Deconstructing the Timber

Volume Paradigm in

Management of the

Tongass National Forest

John P. Caouette, Marc G. Kramer, Gregory J. Nowacki

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical 

Report

PNW-GTR-482

March 2000
Author John P. Caouette is a statistician, U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station, Forestry Sciences Laboratory, 2770 Sherwood

Lane, Suite 2A, Juneau, AK 99801-8545; Marc G. Kramer is a Ph.D. candidate,

Oregon State University, Corvallis, OR 97331; and Gregory J. Nowacki is the regional

ecologist, U.S. Department of Agriculture, Forest Service, Alaska Region, 709 West

Ninth Street, Juneau, AK 99801.
Conservation and Resource

Assessments for the Tongass

Land Management Plan

Revision

Charles G. Shaw III, Technical Coordinator

Deconstructing the Timber Volume Paradigm in

Management of the Tongass National Forest

John P. Caouette

Marc G. Kramer

Gregory J. Nowacki

Published by:

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-482

March 2000
Abstract Caouette, John P.; Kramer, Marc G.; Nowacki, Gregory J. 2000. Deconstructing the

timber volume paradigm in management of the Tongass National Forest. Gen. Tech.

Rep. PNW-GTR-482. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 20 p. (Shaw, Charles G., III, tech. coord.; Conservation and resource assesments for the Tongass land management plan revision).

Timber volume information and associated maps have been widely used in the

Tongass National Forest for land-use planning and timber and wildlife management.

Although considerable effort has been expended to improve timber volume maps, little

has been done to evaluate the suitability of timber volume as a descriptor of forest

character. We established a rough indicator of forest structure that uses trees per acre

and quadratic mean diameter to examine the relation between timber volume and forest structure. Results indicated that timber volume and forest structure are not interchangeable attributes. Results also indicated that the original photinterpreted timber

volume stratification did not always capture differences in timber volume but may have

captured differences in forest structure. The recently revised timber volume stratification provides more reliable timber volume information, but it sacrifices structural information in the process.

Keywords: Tongass National Forest, timber volume, forest structure, trees per acre,

quadratic mean diameter, aerial photointerpretation, canopy texture.

1 Introduction

2 Materials

2 1977 Land and Timber Type Stratification

3 Common Land Unit Stratification

3 Revised 1997 TLMP Volume Stratification 

3 1980s Forest Inventory and Analysis

4 Timber Volume and Forest Structure

4 Trees Per Acre and Mean Diameter Model

5 Forest Structure

9 Timber Volume

9 Comparing Timber Volume and Forest Structure

10 Analysis of Photointerpreted Information

10 1977 Timber Volume Strata

12 1997 Revised Timber Volume Strata

14 Photointerpretation of Canopy Texture

16 Discussion

16 Timber Volume Strata Revisited

17 Conclusion

18 Acknowledgments

18 References

20 Appendix

20 Plot Illustrations, Stand Visualization System 

Contents 
Mapping the Tongass National Forest (Tongass) has generated confusion and controversy for many years. Land-use managers need accurate information for responsible

planning and on-the-ground implementation, but inventorying the Tongass’s 17 million

acres, distributed over remote and rugged island terrain, has been difficult. Aerial

photography has been used to complement or supplement labor-intensive, groundbased surveys to provide forest managers and research scientists with inventories of

the Tongass.

A photointerpreted land and vegetation stratification of the Tongass was created in

1977 (Esca-Tech 1977). The vegetation component of this database, referred to as

“timber type,” delineated forested areas into polygons of relatively homogenous character. Each polygon was assigned photointerpreted estimates of tree size and species,

volume class (hereafter referred to as “stratum”), crown closure, and degree of decadence. In 1988, the land and timber type stratification was converted into digital format,

transferred into a computer-based geographic information system (GIS), and displayed

in color-coded maps. A single attribute of these maps, timber volume stratum, received

extensive use. Timber volume strata were used for estimating Forest-wide timber volumes, planning timber sales, modeling wildlife habitat capability, and implementing

proportionality,

1 as mandated by the 1990 Tongass Timber Reform Act (U.S. Public

Laws, Statutes 1990).

As use of the timber volume stratification increased in Tongass management, questions about its accuracy emerged. Analyses and litigation revealed problems with the

timber volume strata. In 1997, the Forest Service revised the timber volume stratification to provide stronger, more statistically defensible timber volume data (Julin and

Caouette 1997). The revision generated a new volume stratification by using a combination of the original timber volume strata and soil and slope information from the

common land unit (CLU) database (USDA 1990). Although the revised timber volume

stratification provided more reliable volume data, many were still unsatisfied because

they felt the revised stratification did not identify the “highest volume” stands (see

“Discussion” for further explanation).

In short, neither the original nor the revised timber volume strata satisfied all or even

most potential users. Brickell (1989), a USDA Forest Service inventory specialist,

provided several explanations for many problems and shortcomings associated with

the timber volume stratification. Brickell suggested that an often overlooked problem

may be “the recording of stratum only defined by net [board-foot] volume.” He questioned whether timber volume is the best measure to relate to what can be seen in

aerial photographs. He stated that photointerpreters often delineate the forest by

focusing on attributes such as crown closure, crown diameters, tree heights, and

species, and then have to process this information into a single timber volume stratum. Brickell recommended that “the photo variables mentioned can be related not

Introduction

1

1 Proportionality required that high-volume old growth, identified as

timber volume strata 6 and 7, be harvested in proportion to its distribution in the surrounding management area. The provision was

intended to prevent overharvesting of the most valuable forest

(greater than 30,000 net board feet [Scribner rule] per acre).
Materials

only to timber characteristics, but to other characteristics important to other resources.”

Throughout the debate over the accuracy of the timber volume strata, few have heeded Brickell’s recommendation and questioned timber volume as the standard by which

to assess photointerpreted information for the Tongass. Our research posed the often

overlooked question, Is timber volume the best attribute to use for photointerpretation,

mapping, and managing the Tongass National Forest?

The analysis in this paper proceeds as follows: first, we establish a quantitative model

that uses trees per acre (TPA) and quadratic mean diameter (QMD) as rough indicators

of forest structure in the Tongass. We then use this model to analyze:

• The relation between timber volume and forest structure

• The original timber volume strata (4-7) 

• The recently revised timber volume strata (low, medium, and high) 

• Aerial photointerpretation of forest canopy texture 

Our analysis used four Tongass databases: (1) the original land and timber stratification, (2) the CLU stratification, (3) the revised timber volume stratification, and (4) the

1980s Tongass forest inventory and analysis.

The Tongass land and timber type inventory was created in 1977 by contractors under

the direction of the USDA Forest Service (Esca-Tech 1977). Photointerpreters used

stereo pairs of aerial photographs (1:15,840) to delineate the Tongass into several

hundred thousand polygons according to land and forest type.

2 Polygons encompassed a minimum of 5 to 10 acres and averaged 60 acres. The photodelineation of

the forest involved several sequential steps: (1) areas were classified as land or water;

(2) land was classified into forested or nonforested areas; (3) forested land was classified as productive3 or unproductive; and (4) productive forest lands were further

classified by tree size, species composition, timber volume, stand density, and degree

of decadence.

Productive forests occupy roughly 5.1 million acres of the Tongass, most of which was

photointerpreted as being well-stocked (over 70 percent crown closure), old-growth

sawtimber (over 150 years old and 11 inches in average diameter). All productive forest polygons were classified into one of four timber volume strata (4-7): 4 = 8,000-

20,000 net board-foot per acre (NBFT, Scribner 16-foot rule); 5 = 20,000-30,000 NBFT;

6 = 30,000-50,000 NBFT; and 7 = over 50,000 NBFT. Other stand-delineating criteria

included species composition (hemlock, spruce, cedar, etc.) and health (disease,

insects, windthrow, etc.).

2

2 A polygon is an area interpreted as having relatively similar forest

condition.

3 Productive forests are those estimated to be greater than 8,000

net board feet per acre.

1977 Land and Timber

Type Stratification
Another land and vegetation inventory of the Tongass, the CLU (USDA 1990), was

created in the late 1980s and early 1990s. Like timber type, CLU was created by using

aerial photographs (1:15,840) to delineate the Tongass into polygons. CLU polygon

delineation focused on basic site and soil characteristics such as landforms, soil composition, and slope. Polygons with similar land and forest characteristics were grouped

under a particular soil management unit code (SMU). About 800 SMUs were used to

describe over 100,000 CLU polygons across the Tongass.

4 The CLU polygons were

delineated and classified without reference to the original land and timber type polygon

information. With a minimum mapping size of 20 acres, CLU polygons tend to be

larger than land and timber type polygons.

Several options were proposed for improving the timber volume stratification during

revision of the Tongass land management plan (TLMP) (Julin and Caouette 1997).

The selected option followed Brickell’s recommendations (Brickell 1989) to collapse

the original timber volume strata 5, 6, and 7 and use additional Forest-wide data to

improve existing timber volume information. Soil and slope information from the CLU

database was combined with the original timber volume strata to create new timber

volume designations. The new volume stratification provided statistically defensible

differences in Forest-wide volume averages (Julin and Caouette 1997).

In the 1980s, the three Administrative Areas of the Tongass undertook separate

efforts to test, or “ground-truth,” the original timber type strata (USDA 1982). All three

efforts used land and timber type polygons as the fundamental sampling unit in their

designs. Land and timber type polygons were randomly selected and ground-sampled

by using either five-point clusters (Ketchikan and Stikine Areas) or multipoint transects

(Chatham Area). Each point was sampled with a variable radius sampling technique.

Data collected at each point included slope, aspect, elevation, soil type, tree species,

tree diameter, tree height, tree age, crown closure, defect, decadence, disturbance

history, and understory composition. Stand-level attributes such as timber volume,

trees per acre, density, basal area, and quadratic mean diameter were calculated from

field-measured data.5 Using these data, Rogers and van Hees (1992a, 1992b, 1992c)

developed summary statistics for each Administrative Area.

A total of 516 land and timber type polygons were sampled across the Tongass.

6 We

used information from 390 of these polygons. The following polygons were omitted: 59

in nonproductive forests, 60 with three or fewer sample points, 4 in forests significantly

altered since the timber type photointerpretation, and 3 on private lands (Julin and

Caouette 1997). The 390 plots were combined from three separate inventories across

the Tongass, but the data were compatible when used to estimate Forest-wide means

(Brickell 1989).

Common Land Unit

Stratification

3

Revised 1997 TLMP

Volume Stratification

4 Most wilderness areas were not included in the CLU database.

5 Trees less than 9 inches in diameter were excluded.

6 Wilderness areas were not sampled.

1980s Forest Inventory

and Analysis
Timber Volume and

Forest Structure

Trees Per Acre and

Mean Diameter Model

The joint distribution of TPA and mean diameter provided a basis for developing a

quantitative model describing differences in forest condition across the Tongass. There

is strong precedent in forest mensuration for using TPA and mean diameter to quantify

forest conditions. Reineke (1933) used these two measures (log-transformed) to explain

patterns of change in stand density and stocking. McCarter and Long (1986) later

modified Reineke’s work by using TPA and mean diameter to describe differences in

stand volume and height (fig. 1). Although McCarter and Long’s diagram was generated for stands of lodgepole pine (Pinus contorta Dougl. ex Loud.) and may not apply

directly to the Tongass, the complexity of their diagram indicates a potential for using

these two measures to simultaneously model and relate several forest attributes. More

recently, Acker et al. (1998) found TPA and mean diameter useful for quantifying structural differences in Douglas-fir (Psuedostsuga menziesii (Mirb.) Franco) forests in the

Pacific Northwest of the United States.

4

Figure 1—McCarter and Long’s (1986) modification of Reineke’s (1933) stand density

index for lodgepole pine.

Site Height (feet)

Stand density index

Diameter (inches)

Cubic-foot volume (per acre)

Trees per acre
We applied a TPA and mean diameter model to the Tongass by using the 1980s forest

inventory data. The mean TPA and QMD (diameter of a tree having mean basal area)

were calculated for each of the 390 forest inventory plots. Inventory plots were displayed

on a log-transformed two-dimensional scattergram with TPA along the x-axis and

QMD along the y-axis (fig. 2), hereafter referred to as the “TPA-QMD data cluster.”

The word “structure” often is used broadly in discussions of forest character. Foresters

and ecologists intuitively recognize differences in forest structure, but such differences

are difficult to quantify because structure consists of a multitude of features, including

tree sizes and shapes, tree densities, canopy structure, and stand vigor. Kershaw

(1973) considers vegetation structure to have three major components: vertical structure, horizontal structure, and species abundance. Forest ecologists tend to describe

structure in terms of stages of stand development (Alaback 1982, Bormann and

Likens 1979). Oliver and Larson (1996) recognize four forest developmental stages:

(1) stand initiation, (2) stem exclusion, (3) understory reinitiation, and (4) old growth. A

conceptual timeline for these developmental stages has been constructed for the temperate rain forests of southeast Alaska (Nowacki and Kramer 1998; see fig. 3). O’Hara

et al. (1996) use Oliver and Larson’s developmental stages as a basis for a broadscale structural classification system for inland Northwest forests. Although such work

has helped define forest structure, quantification of structure still proves difficult

(Hastings 1997, O’Hara et al. 1996). Acker et al. (1998) have had some success

quantifying structure by using some combination of four forest measures: (1) trees per

acre, (2) mean tree diameter, (3) standard deviation of diameters, and (4) density of

large trees.

5

Forest Structure 

Figure 2—The trees per acre and quadratic mean diameter scattergram, displayed on a log scale, for 390

forest inventory plots.
Although the bivariate distribution of Ln(TPA) and Ln(QMD), hereafter referred to as

“TPA” and “QMD,” may not capture the full range of structural attributes in the Tongass,

these two measures can serve as rough indicators of forest structure. In figure 2,

inventory plots in the lower right portion of the TPA-QMD data cluster are expected to

differ considerably in forest structure from plots in the upper left portion. Inventory

plots in the lower right portion of the data cluster, shown in figure 2, are expected to

be from forests with high tree density and more uniform size distribution, similar to

forests in the stem exclusion development stage (fig. 3); plots in the upper left portion

of the TPA-QMD data cluster (fig. 2) are expected to be from forests with moderate

tree density and more heterogenous size distribution, similar to forests in the oldgrowth development stage (fig. 3).

Stand visualization system (SVS; McGaughey 1997) profile illustrations were generated for six inventory plots selected randomly from various areas of the TPA-QMD data

cluster (fig. 3). Each SVS illustration is a simulated view of trees on a given plot, produced by using measured attributes (species, diameters, and heights) from plot trees.

The appendix further explains the SVS process and assumptions. Differences among

the six SVS inventory plot illustrations (fig. 4) demonstrate that the TPA-QMD data

distribution captures some basic differences in forest structure. In figure 4, inventory

plots 1 and 2 have homogeneous forest structures typical of early stages of stand

development. In contrast, plots 3 and 4 have heterogeneous forests structures typical

of older, more developed stands. Plots 5 and 6 (fig. 4) appear to be less productive

stands, with smaller, more widely spaced trees. These plot illustrations support the

notion that differences in forest structure can be captured by differences in TPA and

QMD. In general, we see a gradient in forest structure coinciding with the long axis of

the TPA-QMD data cluster (fig. 4). This structural gradient is defined by differences in

tree density, average tree size, and the variation of tree sizes.

6

Figure 3—Conceptual timeline portraying developmental stages for temperate rain forests

of southeast Alaska. Shaded bars represent temporal overlap among developmental

stages (Nowacki and Kramer 1998).
7

Figure 4—Six forest inventory plots illlustrated by using stand visualisation system software (McGaughey 1997). Each plot was selected at random to represent various

parts of the scattergram in figure 2.
8

Figure 5—(A) Least-squares regression lines for net board-foot volume ranges. Forest inventory plots are

labeled with a number representing the associated volume range (0=0-5,000; 1=5-15,000; 2=15-25,000;

3=25-35,000; 4=35-45,000; 5=45-55,000; 6=>55,000). (B) Isoclines represent net board-foot volume in relation to the joint distribution of trees per acre and quadratic mean diameter.
To understand the relation between timber volume and the TPA-QMD data cluster, we

overlaid timber volume information for each inventory plot onto the cluster (fig. 5A). Each

inventory plot was assigned a number corresponding to its volume range; for example,

if an inventory plot had a ground-measured volume of 32,000 NBFT, it would fall within the 25,000-35,000 NBFT volume range and be labeled with a “3.” The assigned volume range numbers for all inventory plots were graphed from their respective TPA and

QMD values, and each NBFT volume range was fitted with a best-fit regression line

(fig. 5A, table 1).

The slopes of the best-fit regression lines shown in figure 5A were tested for significant differences by using analysis of covariance (SAS 1994).7 The slopes did not differ

significantly (p=0.51). The slopes of best-fit regression lines (fig. 5A) were averaged,

and the weighted average slope of 0.29 was used to generate a series of equidistant

lines or isoclines across the TPA-QMD data cluster shown in figure 5B. The isoclines

summarize the relation between timber volume and the TPA-QMD data cluster. Forest

inventory plots falling near the same isocline are likely to have similar timber volumes.

Inventory plots falling near an isocline farther from the origin are likely to have higher

timber volume than plots falling near an isocline closer to the origin.

There seems to be no direct correlation between timber volume and forest structure.

In figure 5A, inventory plots with similar timber volumes are well distributed across the

long axis of the TPA-QMD data cluster (i.e., the structural gradient). Plots with similar

timber volumes can be related to many different combinations of TPA and QMD. The

randomly selected plots illustrated in figure 4 provide a more specific demonstration of

the lack of correlation between timber volume and forest structure. Consider plots 2, 3,

9

Timber Volume

Table 1—Statistics for best-fit regression lines shown in figure 5A

Net board-foot Plot Best-fit regression line

volume range label No. Ln(qmd)=a+b*ln(tpa) Slope R2

0 - 5K 0 1 NA NA NA

5 - 15K 1 77 Ln(qmd) = 4.03 - 0.27*Ln(tpa) -0.27 0.62

15 - 25K 2 113 Ln(qmd) = 4.16 - 0.27*Ln(tpa) -0.27 0.58

25 - 35K 3 94 Ln(qmd) = 4.42 - 0.31*Ln(tpa) -0.31 0.74

35 - 45K 4 64 Ln(qmd) = 4.28 - 0.27*Ln(tpa) -0.27 0.73

45 - 55K 5 29 Ln(qmd) = 4.54 - 0.31*Ln(tpa) -0.31 0.70

> 55K 6 12 Ln(qmd) = 4.78 - 0.35*Ln(tpa) -0.35 0.86

7 To meet the assumptions of analysis of covariance, the TPA

and QMD distributions for each volume range were assumed to

have a normal distribution and homogenous variances among

volume ranges.

Comparing Timber

Volume and Forest

Structure
and 4 in figure 4; although these plots have similar timber volumes (table 2), plot 2 is

densely stocked with many medium-sized trees forming a closed, single-storied canopy,

whereas plot 4 is more moderately stocked with trees differing in diameter and height

and forming an open, multilayered canopy. The structural attributes of plot 3 seem to

fall somewhere in between plots 2 and 4. Plots 1, 5, and 6 (fig. 4) also have similar

timber volumes (table 2), yet these plots have large differences in structure (i.e., tree

density and variation of tree sizes).

Do the original timber volume strata (4-7) accurately capture volume differences in the

Tongass or are they more oriented toward differences in forest structure? To address

this question, we graphed each forest inventory plot on TPA-QMD coordinate axes

and labeled each plot with its photointerpreted timber volume stratum (fig. 6A). That is,

an inventory plot located in a polygon designated volume stratum 6 was labeled with a

“6” and graphed according to its ground-measured TPA and QMD values. Because

10

Table 2—Information for the 6 inventory plots illustrated in figure 4

Plot illustration Trees per Quadratic mean Net board-foot volume

(shown in figure 4) acre diameter per acre

1 192 15.6 21, 500

2 135 21.4 67,600

3 94 24.1 44,300

4 57 26.9 58,200

5 69 19.6 18,100

6 121 15.2 17,800

Analysis of

Photointerpreted

Information

1977 Timber 

Volume Strata

Figure 6—(A) The trees per acre and quadratic mean diameter scattergram for 390 forest inventory plots.

Numbers 4 through 7 represent the volume stratum designated for the timber type polygon surrounding the

inventory plot.
11

Figure 6—(B) Mean trees per acre, quadratic mean diameter (log-transformed), and 90-percent confidence

ellipses for inventory plots located within timber type volume strata 4 (n=186), 5 (n=155), and 6/7 (n=49).

(C) Mean trees per acre, quadratic mean diameter (log-transformed), and 90-percent confidence ellipses for

inventory plots located within timber type volume strata 4, 5, and 6/7. The number in the center of each

ellipse is the mean net board-foot volume for inventory plots within each timber type volume stratum. Net

board-foot isoclines are added for reference.
high-volume stands are distributed along the top, outer portion of the TPA-QMD data

cluster (fig. 5A), we would expect plots designated as timber volume strata 6 or 7

(high-volume, >30,000 NBFT) to be distributed along the top, outer portion of the TPAQMD data cluster as well. Instead, the 6s and 7s appear to be more frequently distributed in the upper left portion of the TPA-QMD data cluster (fig. 6A). This distribution

indicates that stands photointerpreted as high volume may have been selected on the

basis of forest structural attributes related to TPA and QMD.

Although the scattergram in figure 6A is instructive, the statistical analysis most appropriate to the design of 1980s forest inventory involves the computation of Forest-wide

means and confidence intervals for those means (see Brickell 1989 for a detailed

explanation). We calculated the Forest-wide means and confidence intervals for TPA

and QMD for all ground-measured plots falling within each photointerpreted timber

volume stratum.8 These means are displayed in figure 6, B and C, with 90-percent

confidence ellipses. Each ellipse encompasses the range of mean TPA and mean

QMD values in which we expect 90 percent of all sample means of the same size to

fall. Non-overlapping ellipses indicate that differences between timber volume strata

are likely significant (α = 0.10) for mean TPA or mean QMD (or both). Data were

analyzed by using a one-way ANOVA9 (PROC GLM; SAS 1994). Scheffe multiple

comparisons contrasts were calculated (α = 0.10) to test the hypothesis of no difference between mean TPA and mean QMD for all possible pairwise contrasts among

timber volume strata 4, 5, and 6/7. Results showed significant differences in all possible pairwise contrasts for both mean TPA and mean QMD (p < 0.10).

Most relevant in this analysis are the differences between mean TPA and QMD for

timber volume strata 5 and 6/7. The separation between strata 5 and 6/7, along the

long axis of the TPA-QMD data cluster, indicates differences in forest structures.

However, the mean timber volumes for strata 5 and 6/7 are similar (fig. 6C). Thus, it

seems that differences between timber volume strata 5 and 6/7 tend to reflect structural differences more than differences in timber volume.

We conducted the same TPA-QMD analysis described above by using the revised

timber volume strata, low, medium, and high (Julin and Caouette 1997). The low,

medium, and high timber volume stratification was created by combining the original

timber volume strata with soil and slope information from CLU. By using the CLU

database, two soil and slope strata, hydric (H) and nonhydric (NH), were intersected

with the original timber volume strata (4-7). The intersection resulted in the formation

of five groups (fig. 7A).

12

8 Timber volume strata 6 and 7 were combined for this analysis.

There were too few samples in timber volume stratum 7 polygons

(12), and the TPA and QMD means for volume strata 6 and 7 were

similar.

9 To meet the assumptions of ANOVA, the TPA and QMD distributions for each volume stratum were assumed to have a normal distribution and homogenous variances among strata.

1997 Revised Timber

Volume Strata
13

Figure 7—(A) Mean trees per acre, quadratic mean diameter, and 90-percent confidence ellipses (log-transformed) for inventory plots located within the intersection of timber type volume strata and common land unit

(CLU) hydric soils strata. The numbers shown in the center of each ellipse represent the timber type volume

strata (4, 5, or 6/7), and the letters represent CLU hydric soils strata (hydric soils [H] and nonhydric soils

[NH]). The timber type and CLU merger creates five groups 4H (n =77), 4NH (n=106), 5-7H (n=34), 5NH

(n=125), and 6/7NH (n=41). (B) Mean trees per acre, quadratic mean diameter (log-transformed), and 90-

percent confidence ellipses for timber type volume strata intersected with CLU hydric soils stratification. The

number in the center of each ellipse is the mean net board-foot volume for inventory plots within each of the

timber type-CLU groupings shown in figure 7A. Net board-foot isoclines are added for reference.
The five groups created from the intersection of original timber volume strata and CLU

soil and slope strata are shown again in figure 7B with their respective mean timber

volumes (NBFT) and timber volume isoclines. The differences in means and the nonoverlapping ellipses in figure 7B indicate that each group may have significant differences in TPA and QMD and, hence, significant information regarding forest structure.

If the goal is strictly to provide efficient timber volume stratification, structural information is superfluous, and one would naturally collapse the five groups into three by

merging groups located along similar NBFT volume isoclines (fig. 7B). The revised

1997 timber volume strata (low, medium, and high) did just that, as shown in figure

7C. The collapsing of five groups into three demonstrates the problems of having a

strict timber volume-based mapping objective. Although the revised timber volume

strata provide significant differences in timber volume (Julin and Caouette 1997),

available information, which could be useful in modeling differences in forest structure,

has been sacrificed.

Canopy texture is generally an obvious and intuitive attribute to recognize and delineate in aerial photographs. Canopy texture, as viewed from aerial photographs, seems

to correspond to differences in forest structure on the ground. To test this theory, we

compared photointerpreted texture classes with ground-measured structural attributes

(TPA and QDM).

14

Photointerpretation of

Canopy Texture

Figure 7—(C) Mean trees per acre, quadratic mean diameter (log-transformed), and 90-percent confidence

ellipses for inventory plots located within new volume timber type-CLU volume strata. The number in the

center of each ellipse is the mean net board-foot volume for inventory plots within each volume strata (low,

medium, and high). Net board-foot isoclines are added for reference.
We had an experienced Tongass photointerpreter assign canopy texture designations

to each of the 390 land and timber type polygons sampled in the 1980s forest inventory. The photointerpreter had no prior knowledge of the polygon location or any previously assigned attributes. Four texture designations ranging from fine to coarse were

defined by forest characteristics visible on aerial photographs (crown density, uniformity

of canopy, crown size, and frequency and size of canopy gaps) (Paine 1981). Finetextured forests have high crown density, uniform crown sizes and heights, small

crowns, and few canopy gaps. In contrast, coarse-textured forests have lower crown

density, nonuniform crown sizes and heights, large crowns, and many canopy gaps.

The middle texture designations, medium-fine and medium-coarse, fall along a gradient between the endpoints. Figure 8 shows the range of canopy textures.

Mean TPA and QMD and 90-percent confidence ellipses for inventory plots within

polygons having the same texture designation are shown in figure 9. Polygons designated as fine or medium-fine had a higher mean TPA and smaller QMD than polygons

with medium-coarse or coarse designations. The differences among means coincide

with the long axis of the TPA-QMD data cluster. Earlier analysis showed that differences along the long axis of the TPA-QMD data cluster appear to be good indicators

of structural differences on the ground. This exercise suggests that canopy texture

corresponds to differences in forest structure on the ground as measured by TPA and

QMD.

15

Figure 8—Forested area south of Eagle Beach, Juneau, Alaska. Canopy textures ranging from fine to

coarse are shown.
Throughout the 1980s and early 1990s, the original timber volume stratification map

(4-7) was a standard tool for land-use planning and managing timber and wildlife

resources. With increased reliance on timber volume stratum information, concerns

about its accuracy began to emerge. To address these concerns, a statistical analysis

of the original timber volume strata was conducted by Brickell (1989). He compared

timber volume strata to ground-truthed forest inventory data and concluded that polygons designated as timber volume strata 5, 6, and 7 (the three highest) did not actually differ significantly in average volume. He stated that strata 5, 6, and 7 could be

combined “without any appreciable loss of precision in the overall volume estimate.”

Furthermore, continued use of strata 5, 6, and 7 to represent distinct volume categories could not be justified statistically. Brickell’s conclusions, along with corroborating evidence from the Kelp Bay timber sale, formed the basis of a lawsuit on using

timber volume strata to administer proportionality (U.S. District Court for the District

of Alaska 1994). The Wilderness Society argued that because there were no statistical differences in ground-measured timber volume among timber volume strata 5, 6,

and 7, the use of timber volume strata for determining proportionality was “arbitrary

and capricious.” The court agreed and the Forest Service was ordered to devise a

more accurate means of determining proportionality than the existing timber volume

stratum map.

16

Figure 9—Mean trees per acre, quadratic mean diameter (log-transformed), and 90-percent confidence

ellipses for inventory plots located within timber type polygons that have been photointerpreted and classified by canopy texture: fine (F) (n=11), medium-fine (MF) (n=91), medium-coarse (MC) (n=222), and coarse

(C) (n=56).

Discussion

Timber Volume Strata

Revisited
Our analysis suggested that timber type photointerpreters may have delineated the

Forest by using structural attributes and translated structural information into timber

volume classes—perhaps under the assumption that structure and timber volume are

closely correlated. Our analyses demonstrated that forest structure and timber volume

are not directly correlated. It is therefore not surprising that timber volume strata, often

based on differences in forest structure, did not correspond to differences in timber

volume. Further analysis indicated that aerial photointerpreters may recognize differences in forest structure by focusing on canopy texture. Differences among texture

classes coincide with the long axis of the TPA-QMD data cluster similar to differences

observed among timber volume strata.

As land managers were revising TLMP during the mid 1990s, many felt it was imperative to address the problems associated with the original timber volume stratum map.

This goal was achieved when Julin and Caouette (1997) revised the timber volume

strata from additional photointerpreted information. Revised timber volume stratum

(low, medium, and high) provided statistically defensible differences in Forest-wide

volume averages. Although the revised timber volume strata provide more reliable

volume data, many interested parties remain unsatisfied. One scientist stated that “the

richest old growth and the less valuable stands are classified into a single category

[high volume]” (Schlickeisen 1998). An appeal of the recently revised TLMP argues that

the revised timber volume strata are unsatisfactory because they do not differentiate

the “characteristics found in the structure of ancient old growth, often extremely highvolume stands” (USDA 1997). Some have even advocated for the Forest Service to

return to using the original timber volume strata.

It seems that many people are dissatisfied with the revised timber volume strata because

they want to see forest structure delineated and mapped for the Tongass.Yet, most

people do not appear to question the role that timber volume goals play in preventing

this from happening. Any forest stratification that has timber volume as its primary

objective will necessarily group together stands of similar timber volume regardless of

differences in forest structure. Our analysis showed that this is what happened in the

timber stratum revision process (Julin and Caouette 1997). During this process, differences in forest structure were collapsed into a single category because they showed

no significant difference in mean timber volume.

Many people associate differences in timber volume with differences in forest structure;

perhaps this is because people naturally associate big trees with high timber volume

without accounting for the sparse stocking that generally characterizes stands of big

trees. Stands of medium-sized trees often have as much or more timber volume than

stands of large trees owing to differences in number of trees. Consider two forest

stands: one is densely stocked with smaller trees and has a uniform canopy, few rotting or dead trees, and a sparse understory; the other is moderately stocked with

many larger, older trees and has many openings in the canopy, standing and down

dead trees, and a large amount of understory vegetation. In many cases, the timber

volume of such stands will reveal nothing of their differences. Stands similar in timber

volume can have a wide range of forest structures.

Conclusion

17
Acknowledgments

Although timber volume information is critical for many management objectives, such

as calculating forestwide harvest rates or the allowable sale quantity and planning timber sales, there are limits to what timber volume can tell us about a forest. To manage a forest for quality wildlife habitat, timber values, alternative harvest prescriptions,

biodiversity, and recreational opportunities, we must develop a system of forest measures that capture differences in forest structure as well as volume. Such a system

can provide managers, scientists, and forest users with richer and more diverse information to help them understand ecological processes and plan for a healthy and sustainable forest.

We thank the following USDA Forest Service personnel: Gene Degayner, Jim Russell,

Bill Pawuk, Bill Wilson, Don Golnick, and Bob Smith for their quest to understand and

solve the problems associated with forest stratification on the Tongass. Many of the

ideas and concepts presented in this paper were built upon their ideas and work. We

thank our peer reviewers, Robert Deal (USDA Forest Service, Juneau, AK), Tim Max

(USDA Forest Service, Portland, OR), Robert Curtis (USDA Forest Service, Olympia,

WA), William Wykoff (USDA Forest Service, Moscow, ID), and Michael Marsden

(USDA Forest Service, Coeur d’Alene, ID). We thank Charles (Terry) Shaw, Kent Julin

(then with USDA Forest Service, Juneau, AK, now with County of Marin, Woodacre,

CA), and Karen Esterholdt (USDA Forest Service, Portland, OR) for providing important and helpful reviews and comments. Rebecca Braun for her many editorial comments, insights, and support; and Kim Hastings and Richard Carstensen for their

attempts to understand and interpret the evolving concepts that led to the development of this paper.

Acker S.A.; Sabin T.E.; Ganio L.M.; McKee W.A. 1998. Development of old-growth

structure and timber volume growth trends in maturing Douglas-fir stands. Forest

Ecology and Management. 104: 265-280.

Alaback, P.B. 1982. Dynamics of understory biomass in Sitka spruce-western hemlock forests of southeast Alaska. Ecology. 63: 1932-1948.

Bormann, F.H.; Likens G.E. 1979. Pattern and processes in a forested ecosystem.

New York: Springer-Verlag.

Brickell, J.E. 1989. Review of forest inventory methodology and results, Tongass

National Forest. [Place of preparation unknown]: U.S. Department of Agriculture,

Forest Service, Northern Region. Unpublished report. 28 p. On file with: Alaska

Region, 709 W. 9th Street, Juneau, AK 99801; and U.S. Department of Agriculture,

Forest Service, Tongass Land Management Planning Office, P.O. Box 21628,

Juneau, AK 99802.

Esca-Tech Corporation. 1977. Under contract with the USDA Forest Service,

General Services Administration, Standard Form 33. On file with: USDA Forest

Service, datalib 27898; and U.S. Department of Agriculture, Forest Service,

Tongass Land Management Planning Office, P.O. Box 21628, Juneau, AK 99802.

Farr, W.; DeMars, D.; Dealy, J. 1989. Height and crown width related to diameter for

open-grown western hemlock and Sitka spruce. Canadian Journal of Forest

Research. 23(3): 1203-1207.

18

References
Hastings, K. 1997. When trees fall in the forest: stand structure following partial disturbance in the spruce-hemlock forest of southeast Alaska. Missoula, MT: University

of Montana. 64 p. M.S. thesis.

Julin, K. R.; Caouette, J. P. 1997. Options for defining old-growth timber volume strata: a resource assessment. In: Julin, Kent R., comp. Assessments of wildlife viability, old-growth timber volume estimates, forested wetlands, and slope stability. Gen.

Tech. Rep. PNW-GTR-392. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station: 24-37. (Shaw, Charles G., III, tech.

coord.; Conservation and resource assements for the Tongass land management

plan revision).

Kershaw, K.A. 1973. Quantitative and Dynamic Plant Ecology. 2d ed. New York:

American Elsevier Publishing Co.

McCarter, J.B.; Long, J.N. 1986. A lodgepole pine density management diagram.

Western Journal of Applied Forestry. 1(1): 6-11.

McGaughey, R.J. 1997. Stand visualization system. USDA Forest Service, Pacific

Northwest Research Station, electronic distribution version. Seattle, WA: University

of Washington. 53 p. Website: http://forsys.cfr.washington.edu/svs.html.

Nowacki, G.J.; Kramer, M.G. 1998. The effects of wind disturbance on temperate rain

forest structure and dynamics of southeast Alaska. Gen. Tech. Rep. PNW-GTR-421.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 25 p. (Shaw, Charles G., III, tech. coord.; Julin Kent R., ed.;

Conservation and resource assessments for the Tongass land management plan

revision).

O’Hara, K.L.; Latham, P.A.; Hessburg, P.; Smith, B. 1996. A structural classification

for inland Northwest forest vegetation. Western Journal of Applied Forestry. 11(3):

97-100.

Oliver, C.D.; Larson, B.C. 1996. Forest stand dynamics. New York: John Wiley and

Sons. 520 p.

Paine, D.P. 1981. Aerial photography and image interpretation for resource management. New York: John Wiley and Sons. 559 p.

Reineke, L.H. 1933. Perfecting a stand-density index for even-aged forests. Journal of

Agricultural Research. 46: 627-638

Rogers, G.; van Hees, W.W.S. 1992a. Timber resources statistics for the Chatham

Area of the Tongass National Forest, Alaska, 1985. Resour. Bull. PNW-RB-186.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 20 p.

Rogers, G.; van Hees, W.W.S. 1992b. Timber resources statistics for the Ketchikan

Area of the Tongass National Forest, Alaska, 1985. Resour. Bull. PNW-RB-184.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 18 p.

Rogers, G.; van Hees, W.W.S. 1992c. Timber resources statistics for the Stikine Area

of the Tongass National Forest, Alaska, 1984. Resour. Bull. PNW-RB-185. Portland,

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research

Station. 19 p.

19
SAS Institute. 1994. A step-by-step approach to using SAS system for univariate and

multivariate statisitcs. Cary, NC. 552 p.

Schlickeisen, Rodger. 1998. Our threatened rainforest. Defenders. 73(2): 34-40.

U.S. Department of Agriculture, Forest Service. 1982. Timber management inventory handbook. Forest Service Handb. 2409.21b R10. Washington DC.

U.S. Department of Agriculture, Forest Service. 1990. Chatham Area intergrated

resource inventory handbook. USDA Forest Service unpublished handbook. Sitka,

AK: Alaska Region. On file with: U.S. Department of Agriculture, Forest Service,

Chatham Area, P.O. Box 204 Siginaka Way, Sitka AK 99835

U.S. Department of Agriculture, Forest Service. 1997. Tongass National Forest land

and resources planning record. Appeal 97-13-0112. Juneau, AK: Alaska Region.

On file with: U.S. Department of Agriculture, Forest Service, Tongass Land Management Planning Office, P.O. Box 21628, Juneau, AK 99802.

U.S. District Court for the District of Alaska. 1994. The Wildlife Society, Alaska

Chapter, and others; Plaintiff vs. Michael A. Barton, Regional Forester, Alaska

Region, United States Forest Service, and others, Defendents, and the Alaska

Foresters Association, Defendent-Intervenor. Order dated April 11, 1994. No. J93-

001-CV(HRH).

U.S. Public Laws, Statutes, etc. 1990. Public Law 101-626. Tongass Timber Reform

Act of 1990. 16 U.S.C. 539d(1990).

The following protocol was used to select and develop the six SVS plot illustrations.

Plots were selected from different regions of the TPA-QMD data cluster without prior

knowledge of their location or contents. The only known attribute of each plot was its

location in the TPA-QMD data cluster. Each plot illustration randomly displays tree

measurement data from the plot over a single square acre. Plot profiles represent a

237- by 20-foot (one-tenth of an acre) cross section across the diagonal of the square

acre. Crown types and trunk colors were assigned to individual trees according to

their species (spruce, hemlock, cedar). Crown size for each individual tree was calculated by using two-thirds of the theoretical maximum crown radius for a tree of any

given diameter. The maximum crown radius was calculated by using a regression

equation (derived from measures of open-grown trees in southeast Alaska [Farr et al.

1989]). Crowns were shown only in the upper one-half of the tree height. Dead trees

less than 50 feet tall were deleted. Dead trees taller than 50 feet were assigned an

SVS snag crown type with an 8-foot radius. A 200-foot reference pole, marked in 20-

foot increments, was added to the center of each illustration.

20

Appendix

Plot Illustrations,

Stand Visualization

System 
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