Descriptive Statistics of Tree 

Crown Condition in the Southern 

United States and Impacts on 

Data Analysis and Interpretation

KaDonna C. Randolph

United States

Department of

Agriculture

Forest Service

Southern

Research Station

General Technical

Report SRS–94


Author

August 2006

Southern Research Station

P.O. Box 2680

Asheville, NC 28802

KaDonna C. Randolph, Mathematical Statistician, 

USDA Forest Service, Southern Research Station, 

Forest Inventory and Analysis, Knoxville, TN 37919. 
Descriptive Statistics of Tree 

Crown Condition in the Southern 

United States and Impacts on Data 

Analysis and Interpretation

KaDonna C. Randolph
Contents

Page

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

Methods..................................................................................................................................................... 1

Data Collection ...................................................................................................................................... 1

Data Summary ....................................................................................................................................... 2

Results....................................................................................................................................................... 3

Species Groups....................................................................................................................................... 5

Individual Species.................................................................................................................................. 5

Discussion.................................................................................................................................................. 8

Influence of Species and Stand Dynamics............................................................................................. 8

Statistical Characteristics ...................................................................................................................... 9

Accommodating the FIA Sampling Design........................................................................................... 13

Interest in the Poorest and Best Crowns................................................................................................. 13

Distinguishing Biological and Statistical Significance ......................................................................... 14

Conclusion ................................................................................................................................................ 14

Acknowledgments .................................................................................................................................... 14

Literature Cited........................................................................................................................................ 14

Appendix................................................................................................................................................... 17

ii
Abstract

The U.S. Department of Agriculture Forest Service, Forest Inventory and 

Analysis Program (FIA) utilizes visual assessments of tree crown condition to monitor changes and trends in forest health. This report describes 

and discusses distributions of three FIA crown condition indicators (crown 

density, crown dieback, and foliage transparency) for trees in the Southern 

United States. Descriptive statistics are presented for all trees combined, 

by hardwood and softwood species groups, and for 53 individual species. 

Implications of these characteristics and other factors for the analysis of the 

phase 3 crown condition indicators are discussed.

Keywords: Crown density, crown dieback, FIA, foliage transparency, forest 

health.

Introduction 

Given the charge of reporting on the status and trends 

in forest ecosystem health in the United States, the U.S. 

Department of Agriculture (USDA) Forest Service, Forest 

Inventory and Analysis Program (FIA) assesses a suite of 

forest health indicators on a network of phase 3 plots systematically located across the United States (Riitters and 

Tkacz 2004). Because a tree’s health is generally reflected 

in the amount and condition of its foliage (Anderson 

and Belanger 1987, Innes 1993), tree crown condition is 

included as one of the FIA forest health indicators. 

Healthy trees typically have crowns that are distributed symmetrically along the stem in a predictable way, but when 

a tree is subjected to stress it reacts by slowing its growth 

and shedding parts of its crown (Millers and others 1989). 

The shedding of parts, termed dieback for the loss of fine 

twigs and defoliation for the loss of leaves, alters not only 

the tree’s appearance but also its rate of photosynthesis and 

carbohydrate production. Thus, the shedding of parts is a 

survival mechanism the tree uses to adjust and conserve its 

energy reserves. 

Each year, FIA monitors the amount of dieback and defoliation present on the phase 3 plots. Nationwide, annual monitoring of this type is relatively new, however, and before 

comprehensive modeling of changes and trends in forest 

health can be accomplished, the basic characteristics of tree 

crown condition must be understood. Thus, the purpose of 

this report is to present a summary of the statistical characteristics of three phase 3 crown condition indicators (crown 

density, crown dieback, and foliage transparency) in the 

Southern United States, and to discuss the implications of 

these and other considerations for the analysis and interpretation of the crown condition data. 

Methods

Data Collection

The data consisted of crown assessments from all forested 

phase 3 plots in Alabama, Georgia, North Carolina, South 

Carolina, Tennessee, and Virginia. Data were collected in 

1995, 1997, 1998, and 1999. Each phase 3 plot is a cluster 

of four 1/60-ha (1/24-acre) circular subplots with subplot 

centers located 36.6 m (120 feet) apart. Crown condition 

was measured for every tree ≥ 12.7 cm (5.0 inches) in diameter at breast height on each subplot. The crown condition 

indicators included in this summary report are (fig. 1): (1) 

crown density—the amount of crown branches, foliage, and 

reproductive structures that blocks light visibility through 

the projected crown outline; (2) crown dieback—recent 

mortality of branches with fine twigs, which begins at the 

terminal portion of a branch and proceeds inward toward the 

trunk; and (3) foliage transparency—the amount of skylight 

visible through the live, normally foliated portion of the 

crown, excluding dieback, dead branches, and large gaps in 

the crown.1

Crown density and foliage transparency cannot be interpreted as exact inverses. Crown density measures the 

amount of sunlight blocked by all biomass produced by the 

tree (both live and dead) in the crown, whereas foliage transparency measures the amount of sunlight penetrating only 

the live portion of the crown. Deductions are made from 

the maximum possible crown density for spaces between 

branches and other large openings in the crown. However, 

large gaps in the crown where foliage is not expected to 

occur are excluded from consideration when foliage transparency is rated. 

1

 U.S. Department of Agriculture Forest Service. 2003. Forest inventory 

and analysis national core field guide, FIA field methods for phase 3 

measurements: crown condition classification. Version 1.7. Internal report. 

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

and Analysis, 201 14th Street., Washington, DC 20250. http://fia.fs.fed.us/

library/field-guides-methods-proc. [Date accessed: March 18, 2005].

Descriptive Statistics of Tree Crown Condition 

in the Southern United States and Impacts on 

Data Analysis and Interpretation

KaDonna C. Randolph
2

All three indicators were visually assessed by Two-person 

field crews, and were recorded in 5-percent increments from 

0 to 100 percent. Higher crown density values, lower foliage 

transparency values, and lower crown dieback values are 

typically associated with better tree health. The trees were 

assessed during the summer, when trees maintain full crown 

foliage, typically between June and mid-August. More 

detailed descriptions of these and other crown condition 

indicators are available in the phase 3 data collection field 

guide (see footnote 1).

Data Summary

In order to correctly calculate means and variances, the 

unequal-sized clusters of trees on the FIA inventory 

plots must be taken into account. The SAS procedure 

SURVEYMEANS (An and Watts 1998) can accommodate 

the FIA survey design through the designation of a 

“cluster” variable, i.e., the primary sampling unit of the 

survey. Plot number was designated as the cluster variable 

and SURVEYMEANS was used to calculate means and 

standard errors for all species combined, hardwood and 

softwood species groups, and individual species with at 

Figure 1—The dashed line is the projected crown outline against which crown density is assessed. 

The dash-dot line within the projected crown outline defines the area of crown dieback. The 

striped areas are areas where foliage is not expected to occur and are not included in the foliage 

transparency estimate. Adapted from Millers and others (1992).
least 25 observations. Other descriptive statistics (skewness, 

kurtosis, minimum, maximum, and percentile values) were 

calculated also. All groupings of the data were examined 

for normality by means of normal probability plots and the 

Kolmogorov-Smirnov (KS) (if ntrees ≥ 2,000) or Shapiro-Wilk 

(if ntrees < 2,000) test. Trees with foliage but by definition 

having no crown, i.e., 0 percent crown density, 99 percent 

crown dieback, and 99 percent foliage transparency were 

omitted from the summary. 

Results

Crown condition was measured for 15,011 trees of 111 

species. Of these trees, 56.9 percent were hardwoods and 

43.1 percent were softwoods. Of the 111 species, 53 had 25 

or more observations. Overall, crown density ranged from

5 to 85 percent with a mean of 44.6 percent (fig. 2; table 1). 

Crown dieback ranged from 0 to 95 percent and had a 

mean of 1.9 percent (fig. 3; table 1). Foliage transparency 

ranged from 0 to 95 percent also, with a mean of 16.9 

percent (fig. 4; table 1). Although the values of each crown 

condition indicator spanned the range of possible values, 

the majority of the observations tended to concentrate in 

a small portion of the range. This was evident in the small 

interquartile ranges and in the low-valued 90th percentiles. 

The interquartile range is a measure of spread in the data 

and is equal to the difference between the 75th percentile and 

the 25th percentile. The widest interquartile range was 10 

percent for crown density. Both crown dieback and foliage 

transparency had interquartile ranges of only 5 percent 

(table 1). The 90th percentiles for crown dieback and foliage 

transparency were 5 and 25 percent, respectively, indicating 

that high values are uncommon for these indicators. The 90th

percentile for crown density was 60 percent, indicating that 

very dense trees are rare.

Skewness and kurtosis are measures of the degree of symmetry and relative peakedness of a distribution, respectively. 

These measures are often interpreted by reference to the 

normal (Gaussian) distribution, which has skewness equal to 

zero and kurtosis equal to three. (Kurtosis is often normalized so that the normal distribution has a kurtosis value of 

zero. The kurtosis values presented here have been normalized in this way.) Positive skewness values indicate that 

the observations to the right of the mean are more spread 

out than those to the left of the mean. Similarly, negative 

skewness values indicate that the observations to the left of 

the mean are more spread out than those to the right of the 

mean. When normalized to zero, positive kurtosis values 

indicate distributions with tails heavier than the normal 

distribution, and negative kurtosis values indicate distributions with tails lighter than the normal distribution. Crown 

density had skewness and kurtosis values most like those of 

Figure 2—Crown density frequency histogram and cumulative frequency distribution for all trees combined for 

Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.
Table 1—Mean crown attributes and other statistics a for all live trees > 4.9 inches d.b.h., by species group for Alabama, 

Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999 b

Crown condition

indicator and

species group

Standard

error

Minimum

Percentile

MaxiPlots Trees Mean Skewness Kurtosis 10 mum th 25th 50th 75th 90th

- - number - - - - - percent - - - - - - - - - - - - - - - -percent - - - - - - - - - - - - -

Crown density 

 All trees 635 15,011 44.6 0.324 0.130 0.296 5 30 40 45 50 60 85

 Hardwoods 576 8,535 46.2 0.325 -0.005 0.311 5 35 40 45 55 60 85

 Softwoods 437 6,476 42.1 0.522 0.249 0.538 5 30 35 40 50 55 85

Crown dieback 

 All trees 635 15,011 1.9 0.093 8.079 105.351 0 0 0 0 5 5 95

 Hardwoods 576 8,535 2.5 0.104 7.287 88.928 0 0 0 0 5 5 95

 Softwoods 437 6,476 1.2 0.122 10.050 149.113 0 0 0 0 0 5 90

Foliage 

 transparency

 All trees 635 15,011 16.9 0.212 1.972 14.400 0 10 15 15 20 25 95

 Hardwoods 576 8,535 15.7 0.232 2.480 22.228 0 10 10 15 20 20 95

 Softwoods 437 6,476 18.4 0.350 1.595 8.906 0 10 15 15 20 25 95

a

The mean and standard error calculations consider the clustering of trees on plots.

b

No data collected for 1996.

Figure 3—Crown dieback frequency histogram and cumulative frequency distribution for all trees combined for 

Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.
Figure 4—Foliage transparency frequency histogram and cumulative frequency distribution for all trees combined for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.

the normal distribution: skewness was 0.130 and kurtosis 

was 0.296. Crown dieback and foliage transparency were 

both positively skewed, with skewness values equal to 8.079 

and 1.972, respectively. The tails of the foliage transparency (kurtosis = 14.400) and crown dieback (kurtosis = 

105.351) distributions were heavier than those of the normal 

distribution. The tail of the crown dieback distribution was 

extremely so. 

The null hypothesis of the KS test (H0: data are normally 

distributed) was rejected for all three indicators (p < 0.01). 

Extremely large sample sizes, however, can lead the normality test to detect deviations from normality that are statistically significant but practically unimportant. This was likely 

the case for crown density since the straight-lined normal 

probability plot (fig. 5), bell-shaped histogram (fig. 2), and 

skewness and kurtosis values are characteristic of a normal 

distribution. On the other hand, the normal probability plots 

for crown dieback (fig. 6) and foliage transparency (fig. 7) 

support the KS test conclusion of nonnormality, with figure 

7 providing additional evidence that foliage transparency is 

right-skewed (Neter and others 1996). 

Species Groups

Hardwood crown density was about 5 percent higher than 

softwood density, both at the mean and at each percentile 

level (table 1). Mean foliage transparency was slightly 

higher for softwoods (18.4 percent) than for hardwoods 

(15.7 percent), but on average both groups exhibited about 

the same crown dieback (table 1). Figures 8, 9, and 10 show 

that hardwood crown density, crown dieback, and foliage 

transparency values were spread across the ranges much 

as the values for softwoods, although hardwood density 

was skewed slightly negatively while softwood density was 

skewed positively. There was much variation in the kurtosis 

values for the three indicators (table 1). The softwood group 

had a much higher crown dieback kurtosis (149.113) than 

did the hardwood group (88.928), though both kurtosis 

values were extremely high. In contrast, foliage transparency 

kurtosis was higher for the hardwoods (22.228) than for the 

softwoods (8.906). The KS test for normality by species 

group was rejected for all three indicators (p < 0.01); but 

as before, skewness, kurtosis, histograms (figs. 8, 9, and 

10), and normal probability plots (fig. 11) indicate that the 

conclusion of nonnormailty is appropriate only for crown 

dieback and foliage transparency. 

Individual Species

For each of the crown condition indicators, the 53 species 

examined individually exhibited a broad range of average 

conditions. Mean crown density ranged from 38.8 percent 

for swamp tupelo to 57.8 percent for eastern hemlock; 

median density ranged from 40 to 55 percent (table 2). Mean 

crown dieback was lowest for eastern hemlock (0.4 percent) 
Figure 7—Normal probability plot for foliage transparency, all trees 

combined for Alabama, Georgia, North Carolina, South Carolina, 

Tennessee, and Virginia, 1995 through 1999.

Figure 5—Normal probability plot for crown density, all trees combined 

for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and 

Virginia, 1995 through 1999.

Figure 6—Normal probability plot for crown dieback, all trees combined 

for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and 

Virginia, 1995 through 1999.

eastern hemlock, a difference of 30 percent (table 2). The 

difference between the lowest and highest values at the 75th

percentile was 25 percent. Even at the lower percentiles of 

crown density, the difference between the lowest and highest 

values was 15 percent, suggesting that there is a tendency 

for some species to maintain less dense crowns than others. 

Differences among the crown dieback and foliage transparency percentiles were less extreme. The greatest differences 

were at the 90th percentile for crown dieback (table 3), and at 

the 90th and 75th percentiles for foliage transparency (table 4). 

Skewness and kurtosis values for the three crown condition 

indicators varied among the individual species. Distributions 

of crown density were both positively and negatively skewed 

(table 2). The most left-skewed distribution was shagbark 

hickory (-1.213) and the most right-skewed distribution 

was baldcypress (0.721). The heaviest tails (kurtosis) for 

the crown density distribution were in shagbark hickory 

(3.233). All of the crown dieback distributions were positively skewed (table 3). Redbay had the least skewed dieback 

distribution (0.687), while loblolly pine had the most skewed 

dieback distribution (12.016). Loblolly pine also had the 

heaviest tails with a kurtosis value of 222.673. The foliage 

transparency distributions were skewed both to the left and 

to the right (table 4). Sweetbay had the most negatively 

skewed foliage transparency distribution (-0.757) and sweet 

birch had the most positively skewed distribution (4.420). 

The tails of the sweet birch foliage transparency distribution were the heaviest of all species with a kurtosis value of 

24.793. 

The tests for normality (KS or Shapiro-Wilk) indicated that 

all crown dieback distributions, all foliage transparency 

and highest for hackberry (4.7 percent); median dieback 

ranged from 0 to 5 percent (table 3). Mean foliage transparency was lowest for laurel oak (13.4 percent) and highest for 

Virginia pine (23.4 percent); median foliage transparency 

ranged from 12.5 to 20 percent (table 4). 

Percentile values indicated that the crown condition indicator distributions differed from species to species in more 

ways than the measure of central tendency. The greatest 

difference among the species was at the 90th percentile of 

crown density. There, the lowest value was 45 percent for 

swamp tupelo and the highest value was 75 percent for 
Figure 8—Frequency histograms and cumulative frequency distributions for crown density, by species group 

for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.

Figure 9—Frequency histograms and cumulative frequency distributions for crown dieback, by species group 

for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.
distributions except one (river birch), and all but 15 crown 

density distributions were nonnormal (alpha = 0.05). The 

species with normally distributed crown density were eastern hemlock, sweet birch, American hornbeam, hackberry, 

American beech, white ash, American holly, water tupelo, 

eastern hophornbeam, redbay, sycamore, live oak, black 

locust, sassafras, and winged elm. An examination of the 

normal probability plots and histograms confirmed the conclusions of nonnormality for crown dieback and indicated 

that the deviations from normality for crown density were 

minimal. The histograms also indicated that the lack of 

normality for foliage transparency was in many cases due to 

one or two outlying observations in the far right tail of the 

distribution. 

Discussion

FIA, in conjunction with the USDA Forest Service, Forest 

Health Monitoring Program, uses the crown condition 

(and other phase 3) indicators to report on the status of and 

changes in forest health in the United States. In this effort, 

there are two basic avenues of inferential analysis: (1) the 

detection of differences among groups of observations at a 

single point in time, and (2) the detection of changes over 

time for a specific group of observations. In each case the 

overall task is to determine the equality of two groups, 

but a number of factors influence the way these analyses 

are implemented and the manner in which the results are 

Figure 10—Frequency histograms and cumulative frequency distributions for foliage transparency, by species 

group for Alabama, Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999.

interpreted. Some of these factors include the influence of 

species and natural stand dynamics on individual tree crown 

condition, the statistical characteristics of the crown condition indicators, i.e., distributional form, the FIA sampling 

design by which the data are collected, the interest in identifying the poorest and best crowns in addition to measures of 

central tendency, and the need to evaluate biological significance relative to statistical significance.

Influence of Species and Stand Dynamics

Before the equality of two groups can be determined, 

confounding influences must be addressed so that real differences between the groups are not obscured. Given that 

tree crown condition is the result of a combination of many 

factors, e.g., species, site conditions, and external stresses, 

these factors need to be controlled to ensure unambiguous 

test results. For example, the data summarized here indicate 

that the crown density indicator varies by species. At the 

species level, the range in average crown density was 19 

percent, from 38.8 percent for swamp tupelo to 57.8 percent 

for eastern hemlock (table 2). Such great variability inhibits 

direct comparisons of species because some species clearly 

tend to have denser crowns than others. Interspecies variability also complicates stand-level analyses in which species 

are combined to calculate plot averages. In a mixed hardwood stand, for example, plots dominated by hickory would 

probably tend to have higher average crown density than 

plots dominated by elm (table 2). Results of a hypothesis test 
indicating statistically significant differences between such 

plots would be confounded by species, and this would make 

it harder to reach definite conclusions about the relative 

health of the plots. 

One way to accommodate the species (and stand condition) 

influences is stratification, i.e., grouping together sets of 

homogeneous observations and making comparisons only 

among those sets. Stratification reduces variation in descriptive statistics and summaries, but it does not necessarily 

facilitate further inferential analyses. In broadscale surveys 

such as the phase 3 program, complete stratification leads to 

small and unbalanced sample sizes that complicate analyses 

or limit interpretations of the results, or have both of these 

effects. To avoid the pitfalls of stratification and still account 

for species and stand influences, Zarnoch and others (2004) 

propose standardizing or residualizing, or both, the crown 

condition indicators prior to inferential analyses. Standardization adjusts the crown indicators for species differences 

by expressing the indicators in terms of standard deviation 

units. This allows the indicators to be combined across species or for direct comparison of the indicators among species. Residualization adjusts the crown indicators for natural 

stand dynamics by redefining the indicators as the residuals 

from a model that predicts crown condition based on tree 

and stand conditions. Residualization allows the combination or comparison of trees from many different plots within 

a given species. Residualized indicators may be standardized 

to allow for comparisons or combinations of many species 

across many plot conditions. 

Statistical Characteristics 

Any data analyzed via inferential hypotheses must meet the 

underlying assumptions of the tests being used. The typical 

hypothesis tests applicable to the crown condition data, e.g., 

the t-test and analysis of variance, or ANOVA, require an 

assumption of normality. For the data examined here, it is 

reasonable to assume that crown density meets this requirement and that the t-test and ANOVA may be safely applied 

to the crown density indicator. On the other hand, crown 

dieback and foliage transparency are best considered nonnormal. The deviation from normality of foliage transparency values is primarily due to a few outlying observations 

that cause right skewness, and since the t-test and ANOVA

are robust against skewness and outliers, these tests may 

also be applied to the foliage transparency indicator as long 

as the sample sizes of the groups being compared are about 

equal and sufficiently large (n ≥ 30). Because of the extreme 

deviation from normality, such tests should not be applied 

to the crown dieback indicator. Other avenues for analyzing 

the crown dieback data include nonparametric techniques or 

categorical methods for ordinal data. 

Figure 11—Normal probability plots for (A) crown density, (B) crown 

dieback, and (C) foliage transparency, by species group for Alabama, 

Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 

through 1999.
10

Table 2—Mean crown density and other statistics a for all live trees > 4.9 inches d.b.h., by species for Alabama, Georgia, 

North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999 b

Species c Standard

error

Minimum

Percentile

MaxiPlots Trees Mean Skewness Kurtosis 10 mum th 25th 50th 75th 90th

- - number - - - - - percent - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - 

Eastern redcedar 50 111 48.0 1.565 -0.141 -0.506 20 35 40 45 55 65 75

Shortleaf pine 91 357 43.0 1.033 -0.155 0.520 5 30 35 45 50 55 75

Slash pine 38 444 40.0 1.015 -0.181 1.228 5 30 35 40 45 50 70

Longleaf pine 32 169 40.8 1.529 -0.021 -0.093 20 30 35 40 45 55 60

Pond pine 9 65 42.4 1.796 -0.327 0.018 20 30 40 45 50 55 60

Eastern white pine 33 134 44.9 1.531 -0.184 -0.203 15 30 35 45 55 60 70

Loblolly pine 254 4,355 42.0 0.696 0.266 0.445 5 30 35 40 50 55 85

Virginia pine 88 641 41.1 1.359 0.117 -0.108 5 25 35 40 50 60 75

Baldcypress 12 68 42.0 2.178 0.721 2.712 15 30 35 40 45 55 85

Eastern hemlock 10 34 57.8 5.000 -0.012 -1.068 30 40 45 55 70 75 85

Boxelder 12 35 48.4 2.628 0.464 0.998 30 35 40 45 55 60 80

Red maple 234 879 46.1 0.627 0.105 0.243 10 35 40 45 55 60 85

Sugar maple 39 106 48.1 1.644 -0.335 -0.280 15 35 40 50 55 60 70

Sweet birch 18 45 50.7 2.624 0.145 0.151 20 35 40 50 60 65 80

River birch 18 37 47.2 1.340 -0.408 0.616 20 40 40 45 55 55 65

American hornbeam 24 54 44.4 2.138 0.099 0.622 15 35 35 45 50 55 70

Pignut hickory 59 137 51.6 1.500 -0.142 -0.167 25 40 45 55 60 65 80

Shagbark hickory 26 57 50.6 1.764 -1.213 3.233 5 35 45 50 60 60 75

Mockernut hickory 80 190 49.5 1.120 -0.491 0.449 10 35 40 50 60 65 80

Hackberry 8 37 40.4 1.813 0.013 -0.693 20 25 30 40 50 55 60

Flowering dogwood 60 103 41.5 1.487 0.247 0.569 10 30 35 40 50 55 80

Persimmon 19 34 40.1 1.430 -0.011 2.198 15 35 35 40 45 50 60

American beech 43 95 55.4 1.343 -0.060 -0.199 20 40 45 55 65 70 85

White ash 25 52 48.8 4.298 0.192 -0.217 15 35 40 45 60 70 80

Green ash 42 125 41.9 2.563 -0.172 0.098 5 25 35 40 50 55 70

American holly 19 50 50.6 2.191 -0.458 0.422 15 32.5 45 52.5 60 65 80

Sweetgum 244 939 47.2 0.663 -0.252 0.370 5 35 40 45 55 60 75

Yellow-poplar 215 766 49.4 0.691 -0.013 -0.027 10 35 40 50 55 65 80

Sweetbay 30 102 40.5 1.360 0.648 0.443 20 30 35 40 45 50 70

Water tupelo 14 68 42.6 1.990 -0.225 0.221 15 30 35 42.5 50 55 70

Blackgum 146 294 45.9 1.068 -0.066 0.172 10 30 35 45 55 60 85

Swamp tupelo 25 184 38.8 1.207 -0.503 0.298 15 30 35 40 45 45 55

Eastern hophornbeam 20 31 47.1 2.345 0.480 0.398 25 35 40 45 55 60 75

Sourwood 101 261 46.9 1.097 0.442 0.178 20 35 40 45 55 60 85

Redbay 9 26 45.0 2.357 0.571 0.489 30 35 40 45 50 55 70

Sycamore 14 25 52.4 2.394 0.610 0.932 30 40 45 55 55 70 85

Black cherry 83 132 43.1 1.177 -0.080 -0.067 10 30 35 40 50 60 70

White oak 184 633 47.8 0.648 0.039 0.768 5 35 40 45 55 60 85

Scarlet oak 78 232 45.6 0.781 0.169 0.328 20 35 40 45 50 60 75

Southern red oak 93 220 45.7 0.982 -0.139 -0.093 15 35 40 45 55 60 70

Laurel oak 23 70 45.7 1.801 0.019 0.123 20 35 40 45 50 60 65

Water oak 134 390 44.6 0.851 0.161 0.286 15 30 40 45 50 60 85

Willow oak 38 91 48.2 1.522 0.175 -0.548 25 35 40 45 55 65 70

Chestnut oak 86 629 46.2 0.885 -0.237 0.517 5 35 40 45 55 60 75

Northern red oak 73 197 46.0 0.941 0.029 0.180 10 35 40 45 50 60 70

Post oak 58 105 42.0 1.324 0.244 0.947 15 30 35 40 45 55 75

Black oak 85 170 45.9 1.043 -0.374 0.916 10 35 40 45 55 60 75

Live oak 13 30 45.7 2.103 -0.558 0.695 25 35 40 45 50 57.5 60

Black locust 28 49 40.1 1.643 0.206 0.479 15 25 35 40 45 55 70

Sassafras 19 42 41.1 2.046 -0.232 0.027 10 25 35 40 50 55 65

Winged elm 28 54 44.3 1.465 0.385 -0.286 25 30 35 45 50 60 70

American elm 27 46 42.3 2.250 -0.830 0.854 5 25 35 40 55 60 60

Slippery elm 18 36 45.6 1.770 0.375 -0.899 35 35 40 45 52.5 55 60

a

The mean and standard error calculations consider the clustering of trees on plots.

b

No data collected for 1996.

c

See appendix for scientific names.
11

Table 3—Mean crown dieback and other statistics a for all live trees > 4.9 inches d.b.h., by species for Alabama, Georgia, 

North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999 b

Species c Standard

error

Minimum

Percentile

MaxiPlots Trees Mean Skewness Kurtosis 10 mum th 25th 50th 75th 90th

- - number - - - - - percent - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - -

Eastern redcedar 50 111 3.6 0.955 2.419 5.948 0 0 0 0 5 10 30

Shortleaf pine 91 357 1.4 0.261 2.237 5.300 0 0 0 0 0 5 15

Slash pine 38 444 0.8 0.351 3.532 16.613 0 0 0 0 0 5 20

Longleaf pine 32 169 0.9 0.302 2.829 7.233 0 0 0 0 0 5 10

Pond pine 9 65 2.2 0.868 3.375 16.750 0 0 0 0 5 5 25

Eastern white pine 33 134 1.9 0.524 6.099 45.546 0 0 0 0 0 5 50

Loblolly pine 254 4,355 0.8 0.130 12.016 222.673 0 0 0 0 0 5 80

Virginia pine 88 641 3.2 0.516 6.673 57.119 0 0 0 0 5 5 90

Baldcypress 12 68 1.0 0.436 2.007 3.348 0 0 0 0 0 5 10

Eastern hemlock 10 34 0.4 0.271 3.039 7.686 0 0 0 0 0 0 5

Boxelder 12 35 2.6 0.837 2.364 6.183 0 0 0 0 5 5 20

Red maple 234 879 1.6 0.169 4.230 28.946 0 0 0 0 0 5 40

Sugar maple 39 106 2.0 0.548 3.309 15.428 0 0 0 0 5 5 25

Sweet birch 18 45 2.0 0.711 1.914 3.897 0 0 0 0 5 5 15

River birch 18 37 3.1 0.699 2.221 6.601 0 0 0 0 5 5 20

American hornbeam 24 54 2.8 0.926 4.990 30.860 0 0 0 0 5 5 40

Pignut hickory 59 137 1.2 0.220 1.670 1.840 0 0 0 0 0 5 10

Shagbark hickory 26 57 2.1 1.270 7.244 53.796 0 0 0 0 0 5 80

Mockernut hickory 80 190 2.3 0.506 7.788 76.640 0 0 0 0 5 5 75

Hackberry 8 37 4.7 1.437 2.909 8.750 0 0 0 0 5 10 40

Flowering dogwood 60 103 4.6 1.127 5.882 37.964 0 0 0 0 5 10 90

Persimmon 19 34 2.1 0.661 2.920 10.622 0 0 0 0 5 5 20

American beech 43 95 0.7 0.206 2.903 8.297 0 0 0 0 0 5 10

White ash 25 52 2.1 0.555 2.012 3.996 0 0 0 0 5 5 15

Green ash 42 125 3.3 0.713 3.683 19.059 0 0 0 0 5 10 45

American holly 19 50 1.3 0.399 1.128 -0.759 0 0 0 0 5 5 5

Sweetgum 244 939 2.3 0.298 8.476 90.445 0 0 0 0 5 5 95

Yellow-poplar 215 766 1.0 0.167 6.921 81.346 0 0 0 0 0 5 45

Sweetbay 30 102 1.6 0.324 1.272 0.579 0 0 0 0 5 5 10

Water tupelo 14 68 2.7 0.984 1.872 3.178 0 0 0 0 5 10 20

Blackgum 146 294 1.8 0.208 2.486 7.648 0 0 0 0 5 5 20

Swamp tupelo 25 184 2.1 0.538 1.969 4.445 0 0 0 0 5 5 20

Eastern hophornbeam 20 31 1.1 0.427 1.379 -0.109 0 0 0 0 0 5 5

Sourwood 101 261 2.6 0.523 5.288 39.170 0 0 0 0 5 5 55

Redbay 9 26 1.7 0.701 0.687 -1.662 0 0 0 0 5 5 5

Sycamore 14 25 1.8 1.305 4.201 18.632 0 0 0 0 0 5 30

Black cherry 83 132 3.6 0.696 3.760 20.436 0 0 0 0 5 10 50

White oak 184 633 3.1 0.346 7.479 80.168 0 0 0 0 5 5 95

Scarlet oak 78 232 4.0 0.484 2.762 11.380 0 0 0 5 5 10 35

Southern red oak 93 220 3.2 0.402 1.802 4.164 0 0 0 0 5 10 25

Laurel oak 23 70 1.8 0.471 1.170 0.400 0 0 0 0 5 5 10

Water oak 134 390 3.2 0.349 2.135 7.315 0 0 0 0 5 7.5 30

Willow oak 38 91 2.4 0.492 1.198 2.038 0 0 0 0 5 5 15

Chestnut oak 86 629 2.7 0.399 8.729 133.572 0 0 0 0 5 5 95

Northern red oak 73 197 4.1 0.489 3.324 17.109 0 0 0 5 5 10 40

Post oak 58 105 3.1 0.479 2.640 10.075 0 0 0 0 5 5 25

Black oak 85 170 4.1 0.504 1.731 3.958 0 0 0 2.5 5 10 30

Live oak 13 30 3.2 1.161 1.174 0.575 0 0 0 0 5 10 15

Black locust 28 49 2.8 0.645 1.274 1.016 0 0 0 0 5 10 15

Sassafras 19 42 3.9 0.810 2.768 10.955 0 0 0 5 5 10 30

Winged elm 28 54 1.5 0.430 3.634 17.825 0 0 0 0 0 5 20

American elm 27 46 4.1 1.266 4.108 17.213 0 0 0 0 5 5 50

Slippery elm 18 36 2.2 0.592 1.292 0.384 0 0 0 0 5 10 10

a

The mean and standard error calculations consider the clustering of trees on plots.

b

No data collected for 1996.

c

See appendix for scientific names.
12

Table 4—Mean foliage transparency and other statistics a for all live trees > 4.9 inches d.b.h., by species for Alabama, Georgia, 

North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999 b

Species c Standard

error

Minimum

Percentile

MaxiPlots Trees Mean Skewness Kurtosis 10 mum th 25th 50th 75th 90th

- - number - - - - - percent - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - -

Eastern redcedar 50 111 17.3 1.465 1.619 6.129 0 10 10 15 20 25 65

Shortleaf pine 91 357 18.2 0.857 0.519 0.253 0 10 15 20 20 25 40

Slash pine 38 444 16.3 0.544 0.907 4.001 5 10 15 15 20 20 40

Longleaf pine 32 169 17.9 1.123 0.258 1.253 0 15 15 15 20 25 30

Pond pine 9 65 21.1 2.748 0.334 -0.601 10 10 15 20 30 30 40

Eastern white pine 33 134 21.7 1.157 3.439 20.230 5 15 15 20 25 30 95

Loblolly pine 254 4,355 17.8 0.424 0.795 4.376 0 10 15 15 20 25 75

Virginia pine 88 641 23.4 1.098 1.541 4.125 5 15 15 20 30 35 75

Baldcypress 12 68 19.6 0.424 0.412 0.902 10 15 15 20 22.5 25 35

Eastern hemlock 10 34 20.6 1.272 0.098 -0.802 10 15 15 20 25 25 30

Boxelder 12 35 14.1 1.366 0.363 -0.855 5 10 10 15 20 20 25

Red maple 234 879 16.3 0.332 0.792 5.272 0 10 15 15 20 25 60

Sugar maple 39 106 15.8 1.081 2.796 17.503 0 10 15 15 20 25 60

Sweet birch 18 45 19.2 1.445 4.420 24.793 10 10 15 20 20 25 75

River birch 18 37 20.4 1.521 0.513 0.310 5 10 15 20 25 30 40

American hornbeam 24 54 14.7 0.988 0.432 0.376 5 10 10 15 20 20 30

Pignut hickory 59 137 14.5 0.498 0.844 1.035 5 10 10 15 15 20 30

Shagbark hickory 26 57 14.9 0.860 -0.273 1.151 0 10 10 15 20 20 25

Mockernut hickory 80 190 13.9 0.453 1.134 4.764 5 10 10 15 15 20 40

Hackberry 8 37 21.9 2.233 2.138 6.412 10 15 15 20 25 30 60

Flowering dogwood 60 103 16.1 0.723 3.032 17.802 5 10 15 15 20 20 60

Persimmon 19 34 17.4 0.918 -0.206 -0.549 10 10 15 20 20 20 25

American beech 43 95 14.8 0.544 0.174 1.382 5 10 15 15 15 20 30

White ash 25 52 18.6 0.940 0.172 -0.474 10 15 15 20 20 25 30

Green ash 42 125 19.3 1.095 1.237 2.873 10 15 15 20 20 25 45

American holly 19 50 17.0 0.890 3.829 21.182 5 10 10 15 20 25 75

Sweetgum 244 939 13.6 0.307 0.508 3.763 0 10 10 15 15 20 50

Yellow-poplar 215 766 14.4 0.406 0.026 1.868 0 10 10 15 15 20 40

Sweetbay 30 102 14.0 1.353 -0.757 2.129 0 10 15 15 15 20 30

Water tupelo 14 68 16.2 0.718 0.101 -0.483 10 10 15 15 20 20 25

Blackgum 146 294 15.4 0.483 0.335 1.867 0 10 10 15 20 20 40

Swamp tupelo 25 184 14.6 1.468 -0.053 1.689 0 5 10 15 15 20 35

Eastern hophornbeam 20 31 13.7 1.193 0.248 -0.602 5 5 10 15 20 20 25

Sourwood 101 261 15.9 0.505 0.051 0.119 5 10 15 15 20 20 30

Redbay 9 26 17.9 1.214 -0.515 -0.068 10 10 15 20 20 20 25

Sycamore 14 25 16.2 2.046 1.041 1.138 5 10 10 15 20 25 35

Black cherry 83 132 19.5 0.892 2.507 12.073 0 10 15 20 25 25 70

White oak 184 633 15.3 0.467 2.615 20.484 0 10 10 15 20 20 75

Scarlet oak 78 232 17.5 0.719 1.357 9.140 5 10 15 20 20 25 60

Southern red oak 93 220 15.1 0.633 -0.462 0.838 0 10 15 15 20 20 25

Laurel oak 23 70 13.4 1.102 0.432 1.354 5 5 10 15 15 20 30

Water oak 134 390 14.5 0.431 0.671 3.765 0 10 10 15 15 20 45

Willow oak 38 91 15.4 0.921 -0.429 0.211 0 5 10 15 20 20 30

Chestnut oak 86 629 18.5 1.188 4.023 24.269 0 10 15 15 20 25 95

Northern red oak 73 197 17.3 0.885 2.576 16.910 0 10 15 15 20 25 70

Post oak 58 105 15.7 0.539 -0.448 2.784 0 10 15 15 20 20 30

Black oak 85 170 16.4 0.623 2.396 20.246 0 10 15 15 20 20 65

Live oak 13 30 13.7 1.447 0.818 0.164 5 10 10 12.5 15 20 25

Black locust 28 49 19.2 1.671 -0.383 0.931 0 10 15 20 25 25 35

Sassafras 19 42 19.4 1.114 0.127 -0.561 10 15 15 20 25 25 30

Winged elm 28 54 17.5 0.950 -0.368 0.814 0 10 15 15 20 25 30

American elm 27 46 19.7 1.723 4.282 24.231 5 10 15 20 20 25 85

Slippery elm 18 36 16.2 0.906 1.011 2.267 10 10 15 15 20 20 30

a

The mean and standard error calculations consider the clustering of trees on plots.

b

No data collected for 1996.

c

See appendix for scientific names.
13

A second assumption of the t-test and ANOVA is an assumption of equal variance (homoscedasticity) among the populations being compared. It is useful to note violations of this 

assumption because the two-sample t-test can be formulated 

to accommodate unequal variances (heteroscedasticity) 

(Montgomery 1997). Since no specific hypothesis tests are 

included here, tests for equal variance among species or 

species groups were not performed. As with the assumption 

of normality, analyses of variance are fairly robust against 

heteroscedasticity and may be applied to populations with 

different variances as long as the sample sizes of the compared groups are about equal. 

Accommodating the FIA Sampling Design

Descriptive statistics for and inferential testing of the crown 

condition data must correctly account for the FIA sampling 

design in which trees are clustered on plots. The clustering 

of trees on plots affects the manner in which the overall 

variance in the data must be allocated. That is, the covariance among trees located on the same plot must be taken 

into consideration when calculating descriptive statistics or 

performing hypothesis tests. The clustering often results in 

unequal sample sizes among the groups under comparison 

(an unbalanced design). As mentioned above, the t-test and 

ANOVA are robust against nonnormality and heteroscedasticity only if the sample sizes are about equal; therefore, 

extra attention must be given to the specification of the 

hypothesis tests when sample sizes are unequal. Statistical software packages are capable of handling unbalanced 

or clustered data, or both, by means of blocked or nested 

designs, but the specific structure of the hypothesis tests 

must be declared by the analyst. 

Interest in the Poorest and Best Crowns

The hypothesis tests so often employed to test for differences between two groups make primary use of the distribution’s central tendency and spread. Unfortunately, only 

comparing differences in central tendencies may overlook 

important features of the differences among groups. This is 

especially true for the crown condition indicators where it is 

differences across the entire distribution, particularly toward 

the distribution tails, that are important in the detection of 

potential forest health problems. Ideally, comparisons of 

crown condition should incorporate analyses that provide a 

broader description of the overall distribution than is provided by the mean and variance alone. FIA has previously 

categorized the distribution of trees into discrete classes of 

“good, average, and poor” (Bechtold and others 1992) and 

compared groups based upon the proportion of observations 

in each of these classes (Stoyenoff and others 1998). This 

approach improves upon examining central tendency alone, 

but it has obvious limitations if the same set of thresholds 

is applied to all species. This is commonly the case because 

species-level, biologically based thresholds have yet to be 

developed for the crown indicator.

When thresholds are applied universally across species, 

shifts of observations into the poor category over time 

indicate declines in forest health; however, since some 

species tend to have “better” crowns than other species, 

species-specific declines could go undetected. Figure 12, 

which shows the cumulative (empirical) distribution function 

of one of the most extreme crown density distributions, 

that of eastern hemlock, illustrates this problem. Eastern 

hemlock tends to have very dense crowns, so the trees are 

concentrated in the good and average categories. For this 

species then, an increase in the proportion of observations 

in the average category would be the first signal of declining 

conditions, whereas analyses primarily focused on detecting 

increases in the poor category would overlook this early 

indication of trouble.

A second limitation of utilizing good, average, and poor 

classes is that information in the extreme tails of the 

distribution is lost when the data are grouped into so 

few categories. Since the crown condition indicators are 

recorded in 5-percent increments, it would be possible to 

leave the data ungrouped and test for differences among 

the 21 categories using categorical methods, e.g., chisquare tests. However, in the ungrouped data the number 

of categories with zero observations can be excessive. This 

Figure 12—Thresholds of crown condition divide the cumulative (empirical) distribution function for eastern hemlock crown density into categories 

of poor, average, and good condition for Alabama, Georgia, North Carolina, 

South Carolina, Tennessee, and Virginia, 1995 through 1999.
14

may give rise to a sparse contingency table, and testing 

differences among contingency tables with many zeroes can 

lead to errant results (Agresti 1996). The potential for this is 

especially great for crown dieback and foliage transparency 

because observations of these indicators tend to concentrate 

in a small range of the overall possible values (figs. 3 and 4). 

In an effort to overcome the limitations of grouping the 

crown condition data and of only considering measures of 

central tendency when testing for differences between two 

groups, Randolph (2004) calculated the differences between 

selected percentiles of two groups’ empirical distribution 

functions and employed bootstrapping to calculate simultaneous confidence intervals for the differences. Calculation 

of confidence intervals for the differences between the 10th, 

25th, 50th, 75th, and 90th percentiles made differences in the 

extreme tails of the distributions, as well as in the degree of 

central tendency, detectable. 

Distinguishing Biological and Statistical 

Significance 

Determining statistically significant differences between two 

groups is only the first step in describing forest health conditions. Reporting the status of and changes in forest health 

must go beyond stating statistical significance and place any 

detected differences in the context of biological significance. 

Although researchers in the past have had varying success in 

relating crown condition to tree vigor as measured by radial 

or basal area growth (e.g., Anderson and Belanger 1987; 

Grano 1957; Juknys and Augustaitis 1998; Kenk 1993), the 

inherent assumption is that crown condition does indeed 

reflect overall tree health. Because little is known about 

the relationship between crown condition and tree vitality, 

thresholds among health categories are now based on isolating observations in the tails of statistical distributions. The 

difficulty with such thresholds is that even in the absence 

of a problem some observations are designated as poor 

(Zarnoch and others 2004). Thus, as repeated measurements 

from the phase 3 plots become available it will be necessary 

to evaluate the relationship between crown condition and 

other measures of vitality so that statistically significant 

changes in crown condition can be placed in a biological 

context. 

Conclusion

Several factors must be addressed in the analysis of the 

phase 3 crown condition indicators. These factors range 

from intrinsic species differences in crown form to the 

sampling procedure used to survey the forest. Incorrect 

conclusions may be drawn about forest health if any one 

of these factors is ignored. Fortunately, most of the factors 

highlighted here can be accommodated with available 

statistical methods, but it is essential that the analytical 

techniques chosen are appropriate given the data and the 

objectives of the analysis. Otherwise, invalid inferences 

may be drawn from the results. Specification of analytical 

techniques for the crown condition data is underway,2

leaving the determination of biological thresholds as the 

most significant remaining issue. As FIA forest health 

assessment continues, development of these thresholds 

will be investigated. In the meantime, the crown condition 

summaries presented here establish a baseline against which 

changing conditions over time may be referenced.

Acknowledgments

The author gratefully acknowledges the comments provided 

by Bill Bechtold, USDA Forest Service, Southern Research 

Station, Asheville, NC, and Michael Schomaker, University 

of Nevada, Las Vegas, during the preparation of this report.

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John Wiley. 290 p.

An, A.; Watts, D. 1998. New SAS procedures for analysis of sample survey 

data. In: Proceedings of the twenty-third annual SAS users group 

international conference. Pap. 247. Cary, NC: SAS Institute Inc. http://

www2.sas.com/proceedings/sugi23/Stats/p247.pdf. [Date accessed: 

June 9, 2005]. 

Anderson, R.L.; Belanger, R.P. 1987. A crown rating method for assessing 

tree vigor of loblolly and shortleaf pines. In: Phillips, D.R., comp. 

Proceedings of the fourth biennial southern silvicultural research 

conference. Gen. Tech. Rep. SE–42. Asheville, NC: U.S. Department 

of Agriculture Forest Service, Southeastern Forest Experiment Station: 

538-543.

Bechtold, W.A.; Hoffard, W.H.; Anderson, R.L. 1992. Summary 

report–forest health monitoring in the South, 1991. Gen. Tech. Rep. 

SE–81. Asheville, NC: U.S. Department of Agriculture Forest Service, 

Southeastern Forest Experiment Station. 40 p.

Grano, C.X. 1957. Growth of loblolly pine seed trees in relation to crown 

density. Journal of Forestry. 55(11): 852.

Innes, J.L. 1993. Forest health: its assessment and status. Oxon, UK: CAB

International. 677 p.

Juknys, R.; Augustaitis, A. 1998. Indicators of crown and their application 

in forest health monitoring. Baltic Forestry. 2: 51-58.

2

 Schomaker, M.E.; Zarnoch, S.J.; Bechtold, W.A. [and others]. Crown 

condition classification: a guide to data collection and analysis. Manuscript 

in preparation. W.A. Bechtold can be reached at the USDA Forest Service, 

Southern Research Station, Asheville, NC.
15

Kenk, G. 1993. Growth in “declining” forests of Baden-Württemberg 

(Southwestern Germany). In: Huettl, R.F.; Mueller-Dombois, D., eds. 

Forest decline in the Atlantic and Pacific region. Berlin; Heidelberg; 

New York: Springer-Verlag: 202-215.

Little, E.L., Jr. 1979. Checklist of United States trees (native and 

naturalized). Agric. Handb. 541. Washington, DC: U.S. Department of 

Agriculture. 375 p.

Millers, I.; Anderson, R.; Burkman, W.; Hoffard, W. 1992. Crown condition 

rating guide. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Area State & Private Forestry; Atlanta: U.S. 

Department of Agriculture, Forest Service, Southern Region. 37 p. 

Millers, I.; Shriner, D.; Rizzo, D. 1989. History of hardwood decline 

in the Eastern United States. Gen. Tech. Rep. NE–126. Radnor, PA: 

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

Experiment Station. 75 p. 

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York: John Wiley. 704 p.

Neter, J.; Kutner, M.H.; Nachtsheim, C.J.; Wasserman, W. 1996. Applied 

linear statistical models. 4th ed. Chicago: Irwin. 1,408 p.

Randolph, K.C. 2004. Inter- and intra-species variation in three crown 

condition indicators for seven tree species in the Southeastern United 

States. Knoxville, TN: The University of Tennessee. 174 p. Ph.D. 

dissertation.

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Central States. Ann Arbor, MI: University of Michigan, School of 

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Research. 34: 1057-1070.
16
17

Appendix

Common and scientific names for the species included in the individual analyses for Alabama, 

Georgia, North Carolina, South Carolina, Tennessee, and Virginia, 1995 through 1999 1

Common name Scientific name 2 Common name Scientific name 2

Softwoods Hardwoods (continued)

 Eastern redcedar Juniperus virginiana Sweetgum Liquidambar styraciflua

 Shortleaf pine Pinus echinata Yellow-poplar Liriodendron tulipifera

 Slash pine P. elliottii Sweetbay Magnolia virginiana

 Longleaf pine P. palustris Water tupelo Nyssa aquatica

 Pond pine P. serotina Blackgum N. sylvatica

 Eastern white pine P. strobus Swamp tupelo N. sylvatica var. biflora

 Loblolly pine P. taeda Eastern hophornbeam Ostrya virginiana

 Virginia pine P. virginiana Sourwood Oxydendrum arboretum

 Baldcypress Taxodium distichum Redbay Persea borbonia

 Eastern hemlock Tsuga canadensis Sycamore Platanus occidentalis

 Black cherry Prunus serotina

Hardwoods White oak Quercus alba

 Boxelder Acer negundo Scarlet oak Q. coccinea

 Red maple A. rubrum Southern red oak Q. falcata var. falcata

 Sugar maple A. saccharum Laurel oak Q. laurifolia

 Sweet birch Betula lenta Water oak Q. nigra

 River birch B. nigra Willow oak Q. phellos

 American hornbeam Carpinus caroliniana Chestnut oak Q. prinus

 Pignut hickory Carya glabra Northern red oak Q. rubra

 Shagbark hickory C. ovata Post oak Q. stellata

 Mockernut hickory C. tomentosa Black oak Q. velutina

 Hackberry Celtis occidentalis Live oak Q. virginiana

 Flowering dogwood Cornus florida Black locust Robinia pseudoacacia

 Persimmon Diospyros virginiana Sassafras Sassafras albidum

 American beech Fagus grandifolia Winged elm Ulmus alata

 White ash Fraxinus americana American elm U. americana

 Green ash F. pennsylvanica Slippery elm U. rubra

 American holly Ilex opaca

1 No data collected for 1996.

2

Little (1979). 

Randolph, KaDonna C. 2006. Descriptive statistics of tree crown condition in the 

Southern United States and impacts on data analysis and interpretation. Gen. Tech. Rep. 

SRS–94. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research 

Station. 17 p. 

The U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis Program 

(FIA) utilizes visual assessments of tree crown condition to monitor changes and trends 

in forest health. This report describes and discusses distributions of three FIA crown 

condition indicators (crown density, crown dieback, and foliage transparency) for trees in 

the Southern United States. Descriptive statistics are presented for all trees combined, by 

hardwood and softwood species groups, and for 53 individual species. Implications of these 

characteristics and other factors for the analysis of the phase 3 crown condition indicators 

are discussed.

Keywords: Crown density, crown dieback, FIA, foliage transparency, forest health.
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