Effect of Habitat-Improvement Thinnings on 

Lumber Products from Coastal Douglas-fir

Dennis P. Dykstra, Patricia K. Lebow, Stephen Pilkerton, R. James Barbour, 

Susan Stevens Hummel, and Stuart R. Johnston

DEPAR TMENT OF AGRICULT URE

United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

Research Paper

PNW-RP-605 

April

2016
Authors

Dennis P. Dykstra is a research forest products technologist (retired), U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station, 

Forestry Sciences Laboratory, 620 SW Main Street, Suite 400, Portland, OR 

97205; Patricia K. Lebow is a mathematical statistician, U.S. Department of 

Agriculture, Forest Service, Forest Products Laboratory, One Gifford Pinchot 

Drive, Madison, WI 53726; Stephen Pilkerton is a operations manager, College 

of Forestry Research Forests, Oregon State University, Corvallis, OR 97331; 

R. James Barbour is a research forest products technologist, U.S. Department of 

Agriculture, Forest Service, Forestry Sciences Laboratory, 620 SW Main Street, 

Suite 400, Portland, OR 97205; Susan Stevens Hummel is a research forester, 

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

Station, Forestry Sciences Laboratory, 620 SW Main Street, Suite 400, Portland, 

OR 97205; Stuart R. Johnston is a forest silviculturist (retired), U.S. Department 

of Agriculture, Forest Service, Siuslaw National Forest, P.O. Box 1148, Corvallis, 

OR 97339.

Cover photograph: Timber-sale purchaser Robert Bateman measures a felled tree 

in preparation for bucking in an Oregon Coast Range Douglas-fir stand.

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Abstract

Dykstra, Dennis P.; Lebow, Patricia K.; Pilkerton, Stephen; Barbour, R. 

James; Stevens Hummel, Susan; Johnston, Stuart R. 2016. Effect of habitatimprovement thinnings on lumber products from coastal Douglas-fir. Res. Pap. 

PNW-RP-605. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 132 p.

We selected 66 sample trees from two thinning treatments, each of which was 

applied at three sites on the Siuslaw National Forest in Oregon. The first commercial thinnings, conducted in 1992 and 1993, had been designed to accelerate the 

development of large trees with large branches and other old-growth characteristics so as to improve habitat for bird species that depend on such characteristics. 

Our sample trees, removed in commercial thinnings from 2008 to 2010, were 

felled and bucked into long (woods-length) logs whose stems were measured 

in detail, with surface defects also measured and located in three dimensions. 

Sample logs from the heavier of the two thinning treatments had larger knots on 

average than logs from the lighter thinning; they had higher average knot density 

and percentage of live knots. The long logs were “bucked” by computer simulation into short (mill-length) logs that were then “sawn” by computer simulation 

into lumber. Logs from the heavier thinning treatment had higher average lumber 

volume recovery, higher average lumber grade recovery, and higher estimated 

lumber value than logs from the lighter thinning. The analysis suggested that 

heavier thinning produced trees with more frequent and larger branches—more 

suitable for nesting and roosting by the target bird species—while at the same 

time yielding larger logs from harvested trees that produce higher product values. 

The product value from trees harvested in such thinnings is likely to be lower 

than values derived from harvests in nearby industrial forests, but comparable to 

other thinning treatments that are applied to similar forests on federal lands. 

Keywords: Habitat-improvement thinning, stem analysis, surface knots, 

lumber quality, lumber volume, branch size, Douglas-fir lumber.
Summary

During the middle 1900s, a substantial amount of forest acreage in the Oregon 

coastal forest was clearcut and replanted, primarily to Douglas-fir (Pseudotsuga 

menziesii [Mirbel] Franco). As a result, large blocks of this forest exhibit little 

diversity with respect to dominant tree species, stand age, and structure. The 

Siuslaw National Forest, which occupies a substantial share of the central and northern coastal forest of Oregon, has launched an extensive thinning program intended 

to promote rapid development of late-successional conditions and improve habitat 

for old-growth-dependent wildlife species such as the northern spotted owl (Strix 

occidentalis caurina) and marbled murrelet (Brachyramphus marmoratus). Forest 

managers, both from government agencies and from private holdings, need to know 

what effect such thinnings might have on timber quality as well as how any changes 

in quality might affect revenues from timber sales.

This report describes an experiment that was designed to collect detailed 

stem and defect information on logs from trees harvested during a second habitatimprovement thinning conducted at three Siuslaw study sites. The stem and defect 

data thus obtained were used in simulation runs to evaluate products that might be 

manufactured from the logs—estimations of quantity, quality, and value from simulated sawing of the logs into lumber. Such information would be useful for foresters 

in estimating the value of standing trees, for loggers in estimating the value of the 

logs they harvest, and for sawmills in estimating the value of the logs they buy. The 

stem and defect data could be used to estimate the production and value of other 

forest products, given appropriate simulation software; or to evaluate the effects of 

thinning operations on stem and crown development in Douglas-fir. The treatments 

in the original 1992-to-1993 commercial thinning (phase 1) produced residual 

stocking densities of approximately 30 (treatment T30), 100 (treatment T100), and 

60 (treatment T60) trees per acre. The second commercial thinning described in this 

later study (phase 2) involved further removals only from treatments T100 and T60. 

Overstory tree stocking on the treatment plots was reduced in the second thinning 

to about half of the relative density measured eight years after the first thinning.

We selected a sample of 66 Douglas-fir trees, which represented about 3 percent 

of the trees harvested in the thinning operations. This sample size is typical of 

wood-quality studies carried out by U.S. Forest Service scientists at the Pacific 

Northwest Research Station (Barbour et al. 1999, Lowell et al. 2012). The random 

sampling procedure used to select the sample trees was stratified so that all diameter classes were represented in the sample at about the same proportion as in the 

population of trees being harvested within each treatment plot and study site. The 

sample trees were felled, cut into long (woods-length) logs, and cable-yarded to 

landings as part of the normal thinning operation. A thorough stem analysis was 
performed on each long log, with every knot or other potential product defect 

measured and located on the log in three dimensions. Stem characteristics such as 

sweep and crook were also recorded, and the inside-bark diameter of each log was 

measured at each assessment point along the stem. Three-dimensional data for the 

long logs were then converted into equivalent data for short (mill-length) logs using 

a bucking simulator developed for this study. The resulting data were then processed with AUTOSAW, a sawing simulator developed by the New Zealand Forest 

Research Institute, to estimate both the quantity and quality of lumber that could 

be recovered from the logs.1 Lumber grades were assigned based on Western Wood 

Products Association rules for dimension and 1-inch Douglas-fir lumber. Product 

prices and equivalent tree and log values from recovered lumber were estimated 

and compared for the two thinning treatments. Below is a summary of the most 

important results from our analysis, emphasizing differences that can be attributed 

to thinning intensity.

Phase 1 Thinning Treatments and Diameter Measurements

• For trees measured immediately before the phase 2 thinning operations, the 

average diameter at breast height (DBH) from the treatment T100 plots in 

the phase 1 operations was found to be significantly smaller than the average DBH from the treatment T60 plots.

• Height and crown ratio were not found to have significant treatment effects. 

No significant differences were detected for any of the variables between 

subplots that were underplanted and those that were not, nor did we find any 

significant interactions between the underplanting and thinning treatments.

Sample Trees and Long Logs

• The 66 sample trees were bucked at the felling site into 185 long logs, of 

which 179 were recovered after yarding; 113 (63 percent) of the long logs 

recovered for measurement were from treatment T100 and 66 (37 percent) were from treatment T60. This is very nearly the same as the ratio of 

sample trees from treatment T100 (64 percent) to those from treatment T60 

(36 percent).

• Treatment, DBH adjusted by treatment, and the position of the log within 

the tree were statistically significant predictors of long-log diameters and 

gross log volume; but for long-log length, only log position and an interaction between log position and adjusted DBH were significant. All means 

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

imply endorsment by the U.S. Department of Agriculture of any product or service.
and upper range limits of those variables were slightly larger for long logs 

from treatment T60 than from treatment T100.

• Sweep was recorded in 38 long logs, or 21 percent of the sample. All were 

butt logs. Treatment was not a statistically significant predictor of either 

maximum sweep offset (the distance to the center of sweep) even though 

both measures were slightly larger overall for logs from treatment T60 than 

for logs from treatment T100.

• Crook was recorded in only six long logs, four of which were from treatment T60 plots. Because crook often results from stem damage, the heavier 

thinning in treatment T60 may have contributed to the greater incidence of 

crook, but the sample was too small to support a firm conclusion.

Short Logs

• The 179 long logs recovered from the sample trees were bucked by a simulator into 327 short logs, of which 324 met the minimum diameter and 

length requirements for lumber production. The resulting short-log gross 

volume was 4,567.3 cubic feet; 19.6 discarded cubic feet were from three 

small unusable logs plus bucking waste, and 112.5 cubic feet were allocated 

to sawing trim. The loss to bucking waste and trim was 2.8 percent of gross 

available volume.

• Among short logs from the sample trees, treatment was a statistically significant predictor of large-end and small-end diameters and of gross log 

volume, but treatment had no apparent effect on log length. As with long 

logs, the position of the log within the tree and the DBH adjusted by treatment were significant predictors of short-log diameters and gross log volume. Log position was the only significant predictor of log length.

• Bucking reduced average sweep offset of 7.65 inches in long logs to only 

1.94 inches in short logs, an amount that we concluded was unlikely to have 

much effect on product recovery.

Surface Defects

• In the 179 long logs for which stem profiles and surface defects were measured, a total of 11,022 defects were tallied. Of these, 10,970 were knots, 

with 41 percent classified as live knots and 59 percent as dead knots. The 

remaining 52 surface defects were small burls (44), forked tops (3), and surface scars (5). Only the knots and forked tops were judged to penetrate the 

surface of the log far enough to influence lumber quality when the log was 

sawn. Forked tops were treated in the sawing simulator as large knots.
• Trees from treatment T60 plots had more knots per tree, a higher average 

live-knot fraction, larger average and minimum knot diameters, and more 

knots per foot of merchandized stem than trees from treatment T100 plots. 

Trees from treatment T60 also tended to have larger DBH and longer stem 

lengths than those from treatment T100.

• Long logs from treatment T60 plots had larger average knot diameters and 

higher average knot densities in all three log-position classes (butt, middle, 

and upper logs) than long logs from treatment T100 plots. Within each 

major group, the smallest average knot density occurred in butt logs and 

the highest average knot density in upper logs, which typically represent 

the part of the tree within the living crown. Average knot diameter differed 

little between middle and upper logs and in some instances was larger in 

middle logs than in upper logs. It was always smaller in butt logs.

• As with long logs, in short logs the percentage of live knots and the average knot density were lowest in butt logs and highest in upper logs. Average 

knot density was also higher in short logs from treatment T60 than from 

treatment T100 except for butt logs. Average knot diameter was larger in 

logs from treatment T60 than in logs from treatment T100 for all position 

classes. The percentage of live knots was higher in upper logs from treatment T60 than from treatment T100 but lower in butt and middle logs.

• Regression analyses on the size of surface knots in tree stems, long logs, 

and short logs found no direct statistically significant treatment effect. 

However, DBH was significantly influenced by treatment and was also a 

significant predictor of knot size in tree stems, so we conclude that there is 

an indirect treatment effect. Results of the statistical analyses suggest that 

trees and logs with larger diameters tended to have larger knots except in 

the butt log; and live knots tended to have larger diameters than dead knots.

• Regression analyses on knot density in tree stems, long logs, and short logs 

found no statistically significant treatment effect. Again, because DBH was 

significantly influenced by treatment and was found to be a significant predictor of knot density, it can be argued that treatment indirectly influences 

knot density either through DBH directly or as an indirect effect of smallend diameter, which is closely correlated with DBH. Tree or log diameter and 

elevation were significant fixed-effect predictors except that for short logs, 

diameter was only significant in butt logs. For both long and short logs the 

live-knot fraction and the interaction between butt-log position and log diameter were significant fixed-effect predictors. For long logs, butt logs tended to 
have lower knot densities than middle or upper logs and knot density tended 

to increase with log diameter and with live-knot fraction. For short logs, knot 

density tended to decrease with increasing butt-log diameter but was not 

influenced by diameter in middle and upper logs. For both log classes, knot 

density tended to increase as the percentage of live knots increased.

Lumber production

• The 324 merchantable short logs produced by the bucking simulator were 

processed into lumber using the AUTOSAW sawing simulator; 2,457 

pieces of lumber were produced totaling 40,341.33 board feet. Logs processed from treatment T100 plots accounted for 59 percent of the lumber 

pieces and 58 percent of the lumber volume, with an average piece volume 

of 16.06 board feet. Treatment T60 produced a slightly higher percentage 

of wide pieces than treatment T100. The average lumber volume per piece 

from treatment T60 was 16.94 board feet, about 5 percent higher than treatment T100. Overall, treatment T60 produced 42 percent of the simulated 

lumber volume even though only 36 percent of the sample trees and 38 percent of the short logs came from treatment T60 plots.

• Because the percentage of wide pieces of lumber (larger than 8 inches) 

from treatment T60 was higher than treatment T100, the volume per piece 

was larger—5 percent overall and 9 percent in the higher lumber grades. 

As a result, the estimated average lumber values were also higher—about 

10 percent higher for all grades and 11 percent for lumber from the higher 

grades. These differentials were even larger for select-structural lumber, the 

highest grade. Select-structural lumber from treatment T60 logs had 14 percent more volume per piece and 16 percent higher estimated value per piece 

than select-structural lumber from treatment T100 logs.

• The position in the tree from which the short log was taken had a substantial influence on both the volume and value of lumber recovered from the 

log. Butt logs produced much more of the higher lumber grades than middle 

or upper logs, with upper logs producing the lowest percentage of higher 

graded lumber (No. 2 and Better). Within all log-position classes, treatment 

T60 logs produced more lumber in the higher grades than treatment T100 

logs. As a result, the average value per short log was 25 percent higher for 

treatment T60 logs. For butt logs the estimated value per log was 32 percent 

higher for treatment T60. Even for middle and upper logs, the differential 

between treatments was substantial; treatment T60 produced 20 percent 
higher value for middle logs and 30 percent higher value for upper logs than 

treatment T100 produced for logs in the same position classes.

• Trees harvested from treatment T60 plots had 32 percent higher estimated 

value from recovered lumber than trees harvested from treatment T100 plots.

• Even though treatment T60 produced higher lumber volume recovery, 

grade recovery, and lumber value per tree and per log compared to logs 

from treatment T100, regression analyses found that treatment was a statistically significant predictor only for lumber grade recovery from short logs. 

However, a measure of diameter (either inverse DBH for trees or inverse 

small-end log diameter for short logs) was a significant predictor of lumber 

volume recovery, grade recovery, and lumber value recovery for both trees 

and logs. Because DBH was found to be strongly influenced by treatment, 

we conclude that the thinning treatment had an indirect influence on all the 

product recovery measures. Knot density, live-knot fraction, and average 

knot diameter were significant predictors of lumber grade and value recovery from trees and short logs. Log position and a measure of sweep were 

both significant predictors of lumber value recovery from short logs.

Stem Profiles and Branch Locations

• We were able to map long-log coordinates for profile points and knot locations into equivalent standing-tree coordinates for all logs from 37 of the 66 

sample trees (56 percent); 11 of the trees had two long logs and 26 had three 

long logs. Partial data could be mapped for 21 of the remaining 29 trees. 

Logs from eight trees could not be mapped because none of their azimuth 

indicators could be read after yarding.

• Of the 179 long logs, azimuth indicators were readable on 132 (74 percent). 

These logs had 75 percent of the knots measured in the study—8,193 of 

10,970 total knots.

• An exploratory statistical analysis of knot azimuths in the reconstructed 

tree stems gave mixed results. In some instances (for the Wildcat site and 

for the light and moderate thinning treatments on all sites combined), the 

analyses supported a conclusion that knots from the sample trees were not 

uniformly distributed around the boles but tended to cluster on the southfacing sides of the trees. In other instances (Cataract and Yachats sites) the 

hypothesis that knots are uniformly distributed about the boles of standing 

trees could not be rejected.

Contents

1 Introduction

2 History of the Study Sites

3 Research Objectives

4 Study Design and Preparation

4 Description of Study Sites

4 Thinning Treatments

7 Selection of Sample Trees

11 Data Collection

11 Long Log Measurements

18 Preparation and Organization of Data

22 Running the Sawing Simulator

22 Analyzing Simulation Outputs

22 Three-Dimensional Stem Profile and Defect Data

23 Sample Trees Felled and Long Logs Produced

25 Effects of Treatment on Stem Growth (Long Logs)

25 Measurement Data

28 Sweep and Crook in Long Logs

28 Effects of Treatment on Stem Growth (Short Logs)

30 Measurement Data

33 Sweep and Crook in Short Logs

33 Effects of Treatment on Surface Defects

34 Surface Knots

41 Size of Surface Knots

43 Density of Surface Knots

45 Effects of Treatment on Simulated Lumber Production

46 Lumber Volume, Grade, and Value

57 Statistical Analysis of Lumber Recovery

70 Treatment Effects on Stem Profiles and Branch Locations

70 Sample Size

73 Statistical Analysis

76 Discussion and Conclusions

76 Differences Attributed to Thinning Intensity
77 Lumber from Simulated Sawing

78 Future Research Needs

79 Acknowledgments

80 Metric Equivalents

80 References

84 Appendix 1: Program for Checking Software Prerequisites

85 Unblocking the Executable File

85 Unsupported Versions of Excel

85 Antivirus Software

86 Appendix 2: Log Bucking Simulator

88 Step 1. Data Selection

88 Step 2. Bucking Simulations

91 Step 3. Correcting Profile Data

94 Step 4. Adjusting Sweep Data

97 Step 5. Adjusting Crook Data

99 Appendix 3: AUTOSAW

102 Short Log Data Conversion

103 AUTOSAW Simulation Processing

104 Sawing Strategies

106 AUTOSAW Files

107 Operation

107 Output Files

109 Compiling AUTOSAW Simulation Results

112 Appendix 4: Three-Dimensional Data for Standing Trees

112 Deriving z-Coordinates for Points Within the Standing Tree

115 Deriving x- and y-Coordinates for Points Within the Standing Tree

125 Software Description for Mapping Profile Points and Surface Defects

129 Glossary
1

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Introduction

For several decades after the mid-1900s, a substantial amount of forest acreage in 

the Oregon Coast Range was clearcut and planted to Douglas-fir (Pseudotsuga menziesii), involving nearly a third of the 630,000-acre Siuslaw National Forest as well 

as large tracts of other federal lands, forests managed by state agencies, and private 

holdings. The goal was to create forest plantations that would provide high yields 

of quality wood products over relatively short rotations. By the late 1980s, many 

coastal forests exhibited high tree density, little understory development, and low 

tree-species, stand-age, and vertical-structure diversity. The adoption of the Northwest Forest Plan (Haynes et al. 2006, Tuchmann et al. 1996) and other changes in 

federal policy that occurred in the early 1990s shifted the emphasis of federal-forest 

management from timber production to a new focus on protection, restoration, and 

enhancement of ecosystem functions and services. Current federal-forest management objectives for designated timberlands still include the production of forest 

products but within an overall framework designed to maintain or enhance the 

ecological integrity of forest ecosystems while also supporting forest-dependent 

social and economic systems (Barbour et al. 1997).

The Siuslaw has begun an extensive thinning program aimed at promoting 

rapid development of late-successional stage conditions in some of its even-aged 

plantations, in part to improve habitat for old-growth-dependent wildlife species 

such as the northern spotted owl (Strix occidentalis caurina) and marbled murrelet 

(Brachyramphus marmoratus).

To support this large-scale undertaking, U.S. Forest Service scientists at the 

Pacific Northwest (PNW) Research Station, working with national forest personnel and Oregon State University, established a set of study sites in the early 1990s 

to evaluate the effectiveness of thinning and underplanting regimes that had 

been designed to promote old-growth characteristics in Douglas-fir plantations 

(Anderson et al. 2007, Chan et al. 2006, Tucker et al. 1993), including larger but 

more widely spaced overstory trees (and thus fewer trees per unit of land area), 

improved vertical structure resulting from age classes (seedlings through midsized 

trees to mature trees), and multiple species both among the canopy trees and in 

the understory. This comprehensive, long-term research has come to be known 

as the STUDS project, an acronym formed from its title, the Siuslaw Thinning 

and Underplanting for Diversity Study. The underlying assumption of the STUDS 

project is that thinning the canopy will allow more light to penetrate into the stand 

so that residual trees retain more of their limbs and develop larger limbs of the type 

favored by the marbled murrelet for nesting and by the northern spotted owl and 

other species for roosting.
2

RESEARCH PAPER PNW-RP-605

In Douglas-fir harvested from second-growth trees, the primary determinants 

of lumber grade, a measure of wood quality, are the percentage of juvenile wood 

and the frequency and size of knots (Barbour and Parry 2001), rather than the 

decay and other defects that characterize older trees. Young trees commonly have 

a much higher percentage of low-density juvenile wood and a smaller percentage 

of clear wood than older trees, primarily because their knots usually go all the way 

to the surface of the stem. In contrast, old trees have knots that are often buried 

deep within the stem so that the outer portion of the stem is mostly clear. For the 

residual trees in young stands, thinning tends to produce more juvenile wood 

because tree growth (especially diameter) accelerates, and more knots because 

more of their limbs are likely to be retained than they would be under a more closed 

canopy. The retained limbs also grow in diameter, increasing the size of knots in 

the lumber and potentially increasing the incidence of grade-limiting defects such 

as spike knots. Because Douglas-fir lumber is generally used in construction rather 

than for finished products, its demand is based largely on its inherent strength and 

stiffness—both of which can be compromised by excessive juvenile wood and the 

presence of knots (Briggs et al 2007). Thus, thinnings can reduce both the quality 

and the value of lumber derived from retained trees when they are finally harvested; 

this effect can increase over time with repeated thinnings and with more aggressive 

habitat-improvement treatments such as those used on the Siuslaw.

Despite the potential importance of juvenile wood in lumber produced from 

young Douglas-fir trees, we did not measure or account for it in this project because 

the methodology used to estimate lumber grades was not capable of incorporating 

information on juvenile wood. The methodology does, however, consider locations, 

density, and sizes of knots and other defects on lumber grade.

This report describes a phase 2 STUDS experiment that was designed to 

evaluate the quantity, quality, and value of lumber from trees left behind by habitatimprovement thinnings (1992 and 1993) on portions of three Siuslaw study sites 

(Chan et al. 2006). The trees sampled were harvested from 2008 to 2010 in a second 

commercial thinning.

History of the Study Sites

The plantations involved in the STUDS project were established in the early 1960s 

by clearcutting old-growth stands, broadcast burning and applying herbicides to 

inhibit competing vegetation, and planting Douglas-fir seedlings; in the mid-1970s, 

the plantations were precommercially thinned. They were selected as study sites 

for the STUDS project in the early 1990s and were commercially thinned in the 

autumn of 1992 and summer of 1993. The 1992-to-1993 operations are referred to 

as the phase 1 thinnings.
3

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Before the phase 1 thinnings, each site was subdivided into four plots, with 

the boundaries of each following natural landforms, such as streams and ridges, to 

facilitate thinning operations on steep slopes. One plot at each site was designated 

an untreated control and the remaining three were randomly assigned to thinning 

treatments. From the initial stand densities of 223 to 277 trees per acre, the phase 1 

treatments included heavy thinning (T30) to a residual density of 30 trees per acre, 

moderate thinning (T60) to a residual density of 60 trees per acre, and light thinning (T100) to a residual tree density of 100 trees per acre. Within each of the four 

plots, two 1-acre subplots were established, one of which was randomly selected 

for underplanting with Douglas-fir and western hemlock (Tsuga heterophylla [Raf.] 

Sarg.). All overstory trees in the 1-acre subplots were tagged with identity numbers 

unique to the site; 40 of the tagged trees within each subplot were selected as overstory measurement trees, meaning that all trees were designated as measurement 

trees in the T30 plots. Total tree height, height to live crown, and diameter at breast 

height (DBH) were measured before the phase 1 thinning, immediately after thinning, four years after thinning, and eight years after thinning (Chan et al. 2006).

The principle focus of this report is the phase 2 thinnings. The logging operations for this second commercial round of thinnings were carried out primarily in 

2008 and 2009 and the final logs were processed and sold in 2010. A complete set 

of measurements, including DBH for all trees within each 1-acre subplot, was taken 

between January and May of 2008, shortly before the thinning operations began. 

For the designated overstory measurement trees, total tree height and height to live 

crown were also measured. Siuslaw personnel also conducted a timber cruise that 

included the 1-acre subplots and their surrounding treatment plots.

Research Objectives

The objectives of the wood-quality assessment for phase 2 of the STUDS project 

were to:

• Develop and implement procedures for acquiring detailed stem and defect 

information to be used in a sawing simulator to estimate the quantity, product sizes, and grades of lumber from the thinning operations

• Compare the size and frequency of lumber defects (primarily knots) in 

trees harvested from the moderate-thinning and light-thinning plots; note 

that some of what are considered defects from the perspective of forest products manufacturing could be considered assets in the context of 

habitat-improvement thinnings, because larger knots suggest the successful 

development of larger limbs on residual trees
4

RESEARCH PAPER PNW-RP-605

• Compare differences in lumber quantity, quality, and value from trees harvested from the moderate-thinning plots versus trees harvested from the 

light-thinning plots

• Develop a dataset that includes three-dimensional coordinates of stem 

profiles, branch locations as indicated by surface knots, and stem-surface 

defects of each sample tree as it stood before felling

Study Design and Preparation

Description of Study Sites

The three STUDS stands—Cataract, Wildcat, and Yachats (Chan et al. 2006)—lie 

on the western slope of the Oregon Coast Range at elevations ranging from 400 to 

1,200 feet above mean sea level (fig. 1). Treatment plots on the two southerly sites 

(Cataract and Yachats) have northwest to northeast aspects whereas the treatment 

plots on the northern site (Wildcat) face southwest. Overstory trees on the three 

sites are essentially all the same age, part of plantations established from 1960 to 

1962.

Figure 2 shows the operational layouts of the three STUDS study sites and the 

locations of the treatment plots, T100 and T60, that were scheduled for a second 

commercial thinning—the other two plots from the first (phase 1) thinning, T30 

and the control, are excluded. The trees on the T30 plots had not yet reached the target basal area that would trigger another thinning and the control plots are intended 

to remain untreated. All three sites were logged with cable systems except for a 

small area of ground skidding on the Yachats site (fig. 2). The figures also show the 

locations of sample trees taken from the 1-acre subplots.

Thinning Treatments

Phase 2 of the STUDS project was designed to build on information from the phase 

1 T100 and T60 plots (Anderson et al. 2007). The phase 2 thinnings on these plots 

were designed to further reduce the relative density of overstory trees to about half 

of the relative density measured in the year 8 assessment following the phase 1 

treatments. Relative density is calculated as BA/√QMD, where BA is the basal area 

per acre and QMD is the quadratic mean diameter of the overstory trees on the plot 

(Curtis 1982). Although the T30 plots from phase 1 were not treated in phase 2, 

they will continue to be measured at intervals as part of the continuing assessment 

process (Anderson et al. 2007).

Before thinning operations began, we conducted an assessment of all residual 

overstory trees on the plots from phase 1 treatments T100 and T60, primarily to 
5

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Eugene

Lebanon

Salem

Astoria

5

Florence

126

Tillamook

6

Corvallis

Pacific Ocean

Cataract

Yachats

Waldport

Hebo

Reedsport

Coos Bay

Wildcat

101

101

34

Newport

20

Siuslaw National

Forest

Study sites (not

drawn to scale)

Yachats Alpine

Portland

5

5

Figure 1—Location map for three wood-quality study 

sites (Cataract, Wildcat, and Yachats) on the Siuslaw 

National Forest where sample trees were harvested 

from 2008 to 2010—15 years after habitat-improvement 

thinning treatments (Chan et al. 2006).

provide data that would signal the existence of significantly different characteristics 

that might have been caused by the different thinning intensities (a long-term focus 

of the STUDS project). Our results (table 1) were consistent with those of Chan et 

al. (2006), who analyzed data measured midway between the phase 1 and phase 

2 treatments. Like Chan et al. (2006), we found that DBH differed significantly 
6

RESEARCH PAPER PNW-RP-605

1200

1000

U.S. Forest Service Road

1000

1040

1080

1120

920 960 840

880

880

920

960

1040

1080

1120

US Forest Service Road

600 560

520

640

680

720

760

800

800

840

840 880 920

960

Cataract Creek

Sample plots in the T100 treatment (100 residual trees per acre)

Scale (feet)

0 250 500

Sample tree (size of circle varies relative to tree diameter

at breast height)

Sample plots in the T60 treatment (60 residual trees per acre)

Ground skidding area

(A)

(B)

(C)

Yarding corridors (cableways)

Treatment boundaries

800

600

700

500

400

600

700

800

400

500

400

300

U.S. Forest Service Road

Figure 2—Operational layout for three wood-quality study sites established on Douglas-fir stands in the Siuslaw 

National Forest 15 years after habitat-improvement thinning treatments: (A) the Cataract site northeast of Florence, 

(B) the Wildcat site north of Hebo, and (C) the Yachats site east of Yachats. North is toward the top in each map.
7

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

between the treatments but we did not find a detectable effect on total tree height 

or crown ratio (the percentage of tree height that constitutes the living crown). For 

crown ratio, Chan et al. (2006) suggested that a trend could be developing, with 

crown ratio in lightly thinned stands tending to decrease over time. We found that 

although the effect was not significant at α = 0.05, it would be significant at α = 

0.10 (p = 0.0933) with the larger average crown ratio corresponding to trees from 

the more heavily thinned plots. We modeled DBH, height, and crown ratio using 

linear mixed-effect models that were based on a split-plot design with (1) fixed 

effects for thinning treatment, underplanting, and an interaction term for thinning 

by underplanting; and (2) random effects for sites, thinning (sites), underplanting 

(thinning sites) and trees (underplanting thinning sites). Using SAS® 9.4 (2013) 

PROC GLIMMIX with the Kenward-Rogers degrees of freedom adjustment, 

we found that the only strongly significant difference was for average DBH (p = 

0.0088), which was significantly smaller for trees from the T100 plots than from the 

T60 plots. No significant differences were detected for any of the variables between 

subplots that were underplanted and those that were not, nor were there any significant interactions between underplanting and thinning treatment. As a result, for the 

remaining analyses we did not differentiate between trees from subplots that were 

underplanted and trees from subplots that were not. The importance of site was 

determined from a combined covariance test for random effects and was significant 

for all three response variables: DBH, height, and crown ratio (table 1). Although 

these results are not part of the present study, we present them here because they 

demonstrate the effects of the two thinning treatments 15 years after the first commercial thinning and just before the beginning of the present study.

Selection of Sample Trees

To maximize the utility of data obtained for our wood-quality assessment, we opted 

to collect detailed stem form and defect information on the harvested logs rather 

than conducting a conventional sawmill recovery study. Stem form and defect data 

can be used in simulation experiments to evaluate a variety of products that might 

be manufactured from logs. In this report we estimate the quantity, quality, and 

value of output from the simulated sawing of the logs into a standard Douglas-fir 

lumber assortment. Given suitable simulation software, the same logs could be 

repeatedly sawn by simulation using different sawmill or lumber parameters, or 

converted by simulation into veneer and other products.

We selected a stratified random sample representing about 3 percent of the trees 

harvested in the phase 2 thinning operations on the three study sites. The 3-percent 

sample is typical of wood-quality studies carried out by the PNW Station (Barbour 

et al. 1999, Lowell et al. 2012). Selection of sample trees was based on data from the 
8

RESEARCH PAPER PNW-RP-605

presale timber cruise and is summarized in table 2. Trees measured in the timber 

cruise were stratified into five DBH classes within each treatment plot, with the 

lowest and highest classes being open ended (table 2). Given that 2,028 trees were 

scheduled for removal during the thinning operation, a 3-percent sample would 

comprise 61 trees but we decided to select 66 trees as a hedge against breakage or 

losses during harvesting. The sample was stratified in proportion to the number of 

trees in each DBH class to be harvested within each treatment plot (table 2), with 

the stipulation that at least one sample tree be selected from each DBH class that 

was scheduled for tree removal within each treatment plot. For selection purposes, 

the subplot trees scheduled for removal in the thinning were sorted into the five 

DBH classes within each treatment plot based on measurements recorded during 

the 2008 assessment of overstory trees. For each DBH class in each treatment plot, 

the specific trees to be included in the sample were then randomly selected from the 

population of trees scheduled for removal from the 1-acre subplots.

The cruise results (table 2) indicated that four treatment plots—two in Cataract, 

one in Yachats, and one in Wildcat—each had no trees designated for removal 

in one DBH class; thus, no sample trees could be selected from that DBH class 

in those plots. Five trees selected as sample trees through the random-selection 

process proved to have been marked as “leave trees” that were to be retained in the 

post-thinning stand. For these, we substituted a sample tree that was in the same 

study site and that had as nearly as possible the same DBH as the originally selected 

tree. For example, one treatment plot (T100 in Wildcat) had only a single tree in the 

23+ DBH class on the two subplots, and although it had been selected as a sample 

Table 1—Effects of light (T100) and moderate (T60) treatments on Douglas-fir stands in the 

Oregon Coast Range 15 years after commercial thinning treatments

Response 

variable

T100a T60b

Treatment 

difference

(95 percent CI)

Treatment 

difference

Site 

importance

Number 

of trees

Mean

(SE)

Number 

of trees

Mean

(SE)

DBH (inches) 558

17.61

(0.3992) 298 19.40(0.4178)

1.79

(0.66, 2.93) 0.0088 < 0.0001

Height (feet) 243

110.63

(5.1141) 219 111.94(5.1213)

1.31

(-3.62, 6.24) 0.5395 < 0.0001

Crown ratio 243 0.368

(0.0199) 219 0.447(0.0200)

0.079

(-0.033, 0.192) 0.0933 < 0.0001

DBH = Diameter at breast height.

SE = Standard error.

CI = Confidence interval.

Crown ratio is the ratio of the length of a tree’s crown to its total height.

a Stand reduced to approximately 100 trees per acre.

b Stand reduced to approximately 60 trees per acre.
9

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 2—Sample trees selected by stratified random sampling based on DBH distributions to test 

the effects of light (T100) and moderate (T60) thinning treatments on Douglas-fir stands in the 

Oregon Coast Range 15 years after commercial thinning

Study site 

(treatment)a

and size of 

area thinned

DBH 

class

Mean 

DBH 

Mean 

estimated 

total height 

Trees 

scheduled for 

removal

Sample trees (stratified random sample)

Total

By DBH 

class

ID tag numbers of sample trees 

selected by DBH class from the 

two 1‐acre subplots within each 

treatment

- - - - Inches - - - - Feet

Cataract (T100)

 6.5 acres

< 10 9.9 89.0 13

13

1 1656

10-14 12.4 101.5 90 3 1009, 1035, 1013

14-18 15.6 111.1 210 6 1655, 1570, 1110, 1514, 1580, 1211

18-23 19.8 116.1 120 3 1501, 1217, 1158

≥ 23b — — — 0 —

Cataract (T60)

 7.5 acres

< 10b — — —

7

0 —

10-14 13.1 97.0 12 1 2604

14-18 16.3 111.7 70 2 2154, 2019

18-23 19.6 112.5 114 3 2146, 2568, 2241

≥ 23 25.3 125.0 9 1 2041

Wildcat (T100)

 6 acres

< 10 8.9 54.0 5

10

1 1247

10-14 12.5 73.0 57 2 1017, 1254

14-18 15.7 94.2 170 5 1285, 1867, 1008, 1226, 1295

18-23 19.7 108.6 65 2 1723, 1504

≥ 23b 24.5 107.0 9 0 —

Wildcat

 (T60)

 6 acres

< 10b — — —

9

0 —

10-14 13.1 79.5 20 1 2133

14-18 15.9 92.5 99 3 2739, 2114, 2658

18-23 19.6 100.3 111 3 2604, 2042, 2687

≥ 23 23.3 116.0 2 2 2732, 2607

Yachats (T100)

 16 acres

< 10 9.0 81.0 6

19

1 1626

10-14 12.7 88.1 58 2 1633, 1041

14-18 16.7 101.8 181 5 1603, 1727, 1114, 1560, 1140

18-23 21.0 113.9 329 9 1202, 1076, 1611, 1659, 1600, 

1604, 1016, 1588, 1111

≥ 23 24.1 116.6 46 2 1159, 1708

Yachats (T60)

 7 acres

< 10b — — —

8

0 —

10-14 12.9 83.5 11 1 2735

14-18 16.0 104.2 56 2 2722, 2022

18-23 20.7 110.8 132 4 2532, 2122, 2733, 2002

≥ 23 24.6 108.7 33 1 2062

Totals 2,028 66 66

DBH = diameter at breast height. a Stand reduced to 100 trees per acre in T100; stand reduced to approximately 60 trees per acre in T60.

b No sample trees were selected in these diameter classes because none were available in the unit and treatment area 

being sampled.
10

RESEARCH PAPER PNW-RP-605

tree, it was marked in the field as a leave tree. Therefore we substituted a second 

sample tree in the 23+ DBH class from the other (T60) Wildcat plot, which had 

been scheduled for only a single sample tree in that DBH class. This means that the 

moderately thinned plot had one more sample tree than selected through the stratified random sampling process, and the lightly thinned plot correspondingly had one 

less sample tree.

Table 2 also shows the tag numbers of all sample trees in the final wood-quality 

sample. Each sample tree was marked in the field as shown in figure 3 with information indicating the study site and treatment type, the unique tree identification 

number, DBH, and the north and south cardinal directions (reference points for 

evaluating possible relationships between direction and the frequency and size 

of knots).

Field crews also recorded the azimuth and distance from each sample tree to 

one of the corners of its subplot. Coordinates of the subplot corners were established using a survey-grade geographical positioning system (GPS) and differential 

Figure 3—A sample tree in one of three woodquality study sites established on Douglas-fir stands 

in the Oregon Coast Range 15 years after habitatimprovement thinning treatments; the Y2 designation 

indicates that the tree was in the Yachats study site (as 

opposed to Wildcat or Cataract) and that the treatment 

(T100) was a light thinning leaving 100 residual trees 

per acre (the moderate T60 treatment would have been 

designated by Y3 rather than Y2).

Y2 = Yachats

treatment 2

(T100)

Tree ID = 1611

White ribbon

Label with Y2-1611

Paint at

breast height

Metal tree tag

(1611) and

checked ribbon
11

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

correction. These coordinates were used to calculate the coordinates of the tree 

as an offset from a subplot corner, enabling the plotting of all sample trees in the 

correct locations within their respective subplots (fig. 2).

Data Collection

Procedures for product-recovery studies have been developed through more than 

100 such studies conducted over nearly 50 years at the PNW Research Station. 

Commonly, such studies involve collection of data in four phases:

1. Tree data, collected when the tree is standing: Information recorded 

includes the location of each tree, ownership, DBH, estimated height, and 

other parameters that might be considered relevant for a particular study.

2. Long-log (woods-length) data, collected when logs are bucked from the 

tree stem at the harvesting site: Their lengths are determined by the logger 

according to the purchaser’s requirements and any specifications that have 

been stipulated in the timber-sale agreement. For this study, data were also 

collected on stem form and surface defects.

3. Short-log (mill-length) data, usually collected when long logs are bucked at 

the mill where they will be processed according to purchaser requirements: 

For this study, long logs were bucked into short logs using computer simulation instead of being followed into the mill as would have been done in 

a conventional product-recovery study. Data from the simulated short logs 

were used as inputs into a sawing simulator.

4. Product data, collected when short logs are sawn: In a conventional 

product-recovery study, each sample log is converted into products in a 

mill and the individual pieces produced are tallied and graded to calculate 

the recovery of quantity and quality for each log and for the collection of 

sample logs as a whole. For this study, the AUTOSAW sawing simulator (FRI 1994, Todoroki 1990) was used instead to simulate the process 

of converting each log into lumber. The sawing simulator was calibrated 

(Barbour et al. 1999, Todoroki et al. 2005) to estimate the grade of each 

piece of lumber according to U.S. grading rules for Douglas-fir lumber as 

described in WWPA (2005).

Long-Log Measurements

Felling and bucking—

At each study site, the sample trees were felled and bucked by the timber-sale 

purchaser in advance of the main thinning operation at that site. As each tree was 

felled and bucked into long logs (fig. 4), labels identifying the study site, treatment, 
12

RESEARCH PAPER PNW-RP-605

tree, and long log were written in indelible ink on heavy, water-resistant paper and 

fastened with aluminum staples to both ends of each log (fig. 5). These measurements were recorded separately as were two inside-bark diameter measurements 

taken at right angles on each end of the log and recorded to the nearest 0.1 inch, and 

length recorded to the nearest 0.1 foot. These measurements were intended primarily to help identify sample logs if labels were destroyed or numbers and letters 

became unreadable during yarding; final diameter and length measurements were 

postponed until the stem analysis.

As the sample trees were being bucked into logs, a horizontal line was painted 

at the top of one side of each log, and a compass reading (N, NE, E, SE, S, SW, W, 

or NW) was added to indicate the orientation of that side of the log when the tree 

was standing; 132 of the 179 long logs that were measured had readings that could 

be recorded as the logs were being measured. The paint used for these marks did 

not always survive the yarding operation, often because large sections of bark were 

dislodged. In addition, the paint that was originally selected was ineffective when 

applied in the rain; in the latter part of the study, this was corrected by recording 

compass readings on the log labels (fig. 5). Some identifying information almost 

A B

Figure 4—At one of three wood-quality study sites established on Douglas-fir stands in the Oregon Coast Range 15 years 

after habitat-improvement thinning treatments, timber-sale purchaser Robert Bateman (A) felling a sample tree (Y2-1159)—

note that although this tree was in a lightly thinned plot (residual tree density 100 trees per acre), the understory appears to 

have responded well to thinning—and (B) measuring a felled tree in preparation for bucking.
13

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

always survived yarding; in fact, only a single log (C3-2041-3) had unreadable 

labeling and had to be identified by the measurements recorded separately during 

felling and bucking. However, five sample logs that were measured during felling 

and bucking were never found at the log yard. Either those logs were broken during 

yarding and never reached the landing, or they arrived with no labels and no staples, 

paint, or other marks that would have identified them as sample logs. Each of these 

lost logs was the topmost log cut from the tree, and all had small-end diameters less 

than 4 inches.

Stem profiles and surface defects—

During the yarding process, the sample logs were segregated from other logs as 

they arrived at the landing, then moved to the purchaser’s log yard (Cataract and 

Yachats sites) or to a roadside clearing (Wildcat site); there they were placed on 

bunk logs so they could easily be measured and the entire log surface inspected. 

Figure 6 shows several sample logs rolled out for measurement at the Wildcat site.

Figure 5—Part of a log from a sample tree harvested on Douglas-fir stands in the Oregon Coast Range 15 years 

after habitat-improvement thinning treatments. The label (stapled to the end of the log when it was cut) indicates 

the side of the stem that faced south (S with arrow) before the tree was felled, that the tree was from plot 3 on the 

Cataract study site (and thus from treatment T60 with a residual density of 60 trees per acre), and that this was 

log 1 (the butt log) from tree 2154.
14

RESEARCH PAPER PNW-RP-605

White paint on

measured knots

Peavy for rolling logs

Paint can Clipboard

Block to stabilize

Me

logs if needed asuring tape

Figure 6—Stem analysis on sample logs from a sample tree harvested on Douglas-fir 

stands in the Oregon Coast Range 15 years after habitat-improvement thinning treatments. Data were recorded along the full length of each log in an initial (0°) orientation, then the log was rolled 180° and defects not visible in the initial orientation were 

measured and recorded.

A Modified Procedure for Measuring Long Logs

Data collection procedures used previously at the PNW Research Station 

to acquire stem data for sawing simulations (Barbour et al. 1999) require a 

minimum of two people working together. For this study we modified those 

procedures into the following five-step procedure to allow one person working 

alone to collect stem data. All measurements are recorded to the nearest 0.1 

foot or the nearest 0.1 inch.

Step 1. Establishing reference points and lines—

On each end of the log, draw an approximately vertical line through the 

pith and extend it to the perimeter (fig. 7). This line serves as a reference for 

measurements and is helpful as a guide when rolling logs that have shape 

irregularities. It is taken as the y-axis, with the axis perpendicular to it taken 

as the x-axis, and the imaginary line joining the geometric centers of the two 

ends of the log taken as the z-axis. Drive nails into the log at points A and B 

in figure 7 and, starting at the large end of the log, lay a measuring tape along 

the distance (labeled the longitudinal axis) between points A and B. Affix a 

chalk line from point A to point B and draw it tight. This line serves as a 

Continued on p.16
15

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

axis

of lo

g

Lo

n

git

u

d

ni al

Pith

Sweep offset at point 

of maximum sweep

Log ID label (one on 

each end of the log)

Line drawn through pith with 

lumber crayon to define end 

points for top and bottom 

longitudinal axes

Offset to knot

Chalk line running

between log ends

A

B

Figure 7—Measurements taken on stem profiles and surface defects of logs from a sample tree harvested on Douglas-fir 

stands in the Oregon Coast Range 15 years after habitat-improvement thinning treatments. Length measurements were 

taken to the nearest 0.1 foot, whereas offset and diameter measurements were taken to the nearest 0.1 inch.
16

RESEARCH PAPER PNW-RP-605

From p. 14

reference for measuring sweep and crook offsets (steps 4 and 5 below), and is 

assumed to be parallel to the z-axis, which joins the two geometric centers of the 

log ends.

The orientation of the log when the reference points and lines are established 

initially is known as the 0° orientation. For logs with sweep, this orientation is 

such that the maximum sweep is positioned in either the vertical or horizontal 

plane and thus can be measured as up, down, left, or right from the central axis 

of the log as viewed from the butt. For example, the log in figure 7 sweeps to the 

right from its central axis. When the log is rolled to measure surface defects on its 

other side, the new orientation is referred to as the 180° orientation.

Step 2. Measuring knots and other surface defects—

To facilitate measurements and verify whether a defect exists at any point, it is 

often necessary to remove bark around suspected surface defects. This is particularly true where knots are fully overgrown by bark. At each point along the log 

where a defect occurs, measure the offset distance from the longitudinal axis to 

the center of the defect along the arc of the log surface (fig. 7), and note whether it 

is left or right of the longitudinal axis when viewed from the large end of the log. 

Also record the distance along the measuring tape (longitudinal axis) to the point 

that corresponds to the center of the defect.

Working along the log in two directions—parallel to the longitudinal axis of 

the log and perpendicular to that axis—measure all surface defects at least 0.5 

inch in average diameter unless they will most likely be trimmed off with the 

outer slab and thus not penetrate into lumber sawn from the log (this sometimes 

occurs, for example, with small burls that form on the surface of a Douglas-fir 

tree). If the defect is a knot, record whether it is alive (sound) or dead (unsound). 

Live knots, also known as ingrown or tight knots, have fibers that are largely intergrown with the fibers of the surrounding xylem. Dead knots usually have a visible 

separation between the knot and the tree stem, or they may appear somewhat 

decayed at the surface. The distinction between live and dead knots is important 

as it influences the grade of lumber manufactured from the log.

After each surface defect is recorded, spray it with white paint (fig. 6) to indicate that the measurement has been completed and insure against double counting.
17

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Step 3. Measuring log diameters—

Measure inside-bark diameters on each end of the log, one along the vertical line 

drawn in step 2, and the other perpendicular to that line with the measuring tape 

placed so that it passes through the pith. For butt logs, use calipers to measure 

the horizontal diameter inside bark at a point 4 feet from the large end of the log. 

This additional measurement, specified in the Forest Service cubic scaling rules 

(USDA FS 2002), helps reduce inaccuracies associated with butt swell near the 

base of the tree.

Use calipers to measure an inside-bark diameter at each point on the longitudinal axis of the log corresponding to the location of a surface defect. If knots 

are clustered closely together, use one diameter measurement. If log cross sections 

are elliptical rather than round, measure both horizontal and vertical diameters 

at each point to improve the accuracy of the sawing simulation. Make sure that 

inside-bark diameter measurements are no farther apart than 4 feet as measured 

along the longitudinal axis of the log; if necessary, take one or more intermediate 

measurements.

Step 4. Measuring sweep and crook—

Sweep is a curvature that extends along the entire length of the log, causing the 

longitudinal axis of the log to deviate from a straight line; sweep can be uniform 

with the maximum sweep occurring at the center of the log, or nonuniform with 

the maximum sweep occurring at a point nearer one end of the log. A crook is a 

curve or bend in a log, often abrupt, that extends only over part of the log length. 

A single log can have multiple crooks.

With the log in the 0° orientation, measure the offset as a perpendicular from 

the chalk line to the longitudinal axis of the log (fig. 7) at the point of maximum 

offset, as measured along the longitudinal axis from the large end of the log and 

as viewed from the large end of the log. For crooks only, measure the length of the 

crook along the longitudinal axis of the log from the crook base toward the top of 

the tree. Note whether the offset direction is right, left, up, or down. Only record 

sweep and crooks when the maximum offset is 2 inches or more.

Step 5. Measuring surface defects after the log has been rolled—

Roll the log 180° and use blocks, if needed, to stabilize the log in the new position 

(fig. 6). Drive nails into the log as shown in figure 7 at points A and B and, starting at the large end of the log, lay a measuring tape along the distance (labeled the 

longitudinal axis) between points A and B. Measure each surface defect that has 

not yet been sprayed and its offset as described in step 2. Measure log diameter at 

the location of each newly detected surface defect as described in step 3 above.
18

RESEARCH PAPER PNW-RP-605

Preparation and Organization of Data

Measurements from the various phases of fieldwork were entered into Microsoft 

Excel® spreadsheets, verified, and processed with additional software in preparation for the sawing simulation. Several utility programs were developed to convert 

data from the Excel spreadsheets into text files that conform to AUTOSAW requirements. This section describes the process by which data were entered, verified, and 

reformatted (fig. 8).

Data entry and verification—

The profile and defect data for each log were entered into a single Excel worksheet 

(fig. 8). All worksheets (and thus all data for the long logs) for each site (Cataract, 

Wildcat, or Yachats) were collected into a single Excel file for that site. Worksheets 

within the files were named using the long-log nomenclature as shown in figure 5; 

for example, C3-2154-1 represents the first (butt) log from tree 2154 in the moderately thinned plot (designated by 3) on the Cataract study site (designated by a C). 

The second long log from the same tree is C3-2154-2.

Measurements were verified by entering the set of profiles and surface defects 

for each log a second time into a verification worksheet within each Excel file. The 

verification worksheet used Excel formulas to compare each measurement as it was 

entered into the verification worksheet with the measurement from the corresponding cell of the original worksheet. Any discrepancies were displayed as errors so 

that they could then be investigated and reconciled with the original, hand-written 

data sheet.

System requirements for STUDS utility software—

Checking software requirements, simulating log bucking, and converting data 

to the AUTOSAW format (fig. 8) all required the development of special utility 

software capable of reading and writing Excel files. These utility programs have the 

following requirements:

• The operating system on the user’s computer must be Windows XP with 

Service Pack 3, or any later version of Windows® (such as Vista, 7, 8, or 

10). Earlier versions of Windows and versions of Windows XP without 

Service Pack 3 are not supported.

• The Microsoft® .NET Framework 4.0 or a later version, in either the client version or the full framework, must be on the user’s computer. Version 

4.0 of the .NET Framework can be downloaded at no cost, but administrative privileges are required to install it. The client profile is a much smaller 

download than the full framework and is adequate for all of the STUDS 

utility programs. Version 4.0 of the Microsoft .NET Framework can only be 
19

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Figure 8—Steps followed to prepare and organize data for the analysis wood quality in long logs, short logs, and 

lumber produced from sample trees harvested on Douglas-fir stands in the Oregon Coast Range 15 years after 

habitat-improvement thinning treatments.

Excel files

with long-log

worksheets

OK?

Upgrade or

use a different

computer

No

Yes

Excel files

with short-log

worksheets

Enter and

verify data

1

Check

software

prerequisites

2

Simulate

log bucking

3

Convert to

AUTOSAW

Data Format

4

Excel file with

lumber data

AUTOSAW

DAT files

Simulate

sawing of logs

into lumber

5

AUTOSAW

OUT files

Convert

output

to Excel

6

Optionally

produce 3-D

profile and

defect data

7

Excel file with

3-D profile and

defect data

i

Information used 

for input or 

produced as output

Action to be taken at 

processing step i

Decision point

Flow of tasks

Flow of inputs

and outputs
20

RESEARCH PAPER PNW-RP-605

installed on a computer running Windows XP with Service Pack 3 or any 

later version of Windows.

• Most of the STUDS utility programs require Microsoft Excel 2002 or a 

later version for reading, manipulating, and writing spreadsheet files. This 

is done by invoking the software through “automation,” a technology that 

facilitates the use of Excel by external software.

To help potential users of the bucking simulator and other software developed 

for the STUDS project determine whether their computer systems meet the requirements listed above, we developed a utility program (app. 1) that checks for the 

required software (fig. 8, step 2).

Simulated log bucking—

Because this study did not include a mill study (in which long logs are normally 

bucked into short logs at the sawmill and then converted into lumber), the process 

of bucking the long logs was simulated in software (fig. 8, step 3) so that data for 

short logs would be available for the sawing simulations. Rather than attempting 

to develop a sophisticated optimal bucking program like those described by Acuna 

and Murphy (2005) or Wang et al. (2009), we decided to use a simple simulator 

that follows the bucking rules used by several of the purchasers of logs from the 

thinning operations. This is because we wanted to emulate typical mill processing 

practices rather than developing an ideal solution that might not be consistent with 

those practices. The bucking simulator was written in the C# (“c-sharp”) programming language using Microsoft Visual Studio® 2010 Professional. Appendix 2 

describes the bucking rules used and also explains how sweep and crook are treated 

during the simulated bucking process.

Software for a Simple Bucking Simulator

Executable File (xlBuckLogs.exe)—

This file can simply be copied into any folder on the user’s computer without formal 

installation; administrative privileges are not required. The user’s computer must 

have Windows (XP with Service Pack 3 or any later version), Microsoft .NET 

Framework 4.0 or later, and Microsoft Excel 2002 or later. If those requirements are 

satisfied, the software will register itself with Windows the first time it is executed. 

Appendix 1 provides instructions on unblocking the executable file.

Supporting File (LogDataFmt.xml)—

This file, which must be present in the same folder as the executable file, defines 

the format of the data columns in the input long-log data worksheets and the output 
21

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

short-log data worksheets. Specifications are defined for Excel columns A through 

AB, which are the columns used for the data worksheets. Entries in all other 

columns are ignored.

Operation—

The user navigates to the folder where the executable file has been placed and 

double-clicks the filename to launch the program.

Input File—

This is a Microsoft Excel spreadsheet file with a filename extension of either XLS 

(2002 or 2003) or XLSX (2007 or later). The file must contain one or more longlog data worksheets that conform to the format described in LogDataFmt.xml. If 

the file contains other worksheets as well, they are simply ignored by the software. 

Each long-log data worksheet must be named in the format Aw-xxxx-y; where A = 

a single letter representing the site (for this study, C is Cataract, W is Wildcat, and 

Y is Yachats); w = a single digit signifying the treatment (for this study, 2 represents the light-thinning treatment T100 and 3 represents the moderate-thinning 

treatment T60 ); xxxx = a four-digit tree number; and y = a single digit representing the index of the long log cut from the tree, where y = 1 for the butt log, 2 for 

the log immediately above the butt log, and 3 for the upper log.

Output File—

This is a Microsoft Excel spreadsheet file containing one worksheet for each short 

log bucked from the long logs described in the input file. The user enters a name 

for this file through the user interface of the simulator. Depending on the version 

installed on the user’s computer, the output file must have a filename extension of 

either XLS or XLSX. The short-log worksheets in the output file have the format 

specified in the LogDataFmt.xml file. Worksheet names are the same as for the 

input file but with “-z” appended to the name, where z is an index number for the 

short log. The simulator assigns a value of 1 for the first short log bucked from the 

large end of the long log, assigns a value of 2 for the next short log bucked from 

the same long log, and continues the numbering system for middle and upper long 

logs. As an example, long log C2-1009-2 might be bucked into three short logs: 

C2-1009-2-1, C2-1009-2-2, and C2-1009-2-3. The output file would then contain 

three worksheets with these names. If a long log is shorter than the maximum 

short-log length plus maximum trim allowance (a total of 21.0 feet for this study), 

it is not bucked by the simulator and all of its data are transferred to the output file 

intact. The worksheet in the output file is then given the same name as the input 

long-log worksheet but with “-1” appended.
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RESEARCH PAPER PNW-RP-605

Running the Sawing Simulator

Sawing simulators require detailed information on the shape and dimensions of 

each log, surface-defect locations and sizes, sweep, and other characteristics that 

could influence the quantity and grade of lumber produced from the log. The threedimensional system used by AUTOSAW to model logs is shown in figure 9. The 

details of data preparation and simulation processing with AUTOSAW (or any 

other simulator) are specific to that particular simulator and would not be relevant 

for a user who has selected another product for running simulations; however, 

these details are available in appendix 3, which describes the input data and the 

outputs resulting from sawing simulations, the software we developed to convert 

log-measurement data into the format required by AUTOSAW, and the software 

used to convert output files from the simulator into Microsoft Excel spreadsheets 

for analysis.

Small end 

of Log

Large

end of Log

Sweep offset at

profile point

Longitudinal

axis of log

(pith)

z-axis

z=0

k

A horizontal line extending through the 

z-axis and to the perimeter at each end of 

the log and at any profile point in the log.

x-axis

A vertical line extending through the z-axis 

and perpendicular to the x-axis.

y-axis

A straight line connecting the geometric 

centers of the two ends of the log.

z-axis

A line (curved unless the log is straight) 

that follows the pith from one end of the 

log to the other.

Longitudinal

axis

Reference points at the geometric centers 

(pith) of the two ends of the log.

A and B

k A knot on the surface of the log.

P The profile point associated with knot k.

Figure 9—The three-dimensional coordinate system used by AUTOSAW to describe logs to be processed by 

simulated sawing; note that (1) this log sweeps to the left as viewed from the large end of the log so the longitudinal 

axis is offset from the z-axis (in a log with no sweep the axes would be identical), and (2) the x-coordinate of profile 

point P is negative because of the sweep.

Analyzing Simulation Outputs

Three-Dimensional Stem Profile and Defect Data

One objective of this study was to develop a dataset that provides three-dimensional 

coordinates of stem profiles, branch locations as indicated by surface knots, and 

surface defects on the stem of each sample tree as it stood before felling. Anticipating that such data could be useful in further studies—not only for wood-quality 
23

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

research but also for any research requiring detailed data on stem form and branch 

locations (such as studies on the development of crown models)—we wrote a computer program in the Microsoft C# language that uses information recorded in the 

long-log data worksheets to calculate three-dimensional coordinates for each profile 

point and surface defect within each tree (fig. 8, step 7).

To convert data from the long-log worksheets into a three-dimensional stemcoordinate dataset for the sample trees, we mapped the three-dimensional log 

coordinates, which were arbitrarily established when each log was measured, into a 

fixed coordinate system that reflects the position of the tree stem as it stood before 

the tree was felled (app. 4). The mapping was somewhat more complicated than 

it might at first seem because the coordinate system for each log was established 

independently from that of every other log, including other logs from the same tree. 

As described above, the z-axis of the log was taken as a line segment joining the 

geometric center of the large end of the log with the geometric center of the small 

end of the log (fig. 9); the x-axis was taken as the horizontal axis and the y-axis was 

taken as the vertical axis, both drawn through the geometric center of the large end 

of the log as it lay in the initial measurement orientation. These axes were arbitrary 

but convenient because they were consistent with the coordinate system required by 

AUTOSAW for describing profile points and the locations of surface knots. However, mapping these log-based coordinates to an equivalent system of coordinates 

for the standing tree required a rather elaborate methodology (app. 4).

Sample Trees Felled and Long Logs Produced

The 66 selected sample trees shown in table 2 were felled and bucked into long logs 

at the felling site, with 185 logs produced (table 3). Most sample trees produced 

three long logs but 14 trees produced only two logs and one produced four logs. The 

number of logs produced from a given tree results from decisions by the logger and 

depends on the length of the tree bole and its characteristics after felling. One log 

that broke during yarding was discarded because it was shorter than the minimum 

merchantable length of 8 feet. Originally 17 feet long, it was the third log from a 

three-log tree, Y2-1603. Five logs were lost altogether; they may have been broken 

during yarding or the tags may have come off, rendering them unidentifiable. The 

lost logs represent 2.7 percent of the sample. In the planning stage of the study, 

several extra sample trees had been selected on the assumption that about 5 percent 

of logs would be lost. The five lost logs were all top logs with small-end diameters 

less than 4 inches; one was the fourth log from the only four-log tree (Y3-2062) and 

the others were all third logs from three-log trees (C3-2146, W3-2732, Y3-1016, and 

Y3-2733).
24

RESEARCH PAPER PNW-RP-605

Table 3 summarizes tree and log counts not only by site but also by treatment. 

We used these categories throughout the analysis in an effort to identify variables 

that influence wood-quality characteristics. The two treatments (T100 = light thinning, T60 = moderate thinning) were both represented on all three sites, but more 

trees were removed from treatment T100 plots to reduce the higher density resulting 

from the lighter phase 1 thinning treatment. Although the treatment plots had been 

subdivided into subplots that were underplanted following the phase 1 thinnings 

and those that were not, a preliminary analysis with underplanting as a classification variable showed no detectable effect on the wood-quality variables. This was 

expected because underplanting at that stage would be unlikely to have had any 

effect on wood quality.

Aspect—

In wood quality studies, aspect is commonly considered a potentially important 

variable because it can influence the size and character of limbs and thus the knots 

that become embedded in lumber. Because of the layout of the STUDS study sites, 

however, aspect was confounded within the site and had a sharply reduced sample 

size. Only the Wildcat site had south-facing slopes; that entire site faces toward the 

southwest (fig. 3). Trees on the Cataract and Yachats sites were on slopes that faced 

Table 3—Long logs produced from sample trees in Douglas-fir stands on the Oregon Coast 

Range 15 years after commercial thinning treatments, summarized by site and treatment

Sample trees felled, classified

 by the number of logs All Logs

 produced per tree sample broken Sample Sample

Two Three Four trees Logs and logs logs

Groupinga logs logs logs felled produced discarded lost measured

Study site

 Cataract 2 18 0 20 58 0 1 57

 Wildcat 8 11 0 19 49 0 1 48

 Yachats 4 22 1 27 78 1 3 74

Treatment

 Light (T100)b 11 31 0 42 115 1 1 113

 Moderate (T60)c 3 20 1 24 70 0 4 66

All sample trees 14 51 1 66 185 1 5 179

a The entries in the table include one log (Y2-1659-2) that was recorded at the felling site as the top log from a two-log tree. With a 

length of 45.8 feet, it was the longest log produced from any of the sample trees. The log broke during yarding and was re-bucked 

at the landing into two merchantable logs with lengths of 33.0 and 9.0 feet. In this table the two resulting logs have been shown as if 

they had been produced at the felling site and the tree has been reclassified as a three-log tree.

b Stand reduced to approximately 100 trees per acre (light thinning).

c Stand reduced to approximately 60 trees per acre (moderate thinning).
25

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

generally northward, with aspects varying from northwest to northeast (fig. 2). As 

a result, the analysis described in this report did not consider aspect as an independent variable.

Competitive status of individual trees—

Although it has not often been considered in wood-quality studies, within-plot 

variation in the competitive status of individual trees can also influence knot 

distribution and knot size. We did not attempt to evaluate competition between the 

sample trees and their neighbors in this study but this could be an important factor 

to consider in future analytical efforts.

Effects of Treatment on Stem Growth (Long Logs)

Measurement Data

Table 4 summarizes basic data for the 179 long logs that were produced from the 66 

sample trees and measured after yarding. It also summarizes the measurements by 

the position of each log within the tree; for the 77 percent of sample trees that had 

three logs, the butt log corresponded to the first log cut from the tree, the middle 

log corresponded to the second log, and the upper log corresponded to the third log. 

The 21 percent with only two logs were recorded as having no middle log—only 

a butt log and an upper log—primarily to associate the upper log designation to 

the part of the tree with a live crown. Although one tree had four logs when it 

was felled, the fourth log was lost during yarding and thus was not measured; our 

analyses treated the third log from that tree as an upper log.

Results of statistical analyses on the long-log data are presented in table 5. The 

interaction tests were run using PROC GLIMMIX in SAS version 9.4 (2013). Fixed 

effects tested for each response variable were treatment, adjusted DBH, log position 

within the tree stem (butt, middle, or upper), and two-factor interactions among 

the variables. Because we found that DBH was influenced by the phase 1 thinning 

treatments (table 1), DBH was adjusted for this analysis to accommodate different ranges for each treatment as described in Milliken and Johnson (2002). And 

because not all sample trees from each treatment had the same number of long logs, 

unique ranges were required for each treatment and long-log position. All trees had 

one butt log and one upper log; 77 percent had a single middle log, 21 percent had 

no middle log, and a single tree had two middle logs (table 3). Trees that had middle 

logs tended to have larger DBH than those that did not. The adjusted DBH used in 

the analysis for a particular log position within a tree (for example, tree x and log 

position y) from a particular treatment was calculated by reducing the observed 

DBH for tree x by the least-squares mean DBH for all trees from that treatment that 

had a long log in position y.
26

RESEARCH PAPER PNW-RP-605

Random effects tested included study site (Cataract, Wildcat, or Yachats), 

site by treatment, site by position, site by treatment by position, and tree (table 5). 

Estimated components of variance associated with each of the random effects that 

are different from zero are represented in the table as σ^ 2

i, where i is a label indicating the random effect (such as site or treatment), and the residual error estimate is 

represented by σ^ 2

ε. The contribution of each random effect to model variation was 

calculated using a likelihood ratio test for a zero-variance component (in PROC 

GLIMMIX using the COVTEST statements), with estimates that were larger than 

zero presented in table 5. Because DBH had been used to stratify the sample, it 

was included in the analysis as a covariate to examine effects at the adjusted DBH 

values. The model for log-length accommodated heterogeneous behavior in the 

residuals that were associated with log position, which was evaluated with a test 

of homogeneity (SAS 9.4 PROC GLIMMIX using the COVTEST statement with 

HOMOGENEITY option).

Log position, either directly or through interaction effects, was strongly significant in characterizing the relationships among the responses in all tests. This 

was expected because logs higher in the tree are generally smaller and shorter 

than those nearer the ground. As can be seen in table 4, log diameters and volumes 

tended to be larger in plots treated with the heavier phase 1 thinning (treatment 

T60) than those treated with the lighter thinning (treatment T100). This is the result 

Table 4—Long logs produced from sample trees in Douglas-fir stands on the Oregon Coast Range 

15 years after commercial thinning treatments, summarized by site, treatment, and log position 

within the tree; does not include data on one broken and discarded log or five lost logs.

Large-end Small-end Gross log

diameter diameter Length volume

Groupings for long 

logs measured Logs Mean Range Mean Range Mean Range Mean Range

- - - Inches - - - - - - Inches - - - - - - - Feet - - - - - - Cubic feet - -

Study site:

 Cataract 57 13.35 6.5–25.7 9.53 3.1–18.2 30.37 9.0–43.0 22.57 2.3–69.6

 Wildcat 48 13.68 6.5–26.1 8.42 3.0–17.1 31.68 15.9–39.0 23.10 3.2–79.4

 Yachats 74 14.64 6.6–25.8 10.04 3.4–19.1 31.24 9.0–39.1 28.26 2.8–87.0

Treatment:

 Light (T100)a 113 13.44 6.5–25.8 9.08 3.0–18.2 30.86 9.0–43.0 23.05 2.3–78.9

 Moderate (T60)b 66 14.89 6.9–26.1 10.06 3.0–19.1 31.45 15.9–41.0 28.51 3.3–87.0

Log position:

 Butt 66 18.18 8.7–26.1 12.68 6.4–19.1 36.58 21.0–41.0 41.38 4.6–87.0

 Middle 47 13.66 8.6–18.3 9.96 6.9–13.7 32.51 16.9–37.2 24.86 5.5–48.2

 Upper 66 9.99 6.5–16.8 5.85 3.0–11.2 24.55 9.0–43.0 8.90 2.3–36.9

All measured logs 179 13.97 6.5–26.1 9.45 3.0–19.1 31.08 9.0–43.0 25.06 2.3–87.0

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).
27

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 5—Results of tests for significant effects of treatment and log position on dimension 

and volume in long logs produced from sample trees in Douglas-fir stands on the Oregon 

Coast Range 15 years after commercial thinning treatments; mixed-effect models were fit 

to account for nesting, with significant random effects estimated for site, tree within site 

and treatment—other random effects were not significant based on likelihood ratio χ2 tests 

(SAS 9.4 PROC GLIMMIX COVTEST option)

Response variable Degrees of

 (179 logs) Effecta freedomb F value p valuec

Long-log large-end Treatment 1,2 53.45 0.0182*

 diameter (inches) Position 2,8 1036.39 < 0.0001*

Treatment × position 2,8 5.55 0.0308*

Adjusted DBH 1,99 590.21 < 0.0001*

Position × adjusted DBH 2,99 86.95 < 0.0001*

σ

^2

tree=0.34 (p = 0.0067), σ^2ε = 1.00

Long-log small-end Treatment 1,2 26.26 0.0360*

 diameter (inches) Position 2,8 708.80 < 0.0001*

Treatment × position 2,8 8.07 0.0120*

Adjusted DBH 1,99 246.65 < 0.0001*

Position × adjusted DBH 2,99 42.54 < 0.0001*

σ

^2

site = 0.51 (p = 0.0424), σ ^ 2tree = 0.45 (p = 0.0006), σ^2ε = 1.05

Long-log Treatment 1,2 0.36 0.6108

 length (feet) Position 2,8 69.80 < 0.0001*

Treatment × position 2,8 0.18 0.8347

Adjusted DBH 1,99 0.56 0.4559

Position × adjusted DBH 2,99 7.53 0.0009*

σ

^2

site,treatment,position = 0.05 (p = 0.8897),

α

^2

tree = 1.77 (p = 0.3568),

(σ^2

ε,butt,σ^2ε,middle,σ^2ε,upper) = (4.61,26.95,61.70) (p < 0.0001)

Gross long-log Treatment 1,2 33.35 0.0287*

 volume (cubic feet) Position 2,8 700.34 < 0.0001*

Treatment × Position 2,8 11.47 0.0045*

Adjusted DBH 1,99 363.15 < 0.0001*

Position × Adj-DBH 2,99 193.52 < 0.0001*

σ

^2

site = 0.97 (p = 0.3959),

 σ^2

site,treatment,position = 0.27 (p = 0.8791),

σ

^2

tree = 7.92 (p = 0.0044), σ^2ε = 20.69

F value = the numerical value of the F-statistic used to test significance in the analysis of variance.

σ

^2

i= the estimated variance of random-effect variable i; (multiple indexes separated by commas indicate interaction effects); 

when i = ε, the variance estimate is for the error term or components of the error term.

DBH = diameter at breast height.

a The tests were carried out with adjustments for DBH assuming different ranges for the groups (Milliken and Johnson, 2002). 

Without DBH adjustments, only position was significant in each model (all others have p > 0.10).

b The conservative containment approach was used for degrees of freedom (this is based on split-plot type designs with 

restricted randomizations); if the less conservative Kenward-Roger approach (Littell et al. 2006) had been used, all tests would 

have had similar declarations of significance.

c An asterisk indicates statistically significant results (p < 0.05). When interactions are significant, probability values for main 

effects are only given for completeness.
28

RESEARCH PAPER PNW-RP-605

that would be expected from such treatments, as heavier thinnings tend to produce 

trees with larger diameters and volumes. When adjusted DBH was included as a 

covariate, the interaction between treatment and log position was highly significant 

for both log diameters and for gross volume. Although not typically a statistically 

significant contributor, site appeared to contribute to the variation in long-log smallend diameter, length, and gross log volume for this study.

Sweep and Crook in Long Logs

Table 6 summarizes data on the sample logs that were recorded as having sweep or 

crook. Of the 179 long logs measured, 38 butt logs (21 percent) had sweep. Slightly 

more than half were from the Yachats site, the source for 41 percent of all sample 

logs; 63 percent were from the lighter phase 1 thinning (treatment T100), which also 

accounted for 63 percent of the sample logs.

Response variables tested in a statistical analysis of sweep—again using 

mixed-effects procedures in SAS (2013)—were sweep offset and the distance from 

the large end of the log to the center of sweep (fig. 7). The variation in sweep offset 

shown in table 6, combined with minimal variation in the distance from the large 

end of the log to the center of sweep, resulted in no statistically significant differences. We did not test log position because all logs with sweep were butt logs. 

Although we found heteroscedasticity in sweep offset among the sites, we also 

found that the treatment effect for sweep offset was not statistically significant, 

and that neither site (as a random effect) nor treatment had a statistically significant 

effect on the distance to the center of sweep.

Only six sample logs (table 6) were recorded as having crook. One of these, 

the only butt log with crook (W3-2042-1), also had sweep. Two-thirds of logs with 

crook were from treatment T60, even though that treatment accounted for only 

slightly more than a third of the sample logs. We did not conduct a statistical analysis for crook because the sample (only six logs) was too small for a satisfactory test.

Effects of Treatment on Stem Growth (Short Logs)

The 179 long logs from sample trees were bucked by the simulator as described in 

appendix 2 into 327 short logs, of which 324 could be used for simulated lumber 

production; three were shorter than the minimum length of 8 feet plus trim and had 

to be discarded. Table 7 summarizes results from the simulated bucking operation. 

Most long logs were bucked into two short logs. Only one, a 43-foot second log 

from a two-log tree, was bucked into three short logs. About 18 percent of long logs 

were not bucked at all because they were already short enough for mill processing. 

Of these, 81 percent were top logs. Both logs from one small two-log tree (DBH 8.4 
29

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

inches, total height 65.7 feet) were too short for bucking. All other trees had at least 

one long log that required bucking.

Log volume and the volumes of bucking waste and trim are shown in table 7 for 

all short logs. Typically, waste and trim are converted into chips during the bucking 

process. Trim is normally removed after sawing, when the pieces of lumber are 

trimmed to length. Waste, which is removed before sawing, consists of excess trim 

(more than 1 foot for this study), bucked pieces that are too short to process (less 

Table 6—Sweep and crook in long logs cut from sample trees in Douglas-fir stands on 

the Oregon Coast Range 15 years after commercial thinning treatments

Distance to

Defect offset defect center Length of

(inches) (feet) defect (feet)a

Defect Logs with

type Groupings defect Mean Range Mean Range Mean Range

Sweep Study site:

 Cataract 9 7.91 4.1–13.5 18.50 18.5–18.5 37.03 36.9–37.2

 Wildcat 9 6.81 4.8–8.5 17.40 12.0–27.5 37.13 36.9–37.4

 Yachats 20 7.91 3.2–13.5 18.45 16.5–19.2 36.95 33.0–38.5

Treatment

 T100b 24 7.48 3.2–13.5 17.82 12.0–18.6 37.06 36.8–37.4

 T60c 14 7.94 5.0–13.5 18.89 16.0–27.5 36.94 33.0–38.5

Log position

 Butt 38 7.65 3.2–13.5 18.21 12.0–27.5 37.01 33.0–38.5

 Middle 0 — — — — — —

 Upper 0 — — — — — —

All logs 38 7.65 3.2–13.5 18.21 12.0–27.5 37.01 33.0–38.5

 with sweep

Crook Study site:

 Cataract 2 4.20 3.9–4.5 12.0 3.0–21.0 5.25 4.5–6.0

 Wildcat 2 3.50 2.5–4.5 8.65 7.5–9.8 10.10 5.2–15.0

 Yachats 2 4.50 4.0–5.0 18.70 13.3–24.1 3.85 2.5–5.2

Treatment:

 T100b 2 4.20 3.9–4.5 12.00 3.0–21.0 5.25 4.5–6.0

 T60c 4 4.00 2.5–5.0 13.68 7.5–24.1 6.98 2.5–15.0

Log position:

 Butt 1 4.50 — 7.50 — 15.00 —

 Middle 4 4.35 3.9–5.0 15.35 3.0–24.1 4.55 2.5–6.0

 Upper 1 2.50 — 9.80 — 5.20 —

All logs 6 4.07 2.5–5.0 13.12 3.0–24.1 6.40 2.5–15.0

 with crook

— = no results. a Sweep is over the entire length of the log but crook occupies only part of the log.

b Stand reduced to approximately 100 trees per acre (light thinning).

c Stand reduced to approximately 60 trees per acre (moderate thinning).
30

RESEARCH PAPER PNW-RP-605

than 8 feet for this study), and tapered ends of logs that are smaller than the minimum processing diameter (4 inches for this study). The Wildcat site had a somewhat higher percentage of waste and trim (table 7) because its trees were generally 

shorter; hence, the top cut in the trees from that site was more often at a point where 

the diameter of the log was too small to satisfy mill standards, requiring additional 

simulated bucking and shorter logs. Eleven such logs came from the Wildcat site 

(out of 91), compared to three logs from Cataract (out of 102) and three logs from 

Yachats (out of 134).

Measurement Data

Table 8 summarizes basic data on short logs after the simulated bucking, organized 

in the same groups as table 4 for long logs. We used a somewhat different definition of the log-position category for short logs than we used for long logs, which 

assumed that every tree has a single butt and single upper log, that a three-log tree 

has a single middle log, and that a four-log tree has two middle logs. Our definition 

for short logs was slightly more complex, and it differed from other recent lumber 

recovery studies such as Lowell et al. (2012) in several ways. First, we chose to 

limit the butt-log designation to a single short log from each tree. Thus, when a 

butt long log was bucked into short logs, only one of them—the one closest to the 

stump—was considered a butt short log. Additional short logs bucked from the butt 

long log were designated as middle or upper logs, depending on how many short 

logs were recovered from the tree stem. Our reasoning was that the part of the tree 

nearest the stump has wood-quality characteristics that differ from any other part 

of the tree, even the section of stem just above it. A second difference was that we 

opted to have at least as many upper logs as middle logs in a tree (as shown by the 

rows in table 9). This was done to ensure that the part of the tree with a live crown 

was represented primarily by upper logs.

Table 7—Bucking summary for logs produced from sample trees in Douglas-fir stands on the Oregon 

Coast Range 15 years after commercial thinning treatments

Short Short Short Short-log Bucking Volume Waste

Long logs logs logs in volume waste in trim and trim

Study site logs bucked discarded saw list (cubic feet) (cubic feet) (cubic feet) (percent)

Cataract 57 102 0 102 1,316.8 4.3 33.1 2.8

Wildcat 48 91 3a 88 1,126.5 8.9 28.1 3.2

Yachats 74 134 0 134 2,124.0 6.4 51.4 2.6

All Sites 179 327 3 324 4,567.3 19.6 112.5 2.8

a Each was the topmost log in the tree. All had small-end diameters less than the minimum diameter of 4 inches. When each log was 

shortened by the bucking simulator to the point where the small-end diameter was 4 inches, the remaining length was less than the minimum 

length of 8 feet plus trim and therefore the entire short log had to be discarded.
31

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

As with long logs, we tested the short-log data for significant fixed effects by 

using the mixed-effects procedure in SAS (2013). The results are presented in table 

10. For this analysis DBH was adjusted in a similar way as in table 5 to account for 

the fact that not all sample trees from a particular treatment had the same number of 

middle and upper logs (table 9). Trees with more logs (and therefore typically more 

total stem length) tended to have larger DBH.

Table 8—Short logs after simulated bucking of long logs from sample trees in Douglas-fir stands on 

the Oregon Coast Range 15 years after commercial thinning treatments; data are summarized by site, 

treatment, and log position within the tree

 Large-end Small-end Gross log

 diameter diameter volume

Long Short

Groupings logs logs Mean Range Mean Range Mean Range Mean Range

 - - - - - - - - - - - Inches - - - - - - - - - - - - - - - - Feet - - - - - - Cubic feet - -

Study site:

 Cataract 57 102 12.61 4.9–25.7 10.52 4.0–18.5 16.71 8.2–20.5 12.91 0.9–39.5

 Wildcat 48 88 12.74 5.2–26.1 10.05 4.0–20.3 16.56 8.5–20.5 12.80 1.1–45.6

 Yachats 74 134 13.90 6.2–25.8 11.40 4.1–20.0 17.10 8.5–20.5 15.85 1.4–43.7

Treatment:

 T100a 113 202 12.74 4.9–25.8 10.38 4.0–20.0 16.91 8.2–20.5 13.17 0.9–43.4

 T60b 66 122 13.90 5.8–26.1 11.37 4.1–20.3 16.70 8.5-20.5 15.63 1.1–45.6

Log position:

 Butt 66 66 18.18 8.8–26.1 14.30 6.4–20.3 18.43 12.5–20.5 23.18 4.6–45.6

 Middle 47 110 14.14 7.7–20.3 12.59 6.4–18.2 17.81 12.5–20.5 17.48 3.8–35.5

 Upper 66 148 10.22 4.9–16.8 7.81 4.0–15.1 15.40 8.2–20.5 7.53 0.9–22.2

All logs 179 324 13.18 4.9–26.1 10.74 4.0–20.3 16.83 8.2–20.5 14.10 0.9–45.6

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).

Table 9—The BMU (Butt-Middle-Upper) classification for short 

logs as defined over the range of short logs cut from sample 

trees in Douglas-fir stands on the Oregon Coast Range 15 years 

after commercial thinning treatments

Total short logs cut Position of the short log within the tree stem

from the tree 1 2 3 4 5 6

2 B U

3 B M U

4 B M U U

5 B M M U U

6 B M M U U U
32

RESEARCH PAPER PNW-RP-605

Table 10—Results of tests for significant effects of treatment and log position on 

dimensions and volumes of short logs produced from sample trees in Douglas-fir 

stands on the Oregon Coast Range 15 years after commercial thinning treatments

Response variable Degrees of

(324 logs) Effecta freedomb F value p valuec

Short-log large-end Treatment 1,2 87.09 0.0113*

 diameter (inches) Position 2,8 1086.86 < 0.0001*

Treatment × position 2,8 8.98 0.0090*

Adjusted DBH 1,244 1440.95 < 0.0001*

Position × adjusted DBH 2,244 65.75 < 0.0001*

σ

^2

site,treatment = 0.01 (p = 0.6888), σ^2tree=0.06 (p = 0.3346),

(σ^2

ε,butt,σ^2ε,middle,σ^2ε,upper) = (0.51,1.06,3.58) (p < 0.0001)

Short-log small-end Treatment 1,2 23.39 0.0402*

 diameter (inches) Position 2,8 771.43 < 0.0001*

Treatment × position 2,8 9.30 0.0082*

Adjusted DBH 1,244 787.62 < 0.0001*

Position × adjusted DBH 2,244 45.69 < 0.0001*

σ

^2

site=0.01 (p = 0.8820),σ^2site,treatment = 0.08 (p = 0.1671)

σ

^2

tree = 0.18 (p = 0.0002),

(σ^2

ε,butt,σ^2ε,middle,σ^2ε,upper) = (0.12,0.89,4.19) (p < 0.0001)

Short-log Treatment 1,2 0.65 0.5053

 length (feet) Position 2,8 74.61 < 0.0001*

Treatment × position 2,8 0.02 0.9797

Adjusted DBH 1,244 3.26 0.0721

Position × adjusted DBH 2,244 2.46 0.0878

σ

^2

tree=0.22 (p = 0.0257),

(σ^2

ε,butt,σ^2ε,middle,σ^2ε,upper) = (0.60,1.29,7.40)(p < 0.0001)

Gross short-log Treatment 1,2 59.19 0.0165*

 volume (cubic feet) Position 2,8 731.93 < 0.0001*

Treatment × position 2,8 12.85 0.0032*

Adjusted DBH 1,244 737.95 < 0.0001*

Position × adjusted DBH 2,244 144.68 < 0.0001*

σ

^2

site = 0.24 (p = 0.4603),

σ

^2

tree = 1.53 (p = 0.0052), 

σ

^2

site,treatment,position = 0.003 (p = 0.9917),

(σ^2

ε,butt,σ^2ε,middle,σ^2ε,upper) = (3.94,10.71,14.59) (p < 0.0001)

DBH = diameter at breast height.

σ

^2

i =the estimated variance of random-effect variable i (multiple indexes separated by commas indicate interaction 

effects); when i = ε, the variance estimate is for the error term or components of the error term. a The tests were carried out with adjustments for DBH assuming different ranges for the groups (Milliken and 

Johnson 2002). Without DBH adjustments, only position was significant in each model (all others have p > 0.10). b The conservative containment approach was used for degrees of freedom (this is based on split-plot type designs 

with restricted randomizations); if the less conservative Kenward-Roger approach (Littell et al. 2006) had been 

used, all tests would have had similar declarations of significance.

c An asterisk indicates statistically significant results (p < 0.05). When interactions are significant, probability 

values for main effects are only given for completeness.
33

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

For the most part, the results of the analysis summarized in table 10 are similar 

to those for long logs (table 5). Log position in conjunction with adjusted tree 

DBH was strongly significant, as was the interaction of log position and treatment 

for large-end diameter, small-end diameter, and gross volume. Unsurprisingly, 

short-log length was significantly influenced by log position but not by treatment or 

adjusted DBH. As with long logs, site appeared to contribute to variation in largeend and small-end diameters and in gross volume for short logs.

Sweep and Crook in Short Logs

Bucking long logs into short logs invariably reduces sweep. After bucking, the 

38 long logs with sweep (table 6) became 76 short logs with sweep (table 11) but 

severity (expressed as sweep offset) decreased from an average of 7.65 inches in 

long logs (equivalent 1.8 percent of log length) to an average of only 1.94 inches in 

short logs (equivalent on average to 0.8 percent of log length). This reduction is such 

that the remaining sweep is unlikely to have much effect on the lumber from the 

affected logs.

Bucking has different effects on crook, depending on its location, length, and 

severity. If bucking occurs within the crook, the effect on the short log may be 

less severe. Alternatively, bucking can remove a short crook altogether (a common 

practice in the mill yard); or it can leave the entire crook in one of the short logs. 

For the six incidents of crook in this study, all were positioned so that bucking had 

no effect on the crook, leaving the entire crook in a single short log. Rather than 

override the bucking algorithm to remove the crook or reduce its severity, we chose 

to leave it in place. This increases the likelihood of negative impacts on the lumber 

recovery from the six affected short logs. It also means that for all three sites and 

both treatments, two measures—crook offset and crook length—were the same for 

short logs (table 11) as they were for long logs (table 6).

Effects of Treatment on Surface Defects

Of the 11,475 profile points recorded for the 179 sample long logs, 453 were diameter measurements (diameters measured at the large and small ends of the logs, 

at four feet from the large end of butt logs, and at intervals along the log when 

necessary to ensure that profile-point locations were no more than four feet apart). 

The remaining 11,022 profile points corresponded to surface defects. Most of these 

(10,970 profile points) were for knots, of which 4,479 (41 percent) were live knots, 

44 were small surface burls, three were forked tops, and five were surface scars 

(two of which had some decay). For the simulated sawing, forked tops were treated 

as large knots and the burls and scars were judged to penetrate the bole only a short 

distance and therefore be removed with the slab when the log was squared up for 
34

RESEARCH PAPER PNW-RP-605

sawing. This left only the knots and forked tops with the potential to influence 

wood quality in our analysis.

Surface Knots

The size, frequency, and distribution of knots can affect the quality of lumber, while 

also serving as indicators of successful habitat improvement for species that depend 

on large branches. To understand the factors that could influence these characteristics, we studied the size and density of surface knots at three levels of disaggregation: the entire merchandized tree stem, long logs, and short logs. We anticipate that 

this information will be valuable to foresters who are designing operations for both 

forest products and habitat improvement and want to know how the size and density 

Table 11—Sweep and crook in the sample short logs cut from sample trees in Douglas-fir 

stands on the Oregon Coast Range 15 years after commercial thinning treatments

Logs Distance to

Defect with Defect offset defect center Length of defect

 type Groupings defect Mean Range Mean Range Mean Range

 - - - Inches - - - - - - - - - - - - - - Feet - - - - - - - - - - -

Sweep Study site:

 Cataract 18 1.96 1.0–3.4 9.84 9.8–9.9 18.49 18.5–18.5

 Wildcat 18 1.90 0.7–5.0 9.66 8.2–11.5 18.49 18.5–18.5

 Yachats 40 1.95 0.8–3.3 9.72 8.2–9.9 18.39 16.5–20.5

Treatment:

 T100a 48 1.89 0.7–3.8 9.74 8.2–9.9 18.49 18.4–18.5

 T60b 28 2.02 1.0–5.0 9.73 8.2–11.5 18.35 16.5–20.5

Log position:

 Butt 38 2.00 0.8–3.8 9.66 8.2–9.8 18.49 16.5–20.5

 Middle 38 1.89 0.7–5.0 9.81 8.2–11.5 18.39 16.5–18.5

 Upper 0 — — — — — —

All logs with sweep 76 1.94 0.7–5.0 9.73 8.2–11.5 18.44 16.5–20.5

Crook Study site:

 Cataract 2 4.20 3.9–4.5 3.75 3.0–4.5 5.25 4.5–6.0

 Wildcat 2 3.50 2.5–4.5 8.65 7.5–9.8 10.10 5.2–15.0

 Yachats 2 4.50 4.0–5.0 9.45 5.6–13.3 3.85 2.5–5.2

Treatment:

 T100a 2 4.20 3.9–4.5 3.75 3.0–4.5 5.25 4.5–6.0

 T60b 4 4.00 2.5–5.0 9.05 5.6–13.3 6.98 2.5–15.0

Log position:

 Butt 1 4.50 — 7.50 — 15.00 —

 Middle 1 3.90 — 3.00 — 4.50 —

 Upper 4 4.00 2.5–5.0 8.30 4.5–13.3 4.73 2.5–6.0

All logs with crook 6 4.07 2.5–5.0 7.28 3.0–13.3 6.40 2.5–15.0

— = no results.

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).
35

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

of knots (indicating branches) on the standing tree would be affected by treatments 

or site variables. Loggers are interested in both trees and long logs because trees are 

what they buy and long logs are what they produce and sell. Forest products companies are interested in both long logs and short logs because long logs are what they 

buy and short logs are what they process into lumber or other products.

We used regression analysis to identify the factors that influence knot size and 

density in tree stems and logs and to quantify those effects in a way that would be 

useful to foresters, loggers, and forest products specialists. As response variables 

we identified two measures that are of general interest in wood-quality studies: knot 

diameter and knot density. Then at each level of disaggregation (tree stems, long 

logs, and short logs) we conducted a regression analysis for each response variable 

using SAS (2013). The fixed-effect model to which the data were fitted with regression analysis was:

E(Y)=β0+β1D+β2LF+β3L+β4E+β5T+β6B+β7U+β8(D*B)+β9(D*U) (1)

where:

Y = the response variable (inverse mean diameter of surface knots or mean density 

of surface knots within tree stems, long logs, or short logs),

βj = parameters estimated via linear regression, j = 0 through 9,

D = an appropriate function of diameter, in inches (DBH, 1/DBH, or √DBH for tree 

stems, small-end diameter—SED, 1/SED, or √SED—for long logs and short logs),

LF = live-knot fraction (live knots divided by total surface knots recorded in each 

merchandized tree stem, long log, or short log),

L = length of the merchandized portion of the tree stem or length of the log, in feet,

E = ground elevation at the base of the tree, in feet above mean sea level,

T = a dummy variable for treatment (T = 0 for trees from treatment T100 

and T = 1 for trees from treatment T60),

B = for logs only, a dummy variable for log position (B = 1 for butt 

logs, B = 0 otherwise),

U = for logs only, a dummy variable for log position (U = 1 for upper logs, U = 0 for 

middle and upper logs),

D*B = for logs only, the log diameter or its transformation (D) multiplied by the 

dummy variable for butt logs (B),

D*U = for logs only, the log diameter or its transformation (D) multiplied by the 

dummy variable for upper logs (U).
36

RESEARCH PAPER PNW-RP-605

We used small-end diameter as a variable in the analysis of logs because it is 

the measure most often used in research on lumber recovery. Large-end and smallend log diameters were highly correlated (ρ = 0.95 for long logs, ρ = 0.96 for short 

logs), so only one of the two could be used as an independent variable in regression 

analysis.

To some extent the live-knot fraction is related to log position because the logs 

from the section of the stem where the live crown is located have larger live-knot 

fractions and also come from the middle-to-upper portion of the tree. However, our 

tests suggested that the relationship between the butt-middle-upper log position and 

the live-knot fraction varied among the sample trees; we therefore included both as 

potential independent variables. Because it is a categorical variable, we represented 

the butt-middle-upper variable by two dummy variables: one for butt logs and the 

other for upper logs, both of which were present in all sample trees. An additional 

variable was not needed for middle logs because by definition they are those for 

which B = U = 0.

To capture variation resulting from the hierarchical structure of the experiment, 

we included random effects for site and site by treatment in initial models, and random effects for tree and tree by log position for long and short logs. For each model, 

a full fixed and random effect model was initially fit. The random effect structure 

was then determined by sequential chi-square tests for variance components equal 

to zero, and random effects that were not significant were dropped (SAS 9.4 PROC 

GLIMMIX, COVTEST statements). In some instances, fitting heterogeneous 

residuals based on log position (butt, middle, upper) improved the model characteristics. After the full model was fitted, the fixed-effect model was determined 

through a backward elimination procedure employing the usual t-tests—a process 

that is typically followed for determination of mixed-effect models when modeling 

longitudinal data (Cheng et al. 2010). For these regression models, we estimated 

degrees of freedom using the Kenward-Roger approach (Littell et al. 2006).

Knots in tree stems—

Table 12 summarizes data on surface knots by site, by treatment, and for all sample 

trees in the study combined. Mean values for several tree-related variables that are 

commonly of interest to foresters (DBH, stem length, and tree elevation) are also 

shown in the table. Merchandized stem length, which was used to calculate knot 

density in knots per foot of merchandized stem, is the sum of the lengths of the 

long logs cut from the tree, excluding any unmerchantable stem sections that were 

bucked out and excluding the one broken and discarded log and the five lost logs 

whose surface knots could not be measured (table 3).
37

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

The largest knots on average were those recorded at the Yachats site, which also 

had the largest trees as measured by DBH and the second-largest trees (behind the 

Cataract site) as measured by merchandized stem length. The Wildcat site had the 

second-largest average knot size even though its trees were shorter than either of the 

other two sites; mean DBH at Wildcat, however, was midway between the others. 

Wildcat also had the largest number of knots per tree and the highest overall knot 

density. Trees from treatment T60 plots had more knots per tree, larger mean knot 

diameters, and more knots per foot of merchandized stem than trees from treatment 

T100 plots.

Knots in long logs—

Table 13 summarizes data on surface knots by site, by treatment, and for all logs 

combined; each major group is further subdivided by the position of the log within 

the tree stem. Although forked tops for this study were treated as large surface 

knots for the simulated sawing, they are not included here because they are not a 

potential consequence of either treatment T60 or treatment T100—often resulting 

instead from breakage of the upper stem, with two or more branches subsequently 

forming the new forked top. If both tops become full boles, a pronounced crotch 

can develop and one or more logs can be recovered from each top. The three forked 

tops observed in sample trees in this study were small, with only one top from each 

tree producing a usable log. The sample size of three forked tops was too small to 

permit a separate statistical analysis.

Table 12—Number, size and density of surface knots in the merchandized stems of sample trees in Douglasfir stands on the Oregon Coast Range 15 years after commercial thinning treatments

 Knots per foot of

 Mean Mean Knots per tree Knot diameter merchandized stem

 Number Mean merchandized tree Live

Groupings of trees DBH stem length elevation Total percent Mean Range Mean Range

 Inches Feet Feet - - - Inches - - - 

Study sites

Cataract 20 16.59 86.55 676 152.9 32.6 0.92 0.73–1.30 1.760 0.950–2.227

Wildcat 19 17.13 80.02 1034 183.2 43.9 1.00 0.69–1.37 2.281 1.269–3.349

Yachats 27 18.29 85.61 740 164.1 44.1 1.16 0.73–1.82 1.907 1.300–2.783

Treatment

T100a 42 16.64 83.03 804 159.8 39.2 1.00 0.69–1.82 1.909 1.269–3.070

T60b 24 18.85 86.48 807 177.4 43.3 1.10 0.82–1.72 2.076 0.950–3.349

All sample 66 17.44 84.28 805 166.2 40.9 1.04 0.69–1.82 1.970 0.950–3.349

 trees

DBH = diameter at breast height.

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).
38

RESEARCH PAPER PNW-RP-605

Table 13—Surface knots in long logs measured after yarding from sample trees in Douglas-fir 

stands on the Oregon Coast Range 15 years after commercial thinning treatments; T100 is a 

light thinning to approximately 100 residual trees per acre, and T60 is a moderate thinning to 

approximately 60 residual trees per acre

Knots Knot Knots per

per log diameter foot of log

Log Long Percent

Groupings position logs Knots Total alive Mean Range Mean Range

- - - Inches - - - 

Study site:

 Cataract Butt 20 1,105 55.3 0.9 0.81a 0.50–4.25 1.507 0.439g–2.081

Middle 17 1,061 62.4 28.7 0.90 0.50–3.40 1.980 1.270–2.961

Upper 20 892 44.6 76.5 1.07 0.50–3.90 1.935 1.333–3.059

All 57 3,058 53.6 32.6 0.93 0.50–4.25 1.798 0.439–3.059

 Wildcat Butt 19 1,389 73.1 2.9 0.84 0.50–3.00 1.975 0.729–3.414

Middle 10 732 73.2 59.3 1.25 0.50–4.55 2.397 1.059–3.303

Upper 19 1,360 71.6 77.7 1.05 0.50–6.25 2.604 f 1.690–3.519h

All 48 3,481 72.5 44.0 1.01 0.50–6.25 2.312 0.729–3.519

 Yachats Butt 27 1,410 52.2 6.9 0.91 0.50–2.60 1.434e 0.649–2.168

Middle 20 1,519 76.0 48.0 1.31 0.50–9.20d 2.224 1.270–3.346

Upper 27 1,502 55.6 75.2 1.31b 0.45c–7.90 2.436 1.386–3.216

All 74 4,431 59.9 44.1 1.16 0.45–9.20 2.013 0.649–3.346

Treatment:

 T100 Butt 42 2,466 58.7 3.7 0.84a 0.50–4.25 1.600e 0.699–2.818

Middle 29 2,056 70.9 42.7 1.11 0.50–5.35 2.168 1.059–3.303

Upper 42 2,190 52.1 76.0 1.13 0.50–7.40 2.215 1.333–3.519h

All 113 6,712 59.4 39.2 1.02 0.50–7.40 1.975 0.699–3.519

 T60 Butt 24 1,438 59.9 3.8 0.89 0.50–3.00 1.631 0.439g–3.414

Middle 18 1,256 69.8 46.8 1.21 0.50–9.20d 2.180 1.270–3.346

Upper 24 1,564 65.2 76.8 1.22b 0.45c–7.90 2.538f 1.462–3.462

All 66 4,258 64.5 43.4 1.10 0.45–9.20 2.111 0.439–3.462

All measured logs:

 Butt 66 3,904 59.1 3.7 0.86a 0.50–4.25 1.612e 0.439g–3.414

Middle 47 3,312 70.5 44.3 1.15 0.50–9.20d 2.172 1.059–3.346

Upper 66 3,754 56.9 76.3 1.16b 0.45c–7.90 2.333f 1.333–3.519h

All 179 10,970 61.3 40.8 1.05 0.45–9.20 2.025 0.439–3.519

a Smallest mean value for knot diameter.

b Largest mean value for knot diameter.

c Smallest individual value for knot diameter.

d Largest individual value for knot diameter.

e Smallest mean value for knots per foot of long log.

f Largest mean value for knots per foot of long log.

g Smallest individual value of knots per foot of long log.

h Largest individual value of knots per foot of long log.
39

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

On average, logs from higher in the tree stem had larger knots than logs from 

lower in the tree (table 13). The smallest mean knot diameters within the major 

groups were measured in butt logs from the Cataract site, treatment T100, and the 

south-facing slopes at the Wildcat site. The largest mean knot diameters occurred 

in upper logs at the Yachats site and in treatment T60. The smallest individual knot 

diameter was almost always 0.5 inches, the minimum diameter for measurement in 

this study; the exception was one knot that measured 0.4 x 0.5 inches for an average 

diameter of 0.45 inches. The largest knot (9.2 inches) was measured on the Yachats 

site. It was in the middle of a three-log tree from treatment T60.

Knot density is shown in table 13 as the number of surface knots per foot of log. 

Within each major group, the smallest average knot density occurred in butt logs 

and the largest occurred in upper logs, which typically represent the part of the tree 

with a living crown. The same result holds for the smallest and largest individual 

knot densities, although the locations of these extreme values did always track with 

the locations of average values; for instance, the smallest individual knot density at 

any site occurred in a log from the Cataract site, but the smallest average density on 

any site was from the Yachats site. For the treatment groups, the smallest average 

density occurred in butt logs from treatment T100 but the smallest individual density occurred in a log from treatment T60; and the largest average density occurred 

in upper logs from treatment T60 but the largest individual density was in an upper 

log from treatment T100. Overall, logs from the Wildcat site (and thus from southfacing slopes) had the highest average densities in all position classes as well as 

both the largest individual and the largest average densities measured in the study.

Knots in short logs—

Table 14 summarizes data on surface knots by site, by treatment, and for all logs 

combined; each major group is further subdivided by the position of the log within 

the tree stem. The total number of knots recorded in long logs (10,970) was reduced 

by 2 percent in short logs (to 10,747) because of knots that were removed, along 

with excess trim, from the long logs when they were bucked into short logs. The 

smallest and largest individual knot diameters recorded for short logs mirrored the 

long-log data (table 13), but not the mean knot diameters and sometimes not the site 

where they occurred; for instance, the smallest mean knot diameter in short logs 

occurred on the Wildcat site, whereas the smallest mean knot diameter in long logs 

was on the Cataract site. The largest mean knot diameter in both short logs and long 

logs occurred in the moderate thinning treatment on the Yachats site.
40

RESEARCH PAPER PNW-RP-605

Table 14—Surface knots in short logs after simulated bucking of long logs from sample trees in 

Douglas-fir stands on the Oregon Coast Range 15 years after commercial thinning treatments; 

T100 is a light thinning to approximately 100 residual trees per acre, and T60 is a moderate 

thinning to approximately 60 residual trees per acre

Knots Knot Knots per

per log diameter foot of log

Log Long Percent

Groupings position logs Knots Total alive Mean Range Mean Range

- - - Inches - - - 

Study site:

 Cataract Butt 20 424 21.2 0.0 0.76 0.50–1.90 1.159 0.195–1.946

Middle 36 1,178 32.7 9.2 0.84 0.50–4.25 1.849 0.683–2.848

Upper 46 1,423 30.9 59.9 1.03 0.50–3.90 2.007 1.186–3.091

All 102 3,025 29.7 31.6 0.91 0.50–4.25 1.785 0.195–3.091

 Wildcat Butt 19 598 31.5 0.0 0.76a 0.50–1.90 1.706 0.162g–3.622

Middle 28 1,163 41.5 16.4 0.97 0.50–4.25 2.335 0.729–3.730

Upper 41 1,568 38.2 75.7 1.13 0.50–6.25 2.581f 1.030–4.080h

All 88 3,329 37.8 41.3 1.00 0.50–6.25 2.314 0.162–4.080

 Yachats Butt 27 577 21.4 4.2 0.83 0.50–1.90 1.149e 0.324–2.005

Middle 46 1,555 33.8 20.7 1.08 0.50–4.85 1.904 0.703–3.647

Upper 61 2,261 37.1 69.3 1.32b 0.45c–9.20d 2.346 1.394–4.032

All 134 4,393 32.8 43.6 1.14 0.50–9.20 1.953 0.324–4.032

Treatment:

 T100 Butt 42 1,040 24.8 1.6 0.78a 0.50-1.90 1.338 0.324–2.919

Middle 68 2,386 35.1 16.0 0.95 0.50–4.25 1.972 0.703–3.647

Upper 92 3,135 34.1 66.0 1.14 0.50–7.40 2.194 1.030–4.080h

All 202 6,561 32.5 37.5 1.00 0.50–7.40 1.941 0.324–4.080

 T60 Butt 24 559 23.3 1.3 0.79 0.50–1.90 1.267e 0.162g–3.621

Middle 42 1,510 36.0 15.6 1.01 0.50–4.85 2.035 0.683–3.730

Upper 56 2,117 37.8 72.5 1.23b 0.45c–9.20d 2.489f 1.294–4.032

All 122 4,186 34.3 42.6 1.07 0.45–9.20 2.093 0.162–4.032

All measured logs:

 Butt 66 1,599 24.2 1.7 0.79a 0.50-1.90 1.312e 0.162g–3.622

Middle 110 3,896 35.4 15.8 0.97 0.50–4.85 1.996 0.683–3.730

Upper 148 5,252 35.5 68.7 1.17b 0.45c–9.20d 2.306 f 1.303–4.080h

All 324 10,747 33.2 39.5 1.03 0.45–9.20 1.998 0.162–4.080

a Smallest mean value for knot diameter.

b Largest mean value for knot diameter.

c Smallest individual value for knot diameter.

d Largest individual value for knot diameter.

e Smallest mean value for knots per foot of long log.

f Largest mean value for knots per foot of long log.

g Smallest individual value of knots per foot of long log.

h Largest individual value of knots per foot of long log.
41

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Compared to long-log data, knot densities for short logs were similar but the 

range of densities was narrower—0.162 to 4.080 knots per foot versus 0.439 to 3.519 

knots per foot—because knots tend to clump in whorls rather than being uniformly 

spaced along the length of Douglas-fir stems. The knot density that results when 

a long log is bucked into short logs depends on where the bucking cuts are made 

relative to these clumps. Our bucking algorithm (app. 2) did not consider whorls or 

other surface features when determining the location of the bucking cuts.

As with long logs, knot density in short logs was smallest in butt logs and largest in upper logs. This held true both for mean values and for individual maximums 

and minimums. Some shifting of the minimum-density values occurred within the 

major groups, however; the smallest individual density occurred on the Cataract site 

(table 13) for long logs but on the Wildcat site for short logs (table 14). The smallest mean density also occurred in treatment T100 for long logs, but not for short 

logs. Conversely, the largest mean and individual short-log densities for short logs 

mirrored the long-log data: on the Wildcat site, treatment T100 (highest individual), 

treatment T60 (highest mean), and upper logs.

Size of Surface Knots

The mean size of knots in a tree is influenced by such factors as genetics, local 

growing conditions (especially stand density), and the management history of the 

stand. One purpose of this study was to determine whether the treatment, elevation, or other measured variables could be associated with knot size. The results of 

regression analyses for tree stems, long logs, and short logs (table 15) showed no 

significant treatment effect and no statistically significant effect of the elevation 

variable. However, DBH was shown to be significantly influenced by treatment 

(table 1) and the square root of either DBH or the closely correlated small-end log 

diameter was significant for all three response variables, therefore suggesting an 

indirect treatment effect. Site was statistically significant as a random effect for all 

three response variables as was tree for both long and short logs.

Knot size in tree stems—

The results of regression analysis for inverse mean knot diameter in tree stems 

suggested a direct relationship between knot diameter and DBH, with an adjustment 

for live-knot fraction. A higher proportion of live knots was associated with a larger 

average knot size. The equation explained about two-thirds of the variance in the 

data on inverse knot diameter in tree stems.

Knot size in long logs—

The results of regression analysis suggested that logs with larger diameters and 

a larger percentage of live knots tend to have larger knots, although the equation 
42

RESEARCH PAPER PNW-RP-605

Table 15—Regression analyses for the inverse mean diameter of surface knots, in inches, from sample trees 

in Douglas-fir stands on the Oregon Coast Range 15 years after commercial thinning treatments; overall fit 

values were calculated on the original scale (inches), R2 and marginal error estimates were based on backtransformed knot diameters, and variance and error components were calculated in terms of inverse knot 

diameters

Overall fit Parameter estimates for individual variables

Level of Standard Degrees of

 analysis Statistic Value Regression variable β

^

j error freedom t p value

Tree stems Adjusted 0.751 Intercept 2.3503 0.1105 52.12 21.27 < 0.0001

 R2-conditional

 Adjusted 0.650 D = √(DBH (inches)) -0.2914 0.0268 61.30 -10.87 < 0.0001

 R2-marginal

RMSE-marginal 0.1441 LF = Live-knot fraction -0.3116 0.0931 62.00 -3.40 0.0012

Observations 66

σ

^2

site=0.004 (p=0.0010), σ^2ε=0.010

Long logs Adjusted 0.737 Intercept 1.8013 0.1040 37.97 17.32 < 0.0001

 R2-conditional

 Adjusted 0.477 D = √(SED (inches)) -0.2126 0.0281 129.1 -7.55 < 0.0001

 R2-marginal

RMSE-marginal 0.2275 LF = Live-knot fraction -0.4671 0.0505 171.1 -9.25 < 0.0001

Observations 179 B*D = Butt log*√SED 0.0462 0.0098 117.6 4.70 < 0.0001

σ

^2

site=0.006 (p<0.0001), σ^2tree =0.006 (p=0.0038), σ^2ε=0.017

Short logs Adjusted 0.783 Intercept 1.4698 0.1291 84.72 11.38 < 0.0001

 R2-conditional

Adjusted 0.469 D = √(SED,inches) -0.0889 0.0342 242.4 -2.60 0.0100

 R2-marginal

RMSE-marginal 0.2350 LF = Live-knot fraction -0.4184 0.0400 315.6 -10.46 < 0.0001

Observations 324 U = Upper log 0.4013 0.1204 258.9 3.33 0.0010

U*D =Upper log*√SED -0.1485 0.0356 250.6 -4.17 < 0.0001

B*D = Butt log*√SED 0.0458 0.0060 259.8 7.66 < 0.0001

σ

^2

site = 0.006 (p = 0.0003), σ^2tree = 0.011 (p < 0.0001), σ^2ε = 0.017

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether βj = 0 (may be approximate).

DBH = diameter at breast height.

SED = small-end diameter of the log.

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

RMSE = the square root of the mean squared error (commonly known as “root mean squared error”).

σ

^2

i = the estimated variance of random-effect variable i; when i = ε, the variance estimate is for the error term or components of the error term.
43

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

explained slightly less than half of the variance in the data on a marginal basis 

(setting random effects to zero). The combination of a negative regression coefficient for the square root of log diameter, log position, and a positive sign for the 

log-position variable suggested that, overall, surface knots in butt logs tend to be 

smaller than those found in middle or upper logs.

Knot size in short logs—

Regression analysis showed statistical significance for the square root of the diameter at the small end of the log, live-knot fraction, log position as defined in table 15 

by the U variable, and adjustments for the square root of the diameter at the small 

end of the log for different log positions. Altogether, the regression suggested that 

knots tend to be larger in logs with larger small-end diameters and in logs with 

larger live-knot fractions (which tend to be middle and upper logs)—at any given 

small-end log diameter, butt logs tend to have the smallest knots and upper logs the 

largest, with knots in middle logs lying between the two extremes. The equation 

explained about half of the observed variance in the data on mean knot diameter in 

short logs.

Density of Surface Knots

Knot density in tree stems—

We hypothesized that knot density within the merchandized tree stem, measured 

in knots per foot of merchandized stem length, could be influenced by the same 

variables as in equation (1) for knot size. However, regression analysis (table 16) 

suggested that only DBH and elevation are significant predictors of mean knot 

density in tree stems, with the equation explaining only a little more than a third of 

the total variance in the data. Even so, the results suggested that knot density tends 

to increase with increasing DBH but is not affected by live-knot fraction or merchandized stem length. The regression relationship described both treatment groups 

without a simple adjustment for treatment; however, a statistical contrast evaluating 

how the predicted DBH values from this regression differed among treatments at 

a common elevation indicated significant differences (1.92 for T100 versus 2.05 

for T60, p-value for difference < 0.0001). When taken together with the finding 

that DBH was strongly influenced by treatment (table 1), this suggested a strong, if 

indirect, treatment effect on knot density in tree stems.

Knot density in long logs—

Regression analysis for knot density showed no statistically significant treatment 

effect. However, because the effect of treatment on DBH was significant (table 1) 

and small-end log diameter is highly correlated with DBH, the treatment arguably 

had an indirect effect on knot density. Small-end log diameter, live-knot fraction, 

elevation, and an adjustment for small-end diameter in butt logs as defined by the 
44

RESEARCH PAPER PNW-RP-605

Table 16—Regression analyses for knot density—as measured in knots per foot of length—for tree stems, 

long logs, and short logs from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after 

commercial thinning treatments; variables whose parameters were not significantly different from zero at 

α = 0.05 have been excluded and the equations refitted without them

Overall fit Parameter estimates for individual variables

Level of Regression Standard Degrees of

 analysis Statistic Value variable β

^

j error freedom t p value

Tree stems Adjusted 0.378 Intercept -0.0608 0.3198 63 -0.19 0.8497

 R2-conditional

Adjusted 0.378 D = DBH (inches) 0.0541 0.0113 63 4.79 < 0.0001

 R2-marginal

RMSE-marginal 0.3748 E = Elevation (feet) 0.0014 0.0003 63 4.65 < 0.0001

Observations 66 

σ

^2

ε = 0.141

Long logs Adjusted 0.770 Intercept 0.4131 0.3932 63.1 1.05 0.2975

 R2-conditional

Adjusted 0.391 D = SED (inches) 0.0539 0.0196 101.2 2.75 0.0071

 R2-marginal

RMSE-marginal 0.5146 LF = Live-knot fraction 0.8713 0.1655 152.8 5.27 < 0.0001

Observations 179 E = Elevation (feet) 0.0012 0.0004 42.8 3.00 0.0045

B*D = Butt log * SED -0.0369 0.0118 31.5 -3.13 0.0037

σ

^2

tree = 0.108 (p < 0.0001), σ^2position = 0.025 (p = 0.0015), σ^2ε = 0.139

Short logs Adjusted 0.693 Intercept 0.9850 0.3050 53.4 3.23 0.0021

 R2-conditional

Adjusted 0.350 LF = Live-knot fraction 0.5648 0.0989 59.9 5.71 < 0.0001

 R2-marginal

RMSE-marginal 0.5912 E = Elevation (feet) 0.0012 0.0004 54.9 3.16 0.0025

Observations 324 B*D = Butt log * SED -0.0429 0.0061 26.0 -7.04 < 0.0001

σ

^2

tree = 0.152 (p < 0.0001), σ^2position = 0.010 (p = 0.0307), σ^2ε = 0.200

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether βj = 0 (may be approximate).

DBH = diameter at breast height.

SED = small-end diameter of the log.

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

RMSE = the square root of the mean squared error (commonly known as “root mean squared error”).

σ

^2

i = the estimated variance of random-effect variable i; when i = ε, the variance estimate is for the error term or components of the error term.
45

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

B*D variable in table 16 all appear to have significantly influenced knot density in 

long logs. The negative sign on the B*D variable implies that butt logs tend to have 

lower knot densities than middle and upper logs; long logs with larger diameters 

and higher percentages of live knots tend to have higher knot densities.

Knot density in short logs—

As with tree stems and long logs, the results of regression analysis showed that the 

treatment variable was not significant. Live-knot fraction, elevation, and small-end 

diameter in combination with log position as defined by the B*D variable in table 

16 were all found to be significant predictors of knot density in short logs—similar 

to the significant predictors in the long-log regression, and with identical signs on 

the coefficients in the two equations. However, because log diameter only appeared 

in the B*D term in this regression, its minus sign was an indicator of an inverse 

relationship between small-end log diameter in butt logs and knot density. Log 

diameter had no statistically significant effect on knot density in middle and upper 

short logs; thus the indirect treatment effect through the correlation between DBH 

and log small-end diameter likely influenced knot density only in butt logs. As with 

the regressions for tree stems and long logs, the fitted equation for short logs was 

highly significant overall but explained only a little more than a third of the variance in the knot-density data.

Effects of Treatment on Simulated Lumber Production

After many preliminary runs to evaluate and refine parameters, we made six final 

simulation runs using AUTOSAW, one run for each site and treatment combination 

(C2, C3, W2, W3, Y2, and Y3, where C is Cataract, W is Wildcat, and Y is Yachats; 

2 corresponds to treatment T100 and 3 corresponds to treatment T60). Segregating 

the runs in this way was an arbitrary but convenient way to avoid exceeding the 

AUTOSAW limit of 99 short logs in a single simulation. Although the 88 Wildcat 

short logs could have been processed in a single run, the saw list for the other two 

sites was too large: 102 logs from Cataract and 134 logs from Yachats. We decided 

to handle all three sites the same way.

The instructions to the simulator (app. 3) were designed to simulate a sawmill 

producing mainly structural grade dimension lumber measuring 2 inches thick and 

4 to 12 inches wide. Occasional 1-inch jacket boards were removed from the outside 

of the log as necessary to produce a properly sized rectangular cant for final sawing. 

Final piece sizes were produced by breakdown of the cant followed by edging.

Although detailed specifications for the AUTOSAW simulations are described 

in appendix 3, we provide a brief summary here. For logs that are not perfectly 

round (and few are), the initial presentation of the log to the saw has previously 
46

RESEARCH PAPER PNW-RP-605

been shown to be an important consideration that can significantly influence lumber 

recovery (Todoroki et al. 2007). For this study it was not possible to determine how 

individual logs would be presented to the saw, because the logs were sold to several 

mills and our study did not include an in-mill recovery component. Therefore we 

arbitrarily assumed that the simulated sawmill would use a vertical bandsaw and 

that each log would be oriented with the small end toward the saw and with the 

y-axis of the log pointing vertically upward in the same configuration as when the 

log was originally measured. For logs with sweep, the orientation was with “horns 

up” (sometimes called sweep down), in which the longitudinal axis at the geometric 

center is located vertically below the z-axis as illustrated in figure 10; this orientation is often used to make the opening cut in Douglas-fir logs when the sawmill is 

not equipped for curve sawing (Monserud et al. 2004). We assumed that the sawmill would use half-taper sawing, a practice in which the cuts are aligned parallel to 

the center of the log. This is a commonly used method for sawing small Douglas-fir 

logs in local mills. Full-taper sawing, in which the cuts are aligned parallel to the 

edge of the log, is more commonly used when higher value logs are being cut. As 

previously mentioned, our goal was not to achieve the maximum possible rate of 

lumber recovery but rather to mimic the rate that would be considered typical of 

local sawmills.

Lumber Volume, Grade, and Value

Our analysis of products from simulated sawing was limited to lumber. Inevitably, some parts of each log are converted at the mill into sawdust or wood chips, 

either of which can be recovered and sold or used at the mill. In general, the value 

of these residual products is quite low, often depending on the local efficiencies 

Figure 10—Horns-up orientation of a log with sweep, as it would be positioned on the headrig carriage for the opening 

cut; the opening cut is made on the far side of the log, beginning outside of point B and moving to a position outside of 

point A.

z-axis A

Longitudinal

axis

B C

Small

end of log

toward

saw

Headrig

carriage (only

partly shown)

A straight line connecting the geometric 

centers of the two ends of the log.

z-axis

Reference points marking the two ends 

of the log.

A and B

The point on the log where the sweep 

offset from the z-axis to the longitudinal 

axis is largest.

C

The geometric center of the log at each 

point along its length (curved unless the 

log is straight).

Longitudinal

axis
47

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

within a particular mill and whether it is part of an integrated processing facility. 

To the extent that an individual sawmill can use or sell the sawdust and wood chips 

it produces, our value estimates would need to be adjusted to reflect some of the 

additional value that would be captured.

The AUTOSAW runs produced one output file for each short log sawn. After 

running each simulation we compiled the results from all output files produced in 

that run into a single Microsoft Excel file using the software described in appendix 

3. Table 17 shows part of a spreadsheet from one of the simulations, and table 18 

summarizes the results from all six runs. The largest number of pieces produced 

was in 2×4 boards (41 percent of pieces and 19 percent of volume) but the largest 

volume produced (39 percent of the total for all simulations) was in 2×12 boards. 

Treatment T60 generally produced a slightly higher percentage of the wider pieces 

than treatment T100; 2×12 boards, for instance, accounted for 42 percent of the 

lumber volume sawn from treatment T60 logs compared to only 36 percent sawn 

from T100 logs. Because the emphasis was on dimension lumber, very few 1-inch 

pieces were produced and these were nearly all 1×4 and 1×6 boards. Only two 1×8 

boards were sawn, both from a single log (C3-2041-2-1). The AUTOSAW output 

lists included no 1×10 or 1×12 boards.

From the simulations, 47 percent of the lumber volume came from the Yachats 

site (table 18). 41 percent of the sample trees were harvested from this site, which 

produced Forty-one percent of the short logs for simulated sawing. On average, 

these logs were somewhat larger in diameter than those from the other sites (table 8) 

and therefore produced more lumber per log.

Data in table 18 also show that 42 percent of the simulated lumber volume for 

all sites came from treatment T60 even though it was the source of only 36 percent 

of the sample trees (table 3) and 38 percent of the short logs (table 8). Logs from 

treatment T60 were generally larger in diameter than those from treatment T100 

and therefore tended to produce more lumber per log.

Results are summarized from the sawing simulation for individual pieces 

of lumber (table 19), for lumber grouped by short logs (table 20), and for lumber 

grouped by sample trees (table 21). Data within each table are organized by site, 

treatment, and for all items (lumber, short logs, or trees) combined. Each table 

includes data on lumber production as measured in pieces, board-foot lumber 

volume, and estimated lumber value. The tables also present data in several grade 

groupings that are commonly used in the Douglas-fir sawmill industry. Although 

the highest dimension-lumber grade, select structural, is shown separately because 

of its importance as a general index of lumber quality, lumber assigned that grade 

is also included in the higher graded grouping (No. 2 and better), an aggregation of 
48

RESEARCH PAPER PNW-RP-605

Table 17—Part of the spreadsheet produced as an aggregation of data from 

AUTOSAW OUT files resulting from a single simulation run; although the 

summary is for all 93 short logs that were derived from sample trees in the 

light treatment (T100) of the Yachats site, the lower part of the spreadsheet 

shows individual pieces of lumber that were cut from only two of the logs 

(selected to illustrate the production of both 2-inch and 1-inch lumber)

AUTOSAW Lumber summary by piece size

Lumber production report Size Pieces Volume

Inches Board feet

Lumber produced from: 2 × 4 330 2,560.00

 Short logs from Yachats, Treatment T100 2 × 6 93 1,307.00

 Number of logs sawn: 93 2 × 8 81 1,730.67

2 × 10 56 1,603.33

2 × 12 158 5,612.00

Volume percentage of lumber by thickness: 1 × 4 55 179.33

 2-inch lumber: 97.8 percent 1 × 6 21 107.50

 1-inch lumber: 2.2 percent 1 × 8 0 0

1 × 10 0 0

1 × 12 0 0

 Totals 794 13,099.83

Lumber 

piece Thick- Autosaw WWPA

Log ID IDa ness Width Length grade Volume gradeb

- - Inches - - Feet Board feet

Y2-1114-1-1 6a 2 4 7 O 4.67 No1 SLF

Y2-1114-1-1 7a 2 6 17 A 17.00 SelStr J&P

Y2-1114-1-1 7b 2 4 17 Q 11.33 No3 SLF

Y2-1114-1-1 8a 2 12 18 C 36.00 No2 J&P

Y2-1114-1-1 9a 2 12 18 D 36.00 No3 J&P

Y2-1114-1-1 10a 2 12 18 A 36.00 SelStr J&P

Y2-1114-1-1 11a 2 10 18 A 30.00 SelStr J&P

Y2-1114-1-1 12a 2 4 8 O 5.33 No1 SLF

Y2-1159-2-2 2a 1 6 10 4 5.00 Com4 COM

Y2-1159-2-2 4a 1 6 10 2 5.00 Com2 COM

Y2-1159-2-2 6a 2 4 7 Q 4.67 No3 SLF

Y2-1159-2-2 7a 2 8 18 D 24.00 No3 J&P

Y2-1159-2-2 8a 2 10 18 C 30.00 No2 J&P

Y2-1159-2-2 9a 2 10 18 E 30.00 Econ J&P

Y2-1159-2-2 10a 2 10 18 D 30.00 No3 J&P

Y2-1159-2-2 11a 2 4 16 O 10.67 No1 SLF

Y2-1159-2-2 11b 2 4 15 P 10.00 No2 SLF

a Piece identifiers are in two parts: a number signifying the order in which sawing cuts were made, 

followed by an alpha character indicating any piece that was subsequently split by the edger. Missing 

numbers indicate pieces that were discarded by the simulator.

b An initial designator indicating the grade (Select Structural, Number 1, Number 2, Number 3, Economy, 

Common 2, Common 4), and a second designator indicating the applicable grading rule (J&P = Joists & 

Planks, SLF = Structural Light Framing, COM = Common Boards). The selection of lumber shown here 

does not include all WWPA grades and rules for which AUTOSAW has been calibrated.
49

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 18—Results from simulated sawing of logs from sample trees in Douglas-fir stands on the 

Oregon Coast Range 15 years after commercial thinning treatments—light thinning (T100) and 

moderate thinning (T60)

Treatment T100 Treatment T60

Short Lumber produced Short Nominal Lumber produced

logs Lumber Number logs lumber Number

Study site sawn size of pieces Volume sawn size of pieces Volume

Inches Board feet Inches Board feet

Cataract 66 2 × 4 180 1,354.67 36 2 × 4 86 610.67

2 × 6 43 643 2 × 6 21 319

2 × 8 52 1,137.33 2 × 8 38 820

2 × 10 49 1,368.33 2 × 10 35 995

2 × 12 51 1,804 2 × 12 63 2,212

1 × 4 21 78.33 1 × 4 17 48

1 × 6 4 24 1 × 6 8 43.50

1 × 8 0 0 1 × 8 2 21.33

All sizes 400 6,409.67 All sizes 270 5,069.50

Wildcat 43 2 × 4 122 882.67 45 2 × 4 150 1,112.67

2 × 6 29 444 2 × 6 41 577

2 × 8 31 678.67 2 × 8 25 520

2 × 10 24 693.33 2 × 10 51 1,318.33

2 × 12 30 1,060 2 × 12 66 2,312

1 × 4 22 78.33 1 × 4 38 121.67

1 × 6 2 7 1 × 6 9 36

1 × 8 0 0 1 × 8 0 0

All sizes 260 3,844 All sizes 380 5,997.67

Yachats 93 2 × 4 330 2,560 41 2 × 4 139 1,066

2 × 6 93 1,307 2 × 6 35 483

2 × 8 81 1,730.67 2 × 8 45 918.67

2 × 10 56 1,603.33 2 × 10 29 768.33

2 × 12 158 5,612 2 × 12 74 2,556

1 × 4 55 179.33 1 × 4 17 53.67

1 × 6 21 107.50 1 × 6 14 75

1 × 8 0 0 1 × 8 0 0

All sizes 794 13,099.83 All sizes 353 5,920.67

All sites 202 2 × 4 632 4,797.33 122 2 × 4 375 2,789.33

2 × 6 165 2,394 2 × 6 97 1,379.

2 × 8 164 3,546.67 2 × 8 108 2,258.67

2 × 10 129 3,665 2 × 10 115 3,081.67

2 × 12 239 8,476 2 × 12 203 7,080.

1 × 4 98 336 1 × 4 72 223.33

1 × 6 27 138.50 1 × 6 31 154.50

1 × 8 0 0 1 × 8 2 21.33

All sizes 1,454 23,353.50 All sizes 1,003 16,987.83
50

RESEARCH PAPER PNW-RP-605

select structural, No. 1 (also called construction), and No. 2 (also called standard) 

dimension lumber. The lower graded grouping (No. 3 and economy) grouping 

consists of the No. 3 (also called utility) and the lowest quality dimension lumber 

(called economy grade). Together, the two groupings account for all of the grades 

for dimension lumber that were assigned by AUTOSAW in the simulations. For 

1-inch jacket boards, we used the five WWPA (2005) common groupings normally 

reserved for pine, spruce, and cedar species. These five grades are grouped together 

because so few jacket boards were produced in the simulation. Prices used to 

determine the estimated lumber values are described below.

Lumber volume recovery—

AUTOSAW has previously been shown to provide reasonably close estimates of 

the volume of lumber from second-growth Douglas-fir logs. Barbour et al. (2003), 

for example, found that AUTOSAW simulations underestimated actual lumber 

production for a set of sample logs by only 10 to 15 percent, likely because the 

simulator avoids producing lumber with wane, which can reduce lumber grade. 

However, tolerance for wane can be adjusted on the simulator; and for this study we 

increased production by setting the maximum allowable wane to 0.4 inch (app. 3), 

twice the allowance used by Barbour et al. (2003). A 0.4-inch allowance for wane is 

not unusual in mills of the type that process Douglas-fir in the area, as verified by a 

database on second-growth Douglas-fir lumber recovery studies maintained by the 

wood-quality research team at the PNW Station. The study reported by Monserud 

et al. (2004) used an even larger maximum wane allowance, 0.47 inch.

Simulation results for individual pieces of lumber (table 19) are shown for all 

grades. Although the largest number of pieces was produced from logs extracted 

from the Yachats site, both the lumber volume per piece and value per piece were 

slightly higher for lumber produced from the Cataract site. The Yachats site had 

slightly larger logs on average than the other two sites (table 8), but it also had 

somewhat larger and more frequent knots (table 14). In addition, 27 percent of logs 

from the Yachats site had sweep compared to only 16 percent from the Cataract site 

and 19 percent from the Wildcat site. Sweep tends to reduce piece length and thus 

reduces both volume and value per piece of lumber recovered. The differences in 

volume and value per piece as shown in table 19, however, appear to be relatively 

minor within all of the aggregations. The simulated lumber production is summarized in number of pieces and in board-foot volume per log (table 20). For each level 

of aggregation, the table also shows the cubic recovery rate (cubic feet of lumber 

produced per cubic foot of log or tree, expressed as a percentage and calculated 

using rough-sawn lumber sizes rather than nominal sizes) and the lumber-overrun 

percentage. Lumber overrun is a common measure used in the sawmill industry, 
51

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 19—Quantity, volume, and value of lumber by piece produced 

from simulated sawing of short logs from sample trees in Douglas-fir 

stands on the Oregon Coast Range 15 years after commercial thinning 

treatments—light thinning (T100) and moderate thinning (T60)

Dimension lumber

Groupings SelStr No2&Btr No3&Econ 1-inch All grades

Pieces of lumber sawn

Cataract 234 476 142 52 670

Wildcat 187 424 145 71 640

Yachats 315 720 320 107 1,147

Treatment T100 423 949 380 125 1,454

Treatment T60 313 671 227 105 1,003

All lumber 736 1,620 607 230 2,457

Lumber volume per piece (board feet)

Cataract 19.65 18.22 18.24 4.14 17.13

Wildcat 18.18 17.47 15.12 3.42 15.38

Yachats 20.69 18.90 15.62 3.88 16.58

Treatment T100 18.61 17.65 16.12 3.80 16.06

Treatment T60 21.22 19.28 16.10 3.80 16.94

All lumber 19.72 18.33 16.11 3.80 16.42

Lumber value (U.S. dollars per piece)

Cataract 6.33 5.47 3.41 1.79 4.75

Wildcat 5.73 5.09 2.79 1.18 4.14

Yachats 6.61 5.51 2.84 1.38 4.38

Treatment T100 5.90 5.15 2.94 1.40 4.25

Treatment T60 6.84 5.73 3.00 1.42 4.66

All lumber 6.30 5.39 2.96 1.41 4.42

SelStr = Select Structural grade lumber.

No2&Btr = a composite category including select Structural, No. 1, and No. 2 grades.

No3&Econ = a composite category including No. 3 and economy grades.

calculated as board feet of lumber produced from a log or tree divided by the 

estimated board-foot volume of the log or tree. In this study, board-foot log volumes 

were estimated using Scribner scaling rules. Because Scribner tends to underestimate the volume of lumber that can be produced from second-growth logs, overrun 

is usually more than 100 percent. To avoid artifacts produced by scaling deductions, 

we used gross volumes in calculating both cubic recovery rate and overrun.

Each of the main groups in table 20 is subdivided into groups representing the 

position of the log within the standing tree (butt, middle, or upper). As one would 

expect, the number of pieces and the volume of lumber cut from the log is highest in 

butt logs and lowest in the upper logs. The average number of pieces sawn per log 
52

RESEARCH PAPER PNW-RP-605

was smallest for Cataract and largest for Yachats. It was larger in logs from treatment T60. Similarly, lumber volume produced per log was highest overall at Yachats, with especially large margins for butt and middle logs. Cubic recovery rates for 

Yachats were also higher than those for the other sites. Despite its relatively high 

recovery rates from butt and middle logs, the Wildcat site had the lowest lumber 

volume per log, primarily because of higher taper and shorter lengths. Lumber 

volume recovered per log was higher in logs from treatment T60 than in those from 

Table 20—Average values (by log position) for selected attributes of lumber produced from simulated sawing 

of short logs from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial 

thinning treatments—light thinning (T100) and moderate thinning (T60)

Lumber Lumber volume Dimension lumber Lumber value, in

pieces (board feet) percentages by grade U.S. dollars per:

Log Dimen- Dimen- Cubic No2& No3& 

Groupings position sion 1-inch sion 1-inch recovery Overrun SelStr Btr Econ Log MLT MBF CCF

- - Percent - -

Cataract Butt 9.5 0.7 185.3 3.3 51.8 160 50.3 82.4 17.6 54.46 281 451 256

Middle 6.8 0.4 135.8 2.2 49.0 169 32.9 71.2 28.8 37.78 268 453 231

Upper 4.0 0.5 58.1 1.5 41.8 172 28.5 76.6 23.4 15.90 262 453 196

All 6.1 0.5 110.4 2.1 46.3 168 34.3 75.8 24.2 31.18 268 453 220

Wildcat Butt 10.5 0.7 198.1 2.8 50.9 163 45.9 88.8 11.2 56.80 279 450 248

Middle 7.4 0.7 143.1 2.6 50.8 167 25.7 65.3 34.7 38.12 261 435 232

Upper 4.0 0.9 44.6 2.9 39.2 189 18.9 71.6 28.4 12.24 252 480 178

All 6.5 0.8 109.1 2.8 45.4 176 26.9 73.3 26.7 30.09 260 459 210

Yachats Butt 12.3 0.7 240.0 3.1 53.3 159 56.7 84.6 15.4 69.78 286 455 266

Middle 9.2 0.8 176.4 3.6 51.9 166 25.0 68.0 32.0 45.68 254 422 230

Upper 4.7 0.9 65.7 2.7 42.2 197 16.3 65.8 34.2 17.06 246 485 187

All 7.8 0.8 138.8 3.1 47.8 179 27.4 70.3 29.7 37.51 257 457 217

Treatment T100 Butt 10.2 0.7 192.7 2.8 51.4 164 47.1 82.1 17.9 55.05 277 451 249

Middle 7.7 0.6 145.5 2.3 50.2 167 26.7 68.5 31.5 38.21 258 431 227

Upper 4.1 0.6 53.2 2.1 41.2 186 20.4 67.5 32.5 13.80 249 464 184

All 6.6 0.6 113.3 2.3 46.4 175 28.1 70.9 29.1 30.59 258 450 212

Treatment T60 Butt 12.2 0.8 244.0 3.5 53.6 155 59.7 90.4 9.6 72.52 292 454 273

Middle 8.2 0.8 169.4 3.8 51.4 167 29.5 68.2 31.8 45.96 265 442 237

Upper 4.7 0.9 64.6 2.8 41.4 188 21.5 76.1 23.9 17.93 259 490 192

All 7.4 0.9 136.0 3.3 47.2 174 31.8 76.2 23.8 38.32 268 467 223

All short logs Butt 10.9 0.7 211.3 3.1 52.2 161 51.6 85.1 14.9 61.40 282 452 258

Middle 7.9 0.7 154.6 2.9 50.7 167 27.8 68.4 31.6 41.17 260 435 231

Upper 4.3 0.8 57.5 2.4 41.3 187 20.8 70.8 29.2 15.36 253 474 187

All 6.9 0.7 121.8 2.7 46.7 175 29.4 72.9 27.1 33.50 261 456 216

SelStr = Select Structural lumber grade.

No2&Btr = a composite category including select Structural, No. 1, and No. 2 grades.

No3&Econ = a composite category including No. 3 and economy grades.

MLT = thousand board-feet lumber tally.

MBF = thousand board feet of gross log volume, Scribner scale.

CCF = hundred cubic feet of gross log volume.
53

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 21—Average values of selected attributes of lumber produced from the simulated sawing of sample 

trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial thinning treatments—light 

thinning (T100) and moderate thinning (T60)

Lumber Lumber volume Dimension lumber Lumber value, in

pieces (board feet) percentages by grade U.S. dollars per:

Dimen- Dimen- Cubic No2& No3& 

Groupings sion 1-inch sion 1-inch recovery Overrun SelStr Btr Econ Tree MLT MBF CCF

- - - Percent - - -

Cataract 30.9 2.6 563.2 10.8 48.3 164 36.1 74.5 25.5 159.04 269 442 229

Wildcat 29.9 3.7 505.2 12.8 48.3 167 31.3 73.6 26.4 139.37 262 436 223

Yachats 38.5 4.0 689.1 15.4 50.0 169 34.2 72.3 27.7 186.14 263 443 231

Treatment T100 31.6 3.0 544.7 11.3 48.3 168 31.6 71.2 28.8 147.14 260 436 221

Treatment T60 37.4 4.4 691.2 16.6 50.3 165 38.1 77.2 22.8 194.79 273 449 240

All sample trees 33.7 3.5 598.0 13.2 49.0 167 33.9 73.4 26.6 164.47 265 441 228

SelStr = Select Structural lumber grade.

No2&Btr = a composite category including select Structural, No. 1, and No. 2 grades.

No3&Econ = a composite category including No. 3 and economy grades.

MLT = thousand board-feet lumber tally.

MBF = thousand board feet of gross log volume, Scribner scale.

CCF = hundred cubic feet of gross log volume.

treatment T100, and higher in logs from trees grown on north-facing slopes than in 

those from trees grown on south-facing slopes.

Results for pieces sawn and volume produced per tree (table 21) generally 

follow the same patterns as exhibited for logs. These values are highest for Yachats 

and lowest for Wildcat and higher for trees from treatment T60 than for those from 

treatment T100. Cubic recovery rates and overrun follow similar trends but differences within and among the groups were relatively small.

Lumber grade recovery—

In second-growth Douglas-fir, decay is uncommon and lumber grades are primarily 

influenced by surface defects such as knots, the presence of juvenile wood (not considered in this study because of AUTOSAW limitations), and sawing imperfections 

such as wane (WWPA 2005). AUTOSAW estimates lumber grade using WWPA 

(2005) rules on knot size and distribution, assuming that surface knots originate 

in the pith at the center of the log and grow to the surface in the shape of a cone 

with its vertex located at the pith and its base at the surface of the log. Although 

AUTOSAW accounts for differences between live and dead knots by modeling the 

live portion of the embedded branch as a cone and the dead portion as a cylinder 

(Todoroki 1997), we could not determine what percentage of an embedded branch 

remained alive without cutting into the log or using expensive procedures such as 

tomography (Taylor et al. 1984). Thus we opted to follow the assumption common 

in studies of simulated Douglas-fir sawing (Todoroki et al. 2005) that 80 percent of 
54

RESEARCH PAPER PNW-RP-605

the length of all such branches was alive—the live portion extending 80 percent of 

the distance to the surface with the remaining 20 percent manifesting as dead knots 

in lumber produced from the outer part of the log.

Although knots are usually the most important determinant of lumber grade in 

second-growth Douglas-fir, wane is also an important consideration. AUTOSAW 

estimates wane from recorded diameters at profile points located along the length of 

the log and uses the estimation to adjust lumber grade, discard pieces with excessive wane, trim them to shorter lengths, or edge them to narrower widths.

Like many sawing simulators, AUTOSAW tends to overestimate grade recovery (table 19) partly because it cannot account for imperfections beyond knots, 

wane, sweep, and crook. It also cannot account for fully embedded branches that 

are hidden beneath the surface of the log—branches (most often occurring in butt 

logs) that were pruned or died early in the life of the tree and were later grown 

over and no longer evident on the surface. Although we removed bark from the 

logs where it appeared that a surface knot might be present, the thick bark typical 

of Douglas-fir can easily hide knots and it is unlikely that we found all of them. 

Although the “All grades” column of table 19 shows few major differences within 

the various aggregations of lumber volume per piece as discussed in the previous 

section, the differences are somewhat larger within individual grade groupings. 

As an example, for all grades combined the mean volume per piece (table 19) 

was somewhat larger for Cataract than for the other two sites; but within both the 

select-structural and No. 2 and better groupings, volume per piece was highest on 

the Yachats site where the logs were generally somewhat larger. But the volume per 

piece graded as No. 3 and economy was substantially larger for Cataract than for 

the other two sites. Also, the larger logs from treatment T60 yielded larger average 

lumber volumes per piece in the select-structural and the No. 2 and better groupings 

than logs from T100, whereas the average volumes per piece were nearly the same 

in other grades for the two treatments.

Figure 11 summarizes dimension-lumber grade recovery from short logs 

according to the location of the log in the tree and the site or the thinning treatment. 

The percentage of select-structural lumber was generally lower in logs originating from higher in the tree. Butt logs from Wildcat produced less select-structural 

lumber and more No. 2 lumber than butt logs from the other two sites; however, 

they also produced less of the two lowest grades. Upper logs from Yachats produced 

the most lumber in the two lowest grades, the consequence of very large knots in 

some of the upper logs from that site. Logs from treatment T60 generally produced 

somewhat more select-structural lumber than those from treatment T100, and also 

produced less lumber in the lowest grade grouping. Although knots on the logs 
55

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

from treatment T60 were generally larger than those from treatment T100, any 

negative effect of the larger knots apparently was mitigated by the fact that logs 

from treatment T60 also tended to have larger diameters and thus produced somewhat wider pieces of lumber.

Tables 20 and 21 show the percentages of total dimension-lumber volume that 

was produced in the three grade groupings. The percentage of No. 2 and better 

lumber plus the percentage of No. 3 and economy add to 100; the select-structural 

grade is a subset of the No. 2 and better grouping, but it also serves as an additional 

measure of wood quality. This grade always constitutes more than half of the No. 2 

and better grouping in butt logs (table 20), always less than half in middle logs; and 

typically less than a third in upper logs. The percentage of lumber graded as No. 

3 and economy was about the same for middle logs and upper logs at Cataract and 

Wildcat but it was largest for upper logs at Yachats. The percentages of lumber in 

the select-structural and the No. 2 and better groupings were substantially higher 

for treatment T60 than for treatment T100.

For trees as a whole (table 21), select-structural lumber constituted somewhat 

less than half of the No. 2 and grouping in all categories with only minor differences occurring among the three sites. However, the difference in grade recovery 

between the two treatments was more marked, with trees from treatment T60 

yielding a larger percentage of lumber in the select-structural and No. 2 and better 

groupings as compared to the No. 3 and economy grouping.

Lumber value recovery—

The price of a piece of lumber depends on dimensions and grade, meaning that 

the volume of lumber recovered by grade at each price level determines the value 

of the logs and trees from which the lumber was processed. Such information is 

of considerable interest both to landowners who sell trees and to loggers who buy 

trees and sell logs. Because lumber prices tend to move rapidly as economic conditions change, the set of prices used for any analysis only represents a snapshot of 

the lumber market at a particular moment in time. For the 2008-to-2009 thinning 

operations in this study, lumber prices had dropped significantly compared with 

much of the preceding decade. For this study, we chose prices reported by sawmills 

in 2010, when the lumber market had recovered to a more normal level. The price 

series were taken from Random Lengths (2011) and WWPA (2011) and represented 

average prices by lumber grade. We assumed that the lumber would be kiln-dried 

and surfaced before being sold.

Estimated lumber value per piece is shown by grade grouping in table 19. 

Variations within a single grade, among sites or other major groups, result from the 

fact that the same grades may be assigned to pieces of different widths and lengths 
56

RESEARCH PAPER PNW-RP-605

0

10

20

30

40

50

60

70

80

90

100

Select Structural

No. 1

No. 2

No. 3

Economy

Butt logs Middle logs Upper logs

Percent dimension lumber volume by grade

C W Y C W Y C W Y

Lumber grades

Study sites

= Cataract

= Wildcat

= Yachats

C

W

Y

(A) Log position and site

0

10

20

30

40

50

60

70

80

90

100

Butt logs Middle logs Upper logs

Percent dimension lumber volume by grade

T100 T60 T100 T60 T100 T60

Treatments

(B) Log position and treatment

T100 = thinned to

100 trees per acre

(light thinning)

T60 = thinned to

60 trees per acre

(moderate thinning)

Figure 11—Recovery of dimension lumber by grade as influenced by log position within sample trees harvested on 

Douglas-fir stands in the Oregon Coast Range 15 years after habitat-improvement thinning treatments, displayed for 

(A) the three study sites and (B) the two thinning treatments.
57

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

(and thus different volumes per piece). As an example, the average value of selectstructural lumber from the Wildcat site was $5.73 per piece compared to $6.61 for 

the same grade of lumber from Yachats. The reason for this 15-percent difference 

is that a higher percentage of select-structural lumber from the Yachats sites was in 

the wider—and higher priced—sizes. Nearly 51 percent of the dimension lumber 

graded as select structural from Yachats was in 2×12 pieces compared to only 38 

percent from Wildcat. Although value per piece is also affected by piece length, the 

average lengths of select-structural 2×12 lumber from the two sites differed by only 

0.08 foot.

As with volume per piece (table 19), lumber value per piece was higher for 

lumber from treatment T60 than from treatment T100, a difference that is especially 

noticeable for lumber in the select-structural and the No. 2 and better grades.

Lumber values are shown in dollars per short log (table 20) or per tree (table 

21) plus three additional measures commonly used in forestry: $/MLT (dollars per 

thousand board feet of lumber tally), $/MBF (dollars per thousand board feet of 

gross Scribner log or tree volume), and $/CCF (dollars per hundred cubic feet of 

gross log or tree volume). These measures were all generally highest for butt logs 

and lowest for upper logs. Lumber value per log (table 20) and per tree (table 21) 

were highest for the Yachats site and lowest for Wildcat, but the value in dollars 

per thousand board feet of lumber tally was highest for Cataract. All measures of 

lumber value were higher for treatment T60 than for treatment T100, with some of 

the differences substantial. The value of lumber recovered per log (table 20), for 

example, was more than 30 percent higher for butt logs from treatment T60 than for 

butt logs from treatment T100, and the value of lumber recovered per tree (table 21) 

was 32 percent higher for treatment T60 than treatment T100.

Statistical Analysis of Lumber Recovery

This section presents the results of statistical analyses on lumber recovery by 

volume, grade, and value for trees and for short logs. We did not include long logs 

because they are not processed directly into lumber. Trees are included because 

they are sold by landowners and their value depends on the products that can be 

recovered from them.

The regression model to which the data were fit in the analysis of lumber 

recovery from tree stems and short logs was:

E(Y)=β0+β1D+β2L+β3LF+β4K+β5KD+β6S+β7T+β8B+β9U+β10(D*B)+β11(D*U) (2)

where:

Y = the response variable, a relevant measure of lumber volume, grade, or value 

recovered from trees or from short logs,
58

RESEARCH PAPER PNW-RP-605

βj = parameters estimated via linear regression, j = 0 through 11,

D = an appropriate function of diameter, in inches (DBH for tree stems, small-end 

diameter for short logs),

L = an appropriate function of the length of the merchandized portion of the tree 

stem or length of the log, in feet,

LF = live-knot fraction (live knots in the tree stem or short log divided by the total 

surface knots recorded in each merchandized tree stem or short log),

K = knot density, measured in knots per foot of merchandized tree stem or short 

log,

KD = average knot diameter, in inches, per tree or per log,

S = the maximum sweep offset recorded for a log, in inches (used for short-log 

analyses only),

T = a dummy variable for treatment (T = 0 for trees from treatment T100 and T = 1 

for trees from treatment T60),

B = for logs only, a dummy variable for log position (B = 1 for butt logs, B = 0 

otherwise),

U = for logs only, a dummy variable for log position (U = 1 for upper logs, U = 0 

otherwise),

D*B = for logs only, the log diameter or its transformation (D) multiplied by the 

dummy variable for butt logs (B),

D*U = for logs only, log diameter or its transformation (D) multiplied by the 

dummy variable for upper logs (U).

For the short-log analyses, equation (2) included a measure of log sweep 

(variable S) but did not include crook. This is because only six of the 324 short 

logs exhibited crook (table 11), a sample that we considered too small for a satisfactory statistical analysis. In contrast, 76 short logs (nearly one out of four) had 

measurable sweep.

Simultaneously with the regression analysis we also evaluated potential random 

effects for site and for site by treatment. For short logs, nonconstant variance related 

to log position was also tested and included in the model where appropriate.

Lumber volume recovery regression analysis—

The regression model described in equation (2) was used to fit data for volume 

recovery from trees and short logs in accordance with the following specifications:

• The response variable was defined as the natural logarithm of the volume of 

green lumber, in cubic feet, produced per tree stem or per short log.

• The function of diameter used as an independent variable was defined as 

the natural logarithm of DBH for tree stems and of small-end diameter for 

short logs, both in inches.
59

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

• The function of length used as an independent variable was defined as the 

natural logarithm of merchandized stem length for tree stems and of log 

length, both in feet.

We used these transformations primarily because of nonlinearities observed 

when the data were plotted; however, we also considered effects on the statistical 

residuals.

Table 22 summarizes results from the regression analyses. For tree stems, none 

of the random effects were statistically significant. Volume recovery per tree was 

strongly related to DBH, tree elevation, and the length of the merchandized stem. 

The estimated parameters for DBH and stem length were both positive numbers, 

indicating that as DBH and stem length increased, the volume of lumber recovered 

from the tree also tended to increase. Because of the logarithmic transformations, 

the effects of these two variables on expected volume recovery were nonlinear. 

DBH had a strong effect on expected volume recovery and this effect increased 

nonlinearly as the diameter increased (fig. 12). Because DBH was significantly 

affected by treatment (table 1), we concluded that treatment had an indirect effect 

on volume recovery. Figure 12 also shows the effect of stem length on volume 

recovery. The regression relationship described both treatment groups without a 

treatment parameter; however, a statistical contrast evaluating the difference in 

the values predicted from this regression at each level of treatment DBH, given the 

assumption of common elevations and tree lengths, indicates significant differences 

in those predicted values on the logarithmic scale (p-value for difference < 0.0001).

For short logs (table 22), site by treatment was significant as a random effect; 

nonconstant variance resulting from the position of the log within the tree was also 

significant and was thus included in the model. In the fixed-effects analysis, the 

volume of lumber produced per log was strongly related to small-end diameter, the 

live-knot fraction, and log length. The effect of small-end diameter was further 

increased in upper logs through the D*U interaction variable from equation (2). 

As shown in figure 13, the influence of small-end log diameter on lumber volume 

recovered from short logs was similar to the influence of DBH on lumber volume 

recovered from trees (fig. 12). The regression parameter for the D*U interaction 

variable was a positive number; however, upper logs would routinely be expected 

to produce somewhat less lumber volume at any small-end diameter because they 

are generally shorter than logs from the butt or middle positions in the tree. For this 

reason, the graph in figure 13 reflects a different average log length for upper logs 

than for butt and middle logs.
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RESEARCH PAPER PNW-RP-605

Lumber grade recovery regression analysis—

The regression model of equation (2) was used to fit data to regression equations for 

grade recovery from trees (table 23) and short logs (table 24) in accordance with the 

following specifications:

• The analysis was limited to dimension lumber because the 1-inch lumber 

from the sawing simulations represented only a small percentage of the 

total lumber produced.

• The response variable was defined as the percentage of total dimension 

lumber produced within each of the three grade groupings.

Table 22—Results of regression analyses for the natural logarithm (loge) of green lumber volume in cubic 

feet produced from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial 

thinning treatments; variables whose parameters were not significantly different from zero at α = 0.05 have 

been excluded and overall fit values were calculated on the original scale (cubic feet)

Overall fit Parameter estimates for individual variables

Level of Regression Standard Degrees of

 analysis Statistic Value variable β

^

j error freedom t p value

Tree stems Adjusted 0.937 Intercept -5.7511 0.4333 62 -13.27 < 0.0001

 R2-conditional

Adjusted 0.937 D = loge of DBH (inches) 2.3544 0.0779 62 30.23 < 0.0001

 R2-marginal

RMSE-marginal 4.6672 E = Elevation (feet) -0.0003 0.0001 62 -3.32 0.0015

Observations 66 L = loge of Merchandized 0.6179 0.1127 62 5.48 < 0.0001

 stem length (feet)

σ

^2

ε = 0.0169

Short logs Adjusted 0.987 Intercept -6.9904 0.1860 305.2 -37.59 < 0.0001

 R2-conditional

Adjusted 0.987 D = loge of SED (inches) 2.2110 0.0289 248.5 101.15 < 0.0001

 R2-marginal

RMSE-marginal 0.6238 LF = Live-knot fraction 0.1115 0.0292 226.3 3.82 0.0002

Observations 324 L = loge of Length (feet) 1.2182 0.0659 312.8 18.48 < 0.0001

U*D = Upper log* 0.0268 0.0113 258.7 2.37 0.0183

 [loge of SED (inches)]

σ

^2

site,treatment = 0.0005 (p = 0.0018),

σ

^2

ε,butt, σ^2ε,middle, σ^2ε,upper = (0.0058,0.0053,0.0365) (p < 0.0001)

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether βj = 0 (may be approximate).

DBH = diameter at breast height.

SED = small-end diameter of the log.

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

RMSE = the square root of the mean squared error (commonly known as “root mean squared error”).

σ

^2

i = the estimated variance of random-effect variable i; when i = ε, the variance estimate is for the error term or components of the error term.
61

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

0

10

20

30

40

50

60

70

80

6 8 10 12 14 16 18 20 22 24 26

Lumb

er volum

e rec

o

v

ery (

cubic feet p

er tree)

Tree diameter at breast height (inches)

Merchantable stem length = 100 feet

Merchantable stem length = 70 feet

Cataract study site observed values

Wildcat study site observed values

Yachats study site observed values

Figure 12—Lumber recovery as influenced by diameter at breast height for sample trees harvested on Douglas-fir stands in 

the Oregon Coast Range 15 years after habitat-improvement thinning treatments; for the two regression lines, merchandized 

stem length was set at approximately one standard deviation above and below the overall average value of 85.8 feet to illustrate the effect of merchandized stem length on volume recovery.
62

RESEARCH PAPER PNW-RP-605

0

5

10

15

20

25

30

2 4 6 8 10 12 14 16 18 20 22

Lumb

er volum

e rec

o

v

ery (

cubic feet p

er short log)

Small-end diameter of short logs (inches)

Regression for butt and middle logs

Regression for upper logs

Cataract study site observed values

Wildcat study site observed values

Yachats study site observed values

Figure 13—Lumber recovery as influenced by small-end diameter and log position within the tree for short logs that were cut from 

sample trees harvested on Douglas-fir stands in the Oregon Coast Range 15 years after habitat-improvement thinning treatments; for the 

two regression lines, log lengths were set at average values—one for butt and middle logs combined and the other for upper logs.
63

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

• When used as an independent variable, diameter was defined as the inverse 

of DBH for tree stems and the inverse of small-end diameter for short logs, 

both in inches.

• The function of length used as an independent variable was the merchandized stem length for tree stems and log length for short logs, both in feet.

Select-structural lumber was fit separately from other grades because of its 

importance as a general quality indicator; however, it is also included along with 

No. 1 and No. 2 lumber in the No. 2 and better grouping. To avoid double counting, 

we opted to calculate recovery rates of lower graded lumber as complements to the 

relevant No. 2 and better grade regression equations using regression analysis. No 

significant random effects were found for either the tree or short-log analysis. To 

better show the relationships between the complementary lumber-grade groupings, 

we somewhat altered the presentation in tables 23 and 24 from what was used for 

other regression analyses in this report.

For lumber produced per tree (table 23), statistically significant variables always 

included inverse DBH, live-knot fraction, and mean knot diameter. The separate 

equation for select-structural lumber also included knot density as measured by 

knots per foot of merchandized stem length. For both select-structural lumber and 

the No. 2 and better grade groupings, the percentage of dimension lumber increased 

with DBH but at a decreasing rate; increased as the live-knot fraction increased; 

and decreased as mean knot diameter increased. The percentage of select-structural 

lumber also decreased as knot density increased. Both regression equations were 

strongly significant but they explained less than a third of total variance in the 

data (No. 2 and better grouping) or slightly more than half of the variance (select 

structural). An analysis of data by log position for lumber produced per short log 

(table 24) showed that the diameter variable was invariably the inverse of small-end 

log diameter. Results were statistically significant in all regression equations, with 

the negative sign indicating that the percentage of lumber produced in the selectstructural or No. 2 and better grades increased with small-end diameter but at a 

decreasing rate. This is evident in figure 14, as is the complementary relationship 

between the No. 2 and better and the No. 3 and economy grade groupings for each 

log position.

Knot density was statistically significant for all regressions except selectstructural lumber from upper logs; however, higher knot density alone would 

be expected to reduce the percentage of dimension lumber in the higher grades. 

Live-knot fraction and average knot diameter were significant in all regressions for 
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RESEARCH PAPER PNW-RP-605

Table 23—Results of regression analyses on dimension-lumber volume recovery by grade from sample trees 

in Douglas-fir stands on the Oregon Coast Range 15 years after commercial thinning treatments; volumes 

for each grade group are expressed as a fraction of the total dimension lumber recovered from the tree and 

variables whose parameters were not significantly different from zero at α = 0.05 have been excluded

Lumber Number 

grade Standard of obser- Adjusted Wald

Group groupa Regression variableb β

^

j error t p value vations R2 statistic p value

Trees Select Intercept 1.1129 0.14253 7.81 < 0.0001 66 0.53 83.56 < 0.0001

 Structural D = 1/DBH ( inches) -6.629 1.0651 -6.22 < 0.0001

LF = Live-knot 0.51922 0.096146 5.4 < 0.0001

 fraction

K = Knots per foot -0.14474 0.028067 -5.16 < 0.0001

KD = Mean knot -0.278 0.077223 -3.6 0.0003

 diameter (inches)

No. 2 & Intercept 1.099 0.14643 7.51 < 0.0001 66 0.3 30.71 < 0.0001

 Better D = 1/DBH (inches) -4.1813 1.1812 -3.54 0.0004

LF = Live-knot 0.47978 0.11443 4.19 < 0.0001

 fraction

KD = Mean knot -0.28679 0.091692 -3.13 0.0018

 diameter (inches)

No. 3 & Intercept -0.099

 Economy D = 1/DBH (inches) 4.1813

LF = Live-knot -0.47978

 fraction

KD = Mean knot 0.28679

 diameter (inches)

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether β_j = 0 (may be approximate).

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

No. 2 & Better = a composite grade group including select Structural, No. 1, and No. 2 lumber grades.

No. 3 & Economy = a composite grade group including No. 3 and Economy lumber grades.

DBH = diameter at breast height.

The horizontal dashed line separates two lumber groups for which only a single regression equation was calculated (for No. 2 & Better), with parameters 

for No. 3 & Economy determined by complementarity since the percentage of volume recovered in the two groups must add to 100 percent.

a The No. 2 & Better grade group and the No. 3 & Economy grade group are complements whose recovery fractions (the dependent variable) must add 

to 1. Therefore lumber recovery for the No. 2 & Better group was fit to the regression parameters and then the values of the parameters for the No. 3 & 

Economy grade group were calculated as complements by construction. Fit statistics for the two groups were computed only for the No. 2 & Better grade 

group.

b Dependent variables were constrained to be between 0 and 1; Tobit regressions were fit with these restrictions (SAS 9.2 PROC QLIM with lower and 

upper bound specifications). A joint Wald test for nonzero coefficients was computed for each regression.
65

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 24—Results of regression analyses on lumber-grade recovery by volume from short logs (by log 

position) from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial 

thinning treatments; volumes for each grade are expressed as a fraction of the total dimension lumber 

recovered from the log and variables whose parameters were not significantly different from zero at α = 0.05 

have been excluded

Lumber Number 

grade Standard of obser- Adjusted Wald

Group groupa Regression variableb β

^

j error t p value vations R2 statistic p value

Butt Select Intercept 1.36434 0.094559 14.43 < 0.0001 66 0.60 97.76 < 0.0001

 short Structural D = 1/SED (inches) -6.78243 1.071274 -6.33 < 0.0001

 logs K = Knots per foot -0.26503 0.033503 -7.91 < 0.0001

No. 2 & Intercept 1.58107 0.21012 7.52 < 0.0001 66 0.22 24.85 < 0.0001

 Better T = Treatment 0.09163 0.04516 2.03 0.0424

D = 1/SED (inches) -2.60379 1.07145 -2.43 0.0151

K = Knots per foot -0.08375 0.03286 -2.55 0.0108

KD = Mean knot -0.54914 0.21253 -2.58 0.0098

 diameter (inches)

No. 3 & Intercept -0.58107

 Economy T = Treatment -0.09163

D = 1/SED (inches) 2.60379

K = Knots per foot 0.08375

KD = Mean knot 0.54914

 diameter (inches)

Middle Select Intercept 1.3438 0.18543 7.25 < 0.0001 110 0.28 44.20 < 0.0001

 short Structural D = 1/SED (inches) -4.4840 1.1071 -4.05 < 0.0001

 logs K = Knots per foot -0.16054 0.032495 -4.94 < 0.0001

LF = Live-knot fraction 0.62045 0.12772 4.86 < 0.0001

KD = Mean knot -0.49489 0.11658 -4.25 < 0.0001

 diameter (inches)

No. 2 & Intercept 2.0129 0.20900 9.63 < 0.0001 110 0.31 57.80 < 0.0001

 Better D = 1/SED (inches) -5.8342 1.2195 -4.78 < 0.0001

K = Knots per foot -0.16404 0.037059 -4.43 < 0.0001

LF = Live-knot fraction 0.99746 0.15217 6.55 < 0.0001

KD = Mean knot -0.65616 0.13305 -4.93 < 0.0001

 diameter (inches)

No. 3 & Intercept -1.0129

 Economy D = 1/SED (inches) 5.8342

K = Knots per foot 0.16404

LF = Live-knot fraction -0.99746

KD = Mean knot 0.65616

 diameter (inches)
66

RESEARCH PAPER PNW-RP-605

Table 24—Results of regression analyses on lumber-grade recovery by volume from short logs (by log 

position) from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial 

thinning treatments; volumes for each grade are expressed as a fraction of the total dimension lumber 

recovered from the log and variables whose parameters were not significantly different from zero at α = 0.05 

have been excluded (continued)

Lumber Number 

grade Standard of obser- Adjusted Wald

Group groupa Regression variableb β

^

j error t p value vations R2 statistic p value

Upper Select Intercept 1.5877 0.32891 4.83 < 0.0001 148 0.19 46.69 < 0.0001

 short Structural D = 1/SED (inches) -4.8108 0.89432 -5.38 < 0.0001

 logs L = Log length (feet) -0.030569 0.013510 -2.26 0.0237

LF = Live-knot fraction 0.66564 0.14361 4.64 < 0.0001

KD = Mean knot -0.67599 0.11512 -5.87 < 0.0001

 diameter (inches)

No. 2 & Intercept 2.2004 0.39077 5.63 < 0.0001 148 0.32 83.14 < 0.0001

 Better D = 1/SED (inches) -3.6982 0.98117 -3.77 0.0002

L = Log length (feet) -0.035119 0.015515 -2.26 0.0236

K = Knots per foot -0.15048 0.059039 -2.55 0.0108

LF = Live-knot fraction 1.4483 0.17893 8.09 < 0.0001

KD = Mean knot -0.78932 0.13201 -5.98 < 0.0001

 diameter (inches)

No. 3 & Intercept -1.2004

 Economy D = 1/SED (inches) 3.6982

L = Log length (feet) 0.035119

K = Knots per foot 0.15048

LF = Live-knot fraction -1.4483

KD = Mean knot 0.78932

 diameter (inches)

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether β_j = 0 (may be approximate).

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

No. 2 & Better = a composite grade group including select Structural, No. 1, and No. 2 lumber grades.

No. 3 & Economy = a composite grade group including No. 3 and economy lumber grades.

SED = small-end diameter of the log.

The horizontal dashed line separates two lumber groups for which only a single regression equation was calculated (for No. 2 & Better), with parameters 

for No. 3 & Economy determined by complementarity since the percentage of volume recovered in the two groups must add to 100 percent.

a The No. 2 & Better grade group and the No. 3 & Economy grade group are complements whose recovery fractions (the dependent variable) must add 

to 1. Therefore lumber recovery for the No. 2 & Better group was fit to the regression parameters and then the values of the parameters for the No. 3 & 

Economy grade group were calculated as complements by construction. Fit statistics for the two groups were computed only for the No. 2 & Better grade 

group.

b Dependent variables were constrained to be between 0 and 1; tobit regressions were fit with these restrictions (SAS 9.2 PROC QLIM with lower and 

upper bound specifications). A joint Wald test for nonzero coefficients was computed for each regression.
67

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

middle and upper logs, and log length was also significant for upper logs. A higher 

percentage of live knots tended to increase the percentage of lumber in the higher 

grades, whereas increased knot diameter had the opposite effect. The log-length 

parameter in upper logs had a negative sign, indicating that longer upper logs had 

lower percentages of dimension lumber in the higher grades.

Although the statistical analysis for short logs generally confirmed the relationships that would be expected, the regression equations usually explained less than 

a third of the total variance in the data. Thus the relationships depicted in figure 14 

should only be considered ideals; actual results for any particular sample of logs 

would likely vary substantially.

Figure 14—Regression curves showing the percentage of dimension lumber produced from butt, middle, and upper logs from sample 

trees harvested on Douglas-fir stands in the Oregon Coast Range 15 years after habitat-improvement thinning treatments. Results 

are shown for each of three grade groupings—Select Structural, No. 2 and Better (Select Structural, No. 1, and No. 2), and No. 3 and 

Economy (No. 3 and Economy grades).

No. 2 & better lumber from butt logs

No. 2 & better lumber from middle logs

No. 2

&

better lumber from upper logs

No. 3 & economy lumber from butt logs

from middle logs

No. 3 & economy lumber

from

upper logs

from upper logs

Select structural lumber from butt logs

Select structural lumber

from middle logs

0

10

20

30

40

50

60

70

80

90

100

2 4 6 8 10 12 14 16 18 20 22

Percentage of dimension lumber rec

o

vered in each grade group

Small-end diameter of short logs (inches)

Select structural lumber

No. 3

&

economy lumber
68

RESEARCH PAPER PNW-RP-605

Lumber value recovery regression analysis—

The regression model as described in equation (2) was used to fit the data to regression equations for value recovery from trees and short logs in accordance with the 

following specifications:

• The response variable was defined as the estimated value of lumber recovered, in dollars per hundred cubic feet of gross cubic volume as calculated 

for the merchandized tree stem or the short log.

• The function of diameter used as an independent variable was defined as 

the inverse of DBH for tree stems and the inverse of small-end diameter for 

short logs, both in inches.

• The function of length used as an independent variable was defined as the 

merchandized stem length for tree stems and log length for short logs, both 

in feet.

Table 25 summarizes results from the regression analyses for both tree stems 

and short logs. When compared to other more significant variables, length was not 

a robust predictor of lumber value recovery for either tree stems or short logs. For 

tree stems, no random effects were statistically significant. The inverse of DBH 

and the percentage of live knots in the tree stem were the most significant predictor variables, with average knot diameter and knot density also being significant. 

Figure 15 shows the effect of DBH on lumber value that was calculated using data 

from the simulated sawing. The regression equation explained about 60 percent of 

the variance.

For short logs (table 25), nonconstant variance resulting from the position of 

the log within the tree was significant and was incorporated into the model. In 

the fixed-effects analysis, the estimated lumber value was strongly related to the 

inverse of small-end diameter, knot density, the live-knot fraction, mean knot 

diameter, and the amount of sweep in the log. Butt logs tend to produce lumber with 

higher value per cubic foot of log volume than upper logs, as was confirmed by the 

signs on the regression parameters associated with the B and U dummy variables 

from equation (2). However, the effect of small-end diameter was modified in upper 

logs through the U*D interaction variable. As shown in figure 16, upper logs with 

a given small-end diameter (such as 7 inches) can actually produce higher lumber 

values per cubic foot than butt or middle logs with the same small-end diameters. 

This result might seem surprising; but because the butt log normally has the largest 

small-end diameter, it would typically produce higher value lumber than middle or 

upper logs from the same tree. Figure 16 also shows average regression values for 

maximum sweep offset. Sweep was limited to butt and middle short logs in this 

study, occurring only in 76 of the 324 short logs produced by the bucking simulator. 
69

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Table 25—Results of regression analyses on the value of lumber (U.S. dollars per 100 cubic feet) 

produced from sample trees in Douglas-fir stands on the Oregon Coast Range 15 years after commercial 

thinning treatments; variables whose parameters were not significantly different from zero at α = 0.05 

have been excluded

Overall fit Parameter estimates for individual variables

Regression Standard Degrees of

Groupings Statistic Value variable β

^

j error freedom t p value

Tree stems Adjusted 0.595 Intercept 369.57 28.232 61 13.09 < 0.0001

 R2-conditional

Adjusted 0.595 D = 1/DBH (inches) -1,642.41 205.20 61 -8.00 < 0.0001

 R2-marginal

RMSE-marginal 18.8969 K = Knots per foot -12.09 5.6562 61 -2.14 0.0366

Observations 66 LF = Live-knot fraction 55.26 19.030 61 2.90 0.0051

KD = Mean knot -37.74 15.258 61 -2.47 0.0162

 diameter (inches)

σ

^2

ε = 386.36

Short logs Adjusted 0.651 Intercept 408.14 13.927 275.3 29.31 < 0.0001

 R2-conditional

Adjusted 0.651 D = 1/SED (inches) -1,231.16 103.18 168.5 -11.93 < 0.0001

 R2-marginal

RMSE-marginal 30.9604 K = Knots per foot -14.97 2.7789 261.3 -5.39 < 0.0001

Observations 324 LF = Live-knot fraction 91.31 10.167 286.2 8.98 < 0.0001

KD = Mean knot -51.89 8.4800 264.5 -6.12 < 0.0001

 diameter (inches)

B = Butt log 12.91 4.6878 186.9 2.75 0.0065

U = Upper log -50.11 13.004 313.4 -3.85 0.0001

U*D = Upper log*1/SED 269.28 118.75 268.9 2.27 0.0242

S = Sweep maximum -11.02 1.9608 154.1 -5.62 < 0.0001

 offset (inches)

σ

^2

ε,butt, σ^2ε,middle, σ^2ε,upper = (542.10,871.38,1264.25) (p = 0.0006)

β

^

j = the estimated regression coefficient for variable j, where j = 0 for the regression intercept and j > 0 for the independent variables in the equation.

t = the “Student’s” t-statistic for testing whether βj = 0 (may be approximate).

R2 = the statistical coefficient of determination (adjusted for the number of model parameters, based on either conditional or marginal residuals as 

determined on the original scale).

RMSE = the square root of the mean squared error (commonly known as “root mean squared error”).

DBH = diameter at breast height.

SED = small-end diameter of the log.

σ

^2

i = the estimated variance of random-effect variable i; when i = ε, the variance estimate is for the error term or components of the error term.
70

RESEARCH PAPER PNW-RP-605

Thus for butt and middle logs with no sweep (dashed lines in fig. 16), the regression 

curves are shifted upward compared to those for butt and middle logs with sweep 

(solid lines), indicating higher expected lumber value recovery for logs without 

sweep than those with sweep.

Treatment Effects on Stem Profiles and 

Branch Locations

Sample Size

Following the procedure described in appendix 4, we mapped long-log coordinates 

for profile points and branch locations into equivalent standing-tree coordinates 

for all logs from 37 sample trees, for two logs each from 11 three-log trees, and for 

one log each from four three-log trees and six two-log trees (table 26). Logs from 

the remaining eight sample trees—of which two were two-log trees and six were 

three-log trees—could not be mapped because their identifying labels were lost, 

destroyed, or rendered unreadable. Altogether, profile-point and branch-location 

Figure 15—Estimated value of lumber recovered from sample trees harvested on Douglas-fir stands in the Oregon Coast Range 15 years 

after habitat-improvement thinning treatments.

120

140

160

180

200

220

240

260

280

300

8 10 12 14 16 18 20 22 24 26

Lumber value recovery 

(U.S. dollars per 100 cubic feet 

of gross stem

v

olume)

Tree diameter at breast height (inches)

Regression for lumber value recovery

Cataract study site observed values

Wildcat study site observed values

Yachats study site observed values
71

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

coordinates were available for 132 of the 179 long logs in the study, or 74 percent of 

the total (table 27). These logs had 75 percent (8,193 of 10,970) of the knots measured in the study.

As shown in tables 26 and 27, the trees and logs with surviving data were 

not evenly represented among the three sites. All but five of the 47 long logs with 

missing azimuth data and all eight of the trees with no data were from the Yachats 

study site—the first site logged using a field procedure that called for painting the 

top of each felled log with a line and a symbol (N, NE, E, SE, S, SW, W, NW) to 

indicate the direction toward which the log had faced in the standing tree. After 

yarding, some indicators were missing or were so badly faded that they had become 

unreadable. For the Cataract and Wildcat sites, we modified the procedure, replacing painted indicators with tags that were stapled to both ends of each log (fig. 5). 

This proved to be a much more robust procedure—all but two logs from two trees 

on the Cataract site and three logs from three trees on the Wildcat site had readable 

Figure 16—Estimated value of lumber recovered from short logs that were cut from sample trees harvested on Douglas-fir stands in the 

Oregon Coast Range 15 years after habitat-improvement thinning treatments.

0

50

100

150

200

250

300

350

3 5 7 9 11 13 15 17 19 21

Lumber value rec

o

very

(U.S. dollars per 100 cubic feet of gross short-log volume)

Small-end diameter of short logs (inches)

Regression for butt logs with sweep

Regression for butt logs without sweep

Regression for middle logs with sweep

Regression for middle logs without sweep

Regression for upper logs (no sweep)

Cataract study site observed values

Wildcat study site observed values

Yachats study site observed values
72

RESEARCH PAPER PNW-RP-605

tags after yarding. As a result, coordinates could be determined for only 45 percent 

of the knots measured on logs from Yachats, compared to about 95 percent Cataract 

and Wildcat combined (all but five top logs).

Data on the locations of knots in standing trees can help wood-quality 

specialists consider how to alter utilization procedures for improved lumber quality. 

For instance, sawing simulations often assume that knots are uniformly distributed 

around the bole of the tree. If instead, they follow a less uniform distribution, 

sawmills could potentially improve lumber quality by modifying the way the logs 

are initially presented to the saw (Benjamin et al. 2009). In the section below, 

we present a preliminary analysis of the way knots were distributed around the 

boles of the sample trees measured for this study. The analysis is limited because 

we consider only the horizontal distribution of knots around the bole, ignoring 

Table 26—Availability of azimuth data for long logs recovered from sample trees in Douglas-fir stands on the 

Oregon Coast Range 15 years after commercial thinning treatments; one four-log tree is treated as a threelog tree because its top log was lost and could not be measured

Data availability for two-log trees

Total

Data availability for three-log trees

Total

 two-log three-log

Grouping No data One log Both logs trees No data One log Two logs All logs trees

All sites 2 6 11 19 6 4 11 26 47

Cataract 0 0 3 3 0 0 2 15 17

Wildcat 0 1 8 9 0 0 2 8 10

Yachats 2 5 0 7 6 4 7 3 20

Treatment T100a 1 4 8 13 5 2 6 16 29

Treatment T60b 1 2 3 6 1 2 5 10 18

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).

Table 27—Stems, long logs, and knots with and without azimuth data for sample trees in Douglas-fir stands 

on the Oregon Coast Range 15 years after commercial thinning treatments

Total Azimuth data available Azimuth data not available

Sample trees Long logs Knots Sample trees Long logs Knots

Sample Long Per- Per- Per- Per- Per- PerGrouping trees logs Knots Count cent Count cent Count cent Count cent Count cent Count cent

All sites 66 179 10,970 58 88 132 74 8,193 75 8 0 47 26 2,777 25

Cataract 20 57 3,058 20 100 55 96 2,971 97 0 0 2 4 87 3

Wildcat 19 48 3,481 19 100 45 94 3,221 93 0 0 3 6 260 7

Yachats 27 74 4,431 19 70 32 43 2,001 45 8 30 42 57 2,430 55

Treatment 42 113 6,712 36 86 82 73 4,922 73 6 14 31 27 1,790 27

 T100a

Treatment 24 66 4,258 22 92 50 76 3,271 77 2 8 16 24 987 23

 T60b

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).
73

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

the vertical distribution of knots within the tree crown. The intention of this 

exploratory analysis is merely to demonstrate how a three-dimensional dataset 

could be used for such evaluations.

Statistical Analysis

As with the mapping of three-dimensional log coordinates, the analysis of the 

distribution of knots around the boles of standing trees was somewhat more complicated than it might at first seem. By definition, azimuths are inherently circular; 

an azimuth of 360° is identical to an azimuth of 0°. Given two knots, one with an 

azimuth of 10° and the other with an azimuth of 350°, the average of the two would 

be 360° (or equivalently, 0°). Conventional analysis, however, would yield an average of 180°, the arithmetic average of 10 and 350. Such difficulties were overcome 

by converting the azimuths to unit vectors, with each knot azimuth represented by 

a vector indicating the direction of the azimuth and having a unit length. Vector 

arithmetic was then used to derive averages and statistical tests. The set of statistical procedures that has been developed for this purpose is commonly referred to as 

circular statistics (Fisher 1993, Jammalamadaka and SenGupta 2001).

Figure 17 provides a set of circular histograms that summarize data for the 

8,193 observations derived by mapping knot coordinates from long-log measurements into equivalent standing-tree coordinates. In the histograms, the knot 

azimuths have been aggregated into 10-degree azimuth sectors with histogram bars 

at the centers of the sectors. A percentage scale was used so that histograms of different sizes could be depicted at the same scale. If all knots were uniformly distributed around the bole of the tree, each bar would be 2.78 percent in length because 

there are 36 bars, one for each 10-degree sector in the circle (1/36 = 0.0278). Each 

histogram shows the circular average knot azimuth by site or treatment along with 

the 95 percent confidence interval for the average.

For the statistical analysis, we used a specialized software package known 

as Oriana (KCS 2010), which was developed specifically to facilitate analysis of 

circular data. The null hypothesis was that knots are uniformly distributed around 

the boles of standing trees. The results, for all data combined and separately by site 

and treatment are summarized in table 28.

Data analyzed for knots from all sites and treatments combined indicated clustering on the south to southwestern sides of trees rather than a uniform distribution 

around the boles. Although the results of the three statistical tests for uniformity 

shown in table 28 were strongly significant, the clustering tendency was relatively 

modest (fig. 17).

For the three sites individually, the results of the statistical tests suggest 

that knots on sample trees from the Cataract and Yachats sites were uniformly 
74

RESEARCH PAPER PNW-RP-605

(B) Cataract

study site

(n = 2,971)

(E) T100

treatment

(n = 4,922)

(F) T60

treatment

(n = 3,271)

0°

90°

180°

270° 4% 3% 3% 4%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

1% 1%

4%

3%

0°

90°

180°

270° 4% 3% 3% 4%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

1% 1%

4%

3%

(A) All sites and

treatments

(n = 8,193)

0°

270° 4% 4% 90°

4%

3% 3%

3%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

180°

1% 1%

0°

270° 3% 3% 90°

3%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

180°

4% 1% 1% 4%

4%

0°

270° 3% 3% 90°

3%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

180°

4% 1% 1% 4%

4%

(C) Wildcat 

study site

(n = 3,221)

0°

270° 3% 3% 90°

3%

3%

2% 2%

2%

2%

(North)

(South)

(West) (East)

1%

1%

4%

180°

4% 1% 1% 4%

4%

(D) Yachats

study site

(n = 2,001)

Figure 17—Distribution of knots around the boles—reconstructed as they would have appeared before 

harvesting—of sample trees harvested on Douglas-fir stands in the Siuslaw National Forest 15 years 

after two habitat-improvement thinning treatments, the first leaving 100 trees per acre and the second 

leaving 60 trees per acre: (A) all study sites and treatments totaling 8,193 knots, (B) Cataract study site 

totaling 2,971 knots, (C) Wildcat study site totaling 3,221 knots, (D) Yachats study site totaling 2,001 

knots, (E) the lighter thinning treatment T100 totaling 4,922 knots, and (F) the moderate thinning 

treatment T60 totaling 3,271 knots. The black bars represent the percentage of knots that faced toward 

each 10-degree azimuth sector when the tree was standing, the red lines represent the mean azimuth for 

the knots in each histogram, and the red arcs and associated shading show the 95 percent confidence 

intervals for the mean.
75

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

distributed around the boles of trees (table 28). All of the sample trees from these 

two sites were located on north-facing slopes, with circular mean knot azimuths of 

359.5° for Cataract and 59.9° for Yachats. In contrast, knots on sample trees from 

the Wildcat site had a statistically significant tendency to concentrate on the southfacing side of the tree with a circular average knot azimuth of 201.5°.

When knot azimuths were grouped by treatment for all sites combined, the 

statistical tests suggested that they were not uniformly distributed around the boles 

but had a larger-than-expected tendency to concentrate on the south-facing sides of 

the trees—nearly due south for treatment T100, and more southwesterly for treatment T60 (fig. 17).

The mixed results from the statistical tests could mean that multiple factors 

influence knot distribution around tree boles. For instance, competition from nearby 

trees or effects from microsite conditions are sometimes more important than knot 

azimuth considered by itself. In addition, the analysis presented here was simplistic 

in that it treated all knots equally; both larger knots resulting from larger branches 

and competition among branches on a single tree can influence the distribution of 

knots. A more sophisticated analysis might consider factors such as knot height 

within the crown, competition from crowns of nearby trees, interactions between 

knot size and azimuth, and differences between dead knots and the live knots that 

are remnants of living branches.

Table 28—Circular statistics used in analyzing knot-azimuth data for sample trees in Douglas-fir stands on 

the Oregon Coast Range 15 years after commercial thinning treatments

Grouped by site Grouped by treatment

Statistic Total Cataract Wildcat Yachats T100a T60b

Number of knot-azimuth observations 8,193 2,971 3,221 2,001 4,922 3,271

Circular mean azimuth, degrees 203.4 359.5 201.5 59.9 181.8 224.2

95 percent confidence interval for the mean:

 Left limit of arc (degrees) 227.1 265.8 214.2 0.6 216.8 251.7

 Right limit of arc (degrees) 179.6 93.3 184.8 119.2 146.8 196.7

Length of mean vector (r) 0.037 0.016 0.110 0.006 0.032 0.050

Circular variance 0.963 0.984 0.890 0.994 0.968 0.950

Circular standard deviation (degrees) 147.2 165.4 120.4 183.6 150.1 140.0

Statistical tests for uniformity:

 Rayleigh test (Z) 11.158*c 0.717 nsd 38.963* 0.070 ns 5.154* 8.321*

 Watson’s test (U2) 0.633* 0.069 ns 2.000* 0.059 ns 0.327* 0.452*

 Kuiper’s test (V) 3.111* 1.310 ns 4.390* 1.322 ns 2.331* 2.340*

a Stand reduced to approximately 100 trees per acre (light thinning).

b Stand reduced to approximately 60 trees per acre (moderate thinning).

c The hypothesis that knots are uniformly distributed around the tree boles is rejected (p < 0.01). d The hypothesis that knots are uniformly distributed around the tree boles is not rejected (p > 0.05).
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Discussion and Conclusions

Differences Attributed to Thinning Intensity

Perhaps the most important point to take away from this report is that we found 

consistent differences, sometimes substantial ones, between the two thinning 

treatments when we analyzed tree-stem and log dimensions and form, knot size and 

frequency, lumber volume and grade recovery rates, and lumber value (based on 

estimated prices of recovered lumber) of both trees and logs. Although not all of the 

analyses showed significant treatment effects, a measure of tree or log diameter was 

always statistically significant. Because DBH was found to be significantly larger 

with more intensive thinning (table 1) and because log diameter is closely correlated 

with DBH, we conclude that the more intensive treatment (reducing the stand to 60 

trees per acre) was superior to the less intensive treatment (reducing to 100 trees per 

acre) in providing larger diameter trees and larger limbs, thereby improving habitat 

for nesting and roosting birds such as the marbled murrelet and spotted owl.

Branching—

Trees in the heavier treatment (T60) retained more branches than those in the lighter 

treatment (T100), possibly because they delayed self-pruning in the more open 

stands or because they produced epicormic branches to take advantage of increased 

light availability (Collier and Turnblom 2001). Those branches tended to be larger 

on average, although our statistical analysis found that branch size was more closely 

related to stem diameter than to treatment group. Over time, as the residual trees 

continue to develop, we expect that some of the retained branches will also continue 

growing and will eventually become suitable for nesting or roosting by the target 

bird species. Additional thinnings in these stands could accelerate the effect.

Logs—

The more intensive thinning treatment (T60) tended to yield logs with larger 

diameters and higher average lumber volume, grade, and value recovery. As a 

consequence, both tree and log values based on estimated lumber recovery were 

substantially higher on average for treatment T60. Even though the treatment T60 

logs tended to have more frequent and larger knots, they also tended to have a 

higher percentage of live knots (which yield higher lumber grades than dead knots). 

In addition, the larger diameters of treatment T60 logs resulted in a larger average 

lumber width as compared to lumber from treatment T100 logs. Because the grading rules for wide pieces are more tolerant of large knots, the net result was larger 

average value per log.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Juvenile wood—

We were unable to determine whether juvenile wood is influenced by thinning 

intensity. Previous studies have shown that an increase in the proportion of juvenile 

wood as a result of increased thinning intensity tends to reduce the value of lumber 

recovered from residual trees (Barbour and Parry 2001). Because the AUTOSAW 

simulator does not consider juvenile wood in assigning lumber grades we did not 

attempt to measure this attribute or incorporate it into our analysis.

Lumber from Simulated Sawing

Results from the simulated sawing indicate that the proportion of lower grade 

lumber (No. 3 and economy) recovered from the thinning treatments was high—

ranging from 24 percent to 30 percent of all lumber produced from the three sites—

compared to about 11 percent for all Douglas-fir logs sawn in western Oregon and 

Washington from 2007 to 2008 (WWPA 2010). The corollary is that the proportion 

of higher grade lumber (Select Structural, No. 1, and No. 2) was much lower—ranging from 70 percent to 76 percent by site—compared to 89 percent for the Douglasfir industry as a whole. This means that attracting purchasers for similar thinning 

operations would likely translate into low stumpage prices for the Forest Service.

The lumber from treatment T60 logs had a much higher estimated value than 

lumber from treatment T100 logs; this differential, which is mostly the result of 

larger average piece width, is not a certainty for future valuations. Engineered 

products like wood I-beams, laminated veneer lumber, and glue-laminated beams 

continue to make inroads in replacing dimension lumber that is wider than about 

8 inches. A question we did not address was whether the differences in knot size 

and distribution would have affected grade recovery and estimated value if lumber 

widths had been limited to 6 or 8 inches. Such a constraint would likely have 

changed the results for grade and value recovery because larger knots are allowed 

in 10- and 12-inch lumber than in smaller pieces. Limiting sawn pieces to the narrower widths would likely reduce lumber grade yields; should this result in an even 

higher proportion of No. 3 and economy lumber, revenues from thinnings might 

not pay the full cost of management activities intended to improve habitat or benefit 

other ecosystem services. Even though our estimated yield of No. 3 and economy 

lumber was much higher than the industry average for Douglas-fir logs (WWPA 

2010), it was nevertheless more optimistic than earlier projections of 27 to 39 

percent for the same thinnings (Barbour et al. 1997). One reason for the difference 

is that the earlier study modeled lumber grade and volume outputs for the entire 

residual stand, but we collected morphological data and modeled lumber grade and 

volume outputs only for a sample of trees actually harvested in the second thinning. 

Regardless of the reason, the combination of empirical data and model projections 
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RESEARCH PAPER PNW-RP-605

in this study present a slightly more hopeful picture than was projected 15 years 

earlier for the same silvicultural treatment.

Future Research Needs

The research procedures and computer programs developed in this study have 

provided useful information on the quantity, quality, and potential value of lumber 

recovered from habitat-improvement thinnings in second-growth Douglas-fir. Even 

so, the results are constrained by the limitations of the sawing simulator that was 

used, a computer program developed originally for use in New Zealand and adapted 

to simulate the production of lumber from second-growth Douglas-fir logs. One 

shortcoming of the simulator is that it was not designed to consider juvenile wood, 

an important determinate of lumber grade in wood from second-growth Douglas-fir 

logs. Thus the results are incomplete because the estimated quality and value yield 

of lumber produced from the thinnings cannot be verified, even though the results 

seem reasonable by comparison with industry averages.

One way to improve estimates of lumber quality and quantity from thinnings 

would be to conduct a full-scale sawmill study in which the lumber produced from 

sample logs is tallied and then graded by certified lumber graders. Such studies are 

expensive but provide valuable information that cannot be obtained any other way. 

The chief disadvantage, other than cost, is that a sawmill study is inherently limited 

by the characteristics of the specific mill selected to host the study.

A second way to improve estimates of the quality and quantity of lumber yields 

from habitat-improvement thinnings would be to use a sawing simulator capable 

of including juvenile wood in its estimation of lumber grade. Several such simulators exist but their cost is high. A potential advantage of using such a simulator 

is that its cost could be spread over a large number of simulation experiments as 

compared to the one-time cost of a sawmill study. The data obtained in the current 

study, for instance, could be used to simulate lumber production under a variety of 

assumptions about sawmill conditions and technologies. This would provide useful 

comparisons with the results produced from the AUTOSAW simulations.

The study reported here considers only one aspect of the information needed 

to effectively manage Oregon coastal forests for the multitude of outcomes that 

are required from public forests. In particular, because the thinnings that form the 

basis of the study were designed to accelerate the development of large trees with 

large branches and other old-growth characteristics that would improve habitat for 

bird species whose survival depends on such characteristics, research to assess the 

resulting thinnings from the perspective of wildlife management would be helpful 

to forest managers who must make decisions about whether to continue this type of 

thinning program.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Finally, the data collected in this study could be used to improve silvicultural 

models that consider conditions influencing tree growth, competition among 

neighboring trees, and development of tree crowns and stem characteristics. The 

dataset and procedures developed to provide three-dimensional coordinates of stem 

profiles, branch locations as indicated by surface knots, and surface defects on the 

stem of each sample tree as it stood before felling could potentially be useful in 

such research.

Acknowledgments

Assistance from the following individuals and organizations is gratefully acknowledged. The research described in this report would not have been possible without 

their full cooperation and support. Special thanks go to timber sale purchaser and 

primary logging operator Robert E. Bateman and his logging and sort-yard assistants, to logging subcontractor for the Yachats study site Gary Van Curler and his 

logging crew, to Paul D. Anderson for encouraging and facilitating the research, 

and to John Champa and Nathan Poage for their exceptional field assistance.

• Robert E. Bateman, owner, Bateman Enterprises Inc., Monroe, Oregon

• Gary Van Curler, owner, Van Curler Logging, Mapleton, Oregon

• Paul D. Anderson, supervisory research forester, Land and Watershed 

Management Program, U.S. Forest Service, Pacific Northwest Research 

Station, Corvallis, Oregon

• John Champa, former silviculture technician, Central Coast Ranger 

District, Siuslaw National Forest, Waldport, Oregon (now at the Council 

Ranger District, Payette National Forest, Council, Idaho)

• Nathan Poage, former postdoctoral fellow, Pacific Northwest Research 

Station, Portland, Oregon (now coordinator, Clackamas Stewardship 

Partners, Clackamas, Oregon)

• Robert Thomas, former contracting officer, Siuslaw National Forest, 

Corvallis, Oregon

• Jim Wolfe, former timber sale administrator, Central Coast Ranger District, 

Siuslaw National Forest, Waldport, Oregon (now forestry technician, 

Chiloquin Ranger District, Fremont-Winema National Forests, Chiloquin, 

Oregon)

• Nathan Pearson, former timber staff employee, Hebo Ranger District, 

Siuslaw National Forest, Hebo, Oregon (now forestry technician, 

Supervisor’s Office, Corvallis, Oregon)
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Metric Equivalents

When you know: Multiply by: To find: 

Inches (in) 25.4 Millimeters (mm) 

Feet (ft) 304.8 Millimeters (mm) 

Feet (ft) 0.3048 Meters (m) 

Miles (mi) 1.609344 Kilometers (km) 

Degrees (°) 0.01745329 Radians (rad) 

Acres (ac) 0.4046873 Hectares (ha) 

Trees per acre (trees/ac) 2.471044 Trees per hectare (trees/ha) 

Cubic feet (ft3) 0.02831685 Cubic meters (m3) 

Board feet (BF) of logsa 0.005 Cubic meters (m3) of logs (approximate) 

Board feet (BF) of rough 0.002071 Cubic meters (m3) of lumber (approximate)

 green lumberb

a Conversions between board-foot and cubic-meter log volumes are approximate because of differences in 

measurement standards and can vary widely from one site to another. For this study the timber cruise volumes 

were tallied both in board feet and in cubic feet, so the conversion factor shown was derived from the ratio 

between the board-foot and cubic-foot total cruise volumes (table 2) and the cubic-foot to cubic-meter conversion 

factor shown above.

b Conversions between board-foot and cubic-meter rough green lumber volumes are approximate because of 

differences in lumber product and measurement standards. The conversion factor shown here was derived by 

calculating lumber volumes in board feet using nominal lumber dimensions and in cubic feet using actual rough 

green lumber dimensions, then converting from cubic feet to cubic meters.

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Appendix 1: Program for Checking Software 

Prerequisites

To run the utility programs that have been developed to organize and prepare data 

for the Siuslaw Thinning and Underplanting for Diversity Study (STUDS) project, 

the user’s computer must have certain software prerequisites in place. The program 

described here queries the user’s system and reports whether or not the prerequisites required to run the STUDS software have been installed.

Program Highlights

Executable File: (STUDS_PrereqCheck.exe)—

This file can be copied into any folder on the user’s computer without formal installation; 

administrative privileges are not required.

Requirements—

The program has been designed to run on any computer with Windows 95 or any later version of 

Windows. There are no other requirements.

Operation—

The user navigates to the 

folder where the executable 

file has been placed and 

double-clicks the filename 

to launch the program. The 

program will query the user’s 

computer for the required 

prerequisites and provide a 

report such as those shown in 

figure 18.

Queries Executed—

The program queries the Windows operating system and 

any service packs that have 

been installed, the Microsoft 

.NET Framework version and 

installation type (client or full 

installation), and the version 

of Microsoft Excel that has 

been installed.

(A)

(B)

Figure 18—Results from running STUDS_PrereqCheck.exe software on two 

different computers: (A) a computer that could potentially run the software if 

it had a version of Microsoft Excel capable of the type of automation required 

by the software and (B) a computer that has all the necessary requirements 

for running the software.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

To determine the version of Excel that has been installed on the user’s system, 

the program attempts to automate Excel. This is the same type of automation 

utilized by the STUDS utility software to read, write, and manipulate Excel files. If 

Excel is available on the user’s system but has not been properly installed and registered under Windows, the query will return a finding of “not installed” because 

Excel cannot be automated by an external program in that condition.

The STUDS prerequisite checker is a very simple program written in the C++ 

programming language. It has been explicitly designed to work on any recent 

version of Windows. It uses only standard Windows programming libraries and has 

no special runtime requirements. The program displays a single window of the type 

shown in figure 18. The size of the report window can vary depending on the user’s 

operating system and the results of the prerequisite checks. To close the window, 

the user simply clicks the X button in the top right-hand corner.

Unblocking the Executable File

Before using this or any of the other STUDS utility software, the executable file 

should be “unblocked.” This can be accomplished by right-clicking the filename 

in Windows Explorer, choosing the Properties item from the popup menu, and 

then clicking the Unblock button in the General tab of the property window. If the 

Unblock button does not appear, unblocking is not required.

Unsupported Versions of Excel

Only Excel 2002 and later versions support the type of automation required by 

this and the other STUDS utility software. In addition, certain recent versions 

of Excel do not support automation at all and the query will return a finding of 

“not installed” if these versions have been installed on the user’s computer. This 

includes Excel Starter, which comes preloaded on some computers and has only 

limited capabilities. Also, several complete versions of Excel can be installed using 

a procedure referred to as Click-to-Run. Although these are full versions of Excel, 

the installation method does not install all components that are necessary for the 

software to be automated. Installing the same versions of Excel with the standard 

Windows Installer rather than with the Click-to-Run installer will permit them to be 

automated.

Antivirus Software

Because the STUDS prerequisite checker queries the user’s operating system and 

the Windows registry, antivirus software on the user’s computer can prevent it from 

running. The user’s guide or help file for the antivirus software package should 

provide a procedure for indicating to the antivirus software that the executable file 

is trusted and can be allowed to run.
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Appendix 2: Log Bucking Simulator

The bucking simulator developed for the Siuslaw Thinning and Underplanting 

for Diversity Study (STUDS) project, xlBuckLogs.exe, uses relatively simple 

rules designed to emulate the bucking process typically used by sawmills in 

western Oregon. This appendix describes the bucking rules used and explains 

how sweep and crook are treated during the simulated bucking process for long 

(woods-length) logs.

User Interface for the Bucking Simulator

When the bucking simulator opens (fig. 19), the user browses to an input file by 

clicking the Browse and Open Excel File button (Step 1) near the top right of the 

window. If the selected file contains one or more long-log data worksheets in the 

proper format, a drop-down box immediately below is loaded with the names of 

all the long-log worksheets in the file. In the image, worksheet C2-1009-1 is shown 

as the currently selected name in this list. After opening an input file, the user 

browses to a folder where the output file should be placed by clicking the Browse 

to Folder button (Step 2). A new folder can be created in this step if desired. After 

selecting the output folder, the user must enter a name for the output Microsoft 

Excel file in a textbox (Step 3). If the user’s computer has Excel 2002 or 2003 

installed, this filename must have an extension of XLS; for Excel 2007 or later the 

filename extension can be either XLS or XLSX. The name entered in figure 19 is 

Cataract_SL.xlsx. When the user clicks the associated Accept Filename button, 

the new file is created and opened for output.

After the output file has been opened, the user can click either of two buttons 

(Step 4) to initiate the bucking simulation. One of these buttons processes only 

the single worksheet currently selected in the drop-down box (the user can change 

the selection in the drop-down box if desired to choose a different worksheet). 

The other button initiates the sequential processing of all long-log worksheets in 

the input file regardless of the current selection in the drop-down box. This is the 

more normal mode of operation. After the bucking simulation has been initiated it 

can be interrupted at any time by clicking the Cancel button, located near the bottom left of the window. During the simulation, a progress bar is displayed to the 

right of the Cancel button with a message indicating the worksheet currently being 

processed out of the total number of long-log data worksheets in the input file.

The large central part of the window in figure 19 is a text box that provides 

feedback to the user. Near the top of the text box in the image is information that 
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

was displayed when the user browsed to the input file to be opened; it indicates the 

folder and name of the input file and the number of worksheets in the file that were 

found to have long-log profile data. Below that is a listing of both the output folder 

selected by the user and the name of the output file, which the report indicates 

has been opened for output. The Time Started message near the middle of the text 

box records the date and time when the user began the bucking simulation. Below 

the Time Started message is a single line of comma-delimited labels followed by 

one line of comma-delimited data for each short (mill-length) log produced by the 

simulation. This report summarizes some of the important long-log inputs and the 

resulting short-log outputs from the simulated bucking. If desired after the simulation has been completed, this summary report, including the initial line of labels, 

can be selected with a mouse and copied to the Windows clipboard, then pasted 

into an empty Excel worksheet as a set of comma-delimited records. This allows 

the summary report to be formatted in Excel so that the details can be more easily 

examined. However, this is only an auxiliary report—the main outputs are in the 

new Excel file 

created by the 

bucking simulator. 

When a simulation has been 

completed, the 

user can click the 

Close the Excel 

Files button to 

close both the 

input and output 

files and then reset 

the simulator to 

process a different input file if 

desired. Alternatively, the user can 

click the Close all 

Files and Exit button to terminate 

the program.

Figure 19—The user interface for a log bucking simulator, shown 

with a simulation underway; the progress bar near the bottom center 

of the window indicates that the 10th long log from the input file is 

currently being “bucked” by the simulator.
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After the bucking simulation has been initiated, the software either processes 

the single file selected in the drop-down box or steps sequentially through the entire 

set of worksheets listed in the drop-down box. For each worksheet, which represents an individual long (woods-length) log to be bucked, the simulator loads all 

data from Excel into an array, then proceeds as follows:

Step 1. Data Selection

From the first data row of the worksheet, the simulator saves data that are constant 

for the entire long log. These data include inside-bark diameters at the large and 

small ends of the log, inside-bark diameter at a point 4 feet from the large end of the 

log if it is a butt log, length of the log, octant of the longitudinal axis along the top 

of the log in the 0° orientation, and sweep data if sweep is present in the log.

Step 2. Bucking Simulations

The methodology described below is a generalization of the bucking rules used by 

several mills that purchased logs from the thinning operations. While processing 

the first data row, the simulator also determines whether to buck the log and if so, 

what the resultant short-log lengths should be. For the STUDS project, any long 

log more than 21.0 feet in length was bucked into two or more short logs. The 

maximum short-log length for the STUDS project was set as 20.0 feet, maximum 

trim as 1.0 foot, normal trim allowance as 0.5 feet, minimum short-log length as 

8 feet, minimum trim allowance as 0.1 foot, minimum short-log diameter as 4 

inches, and increment between short-log lengths as 2 feet. Thus, a log as short as 

8.1 feet including trim could be used by the sawing simulator if its small-end 

diameter was 4.0 inches or larger. Shorter logs would be discarded and sent to 

a whole-log chipper.

Trimmed lengths of short logs are assumed to occur in even 2-foot multiples. 

Thus, a short log 11.3 feet long would have a trimmed length of 10.0 feet plus 1.3 

feet of trim and waste.

After bucking, any trim in excess of the normal trim allowance of 0.5 foot is 

considered waste and is discarded. Thus, the length of the log in the example above 

as used by the sawing simulator would be 10.5 feet including trim, with the additional 0.8 feet of trim being discarded and chipped. Discarded lengths are always 

removed from the smaller end of the log.

Bucked logs with less than the normal trim are permitted as long as the trim 

is at least equal to the minimum trim allowance (0.1 foot for the STUDS project). 

Thus, a bucked log 18.1 feet long is processed as an 18-foot log. Most mills prefer 

to have more than 0.1 foot of trim, so this allowance is somewhat different from 

common practice. Some mills would treat an 18.1-foot log as a 16-foot log with 0.5 
89

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

feet of trim and 1.6 feet of waste. However, the more modern, automated mills are 

capable of sawing logs with as little as 0.1 foot of trim.

Determining the number of short logs—

For logs longer than 21.0 feet, the procedure outlined below is used by the bucking 

simulator to determine how many short logs should be cut from any long log. This 

procedure is not guaranteed to work correctly for all possible situations, but it was 

adequate for all of the long logs in the STUDS project. The methodology begins by 

postulating that two short logs will be cut from the long log. It calculates the total 

length, including trim and waste, of each short log cut from the long log in n equal 

lengths. The maximum trimmed short-log length and the maximum trim are added 

together; if the total length of each short log is larger than this sum, the procedure 

increases n by 1 and total length is recalculated. Otherwise, the current value of n 

is accepted as the number of short logs to be cut from the long log. The trimmed 

log length can then be calculated; the method used ensures that the trimmed length 

is always an exact multiple of I, the increment between trimmed short-log lengths. 

The amount of trim is calculated and adjusted if it exceeds the normal trim for a 

short log. The amount of waste, if any, is also calculated.

Initialize n = 2

Calculate LSL = LLL/n

If LSL > (LT.SL(Max) + TMax),increase n by 1 and recalculate LSL = LLL/n

If LSL ≤ (LT.SL(Max) + TMax), accept n as the number of short logs to be cut from the 

long log

Calculate LT = Truncate(LSL) – Modulo(Truncate(LSL), I)

Calculate T = LSL – LT

If T > TNormal, set W = T – TNormal; then set T = TNormal

If T ≤ TNormal, set W = 0

where:

n = the number of short logs to be cut from the long log,

LLL = the full length of the long log, including trim and waste,

LSL = the full length of a short log, including trim and waste,

LT = the trimmed length of a short log,
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RESEARCH PAPER PNW-RP-605

LT.SL(Max) = the maximum acceptable length of a short log after the trim has been 

removed (20.0 feet for the STUDS project),

TMax = the maximum acceptable trim for a short log (1.0 foot for the 

STUDS project),

TNormal = the normal trim for a short log,

I = the increment between trimmed short-log lengths,

T = the actual amount of trim on a short log,

W = the amount of waste (excess trim) discarded from a short log,

Truncate(a) = a mathematical function that truncates the value of a (a positive number) to the next lower whole integer—if a is already a whole integer, Truncate(a)

returns a,

Modulo(b, c) = a mathematical function that returns the remainder when b (a positive number) is divided by c, another positive number—the result is 0 if b is exactly 

divisible by c.

For the STUDS project it was never necessary to cut more than three short logs 

from any long log; in fact, only one long log, C2-1009-2, was bucked into three 

short logs. The long log was 43.0 feet in length. Following the rule described above, 

it was first cut into two 21.5-foot short logs. Because 21.5 is a larger number than 

(20.0 + 1.0), n was increased to 3 and the long log was cut into three 14.33-foot short 

logs. Because 14.33 is a smaller number than (20.0 + 1.0), the solution was accepted 

with three short logs to be bucked from the long log. The trimmed length was 

calculated as 14 feet with trim of 0.33 feet for each short log. There was no waste.

Determining minimum diameter and length—

The log-bucking simulator considers the minimum diameter (4 inches) and minimum length (8 feet) of short logs. After the number of short logs to be cut from the 

long log is determined as shown above, each short log is considered in turn, beginning with the short log cut from the large end of the long log and proceeding toward 

the small end of the long log. Following is a summary of the procedure that was 

followed for each short log i in the STUDS project:

(a) Recalculate the total length of short log i: LSL(i) = LT(i) + T(i). Note the 

absence of allowance for waste; this is because all waste is taken from 

the small end of the long log rather than being allocated to individual 

short logs.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

(b) If short log i is at the large end of the long log (meaning that i = 1), set 

its large-end diameter to equal the large-end diameter of the long log. 

Otherwise, set its large-end diameter to equal the small-end diameter of 

short log i-1; see (d) below.

(c) Determine the distance from the large end of the long log to the large end 

of short log i, defined as ∑j<iLSL(j) , where the index j refers to the short logs 

that are closer to the large end of the long log than short log i.

(d) Determine the small-end diameter of short log i from the diameters in the 

set of profile points recorded for the long log. If necessary, interpolate linearly between the two closest adjacent profile points.

(e) If the small-end diameter determined in (d) above is less than the minimum 

diameter of 4 inches, reduce the log length by 2.0 feet (I) and determine 

the new small-end diameter as in (d) above. Continue reducing the length if 

necessary until you arrive at a feasible small-end diameter.

(f) If the short-log length was reduced in (e) above, compare the new value of 

LT with the minimum short-log length (8.0 feet). If the short log is less than 

this minimum, discard it. Otherwise add the short log and its data to the list 

of short logs cut from the long log.

Step 3. Correcting Profile Data

Each data row in a long-log worksheet from the input Excel file corresponds to a 

stem-profile measurement consisting of a distance from the large end of the log to 

the profile point and the inside-bark horizontal diameter of the log at the profile 

point. Most rows also include data on surface defects. With the length and position 

of each short log within the long log determined (step 2 above), the data for each 

profile point and surface defect can be associated with an individual short log and 

placed correctly at a specified distance from the large end of the short log. This 

is handled by simple subtraction as shown in figure 20. Any point on the threedimensional log image in the figure can be described using x, y, and z coordinates. 

The x-axis is the reference axis used to measure horizontal log diameters and the 

y-axis, vertical log diameters. The {0, 0} point where these two axes cross can be 

found at the geometric center of the log at its two ends and at any point along the 

z-axis between the two ends. The z-axis runs from the geometric center of the large 

end of the log to the geometric center of the small end of the log, with z = 0 at the 

large end. Thus, the z-coordinate at the small end of the log is equal to the length 

of the log. For a log with no sweep or crook (fig. 20), the z-axis is identical to the 

longitudinal axis of the log. In the presence of sweep or crook, the longitudinal axis 

of the log curves away from the z-axis; this is a complicating factor that is further 

discussed below.
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RESEARCH PAPER PNW-RP-605

When the long log is bucked into two short logs (SL) as shown in figure 20, 

any point in the first short log will have the same z-coordinate value as the same 

point in the long log (LL). However, any point in the second short log will have a 

different z-coordinate than the same point in the long log because the bucking point 

(B) becomes the z = 0 point for the second short log, whereas in the long log point 

B has a positive value zB. The correction for profile points in the second short log is 

computed as zSL2 = zLL – zB.

Longitudinal axis 

of log = z-axis

Large 

end of LL

and SL1

Small end 

of LL and

SL2

z=0 for LL and SL1

Increasing z

z=0 for SL2

LL Long log.

SL Short log.

A horizontal line extending through the 

z-axis and to the perimeter at each end of 

the log and at any profile point in the log.

x-axis

A vertical line extending through the z-axis 

and perpendicular to the x-axis.

y-axis

A straight line connecting the geometric 

centers of the two ends of the log (identical 

to the logitudinal axis for a straight log).

z-axis

A line that follows the pith at the center of 

the log from one end of the log to the other.

Longitudinal

axis

Reference points at the geometric centers 

of the two ends of the log.

A and C

A point at the geometric center of the log 

where the log is to be bucked.

B

Figure 20—The three-dimensional coordinate system used for simulated bucking of a long log into 

two short logs; the bucking point shown will become the small end of the first short log and the large 

end of the second short log.

For logs with sweep, the simple correction described above is an approximation (fig. 21); the z-axis and the longitudinal axis are not identical, with the largest 

difference occurring at the point of maximum sweep offset. This caused a complication for the STUDS project, because measurements from the large end of the long 

log to any profile point or surface defect were made along the longitudinal axis of 

the log rather than directly along the z-axis. However, for all long logs with sweep 

the maximum difference between any measurement along the longitudinal axis and 

the corresponding (correct) measurement along the z-axis was never as much as 

0.1 foot, which was also the limit of precision for our z-axis measurements, so we 

ignored the error. Larger errors could be encountered in logs with severe sweep. 
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Length measurement errors large enough to detect when lengths are measured to 

the nearest 0.1 foot can occur in logs with a maximum sweep offset larger than 

about 4 percent of the length. None of the sample logs in the STUDS project had 

such severe sweep, which is rare because loggers intentionally buck tree stems 

in ways that reduce sweep—sweep is a scaling deduction and a logger’s receipts 

depend on maximizing the net log scale. If logs with that severity of sweep were 

encountered, however, the measurement system used in the STUDS project would 

produce unacceptably large errors for z-coordinates of profile points and surface 

defects located near the small end of the long log.

Unlike z-coordinates, offsets measured to surface defects from the longitudinal 

axis as projected along the top of the log are not affected by bucking because the 

longitudinal axis is the same for the short logs as for the long log. Therefore no 

correction is needed for offset measurements.

Figure 21—Geometry of sweep in a long log and two short logs bucked from it: (A) the long log being 

bucked at the point where the sweep offset is at its maximum, (B) the first short log, and (C) the second 

short log; note that each short log has less sweep than the original long log.

Large 

end of LL

Small end 

of LL

y=0

x=0 z=0 for LL

Longitudinal

axis

M

Increasing z

A

Large end 

of SL1 y=0

x=0 z=0 for SL1

Increasing z

Maximum

sweep offset

for SL1

Small end 

of SL1

A

Increasing z

Small end 

of SL2

z=0 for SL2

Maximum

sweep offset

for SL2

Large end 

of SL2

(A)

(B) (C)

D

LL Long log.

SL Short log.

A horizontal line extending through the 

z-axis and to the perimeter at each end of 

the log and at any profile point in the log.

x-axis

A vertical line extending through the z-axis 

and perpendicular to the x-axis.

y-axis

A straight line connecting the geometric 

centers of the two ends of the log.

z-axis

A line that follows the pith at the center of 

the log from one end of the log to the other.

Longitudinal

axis

Reference points at the geometric centers 

of the two ends of the log.

A and C

A point at the geometric center of the log 

where the log is to be bucked.

B

The z-coordinate at the point of maximum 

sweep offset for the long log.

D

M The maximum sweep offset of the long log.

z-axis
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RESEARCH PAPER PNW-RP-605

Step 4. Adjusting Sweep Data

Bucking long logs into short logs reduces sweep, as can be seen in figure 21. Each 

of the two short logs in the figure has substantially less sweep than the original 

long log. This is because bucking redefines the z-axis in a log with sweep; you can 

see this by comparing the z-axes for the two short logs with the z-axis for the long 

log in figure 21. The software used to prepare data files for the sawing simulator 

requires the position of maximum sweep and the maximum sweep offset for the 

short logs, so this information must be calculated for each short log as the simulator bucks it from the long log. As noted previously, measurements along the arc of 

the surface-to-surface defects at any point on the log are not affected by bucking, 

because the measurements are taken relative to the longitudinal axis along the top 

of the log and it is the same for the short logs as for the long log. However, for any 

short log other than the first, the distance from the large end of the short log to 

the measurement point must be adjusted for the distance between the large end of 

the long log and the large end of the short log. In figure 21, the z-coordinate of the 

bucking point is 0 in the coordinate system of the second short log, whereas it has 

a positive value in the coordinate system of the long log. The bucking simulator 

makes the necessary adjustments for sweep by using the following procedure:

Locating sweep on short logs—

This requires equations that describe the curve representing the longitudinal axis 

of the long log and any short logs bucked from it. For our study, we chose to use a 

parabolic arc with the vertex of the parabola at the point where the maximum sweep 

offset occurs for the long log (fig. 21). Rather than using a single equation for the 

long log, we used two; one for arc AB and another for arc BC. This is because the 

point of maximum sweep may not occur at the center of the long log. To calculate 

the two parabolic arcs we used the vertex form of the parabola as shown in equation 

(3). This formulation ensures that the two arcs, AB and BC, will meet smoothly at 

B, the point of maximum sweep.

As written, the equation assumes that sweep occurs along the x-axis (meaning 

that the sweep offset is left or right of a straight line drawn between the geometric 

centers of the two ends of the log, as shown in fig. 21). If instead it occurs along 

the y-axis (the sweep offset is up or down from the z-axis), the method is the same 

except that y would be substituted in the equation in place of x.

x = (α)(Z − D)

2

 + M (3)

where:

x = the sweep offset along the x-axis of the long log at any point Z along the z-axis 

of the long log between Zref (eq. 4) and D,
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

α = the parameter of the vertex form of the parabola (eq. 4),

Z = the z-coordinate of any point along the z-axis between Zref and D as measured 

from the large end of the long log,

D = the z-coordinate where the maximum sweep offset occurs (in figure 21 this occurs at the bucking point, but in general it could occur at any point along the z-axis),

M = the sweep offset in x- or y-coordinates at point D (the maximum sweep offset)—this value is negative if the sweep direction is down or to the left as viewed 

from the large end of the log, and positive if the sweep direction is up or to the 

right.

The parameter of equation (3) is calculated as follows:

α = −M / (Zref – D)

2 (4)

where: 

Zref = the z-coordinate of the reference point for the arc being computed for the 

long log in figure 21: the z-coordinate at point A (normally 0) if equation (3) is for 

the arc AB, or the z-coordinate at point C if equation (3) is for arc BC.

Note that the maximum sweep offset (M) for the long log in in figure 21 is 

negative because the sweep is to the left of the z-axis as viewed from the large end 

of the log. As a result the value of α calculated in equation (4) will be a positive 

number for the situation of figure 21. In general, when the sweep is to the left or 

downward, α will be a positive number; when the sweep is to the right or upward α 

will be a negative number.

Estimating maximum sweep offset for short logs—

The bucking simulator uses the following iterative procedure to do this for each 

short log:

(a) Calculate an equation for the straight line joining the centers of the two 

ends of the short log. This is the z-axis, shown as a dashed line labeled 

“Increasing z” for each of the two short logs (fig. 21).

(b) Calculate an equation for the straight line perpendicular to the z-axis determined in (a) above.

(c) Set the initial value of the short-log sweep offset to 0. This corresponds to 

the offset at the large end of the short log, where the z-coordinate = 0.

(d) Set the first calculation point at a distance of 500 mm (19.7 inches) from 

the large end of the short log as measured along its z-axis. This is an arbitrary distance, chosen to be somewhat less than the value of the increment 

between trimmed short-log lengths (I); I = 2 feet for the STUDS project.
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RESEARCH PAPER PNW-RP-605

(e) Calculate the long-log sweep offset corresponding to the current z-coordinate using the model of equations (3) and (4).

(f) Using the geometric relationship between the z-axis of the long log and the 

z-axis of the short log, together with the equation calculated in (b) above, 

determine the short-log z-coordinate corresponding to the current point and 

the distance from the longitudinal axis to the z-axis. This is the sweep offset for the short log at the current z-coordinate.

(g) Compare the short-log sweep offset with the largest previously determined 

short-log sweep offset. If the new value is higher, save the value of the offset and the corresponding short-log z-coordinate. Then increase the z-coordinate of the long log by 500 mm and go to (e) above. Otherwise, accept 

the largest previously determined short-log sweep offset as the maximum 

value; record this value and the corresponding short-log z-coordinate, and 

terminate the procedure.

This methodology finds the z-coordinate of the maximum sweep offset within 

a precision of 500 mm for the long log and slightly more than 500 mm for the short 

log, depending on the severity of sweep. If more precision is needed, a smaller step 

length or an optimizing procedure such as successive halving (binary search) could 

be used.

Calculating arc length—

The equation for a parabola has a closed form solution for determining the arc 

length between any two points on the curve. The sweep-adjustment procedure outlined above does not require the arc length, but knowledge of this value can be useful in determining whether errors resulting from the measurement of z-coordinates 

along the longitudinal axis of the log (rather than along the z-axis) are large enough 

to warrant corrections. As mentioned before, the errors in the STUDS project could 

be ignored because the largest possible error was found to be less than the precision 

of the original measurements. Logs with more extreme sweep, however, could have 

larger measurement errors. For a parabola defined as in equations (3) and (4), the 

arc length between points z1 and z2 can be determined as follows (Pahikkala 2009):

L= 1

4α

— (2α ∙ |z1–z2|∙√(4α2(z1–z2)

2

+1)+sinh–1(2α ∙ |z1–z2|)) (5)

where:

L = the arc distance between points z1 and z2,

α = the parameter of the parabola computed as in equation (4).
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

One caveat is that the z-coordinates and value of M used in calculating α in 

equation (4) must be in identical units of measure for α to be used in equation (5). If 

z1 and z2 are measured in feet, M must also be measured in feet; mixing the units of 

measure will yield an incorrect result for the arc length.

Step 5. Adjusting Crook Data

Crooks are similar to sweep in that they cause the longitudinal axis of the log to 

deviate from the z-axis of the log; they differ in that they do not extend over the 

entire length of the log. A single log can have more than one crook, a situation that 

did not occur in the STUDS project. In adjusting the crook offset for a short log, 

three possibilities must be considered:

Logs without sweep—

No adjustment of the crook offset is necessary, because the z-axis for the short 

log is the same as the z-axis for the long log. Crook offsets are measured from 

the z-axis. For short logs other than the first short log bucked from the long log, 

however, the z-coordinate at the offset point must be adjusted as described in 

step 3 above.

Logs with sweep—

The crook offset must be adjusted to account for the difference in sweep between 

the long log and the short log. In the situation shown in figure 22, the crook offset 

for the first short log is less than the crook offset for the long log. The difference 

can be calculated by similar triangles, given that the offset from the z-axis of the 

short log to the bucking point has been calculated as described in step 4 above. For 

a crook located in the second short log, the procedure is essentially the same but 

with some modifications required by the slightly different geometry.

Logs with other complications—

A more complicated situation arises if the bucking point occurs within the crook, 

causing the crook to be split across two short logs and requiring a more complex 

set of adjustments. For the STUDS project that situation did not occur so we did not 

attempt to develop a procedure that would incorporate the necessary adjustments.

When both sweep and crook are present in a single long log, the adjustment 

for sweep is first made as described in step 4 above. Accounting for crook then 

represents an additional modification to the longitudinal axis of the log, which in 

turn affects the three-dimensional coordinates of any surface defects that occur 

along that section of the log. This adjustment is not made by the bucking simulator 
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RESEARCH PAPER PNW-RP-605

Large 

end of LL

and SL1

Small end 

of LL

y=0

x=0 z=0 for LL and SL1

A

z-axis for LL

z-axis for SL1

Total offset

(sweep + crook)

for LL

Total offset

(sweep + crook)

for SL1

LL Long log.

SL Short log.

A horizontal line extending through the 

z-axis and to the perimeter at each end of 

the log and at any profile point in the log.

x-axis

A vertical line extending through the z-axis 

and perpendicular to the x-axis.

y-axis

A straight line connecting the geometric 

centers of the two ends of the log.

z-axis

Reference points at the geometric centers 

of the two ends of the long log.

A and C

A point at the geometric center of the log 

where the long log is to be bucked.

B

P Reference point at the center of the crook.

Figure 22—The three-dimensional coordinate system for a short log that was bucked from a long log with both sweep and crook; note 

that the center of the crook is located at point P between the large end of the long log (A) and the bucking point (B). Although this image 

shows sweep and crook bending in the same direction, in an actual log they might bend in different directions.

but must be incorporated later when the three-dimensional data for the short log are 

determined in preparation for the sawing simulation. The bucking simulator simply 

stores the crook data—with the z-coordinate and crook offset adjusted as described 

in step 5 above—in the data worksheet for the short log where the crook occurs.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Appendix 3: AUTOSAW

AUTOSAW, the program used to simulate the conversion of short (mill-length) 

logs into lumber for the Siuslaw Thinning and Underplanting for Diversity Study 

(STUDS) project, requires log data to be organized as shown in table 29. Each log 

to be sawn requires a single data file, which is a text file with a filename extension of DAT. The file must provide data on profile points, branch whorls and the 

individual branches within each whorl, and pith locations for the log to be sawn. 

Three-dimensional coordinates must be specified in millimeters according to the 

coordinate system shown in figure 9. The coordinates are measured along the 

horizontal (x) and vertical (y) axes for any cross section of the log. These coordinates are determined with respect to the initial (0°) orientation of the log. The 

z-coordinates are measured along an axis running from the geometric center of 

the large end of the log to the geometric center of the small end. For the STUDS 

project, z-coordinates always represent distances along the z-axis as measured from 

the large end of the log.

Data items in an AUTOSAW DAT file (table 29) are delimited by one or more 

blank spaces or by newline characters. Multiple contiguous delimiters are ignored. 

The data are divided into the four blocks described below.

Log ID—

The identifier of the short log is entered on the first line of the file; thus the data 

in table 29 refer to log Y2-1202-1-1. The log ID is preceded by an asterisk, which 

indicates that the file includes data for individual branches based on measurement 

of surface knots. This is the only format supported by AUTOSAW that will produce 

lumber under the U.S. grading rules for Douglas-fir lumber. Without the preceding 

asterisk, AUTOSAW would expect data in a different format and the output would 

only be compatible with the New Zealand lumber grading rules for which AUTOSAW was originally developed.

Profile data—

The second block of data describes the log profile. Up to 40 profile points can be 

entered in the format shown in table 29. Data in this block must be sorted in ascending order according to the z-coordinate, and z-coordinates in adjacent rows of the 

data file must differ by at least 1 mm. The series of negative x-coordinates in the set 

of profile points in table 29 indicates that the log profile sweeps to the left as viewed 

from the large end of the log, similar to what is shown in figure 9. The maximum 

sweep offset for the short log in table 29 as calculated by the bucking simulator 

(app. 2) is 78 mm, or 3.1 inches, and it occurs at a distance of 3200 mm (10.5 feet) 

from the large end of the log. The total length of the log is 5639 mm, or 18.5 feet, 
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RESEARCH PAPER PNW-RP-605

Table 29—Format of an AUTOSAW DAT file with data for one of the short logs from sample trees in 

Douglas-fir stands on the Oregon Coast Range 15 years after commercial thinning treatments

AUTOSAW DAT file contentsa Description

*Y2-1202-1-1 Short-log identifier. The preceding asterisk indicates that branch data are provided.

10 Number of profile points (cannot be more than 40).

0 0 0 245 232 90 Profile points:

-51 0 1219 225 225 0 • The first three numbers in each row are x, y, z coordinates (mm).

-71 0 2073 215 215 0 • The fourth number in each row is the radius of ellipse major axis (mm).

-73 0 2195 212 212 0 • The fifth number in each row is the radius of ellipse minor axis (mm).

-78 0 3200 207 207 0 • The sixth number in each row is the angle of the ellipse major axis (degrees) as

-66 0 4023 203 203 0 measured in a counterclockwise direction from the positive x-axis. Note that for

-29 0 5090 189 189 0 the first profile point this value is 90, indicating that the major axis of the ellipse 

-26 0 5151 192 192 0 is oriented along the y-axis (meaning that the log’s vertical diameter at that point 

-25 0 5182 191 191 0 is larger than its horizontal diameter). For all other profile points the major axis 

0 0 5639 188 188 0 is oriented along the x-axis.

5 The number of branch whorls in the log (cannot be more than 48).

-71 0 2073 1 Data for whorl 1: x, y, z coordinates and number of branches (last digit; cannot be 

more than 12 per whorl but multiple whorls can have the same z-coordinate).

172 2.07 8 0.00 43 Data for branch 1 of whorl 1:

172 =length of the live portion of the branch as measured from the 

pith toward the log surface (mm).

2.07 =angle to the center of the branch about the central axis of the log as 

measured from the positive x-axis (radians).

8 =mean radius of the surface knot (mm).

0.00 =rake angle of the branch (radians); 0.00 indicates that the branch is 

perpendicular to the longitudinal axis of the log.

43 =length of the dead portion of the branch as measured from the top of the 

 embedded live portion of the branch to the log surface (mm).

-73 0 2195 1 Whorl 2 has 1 branch.

170 3.93 22 0.00 42 Data for branch 1 of whorl 2.

-78 0 3200 1 Whorl 3 has 1 branch.

166 3.95 6 0.00 41 Data for branch 1 of whorl 3.

-660 4023 3 Whorl 4 has 3 branches.

204 3.80 10 0.00 0 Data for branch 1 of whorl 4. This branch was recorded as having a live surface 

 knot, so the live length is 204 mm and the dead length is 0 mm.

163 2.84 10 0.00 41 Data for branch 2 of whorl 4.

163 0.04 10 0.00 41 Data for branch 3 of whorl 4.

-270 5144 4 Whorl 5 has 4 branches.

151 2.78 20 0.00 38 Data for branch 1 of whorl 5.

153 2.15 15 0.00 38 Data for branch 2 of whorl 5.

153 3.89 10 0.00 38 Data for branch 3 of whorl 5.

152 -0.83 9 0.00 38 Data for branch 4 of whorl 5.

2 Number of pith coordinates (cannot be more than 60)b.

0 0 0 x, y, z coordinates of the pith at the large end of the log.

0 0 5639 x, y, z coordinates of the pith at the small end of the log.

a Data entries are delimited by a space or return key and multiple contiguous delimiters are ignored by AUTOSAW.

b The number of pith coordinates is always 2 for this study, with one set of coordinates located at each end of the log. Intermediate pith locations 

are assumed to lie along the longitudinal axis of the log, at the geometric center of the log cross-section at each profile point.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

inclusive of sawmill trim. AUTOSAW will treat it as an 18-foot log with 6 inches of 

trim at the small end of the log.

Branch data—

The third data block describes branch data for the log as determined from surface 

knots. AUTOSAW assumes that each branch is associated with a whorl. A log can 

have as many as 48 whorls, and each whorl can have up to 12 branches. For the 

STUDS project, we assumed that a whorl consisted of all branches located within 

100 mm (about 4 inches) of each other as measured along the z-axis. The z-coordinate of the whorl was taken to be the arithmetic average of the z-coordinates of all 

the branches within the whorl.

The data entered for each branch are also described in table 29—see the 

detailed explanation for the data of branch 1 in whorl 1. Note that the lengths of 

the live and dead portions of the branch are provided. For the STUDS project, we 

measured only surface knots and therefore have no information on the live and dead 

lengths of each branch embedded within the log unless the knot was recorded as a 

live knot; then the entire branch was recorded as alive and the length of the dead 

portion was recorded as zero. Following the practice used in previous studies of 

Douglas-fir sawing with AUTOSAW (Todoroki et al. 2005), we assumed that 80 

percent of the length of any embedded branch corresponding to a dead surface knot 

is alive. The live portion begins at the pith and extends 80 percent of the distance to 

the surface of the log; the final 20 percent of the embedded branch is assumed to 

be dead.

The position of each branch is entered as an angle in radians measured from 

the positive x-axis of the log profile at the branch (fig. 23). This angle is calculated 

based on the measured position of the knot on the surface of the log under the 

assumption that the branch grows from the pith and that the pith is located at the 

geometric center of the log cross section at the branch. For the STUDS project, 

we assumed that the rake angle of each branch was 0° from horizontal, implying 

that the branches grow out horizontally from the standing tree. This assumption 

simplifies certain calculations and is consistent with previous studies that involved 

simulated sawing of Douglas-fir with AUTOSAW. However, a more sophisticated 

model would likely vary the rake angle according to the position of the branch 

within the tree crown.

Pith data—

The final section of the DAT file defines the location of the pith. For the STUDS 

project we included only two sets of pith coordinates for each log—those for the 
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RESEARCH PAPER PNW-RP-605

large end and those for the small end. The location of the pith is used by AUTOSAW to locate and calculate the size of internal knots based on the branch angle 

and the average radius of each surface knot. Given that we did not deconstruct each 

log and thus could not determine the exact location of the pith at each profile point, 

we simply assumed that the pith was located along the longitudinal axis of the 

log (at the geometric center of the log cross sections corresponding to the set of 

profile points).

Short Log Data Conversion

Conversion of data from short-log Microsoft Excel files into the format required by 

AUTOSAW was accomplished for the STUDS project with a program written in 

the C# programming language and using Excel automation in the same way as the 

bucking program described in appendix 2. Following is a brief description of the 

data-conversion program:

Executable file (xlAutosawDAT.exe)—

Like the bucking simulator, this file can simply be copied into any folder on the 

user’s computer without formal installation; administrative privileges are not 

required. The user’s computer must meet the same requirements as for the bucking 

simulator (app. 2), and the executable file may need to be unblocked before first use 

(app. 1).

Supporting file (LogDataFmt.xml)—

This file must be present in the same folder as the executable file. It is identical to 

the file of the same name used with the bucking simulator (app. 2).

φ

P

k

RMajor

RMinor Branch angle (expressed in radians), 

always measured counterclockwise 

from the horizontal axis.

φ

k Surface knot.

P The point in the pith from which the 

branch grew.

RMajor The radius of the major axis of an 

ellipse representing the cross-section 

of the log.

RMinor The radius of the minor axis of an 

ellipse representing the cross-section 

of the log.

Figure 23—Geometry of the log cross section at an arbitrary profile point as defined in AUTOSAW; 

note that the ellipse shown here has its major axis oriented horizontally but AUTOSAW permits it to be 

oriented at any angle (orientation is specified individually for each profile point).
103

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Operation—

The user navigates to the folder where the executable file has been placed and 

double-clicks the filename to launch the program. The user interface for this 

program is similar to the one for the bucking simulator (app. 2).

Input file—

This is an Excel spreadsheet file with a filename extension of either XLS (Excel 

2002 or 2003) or XLSX (Excel 2007 or later). The file must contain one or more 

short-log data worksheets that conform to the format described in LogDataFmt.xml. 

Each short-log data worksheet must be named in the format Aw-xxxx-y-z, where A 

= a single letter representing the site; w = a single digit signifying the treatment (in 

the STUDS project; 2 represents the light-thinning treatment T100 and 3 represents 

the moderate-thinning treatment T60); xxxx = a four-digit tree number; y = a single 

digit representing the index of the long (woods-length) log from which the short log 

was cut; and z = a single digit representing the index of the short log (1 = the short 

log cut from the large end of the long log; 2 = the next short log cut from the same 

long log; and so on).

Output file—

For each short log to be sawn (represented by a short-log data worksheet in the input 

file), a text file having the format described in table 29 is written to a folder specified by the user. The filename extension is always DAT, as required by AUTOSAW. 

Because AUTOSAW was written for the MS-DOS® operating system that predated 

Windows, the main part of the output filename can be no more than 8 characters in 

length. However, a short-log name as used in the STUDS project was 11 characters 

long, including several hyphens. To observe the name-length limit while fully 

identifying each short log, we specified a separate folder for each site. Because tree 

numbers within each site were unique regardless of the treatment, this permitted 

the first three characters of each short-log name to be dropped so that the remaining 

characters could be used to form the output filename. As an example, the AUTOSAW data for short-log C2-1009-2-3 were written to a file named 1009-2-3.DAT that 

was created in a folder named C_DAT.

AUTOSAW Simulation Processing

The AUTOSAW sawing simulator (FRI 1994) is proprietary software that was 

used for the STUDS project under license from Scion, the New Zealand Forest 

Research Institute. Its use for the STUDS project corresponds to step 5 in figure 8. 

AUTOSAW was developed for the MS-DOS® operating system, a command-line 

system that has been supplanted by the Windows graphical-interface operating 

systems. Current versions of Windows retain the ability to run MS-DOS® programs 
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RESEARCH PAPER PNW-RP-605

in a command window opened with the Windows cmd.exe processor. However, 

because of security issues, Windows Vista and later versions prevent the execution of MS-DOS® programs that use full-screen graphics. This is an issue with 

AUTOSAW, which was designed to use full-screen graphics to display each log as 

sawing is being simulated. For the STUDS project, we installed Microsoft Hyper-V 

on a computer running Windows 8.1. Hyper-V is an optional component of 64-bit 

versions of Windows 8 or later that permits secondary operating systems to run 

in a protected window. We could then install Windows XP as a secondary operating 

system under Hyper-V. Because Windows XP permits MS-DOS® programs 

to use full-screen graphics, we were thus able to run AUTOSAW in the 

Hyper-V window.

Sawing Strategies

AUTOSAW can implement either of two sawing strategies: cant sawing or live 

sawing. The two strategies are illustrated in figure 24, which shows the simulation 

results for a single log sawn both ways. Some pieces of lumber cut from the log are 

shorter than the trimmed length of the log (18 feet) because AUTOSAW accounts 

for taper as defined by the set of profile points entered into the DAT file (described 

above and illustrated in table 29). Only the large-end and small-end diameters are 

shown in the figure; although the intermediate profile points have been omitted for 

clarity, they also influence lumber production. Following is a brief description of 

each sawing method as applied to the specific log shown in the figure.

• For the cant sawing strategy, an initial cut (cut 1) was made to remove the 

top slab (fig. 24). Then a 1-inch piece was removed in cut 2 immediately 

below the slab so that most of the cant would lie inside the cylinder defined 

by the small-end diameter of the log. The log was then turned 180° and a 

cut made to remove the bottom slab (cut 3). This was followed by removal 

of an additional 1-inch piece in cut 4. The log was then turned 90° and 

another slab (cut 5) was removed. This exposed the cant, which was cut 

into the pieces numbered 6 through 11 by the AUTOSAW simulator. One 

piece (cut 11) was converted into two pieces with different lengths by the 

simulated edging process to adjust for log taper. The total volume recovered 

from the two 1-inch boards and seven 2-inch pieces was 149.33 board feet.

• For the live sawing strategy, an initial cut (cut 1) was made to remove the 

top slab. Then a series of 2-inch cuts (2 through 5) was made to remove 

pieces until the center of the log was reached, after which the log was 

turned 180° and the bottom slab was removed (cut 6). This was followed by 

a series of cuts made to remove the pieces numbered 7-11. Although pieces 

from the latter series were all 1-inch boards in this study, the cuts could 
105

Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Figure 24—Comparison of AUTOSAW results for lumber produced by 

(A) cant sawing and (B) live sawing. Saw kerfs are shown as gaps between 

pieces; pieces discarded by the edger or as slabs are shaded gray; lumber produced by the sawing simulator is white; the solid outside ellipse defining each 

image represents the large-end diameter of the log; the dashed inner ellipse 

represents the small-end diameter; and the index numbers within each image 

indicate the order in which cuts were made by the sawing simulator—with 

sub-index values (such as 11a, 11b) indicating that a single sawn piece was cut 

into multiple pieces by the edger.

(B)

(A)

2x8x18

2x10x182x10x182x10x18

3

4 4 4

6 5

6

6

7

7

7

12 11b 10 9 8

11

11

11a

2x4x7

1

2 2 2 1x6x10

1x6x10

2x4x16 2x4x15

1x4x6

1x6x15

1x6x18

1x4x10 1x8x18

1x4x18 1x4x18 1x4x17

2x8x18

2x12x18

6

1

2

2 2

3 3

4a 4b 4c 4

11 11a 11b 11c 11

10a 10b 10

9 9a 9b 9

8 8 8

7 7 7

2x4x15 2x4x18 2x4x17

2x4x6

1x4x10

5 5 5

4

3
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RESEARCH PAPER PNW-RP-605

have been spaced differently to produce dimension lumber. Altogether, 

four cuts were converted into multiple pieces with unequal lengths by the 

simulated edging process to adjust for log taper. The total volume recovered 

from the nine 1-inch boards and six 2-inch pieces was 152.17 board feet.

For this study we elected to use cant sawing for two reasons. First, all previous 

North American studies using AUTOSAW with unpruned Douglas-fir logs have 

used cant sawing (Barbour et al. 1997, 1999; Barbour and Parry 2001; Todoroki et 

al. 2005). Second, cant sawing more closely resembles the cutting strategy commonly employed by sawmills using timber from the Siuslaw National Forest. During a typical shift, however, an individual sawyer would be just as likely to employ 

both cant and live sawing as well as any of several other strategies. The optimal 

sawing method can vary from one log to the next (Blackwell and Walker 2006).

AUTOSAW Files

In addition to a data file for each short log to be sawn as described above, the sawing simulator requires the main executable file and several supporting binary files, a 

set of auxiliary files, a parameter file, and a command file (described below).

Executable file (AUTOSAW.EXE)—

Because this is an MS-DOS® program, it can be installed simply by copying the 

executable file and several other required files into a folder on the hard drive. We 

installed the executable file in a folder named C:\Autosaw in the file system of the 

Hyper-V window. Several additional executable files (DPMIInst.exe, DPMILoad.

exe, and RTM.exe) plus a binary overlay file (DPMI16bi.ovl) were also required in 

the same folder.

Auxiliary files—

Under the main AUTOSAW folder, a subfolder named INIT must be created and 

several graphic-support files, font files, and a binary definition file must be copied 

there (FRI 1994). In addition, the folder must contain six AUTOSAW definition 

files, which are text files that provide information used by the sawing simulator. 

These files—Autosaw.def, Autoset.def, Autoedge.def, Cantsaw4.can, Edge2.def, and 

Livesaw4.liv—can be revised using a text editor to change default settings. However, we left them unchanged.

Parameter file—

AUTOSAW requires a parameter file to provide information about the sawmill 

being simulated, the type of products to be sawn, settings for various parameters 

used during the sawing simulation, and the lumber grading rules to be used. The 
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

file can have any name but must have an extension of either CAN or LIV, depending 

on whether cant sawing or live sawing is to be simulated. Because we elected to use 

cant sawing, we created a parameter file named STUDS.CAN. Information from the 

STUDS.CAN file used for our simulations is summarized in table 30.

Command file—

We ran AUTOSAW in automatic-simulation mode, rather than interactive mode, 

because of the large number of logs to be sawn. This mode requires a text file with 

an extension of AUT that provides information to AUTOSAW on the type of sawing to be done and the number and location of the data files for the short logs to be 

processed. As an example, the format of the AUT file is:

STUDS.CAN

4

C:\AUTOSAW\STUDS\Y_DAT\1016-1-1.dat

C:\AUTOSAW\STUDS\Y_DAT\1016-1-2.dat

C:\AUTOSAW\STUDS\Y_DAT\1016-2-1.dat

C:\AUTOSAW\STUDS\Y_DAT\1016-2-2.dat

These instructions tell AUTOSAW that cant sawing is to be simulated and the 

sawing parameters are to be found in STUDS.CAN, which is located in the same 

folder as the AUT file. Four logs are to be sawn and the DAT files for these logs are 

found in the C:\AUTOSAW\STUDS\Y_DAT folder.

Operation

To execute a simulation run, the user opens a command window by executing the 

cmd.exe command-line processor, then navigates to the folder where the AUT file is 

located and launches AUTOSAW by typing the filename (Autosaw.exe) at the command line and pressing the Enter key. If the executable file is located in a different 

folder than the AUT file, the location of the executable file can be specified by prefixing either a relative or absolute path to the executable filename. For instance, if 

Autosaw.exe is located in the C:\Autosaw folder, typing C:\Autosaw\Autosaw.exe at 

the command line will launch the simulator. After the simulator has been launched, 

the user chooses the option to initiate an automated simulation run and enters the 

name of the AUT file. AUTOSAW reads the file and processes the logs one by one 

according to the parameters specified in the CAN file.

Output Files

For each log specified in the AUT file, an AUTOSAW simulation produces one file 

with an extension of CON and another with an extension of OUT. It also produces a 

single file with the same base name as the AUT file but with an extension of SWN. 

This file contains a summary of the total volume of lumber produced from each log 
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RESEARCH PAPER PNW-RP-605

Table 30—Summary of sawing instructions used for the AUTOSAW simulations as specified 

in the STUDS.CAN parameter file; most numerical data except nominal lumber dimensions are 

specified in millimeters and conversions are shown in parentheses to units of measure that are 

commonly used in Douglas-fir sawmills

Specification Description

Log presentation Small end toward the saw

Headrig saw Vertical bandsaw

Log position Centered on the carriage with the log rotated into the same position

 as initially measured (y-axis pointing vertically upward) except

 that logs with sweep are rotated instead to the “horns up” orientation.

Sawing method Half-taper cant sawing (cants are sawn with edges parallel 

 to the center; i.e., the longitudinal axis, of the log)

Headrig sawkerfa 3.6 mm (0.142 inch)

Cant breakdown sawkerf a 3.6 mm (0.142 inch)

Edger sawkerf a 3.8 mm (0.150 inch)

Trim increment 304.8 mm (1 foot)—each piece length is an even multiple of 

 this after trimming

Minimum piece length 1829 mm (6 feet)

Maximum wane permitted 10 mm (0.4 inch) along each edge

Pith diameter 10 mm (0.4 inch)

Cant width (nominal) 4, 6, 8, 10, or 12 inches

Cant width (rough-sawn)a,b,c 97, 150, 198, 249, or 300 mm (3.82, 5.91, 7.80, 9.80, or 11.81 inches).

Minimum log small-end 101.6, 152.4, 203.2, 254.0, or 304.8 mm (4, 6, 8, 10, or 12 inches)

 diameter for corresponding 

 cant widths

Lumber thickness (nominal) 1 or 2 inches

Lumber thickness (rough-sawn)a,b 24.6 or 46.5 mm (0.97 or 1.83 inch)

Lumber width (nominal) 4, 6, 8, 10, or 12 inches

Lumber width (rough-sawn)a,b 97, 150, 198, 249, or 300 mm (3.82, 5.91, 7.80, 9.80, or 11.81 inches)

Edging methodology Edge to maximize volume, testing all possible width combinations

Lumber grading rules:

 1-inch boards Common boards (WWPA 2005)

 2 × 4 pieces Structural light framing (WWPA 2005)

 2 × 6, 2 × 8, 2 × 10, 2 × 12 pieces Joists and planks (WWPA 2005)

Lumber grades:

 1-inch boards Common No. 1, 2, 3, 4, 5

 2 × 4 pieces Select Structural, No. 1 (construction), No. 2 (standard), 

 No. 3, economy

 2 × 6, 2 × 8, 2 × 10, 2 × 12 pieces Select Structural, No. 1 (construction),

 No. 2 (standard), No. 3, economy

a Median values from eight sawmill studies on second-growth Douglas-fir as recorded in the product-recovery database located at the 

U.S. Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main Street, Portland, OR 97205.

b These dimensions are for rough-sawn lumber before it shrinks from drying and is reduced by surfacing.

c Although cant widths are specified as equal to lumber widths, AUTOSAW expands the cant width to account for the internal saw kerfs 

that will be needed when the cant is reduced into individual pieces.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

in the simulation, measured in cubic meters. Rather than use information from this 

file, we wrote the program described below to aggregate detailed results from the 

OUT files into a single Excel file.

CON and OUT files have the same base name as the AUT file, up to the first six 

characters of the filename stem, plus an index number that begins at 1 for the first 

DAT file specified in the AUT file and runs up to the number of logs sawn in the 

simulation. For example, if an AUT file named Cataract.AUT lists 30 short-log DAT 

files to be sawn, the CON files would be named Catara1.CON, Catara2.CON, ... 

Catara29.CON, Catara30.CON. The OUT files from the same simulation run would 

have corresponding names but with an OUT extension. Because the maximum 

value of the index is 99, no more than 99 DAT files can be specified in an individual 

AUT file. Multiple AUT files can be used to run all logs for the study.

Each CON (continuation) file is a binary file that stores a record of the sawing 

simulation for an individual log. The CON file can be loaded into AUTOSAW using 

its Continue with Old Run option and the results of the simulation can then be 

visualized or processed further. This is how the images in figure 24 were produced.

Each OUT file is a text file listing each piece of lumber produced in the simulation along with its dimensions and assigned grade. The software discussed below 

uses these files to compile a spreadsheet with complete results for all of the logs 

processed in an entire simulation run.

Compiling AUTOSAW Simulation Results

Because an AUTOSAW simulation produces an OUT file for each log sawn, we 

wrote a program to aggregate the product information for all logs sawn during a 

simulation run into a single Excel file. This corresponds to step 6 in figure 8. The 

conversion program was written in the C# programming language and uses Excel 

automation in the same way as the bucking program described in appendix 2. 

Following is a brief description of the output-aggregation program:

Executable file (xlFromOutfiles.exe)—

This file can simply be copied into any folder on the user’s computer without formal 

installation; administrative privileges are not required. The user’s computer must 

meet the same requirements as the bucking simulator (app. 2), and the executable 

file may need to be unblocked before its first use (app. 1).

Supporting files (Template.xls and Template.xlsx)—

These two Excel files must be present in the same folder as the executable file. The 

XLS file is used as a template to produce an output file if the user’s computer has 

Excel 2002 or 2003 installed, and the XLSX file is used as a template for Excel 

2007 or later. The contents of the two template files are identical. Each contains 
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RESEARCH PAPER PNW-RP-605

two worksheets, one named Autosaw Results and the other named WWPA Grades. 

Data from the AUTOSAW OUT files are written to the first worksheet, with each 

row corresponding to a single piece of lumber. Formulas in that worksheet then use 

information from the second worksheet to convert AUTOSAW grades into the corresponding WWPA (2005) grades and to create a report summarizing the lumber 

produced by the simulation.

Operation—

The user navigates to the folder where the executable file has been placed and 

double-clicks the filename to launch the program. The user interface for this program is shown in figure 25. In step 1 the user browses to a folder containing output 

files from an AUTOSAW simulation and selects any file with an OUT extension. 

The program then infers the root name of the OUT files (step 2); in figure 25 this is 

W2-, implying that the OUT files are for the lightly thinned treatment at the Wildcat 

site and that individual OUT files are named W2-1.out, W2-2.out, and so on. The 

root name can never be longer than six characters, because AUTOSAW reserves 

two characters for the file index values and the names of OUT files cannot exceed 

eight characters under MS-DOS® naming rules. In step 3, the program determines 

the number of OUT files with that root name in the folder; the file index values in 

figure 25 run from 1 through 43. The user can enter a short description of the logs 

from which the lumber was produced (step 4) and then click the Aggregate button 

(step 5) to create the Excel file.

Input files—

This is a set of OUT files from an AUTOSAW simulation. These files must be 

located in a single folder and they must all have the same root name.

Output files—

Two files are produced: _LogList.txt and either Root.xlsx or Root.xls, where Root 

is replaced by the root name of the set of OUT files described above. The Excel file 

created as the primary output has the extension XLSX or XLS depending on the 

version installed on the user’s computer. For the illustration in figure 25 the output 

filename would be W2-.xlsx or W2-.xls.

The _LogList.txt file is a simple text file that provides a one-to-one mapping of 

OUT files to the corresponding short-log identifiers, such as the following example 

for two of the logs being processed: 

Filename Log ID

W2-1.out W2-1008-1-1

W2-2.out W2-1008-1-2
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Figure 25—The user’s interface for the xlFromOutfiles program, which aggregates multiple output files from AUTOSAW into a 

single Microsoft Excel file.

The Excel file produced by the program contains a complete listing of all lumber produced by the AUTOSAW simulation as well as a summary of the number 

of pieces and volume of lumber in each piece size, and a separate summary of the 

volume produced by lumber grade.
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Appendix 4: Three-Dimensional Data for Standing Trees

This appendix describes the procedures and assumptions used to convert data from 

long-log Microsoft Excel worksheets into a three-dimensional dataset in a single 

Excel worksheet comprising information on profile points and branch locations for 

standing trees. We developed a computer program in the C# programming language 

(xlLogToTree3D.exe) to reassemble long logs into the standing trees from which 

they came and then derive three-dimensional coordinates for profile points and 

branch locations (surface knots). We used long-log (woods-length) data rather than 

short (mill-length) log data because they are more complete. The long-log worksheets include information on sections of logs that were trimmed off and discarded 

during the simulated bucking process (app. 2), whereas the short-log worksheets 

by definition omit this information. By combining data for all long logs from each 

sample tree and accounting for short sections of tree stems that were bucked out and 

left in the woods, we were able to put together three-dimensional data on profile 

points and branch locations (surface knots) for individual trees up to the height at 

which each was topped (fig. 26). We refer to this as merchantable height of the tree, 

although in some instances it was beyond the limit of merchantability, defined for 

this study as the height at which the stem diameter drops to four inches. Occasionally the stem diameter at the merchantable height determined in this way was larger 

than the minimum merchantable diameter because breakage or defects located near 

the top of the tree prevented the logger from including the top log all the way to the 

4-inch limit.

Constructing a virtual tree from the individual long logs requires knowledge 

about how the logs originally fit together within the tree. From the methodology 

used to record data for each long log, we know the location of each log within the 

tree stem and, for most logs, the azimuth toward which the longitudinal axis of the 

log faced in the standing tree. At each measured profile point we can also calculate 

coordinates for the center of the log and for any point on the surface of the log (app. 

3). Because the measurement methodology was designed to provide this information in log coordinates for the sawing simulator, a different procedure is required to 

convert the log-based coordinates into tree coordinates.

Deriving z-Coordinates for Points Within the Standing Tree

As shown in figure 26, the known locations of the logs within the standing tree 

can be used in a relatively simple way to derive a tree-based z-coordinate for each 

profile point. This z-coordinate measures the height above the ground of any point 

within the measured tree stem. Given that each log-based z-coordinate was measured as a distance from the large end of each log, the corresponding tree-based 

z-coordinate can be calculated as follows:
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Tree topped at

minimum

merchantable

diameter

Unmerchantable

section bucked

out and discarded

in woods

zt(1, k1) = z1(k1)

 + S k1

k2

zt(3, k2) = z3(k2) 

 + S

 + L1

 + L2

 + LD

x-axis

y-axis

z3(k2)

z1(k1)

S

L1

L2

LD

M

L3

L1, L2, L3 Lengths of logs 1, 2, and 3.

LD Length of a discarded section between

logs 2 and 3. 

S Height of the stump above the ground.

M Height to the merchantable top of the tree.

k1, k2 Knots 1 and 2 located at different heights

on the tree.

z-axis An approximately vertical line connecting

the geometric centers of the two ends of

each log within the reassembled tree.

x-axis A horizontal line extending through the 

z-axis at any point within the tree, with 

coordinate values increasing toward the 

east.

y-axis A horizontal line extending through the

z-axis and perpendicular to the x-axis,

with coordinate values increasing toward

the north.

zt(i, kj) The z-coordinate in the tree coordinate

system for point kj within log i.

zi(kj) The z-coordinate in the log coordinate

system for point kj within log i.

Felling or bucking point.

Profile point.

Unused section of the bole.

Figure 26—The methodology for determining coordinates of profile points and branch locations in a standing tree from long-log 

data; note that—unlike z-coordinates—the x- and y-coordinates are independent of the height of the profile point in the tree but 

must be determined relative to axes that are constant for the entire tree stem.
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zt (i,P)=zi(P)+∑j=0

i-1 Lj

where:

zt (i, P) = the tree-based z-coordinate for any point P measured within log i,

zi (P) = the log-based z-coordinate for point P measured within log i,

Lj = stump height, S, when j = 0, otherwise the total length of log j.

We did not measure stump height, so we assume it is 1 foot for all sample trees 

in the STUDS project. Based on field observations this is a reasonable average, but 

of course it does not capture the actual variation inherent when trees are felled on 

varying slopes.

In addition to the calculation in the above equation, the tree-based z-coordinate 

must be adjusted for any unmerchantable sections of the tree stem that were bucked 

out below log i and discarded. Figure 26 shows one such section between logs 2 

and 3. Of the 66 sample trees in this study, the logger discarded a portion of the 

merchantable part of the stem for only four trees, and one broken log was rebucked 

at the landing to discard the broken section, resulting in the second and third merchantable logs from what had been a two-log tree. Data for the discarded sections 

were recorded as summarized in this tabulation: 

Discarded sections

Tree Location Length (feet) Description

C2-1035 Between log 1 and log 2 14.0 Broken, crooked piece

C2-1570 Between log 2 and log 3 12.0 Lengthwise split through the stem

Y2-1659 Between log 2 and log 3 3.8 Broken log rebucked at the landing

Y3-2002 Between log 2 and log 3 4.0 Stem crushed at this point

Y3-2532 Between log 2 and log 3 3.0 Short broken piece

We did not measure knots or other surface defects within the discarded sections, so deriving profile or branch data for those sections was not possible. Measurements for each discarded section were limited to the length of each discarded 

piece and the diameters at both ends of the piece.

The methodology described above and shown in figure 26 for converting 

log-based z-coordinates to tree-based z-coordinates involves two approximations. 

First, z-coordinates for each log were measured along the longitudinal axis of the 

log rather than along the z-axis—the longitudinal axis is a projection of the center 

of the log onto the surface of the log along its length (fig. 7), whereas the z-axis is a 

straight line joining the geometric centers of the two ends of the log. For a log with 

sweep or crook, this means that the measured z-coordinate is slightly longer than 

the corresponding distance along the z-axis. For this study, we ignore this error 

because for our sample logs it was always less than 0.1 foot—the limit of precision 

for our length measurements (app. 2). If larger errors were encountered, a correction 

would be necessary.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

A further approximation in the above methodology is that the z-axes of individual logs within a tree may not all be vertical. This would cause slight differences 

between the assumed vertical-length measurements shown in figure 26 and the 

actual distances along the z-axes of the logs. For most Douglas-fir trees, any such 

error is likely to be very small; also, because our measurement procedures did not 

capture the information that would be necessary to adjust for this type of error, we 

had no other option but to ignore it.

Deriving x- and y-Coordinates for Points Within the Standing Tree

The methodology used to derive tree-stem x- and y-coordinates for profile points 

and branch locations (surface knots) is more complex than the procedure outlined 

above for deriving z-coordinates, and is also potentially subject to somewhat larger 

errors. The procedure for calculating x- and y-coordinates for logs as described in 

appendix 3 uses an arbitrary log-based coordinate system that is appropriate for 

the sawing simulator but is not guaranteed to correctly orient logs from a particular 

tree relative to one another. Reconstructing tree stems from their constituent logs 

requires the establishment of a single, tree-based coordinate system that can be 

used to calculate x- and y-coordinates for all profile points and branch locations in 

all logs cut from the tree. The reason for this is illustrated in figure 27. When the 

upper log identified as log 2 in figure 27 was measured in its 0° orientation following the procedure outlined in figure 7, its longitudinal axis lay along a surface that 

had faced toward the west (azimuth 270°) in the standing tree. The longitudinal 

axis for the butt log (identified as log 1 in fig. 27), however, lay along a surface 

that had faced toward the north (azimuth 0°) in the standing tree. If the two logs 

are to be virtually reassembled as they originally fit within the tree, one of them 

must be “rolled” by 90° (the acute angle between azimuths of 270° and 0°) and its 

coordinate system reoriented to match that of the other log. This is accomplished 

by rolling the upper log until the surface that in the standing tree had faced toward 

the north is at the top of the log. Then the x- and y-axes of log 2 are rotated to match 

those of log 1. Once this has been done, the coordinates of all objects within log 2 

can be recalculated using the tree-stem coordinate system and they will be consistent with the coordinates of objects within log 1.

Rather than base tree-stem coordinates on the axes established arbitrarily for 

one log from each tree, we decided instead to establish a coordinate system that is 

identical for all sample trees measured for the entire study. This coordinate system 

defines the x-axis as running east and west, with a positive value for x toward the 

east; and the y-axis as running north and south, with a positive value of y toward 

the north. The two axes intersect at every point along the z-axis of the tree, where 

x = y = 0. Thus, in figure 26 the x-axis shown at the top of the stump is a west-east 
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RESEARCH PAPER PNW-RP-605

line and the y-axis is a south-north line. The z-axis is assumed to extend vertically 

upward from the center of the stump, which as noted earlier, is assumed to be a foot 

above ground level. In three-dimensional coordinates, denoted {x, y, z}, the center 

of the stump (or equivalently the center of the large end of the butt log) is always at 

point {0, 0, 304.8}, with coordinates expressed in millimeters. Similarly, if the butt 

log has a length of 11,277.6 mm (37 feet), the center of the small end of the log would 

have coordinates {0, 0, 11582.4}. For this exercise all coordinate values are expressed 

in millimeters even though the original measurements were in feet and inches.

(A) (C)

Longitudinal

axis (faced

west in tree)

x-axis

y-axis x-axis

y-axis

y-axis

x-axis

Faced north in tree

Longitudinal

axis (faced

north in tree)

(B)

Log ends

do not match

Log ends

match

y-axis

x-axis

A horizontal line extending through the pith 

and to the perimeter at each end of the log 

and at any profile point in the log.

x-axis

A vertical line perpendicular to the x-axis 

extending through the pith at each end of 

the log and at any profile point in the log.

y-axis

A line along the length of a log parallel to 

the path followed by the pith at the center of 

the log; a straight line if the log is straight or 

a curved line if the log has sweep or crook.

Longitudinal

axis

Figure 27—Reconstructing a tree stem from its long logs after simulated bucking requires that x- and y-axes for the logs align correctly, 

as in this example: (A) the longitudinal axis used as a reference to establish the x- and y-axes for log 2 faced west in the standing tree; 

whereas (B) the longitudinal axis for log 1 faced north, causing data for the two logs to be based on different coordinate systems; 

and requiring (C) the virtual “rolling” of one log as needed until the two coordinate systems matched, then recalculating the x- and 

y-coordinates of objects within the rolled log.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Mirror images—

The x- and y-axes defined as described above for the tree stem are mirror images of 

the axes that were established when measuring individual logs. This has implications for the way the log axes must be rotated to ensure an equivalent tree-stem 

coordinate system, and also affects numerical values along the x-axis. To visualize 

the effect on x-coordinates, consider log 1 in figure 27. The y-axis of the log points 

toward the direction that the top surface of the log faced in the standing tree; this 

is shown in the image as north. Because of the way the log-coordinate system was 

established, the positive values of the x-axis increase toward the right, which in the 

standing tree was the surface of the log that faced west. In tree-stem coordinates 

the y-axis also points toward north, but positive values of the x-axis increase toward 

the east as shown in figure 26. As a result, for log 1 in figure 27, surface defects 

on the side of the log that faced west in the standing tree will have positive log 

x-coordinates but negative tree-stem x-coordinates.

Rotating axes and calculating center coordinates—

The initial step in computing x-and y-coordinates for objects at any log profile point 

is to derive the coordinates at the center of the stem corresponding to the profile 

point. The procedure we followed for doing this is illustrated in figure 28 and 

described below.

1. In figure 28, point C is at the center of the log and corresponds to a profile 

point for which coordinates of surface defects or other features are needed. 

Point C is the base from which these coordinates must be calculated for the 

profile point. The azimuth of the y-axis established when the log was measured was 315°. The log in the example has both a sweep offset of Δs parallel to the x-axis at point C and a crook offset of Δc parallel to the y-axis at 

point C.

2. The azimuth of the original y-axis as shown in figure 28 is the direction 

toward which the top surface of the log in its initial measurement orientation would have faced when the log was in the standing tree. For tree-stem 

coordinates, the y-axis should point north, corresponding to an azimuth of 

0°, so the y-axis in the figure must be rotated from 315° to 0°. Because the 

x- and y- axes are orthogonal by definition, both must be rotated equally.

3. If it seems odd that the original y-axis with an azimuth of 315° is shown to 

the right of the rotated y-axis at 0°, remember that our point of reference 

for log coordinates (shown in the image) is from the large end of the log 

looking toward the small end of the log and thus toward the top of the tree. 

But for stem coordinates, the point of reference is the tree as it stood on the 

stump (fig. 26). Therefore the x- and y-axes shown in figure 28 represent 
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RESEARCH PAPER PNW-RP-605

mirror images of the axes as they would be viewed from the perspective of 

the standing tree.

4. The point around which the x- and y-axes rotate is the z-axis. This point lies 

outside of the log cross section because of sweep and crook at this profile 

point. For an example of such a situation, refer to figure 21, where the z-axis 

is located outside the log at point D and is associated with the profile point 

centered on the bucking point.

5. Rotation of the x- and y-axes is shown in figure 28, where the original axes 

are drawn in red and the rotated axes in blue.

6. Because of the difference in perspective between log coordinates and treestem coordinates, the x-axis must be “flipped” from west to east after the 

Azimuth of y-axis

established when

the log was

measured

315°

0°

Azimuth of y-axis

used to reassemble

logs into tree stem

+x Original x-axis

+y Original y-axis

Rotated y-axis

Rotated and flipped

x-axis +x

Cross-section of log

at profile point

C

B

Rotation of xand y-axes

+y

A

∆c

∆s

An imaginary straight line between the 

geometric centers of the two ends of a log.

z-axis

A Location of the z-axis, offset from C by sweep 

and crook. In this figure the axis is a single point 

because it is perpendicular to the plane of the 

page.

C A point at the center of a log for which the 

locations of surface defects are to be calculated 

in tree-stem coordinates.

B A point on the original y-axis where it intersects 

a line drawn through point C and parallel to the 

original x-axis.

∆c The crook offset (A to B) in original coordinates. 

In this example the crook offset is parallel to the 

original y-axis and in the positive y direction 

(upward).

∆s The sweep offset (B to C) in original coordinates. In this example the sweep offset is 

parallel to the original x-axis and in the negative 

x direction (leftward).

In original log coordinates, a horizontal line 

extending through the geometric center of the 

log at each end; in rotated coordinates, a 

horizontal line extending through the geometric 

center of the tree stem at any point and pointing 

east.

x-axis

In original log coordinates, a vertical line 

perpendicular to the x-axis and intersecting the 

geometric center of the log at each end; in 

rotated coordinates, a horizontal line perpendicular to the x-axis extending through the 

geometric center of the tree stem at any point 

and pointing north.

y-axis

Figure 28—Cross sectional view of a log at an arbitrary stem profile point after simulated bucking, showing the procedure used to rotate 

the x- and y-axes and flip the x-axis, thereby converting the log coordinate system into a tree-stem coordinate system with the y-axis 

pointing north and the x-axis pointing east.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

rotation described in steps 2 to 5 has been carried out. This is necessary 

because positive x-coordinates increase toward the west in the log coordinate system but increase toward the east in the stem coordinate system (fig. 

26). The difference is a result of the mirror-image relationship between log 

coordinates and stem coordinates.

7. From the geometry shown in figure 28, the x- and y-coordinates at point C, 

in tree-stem coordinates, can be calculated as shown below while simultaneously rotating the x- and y-axes about the z-axis and flipping the x-axis. 

The calculations differ for the two types of offsets (left/right or up/down) 

because they have different geometric relations.

xC = ∑i=0

n s1i ∙ s2i ∙ ∆i ∙ fxi (θ)

yC = ∑i=0

n s2i ∙ ∆i ∙ fyi (θ)

where:

xC = the x-coordinate at the center of the log profile (point C in fig. 28),

yC = the y-coordinate at the center of the log profile,

n = the number of sweep and crook offsets measured at the current profile point 

(n = 2 in fig. 28, which shows one sweep offset and one crook offset),

s1i = –1 if the offset direction of ∆i is left or right (for example ∆s in fig. 28), or +1 

if the offset direction is up or down (for example, ∆c in fig. 28),

s2i = –1 if the offset direction of ∆i is left or down, or +1 if the direction is right 

or up,

∆i = the sweep or crook offset at point i as determined when the log was measured, 

fxi (θ) = cos(θ) if the offset direction of ∆i is left or right, or sin(θ) if the offset 

direction is up or down,

fyi (θ) = sin(θ) if the offset direction of ∆i is left or right, or cos(θ) if the offset 

direction is up or down,

θ = the azimuth of the original y-axis before the rotation (315° in fig. 28).

In the equation for x, the –1 value of s1i for left and right offsets flips the x-axis 

from west to east as described in point 6 above.
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RESEARCH PAPER PNW-RP-605

To illustrate the procedure outlined above, consider the situation illustrated in 

figure 28. At the profile point shown, adjustments must be made for one crook offset and one sweep offset. The extent of each offset at the profile point is calculated 

from the original measurement data using the procedure described in appendix 2. 

Offset calculations at any profile point use the same methodology for both sweep 

and crook except that sweep extends over the entire length of the log whereas crook 

has a specified beginning and end within the log. Suppose that for the situation in 

figure 28 the two offsets have been determined to be the following:

• Crook offset: up 117 mm, or about 4.6 inches (the distance from point A 

to point B)

• Sweep offset: left 148 mm, or about 5.8 inches (the distance from point B 

to point C)

The “up” and “left” qualifiers are expressed in terms of the original x- and 

y-axes that were established when the log was measured. Using the equations for 

xC and yC as described in step 7 above, the tree-stem coordinates at point C are 

calculated as shown below, where i = 1 for the crook offset and i = 2 for the sweep 

offset. These specific values for i are arbitrary; the order in which the offsets are 

calculated makes no difference to the outcome. Variables in the equations are 

established as: s11 = +1 because offset 1 (crook) has an up or down direction; s21 = 

+1 because offset 1 (crook) is up; ∆1 = 117; fxi (θ) = sin(θ) because offset 1 (crook) 

has an up or down direction; fy1 (θ) = cos(θ) because offset 1 (crook) has an up or 

down direction; s12 = –1 because offset 2 (sweep) has a left or right direction; s22 = 

–1 because offset 2 (sweep) is to the left; ∆2 = 148; fx2(θ) = cos(θ) because offset 2 

(sweep) has a left or right direction; fy2 (θ) = sin(θ) because offset 2 (sweep) has a 

left or right direction.

Then calculate the coordinates of point C as follows:

x = [s11∙s21∙∆1∙fx1 (θ)] + [s12∙s22∙∆2∙fx2(θ)]

= [+1∙ + 1∙117∙sin(315°)] + [-1∙-1∙148∙cos(315°) ] = -82.7 + 104.7 = 22.0 mm

y = [s21∙∆1∙fy1 (θ)] + [s22∙∆2∙fy2(θ)]

= [+1∙117∙cos(315°) ] + [-1∙148∙sin(315°)] = + 82.7 + 104.7 = 187.4 mm

Calculating coordinates of surface defects—

Below is a description of the procedure for calculating the coordinates of surface 

defects on the reassembled tree stem, as illustrated in figure 29.

1. Locations of surface defects have been measured as offsets from the longitudinal axis of the log, measured along the curve of the log surface (fig. 

7). Figure 29 shows a surface knot on the profile point from figure 28. The 

location of the surface knot has been recorded as left from the longitudinal 
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Figure 29—Cross-sectional view of a log showing the procedure for calculating the tree-stem coordinates of a surface knot when 

the offset to the knot from the original y-axis of the log is known.

axis a distance of dk. The first step in the calculation procedure is to determine the angle between the longitudinal axis of the log (the original y-axis) 

and a line drawn from the center of the log through the center of the surface 

knot. In figure 29, this angle is denoted θ. It can be calculated as:

 θ = dk ∙ 360 / (π ∙ DC)

where:

θ = the angle in degrees between the original y-axis and a line drawn from 

the center of the log at point C through the center of the surface knot,

dk = the offset distance from the original y-axis to the center of the surface 

knot as measured along the curve of the log surface (units of measure must 

be the same as those for DC below),

360 = the number of degrees in a full circle,

π = the ratio of the circumference of a circle to its diameter,

DC = the diameter of the log at profile point C (units of measure must be the 

same as those for dk above).

315°

0°

+x Original x-axis

+y Original y-axis

Rotated y-axis

Rotated and

flipped x-axis +x

Cross-section of log

at profile point

C +y

dk

θ

Surface

knot

C A point at the center of a log for which the 

locations of surface defects are to be calculated 

in tree-stem coordinates.

θ The angle corresponding to offset dk.

dk Offset to a surface knot as measured from 

the original y-axis of the log.

In original log coordinates, a horizontal line 

extending through the geometric center of the 

log at each end; in rotated coordinates, a 

horizontal line extending through the geometric 

center of the tree stem at any point and pointing 

east.

x-axis

In original log coordinates, a vertical line 

perpendicular to the x-axis and intersecting the 

geometric center of the log at each end; in 

rotated coordinates, a horizontal line perpendicular to the x-axis extending through the 

geometric center of the tree stem at any point 

and pointing north.

y-axis
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RESEARCH PAPER PNW-RP-605

2. Calculate αk, the azimuth that the surface knot faced in the standing tree, 

using αy to denote the azimuth of the original y-axis (315° in fig. 29): If the 

surface knot is to the left of the original y-axis of the log, αk = αy + θ; if it 

is to the right of the original y-axis, αk = αy – θ. If the result is larger than 

360, subtract 360 from αk; if the result is less than 0, add 360 to αk.

3. Calculate the tree-stem coordinates of the surface knot as:

 xk=xC+(D—C

2 )∙Sin(αk

)

 yk=yC+(D—C

2 )∙Cos(αk )

where:

xk = the x-coordinate at the center of the surface knot,

yk = the y-coordinate at the center of the surface knot,

xC = the x-coordinate of the center of the log profile (point C in fig. 29),

yC = the y-coordinate of the center of the log profile,

DC = the diameter of the log at profile point C,

αk = the azimuth faced by the surface defect in the standing tree as derived 

in step 2 above.

Rotating elliptical diameters—

The final adjustment needed to complete the conversion of log-based data into the 

equivalent tree-stem data is to rotate the elliptical diameters at the two ends of each 

log and adjust the diameter measurements along the rotated x- and y-axes. In the 

Siuslaw Thinning and Underplanting for Diversity Study (STUDS) project, we took 

both horizontal and vertical diameter measurements only at the two ends of each 

log. Diameters at all other profile points were recorded from only a single horizontal-diameter measurement. For the vertical measurements, therefore, we were 

forced to assume that the log cross section is circular. However, the dual measurements at each end of the log permitted us to use elliptical geometries for those two 

profile points in each log. This was consistent with AUTOSAW, which assumes that 

log cross sections are elliptical.

Figure 30 illustrates the diameter corrections required when the x- and y-axes 

are rotated with respect to a log with an elliptical cross section. As before, we rotate 

the axes from their original positions when the log was measured into a new position that has the y-axis oriented toward north. The figure shows the original y-axis 

being rotated through an angle θ, which is equal to the azimuth of the original 

y-axis. The original azimuth of 315° is shown to the right of north because of the 

mirror-image effect discussed earlier. The x- axis is of course rotated through the 

angle θ as well; however, the x-axis does not need to be flipped because we are not 

calculating coordinates. Diameter measurements are absolute values and are invariant regardless of how the log profile is positioned along the x-axis.
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

The original diameter measurements shown in figure 30 are Dh (the horizontal 

measurement) and Dv, (the vertical measurement). These are assumed to represent 

the lengths of the major and minor axes of the ellipse; in figure 30, Dh is the length 

of the major axis and Dv is the length of the minor axis. After the axes have been 

rotated, the diameter measurements along the rotated axes are Dhr (the rotated horizontal measurement) and Dvr

 (the rotated vertical measurement). These quantities 

can be calculated using the following equations derived from the standard formula 

for the rotation of an ellipse:

Dhr = Dh∙Dv

2 ∙ √ [Dv

—2 ∙ cos(θ1)

]

2 

+ [Dh—2 ∙ sin(θ1)

]

2

 )

Dvr = Dh∙Dv

2 ∙ √ [Dv

—2 ∙ cos(θ2)

]

2 

+ [Dh—2 ∙ sin(θ2)

]

2

 )

where:

Dhr = the rotated horizontal diameter measurement,

Dvr = the rotated vertical diameter measurement,

Dh = the original horizontal diameter measurement,

315°

+x Original x-axis

+y Original y-axis

+x Rotated x-axis

Dvr

Dh

Dv

Dhr

Rotated y-axis

0°+y

θ Cross-section 

of log at profile 

point

In original log coordinates, a horizontal line 

extending through the geometric center of 

the log at each end; in rotated coordinates, 

a horizontal line extending through the 

geometric center of the tree stem at any 

point and pointing east.

x-axis

In original log coordinates, a vertical line 

perpendicular to the x-axis and intersecting 

the geometric center of the log at each end; 

in rotated coordinates, a horizontal line 

perpendicular to the x-axis extending 

through the geometric center of the tree 

stem at any point and pointing north.

y-axis

θ The angle through which the axes of the log 

must be rotated in order to convert log 

coordinates into tree-stem coordinates.

Dv Vertical diameter of an elliptical log before 

the axes have been rotated.

Dvr Vertical diameter of an elliptical log after the 

axes have been rotated.

Dh Horizontal diameter of an elliptical log 

before the axes have been rotated.

Dhr Horizontal diameter of an elliptical log after 

the axes have been rotated.

Figure 30—Geometry of an elliptical profile point after simulated bucking, showing the horizontal and vertical diameter 

measurements and the equivalent measurements after the axes have been rotated.
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RESEARCH PAPER PNW-RP-605

Dv = the original vertical diameter measurement,

θ1 = the azimuth of the original y-axis established when the log was measured (in 

figure 30, θ1 = 315°),

θ2 = θ1 + 90°; if θ2 > 360°, θ2= θ1 – 270° (in fig. 30, θ2 = 45°).

Potential errors—

As noted before, the methodology for calculating tree-stem x- and y-coordinates 

is potentially subject to somewhat larger errors than the methodology for calculating tree-stem z-coordinates. The reason for this is demonstrated in figure 26. The 

potential for error stems from the fact that individual logs may not be perfectly 

vertical within the tree stem, resulting in an {x, y} coordinate at the top of a log 

that is not located directly above the {x, y} coordinate at the bottom of the log. 

This potential may be especially great for discarded sections, which are frequently 

crooked or irregularly shaped (fig. 26). Because our measurement methodology was 

not designed to provide information that can be used to compensate for this type of 

error, the calculated x- and y-coordinates for the stem above a log or discarded section with a nonvertical z-axis were offset from the true coordinates by an unknown 

amount. Because the quantity or direction of any such shifts cannot be estimated, 

we had no choice but to ignore them. Any study that relies on data gathered from 

individual logs rather than from the standing tree stem will include the possibility 

of this type of error. Stem-length or whole-tree harvesting could potentially be used 

but both would require a measurement apparatus designed to provide a fixed reference for the z-axis of the stem.

Depth data for surface defects—

Some surface defects are exposed as gouges or gashes that cut into the stem of the 

tree. Our data forms did not provide for these defects because AUTOSAW does not 

use this kind of data and because they are uncommon in second-growth Douglasfir. However, wherever we found an open wound in the side of a log, we recorded 

its depth on the data form as a note. Recognizing its potential value for future 

stem analyses, we converted the information from these notes into data in a Depth 

column within the surface-defect section of the output files produced by the threedimensional software described below. Only four instances of this type of information were recorded, as shown in the following tabulation: 

Distance Distance

Long from large from large

 log end (feet) Short log end (feet) Description

C3-2019-1 16.0 C3-2019-1-2 3.5 Gash from forked top 5 inches deep

W2-1247-1 22.2 W2-1247-1-2 5.8 Rotten scar 3 inches deep

W2-1285-2 1.9 W2-1285-2-1 1.9 Forked top scar 1.5 inches deep

Y2-1588-3 12.3 Y2-1588-3-1 12.3 Scar from forked top 2 inches deep
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Effect of Habitat-Improvement Thinnings on Lumber Products from Coastal Douglas-fir

Software Description for Mapping Profile Points 

and Surface Defects

Developing three-dimensional coordinates for profile points and stem defects 

(mostly indicating branch locations) is difficult to execute by hand but reasonably 

straightforward to implement in a computer program. To reassemble individual 

logs into the standing trees from which they came and then calculate the threedimensional coordinates, we developed a computer program in the C# programming language that uses Excel automation in the same way as the bucking simulator 

described in appendix 2. The program uses the methodology described in this 

appendix. Following is a brief description of the software:

Executable file (xlLogToTree3D.exe)—

This file can be copied into any folder on the user’s computer without formal installation; administrative privileges are not required. The user’s computer must meet 

the same requirements as the bucking simulator (app. 2), and the executable file 

may need to be unblocked before its first use. Appendix 1 provides instructions on 

unblocking the executable file.

Supporting files—

The LogDataFmt.xml file must be present in the same folder as the executable file. 

It is identical to the LogDataFmt.xml file that is used with the bucking simulator.

The Template3D.xls and Template3D.xlsx Excel files must be present in the 

same folder as the executable file. The XLS file is used as a template to produce an 

output file if the user’s computer has Excel 2002 or 2003 installed, and the XLSX 

file is used as a template for Excel 2007 or later. The contents of the two template 

files are identical. Each contains two worksheets, one named ProfileData and the 

other named DefectTypes. Three-dimensional data are written to the first worksheet, with each row corresponding to a measurement point within the tree stem at 

a specified distance above the ground. The second worksheet contains a listing of 

the defect codes and corresponding descriptions for all surface defects to be incorporated into the three-dimensional dataset.

Operation—

The user navigates to the folder where the executable file has been placed and double-clicks the filename to launch the program. The user interface for this program 

is shown in figure 31. When the window opens, the user clicks the Browse button to 

locate a file of log-data worksheets to be processed. When the file is opened, a list 

of worksheets appears in the drop-down box below the Browse button. Information 

about the file is recorded in the large text box near the center of the window; in figure 31, the report indicates that the file contains 48 worksheets with log-profile data. 
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An output filename is entered in the text box (Wildcat_LL_Tree3D.xlsx in fig. 31) 

and the Accept button is clicked. The output file is created or opened if it already 

exists and its status is reported in the large text box. The user then clicks one of the 

two processing buttons (step 3 in fig. 31)—normally, all worksheets are processed 

but only a single worksheet can be processed if desired. As each worksheet is 

processed, its status is reported in the large text box; if errors are encountered they 

will also be described. As the worksheets are being processed, the user can click 

the Cancel button to halt processing (for instance, if an error is reported) and check 

the partial output. If processing is allowed to complete, a summary appearing in 

the large text box reports the number of logs reassembled into each tree stem. For 

the example in figure 31, the 48 logs were aggregated into 19 tree stems—10 trees 

with three logs each and the rest with two logs each. After processing has been 

completed, the user can optionally close the Excel files (step 4 in fig. 31), and then 

process log-profile data from a different input file; otherwise the user would click 

the Exit button to close all files and exit the application.

Input file—

This is an Excel spreadsheet file with a filename extension of either XLS (Excel 

2002 or 2003) or XLSX (Excel 2007 or later). The file must contain one or more 

long-log or short-log data worksheets that conform to the format described in 

LogDataFmt.xml. Each long-log data worksheet must be named in the format 

Aw-xxxx-y, where A = a single letter representing the site (for the STUDS project, 

C is Cataract, W is Wildcat, and Y is Yachats); w = a single digit signifying the 

treatment (for the STUDS project 2 represents the light-thinning treatment T100 

and 3 represents the moderate-thinning treatment T60); xxxx = a four-digit tree 

number; and y = an integer representing the index of the long log (1 = the first long 

log cut from the stem; 2 = the next long log; and so on). If the data are for short 

logs, each worksheet must be named in the format Aw-xxxx-y-z, where Aw-xxxx-y 

is the same as for long logs and z = an integer representing the index of the short 

log (for example, 1 = the first short log cut from long-log y and 2 = the next short 

log cut from that same long log). Long-log and short-log data worksheets should not 

be mixed within a single input file. Stem data aggregated from long logs is likely to 

be more complete than stem data aggregated from short logs because bucking often 

results in discarded pieces that will not be accounted for if short-log data are used.

Output—

This is a single Excel file with two worksheets as described above for the Template3D supporting files. The filename is specified by the user and must have an 

XLS extension if the user’s computer has Excel 2002 or 2003 installed; it may have 

either an XLS or XLSX extension if the user’s computer has Excel 2007 or later 
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Figure 31—User interface for xlLogToTree3D.exe, the program created to aggregate log data into the equivalent data for the 

standing tree after a simulated bucking.
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installed. Within the ProfileData worksheet, each row provides data for an individual profile point within the tree stem. Although each data row can provide data 

for only a single surface defect, multiple data rows can have the same z-coordinate. 

Data within a specific row include the {x, y, z} coordinate at the center of the stem 

cross section at the profile point, the diameter of the stem, the azimuth that the 

surface defect would have faced in the standing tree, the defect-type code from 

the DefectTypes worksheet, the {x, y} coordinate of the defect (the z-coordinate is 

the same as that for the center of the stem), the surface dimensions of the defect, 

the depth of the defect if recorded in a note on the data sheet, and the estimated 

live and dead lengths within the log if the object is a branch or surface knot. All 

measurements are in millimeters except the azimuth, which is in degrees. For 

cross-reference purposes, each row also lists the site, treatment, long-log index, 

short-log index if the data are for short logs, the data page and data record from the 

original data-entry sheet, and the tag identifier of the log from which the data row 

was taken. Any notes attached to the original data row are also transferred to the 

output file.

Data storage—

All of the three-dimensional data are stored in a single worksheet of the Excel 

output file described above rather than being organized into separate worksheets by 

tree. The reason for this is that the worksheet can then be imported into a database 

as a single data table. Database queries can then be written to access the data by 

tree, long log, short log, or according to any aggregation or disaggregation criteria 

that might be desired.
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Glossary

1-inch lumber—Lumber sawn into a nominal thickness of 1 inch. In Douglas-fir, 

widths of 1-inch lumber commonly vary from 4 to 12 inches (nominal measure), 

although both smaller and larger pieces are sometimes sawn.

Aspect—The compass direction toward which the slope of a land feature faces, 

often expressed in general terms such as “north-facing” or “west-facing.”

Azimuth—A measure of direction on the earth, taken as the compass angle from 

due north to the object of interest. Thus, due north has an azimuth of 0 degrees; 

due east, 90 degrees; due south, 180 degrees; and due west, 270 degrees.

Bucking—The act or process of cutting a tree stem into long logs for yarding and 

transport, or of cutting a long log into short logs for processing in a mill.

Burl—A rounded knotty growth on a tree stem associated with deformed wood 

grain in the immediate vicinity of the burl.

Butt log—The first log removed from a tree stem immediately above the stump. 

See also log position.

Cable yarding—A method for transporting woods-length logs from the felling site 

to a landing (archaic: yard) where the logs can be loaded onto trucks for transport 

by road to a processing facility. Typically cable yarding is used at sites where the 

terrain is too steep to permit the use of ground-skidding equipment.

Cant—A thick block of wood sawn from a log that is later sawn further into 

individual pieces of lumber.

Crook—A curve or bend that extends only partway along the log length. A single 

log can have more than one crook.

Crown ratio—The ratio of tree-crown length to total tree height.

Dimension lumber—Softwood lumber sawn into nominal thicknesses ranging 

from 2 to 5 inches, with 2 inches being the most common nominal thickness. 

In Douglas-fir, dimension lumber typically varies in width from 4 inches to 12 

inches (nominal measure), although both smaller and larger pieces are sometimes 

sawn.

DBH—Diameter of a tree measured at breast height, generally taken as a point 4.5 

feet above the average ground level at the base of the tree.
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Dead knot—A knot whose fibers are only partially intergrown with the fibers of 

the surrounding xylem; also known as a loose knot. Dead knots usually have a 

visible separation between the knot and the surrounding tissue or may appear 

somewhat decayed at the surface.

Forked top—A defect in a tree stem caused by branching of the stem into two tops. 

When the tree is felled, the more poorly formed of the two is usually removed 

and the result is a surface defect similar to a large knot.

Heteroscedasticity—In statistical analysis, a condition in which the variability of a 

variable is unequal across the range of values of a second variable that predicts it.

Jacket board—A board produced incidentally when sawing a log into lumber. 

Jacket boards are produced from near the outside of the log (hence the term 

“jacket”) during the process of converting the round log into a squared cant for 

subsequent sawing.

Knot—A hard mass of wood where a branch has been severed from the stem of a 

tree. Knots are the most common defects in lumber manufactured from secondgrowth Douglas-fir.

Live knot—A knot with fibers that are largely intergrown with the fibers of the 

surrounding xylem; also known as an ingrown or tight knot.

Log position—The position within the tree stem from which the log was taken. For 

this study we classified logs as butt, middle, or upper logs. For long logs, the butt 

log is the first log above the stump and the upper log is the top log removed from 

the stem. Any logs between these two are classified as middle logs.

Long log—A log created by bucking a tree stem at the felling site into logs that 

can be yarded to a landing and then transported to a mill in preparation for 

processing into lumber or another product; also known as a woods-length log.

Lumber recovery—A measure of the quantity of lumber recovered from sawing a 

specified quantity of logs.

Merchandizing—The process of converting felled tree stems into logs.

Merchantable—A tree or stand that has attained sufficient size, quality, and 

volume to make it suitable for harvesting and conversion into wood products.

Mill-length log—See the definition for short log.

Middle log—A log extracted from the middle portion of a tree stem. See also 

log position.
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Offset—A distance measured from a reference line to the location of an object such 

as a knot on the surface of the log.

Overrun—The excess amount of lumber sawn from logs as compared to the 

amount predicted by a log-scaling rule. 

Precommercial thinning—A thinning operation carried out to reduce the density 

of a stand of trees before the trees have reached sufficient size, quality, or volume 

to be suitable for conversion into wood products.

Recovery (product, volume, grade, or value)—Any measure of product recovered 

from a manufacturing process. In lumber, product recovery is usually expressed 

as the amount of lumber (pieces or volume) recovered per unit (usually volume) 

of logs used. Recovery may be further specified as the fractions of different 

grades of lumber recovered per unit of logs used, or as the value of lumber 

recovered from a specified volume of logs or trees.

Sample log—Any of the logs cut from a sample tree and measured to provide 

stem and defect data. For this study, only long logs were measured; stem and 

defect data were then computed for the corresponding short logs by a bucking 

simulator.

Sample tree—For this study, a tree selected from the population of numbered trees 

located within 1-acre subplots and marked for removal in the thinning operation.

Short log—A log created by long-log bucking, usually at a mill where the resulting 

short log will be converted into lumber or other product; also known as a milllength log. In this study, short logs were produced mathematically by using a 

computer simulation program to buck long logs.

Sweep—Curvature of a log that extends along its entire length, causing the 

longitudinal axis of the log to deviate from a straight line. Sweep can be uniform 

with the maximum sweep occurring at the center of the log, or nonuniform with 

the maximum sweep occurring at a point nearer one end of the log.

Treatment T30—A treatment in the original 1992 to 1993 commercial thinning on 

the STUDS project areas that was designed to achieve a residual stocking density 

of approximately 30 trees per acre.

Treatment T60—A treatment in the original 1992 to 1993 commercial thinning on 

the STUDS project areas that was designed to achieve a residual stocking density 

of approximately 60 trees per acre.
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Treatment T100—A treatment in the original 1992 to 1993 commercial thinning 

on the STUDS project areas that was designed to achieve a residual stocking 

density of approximately 100 trees per acre.

Upper log—A log removed from the upper part of a tree stem. See also log 

position.

Wane—Missing wood along the edge of a piece of lumber, commonly caused by 

sawing near the outside of the round log.

Woods-length log—See the definition for long log.

WWPA—Western Wood Products Association, a trade association representing 

softwood lumber manufacturers in the Western United States. WWPA also 

serves as the principle authority for defining lumber grading rules for western 

tree species.
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