diameter at breast height (d.b.h.): the diameter of a tree stem, located at 

4.5 feet above the ground (breast height) on the uphill side of a tree. The 

point of diameter measurement may vary on abnormally formed trees.

diameter at root collar (d.r.c.): the diameter of a woodland tree species, 

measured outside bark at the groundline or stem root collar.

dripline: the vertical projection of the perimeter of a tree crown.

epicormic branches: recent branches or shoots arising from dormant 

buds on the main stem of a tree. Epicormic branching often follows stress 

or damage to the tree. Epicormic branches remain classified as such until 

they reach the size of regular branches. For trees that had 1.0 inch or larger 

branches when the epicormic branches formed, epicormic branches are 

considered regular branches once they reach 1.0 inch in diameter.

five-foot rule: the rule that establishes the position of a live crown base 

when scattered branches are present in the lower portion of a tree crown. If 

the point of attachment (to the bole) of any live branch is < 5 feet below the 

obvious live crown baseline, a new base is established at the lowest foliage 

of this branch. This process is repeated until no qualifying branches occur 

within 5 feet of the live crown base.
76

forest: land that is or was formerly at least 10-percent stocked with forest 

trees of any size and is not currently developed for a nonforest use. The 

minimum area for classification as forest land is 1 acre. Roadside, streamside, and shelterbelt strips of timber must have a crown cover width of 

at least 120 feet to qualify as forest land. Unimproved roads and trails, 

streams and other bodies of water, or natural clearings in forested areas 

shall be classified as forest if < 120 feet in width or 1 acre in area. Grazed 

woodlands, reverting fields, and pastures that are not actively maintained 

are included if the above qualifications are satisfied. 

forest type: a classification of forest land based upon and named for the 

tree species that forms the majority of live-tree stocking. A forest type classification for a field location indicates the predominant live-tree species 

cover for the field location; hardwoods and softwoods are first grouped to 

determine predominant group, and forest type is selected from the predominant group.

indicator: a characteristic of the environment that can be correlated with 

exposure to an environmental stressor. Indicators may be biotic or abiotic.

live crown base: the point where an imaginary line perpendicular to the 

main stem intersects the lowest live foliage of the obvious live crown. For 

trees mature enough to have a live branch at least 1.0 inch in diameter, 

the obvious live crown base is defined by branches above the 1.0-inch 

minimum. For trees and saplings that have no branches larger than the 1.0-

inch minimum, smaller branches and twigs are included. In cases where no 

obvious live crown is apparent, the 5-foot rule is used to establish the base.

Epicormic branches are not considered as part of the live crown base until 

they reach the size of regular branches. For trees that had 1.0-inch branches 

when the epicormic branches formed, epicormic branches are considered to 

be regular branches once they reach 1.0 inch in diameter.

live crown length: the distance from the live crown base to the live crown 

top.

live crown top: the point where an imaginary line perpendicular to the 

main stem intersects the highest live foliage of a tree.

macroholes: large spaces between branches. These are usually the result of 

branch mortality but may also be due to species characteristics. Macroholes 

are included in the crown outline and reduce crown density estimates. 

Macroholes are usually ignored when estimating dieback and transparency, 

but are included in dieback and transparency estimations if they are recent. 
77

microholes: small spaces within the crown of a tree where light passes 

between layers of leaves or closely spaced branches. These areas are 

considered for all crown measurements.

obvious live crown: the portion of the tree crown where most live branches 

or twigs are typically concentrated for a given tree species and tree size at a 

particular site. Epicormic twigs and straggler branches are excluded.

overstory canopy zone: the height zone bounded by the average height to 

the live crown bases and the average height to the live crown tops of overstory trees in a forest stand. 

sapling: a live tree 1.0 to 4.9 inches in diameter at d.b.h. (or d.r.c.).

snag branch: a dead branch above the live crown base without twigs or 

sprigs.

sprig: a woody or nonwoody lateral shoot, without secondary branching, 

< 1.0 inch in diameter at the base above the swelling at the point of attachment to a branch or crown stem.

stocking: the degree of occupancy of land by trees, measured by basal area 

and/or the number of trees in a stand by size or age and spacing, compared 

to the basal area and/or number of trees required to fully utilize the growth 

potential of the land; that is, the stocking standard. 

straggler branch: a live branch that occurs more than 5 feet below the 

base of the obvious live crown.

tree: a woody perennial plant, typically large, with a single well-defined 

stem carrying a more or less definite crown, defined as attaining a minimum diameter of 5.0 inches and a minimum height of 15 feet at maturity.

twig: any woody lateral growth, with secondary branching, < 1.0 inch 

in diameter at the base above the swelling at the point of attachment to a 

branch or crown stem.

woodland: forest land with 10 percent or more crown cover in woodland 

species, or timber and woodland species combined, but < 5-percent crown 

cover in timber species; or forest land with a minimum of 40 seedlings, or 

saplings, or both per acre of woodland species.
78

woodland species: a shrublike tree species. The Forest Inventory and 

Analysis Program measures the stem diameters of woodland species at the 

root collar. 


Schomaker, Michael E.; Zarnoch, Stanley J.; Bechtold, William A.; Latelle, 

David J.; Burkman, William G.; Cox, Susan M. 2007. Crown-condition

classification: a guide to data collection and analysis. Gen. Tech. Rep. SRS102. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 78 p.

The Forest Inventory and Analysis (FIA) Program of the Forest Service, U.S. 

Department of Agriculture, conducts a national inventory of forests across the 

United States. A systematic subset of permanent inventory plots in 38 States is 

currently sampled every year for numerous forest health indicators. One of these 

indicators, crown-condition classification, is designed to estimate tree crown

dimensions and assess the impact of crown stressors. The indicator features eight 

tree-level field measurements in addition to variables traditionally measured in 

conjunction with FIA inventories: vigor class, uncompacted live crown ratio, 

crown light exposure, crown position, crown density, crown dieback, foliage 

transparency, and crown diameter. Indicators of crown health derived from the 

crown data are intended for analyses at the State, regional, and national levels, 

and contribute to the core tabular output in standard FIA reports. Crown-condition measurements were originally implemented as part of the Forest Health 

Monitoring (FHM) Program in 1990. Except for crown diameter, these measurements were continued when the FIA Program assumed responsibility for FHM 

plot-based detection monitoring in 2000. This report describes in detail the data 

collection and analytical techniques recommended for crown-condition classification.

Keywords

tree health indicators, tree crown measurement.

: Forest health indicators, tree crown condition, tree crown health,
The Forest Service, United States Department of

Agriculture (USDA), is dedicated to the principle 

of multiple use management of the Nation’s forest

resources for sustained yields of wood, water, forage, wildlife, 

and recreation. Through forestry research, cooperation with

the States and private forest owners, and management of the 

National Forests and National Grasslands, it strives—as directed 

by Congress—to provide increasingly greater service to a 

growing Nation.

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