AN ABSTRACT OF THE DISSERTATION OF
Dzhamal Yusmenov Amishev for the degree of Doctor of Philosophy in Forest
Engineering presented on June 2, 2008.
Title: In-Forest Log Segregation Based on Acoustic Measurement of Wood
Stiffness
Abstract approved:
___________________________________________________________________
Glen E. Murphy
To remain competitive, the forest products industry needs to look for new and
innovative processes and technologies to not only reduce costs but also to recover
more value through the entire seedling-to-customer forest products supply chain. It
is well recognized that measuring wood properties of logs in real time during
harvesting would lead to improved log allocation decisions and increased value
recovery. Wood stiffness is certainly one of the attributes defining the quality of
forest products such as veneer and lumber. Accurately assessing stiffness in realtime can be a challenge for log supply managers requiring logs segregated into
different product classes based on stiffness.
Acoustic technology has proven to be a well established non-destructive technique
for assessing potential product performance by identifying logs with high stiffness.
Launched by the worldwide trend towards increased mechanization of forest
harvesting operations, providing a platform for innovative measurement systems,
the interest in incorporating technologies for measuring internal stem features into a
harvester head is rapidly growing. Therefore, the purpose of this study was to
provide forest products stakeholders with comprehensive scientific information on
the potential capabilities, limitations, and applicability of acoustic technology to
improve value recovery from Douglas-fir stands by means of in-forest sorting of
veneer quality logs.
This dissertation:
•
Demonstrated that recovery of high quality green veneer from Douglas-fir
peeler logs could be accurately predicted using resonance-based acoustic
velocity measurements,
•
modeled the predictive capabilities of spatial as well as internal and external
log and tree characteristics in terms of veneer quality and analyzed their
effects on acoustic velocity measurements of Douglas-fir wood stiffness,
•
determined whether time of flight acoustic technology could be used for
pre-harvest veneer quality assessment of Douglas-fir stands in terms of
stiffness requirements,
•
described influential factors arising from incorporating acoustic instruments
on a mechanized harvester head and suggested optimal procedures for
scanning in terms of feasibility and harvester productivity,
•
presented a general methodology to estimate breakeven prices of Douglasfir peeler logs based on the net return obtained when logs from stiffness
graded stands using acoustic technology are processed and converted into
end products.
© Copyright by Dzhamal Yusmenov Amishev
June 2, 2008
All Rights Reserved
In-Forest Log Segregation Based on Acoustic Measurement of Wood Stiffness
by
Dzhamal Yusmenov Amishev
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented June 2, 2008
Commencement June 2009
Doctor of Philosophy dissertation of Dzhamal Yusmenov Amishev presented on
June 2, 2008.
APPROVED:
Major Professor, representing Forest Engineering
Head of the Department of Forest Engineering
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of my
dissertation to any reader upon request.
Dzhamal Yusmenov Amishev, Author
ACKNOWLEDGEMENTS
The research presented in this dissertation would not have been possible without
the funding provided to me by the Forest Engineering Department of Oregon State
University and the USDA CSREES Center for Wood Utilization Research. I am
also very grateful to the family of Gordon G. Carlson for the fellowship I received
during my time here in Corvallis.
First and foremost, I want to extend my thanks to my major professor, Dr. Glen
Murphy. I am not sure whether words can express the breadth and depth of the
respect and gratitude I hold for Glen. I was blessed to have him as my advisor. His
guidance, motivation and knowledge allowed me to successfully finish this work.
Thanks for always having an open door and taking the time to help me through the
challenging research work with all this patience and understanding. It has been
truly inspiring to work with a professional like you. Beyond this, Glen has been a
special and great friend, an excellent mentor about all aspects of life and I am
grateful for having that.
My advisory committee of Dr. Barb Lachenbruch, Dr. Loren Kellogg, Dr. David
Mok, Dr. Todd Scholz, and Dr. John Sessions provided crucial input, guidance, and
encouragement which is much appreciated. I want to especially thank Dr. Sessions
for always accommodating any needs and circumstances, for sharing his impressive
knowledge and for being so supportive.
Thanks are extended to Roseburg Forest Products and especially the Oregon
Logging Manager Donald Persyn, for providing the stands, equipment, tools and
personnel to assist with this study. I am grateful to the OSU College of Forestry for
providing a stand in the College Research Forests, as well as funding for equipment
and personnel. I would like to express my gratitude to Dustin Boos for helping me
with data collection and processing, as well as to Francisca Belart for her help with
sampling and measurements, data entry and processing.
I would like to also thank the outstanding forestry faculty and staff of the
Department of Forest Engineering at OSU for providing field and laboratory
assistance, friendship, and much support. I would like to especially thank Dr. Steve
Tesch, Rayetta Beall, Yvonne Havill, Lesley Nylin for their endless support.
Faculty and professionals like Dr. Kevin Boston, Jim Kiser, Jeff Wimer, Geoff
Huntington, Steve Pilkerton, Dr. Darius Adams and others have greatly influenced
my scientific thinking and view of the world and I am grateful for that.
Thanks to my fellow students and friends Francisca Belart, Josh Clark, Mike
Vanderberg, Henk and Elise Stander, Matt Thompson, Mauricio and Marlene
Acuna, Sang-Kyun Han, Brian Wing, Matt and Jenny Meadows for being so
supportive and encouraging. I am very grateful to Robyn Murphy for always being
there for me and my family.
I would like to acknowledge my mother Giulfie, my father Yusmen, my brother
Beyhan, and my special cousin Rafet. Despite the distance we lived apart during
this time, I could always count on their support and advice.
To my wife, Nadezhda, goes my deepest appreciation for all her patience, support,
assistance, and unconditional love throughout these years. Along with her, I want to
dedicate this work to my son, Teoman. Always having you two by my side has
been emboldening and strengthening no matter the challenge. You have been my
constant and never ending inspiration and being with you makes me the luckiest
person in this universe.
CONTRIBUTION OF AUTHORS
I would like to recognize the considerable contribution of Dr. Glen Murphy on all
the papers cited in this document. His contribution included conceptual
formulation, study design, assistance with data collection and processing, and
reviews for each chapter.
TABLE OF CONTENTS
Page
CHAPTER 1: GENERAL INTRODUCTION TO DISSERTATTION TOPIC…..1
1.1 LITERATURE REVIEW……………………………………………………1
1.1.1 Global competition……………………………………………………….1
1.1.2 Wood quality……………………………………………………………..3
1.1.3 Acoustic technology and its forest products applications………………..4
1.1.4 Mechanization and technologies in forest operations…………………...10
1.2 RESEARCH OBJECTIVES………………………………………………...13
1.3 SCOPE OF INFERENCE…………………………………………………...14
1.4 ORGANIZATION…………………………………………………………..14
1.5 LITERATURE CITED……………………………………………………...17
CHAPTER 2: IN-FOREST ASSESSMENT OF VENEER GRADE
DOUGLAS-FIR LOGS BASED ON ACOUSTIC MEASUREMENT OF
WOOD STIFFNESS……………………………………………………………....23
2.1 ABSTRACT……………………………………………………….………...24
2.2 INTRODUCTION…………………………………………………………...25
2.3 MATERIALS AND METHODS……………………………………………27
2.3.1 Study sites……………………………………………………………….27
2.3.2 Acoustic velocity measurements………………………………………..30
TABLE OF CONTENTS (Continued)
Page
2.3.3 Data analysis…………………………………………………………….31
2.4 RESULTS AND DISCUSSION…………………………………………….31
2.5 CONCLUSIONS……………………………………………………………43
2.6 LITERATURE CITED……………………………………………………...45
CHAPTER 3: PRE-HARVEST VENEER QUALITY EVALUATION OF
DOUGLAS-FIR STANDS USING TIME OF FLIGHT ACOUSTIC
TECHNIQUE……………………………………………………………………...48
3.1 ABSTRACT…………………………………………………………………49
3.2 INTRODUCTION…………………………………………………………...51
3.3 MATERIALS AND METHODS……………………………………………54
3.3.1 Study sites……………………………………………………………….54
3.3.2 Acoustic velocity measurement tools……………………………………56
3.3.3 Data Analysis……………………………………………………………57
3.4 RESULTS AND DISCUSSION…………………………………………….58
3.5 CONCLUSIONS…………………………………………………………….69
3.6 LITERATURE CITED……………………………………………………...71
CHAPTER 4: IMPLEMENTING ACOUSTIC TECHNOLOGY ON
MECHANICAL HARVESTERS/PROCESSORS FOR REAL-TIME WOOD
STIFFNESS ASSESSMENT: OPPORTUNITIES AND CONSIDERATIONS….75
TABLE OF CONTENTS (Continued)
Page
4.1 ABSTRACT…………………………………………………………………76
4.2 INTRODUCTION…………………………………………………………...78
4.3 MATERIALS AND METHODS……………………………………………85
4.3.1 Study sites and data collected……………………………………………85
4.3.2 Acoustic velocity measurement tools and harvesting equipment……….88
4.3.3 Data analysis…………………………………………………………….89
4.4 RESULTS AND DISCUSSION…………………………………………….90
4.5 CONCLUSIONS…………………………………………………………...101
4.6 LITERATURE CITED…………………………………………………….104
CHAPTER 5: ESTIMATING BREAKEVEN PRICES FOR DOUGLAS-FIR
VENEER QUALITY LOGS FROM STIFFNESS GRADED STANDS
USING ACOUSTIC TOOLS………………………………………………….....109
5.1 ABSTRACT………………………………………………………………..110
5.2 INTRODUCTION………………………………………………………….112
5.3 MATERIALS AND METHODS…………………………………………..116
5.3.1 Study sites……………………………………………………………...116
5.3.2 General price estimation procedure…………………………………….117
5.3.3 Veneer recovery, prices, and revenues…………………………………118
5.3.4 Chip and core recovery, prices, and revenues………………………….119
TABLE OF CONTENTS (Continued)
Page
5.4 RESULTS…………………………………………………………………..123
5.5 DISCUSSION AND CONCLUSIONS…………………………………….129
5.6 LITERATURE CITED…………………………………………………….133
CHAPTER 6: GENERAL CONCLUSIONS…………………………………….138
BIBLIOGRAPHY………………………………………………………………..145
APPENDIX………………………………………………………………………154
Appendix A: Data from Stand A (“Putney”, located near Bellfountain, OR)…154
Appendix B: Data from Stand B (“Brohme Flats”, located near
Sutherlin, OR)…………….………………………………………………….171
Appendix C: Data from Stand C (“Collins Flats”, located near Tiller, OR)…..188
Appendix D: Data from Stand D (“Weatherly Ridge”, located near
Elkton, OR)…………………………………………………………..…...….205
Appendix E:Data from Stand E (“Burchard Creek”, located near
Elkton, OR)……………….………………………………………………….222
Appendix F: Data from Stand F (“Letz Go”, located near Lorane, OR)………239
Appendix G: Data from Stand G (“B&M Block – Dunn Forest”, located
near Corvallis, OR)………………………………………………….……….256
LIST OF FIGURES
Figure
Page
1.1 Types of stress waves in semi-infinite elastic material…………………………6
1.2 The four research areas of focus addressed in this dissertation…………….....15
2.1 Distribution of the veneer log lengths produced across the seven study
sites………………………………………………………………………….....32
2.2 Distribution of the HM200 log acoustic velocities across the seven study
sites…………………………………………………………………………….33
2.3 Veneer grades (G1, G2, and G3) recovery for the seven trial stands (A to G)
from the OSU stiffness measurement study…………………………………...34
2.4 Relationship between G1/G2 veneer recovery and on-site average log
velocity (mixed logs; some with bark on, some with most bark missing)…….35
2.5 Relationship between G1/G2 veneer recovery and “bark-removal-adjusted”
on-site average log velocity (it is assumed that all bark is removed)…………36
2.6 Relationship between G1/G2 veneer recovery and log dynamic MOE……….37
2.7 Overall relationship between tree-length HM200 acoustic velocity
readings with the limbs on and with the limbs and top off for the
seven trial stands from the OSU acoustic stiffness study……………………..39
3.1 Distribution of the ST300 tree acoustic velocities across the seven study
sites……………………………………………………………………………60
3.2 Distribution of the veneer log lengths produced across the seven study
sites……………………………………………………………………………60
3.3 Director ST300 acoustic velocity averages for the seven study sites
(A to G). LSD.05 = Least Significant Difference between stand velocity
means at p=0.05 level of significance. Means with the same lower-case
letter (a to e) are not significantly different…………………………………...62
3.4 Veneer grades (G1, G2, and G3) recovery for the seven study sites
(A to G) from the OSU stiffness measurement study…………………………63
LIST OF FIGURES (Continued)
Figure
Page
3.5 Relationship between G1/G2 veneer recovery and on-site average
ST300 stem acoustic velocity…………………………………………………64
3.6 Relationship between tool x site average stem velocity and G1/G2
veneer recovery………………………………………………………………..66
4.1 Relationship between acoustic velocities in the grip of a
harvester/loader grapple and those on the ground with no contact to
harvesting equipment for seven study sites in Western Oregon………………92
4.2 Average percent difference between whole tree acoustic velocities
and those measured on each subsequent log produced from that tree.
The “error bars” represent the range in percent difference for each log………94
4.3 Percent trees above a hypothetical acoustic velocity threshold value for
stiffness quality assessment from the validation data set (VDS) of the
seven trial stands. The three curves represent the actual, k-NN and linear
regression method predicted percent, respectively…………………………....96
5.1 Percentage of processed log volume in green veneer produced, chippable
product, and unpeeled core section for each of the seven study sites………..124
5.2 Gross return ($/M 3/8) including revenue from green veneer, chips, and
cores………………………………………………………………………….126
5.3 Relationship between breakeven veneer quality log prices and on-site
average log acoustic velocity for seven Douglas-fir study sites……………..128
LIST OF TABLES
Table
Page
2.1 Characteristics of the seven study sites………………………………………..29
2.2 Log summary statistics for the seven research sites…………………………...32
2.3 Summary for the regression between HM200 acoustic velocity (km/s)
measurements with limbs and tops removed and the explanatory variables:
HM200 acoustic velocity (km/s) measurements with limbs and tops on,
branches basal area (cm2) and length difference (m)………………………….40
2.4 Difference between whole tree acoustic velocity with limbs on and
each subsequent log produced from that tree (km/sec)………………………..41
2.5 Some statistics for the difference between whole tree acoustic velocity
with limbs on and different length butt logs (Log 1) produced (km/sec)……..42
3.1 Characteristics of the seven study sites………………………………………..55
3.2 Stem and log summary statistics for the seven study sites…………………….59
3.3 Components of variation for differences between stands, between trees and
between sides of a tree and their percentage contribution to the total
variation………………………………………………………………………..61
4.1 Characteristics of the seven study sites………………………………………..87
4.2 Log summary statistics for seven research sites……………………………….91
4.3 Proportion of trees (%) above a hypothetical acoustic velocity threshold
value (km/sec) for stiffness quality assessment from the validation data
set (VDS) of the seven trial stands. The three forecasting methods are the
actual (A) percent, k-Nearest-Neighbor (NN) and linear regression (R)
method, respectively…………………………………………………………...97
4.4 The RMSE for the forecast acoustic velocity and the proportion of trees for
which quality, based on a 3.81 km/sec cutoff value, was incorrectly
forecasted by the k-Nearest-Neighbor (NN) and linear regression (R)
method for the seven trial stands………………………………………………98
LIST OF TABLES (Continued)
Table
Page
4.5 Summary output for the regression model between HM200 acoustic
velocity (km/s) measurements for consecutive logs up the tree stem as
the response variable and whole tree HM200 acoustic velocity (km/s)
measurements with limbs and tops on and HM200 acoustic velocity (km/s)
measurements of previously produced logs from that stem as
explanatory variables………………………………………………………….99
5.1 Produced and transported log summary statistics for the seven research
sites…………………………………………………………………………...120
5.2 Estimated number of logs and net volume (Scribner board feet and
cubic meters) input for the production of the veneer reported. Average
stand basic density (BD) in kg/m3 was calculated from discs collected
from a subsample of trees…………………………………………………….123
5.3 Veneer grades, prices in dollars per thousand square feet, 3/8-inch basis
($/M 3/8), and percent recovery in each grade from the total veneer
produced for each of the seven study sites…………………………………...125
5.4 Total gross revenue, veneer manufacturing costs, and net revenue
calculated for each study site. Net revenue per net thousand board feet
($/MBF) was calculated based on estimated input for veneer production…...127
A.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite
sides of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R* - remarks (1 = dead, 2 = double leader, 3 = broken top). Disks from the
bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….154
A.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and
top of each produced log were taken from the 40 trees highlighted and
green density was measured………………………………………………….159
LIST OF TABLES (Continued)
Table
Page
A.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log, 4 = fourth
log); HM200 acoustic velocity readings in the grapples (up) and on the
ground (down) on the unprocessed portion of the stem once a log is
produced (1 = the stem portion after log 1 is cut, 2 – stem portion after
log 2 is cut, 3 = stem portion after log 3 is cut – only one selected tree for
“in-grapple” measurements - #156 - produced 4 logs and had an HM200
reading up (11614 ft/sec) and down (11450 ft/sec) on the stem portion
after log 3 was cut). Disks from the bottom and top of each produced
log as well as “in-grapple” measurements were taken from the 40 trees
highlighted……………………………………………………………………164
A.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered…………...169
A.5 Green veneer produced from processed logs delivered exclusively from
site A: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….170
B.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R - remarks (1 = dead, 2 = double leader, 3 = broken top, 4 = missing
data). Disks from the bottom and top of each produced log as well as
“in-grapple” measurements were taken from the 40 trees highlighted………171
B.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and
top of each produced log were taken from the 40 trees highlighted and
green density was measured…………………………………………………176
LIST OF TABLES (Continued)
Table
Page
B.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….181
B.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered…………...186
B.5 Green veneer produced from processed logs delivered exclusively from
site B: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….187
C.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R - remarks (1 = dead, 2 = double leader, 3 = broken top, 4 = missing
data, 5 = butt rot). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees
highlighted……………………………………………………………………188
C.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and
top of each produced log were taken from the 40 trees highlighted and
green density was measured………………………………………………….193
C.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….198
LIST OF TABLES (Continued)
Table
Page
C.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered……..…….203
C.5 Green veneer produced from processed logs delivered exclusively from
site C: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….204
D.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R - remarks (1 = dead, 2 = double leader, 3 = broken top, 4 = missing
data, 5 = butt rot). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees
highlighted……………………………………………………………………205
D.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and
top of each produced log were taken from the 40 trees highlighted and
green density was measured………………………………………………….210
D.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….215
D.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered….………..220
D.5 Green veneer produced from processed logs delivered exclusively from
site D: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent………………………………………………………………..…...221
LIST OF TABLES (Continued)
Table
Page
E.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R - remarks (1 = dead, 2 = double leader, 3 = broken top, 4 = missing
data, 5 = butt rot). Disks from the bottom and top of each produced log
as well as “in-grapple” measurements were taken from the 40 trees
highlighted……………………………………………………………………222
E.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and
top of each produced log were taken from the 40 trees highlighted and
green density was measured………………………………………………….227
E.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….232
E.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered…………...237
E.5. Green veneer produced from processed logs delivered exclusively from
site E: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….238
F.1 Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (limbs on) and merchantable length
(no limbs) and the respective HM200 acoustic velocity measurements on
the ground (down) or in the grapples of the loader (up), R - remarks
(1 = dead, 2 = double leader, 3 = broken top, 4 = missing data, 5 = butt
rot). Disks from the bottom and top of each produced log as well as
“in-grapple” measurements were taken from the 40 trees highlighted……....239
LIST OF TABLES (Continued)
Table
Page
F.2 Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom
and top of each produced log were taken from the 40 trees highlighted
and green density was measured……………………………………………..244
F.3 Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….249
F.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF),
average small end diameter and average length of logs delivered…………...254
F.5 Green veneer produced from processed logs delivered exclusively from
site F: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….255
G.1 Measurements from 182 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable
length (with no limbs) and the respective HM200 acoustic velocity
measurements on the ground (down) or in the grapples of the loader (up),
R - remarks (1 = dead, 2 = double leader, 3 = broken top, 4 = missing
data, 5 = butt rot). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees
highlighted……………………………………………………………………256
G.2 Measurements from 182 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom
and top of each produced log were taken from the 40 trees highlighted
and green density was measured……………………………………………..261
LIST OF TABLES (Continued)
Table
Page
G.3 Measurements from 182 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree
starting at the butt log (1 = base, 2 = second log, 3 = third log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down)
on the unprocessed portion of the stem once a log is produced (1 = the
stem portion after log 1 is cut, 2 – stem portion after log 2 is cut). Disks
from the bottom and top of each produced log as well as “in-grapple”
measurements were taken from the 40 trees highlighted…………………….266
G.4 Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs, gross volume (Scribner long log board feet (BF), average small end
diameter and average length of logs delivered……………………………….271
G.5 Green veneer produced from processed logs delivered exclusively from
site G: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent…………………………………………………………………….272
IN-FOREST LOG SEGREGATION BASED ON ACOUSTIC
MEASUREMENT OF WOOD STIFFNESS
CHAPTER 1
GENERAL INTRODUCTION TO DISSERTATION TOPIC
1.1 LITERATURE REVIEW
1.1.1 Global competition
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) is one of the
most important raw material resources for the forest products industries of the
United States, Canada, New Zealand, and parts of Europe (Gartner et al., 2002).
The unique attributes (appearance, strength, and machinability) of its wood have
established and maintained the Pacific Northwest as a major factor in domestic and
international markets for forest products. It is expected that international and U.S.
wood product markets, especially high-quality structural lumber markets, will
continue demanding Douglas-fir logs (Schuler and Craig, 2003). Over the last few
decades, however, as demand for high-quality timber has been rapidly increasing,
the availability of old growth Douglas-fir and other softwoods has been
diminishing across North America and timber resources have gradually shifted to
intensively managed young growth stands (Adams et al., 2002; Zhang et al., 2004).
Younger stands yield lower quality timber in comparison to old growth stands
because of the higher proportion of juvenile wood, which in turn affects properties
2
such as strength and dimensional stability (Gartner et al., 2002). Moreover,
harvesting younger trees increases the variability in product performance (Carter et
al., 2004) and limits log producers in their ability to meet market demand for wood
products (Murphy, 2003; Murphy et al., 2003).
As global forest products markets are becoming increasingly competitive and
complex, the successful transformation of managed second-growth stands into
quality products is crucial for the existence of a robust forest industry (Kellogg,
1989; Barbour and Kellogg, 1990; Eastin, 2005). Forest products companies face
increasing global competition not only from other wood producers, but also from
other industries, such as steel, aluminum, plastics, and composites (Marshall and
Murphy, 2004; Eastin, 2005; Murphy et al., 2005). To be competitive, forest
products companies need to control costs, sort and allocate logs to the most
appropriate markets, and maximize the value of the stands at the time of harvest.
Beyond the silvicultural aspects of production forestry, harvesting and
transportation of timber constitutes a large portion of the cost of the wood at the
time it is delivered to the manufacturing facility (Marshall and Murphy, 2004).
Reducing these costs could substantially increase the competitiveness of US wood
producers. Cost reduction could be achieved by optimizing harvesting operations
(Boston and Murphy, 2003; Conradie et al., 2004; Haynes and Visser, 2004) and
improving the coordination among harvesting, transportation, and product
manufacturing. Good measurements and predictions of both the external and
internal properties of the wood in each stem are essential to optimally match logs to
3
markets (Clarke et al., 2002). Identifying stem quality in the stand (Acuna and
Murphy, 2006a), determining its most appropriate use and processing operation,
and consequently shipping the product to the right location are steps to achieving
reduced costs and increased product values (Murphy et al., 2005). Effective
management of timber supply and the allocation of logs according to “fitness for
purpose” are fundamental in capturing greater value from the forest product value
chain (Dickson et al., 2003).
1.1.2 Wood quality
Wood quality can be defined according to attributes that make wood valuable for a
given use by society (Gartner, 2005). Where at one time tree dimensions and
external quality characteristics (such as branch size, sweep, and scarring) may have
been sufficient to specify a log-sort, consideration is now being given to specifying
such wood properties as density, stiffness, spiral grain, extractives content, and
consumption of energy for processing (Andrews, 2002; Young, 2002). Recently,
these internal “attributes” of the wood are being accounted for more frequently and
are an important consideration in estimating timber value (Tsehaye et al., 2000;
Hauksson et al., 2001; Acuna and Murphy, 2007). These additional potential
specifications required by the future wood users add extra complexity to the already
complex task of log producing and sorting. Studies have reported that wood
producers are sorting logs according to external and internal properties (Matheson
et al., 2002; Edlund and Warensjo, 2005). Technologies capable of capturing
4
internal log features such as microwave, X-ray, computer-aided tomography,
ultrasound, near-infrared (NIR) spectroscopy and nuclear magnetic resonance
(NMR) have been investigated for their potential for log scanning (Schmoldt et al.,
2000; Rayner, 2001; So et al., 2004; Acuna and Murphy, 2006b) in sawmills.
Wood modulus of elasticity (MOE), also known as stiffness, is an important
mechanical property and is the most frequently used indicator of the ability of
wood to resist bending and support loads. Stiffness in raw timber material is highly
variable and dependent upon site, genetics, silviculture, and location within the tree
and stand. It has long been recognized as a critical product characteristic in both
solid wood and pulp and paper processing (Eastin, 2005). It is a particularly
important parameter in the conversion of raw timber material into veneer and
plywood products, which require high stiffness wood. With the ever growing use of
engineered wood products such as roof trusses and laminated veneer lumber (LVL)
the demand for high-MOE lumber and veneer has increased.
1.1.3 Acoustic technology and its forest products applications
According to Ross et al. (1998), “nondestructive materials evaluation (NDE) is the
science of identifying the physical and mechanical properties of a piece of material
without altering its end-use capabilities and using this information to make
decisions regarding appropriate applications”. Assessment of lumber quality via
visual means is likely the most prevalent NDE method. Many physical NDE
5
techniques exist such as electrical resistance, dielectric and vibrational properties,
acoustical emission, wave propagation, X-ray (Ross et al., 1998). The use of such
technologies could lead to greater profitability for the forest industry. Acoustic
technologies have been well established as material assessment means in the past
several decades, and their use has become widely accepted by the wood industry
for quality control and product grading (Wang et al., 2007a).
Ross (1985) provided a detailed explanation of general stress wave behavior
principles. When subjected to a compressive force at one end, molecular particles at
that end begin moving longitudinally in a wave fashion down a long, thin
viscoelastic bar. As the wave moves along the bar, particles at the leading edge
become excited and those at the trailing edge come to rest and thus a slight
longitudinal particle movement is produced. Upon reaching the end, the wave is
reflected and the wave movement changes direction. During this process the wave
speed remains constant but the energy associated with each pass dissipates, particle
motion decreases until all particles come to rest. A generally accepted formula to
calculate dynamic modulus of elasticity (MOEd) of wood using stress wave
velocity and measured density is as follows (Pellerin and Ross, 2002):
MOEd = C2*ρ
, where
C – stress wave speed
ρ – mass density.
Although wood is an anisotropic, non-homogenous material and this relationship is
for a one-dimensional case, it has been adopted in the wood science field.
6
Wang et al. (2007a) explained the fundamentals of wave propagation in wood in
even greater detail stating that it “is a complex dynamic process controlled by the
properties, orientation, and microstructure of wood fiber and, perhaps more
importantly, by the geometric form of the material.” They defined three types of
stress waves initiated by a surface impact: (1) a longitudinal (compressive or Pwave), (2) shear wave (S-wave), and (3) surface wave (Rayleigh wave) (Fig. 1.1).
Figure 1.1* Types of stress waves in semi-infinite elastic material.
* - This figure from the Wang et al. (2007a) article is shown for the purposes of better visualization
of the concepts presented
Further they describe the P-wave as the oscillation of particles along the direction
of wave propagation, while in an S-wave the motion of the particles conveying the
wave is perpendicular to that direction. In a Rayleigh wave the particles move both
up and down and back and forth, tracing elliptical paths in the region adjacent to
the surface. Even though most energy is carried by shear and surface waves, the
longitudinal wave travels the fastest and is the easiest to detect (Wang et al.,
2007a). They also provide details about the partial differential equations governing
7
the free motion of a P-wave in a slender rod and derive the three-dimensional
longitudinal wave equation in an unbounded isotropic medium:
C=
(1 − ν ) * E
, where
(1 + ν )(1 − 2ν ) * ρ
C – longitudinal wave velocity,
ν – Poisson’s ratio,
E – longitudinal modulus of elasticity,
ρ – mass density of the material.
According to the authors, the equation implies that the dilatation (differentiating
longitudinal wave in a slender rod from the wave in an infinite or unbounded
isotropic elastic medium) is propagated through the medium with velocity C,
dependent on two elastic parameters (MOE and Poisson’s ratio) and density.
They state, however, that “direct application of fundamental wave equations in
wood, particularly in standing trees, has been complicated by the fact that wood is
neither homogeneous nor isotropic”. Wood properties in live trees vary from pith to
bark as wood transforms from juvenile wood to mature wood and the
corresponding change in microfibril angle and specific gravity (So et al., 2002);
properties differ between heartwood and sapwood; strength and density change
even within each annual growth ring as earlywood is followed by latewood;
properties also change from butt to top within a tree and differ between trees.
8
It is expensive to process logs or purchase timber stands that have low yield of
product with the stiffness and strength levels desired. For many years, the
sawmilling industry has utilized acoustic technology for lumber assessment and
devices, such as the in-line commercialized Metriguard® stress-wave grade sorter,
have been widely used. Nondestructive testing (NDT) instruments that are compact
and easy to operate and are based on acoustic principles have been developed for
measuring stiffness of logs and standing trees (Dickson et al., 2004). Acoustic NDT
tools have been successfully used for evaluation of mechanical properties of
various wood products (structural lumber, poles, pulp logs, decay detection, etc.)
and species as well as in tree selection and breeding based on stiffness (Huang et
al., 2003).
The most widely implemented acoustic techniques among industry and researchers
are the “time of flight” (TOF) for standing trees and “resonance-based” for logs
(Lindstrom et al., 2002). Originally intended for decay detection in trees, TOF is
currently the most popular method for directly measuring stiffness on standing trees
(Andrews, 2002) with the caveat that it measures acoustic velocity only in the
outerwood portion between the inserted probes in the lower part of the tree
(Wagner et al., 2003). A time-of-flight (TOF) acoustic measurement system was
developed and as described by Wang et al. (2007a), the prototype system includes
two probes (input and receive prove), two acoustic sensors (start and stop sensor), a
portable two-channel scopemeter, and a hand-held hammer. In the field, the probes
are inserted into the tree trunk (probes pierce bark and cambium and extend into
9
sapwood) and aligned within a vertical plane on the same face of the tree. Once the
acoustic energy is directed by a mechanical impact on the input probe, the resulting
acoustic waves are detected by the sensors and transmitted to the scopemeter. In
this system, a slope-detection method is used for obtaining good quality acoustic
signals as opposed to the more sensitive to signal amplitude variations simple
voltage threshold detection method used in traditional TOF devices thus having
good repeatability and consistence (Wang et al., 2007a). Past research has indicated
high correlation between yield of structural grades of lumber and acoustic velocity
of standing trees (Lindstrom et al., 2002; Grabianowski et al., 2006; Wang et al.,
2007a) and processed logs (Ross et al., 1997; Joe et al., 2004; Wang et al., 2007b).
Taking the measurement on a felled whole stem instead of a log has its advantages,
i.e. more volume is measured per hit, optimization of log manufacturing is possible,
and no extra handling is required. However, measurement accuracy on standing
trees is often reduced. According to Wang et al. (2007a) who describe the TOF
technique in detail, “the accuracy of TOF measurement depends on accurate
identification of the arrival times of the acoustic wave signals, each from a start
sensor and a stop sensor”.
The “resonance-based” technique is performed by measuring the velocity of an
acoustic wave, originating from a hammer strike on the end of the specimen and is
used as a direct indicator of the dynamic modulus of elasticity (MOE), a measure of
the material’s stiffness (Carter et al., 2004). For logs, a resonance based acoustic
method is used to measure longitudinal wave velocity. Resonance data are obtained
10
using resonance acoustic tools, one of which is the Director HM200 (CHH Fibregen, New Zealand). In this tool, acoustic signals are analyzed by a built-in fast
Fourier transformation (FFT) program following impact. Log acoustic velocity is
determined on the basis of the following equation:
CL = 2*f0*L
, where
CL is acoustic velocity of logs (m/s),
f0 - fundamental natural frequency of an acoustic wave signal (Hz),
L - log length (end-to-end) (m).
In contrast to the TOF method, the resonance technique stimulates many acoustic
pulse reverberations in a log, resulting in repeatable velocity measurements making
this approach inherently accurate and robust NDE technique for measuring long,
slender wood members.
1.1.4 Mechanization and technologies in forest operations
Worldwide there is a trend towards increased mechanization of forest harvesting
operations, particularly as harvested tree size decreases. This is especially true
where harvested tree size is decreasing and the capability of one or two machines to
fell, delimb, buck, and sort a tree or a group of trees is an appealing advantage. This
trend towards mechanization of forest harvesting operations is observed for various
forest types, terrains and climatic conditions (Raymond, 1988; Nordlund, 1996;
Godin, 2001) leading to near elimination of motor-manual felling in thinning
11
operations and continuously increasing sales of harvesters and processors. This
tendency has arisen for a number of reasons; (1) to reduce the impacts of smaller
trees on productivity and costs, (2) to improve worker safety, (3) to reduce
environmental impacts, and (4) to overcome the difficulties some regions face with
labor force deficiency (Murphy 2003). Harvesting machines are frequently fitted
with on-board computer technology and rudimentary sensor systems for measuring
external stem dimensions – usually diameters over bark along the stem and stem
length – providing opportunities to:
•
Make better informed decisions about allocating each tree to the most
suitable markets.
•
Reduce the variability in product performance by sorting for niche uses.
•
Reduce the number of handlings of the same stem/log thus reducing
manufacturing and transportation costs and increasing the end product
value.
There is a growing interest in incorporating technologies for measuring internal
stem features into a harvester head (Carter and Sharplin, 2006). Ongoing research
efforts are addressing potential challenges, opportunities and considerations from
installing NIR and acoustic instruments on mechanized harvesters (Carter, 2007).
These preliminary reports indicate that, despite a number of influential factors and
working protocol uncertainties, there is a great potential for these technologies to
demonstrate reliable performance and logs produced using harvesters/processors
12
could be segregated for internal wood quality in the forest. Possible fitting of
acoustic measuring systems to mechanized harvesters could prove beneficial for
increased value recovery from forest stands. There is no research published for
evaluating costs and benefits from such implementation. Measuring wood
properties of logs in real time during harvesting would lead to improved log
allocation decisions early in the supply chain, improved value recovery for the
forest owner, and optimal matching of wood to markets.
To date very little research has investigated the correlation between stress-wave
acoustic velocities in raw timber and those in veneer produced from that timber,
especially for Douglas-fir. Few studies have investigated the effects of spatial as
well as internal and external log characteristics on Douglas-fir wood stiffness and
no research has evaluated those effects from a veneer quality standpoint. Only a
small number of research studies, all of them focusing on external stem features,
have investigated the use of scanning technology with mechanical harvesting
equipment and the best procedures for scanning and optimal bucking with
mechanized harvesters. The author is unaware of any research efforts that have
estimated relative premium prices for Douglas-fir peeler grade logs based on
stiffness differences assessed using acoustic technology. A large knowledge
deficiency exists concerning the potential for in-forest sorting of Douglas-fir veneer
quality logs based on acoustic measurement of wood stiffness.
13
1.2 Research Objectives
The ultimate objective of this study was to increase the knowledge about
recovering more value from a forest stand and consequently to enhance the
competitiveness of the forest products industry. More specific objectives included:
•
Investigating relationships between Douglas-fir stem and log internal
mechanical properties based on acoustic technology.
•
Evaluating the predictive ability of log acoustic velocity in terms of green
veneer grade recovery.
•
Quantifying the spatial variability in stem internal characteristics and
quality within Douglas-fir stands.
•
Determining the influence of internal and external Douglas-fir tree
characteristics on the quality and accuracy of acoustic velocity readings.
•
Determining and investigating the factors arising from incorporating
acoustic instruments on a mechanized harvester head that might influence
acoustic signal and velocity readings quality.
•
Investigating the issues and considerations related to suggested working
procedures for performing real-time acoustic measurements in regards to
harvesting productivity impacts and processing decisions.
•
Estimating relative breakeven prices of Douglas-fir peeler logs that a log
purchaser could afford to pay based on stand acoustic assessment of veneer
stiffness differences.
14
1.3 Scope of Inference
In terms of the spatial variability in stem stiffness and strength, conclusions would
be limited to the actual Douglas-fir stands sampled throughout Western Oregon.
When the effect of stiffness on the market value of logs is considered, inferences
would be applicable to any Douglas-fir timber markets. Regarding the use of
acoustic tools for stiffness sorting and its related benefits in wider applications,
leading to their successful incorporation into mechanized harvesting heads,
findings would be pertinent to any similarly equipped mechanized harvesting
system.
1.4 Organization
This dissertation is a comprehensive study of an important mechanical wood
property and acoustic sensor technology to improve value recovery from Douglasfir stands and ultimately enhance the competitiveness of the forest products
industry. A comprehensive approach was employed to investigate the possibilities
for: 1) resonance-based acoustic velocity measurements on felled stems or logs, 2)
time of flight acoustic velocity measurements on standing trees, 3) integrating
acoustic technology into the design of mechanized harvesting systems, and 4)
determining the breakeven monetary value of logs from stiffness graded stands
using acoustics (Fig. 1.2).
This dissertation has been written in a manuscript format and is made up of four
distinct manuscripts. While some redundancy exists because each manuscript is
15
designed to stand alone, it has been written using a logical sequence that allows the
reader to get a broad understanding of the effect of internal wood properties
(mainly wood stiffness) and advanced sensor technologies (mainly acoustic
techniques) on value recovery. The following is a synopsis of each chapter,
corresponding research questions, and significance.
Figure 1.2. The four research areas of focus addressed in this dissertation.
Chapter 2 summarizes the results of the investigation into modeling the effects of
within tree spatial as well as internal and external log characteristics – in particular,
height within stem, log length and diameter, green density, presence of bark, and
16
presence and size of branches - on acoustic velocity measurements of Douglas-fir
wood stiffness from a range of sites in Western Oregon. The goal was to determine
whether recovery of high quality green veneer from Douglas-fir peeler logs could
be accurately predicted using resonance-based acoustic velocity measurements.
Chapter 3 describes an investigation on determining whether “time of flight” (TOF)
acoustic technology could be used for pre-harvest veneer quality assessment of
Douglas-fir stands in terms of stiffness requirements. The predictive capabilities in
terms of veneer quality and the effect of spatial (altitude, latitude, and longitude)
and commonly measured tree characteristics (diameter at breast height DBH, tree
height) on the accuracy of TOF acoustic measurements in second-growth Douglasfir stands were also evaluated.
Chapter 4 investigates the potential implementation and use of acoustic technology
with mechanical harvesting equipment and the best procedures for scanning in
terms of feasibility and productivity of such equipped mechanized harvesters.
Factors arising from incorporating acoustic instruments on a mechanized harvester
head that might influence acoustic signal and velocity readings quality were
determined and investigated. Working procedures were suggested and the issues
and considerations with these working procedures in regards to measurement
feasibility, harvesting productivity impacts, and processing decisions were
investigated.
17
Chapter 5 presents a general methodology to estimate breakeven prices of Douglasfir peeler logs based on the net return obtained when logs from stiffness graded
stands using acoustic technology were processed and converted into end products
(green veneer and pulp). For green veneer, a number of different grades and their
prices were used to estimate the price that markets would be willing to pay for such
logs. For chippable material recovered, the price is estimated from the net product
value per metric ton which considers pulp selling price and nonwood and fixed
cost.
Chapter 6 is a concluding chapter that summarizes major findings of the four
previous chapters, links them together conceptually, highlights the contributions
made with this work, assesses management implications, and suggests further
research direction. It is followed by a bibliography listing all references used
throughout the dissertation.
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18
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Forest Products Journal 54(3):6-16.
Tsehaye, A., A.H. Buchanan and J.C. Walker. 2000. Selecting trees for structural
timber. Holz als Roh- und Werkstoff 58:162-167.
Wagner, F.G., T.M. Gorman and S.Y. Wu. 2003. Assessment of intensive stresswave scanning of Douglas-fir trees for predicting lumber MOE. Forest
Products Journal 53(3):36-39.
Wang, X., R.J. Ross and P. Carter. 2007a. Acoustic evaluation of wood quality in
standing trees. Part I. Acoustic wave behavior. Wood and Fiber Science
39(1):28-38.
Wang, X., P. Carter, R.J. Ross and B.K. Brashaw. 2007b. Acoustic assessment of
wood quality of raw forest materials – a path to increased profitability.
Forest Products Journal 57(5):6-14.
22
Young, G.G. 2002. Radiata pine wood quality assessments in the 21st century. New
Zealand Journal of Forestry 47(3):16-18.
Zhang, S.Y., Y. Qibin and J. Beaulieu. 2004. Genetic variation in veneer quality
and its correlation to growth in white spruce. Canadian Journal of Forest
Research 34:1311-1318.
23
CHAPTER 2
IN-FOREST ASSESSMENT OF VENEER GRADE DOUGLAS-FIR LOGS
BASED ON ACOUSTIC MEASUREMENT OF WOOD STIFFNESS
Dzhamal Y. Amishev and Glen E. Murphy
Department of Forest Engineering
Oregon State University
Corvallis, OR 97331-5706
USA
Forest Products Journal (in review, submitted January 2008)
Forest Products Society
2801 Marshall Court
Madison, WI 53705-2295
USA
24
2.1 ABSTRACT
Acoustic technology has proven to be a well established non-destructive technique
for assessing potential product performance by identifying logs with high stiffness.
In an ongoing endeavor to optimize merchandizing and enhance timber value
recovery, seven second growth Douglas-fir stands of similar age class in Western
Oregon were sampled, totaling 1,400 trees and more than 3,000 logs. This research
investigated the effects of spatial as well as internal and external log characteristics
on Douglas-fir wood stiffness.
In-forest log acoustic measurements, as well as dynamic modulus of elasticity
values correlated well with the actual G1/G2 veneer grade recovery (R2 of 0.91 and
0.82, respectively) once bark removal adjustments were made. External log
characteristics such as diameter and length were found to have limited predictive
capability in terms of acoustic velocity and hence wood stiffness. The presence and
size of branches was found to be negatively correlated to acoustic velocity readings
and the addition of the tree length difference improved the regression model. Logs
produced from the lowest part of the tree had the largest acoustic velocity and
velocity decreased in each subsequent log along the length of a tree stem.
Keywords: Douglas-fir, stiffness, stress wave method, sound velocity, dynamic
modulus of elasticity
25
2.2 INTRODUCTION
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is of great economic
importance for the forest products industries of the United States, Canada, New
Zealand, and parts of Europe (Gartner et al., 2002). The area of non-reserved
timberland presently stocked with Douglas-fir ranges from 134,000 and 330,000 ha
in Germany and France, respectively, to about 4.5 million ha in Canada, and to
more than 14 million ha in the United States (Hermann and Lavender, 1999). It is
expected that international and U.S. wood product markets, especially high-quality
structural lumber markets, will continue to demand Douglas-fir logs (Schuler and
Craig, 2003).
Timber resources in the Pacific Northwest have gradually shifted from unmanaged
old growth to intensively managed young growth stands (Adams et al., 2002).
Younger stands yield lower quality timber in comparison to old growth stands
because of the higher proportion of juvenile wood, which in turn affects properties
such as strength and dimensional stability (Gartner, 2005). Moreover, harvesting
younger trees increases the variability in product performance (Carter et al., 2004)
and limits log producers in their ability to meet market demand for wood products
(Murphy et al., 2003). As world log markets are becoming increasingly competitive
and complex, forest products companies face increasing global competition not
only from other wood producers, but also from other industries, such as steel,
26
aluminum, plastics, and composites (Eastin, 2005; Marshal and Murphy, 2004). To
be competitive, forest products companies need to control costs, sort and allocate
logs to the most appropriate markets, and maximize the value of the stands at the
time of harvest. Beyond the silvicultural aspects of production forestry, harvesting
and transportation of timber constitutes a large portion of the cost of the wood at
the time it is delivered to the manufacturing facility (Marshal and Murphy, 2004).
Identifying stem quality in the stand (Acuna and Murphy, 2006), determining its
most appropriate use and processing operation, and consequently shipping the
product to the right location are steps to achieving reduced costs and increased
product values (Murphy et al., 2005). Optimally matching wood quality to markets
can mean cutting logs for very specific end uses and classifying them into several
categories or “sorts” to improve product uniformity, productivity and profitability
along the seedling to customer supply chain. Research into technologies for
measuring stem quality attributes is progressing on a number of fronts with varying
levels of success; e.g. acoustics, optical and laser scanning, x-ray, microwave,
ultrasound and near infrared (NIR) spectroscopy (Carter et al., 2004).
Stiffness is correlated to strength and is the most frequently used indicator of the
ability of wood to support loads. Wood stiffness and strength have long been
recognized as crucial product variables in both solid wood and pulp and paper
processing (Eastin, 2005). Raw timber material is highly variable in these
properties, dependent upon site, genetics, silviculture, and location within the tree
and stand. Nondestructive testing (NDT) and evaluation of wood products for
27
stiffness and strength has been proven and commercialized for years (Wang et al.,
2004b). Initial research trials indicate high correlation between yield of structural
grades of lumber and acoustic velocity of logs processed as measured using
acoustic techniques (Wang et al., 2007a; Carter et al., 2004; Wang et al., 2001;
Ross et al., 1997). Segregation of logs based on tools that measure stiffness is
already being used by some forest companies to improve the value of lumber
recovery (Wang et al., 2007b; Dickson et al., 2004).
Good measurements and predictions of the external and internal properties of the
wood in each stem are crucial to optimally match logs to markets (Clarke et al.,
2002). In 2006, a study was launched to determine whether log stiffness could be
successfully measured in-forest using acoustic technology and to evaluate the
factors influencing its accuracy. This paper summarizes the results of the
investigation into modeling the effects of spatial as well as internal and external log
characteristics on Douglas-fir wood stiffness from a range of sites in Western
Oregon.
2.3 MATERIALS AND METHODS
2.3.1 Study sites
In summer 2006, six Roseburg Forest Products company (RFP) stands, located in
the Coastal (A - near Bellfountain, D and E - near Elkton, and F – near Lorane,)
and Cascade (B – near Sutherlin and C – near Tiller) Ranges of Oregon, were
28
harvested as part of two studies evaluating novel technologies for in-forest
measurement of wood properties. In summer 2007, a seventh stand (G - near
Corvallis), located within Oregon State University’s McDonald-Dunn College
Forest, was also harvested as part of these studies. All sites were second growth
Douglas-fir stands of similar age class (50 – 70 years) chosen to cover a range of
elevations and tree sizes (Table 2.1). Site G had been commercially thinned on
three occasions. Sites A to F had no commercial thinning but may have received a
pre-commercial thinning. Two hundred trees from each stand were sampled
totaling 1,400 trees and more than 3,000 logs. Only veneer grade lengths were cut
(18, 27, and 35 ft or 5.5, 8.2, and 10.7 m, respectively); no sawlogs or pulp logs
were produced. Measurements included, but were not limited to tree length,
merchantable length, diameter at breast height (DBH), biggest branch diameter on
each 20 ft (6.1 m) segment of the tree, acoustic velocity of the standing tree (using
the Director ST300® tool), acoustic velocity of the whole stem with and without the
branches (using the Director HM200® tool), and acoustic velocity of each log made
out of the stem. Approximately 100 mm thick disks were collected from a subsample (40 trees per stand) of the trees. These samples were taken at different
heights from the tree, one from the base and one from the top of each log. More
than 800 disks were collected. The disks were labeled, placed in bags, and stored in
a cold room. Green densities, as well as sapwood/heartwood ratios, were calculated
for each disk.
29
Table 2.1 Characteristics of the seven study sites.
Site Elevation
Stand
DBH Range of
Site Location
of the site
Age
trees selected
Latitude/Longitude
(m)
(years)
(cm)*
A
180
62
19.3 – 96.8 (52.2) 44° 24.04’N / 123° 23.24’W
B
900
66
16.5 – 69.6 (36.3) 43° 22.58’N / 123° 03.54’W
C
1040
56
17.5 – 79.0 (50.6) 42° 58.56’N / 122° 48.52’W
D
220
54
14.2 – 66.8 (39.5) 43° 40.09’N / 123° 43.19’W
E
120
51
15.5 – 59.4 (32.0) 43° 40.16’N/ 123° 44.58’W
F
290
53
16.3 – 77.2 (38.9) 43° 48.40’N / 123° 18.34’W
G
280
72
15.0 – 78.5 (41.6) 44° 42.55’N/ 123° 19.58’W
* Average DBH in parentheses
Dynamic MOE was obtained by using the green density and the velocity of the
acoustic wave through the material, expressed by the following formula:
MOE d =
ρ
g
*V 2 where
MOEd - dynamic modulus of elasticity (lb/in2 (Pa))
ρ - density of the material (lb/ft3 (kg/m3))
g - acceleration due to gravity (386 in/s 2 (9.8 m/s 2))
V - velocity of the wave through the material (ft/s (m/s))
30
After the in-forest measurements on the logs were completed, they were transported
to a veneer mill, debarked, cut into 8 ft (2.4 m) bolts, kiln-heated, shape scanned,
and peeled into veneer sheets. They were then scanned for defects and moisture,
sorted into moisture classes, dried and then sorted into several veneer grades (G1,
G2, G3, AB, C+, C, D, X, and XX) based on in-line acoustic measurement of wood
stiffness using the Metriguard® grade sorter. Percent veneer recovery in all grades
was calculated.
2.3.2 Acoustic velocity measurement tools
The acoustic velocity of the standing trees was measured using the Director
ST300® (CHH Fibre-gen, New Zealand) tool which is a time-of-flight (TOF)
acoustic measurement system, developed by a research team for measuring acoustic
velocity in standing trees (Wang et al. 2004a). The system and the working
protocol are described in detail by Wang et al., 2007a. Findings, based on standing
tree measurements in stands A to G, however, will be presented in a separate paper.
The same authors also describe the resonance based acoustic tool (Director
HM200®, CHH fibre-gen, New Zealand) used to measure longitudinal wave
velocity in the logs. They point out that the latter method is a well-established NDT
technique “for measuring long, slender wood members”.
31
2.3.3 Data analysis
Statistical analyses of the data were undertaken following either a simple linear
least squares regression analysis or a stepwise multiple regression methodology
described by Ramsey and Shafer (2002). It included the following steps: graphical
analysis of the data, examination of the correlation matrix, fitting of the linear
model, exploration of the residuals, significance test of the variables, and
improvement of the final regression model. Both SAS® 9.1 statistical software
(SAS, 2004) and the Data Analysis Tool Pak of MS Excel was used for the analysis
and a p-value of 0.05 was used as the threshold for determining significance of
explanatory variables.
2.4 RESULTS AND DISCUSSION
Stand A produced the largest number of logs totaling 572 while Stand G yielded
the least with 353 logs; the average log length was 9.2 m ranging from 8.5 m for
Site F to 9.5 m in Site B; HM200 acoustic velocity averaged 3.77 km/sec
throughout the 3077 total logs and ranged from 2.73 to 4.69 km/sec (Table 2.2).
The variation and distributions of the log lengths and the HM200 acoustic
velocities across all sites are shown in figures 2.1 and 2.2, respectively.
32
Table 2.2 Log summary statistics for the seven research sites.
Study Sites
Site A
Log Count
Total
#
572
Log Length
HM200 Acoustic Velocity
Average
Average Min
Max
m
km/sec
9.4
3.92
3.03
4.58
Site B
399
9.5
3.77
2.80
4.69
Site C
458
9.2
3.46
2.73
4.23
Site D
447
9.2
3.76
2.98
4.63
Site E
395
9.3
3.84
2.88
4.48
Site F
453
8.5
3.82
2.96
4.47
Site G
353
9.3
3.77
2.78
4.32
Overall
3077
9.2
3.77
2.73
4.69
400
350
300
Frequency
250
5.5 m
8.2 m
200
10.7 m
150
100
50
0
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Figure 2.1 Distribution of the veneer log lengths produced across the seven study
sites.
33
120
100
Frequency
80
Site A
Site B
60
Site C
Site D
Site E
40
Site F
Site G
20
Site G
0
3.0
Site E
3.1
3.3
3.4
3.5
3.6
Site C
3.7
3.8
3.9
Log Acoustic Velocity (km/s)
4.0
4.1
4.2
Site A
4.4
4.5
More
Figure 2.2 Distribution of the HM200 log acoustic velocities across the seven study
sites.
Although no particular trend was observed in terms of the spatial location of the
stands, not all sites yielded the same quantity and/or quality of veneer (Fig. 2.3).
While the overall G1 and G2 (the highest quality grades) veneer grade recovery
percentage for sites A, B, D, and E was about the same (around 50%), the other
three sites (C, F, and G) were considerably lower (32, 37, and 37%, respectively).
This highlights the variation in internal wood properties between stands and
emphasizes the need for preceding log quality information in order to make
informed management decisions.
34
70
60
50
40
G3
G2
G1
30
20
10
0
A
B
C
D
E
F
G
Figure 2.3 Veneer grades (G1, G2, and G3) recovery for the seven trial stands (A to
G) from the OSU stiffness measurement study.
Both log acoustic velocity and dynamic MOE were good predictors of the
subsequent veneer grade recovery from the processed logs. Initial investigation of
the relationship between G1/G2 veneer grade recovery and average acoustic
velocity for the delimbed logs measured on site yielded a correlation coefficient R2
of 0.52 and the linear model was not significant with a p-value of 0.07. It was
observed, however, that the two “outlier” points (Fig. 2.4) coincided with the
stands (F and G) where trees were processed using a mechanized Waratah
processor head which resulted in most of the log bark being removed; trees in
stands A to E were felled, bucked and delimbed manually with a chain saw, leaving
most of the bark on the logs. A study in a radiata pine stand in New Zealand
(Lasserre, 2005) found that removal of bark significantly increased acoustic
35
velocity by on average 4.1%. In order to evaluate the effect of bark removal on
Douglas-fir logs acoustic measurement, the authors of the current paper conducted
a short experiment in a mill yard on 81 Douglas-fir logs and found an average
increase of 4.6% for debarked logs (Murphy and Amishev, in review). Once the
average acoustic velocity values for sites A to E were adjusted by 4.6%, the
regression model was significant with an R2 of 0.91 (Fig. 2.5).
60
G 1/G 2 Veneer Recovery (%)
50
40
30
20
10
y = 40.939x - 110
R2 = 0.52
0
3.40
3.50
3.60
3.70
3.80
3.90
4.00
OSU Average Log Velocity (km/sec)
Figure 2.4 Relationship between G1/G2 veneer recovery and on-site average log
velocity (mixed logs; some with bark on, some with most bark missing).
36
60
G 1/G 2 Veneer Recovery (%)
50
40
30
20
10
y = 48.148x - 143
R2 = 0.91
0
3.50
3.60
3.70
3.80
3.90
4.00
4.10
4.20
OSU AdjustedAverage Log Velocity (km /sec)
Figure 2.5 Relationship between G1/G2 veneer recovery and “bark-removaladjusted” on-site average log velocity (it is assumed that all bark is removed).
Green density measurements were combined with the acoustic velocity to explore
the relationship between dynamic modulus of elasticity (MOE) and veneer recovery
(Fig. 2.6).
37
60
G 1 /G 2 V e n e e r R e c o v e ry (% )
50
40
30
20
10
Veneer Recovery = 8.65*MOE - 61
2
R = 0.82
0
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
Average Dynamic MOE (GPa)
Figure 2.6 Relationship between G1/G2 veneer recovery and log dynamic MOE.
Yielding a significant linear model with an R2 of 0.82, dynamic MOE was found to
be strongly correlated with veneer grade recovery. The “outlier” (stand F),
designated with a triangle instead of a diamond on fig. 2.6, had logs with green
density values that were 5% greater on average than the rest of the sites.
Some forest managers in the Pacific North West, as well as researchers overseas
(e.g. Leith Knowles, New Zealand Scion Research, personal communication)
believe that there is a correlation between diameter and log acoustic velocity.
Investigating the relationship between tree DBH and the acoustic velocity of the
felled trees revealed that, overall, there was a very weak, non-statistically
significant relationship with an R2 of 0.10. There was a weak trend for the
measured acoustic velocity to decrease with increasing tree DBH. The relationship
38
between diameter and acoustic velocity measurements for fixed-length butt logs
was investigated as well, thus removing the diameter-length interaction factor. In
this case, the measured DBH was found to be very weakly related to the acoustic
velocity readings with R2 ranging from 0.15 for the 5.5 m butt logs to 0.19 for the
10.7 m butt logs. Also, an investigation of the relationship between tree length and
acoustic velocity showed no relationship between these two variables with an R2 of
0.006. In other words, both DBH and log length have limited predictive capability
in terms of acoustic velocity and hence wood stiffness.
Two models were developed to assess the impact of branches on acoustic readings
once the cross-sectional areas of the measured tree branches were calculated and
then summed for each tree. In the first model, simply by regressing HM200
acoustic velocity measurements with the limbs on against those taken with the
limbs removed, the relationship was fairly strong (Fig. 2.7) with an R2 of 0.67.
39
4.9
Limbs Off Stem Velocity (km/sec)
4.7
4.5
4.3
4.1
3.9
3.7
3.5
3.3
3.1
2.9
y = 0.7657x + 0.967
R2 = 0.67
2.7
2.5
2.5
2.7
2.9
3.1
3.3
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
Limbs On Stem Velocity (km/sec)
Figure 2.7 Overall relationship between tree-length HM200 acoustic velocity
readings with the limbs on and with the limbs and top off for the seven trial stands
from the OSU acoustic stiffness study.
Potentially influential points were identified using both the Cook’s Distance
diagnostic and the studentized residual statistic test (cutoff value of 3) in SAS.
After examining them 28 points of the total 1,375 observations were identified as
outliers and removed from the sample based on additional indications (sampling
errors) regarding the validity of those measurements. The resultant model yielded
an R2 of 0.76 meaning that the velocity measurements with the limbs on correlated
fairly well with those with the limbs removed. Lasserre (2005) reported that
acoustic velocity for radiata pine logs with the branches still attached was 2.7%
lower compared to that after they had been removed. In our model, for Douglas-fir
logs, we found the same change (2.7%) in velocity. This model, however, only
accounts for the “presence” of branches and non-merchantable tops but not for the
40
effect of their size (basal area difference and tree length difference). The second
model included total branch basal area for each tree as well as the length difference
between total and merchantable tree lengths as explanatory variables. The resultant
R2 was 0.83 with the model, intercept and slope coefficients all being highly
significant (Table 2.3). These results highlight the need for consistency in acoustic
measurement procedures, particularly for logs with large limbs.
Table 2.3 Summary for the regression between HM200 acoustic velocity (km/s)
measurements with limbs and tops removed and the explanatory variables: HM200
acoustic velocity (km/s) measurements with limbs and tops on, branches basal area
(cm2) and length difference (m).
Coefficients
Value
Standard Error
t Stat
P-value
Intercept
0.925
0.04992
18.532
2.387E-67
Limbs on velocity
0.762
0.01287
59.237
0
Branches BA
-0.00048
6.718E-05
-7.1605
1.425E-12
Length difference
-0.01629
8.714E-04
-18.691
2.439E-68
Depending on the site and log lengths, an average tree yielded 1 to 4 logs with most
trees yielding 2 or 3 logs in our trials. The correlation between the acoustic velocity
measurement taken on the felled whole stem with the limbs and tops present and
the acoustic velocity reading on the logs produced from that stem was investigated.
The analysis showed that logs produced from the lowest part of the tree are
different than those produced from higher sections. On average, the butt log had the
41
largest acoustic velocity (Table 2.4) and it decreased in each subsequent log along
the length of each tree stem.
Table 2.4 Difference between whole tree acoustic velocity with limbs on and each
subsequent log produced from that tree (km/sec).
Log 1
Log 2
Log 3
Log 4
Average
-0.201
-0.051
0.205
0.381
Minimum
-0.940
-0.950
-0.560
-0.210
Maximum
0.690
0.980
0.800
0.680
The correlations between whole tree acoustic velocity readings with limbs and tops
present and those taken on the logs produced were statistically significant and quite
strong (R2 ranged from 0.60 to 0.72) for all the logs along the stem, except for the
fourth log whenever such was produced. However, one might ask whether the
length of those logs matters. Investigating this relationship only for the butt log it
was found that longer logs had higher acoustic velocities than shorter ones (Table
2.5) as compared to the acoustic velocity reading on the whole tree. Those
regression models were all significant as well and had coefficients of determination
R2 ranging between 0.57 and 0.61, meaning that a whole tree measurement can be a
fairly good predictor of the wood stiffness for each log produced based on its
length and position up the stem.
42
Table 2.5 Some statistics for the difference between whole tree acoustic velocity
with limbs on and different length butt logs (Log 1) produced (km/sec).
Log 1 length (m)
5.5
8.2
10.7
Average
-0.117
-0.197
-0.207
Minimum
-0.600
-0.870
-0.940
Maximum
0.578
0.130
0.690
These findings are consistent with what other researchers have reported for other
species; Xu and Walker (2004) found that low-stiffness wood zone forms from the
base to about 2.7 m stem height in a sample of 62 radiata pine trees, resulting in
limited structural usability of that portion of the tree; Xu et al. (2004) concluded
that the cell ultrastructure and more specifically microfibril angle is responsible for
the “rapid increase of wood stiffness in vertical direction, especially in corewood
zone”, reporting that in the S3 layer it dropped from over 80o at the bottom to about
51o at the top of a radiata pine butt log.
This research has primarily focused on the use of acoustic technology for
evaluating internal properties on Douglas-fir veneer grade logs. Other researchers
have found the use of acoustic tools to be viable in assessing stiffness in radiata
pine sawlogs and green lumber (Grabianowski et al., 2006), structural lumber logs
and boards (Carter et al. 2006) and young seedling clones (Lindstrom et al., 2004);
Eucalyptus dunnii veneer and LVL logs and structural lumber boards (Joe et al.,
43
2004). Acoustic techniques have been successfully used and implemented for nondestructive evaluation of mechanical properties of other wood products (structural
lumber, poles, pulp logs, decay detection, etc.) and species as well as in tree
selection and breeding based on stiffness (Huang et al., 2003).
2.5 CONCLUSIONS
The objective of this study was to determine whether acoustic technology could be
used for in-forest assessment of veneer grade log stiffness and whether spatial and
within tree characteristics could influence the accuracy of those measurements in
second-growth Douglas-fir stands in Oregon. Acoustic velocity and dynamic MOE
were found to correlate very well with the mill veneer recovery. Hence, despite the
inherent variability between and within trees, acoustic technology could be a
promising and valuable tool in assessing log dynamic modulus of elasticity (MOE)
early in the supply chain even on a whole-tree basis. These measurements could be
incorporated into optimal bucking decision-making based on market requirements
for wood stiffness by taking into account the effect of log position and length,
branch size, bark presence/absence, green density. All these variables were found to
influence acoustic velocity measurements and their influence could be predicted
based on linear models developed in this study.
44
Segregation of logs based on acoustic tools that measure stiffness is already being
used by some forest companies to improve the value of lumber recovery (Dickson
et al., 2004). Preliminary results from our acoustics trials show that in-forest sorting
of logs are likely to lead to improvements in recovery of higher value Douglas-fir
veneer grades. Our initial studies strive to address an array of questions related to
the technical feasibility of using acoustic technology in forest environments. Much
more work, however, needs to be undertaken to examine the costs, benefits and
economic viability of this technology.
45
2.6 LITERATURE CITED
Acuna, M.A. and G.E. Murphy. 2006. Geospatial and within tree variation of
wood density and spiral grain in Douglas-fir. Forest Products Journal
56(4):81-85.
Adams, D.M., R.R. Schillinger, G. Latta and A. Van Nalts. 2002. Timber harvest
projections for private land in Western Oregon. Oregon State Univ., Forest
Res. Lab. Reseach Contribution 37. 44 pp.
Carter, P., D. Briggs, R.J. Ross and X. Wang. 2004. Acoustic Testing to Enhance
Western Forest Values and Meet Customer Wood Quality Needs. In
Harrington, C.H. & Schoenholtz, S., (eds.) Productivity of Western Forests:
A Forest Products Focus. USDA Forest Service PNW Res. Sta. Gen. Tech.
Rep. PNW-GTR-642. pp. 121-129
Carter, P., S.S. Chauhan and J.C.F. Walker. 2006. Sorting logs and lumber for
stiffness using Director HM200. Wood and Fiber Science 38(1):49-54.
Clarke, C.R., R.D. Barnes and A. R. Morris. 2002. Effect of environment on wood
density and pulping of five pine species grown in Southern Africa. Paper
presented at the 2002 Technical Association of the Pulp and Paper Industry
of Southern Africa Conference, Durban, October 2002.
(http://tappsa.co.za/archive/APPW2002/Title/Effect_of_environment_on_w
ood_/effect_of_environment_on_wood_.html),(Accessed March, 2006).
Dickson, R.L., A.C. Matheson, B. Joe, J. Ilic and J.V. Owen. 2004. Acoustic
segregation of Pinus radiata logs for sawmilling. New Zealand Journal of
Forestry Science 34(2):175-189.
Eastin, I. 2005. Does lumber quality really matter to builders? In: Harrington, C.A.,
Schoenholtz, S.H., (eds.) Productivity of Western Forests: A Forest
Products Focus. USDA Forest Service PNW Res. Sta. Gen. Tech. Rep.
PNW-GTR-642. pp. 131-139.
Gartner, B.L., M.N. North, G.R. Johnson and R. Singleton. 2002. Effects of live
crown on vertical patterns of wood density and growth in Douglas-fir.
Canadian Journal of Forest Research 32:439-447.
Gartner, B.L. 2005. Assessing wood characteristics and wood quality in intensively
managed plantations. Journal of Forestry 100(2):75-77.
46
Grabianowski, M., B. Manley and J. Walker. 2006. Acoustic measurements on
standing trees, logs and green lumber. Wood Science and Technology
40:205-216.
Hermann, R.K. and D.P. Lavender. 1999. Douglas-fir planted forests. New Forests.
17: 53-70.
Huang, C.-L., H. Lindstrom, R. Nakada and J. Ralston. 2003. Cell wall structure
and wood properties determined by acoustics – a selective review. Holz als
Roh- und Werkstoff 61:321-335.
Joe, B., R. Dickson, C. Raymond, J. Ilic and C. Matheson. 2004. Prediction of
Eucalyptus dunnii and Pinus radiata timber stiffness using acoustics.
RIRDC Publication No 04/013. RIRDC, Kingston, Australia. 121 pp.
Lindstrom, H., P. Harris, C.T. Sorensson and R. Evans. 2004. Stiffness and wood
variation of 3-year old Pinus radiata clones. Wood Science and Technology
38:579-597.
Lasserre, J. P. 2005. Influence of initial stand spacing and genotype on Pinus
radiata corewood properties. M.S. thesis, Univ. Canterbury, New Zealand,
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Marshal, H.D. and G.E. Murphy. 2004. Economic evaluation of implementing
improved stem scanning systems on mechanical harvesters/processors. New
Zealand Journal of Forestry Science 34(2):158-174.
Murphy, G.E. and D.Y. Amishev. (in review). Effects of bark removal on acoustic
velocity of Douglas-fir logs. New Zealand Journal of Forestry Science
(submitted January 2008)
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effect on variables that gauge the economics of harvesting production. New
Zealand Journal of Forestry Science 32(2):281-292.
Murphy, G.E., H.D. Marshal and A.W. Evanson. 2005. Production speed effects on
log-making error rates and value recovery for a mechanized processing
operation in radiata pine in New Zealand. Southern African Forestry
Journal 204: 23-35.
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data analysis. 2nd edition Duxbury Press. Florence, KY. 742 pp.
47
Ross, R.J., K.A. McDonald, D.W. Green and K.C. Schad. 1997. Relationship
between log and lumber modulus of elasticity. Forest Products Journal
47(2):89-92.
SAS Institute Inc., 2004. SAS/STAT User’s Guide: Version 9.1.3. Cary, NC, USA.
Schuler, A. and A. Craig. 2003. Demographics, the housing market, and demand
for building materials. Forest Products Journal 53(5):8-17.
Wang, X., R.J. Ross and M. McClellan. 2001. Nondestructive evaluation of
standing trees with a stress wave method. Wood and Fiber Science
33:522-533.
Wang, X., N. Sharplin, P. Carter and R.J. Ross. 2004a. Method and apparatus for
evaluation of standing timber. U.S. provisional application: 60/538,376.
Wang, X., R.J. Ross and P. Carter. 2004b. Assessment of standing tree quality –
from baseline research to field equipment. In: Forest Products Society 2004
Annual Meeting – Technical Forum. Grand Rapids, Michigan, USA.
Wang, X., R.J. Ross and P. Carter. 2007a. Acoustic evaluation of wood quality in
standing trees. Part I. Acoustic wave behavior. Wood and Fiber Science
39(1):28-38.
Wang, X., P. Carter, R.J. Ross and B.K. Brashaw. 2007b. Acoustic assessment of
wood quality of raw forest materials – a path to increased profitability.
Forest Products Journal 57(5):6-14.
Xu, P., L. Donaldson, J. Walker, R. Evans and G. Downes. 2004. Effects of density
and microfibril orientation of low-stiffness wood in radiata pine butt logs.
Holzforschung 58:673-677
Xu, P. and J. Walker. 2004. Stiffness gradients in radiata pine trees. Wood Science
and Technology 38(1):1-9.
48
CHAPTER 3
PRE-HARVEST VENEER QUALITY EVALUATION OF DOUGLAS-FIR
STANDS USING TIME OF FLIGHT ACOUSTIC TECHNIQUE
Dzhamal Y. Amishev and Glen E. Murphy
Department of Forest Engineering
Oregon State University
Corvallis, OR 97331-5706
USA
Wood and Fiber Science Journal (in review, submitted March 2008)
Society of Wood Science and Technology
One Gifford Pinchot Drive
Madison, WI 537026-2398
USA
49
3.1 ABSTRACT
Acoustic technology has been successfully used as a non-destructive technique for
assessing mechanical properties of various wood products and species as well as in
tree selection and breeding based on stiffness. In an ongoing endeavor to optimize
merchandizing and enhance timber value recovery, seven second growth Douglasfir stands of similar age class in Western Oregon were sampled, totaling 1,400 trees
and more than 3,000 logs. The objectives of this research were to (i) investigate the
spatial variability of TOF acoustic velocities in standing Douglas-fir trees (ii)
develop relationships between average TOF acoustic velocities of standing
Douglas-fir trees and actual veneer produced and (iii) determine the influence of
diameter at breast height (DBH) on TOF sound speeds.
Spatial location of the stands in terms of their latitude, longitude, or altitude had no
predictive capability regarding their veneer quality. Standing tree TOF acoustic
velocity and the actual G1/G2 veneer produced using a stress-wave grade sorter had
no significant correlation. Significant differences were found between the three
different Director ST300® tools used along the duration of the study as well as
between the two opposite side measurements within trees. DBH correlated poorly
with both acoustic velocity and G1/G2 veneer recovery.
50
Keywords: Pseudotsuga menziesii, stiffness, impact based tool, sound velocity,
dynamic modulus of elasticity.
51
3.2 INTRODUCTION
Over the last few decades, as demand for high-quality timber has been rapidly
increasing, the availability of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco)
and other softwood old growth saw logs has been diminishing across North
America and timber resources have gradually shifted to intensively managed young
growth stands (Adams et al., 2002; Zhang et al., 2004). Due to the higher
proportion of juvenile wood, younger stands usually yield lower quality timber
(Gartner, 2005) with greater variability in product performance (Carter et al.,
2005). It has been long recognized that, as world log markets are becoming
increasingly competitive and complex, the successful transformation of managed
second-growth stands into quality products is crucial for the existence of a robust
forest industry (Kellogg, 1989; Barbour and Kellogg, 1990; Eastin, 2005). Good
measurements and predictions of both the external and internal properties of the
wood in each stem are essential to optimally match logs to markets (Clarke et al.,
2002). Assessing a forest stand quality (Acuna and Murphy, 2006), determining its
most appropriate use, time of harvest and processing technique, and consequently
distributing the products to the right location are all important management
decisions to achieve reduced costs and increased product values (Murphy et al.,
2005).
52
Wood modulus of elasticity, also known as stiffness, is one of the most important
mechanical properties and is the most frequently used indicator of the ability of
wood to resist bending and support loads. It has long been recognized as critical
product variable in both solid wood and pulp and paper processing (Eastin, 2005)
despite its high variability dependent upon site, genetics, silviculture, and location
within the tree and stand. It is a particularly important parameter in the conversion
of raw timber material into veneer and plywood products, requiring high stiffness
wood. With the ever growing use of engineered wood products such as roof trusses
and laminated veneer lumber (LVL) the demand for high-MOE lumber and veneer
has increased.
Nondestructive testing (NDT) instruments that are compact and easy to operate and
are based on acoustic principles, have been developed for measuring stiffness of
logs and standing trees (Dickson et al., 2004). Acoustic NDT tools have been
successfully used for evaluation of mechanical properties of various wood products
(structural lumber, poles, pulp logs, decay detection, etc.) and species as well as in
tree selection and breeding based on stiffness (Huang et al., 2003). Past research
has indicated high correlation between yield of structural grades of lumber and
acoustic velocity of standing trees (Wang et al., 2001; Lindstrom et al., 2002;
Grabianowski et al., 2006; Lasserre et al., 2007; Wang et al., 2007a) and processed
logs (Ross et al., 1997; Joe et al., 2004; Wang et al., 2007b; Waghorn et al., 2007;
Amishev and Murphy, in review). The most widely implemented acoustic
techniques among industry and researchers are the “time of flight” (TOF) for
53
standing trees and “resonance-based” for logs (Lindstrom et al., 2002). Originally
intended for decay detection in trees, TOF is currently the most popular method for
directly measuring stiffness on standing trees (Andrews, 2002) with the caveat that
it measures acoustic velocity only in the outerwood portion between the inserted
probes in the lower part of the tree (Wagner et al., 2003).
To date very little research has investigated the correlation between stress-wave
acoustic velocities in raw timber and those in veneer produced from that timber,
especially for Douglas-fir. Rippy et al. (2000) found moderate correlations between
acoustic speeds in Douglas-fir logs and those in veneer produced from those logs;
Amishev and Murphy (in review) found strong correlations between in-forest
acoustic measurements on Douglas-fir logs and the actual veneer recovered from
those logs based on an in-line commercialized Metriguard® stress-wave grade
sorter. The authors of this paper are aware of ongoing research efforts by the
University of Washington Stand Management Cooperative (SMC, 2008) to
investigate the relationship between stress-wave velocities in standing Douglas-fir
trees and acoustic speeds in veneer produced from those trees (84 trees, producing
186 wood logs and more than 5,000 veneer sheets). At the time of preparing this
manuscript, however, no results were available and no other published research was
found with the exception of Wagner et al. (1998, cited in Wagner et al., 2003), who
reported a low coefficient of determination (R2 = 0.14) between standing Douglasfir trees and the produced veneer.
54
The objectives of this study were to (i) investigate the spatial variability (within
stands and between stands) of TOF acoustic velocities in standing Douglas-fir trees
(ii) develop relationships between average TOF acoustic velocities of standing
Douglas-fir trees measured using the Director ST300® (CHH Fibre-gen, New
Zealand) tool and actual veneer produced and (iii) determine the influence of
diameter at breast height (DBH) on TOF sound speeds over a range of sites in
Western Oregon.
3.3 MATERIALS AND METHODS
3.3.1 Study sites
In summer 2006, six Roseburg Forest Products company (RFP) stands, located in
the Coastal (A - near Bellfountain, D and E - near Elkton, and F – near Lorane,)
and Cascade (B – near Sutherlin and C – near Tiller) Ranges of Oregon, were
harvested as part of two studies evaluating novel technologies for in-forest
measurement of wood properties. In summer 2007, a seventh stand (G - near
Corvallis), located within Oregon State University’s McDonald-Dunn College
Forest, was also harvested as part of these studies. All sites were second growth
Douglas-fir stands of similar age class (50 – 70 years) chosen to cover a range of
elevations and tree sizes (Table 3.1). Site G had been commercially thinned on
three occasions. Sites A to F had no commercial thinning but may have received a
pre-commercial thinning. Two hundred trees from each stand were sampled,
55
totaling 1,400 trees converted into more than 3,000 logs. Only veneer grade log
lengths were cut (18, 27, and 35 ft or 5.5, 8.2, and 10.7 m, respectively); no
sawlogs or pulp logs were produced. Prior to felling, each tree was numbered for
unique identification and DBH was measured and recorded. On a subsample of
about 100 (varying because of either measuring additional non-selected trees or
unavailability of a working TOF tool) randomly selected trees, acoustic velocity of
the standing trees was measured using the Director ST300® tool.
Table 3.1 Characteristics of the seven study sites.
Site
Elevation
Stand
DBH Range of
Site Location
of the site
Age
trees selected
Latitude/Longitude
(m)
(years)
(cm)*
A
180
62
20.1 - 86.1 (51.7)
44° 24.04’N / 123° 23.24’W
B
900
66
16.5 – 65.0 (35.0)
43° 22.58’N / 123° 03.54’W
C
1040
56
17.5 – 79.0 (50.7)
42° 58.56’N / 122° 48.52’W
D
220
54
18.8 – 66.8 (39.8)
43° 40.09’N / 123° 43.19’W
E
120
51
17.3 – 55.9 (29.8)
43° 40.16’N/ 123° 44.58’W
F
290
53
16.3 – 77.2 (38.0)
43° 48.40’N / 123° 18.34’W
G
280
72
15.0 – 62.9 (40.6)
44° 42.55’N/ 123° 19.58’W
* Average DBH in parenthesis
56
After felling, measurements included: total tree length (if broken the tree length
was measured to the point of breakage), merchantable length, biggest branch
diameter on each 20 ft (6.1 m) segment of the tree, acoustic velocity of the whole
stem with and without the branches (using the Director HM200® tool), and acoustic
velocity of each log made out of the stem. Approximately 100 mm thick disks at
different heights from the tree were collected for green density measurements from
a subsample (40 trees per stand) of the trees, totaling more than 800 disks.
After the in-forest measurements on the logs were completed, the logs were
transported to a veneer mill, debarked, cut into 8 ft (2.4 m) bolts, kiln-heated, shape
scanned, and peeled into veneer sheets. The sheets were then scanned for defects
and moisture, sorted into moisture classes, dried and then sorted into several veneer
grades (G1, G2, G3, AB, C+, C, D, X, and XX) based on in-line acoustic
measurement of wood stiffness using the Metriguard® grade sorter. Percent veneer
recovery in all grades was calculated.
3.3.2 Acoustic velocity measurement tools
The acoustic velocity of the standing trees was measured using the Director
ST300® (CHH Fibre-gen, New Zealand) tool which was specifically designed for
measuring TOF acoustic velocity in standing trees (Wang et al. 2004a). The system
and the working protocol that was followed in this research are described in detail
by Wang et al., 2007a. Measurements were taken on two faces of each tree and
57
multiple readings (at least three) were taken in each hitting position in order to get a
consistent “averaged” measurement. A total of three Director ST300® tools were
used in the duration of the study. Tool 1 (borrowed from Forest Products
Laboratory in Madison, WI) was used to measure all trees in sites A, B, and C and
some trees at sites D and E. Partly through sites D and E Tool 1’s rubber cover fell
off making it unusable and a new factory upgraded tool (Tool 2) was borrowed
from Roseburg Forest Products Company (RFP) to finish off sites D and E and to
measure all the trees in site F. The following year, yet another factory upgraded
tool (Tool 3) was borrowed from RFP for measuring all trees in site G.
Wang et al.(2007a) also describe the resonance based acoustic tool (Director
HM200®, CHH fibre-gen, New Zealand) used to measure longitudinal wave
velocity in the logs. They point out that the latter method is a well-established NDT
technique “for measuring long, slender wood members”. Results, based on log
measurements with the Director HM200® in stands A to G are presented in a
separate paper (Amishev and Murphy, in review).
3.3.3 Data analysis
Statistical analyses of the data were undertaken following either a simple linear
least squares regression analysis or a stepwise multiple regression methodology
described by Ramsey and Shafer (2002). They included the following steps:
graphical analysis of the data, examination of the correlation matrix, fitting of the
58
linear model, exploration of the residuals, significance test of the variables, and
improvement of the final regression model. Mean separations were examined using
Fisher's least significant difference method. Both SAS® 9.1 statistical software
(SAS, 2004) and the Data Analysis Tool Pak of MS Excel was used for the analysis
and a p-value of 0.05 was used as the threshold for determining significance of
explanatory variables.
3.4 RESULTS AND DISCUSSION
Stand F had the largest number of trees sampled (105) with the Director ST300®
tool while only 90 trees were sampled in Stand D because of temporary
unavailability of a TOF acoustic tool. Stand A produced the largest number of logs,
totaling 292, while Stand B yielded the least with 190 logs. The average log length
was 9.2 m ranging from 8.6 m for Site F to 9.4 m in Sites B and G. ST300 acoustic
velocity averaged 4.36 km/sec for all 698 trees and ranged from 3.16 to 6.26
km/sec (Table 3.2). The variation and distributions of the ST300 acoustic velocities
and the log lengths across all sites are shown in figures 3.1 and 3.2, respectively.
59
Table 3.2 Stem and log summary statistics for the seven study sites.
Site A
Trees
Sampled
Total
#
101
Log Count
Total
#
292
Site B
102
190
9.4
4.15
3.28
4.83
Site C
100
225
9.0
3.99
3.16
4.67
Site D
90
200
9.3
4.25
3.57
5.34
Site E
100
194
9.3
4.63
3.56
6.26
Site F
105
231
8.6
4.60
3.78
5.54
Site G
100
192
9.4
4.78
3.38
5.68
Overall
698
1524
9.2
4.36
3.16
6.26
Study
Sites
ST300 Acoustic
Log Length
Velocity
Average
Average Min Max
m
km/sec
9.3
4.15
3.25 4.75
The ST300 acoustic velocity data were approximately normally distributed around
the mean for each stand (fig. 3.1). Across all sites, the most frequently produced
log length was 10.7 m (fig. 3.2).
60
25
Frequency
20
Site A
15
Site B
Site C
Site D
10
Site E
Site F
Site G
5
Site G
Site F
Site E
Site D
Site C
Site B
Site A
0
3.6 3.7
3.8 4.0
4.1 4.2
4.3
4.4
4.5
4.6
4.7
Tree acoustic velocity (km/s)
4.8
4.9
5.0
5.1
5.3 More
Figure 3.1 Distribution of the ST300 tree acoustic velocities across the seven study
sites.
200
180
160
140
Frequency
120
5.5 m
8.2 m
100
10.7 m
80
60
40
20
0
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Figure 3.2 Distribution of the veneer log lengths produced across the seven study
sites.
61
Components of variations in ST300 acoustic velocity (Table 3.3) indicate that the
major source of variation was that between the different stands, contributing more
than 43% of the total variation.
Table 3.3 Components of variation for differences between stands, between trees
and between sides of a tree and their percentage contribution to the total variation.
Variance (σ2) components
Between
Stands
Variance
Percentage of total
Trees
Random
Sides
Error
Total
0.08973 0.06483 0.02888 0.02447 0.20791
43.2
31.2
13.9
11.8
100
Variation between trees totaled 31% while variation between the two sides within
trees contributed less than 14 %. These statistics are similar to what Toulmin and
Raymond (2007) reported for components of variance in acoustic velocity
measurements in radiata pine stands using the TreeTap TOF tool. Although not
recorded, substantial variability was observed from hit to hit within each side of a
tree; Mahon et al. (in review) showed that variation from hit-to-hit using the
“same-face method” contributed almost as much as the between tree variation in
standing tree acoustic velocities. They also suggested that using the “opposite-face
method” would likely reduce this variation by more than 60% using the FAKOPP
TreeSonic microsecond timer device.
62
Investigating the spatial variability of the standing tree sound velocity revealed that
site C had the lowest ST300 acoustic velocity and site G was found to be
significantly higher than all the other stands based on Fisher’s Least Significant
Difference (LSD) method (Fig. 3.3). The average sound velocities for site E and
site F were not significantly different and the same was true for site A and site B.
No significant difference was found between sites in the Coastal and those in the
Cascade Ranges of Western Oregon. No significant trend was observed in terms of
geographic spatial location of the sites (latitudinal, longitudinal, or altitudinal).
Figure 3.3 Director ST300 acoustic velocity averages for the seven study sites (A to
G). LSD.05 = Least Significant Difference between stand velocity means at p=0.05
level of significance. Means with the same lower-case letter (a to e) are not
significantly different.
63
The quantity and the quality of the produced veneer was not the same among the
different sites (Fig. 3.4). While the overall G1 and G2 (the highest quality grades)
veneer grade recovery percentage for sites A, B, D, and E was about the same
(around 50%), the other three sites (C, F, and G) were considerably lower (32, 37,
and 37%, respectively). No significant relationship was observed between G1/G2
veneer recovery and the spatial location of the sites (latitudinal, longitudinal, or
altitudinal). This highlights the variation in internal wood properties between stands
and emphasizes the need for pre-harvest stand quality information in order to make
informed management decisions.
70
60
50
40
G3
G2
G1
30
20
10
0
A
B
C
D
E
F
G
Figure 3.4 Veneer grades (G1, G2, and G3) recovery for the seven study sites (A to
G) from the OSU stiffness measurement study.
64
Investigating the relationship between stand average ST300 acoustic velocity and
actual G1/G2 veneer recovery percentage yielded a non-significant regression
model with no correlation (R2 = 0.03) between them (Fig. 3.5).
60
G1/G2 Veneer Recovery (%)
50
40
30
20
10
y = -4.69x + 64.5
R2 = 0.03
0
3.9
4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
OSU Average ST300 Velocity (ft per s)
Figure 3.5 Relationship between G1/G2 veneer recovery and on-site average ST300
stem acoustic velocity.
This result suggests that stand average acoustic velocities for Douglas-fir measured
by the TOF method using the Director ST300® tool may be of limited value in the
efforts to identify stand quality in terms of veneer stiffness parameters prior to
harvest. This is in agreement with the findings of Wagner et al. (1998, cited in
Wagner et al., 2003), who reported a low coefficient of determination (R2 = 0.14)
between standing Douglas-fir trees and the produced veneer. When correlating
standing tree stress wave velocity and lumber cut from logs in radiata pine
Matheson et al. (2002) found mixed results, reporting correlations of R2 = 0.33
65
(control seedlot) and R2 = 0.01 (orchard lot). Joe et al. (2004) also reported
moderate relationships between Eucalyptus dunni standing tree acoustics and
machine graded MOE (R2 of 0.40 to 0.44). In a more recent study, Grabianowski et
al. (2006) reported that standing tree acoustic velocities correlated well with lumber
cut both adjacent to the bark and corewood with R2 values of 0.89 and 0.74,
respectively. Wang et al., (2007a) suggested that the acoustic velocity of standing
trees measured by the TOF method may be used with confidence to derive
equivalent log acoustic velocity (and corresponding lumber stiffness), reporting
coefficients of determination ranging from 0.71 to 0.93 for five conifer species. Our
findings indicate that this might not be true for Douglas-fir with a coefficient of
determination R2 value of 0.25 between sound velocities of 698 standing trees
measured by the TOF method (ST300 tool) and the corresponding speeds in the
butt logs, measured by the resonance-based method (HM200 tool) which, in turn,
were found to be strongly correlated to actual veneer recovery (Amishev and
Murphy, in review).
As mentioned earlier, along the duration of the study we had the misfortune of
having to change our standing tree acoustic velocity device and ended up using a
total of three Director ST300® TOF tools. The first one was used on a total of 425
trees (all trees in sites A, B, C, on 75 trees in site D and 47 trees in site E), the
second factory upgraded device was used on 173 trees (15 trees in site D, 53 in site
E and all trees in site F), while a third upgraded tool was used for site G. This fact
certainly limits the power of our conclusions in terms of the reliability of TOF
66
acoustic device tree measurements to predict the end-use veneer characteristics
produced. In fact, an ANOVA analysis and means separation procedure revealed a
significant difference between the three tools in terms of their averaged
measurements (4.13, 4.71, and 4.78 km/sec, for tools 1, 2, and 3, respectively;
LSD.05 = 0.059 km/sec). Significant difference was also found between acoustic
velocity measurements taken on one side of the trees and those on the opposite side
although the choice of tree sides for the measurements was not related to any factor
(e.g. windward and leeward, north and south).
60
G1/G2 Veneer recovery (%)
55
50
45
40
35
30
25
3.60 3.70 3.80 3.90 4.00 4.10 4.20 4.30 4.40 4.50 4.60 4.70 4.80 4.90 5.00 5.10 5.20
Average stem velocity (km/sec)
Tool 1
Linear (Tool 1)
Tool 2
Linear (Tool 2)
Tool 3
Linear (Tool 3)
Figure 3.6 Relationship between tool x site average stem velocity and G1/G2
veneer recovery.
67
Considering the significant effect of the different tools on the standing tree
measurements, the relationship between average acoustic velocities for each tool x
site (where applicable) and actual veneer recovery was investigated (Fig. 3.6).
Although no statistically significant relationship was found for any one of the tool x
site averages, an interesting trend was observed between the fitted linear correlation
lines for tool 1 and tool 2 (tool 3 linear correlation line was hypothesized to follow
the same slope as that of tools 1 and 2, going through its only data point for site G).
It is possible that these tools have similar slopes but different intercepts. In other
words, there might be a potential for those devices to produce data compatible with
the final veneer characteristics, but there was great inconsistency between the
different tools. Latter trials in additional Roseburg Forest Products Douglas-fir
stands conformed to our findings on these devices (Donald Persyn, personal
communication).
Other research studies have investigated the significance of diameter at breast
height (DBH) on the relationship between standing tree acoustic velocity and
lumber machine graded MOE. Joe et al. (2004) found no significant relationships
when correlating DBH with acoustic velocity and machine graded MOE values.
When examining the relationship between acoustic velocity, outerwood density,
and DBH in radiata pine stands aged 8, 16, and 25 years, Chauhan and Walker
(2006) reported R2 values of 0.02, 0.07, and 0.18, respectively. Toulmin and
Raymond (2007) also reported minimal relationships between DBH and standing
tree acoustic velocities in radiata pine with R2 values of 0.07, 0.09, and 0.04 for 10,
68
15, and 20 year old stands, respectively. When regressing standing tree acoustic
velocity to tree DBH, we found similar values for the R2 averaging 0.21 for all
trees, ranging from 0.07 for trees from site G to 0.32 for site A trees. In other
words, DBH seems to have limited predictive capability in terms of tree acoustic
velocity and hence wood stiffness.
Other research studies have reported that acoustic velocity may be influenced by
other factors such as initial stand spacing, genotype, climatic conditions,
silvicultural treatments, management intensity and even temperature. Those factors
and their effects were not investigated in our study.
This research has primarily focused on the use of acoustic technology for
evaluating internal properties of Douglas-fir stands in terms of their veneer quality.
Acoustic techniques have been successfully used and implemented for nondestructive evaluation of mechanical properties of other wood products (structural
lumber, poles, pulp logs, decay detection, etc.) and species as well as in tree
selection and breeding based on stiffness (Huang et al., 2003). Recent research
studies have also found the use of acoustic tools to be viable in assessing stiffness
in Douglas-fir veneer grade logs (Amishev and Murphy, in review), radiata pine
sawlogs and green lumber (Grabianowski et al., 2006), structural lumber logs and
boards (Carter et al. 2006) and young seedling clones (Lindstrom et al., 2004),
Eucalyptus dunnii veneer and LVL logs and structural lumber boards (Joe et al.,
2004).
69
However, in a congruous manner with the results reported by Wagner et al. (1998,
cited in Wagner et al., 2003), this research contradicts the general agreement about
the usefulness of the TOF acoustic technology in a particular case - accomplishing
a reliable pre-harvest veneer quality evaluation of Douglas-fir stands.
3.5 CONCLUSIONS
The objective of this study was to determine whether TOF acoustic technology
could be used for pre-harvest veneer quality assessment of Douglas-fir stands in
terms of stiffness requirements and whether spatial and within tree characteristics
could be good predictors or influence the accuracy of those measurements in
second-growth Douglas-fir stands in Oregon. Standing tree acoustic velocity using
the Director ST300® TOF device and the actual G1/G2 veneer produced using an
in-line commercialized Metriguard® stress-wave grade sorter were found to have
no significant correlation. Significant differences were found between the three
different Director ST300® tools used along the duration of the study as well as
between the two opposite side measurements within trees. Substantial variability in
velocity readings was observed from hit to hit in each measurement position within
tree sides. Hence, TOF acoustic technology did not prove to be a promising and
valuable tool in assessing standing tree dynamic modulus of elasticity (MOE) early
in the supply chain on a single-tree or whole-stand basis. Spatial location of the
70
stands in terms of their latitude, longitude, or altitude had no predictive capability
regarding their veneer quality. The same was true for DBH according to its poor
correlation with both acoustic velocity and G1/G2 veneer recovery.
Segregation of logs based on acoustic tools that measure stiffness is already being
used by some forest companies to make informed management decisions and
improve the value of lumber recovery (Dickson et al., 2004). Although preliminary
results from our acoustics trials show that in-forest sorting of logs are likely to lead
to improvements in recovery of higher value Douglas-fir veneer grades (Amishev
and Murphy, in review), pre-harvest veneer quality evaluation of Douglas-fir stands
using TOF acoustic devices is, at this stage, not reliable. More efforts are required
to identify the reasons for the large variability and inconsistency of TOF devices on
Douglas-fir trees.
Our initial studies strive to address an array of questions related to the technical
feasibility of using acoustic technology in forest environments. Much more work,
however, needs to be undertaken to examine the costs, benefits and economic
viability of this technology.
71
3.6 LITERATURE CITED
Acuna, M.A. and G.E. Murphy. 2006. Geospatial and within tree variation of
wood density and spiral grain in Douglas-fir. Forest Products Journal
56(4),81-85.
Adams, D.M., R.R. Schillinger, G. Latta and A. Van Nalts. 2002. Timber harvest
projections for private land in Western Oregon. Oregon State Univ., Forest
Res. Lab. Reseach Contribution 37. 44 pp.
Amishev, D.Y. and G.E. Murphy. (in review). In-forest sssessment of veneer grade
Douglas-fir logs based on acoustic measurement of wood stiffness. Forest
Products Journal (submitted January 2008).
Andrews, M., 2002. Wood quality measurement – son et lumière. New Zealand
Journal of Forestry 47:19-21.
Barbour, R.J. and R.M. Kellogg. 1990. Forest management and end-product
quality: a Canadian perspective. Canadian Journal of Forest Research
20:405-414.
Carter, P., D. Briggs, R.J. Ross and X. Wang. 2004. Acoustic testing to enhance
Western forest values and meet customer wood quality needs. In:
Harrington, C.H. & Schoenholtz, S., (eds.) Productivity of Western Forests:
A Forest Products Focus. USDA Forest Service PNW Res. Sta. Gen. Tech.
Rep. PNW-GTR-642. pp. 121-129.
Carter, P., S.S. Chauhan and J.C.F. Walker. 2006. Sorting logs and lumber for
stiffness using Director HM200. Wood and Fiber Science 38(1):49-54.
Chauhan, S.S. and J.C.F. Walker. 2006. Variations in acoustic velocity and density
with age, and their interrelationships in radiata pine. Forest Ecology and
Management 229:388-394.
Clarke, C.R., R.D. Barnes and A. R. Morris. 2002. Effect of environment on wood
density and pulping of five pine species grown in Southern Africa. Paper
presented at the 2002 Technical Association of the Pulp and Paper Industry
of Southern Africa Conference, Durban, October 2002.
(http://tappsa.co.za/archive/APPW2002/Title/Effect_of_environment_on_w
ood_/effect_of_environment_on_wood_.html),(Accessed March, 2006).
72
Dickson, R.L., A.C. Matheson, B. Joe, J. Ilic and J.V. Owen. 2004. Acoustic
segregation of Pinus radiata logs for sawmilling. New Zealand Journal of
Forestry Science 34(2):175-189.
Eastin, I. 2005. Does lumber quality really matter to builders? In: Harrington, C.A.,
Schoenholtz, S.H., (eds.) Productivity of Western Forests: A Forest
Products Focus. USDA Forest Service PNW Res. Sta. Gen. Tech. Rep.
PNW-GTR-642. pp. 131-139.
Gartner, B.L. 2005. Assessing wood characteristics and wood quality in intensively
managed plantations. Journal of Forestry 100(2):75-77.
Grabianowski, M., B. Manley and J. Walker. 2006. Acoustic measurements on
standing trees, logs and green lumber. Wood Science and Technology
40:205-216.
Huang, C.-L., H. Lindstrom, R. Nakada and J. Ralston. 2003. Cell wall structure
and wood properties determined by acoustics – a selective review. Holz als
Roh- und Werkstoff 61:321-335.
Joe, B., R. Dickson, C. Raymond, J. Ilic and C. Matheson. 2004. Prediction of
Eucalyptus dunnii and Pinus radiata timber stiffness using acoustics.
RIRDC Publication No 04/013. RIRDC, Kingston, Australia. 121 pp.
Kellogg, R.M. 1989. Second growth Douglas-fir: its management and conversion
for value. Forintek Canada Corp., Vancouver, B.C. Spec. Publ. SP-32.
173 pp.
Lindstrom, H., P. Harris and R. Nakada. 2002. Methods for measuring stiffness of
young trees. Holz als Roh- und Werkstoff 60:165-174.
Lindstrom, H., P. Harris, C.T. Sorensson and R. Evans. 2004. Stiffness and wood
variation of 3-year old Pinus radiata clones. Wood Science and Technology
38:579-597.
Lasserre, J.P., E.G. Mason and M.S. Watt. 2007. Assessing corewood acoustic
velocity and modulus of elasticity with two impact based instruments in 11year-old trees from a clonal-spacing experiment of Pinus radiata D. Don.
Forest Ecology and Management 239:217-221.
Mahon, J.M., Jr., L. Jordan, L.R. Schimleck, A. Clark III and R.F. Daniels. (in
review). A comparison of sampling methods for a standing tree acoustic
device. Southern Journal of Applied Forestry (submitted June, 2007).
73
Matheson, A.C., R.L. Dickson, D.J. Spencer, B. Joe and J. Ilic. 2002. Acoustic
segregation of Pinus radiata logs according to stiffness. Annals of Forest
Science 59:471-477.
Murphy, G.E., H.D. Marshall and A.W. Evanson. 2005. Production speed effects
on log-making error rates and value recovery for a mechanized processing
operation in radiata pine in New Zealand. Southern African Forestry
Journal 204: 23-35.
Persyn, D. (Pers. Comm.). Roseburg Forest Products Co Oregon logging manager.
Ramsey, F.L. and D.W. Shafer. 2002. The statistical sleuth: A course in methods of
data analysis. 2nd edition Duxbury Press. Florence, KY. 742 pp.
Rippy, R.C., F.G. Wagner, T.M. Gorman, H.D. Layton and T. Bodenheimer. 2000.
Stress-wave analysis of Douglas-fir logs for veneer properties. Forest
Products Journal 50(4):49-52.
Ross, R.J., K.A. McDonald, D.W. Green and K.C. Schad. 1997. Relationship
between log and lumber modulus of elasticity. Forest Products Journal
47(2):89-92.
SAS Institute Inc., 2004. SAS/STAT User’s Guide: Version 9.1.3. Cary, NC, USA.
SMC Newsletters, 2008. Non-destructive evaluation of wood quality in standing
Douglas-fir trees and logs. University of Washington, Seattle, WA.
http://www.cfr.washington.edu/research.smc/ (Accessed Feb. 06, 2008).
Toulmin, M.J. and C.A. Raymond. 2007. Developing a sampling strategy for
measuring acoustic velocity in standing Pinus radiata using the TreeTap
time of flight tool. New Zealand Journal of Forestry Science 37(1):96-111.
Waghorn, M.J., E.G. Mason and M.S. Watt. 2007. Influence of initial stand density
and genotype on longitudinal variation in modulus of elasticity for 17-yearold Pinus radiata. Forest Ecology and Management 252:67-72.
Wagner, F.G., T.M. Gorman, R.C. Rippy, H.D. Layton, T. Bodenheimer, A.
Keenan, C.E. Keegan and R.J. Ross. 1998. Nondestructive evaluation of
veneer properties from standing trees. Unpublished report. Univ. of Idaho,
Moscow, ID.
Wagner, F.G., T.M. Gorman and S.Y. Wu. 2003. Assessment of intensive stresswave scanning of Douglas-fir trees for predicting lumber MOE. Forest
Products Journal 53(3):36-39.
74
Wang, X., R.J. Ross and M. McClellan. 2001. Nondestructive evaluation of
standing trees with a stress wave method. Wood and Fiber Science
33:522-533.
Wang, X., N. Sharplin, P. Carter and R.J. Ross. 2004a. Method and apparatus for
evaluation of standing timber. U.S. provisional application: 60/538,376.
Wang, X., R.J. Ross and P. Carter. 2007a. Acoustic evaluation of wood quality in
standing trees. Part I. Acoustic wave behavior. Wood and Fiber Science
39(1):28-38.
Wang, X., P. Carter, R.J. Ross and B.K. Brashaw. 2007b. Acoustic assessment of
wood quality of raw forest materials – a path to increased profitability.
Forest Products Journal 57(5):6-14.
Zhang, S.Y., Y. Qibin and J. Beaulieu. 2004. Genetic variation in veneer quality
and its correlation to growth in white spruce. Canadian Journal of Forest
Research 34:1311-1318.
75
CHAPTER 4
IMPLEMENTING ACOUSTIC TECHNOLOGY ON MECHANICAL
HARVESTERS/PROCESSORS FOR REAL-TIME WOOD STIFFNESS
ASSESSMENT: OPPORTUNITIES AND CONSIDERATIONS
Dzhamal Y. Amishev and Glen E. Murphy
Department of Forest Engineering
Oregon State University
Corvallis, OR 97331-5706
USA
International Journal of Forest Engineering (in review, submitted May 2008)
Forest Products Society
2801 Marshall Court
Madison, WI 53705-2295
USA
76
4.1 ABSTRACT
Acoustic technology has been successfully used as a non-destructive technique for
assessing mechanical quality of various wood products and species based on
stiffness. Many mechanical harvester/processor manufacturers have implemented
mechanical sensors to measure tree diameter and length as well as optimal bucking
algorithms on their equipment. There is a growing interest in incorporating
technologies for measuring internal stem features into a harvester head. The
objectives of this study, therefore, were to (i) determine and investigate the factors
arising from incorporating acoustic instruments on a mechanized harvester head
that might influence acoustic signal and velocity readings quality and (ii)
investigate the issues and considerations with suggested working strategies in
regards to harvesting productivity impacts and processing decisions.
It was found that, taking into account some feasibility considerations, the hold of
the machine grapple would not compromise the accuracy of the resonance-based
acoustic velocity readings. There were three working procedures suggested for
measuring resonance-based acoustic velocity: (1) after the stem is delimbed and run
through the measuring equipment, (2) once a portion of the stem is measured and
the length of its unmeasured portion is forecasted and (3) after the tree is felled by
the harvester and before any further processing is done.
77
No matter the working procedure, it was found that logs produced from lower
sections of the tree are stiffer than those from upper portions. If the processor head
traverses the stem partially or completely, the removal of bark and branches and
their effect on acoustic velocity readings should be taken into account. Forecasting
routines could be developed to get around imperfect and even non-existing
information about tree length with the second or third working procedure. Results
yielded by the two methods used for stem height (and consequently acoustic
velocity) prediction in this study (linear regression model and a k-nearest-neighbor)
were considered as rather promising. Testing feasibility concerns with the
resonance-based acoustic technique were observed if the entire stem was intact to
the very top offshoot bud.
Keywords: Douglas-fir, dynamic modulus of elasticity, sound velocity, veneer
quality, mechanized harvesting, implications
78
4.2 INTRODUCTION
Wood quality can be defined according to attributes that make wood valuable for a
given use by society (Gartner 2005). Traditionally, tree species, log dimensions and
external quality characteristics such as knot size and distribution, sweep, taper,
scarring and decay have been used to specify a particular log-type. In recent years,
however, mills and markets have begun to include additional characteristics to
specify the logs they require with consideration now being given to such wood
properties as stiffness, strength, density, spiral grain, extractives content, and
consumption of energy for processing (Andrews 2002, So et al. 2002; Young
2002). These additional potential specifications required by the future wood users
add extra complexity to the already complex task of log producing and sorting.
Technologies capable of capturing internal log features such as microwave, X-ray,
computer-aided tomography, ultrasound, near-infrared (NIR) spectroscopy and
nuclear magnetic resonance (NMR) have been investigated for their potential for
log scanning (Schmoldt et al. 2000, Rayner 2001, So et al. 2004, Acuna & Murphy
2006) in sawmills.
Wood modulus of elasticity (MOE), also known as stiffness, is an important
mechanical property and is the most frequently used indicator of the ability of
wood to resist bending and support loads. Stiffness in raw timber material is highly
variable and dependent upon site, genetics, silviculture, and location within the tree
79
and stand. It has long been recognized as a critical product characteristic in both
solid wood and pulp and paper processing (Eastin, 2005). It is a particularly
important parameter in the conversion of raw timber material into veneer and
plywood products, which require high stiffness wood. With the ever growing use of
engineered wood products such as roof trusses and laminated veneer lumber (LVL)
the demand for high-MOE lumber and veneer has increased.
For many years, the sawmilling industry has utilized acoustic technology for
lumber assessment and devices, such as the in-line commercialized Metriguard®
stress-wave grade sorter, have been widely used. Acoustic nondestructive testing
(NDT) instruments have been successfully used for evaluation of mechanical
properties of various wood products (structural lumber, poles, pulp logs, decay
detection, etc.) and species as well as in tree selection and breeding based on
stiffness (Huang et al. 2003). Acoustic NDT tools that are compact and easy to
operate and are based on acoustic principles have been developed for measuring
stiffness of both logs and standing trees (Dickson et al. 2004). The most widely
implemented acoustic techniques among industry and researchers are the “time of
flight” (TOF) technique for standing trees and “resonance-based” technique for
logs (Lindstrom et al., 2002). According to Wang et al. (2007a) who describe the
TOF technique in detail, “the accuracy of TOF measurement depends on accurate
identification of the arrival times of the acoustic wave signals, each from a start
sensor and a stop sensor”. The same authors also claim that the inherent accuracy
of the “resonance-based” method presents a substantial advantage over a TOF
80
system in log measurement applications, because, in contrast to TOF, the resonance
technique stimulates many acoustic pulse reverberations, resulting in a very robust
and repeatable velocity measurement. While researchers around the world are in
general agreement about the merits and significance of the resonance-based
acoustic instruments and their potential applications for a variety of tree species and
products (Ross et al. 1997, Huang et al. 2003, Joe et al. 2004, Wang et al. 2007b,
Amishev & Murphy (in review)a), past research have reported mixed results in
regards to the TOF acoustic tools. Coefficients of correlation R2 ranging from 0.01
to 0.44 (Matheson et al. 2002, Joe et al. 2004) have indicated low predictive
capabilities while Grabianowski et al. (2006) and Wang et al. (2007a) suggested
that the TOF technique may be used with confidence to derive equivalent lumber
stiffness values with R2 reported in the range of 0.71 to 0.93. In terms of TOF
average acoustic velocities for Douglas-fir, findings by Wagner et al. (1998, cited
in Wagner et al. 2003) and Amishev & Murphy (in review)b) indicate that the TOF
method may be of limited value in the efforts to identify standing tree quality in
regards to veneer stiffness parameters. In contrast to the robust and repetitive
measurements yielded by resonance-based acoustic tools for a particular tree, the
TOF acoustic instruments have been reported to produce readings with substantial
inherent variability both between sides of the tree/log and between hits within each
side of the tree/log and even between different devices from the same manufacturer
(Toulmin & Raymond 2007, Mahon et al. (in review), Amishev & Murphy (in
review)b).
81
Worldwide forest harvesting has become increasingly mechanized during the last
few decades. This is especially true where harvested tree size is decreasing and the
capability of one or two machines to fell, delimb, buck, and sort a tree or a group of
trees is an appealing advantage. This trend towards mechanization of forest
harvesting operations is observed for various forest types, terrains and climatic
conditions (Raymond 1988, Nordlund 1996, Godin 2001) leading to near
elimination of motor-manual felling in thinning operations and continuously
increasing sales of harvesters and processors. Drivers for this shift from the
traditional motor manual harvesting systems to mechanical harvesting systems
generally include productivity/cost improvement goals or labor-related issues.
Among other things, mechanization also provides a platform for innovative
measurement systems which could lead to improved log segregation based on a
wider range of wood properties (Murphy 2003a).
Most modern mechanical harvesting systems use mechanical sensors, some
combining these with photocells to measure diameter and length (Andersson and
Dyson 2002). Many mechanical harvester/processor manufacturers have also
implemented optimal bucking algorithms on their equipment. While operators can
visually assess changes in quality along the length of each stem, they are incapable
of evaluating internal wood properties without the assistance of a proper scanning
technology. To produce accurate optimal bucking decisions these systems require
accurate and detailed information on stem shape and quality characteristics.
82
According to the breakeven values reported by Marshall & Murphy (2004),
substantial investments could be made in improved stem scanning systems.
There is a growing interest in incorporating technologies for measuring internal
stem features into a harvester head (Carter & Sharplin 2006). Ongoing research
efforts are addressing potential challenges, opportunities and considerations from
installing NIR and acoustic instruments on mechanized harvesters (Murphy et al.
2007, Carter 2007). These preliminary reports indicate that, despite a number of
influential factors and working protocol uncertainties, there is a great potential for
these technologies to demonstrate reliable performance and logs produced using
harvesters/processors could be segregated for internal wood quality in the forest. At
the time of preparing this manuscript, however, no research results have been
published regarding the implementation of such technologies for real-time
assessment of internal wood properties.
In regards to incorporating acoustic technology into a harvester head, either of the
two techniques, TOF or resonance, could be used. Findings by Mahon et al. (in
review) suggest that placing the two probes on either side of the tree instead of
longitudinally on the same side may result in reduced variability and increased
confidence in TOF acoustic readings. The TOF technique also does not require
stem length information prior to gathering acoustic data. This is likely the reason
for Carter (2007) to evaluate the performance and endeavor to improve a TOF
prototype device (Director PH330) installed on a harvester head. The resonance-
83
based approach, in contrast to the TOF technique, would require stem length
information to ensure accurate measurements. This may necessitate measuring the
entire stem before an acoustic reading is taken resulting in double handling with
considerable consequences for machine productivity and costs. The consistency of
resonance acoustic readings and their strong correlation with veneer recovery, at
least for Douglas-fir logs, supports study of the potential implications from
incorporating resonance-based acoustic technology into a mechanized
processor/harvester for real-time wood stiffness assessment. The resonance based
approach is the focus of the remainder of this paper.
Only a small number of research studies, all of them focusing on external stem
features, have investigated the use of scanning technology with mechanical
harvesting equipment (Tian & Murphy 1997, Löfgren & Wilhelmsson 1998, Möller
et al. 2002, Murphy 2003b, Marshall & Murphy 2004) and the best procedures for
scanning and optimal bucking with mechanized harvesters (Berglund & Sondell
1985, Näsberg 1985, Liski & Nummi 1995). There are three working procedures
suggested in those studies that would also be applicable for performing acoustic
measurements:
1. The first procedure would involve the complete delimbing and shape scanning
of the stem after the tree is felled and subsequently performing the acoustic
measurement for stiffness quality characterization of the tree. This method
would guarantee a good-quality acoustic result but would require the harvester
84
head to traverse the entire length of the tree at least twice (three times if it has
to start processing from the butt) which would have considerable consequences
for machine productivity and costs (Murphy 2003a, Marshall & Murphy 2004).
2. The second procedure involves delimbing and measuring a portion of the stem
and forecasting the taper of its unmeasured portion for length estimation. Based
on the forecast length to the top of the stem an acoustic measurement would be
performed. The acoustic measurement may be affected by the presence of
branches on the undelimbed portion of the stem. Berglund & Sondell (1985)
reported that, using this strategy, productivity impacts could be reduced and
value losses minimized. Näsberg (1985) found that loss in value due to
incomplete information was less than 2% using a similar forecasting procedure.
Work by Murphy (2003b) later expanded by Marshall & Murphy (2004)
suggested that automated partial scanning methods coupled with stem
dimensions forecasting and optimization equipment have a great potential in
achieving optimal value from every stem.
3. The third procedure involves measuring the acoustic velocity after the tree is
felled but before any further processing is done. This method would inherently
entail imperfect and even non-existing information about tree length, as well as
acoustic measurements of undelimbed stems. Scandinavian researchers Liski &
Nummi (1995) developed a linear mixed model that used data from previous
stems plus a number of known measurements on the current stem for predicting
85
stem curve characteristics in Norway spruce. They found that value losses
decreased as the length of the known portion of the stem increased. Studies
performed on Scots pine in Sweden (Möller et al. 2003) and Finland (Nummi &
Möttönen 2003) on prediction models for accurately forecasting a number of
wood quality characteristics during the stem processing operation also yielded
promising results. With no scanning, tree height has to be predicted based on
already available information from previously processed trees and
measurements acquired while the tree is being felled, such as DBH or butt
diameter.
The objectives of this study, therefore, were to (i) determine and investigate the
factors arising from incorporating acoustic instruments on a mechanized harvester
head that might influence acoustic signal and velocity readings quality and (ii)
investigate the issues and considerations with these working strategies in regards to
harvesting productivity impacts and processing decisions.
4.3 MATERIALS AND METHODS
4.3.1 Study sites and data collected
In summer 2006, six Roseburg Forest Products company (RFP) stands, located in
the Coastal (A - near Bellfountain, D and E - near Elkton, and F – near Lorane,)
and Cascade (B – near Sutherlin and C – near Tiller) Ranges of Oregon, were
86
harvested as part of two studies evaluating novel technologies for in-forest
measurement of wood properties. In summer 2007, a seventh stand (G - near
Corvallis), located within Oregon State University’s McDonald-Dunn College
Forest, was also harvested as part of these studies. All sites were second growth
Douglas-fir stands of similar age class (50 – 70 years) chosen to cover a range of
elevations and tree sizes (Table 4.1). Site G had been commercially thinned on
three occasions. Sites A to F had no commercial thinning but may have received a
pre-commercial thinning. Two hundred trees from each stand were sampled,
totaling 1,400 trees converted into more than 3,000 logs. Only veneer grade log
lengths were cut (18, 27, and 35 ft or 5.5, 8.2, and 10.7 m, respectively); no
sawlogs or pulp logs were produced. Prior to felling, each tree was numbered for
unique identification and diameter at breast height (DBH) was measured and
recorded. On a subsample of about 100 (varying because of either measuring
additional non-selected trees or unavailability of a working TOF tool) randomly
selected trees, acoustic velocity of the standing trees was measured using the
Director ST300® tool.
87
Table 4.1. Characteristics of the seven study sites.
Site
Elevation
Stand
DBH range of
Site location
of the site
age
trees selected
Latitude/Longitude
(m)
(years)
(cm)*
A
180
62
19.3 – 96.8 (52.2)
44° 24.04’N / 123° 23.24’W
B
900
66
16.5 – 69.6 (36.3)
43° 22.58’N / 123° 03.54’W
C
1040
56
17.5 – 79.0 (50.6)
42° 58.56’N / 122° 48.52’W
D
220
54
14.2 – 66.8 (39.5)
43° 40.09’N / 123° 43.19’W
E
120
51
15.5 – 59.4 (32.0)
43° 40.16’N/ 123° 44.58’W
F
290
53
16.3 – 77.2 (38.9)
43° 48.40’N / 123° 18.34’W
G
280
72
15.0 – 78.5 (41.6)
44° 42.55’N/ 123° 19.58’W
* Average DBH in parentheses.
After felling, measurements included: total tree length (if broken the tree length
was measured to the point of breakage), merchantable length, biggest branch
diameter on each 20 ft (6.1 m) segment of the tree, acoustic velocity of the whole
stem with and without the branches (using the Director HM200® tool), and acoustic
velocity of each log made out of the stem. A subsample of 40 randomly selected
trees was used to evaluate the impacts of harvesting equipment on acoustic velocity
readings when a tree/log was in the grip of a processor/loader grapple. Sound
velocities (using the Director HM200® tool) on these trees and each of the
88
subsequently produced logs were measured both in the grip of a harvester/loader
grapple and on the ground with no contact to harvesting equipment.
After the in-forest measurements on the logs were completed, the logs were
transported to a veneer mill, debarked, cut into 8 ft (2.4 m) bolts, kiln-heated, shape
scanned, and peeled into veneer sheets. The sheets were then scanned for defects
and moisture, sorted into moisture classes, dried and then sorted into several veneer
grades (G1, G2, G3, AB, C+, C, D, X, and XX) based on in-line acoustic
measurement of wood stiffness using the Metriguard® grade sorter. Percent veneer
recovery in all grades was calculated.
4.3.2 Acoustic velocity measurement tools and harvesting equipment
The acoustic velocity of the standing trees was measured using the Director
ST300® (CHH Fibre-gen, New Zealand) tool which was specifically designed for
measuring TOF acoustic velocity in standing trees (Wang et al. 2004). The system
and the working protocol that was followed in this research are described in detail
by Wang et al. (2007a) and additional details for this study are specified in
Amishev & Murphy (in review)b). Wang et al.(2007a) also describe the resonance
based acoustic tool (Director HM200®, CHH fibre-gen, New Zealand) used to
measure longitudinal wave velocity in logs.
89
Various pieces of harvesting equipment were used to evaluate the “grapple effect”
on acoustic readings for the seven stands studied with the same machine being
available and used throughout each particular site. Track-mounted knuckle-boom
loaders were used in sites A, B, and C. Sites D and E utilized a rubber-tired truckmounted knuckle-boom loader. Waratah processor heads mounted on tracked
carriers were used in sites F and G.
4.3.3 Data analysis
Statistical analyses of the data were undertaken following either a simple linear
least squares regression analysis or a stepwise multiple regression methodology
described by Ramsey and Shafer (2002). They included the following steps:
graphical analysis of the data, examination of the correlation matrix, fitting of the
linear model, exploration of the residuals, significance test of the variables, and
improvement of the final regression model. Mean separations were examined using
Fisher's least significant difference method. Both SAS® 9.1 statistical software
(SAS, 2004) and the Data Analysis Tool Pak of MS Excel were used for the
analysis and a p-value of 0.05 was defined as the threshold for determining
significance of explanatory variables. Potentially influential points were identified
using both the Cook’s Distance diagnostic and the studentized residual statistic test
(cutoff value of 3) in SAS® 9.1.
90
Two methods were used for stem height (and consequently acoustic velocity)
prediction: a random coefficient regression model (R) and a k-nearest-neighbor
(NN) prediction. For this purpose, a sample of 100 trees from each stand was
designated as the training data set (TDS) and used to predict the stem height value
for each of the remaining trees in that stand, referred to as the validation data set
(VDS). The first method consisted of developing a linear regression model from
TDS with tree height (or a function of it) being the response variable and DBH (or
a function of it) being the explanatory variable. The model was then applied to
predict tree height from DBH data in VDS and adjust acoustic velocity values by
the predicted-to-actual tree height ratio. The NN approach involved locating the k
closest members (the k-nearest-neighbors) of TDS in terms of DBH, calculating the
weighted average of the corresponding tree heights and using that value as the
predicted tree height based on the DBH value for each tree in VDS. Acoustic
velocity was then adjusted in the same manner as with the regression method.
4.4 RESULTS AND DISCUSSION
Stand A produced the largest number of logs totaling 572 while Stand G yielded
the least with 353 logs; the average log length was 9.2 m ranging from 8.5 m for
Site F to 9.5 m in Site B; HM200 acoustic velocity averaged 3.77 km/sec
throughout the 3077 total logs and ranged from 2.73 to 4.69 km/sec; and ST300
91
acoustic velocity averaged 4.36 km/sec for all 698 sampled trees and ranged from
3.16 to 6.26 km/sec (Table 4.2).
Table 4.2 Log summary statistics for seven research sites.
Study
Sites
HM200 Acoustic
ST300 Acoustic
Log Length
Velocity
Velocity*
Average
Average Min Max Average Min Max
m
km/sec
km/sec
9.4
3.92
3.03 4.58
4.15
3.25 4.75
Site A
Log Count
Total
#
572
Site B
399
9.5
3.77
2.80
4.69
4.15
3.28
4.83
Site C
458
9.2
3.46
2.73
4.23
3.99
3.16
4.67
Site D
447
9.2
3.76
2.98
4.63
4.25
3.57
5.34
Site E
395
9.3
3.84
2.88
4.48
4.63
3.56
6.26
Site F
453
8.5
3.82
2.96
4.47
4.60
3.78
5.54
Site G
353
9.3
3.77
2.78
4.32
4.78
3.38
5.68
Overall
3077
9.2
3.77
2.73
4.69
4.36
3.16
6.26
* Only a subsample of trees measured (see Amishev & Murphy (in review)b
Detailed information regarding the variation and distributions of log lengths,
HM200 and ST300 acoustic velocities for this study is presented in Amishev &
Murphy ((in review)a, (in review)b).
The apparent difference between hand-held acoustic tools and their potential
counterparts integrated into a harvester head is the fact that in the latter the tree/log
would be in the grip of a metal grapple. One of the challenges pointed out by Carter
(2007) in regards to the TOF instrument is capturing good-quality signals while the
chainsaw is operating; this would certainly be an important consideration with a
resonance-based tool as well. Considering similar factors in our study, the
92
relationship between acoustic velocities in the grip of a harvester/loader grapple
and those on the ground with no contact to harvesting equipment was investigated.
Yielding a significant linear model with an R2 of 0.86, acoustic velocity readings in
the grip of a metal grapple were found to be strongly correlated with acoustic
velocities measured on the same tree/log laid on the ground.
4.9
4.7
On-Ground Velocity (km/sec)
4.5
4.3
4.1
3.9
3.7
3.5
3.3
3.1
2.9
y = 0.93x + 0.26
R2 = 0.92
2.7
2.5
2.5
2.7
2.9
3.1
3.3
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
In-Grapple Velocity (km/sec)
Figure 4.1 Relationship between acoustic velocities in the grip of a harvester/loader
grapple and those on the ground with no contact to harvesting equipment for seven
study sites in Western Oregon.
Potentially influential data points were identified and, after examining those, 15 out
of the total 889 observations were identified as outliers and removed from the
sample based on additional indications (lower confidence for acoustic readings and
sampling errors) regarding the validity of those measurements. The resultant model
93
yielded an R2 of 0.92 meaning that the hold of the grapples does not compromise
the accuracy of the resonance-based acoustic velocity readings (Fig. 4.1).
Although not recorded and investigated, during the study it was observed that in
several cases lower confidence readings (and sometimes no readings) were
produced by the Director HM200® tool while the tested specimen was in the
grapples. A slight release in strength of the grip or changing the grip position
further up along the length of the tree/log was needed to warrant a good-quality
signal. These factors should be taken into account in designing an acoustic device
to be incorporated into a harvester head for real-time stiffness-based wood
segregation in the forest.
Another aspect to be considered which is valid for all three working procedures is
the tree structure effect on stiffness. In other words, is it possible to predict stiffness
characteristics for each of the logs to be produced from a tree based on a single
measurement for the whole stem? The correlations between whole tree acoustic
velocity readings and those taken on the logs produced were statistically significant
and quite strong (R2 ranged from 0.60 to 0.72) for all the logs along the stem. The
analysis revealed that acoustic velocities of logs produced from different sections
of the tree are unequal and, on average, the butt log had the largest acoustic
velocity relative to that of the whole tree (6.4% higher) and it decreased in each
subsequent log along the length of a tree stem and the topmost log had 10% lower
velocities than the whole tree (Fig. 4.2).
94
40
Acoustic Velocity Difference (%)
30
20
10
0
-10
-20
Log 1
Log 2
Log 3
Log 4
-30
Figure 4.2 Average percent difference between whole tree acoustic velocities and
those measured on each subsequent log produced from that tree. The “error bars”
represent the range in percent difference for each log.
Studies on radiata pine have found that a low-stiffness wood zone forms from the
base to about 2.7 m tree height (Xu & Walker 2004, Xu et al. 2004) and recent
findings suggest this might be valid for Douglas-fir as well (Amishev & Murphy
(in review)a). This tree structure peculiarity should definitely be considered in
designing an acoustic tester for harvester head and is a valid consideration for any
working procedure employed.
With the first two working procedures, if either complete or partial
scanning/processing is performed, any alterations to the stem by the harvester head
should be considered in regards to their influence on acoustic velocity readings.
95
One such alteration that was observed during this study is the partial and in some
cases the near-complete removal of the bark from tree stems while delimbing and
shape scanning is performed. This is especially true early in the growing season
when increased sap flow is initiated through the cambial layer of the trees. Studies
in radiata pine (Lasserre, 2005) and Douglas-fir (Murphy and Amishev, in review)
stands have reported that removal of bark significantly increased acoustic velocity
by on average 4.1% and 4.6%, respectively. This should be accounted for to
achieve a superior bucking decision for maximum value recovery from each stem.
The question remains to ascertain whether bark is consistently removed by
harvesting equipment across different conditions and circumstances and whether a
change in the design of the feeding wheels/cutting knives of the harvester head
would benefit the handling of this variance.
With the second and third working procedure, the issue of unavailable or imperfect
information may be overcome by forecasting the length of the tree stem based on
other already available information about the particular tree and/or the stand that it
is part of. The two forecasting techniques, the regression model and the k-nearestneighbor (NN) prediction, were evaluated. For the NN method, different values for
the k parameter were explored and k = 5 was applied for the final predictions.
Increasing this parameter did not result in significant prediction improvements
while values, lower than k = 5, yielded substantially poorer results. In their practice,
mills and forest products companies use cutoff values for acoustic velocity to
segregate different quality logs and products. In this study, when a resonance-based
96
acoustic velocity threshold value of 3.81 km/sec (12500 ft/sec) for stiffness quality
control is assumed, the seven sites would yield unequal numbers of good quality
trees to be accepted for veneer processing (Fig. 4.3).
Proportion above minimum threshold (%)
90
80
70
60
50
40
30
20
10
0
Site A
Site B
Actual
Site C
Site D
Nearest Neighbor Predicted
Site E
Site F
Site G
Regression Model Predicted
Figure 4.3 Percent trees above a hypothetical acoustic velocity threshold value for
stiffness quality assessment from the validation data set (VDS) of the seven trial
stands. The three curves represent the actual, k-NN and linear regression method
predicted percent, respectively.
The two prediction methods performed similarly to each other and followed the
actual distribution trend in an adequate manner. Both methods underestimated the
sites with greater proportion of good-quality trees and overrated the mediocre sites.
Similar results were observed with other acoustic velocity cutoff values (Table 4.3).
The accuracy of the velocity predictions was evaluated by calculating Root Mean
97
Square Error (RMSE) while the accuracy of the quality prediction was expressed as
the percentage of trees for which quality, based on the 3.81 km/sec cutoff value,
was inaccurately predicted.
Table 4.3 Proportion of trees (%) above a hypothetical acoustic velocity threshold
(AVT) value (km/sec) for stiffness quality assessment from the validation data set
(VDS) of the seven trial stands. The three forecasting methods (FM) are the actual
(A) percent, k-Nearest-Neighbor (NN) and linear regression (R) method,
respectively.
AVT
FM
Percent of trees above AVT
Site A Site B Site C Site D Site E Site F Site G
A
NN
3.58
R
A
NN
3.73
R
A
NN
3.89
R
A
NN
4.04
R
Trees in VDS
96
75
74
87
63
59
71
56
48
37
44
35
100
81
55
57
60
46
45
31
36
31
12
29
25
100
15
29
17
7
21
13
0
16
9
0
11
6
100
70
62
60
49
49
49
19
40
40
8
33
30
100
70
76
69
39
59
60
18
49
49
4
39
37
100
80
55
56
52
49
46
23
41
39
8
32
32
100
34
32
29
16
23
21
2
16
13
1
7
7
83
On average, according to the RMSE values, the regression approach performed
slightly better (Table 4.4), but incorrectly predicted the quality of 0.3% more trees
than the NN method. Accurately predicting the stiffness quality of more than 70%
of the trees sampled could be considered as rather promising. Any breakages along
the stem should also be accounted for most probably by operator inputs while
processing.
98
Table 4.4 The RMSE for the forecast acoustic velocity and the proportion of trees
for which quality, based on a 3.81 km/sec cutoff value, was incorrectly forecasted
(Inc. Pred.) by the k-Nearest-Neighbor (NN) and linear regression (R) method for
the seven trial stands.
Statistics
Forecast
OSU Trial Stands
Parameter
method
A
B
C
D
E
F
G
Overall
RMSE
NN
0.643
0.692
0.588
0.687
0.683
0.715
0.583
0.660
(km/sec)
R
0.606
0.670
0.565
0.662
0.685
0.633
0.597
0.633
Inc. Pred.
NN
28
40
17
35
35
30
18
29.3
(%)
R
36
36
11
37
37
28
20
29.6
If the second working procedure involves producing a log from the partially
scanned portion of the stem and acquiring an acoustic velocity reading on that log,
this additional information could be used for predicting acoustic velocity of the
logs to be produced further up the stem. Based on HM200 acoustic readings for the
whole stem with the limbs still attached to the tree and velocity measurements for
the first processed log, a linear regression model was developed for the prediction
of acoustic speed of the second log (Table 5), yielding a coefficient of
determination R2 of 0.74. Adding the length of the first log (5.5, 8.2, or 10.7 m) as
an indicator variable resulted in a slight improvement of the model with R2 = 0.77.
Also, if another measurement is taken on the second log produced, the acoustic
velocity of the third log could be predicted including this additional parameter in
the model (Table 4.5). In fact, the acoustic velocity value for the first log in this
99
case was not a significant explanatory variable and upon its removal the resultant
model yielded an R2 of 0.79.
Table 4.5 Summary output for the regression model between HM200 acoustic
velocity (km/s) measurements for consecutive logs up the tree stem as the response
variable and whole tree HM200 acoustic velocity (km/s) measurements with limbs
and tops on and HM200 acoustic velocity (km/s) measurements of previously
produced logs from that stem as explanatory variables.
Predicted
Regression
Variable
Statistics
Intercept
Limbs on
Log 1
Log 2
Coefficient
0.16
0.62
0.318
---
Log 2
Log 3
Explanatory Variables
Standard Error
0.0629
0.0238
0.02064
---
t Stat
2.5395
26.0315
15.3836
---
p-value
0.0112
7.64E-49
1.3E-118
---
Coefficient
-0.308
0.334
---
0.677
Standard Error
0.0893
0.0392
---
0.042
t Stat
-3.4518
8.4922
---
16.1227
p-value
0.0006
2.63E-16
---
6.1E-47
The presence of branches attached to the tree, and their effect on acoustic velocity
readings may be of great importance and must be considered should the second or
third working procedure for stiffness measurements be adopted. In a congruent
manner, Lasserre (2005) and Amishev & Murphy (in review)a) reported that
acoustic velocity for radiata pine and Douglas-fir logs with the branches still
attached was 2 to 3 % lower compared to the velocity after they had been removed.
100
Another observation during this study which might play a crucial role in selecting a
working procedure is the influence of the tree top on acoustic velocity readings.
More explicitly, it was observed that if the entire stem was intact to the very top
offshoot bud, resonance-based acoustic velocity readings could not be acquired or
they had a low confidence level (not recorded). Severing the very top portion of the
tree (up to at least 2 cm in diameter) was necessary to ensure a good quality
acoustic velocity measurement. This might be due to the dissipation of the acoustic
wave energy into the smallest offshoots and not rebounding back to the signal
receiver.
This research has primarily focused on the use of acoustic technology for in-forest
evaluation of internal properties of Douglas-fir trees/logs in terms of their veneer
quality.
Current research efforts strive to evaluate the performance of a TOF prototype
device (Director PH330) installed on a harvester head (Carter 2007). Recent studies
on Douglas-fir, however, have indicated that the resonance-based acoustic
technique might be a better alternative in terms of its veneer quality assessment
(Amishev & Murphy (in review)a, Amishev & Murphy (in review)b). There are
number of factors to be accounted for if this technology is to be implemented on a
mechanized harvester/processor for real-time stiffness evaluation and an optimal
working method to be adopted with it. Some of them were identified and examined
in this manuscript.
101
4.5 CONCLUSIONS
The objectives of this study were to determine the most suitable acoustic technique
for segregating veneer quality Douglas-fir logs, to investigate influential factors in
regards to installing such technology on a processor/harvester head, and to evaluate
suggested working procedures based on feasibility and productivity considerations.
Both the TOF and the resonance-based technique have advantages and
disadvantages but research findings have suggested that the resonance-based
acoustic method is a more reliable option in this particular case unless improved
TOF instruments are developed and utilized.
Investigating the relationship between acoustic velocities in the grip of a
harvester/loader grapple and those on the ground with no contact to harvesting
equipment revealed that the hold of the machine grapple would not compromise the
accuracy of the resonance-based acoustic velocity readings with proper attention
given to some feasibility concerns.
There were basically three working procedures examined:
1. Measure acoustic velocity once the stem is delimbed and run through the
measuring equipment.
102
2. Measure a portion of the stem and forecast the taper of its unmeasured
portion for length estimation. Based on this information an acoustic
measurement would be performed.
3. Perform the acoustic testing after the tree is felled by the harvester and
before any further processing is done.
No matter the working procedure, however, it was revealed that logs produced from
upper sections of the tree are less stiff than those from lower portions which is
important if optimal bucking decisions based on stiffness are to be accomplished. If
the processor head traverses the stem partially or completely, the removal of bark
and branches and their effect on acoustic velocity readings should be taken into
account.
If the second or third working procedure is selected, it would inherently entail
imperfect and even non-existing information about external tree characteristics and
particularly tree length. Forecasting routines could be developed to accommodate
this issue and the two methods used for stem height (and consequently acoustic
velocity) prediction in this study (linear regression model and a k-nearest-neighbor)
were considered as rather promising. Testing feasibility concerns with the
resonance-based acoustic technique were observed if the entire stem was intact to
the very top offshoot bud.
103
This research has significant implications for the mechanized harvesting of
Douglas-fir stands. Further research needs to be undertaken to determine how
broadly these findings and considerations can be applied. Much more work needs
to be carried out to examine the costs, benefits, the technical feasibility and
economic viability of this challenging endeavor.
104
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109
CHAPTER 5
ESTIMATING BREAKEVEN PRICES FOR DOUGLAS-FIR VENEER
QUALITY LOGS FROM STIFFNESS GRADED STANDS USING
ACOUSTIC TOOLS
Dzhamal Y. Amishev and Glen E. Murphy
Department of Forest Engineering
Oregon State University
Corvallis, OR 97331-5706
USA
Forest Products Journal (in review, submitted June 2008)
Forest Products Society
2801 Marshall Court
Madison, WI 53705-2295
USA
110
5.1 ABSTRACT
Where traditionally tree dimensions and external quality characteristics (such as
branch size, sweep, and scarring) may have been sufficient to specify a log-sort,
consideration is now being given to specifying such wood properties as density,
stiffness, spiral grain, extractives content, and consumption of energy for
processing. Recently, these internal “attributes” of the wood are being accounted
for more frequently and are an important consideration in estimating timber value.
These additional specifications required by wood buyers add extra complexity to
the already complex task of log producing and sorting and it has been shown that,
without any premium prices and incentives, such requirements for log grades can
reduce the total value by as much as 40 percent.
This paper presents a general methodology to estimate relative breakeven prices of
Douglas-fir peeler logs that a mill or any other log purchaser could afford to pay
based on acoustic assessment of veneer stiffness differences.Green veneer was the
largest source of revenue averaging about 80% as compared to that from chippable
material and unpeeled cores combined. Smaller trees incurred higher manufacturing
costs, up to a 40 percent difference between the largest delivered average-size log
and the smallest. The sample with the greatest net revenue (1144.79 $/MBF) was 3
percent higher than the second one and more than 16 percent higher than the
lowest.
111
These results show that stand stiffness grading based on acoustic velocity
measurements on Douglas-fir peeler logs could be used as a surrogate measure for
potential net returns and hence for a premium price to be afforded on logs from
such stands.
Keywords: Pseudotsuga menziesii, financial analysis, product recovery, sound
velocity, dynamic modulus of elasticity
112
5.2 INTRODUCTION
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) is one of the
most important raw material resources for the forest products industries of the
United States, Canada, New Zealand, and parts of Europe (Gartner et al., 2002).
The unique attributes (appearance, strength, and machinability) of its wood have
established and maintained the Pacific Northwest as a major factor in domestic and
international markets for forest products. International and U.S. wood product
markets, especially high-quality structural lumber and veneer markets, are likely to
continue to demand Douglas-fir logs (Schuler and Craig, 2003).
Over the last few decades, however, as demand for high-quality timber has been
rapidly increasing, the availability of old growth Douglas-fir and other softwoods
has been diminishing across North America and timber resources have gradually
shifted to intensively managed young growth stands (Adams et al., 2002; Zhang et
al., 2004). Due to the higher proportion of juvenile wood, younger stands usually
yield lower quality timber (Gartner, 2005) with greater variability in product
performance (Carter et al., 2006). As global forest products markets are becoming
increasingly competitive and complex, the successful transformation of managed
second-growth stands into quality products is crucial for the existence of a robust
forest industry (Kellogg, 1989; Barbour and Kellogg, 1990; Eastin, 2005). Good
measurements and predictions of both the external and internal properties of the
113
wood in each stem are essential to optimally match logs to markets (Clarke et al.,
2002). Assessing a forest stand’s quality (Acuna and Murphy, 2006), determining
its most appropriate use, time of harvest and processing technique, and
consequently distributing the products to the right location are all important
management decisions to achieve reduced costs and increased product values
(Murphy et al., 2005).
Wood modulus of elasticity, also known as stiffness, is one of the most important
mechanical properties and is the most frequently used indicator of the ability of
wood to resist bending and support loads. It has long been recognized as a critical
product variable in both solid wood and pulp and paper processing (Eastin, 2005)
despite its high variability dependent upon site, genetics, silviculture, and location
within the tree and stand. It is a particularly important parameter in the conversion
of raw timber material into veneer and plywood products, requiring high stiffness
wood. With the ever growing use of engineered wood products such as roof trusses
and laminated veneer lumber (LVL) the demand for high-MOE lumber and veneer
has increased.
For many years, the sawmilling industry has utilized acoustic technology for
lumber assessment and devices, such as the in-line commercialized Metriguard®
stress-wave grade sorter, have been widely used. Nondestructive testing (NDT)
instruments that are compact and easy to operate and are based on acoustic
principles, have been developed for measuring stiffness of logs and standing trees
114
(Dickson et al., 2004). Acoustic NDT’s have been successfully used for evaluation
of mechanical properties of various wood products (structural lumber, poles, pulp
logs, decay detection, etc.) and species as well as in tree selection and breeding
based on stiffness (Huang et al., 2003). Past research has indicated high correlation
between yield of structural grades of lumber and acoustic velocity of standing trees
(Wang et al., 2001; Lindstrom et al., 2002; Grabianowski et al., 2006; Lasserre et
al., 2007; Wang et al., 2007a) and processed logs (Ross et al., 1997; Joe et al.,
2004; Wang et al., 2007b; Waghorn et al., 2007; Amishev and Murphy, (in
review)a).
Worldwide forest harvesting has become increasingly mechanized during the last
few decades. This is especially true where harvested tree size is decreasing and the
capability of one or two machines to fell, delimb, buck, and sort a tree or a group of
trees is an appealing advantage. Drivers for this shift from the traditional motor
manual harvesting systems to mechanical harvesting systems generally include
productivity/cost improvement goals or labor-related issues. Among other things,
mechanization also provides a platform for innovative measurement systems which
could lead to improved log segregation based on a wider range of wood properties
(Murphy, 2003). In recent years, mills and markets have begun to include
additional characteristics to specify the logs they require with consideration now
being given to such wood properties as stiffness, strength, density, spiral grain,
extractives content, and consumption of energy for processing (Andrews, 2002; So
et al., 2002; Young, 2002). Segregation of logs, based on hand-held acoustic tools
115
that measure stiffness, is already being used by some forest companies to improve
the value of lumber recovery (Green and Ross, 1997; Matheson et al., 2002) and
internal wood properties of logs are likely to be more commonly measured and
specified by markets in the near future. Amishev and Murphy (in review)a) have
demonstrated that acoustic technology could be a promising and valuable tool for
in-forest assessment of veneer grade Douglas-fir log stiffness early in the supply
chain even on a whole-tree basis. Amishev and Murphy (in review)b) have
investigated some of the influential factors in regards to installing such technology
on a processor/harvester head, and evaluated suggested working procedures based
on feasibility and productivity considerations.
Although wood producers are already sorting logs according to both external and
internal properties (Jappinen, 2000; Matheson et al., 2002), there is scarce evidence
of markets paying premium prices for logs with superior internal characteristics,
such as high stiffness. The economic importance of different wood properties
varies with the products milled from the stems measured in a recovery study, the
grading methods applied, and the price structure used (Aubry et al., 1998). Only a
few comprehensive veneer recovery reports on Douglas-fir have been published in
the literature (Lane et al., 1973; Fahey, 1974; Fahey and Willits, 1991; Fahey et al.,
1991) and even fewer studies were found that link wood characteristics and their
effect on product value (Green and Ross, 1997; Willits et al., 1997). Such studies
are crucial sources of information in estimating the effect of different wood
characteristics on economic value. Although product standards, mill equipment,
116
and the size and quality of the resource have changed since these comprehensive
studies were performed, relationships reported in them are still useful in financial
analyses.
Acuna and Murphy (2007) have performed a financial analysis to estimate the
premium price that markets would be willing to pay for Douglas-fir sawlogs and
pulp logs with different wood densities. No studies have estimated relative
premium prices for Douglas-fir peeler grade logs based on stiffness differences
assessed using acoustic technology. This manuscript reports the results from a
financial analysis aimed at estimating relative breakeven prices of Douglas-fir
peeler logs that a mill or any other log purchaser could afford to pay based on
acoustic assessment of veneer stiffness differences.
5.3 MATERIALS AND METHODS
5.3.1 Study sites
Seven second growth Douglas-fir stands (A through G) of similar age class (50 –
70 years) chosen to cover a range of elevations and tree sizes were harvested. Two
hundred trees from each stand were sampled totaling 1,400 trees and more than
3,000 logs. Only veneer grade lengths were cut (18, 27, and 35 ft or 5.5, 8.2, and
10.7 m, respectively); no sawlogs or pulp logs were produced. The average log
length was 9.2 m, the average log acoustic velocity was 3.77 km/sec (12,363
117
ft/sec), ranging from 2.73 to 4.69 km/sec (8,957 to 15387 ft/sec). Detailed site
description and statistics may be found in Amishev and Murphy (in review)a).
After the in-forest measurements on the logs were completed, they were transported
to a veneer mill, debarked, cut into 8 ft (2.4 m) bolts, kiln-heated, shape scanned,
and peeled into veneer sheets. They were then scanned for defects and moisture,
sorted into moisture classes, dried and then sorted into several veneer grades based
on in-line acoustic measurement of wood stiffness using a Metriguard® grade
sorter. Percent veneer recovery in all grades was calculated.
5.3.2 General price estimation procedure
The final set of breakeven relative log purchase prices for the seven stands sampled
was estimated by determining the residual stand values (total revenues minus
processing costs) and applying them on a per net thousand board feet ($/MBF)
basis. The particular sequence of steps needed to estimate those prices for each of
the seven stands was as follows:
1. Calculate and summarize the amount of veneer (3/8-inch basis) produced in
each grade including full sheets (54-inch x 8-foot), half sheets (27-inch x 8foot), strips and fish tails.
2. Determine the proportion and actual amounts of veneer, peeler core and
chippable material based on previously published veneer recovery reports.
3. Calculate revenue acquired from veneer, chippable material and cores
based on prices and costs updated to year 2007.
118
4. Determine the breakeven log purchase price based on the sample residual
value and investigate their relationship with stand average log acoustic
velocity.
5.3.3 Veneer recovery, prices, and revenues
Nine veneer grades, plus waste, were used in this particular study (AB, G1, G2, G3,
C+, C, D, X, and XX). The prices used in this report are green veneer prices from
Random Lengths (2007) converted to a 3/8-inch basis and are used only for
illustration of relative trends, reflecting the normal ratio of values for each grade.
For each site the veneer produced in each grade was multiplied by its respective
price and a total site veneer revenue was calculated and further converted to a
sample plot revenue per thousand square feet, 3/8-inch basis ($/M 3/8). At the time
of generating the veneer yield reports, a portion of the veneer sheets were still in
process without assigned grade. That volume was allocated to specific grades in
each site respective to the percent recovery in each grade. The average cost (41 $/M
3/8) of processing logs into green veneer was estimated using values reported in
Spelter (1989) and Mitchell (pers. com.) and updated using a sawmill producer
price index to 2007 values (PPI, 2008). However, a single veneer manufacturing
cost regardless of the log size would not accurately reflect the differences
associated with handling different size material. Briggs and Fight (1992) have
developed an equation for estimating veneer manufacturing costs based on small
end diameter inside bark (inches) and the ratios between costs calculated using that
119
equation for the seven study sites were applied to the average updated costs above
to account for those size differences. No profit allowance was included in revenue
calculations.
5.3.4 Chip and core recovery, prices, and revenues
Not all logs produced and measured on site were transported to the same mill
where they were converted into veneer as part of this study. From logger/truck
ticket information, which provided data on the total number of logs, their weighted
average diameter and length, as well as gross Scribner volume in thousand board
feet (MBF), we were able to determine that some logs were left in the forest or
transported elsewhere (Table 5.1). We were also able to determine that the veneer
produced and reported from each study site was not the yield of the total logs
delivered, but came from a portion of them. We attribute this to limitations on
available kiln volumes and the need for a separate kiln load for each study site.
Unfortunately, except for site G, there is no information regarding the exact number
of logs used for the reported yield of produced veneer. The distribution of the
delivered and processed logs in terms of size and stiffness quality was assumed to
be representative of those produced on site.
120
Table 5.1 Produced and transported log summary statistics for the seven research
sites.
Study
Sites
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Overall
Produced on site
Log
Log
Count
Length
Total
Average
#
m
572
9.39
399
9.50
458
9.17
447
9.22
395
9.33
453
8.49
353
9.33
3077
9.20
Log
Count
Total
#
357
397
310
447
389
379
168
2447
Transported to mill
Log
Small End
Length
Diameter
Average
Average
m
cm
8.57
24.37
9.22
22.25
8.29
24.27
8.95
23.71
9.05
19.04
8.23
23.35
10.52
31.24
8.87
23.36
Scribner
Volume
Gross BF
35690
38430
30720
49120
27710
37410
34060
253140
For this reason, veneer and chip recovery models from previous studies had to be
used in conjunction with the already available produced veneer volumes and
delivered log summary statistics. The following steps were carried out. Fahey et al.
(1991) have established percent cubic recovery equations for green veneer (GV)
and core (C):
GV = 101.2 - 360.8/D – 1.6*LLAD2),
C = -6.7+252.7/D), where
D = small end diameter (inches),
LLAD = large limb average diameter (in) (assumed to be 2.5 inches in this study).
In deriving these equations they utilized a 5.6 inches constant size of the unpeeled
core while in our study this size was around 3 inches. Hence using these equations
to estimate core recovery would not be correct. Regardless of the unpeeled core
size, however, the volume of the core and produced veneer summed together
121
should be the same for a log with known diameter and length. Hence, from Fahey
et al. (1991) combined equations (GV + C = 94.5-108.1/D - 1.6*LLAD2), total
cubic recovery percent of veneer and core together and the percent chippable
material recovered were calculated (Chip = 100 – GV – C). The core only recovery
percent from Fahey et al. (1991) model was calculated again and using the
quadratic ratio of the core diameters (3 to 5.6 inches) percent unpeeled core
recovery was adjusted and veneer recovery percent was calculated by subtracting
adjusted percent core from the total. Since the actual veneer volume produced is
known (from the square footage), the total net log volume input in cubic meters is
calculated as well as the cubic volumes for core and chippable material. Also, using
core diameter (3 inches), average log length, and the calculated adjusted core
volume, the number of logs that were used for the veneer production was estimated.
With that information, the total net MBF log input was calculated for each stand
using Spelter (2002)’s “m3 to MBF” conversion factors:
(1,000/((10.16 – 0.04 * D – 88.18/D + 290.58/D2) * 35.31), where
D = small end log diameter (in).
Once the volume of chippable material is known, the net pulp value (NPV) per
cubic meter of pulp was calculated using the methodology presented by Briggs and
Fight (1992) and later used by Acuna and Murphy (2007):
122
NPV = BD * Y * (SP – NWC – FC * (BDN / BD)), where
NPV = Net pulp value ($/m3),
BD (BDN) = (Normal) Basic density (kg/m3),
Y = pulp yield ratio,
SP = selling price ($/metric ton)
NWC = nonwood costs ($/metric ton)
FC = fixed costs ($/metric ton)
Various sources were used to retrieve input values and all values were updated to
2007 values using either consumer or producer price indices. The TD Bank
Financial Group reports a pulp selling price (SP) of $720/mt (metric ton) (TD Bank
Financial Group, 2008). Nonwood costs (NWC) and fixed costs (FC) were acquired
from Briggs and Fight (1992) and updated (NWC = $263/mt, FC = $173/mt). Yield
(Y) was assumed as 0.5, and the “normal” basic density (BDN) was estimated as
the species average (450 kg/m3) from Bowyer et al. (2003). Stand average basic
densities (BD) were calculated from the disks collected from a subsample of trees
in each stand using the water displacement method (Saranpää, 2003).
The unpeeled cores could be used for either fence posts or even stud grade lumber
(Fahey and Willits, 1991) or chipped and included in the chippable volume. The
latter option was chosen for the current study.
123
5.4 RESULTS
Of the total 3077 logs produced from the seven study sites, 80 percent were
actually delivered to the mill (Table 1) and an estimated 57 percent of them were
followed through the whole veneer production process, ranging from less than 43
percent of the site D logs to about 74 percent of the logs in site B (Table 5.2).
Table 5.2 Estimated number of logs and net volume (Scribner board feet and cubic
meters) input for the production of the veneer reported. Average stand basic density
(BD) in kg/m3 was calculated from discs collected from a subsample of trees.
Estimated input for veneer production
Study
Log Count Scribner Cubic
Basic
Sites
Total
Volume Volume Density
#
Net BF
m3
kg/m3
Site A
282
25867
195.7
517
Site B
295
24340
195.3
486
Site C
276
24274
184.2
480
Site D
191
17337
133.6
484
Site E
282
17362
150.5
514
Site F
256
20723
161.3
498
Site G
168
34060
218.3
523
Total
1752
163963 1238.9
--Because of size and length differences of the delivered logs, the estimated input of
logs in terms of volume ranged from 133.6 m3 from site D to 218.3 m3 from site G
and corresponding Scribner volumes of 17,337 BF to 34,060 BF, respectively.
Assuming that the estimated log input is representative of the stand log distribution
produced on site, the average basic density ranged from 480 kg/m3 in site C to 523
kg/m3 in site G.
124
Figure 5.1 shows the estimated proportion of logs from each stand associated with
green veneer, chippable product, and unpeeled core section.
100%
90%
Percentage from total
80%
70%
60%
50%
40%
30%
20%
10%
0%
A
B
C
D
E
F
G
Study Sites
Veneer
Chippable
Core
Figure 5.1 Percentage of processed log volume in green veneer produced, chippable
product, and unpeeled core section for each of the seven study sites.
The processed logs from site G had the biggest proportion of green veneer
recovered (68 percent) while site E had the lowest recovery with a little over 56
percent of the logs converted into green veneer. On the contrary, the proportion of
the unpeeled core section was greatest in site E with almost 14 percent while site G
had less than 7 percent. Chippable material was more or less constant across the
stands, ranging from 26 percent in site G to almost 30 percent in site E. These
125
values are in agreement with previous studies on veneer yields from similar sized
resource (Fahey and Willits, 1991; Fahey et al., 1991; Ross et al., 2004)
Not only was the proportion of green veneer recovered different, but the quality of
the veneer among the stands was different as well (Table 5.3).
Table 5.3 Veneer grades, prices in dollars per thousand square feet, 3/8-inch basis
($/M 3/8), and percent recovery in each grade from the total veneer produced for
each of the seven study sites.
Veneer
Grades
Market
Prices
($/M 3/8)
480
270
255
240
225
195
180
105
75
A
%
0.36
23.57
14.71
4.10
4.59
18.27
6.29
2.41
0.32
AB
G1
G2
G3
C+
C
D
X
XX
In
process
--25.38
Total volume (3/8's) 128169
Veneer grades recovered from the trial stands
B
C
D
E
F
%
%
%
%
%
0.02
0.01
2.45
0.21
0.03
19.86
6.86 19.13 23.54
8.36
21.23
13.68 22.32 18.89
24.62
5.70
7.34 12.55 10.67
13.53
8.63
4.26
1.11
6.24
5.47
20.63
26.16
8.87 17.52
22.31
3.76
14.20
9.02
5.63
8.38
3.10
3.69
7.75
3.31
4.57
0.63
0.42
0.78
0.79
0.91
16.44
23.38
123093 120416
16.01
86533
G
%
2.44
7.08
29.92
14.42
3.03
16.80
12.11
12.42
1.77
13.21
11.82
0.00
87734 103808 153399
As with any other product, quality certainly matters considering the difference in
market prices buyers are willing to pay for a better quality product. In this case, the
best quality veneer (AB) is worth more than twice as high as a mediocre one (C and
D), while the G grades are in between. As already mentioned, the green veneer “In
process” was proportionally allocated to the respective veneer grades within each
126
stand. While around 50 percent of the green veneer produced from stands A, B, D,
and E was in the more valuable and “desirable” AB, G1, and G2 grades, the
proportion of these grades in sites F and G was less than 40 percent and only 27
percent in site C. However, the proportion of green veneer in the G3 grade, which
is worth only 6 percent less than G2, was substantially higher in sites D, E, F, and
G than in the other three sites. Site G yielded the largest volume amount of green
veneer produced (153,399 square feet, 3/8-inch basis) while sites D and E yielded
only about 60 percent of that amount.
Return ($/M 3/8)
300
200
100
0
A
Veneer
B
Chippable
C
Core
D
E
F
G
Study Sites
Figure 5.2 Gross return ($/M 3/8) including revenue from green veneer, chips, and
cores.
Green veneer was the largest source of revenue as compared to that from chippable
material and unpeeled cores (Fig. 5.2).
127
Although AB, G1, and G2 veneer grades were worth the most, revenue from the
lower-value grades may be a large contributor to the gross return from these stands;
revenue from G3, C+, and C veneer grades combined accounted for 37, 45 and 48
percent of the total veneer revenue in sites E, F, and C, respectively. Greater
amounts of higher density chippable material can also substantially increase a
stand’s value and partially overcome mediocre veneer characteristics; while
revenue from chips and cores (which were also considered chippable) from most
sites averaged 16 percent of the total gross return, they accounted for more than 20
percent in site E, providing a 6.5 percent increase in gross revenue ($/M 3/8) over
the “runner-up” site D.
Smaller trees incurred higher manufacturing costs, up to a 40 percent difference
between the largest delivered average-size log from stand G and the smallest from
site E (Table 5.4).
Table 5.4. Total gross revenue (GR), veneer manufacturing costs (VMC), net
revenue (NR) calculated, and average stand acoustic velocity (ASV) for each study
site. Net revenue per net thousand board feet ($/MBF) was calculated based on
estimated input for veneer production.
Revenues and costs
GR ($/M 3/8)
VMC ($/M 3/8)
NR ($/M 3/8)
NR ($/net MBF)
ASV (km/sec)
A
B
258.94 265.79
52.46 57.78
206.48 208.01
1074.2 1104.6
4.12
3.95
Study sites
C
D
E
F
G
237.15 265.95 283.25 259.24 248.69
52.70 54.02 67.49 54.91 41.00
184.45 211.93 215.76 204.33 207.69
960.8 1110.6 1144.8 1074.7 982.2
3.62
3.93
4.02
3.82
3.77
128
The cost difference, however, was not enough to offset the greater gross revenues
and site E (1144.79 $/MBF) was still 3 percent higher in net revenues than the
second largest site D and more than 16 percent higher than the lowest site C
(960.77 $/MBF). The net revenue values are in fact the estimated breakeven prices
that a log purchaser could afford to pay for Douglas-fir peelers from those stiffness
graded stands using a resonance-based acoustic technique. Furthermore,
investigating the relationship between those estimated breakeven log prices and the
average stand log acoustic velocity revealed a fairly strong relationship with a
coefficient of correlation R2 of 0.62 (Fig. 5.3).
1200
N et revenue ($/net MB F)
1150
1100
1050
1000
950
y = 322.14x - 188.84
2
R = 0.62
900
3.50
3.60
3.70
3.80
3.90
4.00
4.10
4.20
On-site average log velocity (km/sec)
Figure 5.3 Relationship between breakeven veneer quality log prices and on-site
average log acoustic velocity for seven Douglas-fir study sites.
129
Moreover, if site A is removed from the regression model possibly because of
concerns about whether the actual log input for veneer production is representative
of the stand log distribution produced on site, not only the adjusted R2 increases to
0.89, but the model coefficient statistics are improved. There is no evidence,
however, suggesting that site A is indeed an outlier other than its effect on the
regression model. Also, as Acuna and Murphy (2007) have reported, pulp as an end
product was more sensitive to variations in basic density of logs than lumber and
veneer. Smaller-sized logs, which are usually associated with higher manufacturing
costs, yield larger corresponding proportions of unpeeled core and chippable
material and, provided it has higher basic density, their potential revenue
contribution may offset those costs to a large extent.
5.5 DISCUSSION AND CONCLUSIONS
This research has primarily focused on the use of acoustic technology for
evaluating internal properties on Douglas-fir veneer grade logs. The objective of
this study was to estimate relative breakeven prices of Douglas-fir peeler logs that a
mill or any other log purchaser could afford to pay based on acoustic assessment of
veneer stiffness differences. It was shown that this task is achievable and although
markets do not yet pay a premium for higher stiffness peeler logs, log purchasers
could afford to pay such prices. Based on the results of this study, it is suggested
that stand stiffness grading based on acoustic velocity measurements on Douglas-
130
fir logs could be used as a surrogate measure for potential net returns from that
forest stand and hence for a premium price to be afforded on such stands.
A previous study has shown that the requirement of minimum levels of basic
density for log grades can reduce the total value by as much as 40 percent (Acuna
and Murphy, 2005). It was also shown that it is possible to estimate relative
Douglas-fir log prices based on wood density (Acuna and Murphy (2007).
Therefore, it is important to develop techniques to allow log purchasers to estimate
relative log prices they could afford to pay for logs with different internal
characteristics (wood stiffness, density, etc.), especially with the promising
potential of newer technologies (acoustics, NIR) to accurately measure those
features. The authors are unaware of any studies or ongoing research efforts that
have tried to estimate breakeven premium prices that log buyers could afford to pay
for logs with higher stiffness as measured using acoustic tools. No studies have
been published on analyzing the economic effects of optimal log bucking based on
stiffness grade differentiated prices.
There are several limitations associated with this study which could have affected
the results and further conclusions: only an unknown estimated portion of the
sampled trees was actually followed through the full process of veneer production
while stiffness grading of the logs was performed on the full sample; only three
peeler log lengths were used throughout the study and no other product recovery
from those stands was targeted; the market was assumed to be “supply-
131
constrained”; only green veneer and chips (since unpeeled cores were added to the
chippable portion) were considered as end products to calculate net returns;
equations to calculate the proportion of green veneer; chippable material and cores
were extracted from a previous study (Fahey et al. (1991) performed with different
mill equipment, size and quality of the resource; the non-linear character of the
Scribner volume measure presents difficulties in accurately transforming costs and
revenues from one unit to another; basic density was calculated from a small
subsample; and all logs were converted into veneer for the purposes of the study, in
practice logs below a set threshold for veneer quality may have been converted into
alternative products.
Future research in this area should strive to perform a similar analysis with data,
resulting from closer tracking of sampled material until its end-product conversion
not only on stand average level, but even on tree and log-by-log level. This might
be a challenging task to perform especially with proper consideration given to
productivity and efficiency constraints. Also, a wider range of end products and
market scenarios should be considered when analyzing the effect of acoustically
measured stiffness on net revenue. The impact of additional internal and external
wood characteristics on those revenues should be evaluated.
In conclusion, despite the assumptions and limitations of this study, the results
presented here indicate the potential impacts of perceived future market
requirements and the way future forest product industry decision-making could be
132
affected by them. The shift of wood markets towards new end product
characteristic requirements is already taking place on a smaller scale in some places
and future global market change should be expected.
133
5.6 LITERATURE CITED
Acuna, M.A. and G.E. Murphy. 2005. Optimal bucking of Douglas-fir taking into
consideration external properties and wood density. New Zealand Journal
of Forestry Science 35(2):139-152.
Acuna, M.A. and G.E. Murphy. 2006a. Geospatial and within tree variation of
wood density and spiral grain in Douglas-fir. Forest Products Journal
56(4):81-85.
Acuna, M.A. and G.E. Murphy. 2007. Estimating relative log prices of Douglas-fir
through a financial analysis of the effects of wood density on lumber
recovery and pulp yield. Forest Products Journal 57(3):60-65.
Adams, D.M., R.R. Schillinger, G. Latta and A. Van Nalts. 2002. Timber harvest
projections for private land in Western Oregon. Oregon State Univ., Forest
Res. Lab. Reseach Contribution 37. 44 pp.
Amishev, D.Y. and G.E. Murphy. (in review)a. In-forest sssessment of veneer
grade Douglas-fir logs based on acoustic measurement of wood stiffness.
Forest Products Journal (submitted January 2008).
Amishev, D.Y. and G.E. Murphy. (in review)b. Pre-harvest veneer quality
evaluation of Douglas-fir stands using time of flight acoustic technique.
Wood and Fiber Science (submitted March, 2008).
Andrews, M., 2002. Wood quality measurement – son et lumière. New Zealand
Journal of Forestry 47:19-21.
Aubry. C.L., W.T. Adams and T.D. Fahey. 1998. Determination of relative
economic weights for multitrait selection in coastal Douglas-fir. Canadian
Journal of Forest Research 28:1164-1170.
Barbour, R.J. and R.M. Kellogg. 1990. Forest management and end-product
quality: a Canadian perspective. Canadian Journal of Forest Research
20:405-414.
Bowyer, J.L., R. Shmulsky and J.G. Haygreen. 2003. Forest Products and Wood
Science: An Introduction. Fourth edition. Iowa State University Press.
554 pp.
134
Briggs, D.G. and R.D. Fight. 1992. Assessing the effects of silvicultural practices
on product quality and value of coast Douglas-fir trees. Forest Products
Journal 42(1):40-46.
Carter, P., S.S. Chauhan and J.C.F. Walker. 2006. Sorting logs and lumber for
stiffness using Director HM200. Wood and Fiber Science 38(1):49-54.
Clarke, C.R., R.D. Barnes and A. R. Morris. 2002. Effect of environment on wood
density and pulping of five pine species grown in Southern Africa. Paper
presented at the 2002 Technical Association of the Pulp and Paper Industry
of Southern Africa Conference, Durban, October 2002.
(http://tappsa.co.za/archive/APPW2002/Title/Effect_of_environment_on_w
ood_/effect_of_environment_on_wood_.html),(Accessed March, 2006).
Dickson, R.L., A.C. Matheson, B. Joe, J. Ilic and J.V. Owen. 2004. Acoustic
segregation of Pinus radiata logs for sawmilling. New Zealand Journal of
Forestry Science 34(2):175-189.
Eastin, I. 2005. Does lumber quality really matter to builders? In: Harrington, C.A.,
Schoenholtz, S.H., (eds.) Productivity of Western Forests: A Forest
Products Focus. USDA Forest Service PNW Res. Sta. Gen. Tech. Rep.
PNW-GTR-642. pp. 131-139.
Fahey, T.D. 1974. Veneer recovery from second-growth Douglas-fir. Res. Pap.
PNW-173. USDA Forest Service, PNW Research Station. 22 pp.
Fahey, T.D. and S. Willits. 1991. Veneer recovery of Douglas-fir from the coast
and Cascade ranges of Oregon and Washington. Res. Pap. PNW-439.
USDA Forest Service, PNW Research Station. 32 pp.
Fahey, T.D., J.M. Cahill, T.A. Snellgrove and L.S. Heath. 1991. Lumber and
veneer recovery from intensively managed young growth Douglas-fir. Res.
Pap. PNW-RP-437. USDA Forest Service, PNW Research Station. 25 pp.
Gartner, B.L., M.N. North, G.R. Johnson and R. Singleton. 2002. Effects of live
crown on vertical patterns of wood density and growth in Douglas-fir.
Canadian Journal of Forest Research 32:439-447.
Gartner, B.L. 2005. Assessing wood characteristics and wood quality in intensively
managed plantations. Journal of Forestry 100(2):75-77.
Grabianowski, M., B. Manley and J. Walker. 2006. Acoustic measurements on
standing trees, logs and green lumber. Wood Science and Technology
40:205-216.
135
Green. D.W. and R. Ross. 1997. Linking log quality with product performance.
Role of wood production in ecosystem management. In: Proceedings of the
Sustainable Forestry Working Group at the IUFRO All Division 5
Conference, Pullman, Washington. July 1997. pp. 53-58.
Huang, C.-L., H. Lindstrom, R. Nakada and J. Ralston. 2003. Cell wall structure
and wood properties determined by acoustics – a selective review. Holz als
Roh- und Werkstoff 61:321-335.
Jappinen, A. 2000. Automatic sorting of sawlogs by grade. PhD thesis.Department
of Forest Management & Products, Swedish Agricultural University,
Uppsala, Sweden.
Joe, B., R. Dickson, C. Raymond, J. Ilic and C. Matheson. 2004. Prediction of
Eucalyptus dunnii and Pinus radiata timber stiffness using acoustics.
RIRDC Publication No 04/013. RIRDC, Kingston, Australia. 121 pp.
Kellogg, R.M. 1989. Second growth Douglas-fir: its management and conversion
for value. Forintek Canada Corp., Vancouver, B.C. Spec. Publ. SP-32.
173 pp.
Lindstrom, H., P. Harris and R. Nakada. 2002. Methods for measuring stiffness of
young trees. Holz als Roh- und Werkstoff 60:165-174.
Lane, P.H., R.O. Woodfin, Jr., J.W. Henley and M.E. Plank. 1973. Veneer recovery
from old-growth coast Douglas-fir. Res. Pap. PNW-162. Portland, OR:
USDA Forest Service, PNW Research Station. 44 pp.
Lasserre, J.P., E.G. Mason and M.S. Watt. 2007. Assessing corewood acoustic
velocity and modulus of elasticity with two impact based instruments in 11year-old trees from a clonal-spacing experiment of Pinus radiata D. Don.
Forest Ecology and Management 239:217-221.
Matheson, A.C., R.L. Dickson, D.J. Spencer, B. Joe and J. Ilic. 2002. Acoustic
segregation of Pinus radiata logs according to stiffness. Annals of Forest
Science 59:471-477.
Mitchell, W.L. (pers. com.). The Beck Group – An international planning,
consulting, and benchmarking firm to the forest products industry. Portland,
OR, USA.
Murphy, G.E., H.D. Marshall and A.W. Evanson. 2005. Production speed effects
on log-making error rates and value recovery for a mechanized processing
operation in radiata pine in New Zealand. Southern African Forestry
Journal 204: 23-35.
136
PPI. 2008. Producer price index industry (Sawmill) data. Bureau of labor statistics.
Random Lengths. 2007. Eugene, OR. Random Lengths Publications Inc. Weekly.
Ross, R.J., K.A. McDonald, D.W. Green and K.C. Schad. 1997. Relationship
between log and lumber modulus of elasticity. Forest Products Journal
47(2):89-92.
Ross, R.J., J.R. Erickson, B.K. Brashaw, X. Wang, S.A. Verhey, J.W. Forsman and
C.L. Pilon. 2004. Yield and ultrasonic modulus of elasticity of red maple
veneer. Forest Products Journal 54(12):220-225.
Saranpää, P. 2003. Wood density and growth. In: J.R. Barnett and G. Jeronimidis
(eds.). Wood quality and its biological basis. Blackwell Publishing Ltd.,
Oxford, UK. pp. 87–117.
Schuler, A. and A. Craig. 2003. Demographics, the housing market, and demand
for building materials. Forest Products Journal 53(5):8-17.
So, C.L., L.H. Groom, T.G. Rials, R. Snell, S. Kelley, and T. Meglen. 2002. Rapid
assessment of the fundamental property variation of wood. In Outcalt, K.W.
(Ed.) “Proceedings of the 11th Biennial Southern Silvicultural Research
Conference”. USDA Forest Service, Southern Research Station, General
Technical Report SRS-48. 622 p.
Spelter, H. 1989. Plywood manufacturing cost trends, excluding wood, in Western
U.S. mills: 1975-1988. FPL-GTR-64. USDA Forest Service, Forest
Products Lab. 12 pp.
Spelter, H. 2002. Conversion of board foot scaled logs to cubic meters in
Washington State, 1970–1998. Gen. Tech. Rep. FPL-GTR-131. USDA
Forest Service, Forest Products Lab. 6 pp.
TD Bank financial group. 2008. TD Commodity price report. April, 2008.
www.td.com/economics. (Accessed April 2008).
Waghorn, M.J., E.G. Mason and M.S. Watt. 2007. Influence of initial stand density
and genotype on longitudinal variation in modulus of elasticity for 17-yearold Pinus radiata. Forest Ecology and Management 252:67-72.
Wang, X., R.J. Ross and M. McClellan. 2001. Nondestructive evaluation of
standing trees with a stress wave method. Wood and Fiber Science
33:522-533.
137
Wang, X., R.J. Ross and P. Carter. 2007a. Acoustic evaluation of wood quality in
standing trees. Part I. Acoustic wave behavior. Wood and Fiber Science
39(1):28-38.
Wang, X., P. Carter, R.J. Ross and B.K. Brashaw. 2007b. Acoustic assessment of
wood quality of raw forest materials – a path to increased profitability.
Forest Products Journal 57(5):6-14.
Willits. S.A., E.C. Lowell and G.A. Christensen. 1997. Lumber and veneer yields
from small-diameter trees. In: Proceedings of the Sustainable Forestry
Working Group at the IUFRO All Division 5 Conference, Pullman,
Washington, July 1997. pp. 73-79.
Young, G.G. 2002. Radiata pine wood quality assessments in the 21st century. New
Zealand Journal of Forestry 47(3):16-18.
Zhang, S.Y., Y. Qibin and J. Beaulieu. 2004. Genetic variation in veneer quality
and its correlation to growth in white spruce. Canadian Journal of Forest
Research 34:1311-1318.
.
138
CHAPTER 6
GENERAL CONCLUSIONS
The ultimate objective of this dissertation was to build information on recovering
more value from a forest stand and consequently enhance the competitiveness of
the forest products industry. It is well recognized that measuring wood properties of
logs in real time during harvesting would lead to improved log allocation decisions
early in the supply chain, improved value recovery for the forest owner, and
optimal matching of wood to markets. There are a number of wood properties and
attributes which define the quality of forest products such as veneer, lumber and
pulp. Wood stiffness, which is the most frequently used indicator of the ability of
wood to resist bending and support loads, is certainly one of them. It is a
particularly important parameter in the conversion of raw timber material into
veneer and plywood products, which require high stiffness wood. To date very little
research has investigated the correlation between stress-wave acoustic velocities in
raw timber and those in veneer produced from that timber, especially for Douglasfir. Moreover, a large knowledge deficiency exists concerning the potential for inforest sorting of Douglas-fir veneer quality logs based on acoustic measurement of
wood stiffness.
Results presented in this dissertation build information on the potential capabilities,
limitations, and applicability of acoustic technology to improve value recovery
139
from Douglas-fir stands by means of in-forest sorting of veneer quality logs. A
unique integrative approach was employed to investigate the predictive capabilities
of acoustic technology in terms of Douglas-fir veneer quality, its applicability and
limitations in a rugged forest working environment as an incorporated part of a
mechanized harvester head, as well as the opportunities it would provide for
estimating breakeven resource prices according to end-use requirements. More
specifically, the conceptual linkage between the chapters consists of the following:
the two main acoustic techniques (TOF and resonance) were evaluated
independently from each other in terms of their predictive capabilities regarding
Douglas-fir veneer quality; after it was shown that acoustic technology is a
promising tool (the resonance method in particular), possible alternatives for
acquiring more value from the forest stands early in the supply chain were
considered and namely incorporating such a technology on a harvester head for
real-time log sorting based on wood stiffness requirements; finally, assuming this
incorporation is achievable, a method was developed for estimating breakeven
(premium) prices that a log purchaser could afford to pay for such stiffness
segregated logs and thus “reward” forest stand owner/contractors for their
additional effort into sorting that raw material.
Chapter 2 summarizes the results of the investigation into modeling the effects of
within tree spatial as well as internal and external log characteristics – in particular,
height within stem, log length and diameter, green density, presence of bark, and
presence and size of branches - on acoustic velocity measurements of Douglas-fir
140
wood stiffness from a range of sites in Western Oregon. The goal was to determine
whether recovery of high quality green veneer from Douglas-fir peeler logs could
be accurately predicted using resonance-based acoustic velocity measurements. It
was found that in-forest log acoustic measurements, as well as dynamic modulus of
elasticity values correlated well with the actual G1/G2 veneer grade recovery (R2 of
0.91 and 0.82, respectively) once bark removal adjustments were made. External
log characteristics such as diameter and length were found to have limited
predictive capability in terms of acoustic velocity and hence wood stiffness. Logs
produced from the lowest part of the tree had the largest acoustic velocity and it
decreased in each subsequent log along the length of a tree stem. It was concluded
that resonance-based acoustic technology could be a promising and valuable tool in
assessing log dynamic modulus of elasticity (MOE) early in the supply chain even
on a whole-tree basis.
Chapter 3 describes an investigation on determining whether “time of flight” (TOF)
acoustic technology could be used for pre-harvest veneer quality assessment of
Douglas-fir stands in terms of stiffness requirements. The predictive capabilities in
terms of veneer quality and the effect of spatial (altitude, latitude, and longitude)
and commonly measured tree characteristics (diameter at breast height DBH, tree
height) on the accuracy of TOF acoustic measurements in second-growth Douglasfir stands were also evaluated. Results suggested that spatial location of the stands
in terms of their latitude, longitude, or altitude had no predictive capability
regarding their veneer quality. Diameter at breast height (DBH) was poorly
141
correlated to both acoustic velocity and G1/G2 veneer recovery. A regression
analysis between sound velocities of 698 standing trees measured by the TOF
method and the corresponding speeds in the butt logs, measured by the resonancebased method, yielded a coefficient of determination R2 of 0.25. Significant
differences were found between different TOF acoustic tools of the same make
used along the duration of the study as well as between the two opposite side
measurements within trees. Hence, it was concluded that TOF acoustic technology
may be of limited value in the efforts to identify stand quality in terms of veneer
stiffness parameters prior to harvest.
Chapter 4 investigates the potential implementation and use of acoustic technology
with mechanical harvesting equipment and the best procedures for scanning in
terms of feasibility and productivity of such equipped mechanized harvesters.
Investigating the relationship between acoustic velocities in the grip of a
harvester/loader grapple and those on the ground with no contact to harvesting
equipment revealed that the hold of the machine grapple would not compromise the
accuracy of the resonance-based acoustic velocity readings with proper attention
given to some feasibility concerns. There were three working procedures suggested
for measuring resonance-based acoustic velocity: (1) after the stem is delimbed and
run through the measuring equipment, (2) once a portion of the stem is measured
and the length of its unmeasured portion is forecasted and (3) after the tree is felled
by the harvester and before any further processing is done. Regardless of the
working procedure, tree structure and presence of bark and branches and their
142
effect on acoustic velocity readings should be taken into account. Forecasting
routines for stem height (and consequently acoustic velocity) prediction (linear
regression model and a k-nearest-neighbor) were considered and found as rather
promising in accommodating imperfect and even non-existing information about
tree length, associated with the second or third working procedure.
Chapter 5 presents a general methodology to estimate breakeven prices of Douglasfir peeler logs based on the net return (with no profit allowance included) obtained
when logs from stiffness graded stands using acoustic technology are processed and
converted into end products (green veneer and pulp). For green veneer, a number of
different grades and their prices were used to estimate the price that markets should
be willing to pay for such logs. For chippable material recovered, the price was
estimated from the net product value per metric ton which considered pulp selling
price and nonwood and fixed costs. There were several limitations associated with
this study which could have affected the results and further conclusions: only an
unknown estimated portion of the sampled trees was actually followed through the
full process of veneer production while stiffness grading of the logs was performed
on the full sample; only three peeler log lengths were used throughout the study
and no other product recovery from those stands was targeted; the market was
assumed to be “supply-constrained”; only green veneer and chips (since unpeeled
cores were added to the chippable portion) were considered as end products to
calculate net returns; and all logs were in fact converted into veneer for purposes of
the study while in practice logs below a set threshold for veneer quality may have
143
been converted into alternative products. It was shown, however, that this objective
is achievable and although markets do not yet pay a premium for higher stiffness
peeler logs, log purchasers could afford to pay such prices. The findings of this
chapter indicated the potential impacts of perceived future market requirements and
the way future forest product industry decision-making could be affected by them.
This study strived to address an array of questions related to the technical
feasibility of using acoustic technology in forest environments. Further research
needs to be undertaken to determine how broadly these findings and considerations
can be applied. The next steps in this research should include the investigation of
the accuracy of acoustic technology in predicting veneer quality on a stem-by-stem
and even on a log-by-log basis. Alternative sampling procedures such as transverse
versus longitudinal velocity measurement with both resonance-based and time of
flight techniques should be investigated. Regarding its incorporation into a
mechanized harvester, several directions should be followed: sampling preparation
and stem scanning speed, reliability of the measurement readings, potential
productivity loss and costs as well as value gains associated with real time quality
assessment and grade sorting. When assessing the possible gains in value recovery,
a wider range of end products and market scenarios should be considered, the
impact of inaccurate measurements or predictions on optimal log merchandizing as
well as the impact of increased number of quality requirements on the cost of raw
material output should be evaluated.
144
To remain competitive, the forest products industry needs to look for new and
innovative processes and technologies to not only reduce costs but also to increase
value through the entire seedling-to-customer forest products supply chain. This
dissertation presented four studies which investigated areas either not studied
before or applied new or standard solution techniques to meet the objectives
established in each study. Its unique contribution is the breadth and scope of the
topics that were evaluated and the linkage between investigating the predictive
capabilities of a promising sensor technology in terms of an essential wood
property, its applicability and limitations when incorporated into a mechanized
equipment, as well as the opportunities it would provide for estimating breakeven
resource prices according to end-use requirements. It is expected that the
knowledge from these studies will help improve value recovery and
competitiveness of the forest products industry.
145
BIBLIOGRAPHY
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wood density and spiral grain in Douglas-fir. Forest Products Journal
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multivariate analysis to predict wood density of Douglas-fir from chain saw
chips. Forest Products Journal 56(11/12):67-72.
Acuna, M.A. and G.E. Murphy. 2007. Estimating relative log prices of Douglas-fir
through a financial analysis of the effects of wood density on lumber
recovery and pulp yield. Forest Products Journal 57(3):60-65.
Adams, D.M., R.R. Schillinger, G. Latta and A. Van Nalts. 2002. Timber harvest
projections for private land in Western Oregon. Oregon State Univ., Forest
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Amishev, D.Y. and G.E. Murphy. (in review)a. In-forest sssessment of veneer
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Eucalyptus dunnii and Pinus radiata timber stiffness using acoustics.
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149
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velocity and modulus of elasticity with two impact based instruments in 11year-old trees from a clonal-spacing experiment of Pinus radiata D. Don.
Forest Ecology and Management 239:217-221.
Lindstrom, H., P. Harris and R. Nakada. 2002. Methods for measuring stiffness of
young trees. Holz als Roh- und Werkstoff 60:165-174.
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154
APPENDIX
Appendix A. Data from Stand A (“Putney”, located near Bellfountain, OR).
Details: Loader - Knuckle boom Hitachi 270 LC; cut and measured in July 5 - July
12, 2006; ST300 tool borrowed from Forest Products Laboratory in Wisconsin.
Table A.1. Measurements from 200 trees sampled: diameter at breast height
(DBH), ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable length (with no
limbs) and the respective HM200 acoustic velocity measurements on the ground
(down) or in the grapples of the loader (up), R* - remarks (1 = dead, 2 = double
leader, 3 = broken top). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
ST300
R*
Tree
DBH
#1
1
#
2
(in)
3
22.0
23.7
19.1
14.5
17.1
21.5
20.9
22.8
18.9
30.2
21.9
19.5
14.3
27.4
12.6
10.4
28.1
29.4
30.2
29.9
29.5
18.6
26.7
25.2
25.9
ft/s
4
ft/s
5
13692
13433
14143
13771
12803
13173
12704
13542
12798
13615
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
#2
Limbs on
Tree
length
ft
6
96.0
115.0
125.0
95.0
91.0
98.0
97.0
106.0
117.0
110.0
94.0
117.0
88.0
100.0
60.0
80.0
89.0
106.0
106.0
93.0
100.0
101.0
83.0
104.0
79.0
HM200
(down)
(up)
ft/s
ft/s
7
8
12959
12073
11253
12992
13025
12828
12402
13320 13419
10630
12730
13222 13222
12139
12762 12785
11877
11877 12172
11024
12500
12434
11614
10466
11549
13091 13255
13222
13419
12664
Limbs off
Merch
length
ft
9
96.0
115.0
125.0
95.0
89.0
89.0
91.5
97.0
117.0
105.0
88.0
105.0
70.0
97.0
45.0
62.0
80.0
105.0
105.0
89.0
97.0
80.0
77.0
101.0
70.0
HM200
(down)
(up)
ft/s
ft/s
10
11
12992
12172
12785
12992
12992
12598
12533
13222 13189
12730
12795
13419 13419
12369
12828 12927
11877
12238 12238
13451
12566
12303
11745
12434
13025
13484 13615
12828
13189
12631
155
Table A.1. Continued…
1
2
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
3
13.0
22.1
14.8
18.8
24.0
18.7
9.4
32.5
25.7
16.5
11.1
27.5
33.9
11.4
21.3
22.3
32.1
27.6
17.1
16.8
7.6
28.8
22.2
23.4
11.4
17.8
24.9
21.0
20.0
24.4
10.3
22.0
22.1
25.3
22.5
23.4
25.3
25.3
29.4
16.0
38.1
22.8
16.1
30.6
24.2
14.5
4
5
14354
12798
12740
14564
12525
13138
13123
11885
11752
13283
13073
11794
13457
14258
14123
13824
12456
13366
6
84.0
98.0
84.0
77.0
113.0
74.0
75.0
121.0
88.0
95.0
93.0
101.0
119.0
89.0
97.0
120.0
78.0
115.0
93.0
106.0
57.0
111.0
121.0
127.0
103.0
130.0
86.0
95.0
104.0
125.0
64.0
118.0
97.0
95.0
94.0
99.0
94.0
104.0
112.0
97.0
115.0
111.0
83.0
118.0
91.0
97.0
7
12073
12637
13189
13517
12533
13681
13878
12533
13484
13320
13419
13091
12139
13648
12730
12172
12697
12500
13320
11713
13056
12828
13287
12533
14337
12697
12730
13648
12927
12270
14501
12336
11680
13287
13156
12664
12402
12172
12205
13025
12073
12598
13944
13123
13419
13648
8
13976
12598
13648
13189
12270
12730
13714
11745
12500
9
65.0
96.0
80.0
70.0
109.0
70.0
70.0
105.0
80.0
89.0
62.0
97.0
105.0
70.0
88.0
105.0
70.0
105.0
89.0
97.0
18.0
105.0
88.0
88.0
70.0
123.0
62.0
88.0
97.0
123.0
53.0
105.0
88.0
89.0
89.0
97.0
88.0
88.0
105.0
88.0
105.0
97.0
80.0
115.0
88.0
88.0
10
12171
12434
12861
13451
12434
13451
13747
12631
12927
12992
13517
13091
12424
13944
12205
12631
12795
12533
13353
11942
13123
12795
13451
13058
14731
12730
13189
13615
12959
12238
14108
12621
12992
13058
12992
12697
12500
11909
12369
12861
11909
12631
14009
12992
13419
13681
11
13911
12795
12927
13091
12424
12238
13747
12959
12500
156
Table A.1. Continued…
1
1
2
2
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
3
30.5
20.2
13.5
35.3
28.3
21.5
9.4
22.7
25.6
13.9
24.2
22.7
21.1
11.6
21.9
15.9
18.9
17.8
18.3
29.4
26.1
26.1
19.8
25.8
9.6
15.7
25.0
28.0
16.7
12.4
25.6
17.2
16.8
13.7
26.3
24.3
16.1
20.1
18.0
19.2
12.7
22.4
17.8
8.8
24.9
4
5
11807
13508
14099
15473
13036
13758
15118
13761
13715
13802
13877
13433
13608
14613
13384
13850
6
110.0
89.0
86.0
103.0
119.0
97.0
77.0
77.0
103.0
55.0
115.0
133.0
86.0
73.0
80.0
98.0
91.0
87.0
89.0
106.0
103.0
139.0
97.0
132.0
85.0
98.0
113.0
125.0
105.0
89.0
126.0
30.0
83.0
95.0
112.0
99.0
93.0
108.0
111.0
106.0
92.0
109.0
102.0
90.0
104.0
7
12566
12762
12664
12402
13220
12894
13517
12828
13287
13156
12073
12697
13091
13845
13123
13583
13156
13222
12568
13648
12861
11942
13419
12500
13484
13353
12894
12270
13583
12238
12073
13608
13550
13222
13025
12631
13353
12828
13156
12730
13189
12762
13451
12959
13156
8
12126
13123
13812
12762
12861
13419
13780
11453
9
105.0
80.0
70.0
97.0
115.0
97.0
45.0
70.0
88.0
53.0
105.0
97.0
80.0
70.0
71.0
89.0
88.0
80.0
88.0
105.0
97.0
132.0
88.0
123.0
70.0
97.0
105.0
124.0
97.0
80.0
97.0
18.0
80.0
70.0
105.0
97.0
88.0
95.0
105.0
97.0
89.0
105.0
97.0
53.0
97.0
10
12631
12927
13123
12500
13353
12959
14337
12795
12434
13091
11975
13353
12927
14140
13025
13714
13058
13320
12730
13550
12795
12270
13189
12500
12550
13353
12927
12303
13648
12795
13550
11713
13484
13583
12123
12500
13189
13270
13189
12959
13419
12631
13353
13911
13123
11
12205
13091
14140
12434
12894
13386
13714
13222
157
Table A.1. Continued…
1
3
2
2
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
3
7.6
17.1
8.4
8.9
16.9
27.0
22.9
16.9
27.0
24.9
26.1
17.1
19.8
18.8
22.4
21.0
8.2
31.4
29.9
22.3
19.1
15.1
15.5
25.4
24.3
20.1
22.4
23.8
26.5
18.9
26.1
23.7
29.6
26.2
19.8
25.2
17.7
16.6
18.2
23.2
23.4
9.9
14.6
15.3
16.6
15.5
4
5
14466
14737
15025
12982
12507
14474
13581
13980
12459
14549
14562
14764
12791
14298
14678
13378
14774
14431
12110
13563
14088
13494
13699
13744
14028
12545
13080
13754
13451
13145
14058
14981
15222
13916
13701
14612
13840
14614
15285
15443
13554
13626
13857
14150
10844
11956
13031
11794
13649
12758
13830
13392
13485
11820
12935
14357
12458
14550
14209
12876
13116
14061
13246
12899
13650
13566
13136
13199
13236
12383
13644
14253
14706
14167
13227
13528
14003
13698
13536
13699
13501
14461
6
59.0
95.0
85.0
100.0
102.0
94.0
106.0
101.0
102.0
104.0
99.0
107.0
103.0
118.0
125.0
106.0
78.0
108.0
97.0
103.0
103.0
102.0
94.0
102.0
108.0
89.0
117.0
91.0
127.0
112.0
110.0
106.0
108.0
116.0
86.0
102.0
102.0
101.0
95.0
118.0
110.0
84.0
133.0
86.0
97.0
125.0
7
13911
13615
13615
13747
13541
12861
12664
13025
12270
12270
12553
12861
13156
12238
13517
13517
13287
12139
13253
13681
12566
13878
13484
12303
13615
12927
12106
13255
12172
12500
12959
13225
12697
13356
13353
12927
13780
13287
13714
12894
12730
13615
11680
13517
14270
12795
8
13091
12270
13845
12762
13419
13386
13780
12959
12730
13615
13780
14436
13058
9
35.0
88.0
35.0
54.0
97.0
88.0
105.0
97.0
97.0
97.0
97.0
105.0
97.0
115.0
123.0
105.0
35.0
105.0
89.0
97.0
97.0
97.0
89.0
97.0
105.0
89.0
105.0
88.0
123.0
106.0
88.0
97.0
105.0
106.0
81.0
97.0
97.0
88.0
93.0
115.0
105.0
54.0
123.0
80.0
88.0
105.0
10
13911
13615
14206
13911
13648
12730
12467
13025
12041
12500
12566
12702
13287
12992
13484
13419
13812
11979
13550
13550
12730
13944
13648
12402
13583
12927
12467
13189
12106
12500
13189
13222
12795
13583
13287
12894
13878
13517
13287
12861
12927
13615
11877
13386
14239
13320
11
12894
12041
13714
12598
13648
13320
13517
12927
12927
13550
13484
14567
13419
158
Table A.1. Continued…
1
3
2
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3
25.6
17.7
12.3
28.2
26.1
25.3
23.7
22.9
22.1
22.9
20.2
24.0
12.9
24.7
11.0
19.3
18.3
24.1
18.6
27.3
23.1
19.4
12.5
12.4
7.9
12.0
19.8
26.5
19.9
21.9
10.0
28.6
11.9
12.7
8.1
14.6
12.5
24.5
4
13227
14684
15028
12933
13805
13453
12816
14476
12535
14235
14285
13527
13913
10665
14886
13773
13197
15404
14521
13155
13047
14973
15226
14195
13957
15577
14253
13325
14523
13275
14503
12955
15488
15373
14634
14746
13678
13336
5
12619
13309
15025
12563
13131
13146
12784
13005
12484
12741
13898
14126
13480
13142
14605
13452
11058
13220
14021
13520
12416
13653
14594
13283
12493
14526
13320
12851
13924
12910
13760
13398
15428
12596
15090
13712
13855
12899
6
88.0
89.0
94.0
89.0
114.0
70.0
91.0
94.0
95.0
98.0
98.0
100.0
69.0
123.0
103.0
97.0
54.0
118.0
96.0
97.0
125.0
81.0
79.0
92.0
18.0
89.0
90.0
99.0
86.0
88.0
80.0
76.0
75.0
83.0
83.0
78.0
86.0
120.0
7
12631
13848
13944
12861
12336
12795
11581
12500
13517
12959
13257
12828
13222
12205
13287
13025
12762
12762
13058
12172
11253
13123
13353
13025
13419
12992
12664
12533
13287
13123
11680
12828
13747
13355
13664
13156
13035
12172
8
13386
12730
12595
13058
12730
9
88.0
89.0
80.0
80.0
105.0
62.0
88.0
88.0
89.0
97.0
97.0
97.0
53.0
123.0
70.0
89.0
45.0
115.0
80.0
89.0
106.0
80.0
70.0
88.0
18.0
80.0
89.0
97.0
80.0
88.0
48.0
70.0
70.0
70.0
35.0
72.0
80.0
115.0
10
12730
13550
14173
12762
12621
12861
11811
12730
13550
12959
13257
12697
13189
12205
13583
13123
12434
12795
13386
12172
11549
13025
13583
13058
13615
13353
12566
12566
13320
13189
13255
12861
13583
13550
13714
13222
13035
12238
11
13747
12730
13259
13484
12861
159
Table A.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
0.00
1.25
1.50 2.25
2.50
0.00
1.75
2.25 2.50
2.75 3.00
1.75
1.00
1.50 1.75
1.75 2.25
1.75
0.00
0.75
0.75 1.25
1.25
0.00
0.00
0.75 1.50
1.50
1.00
1.00
1.25 1.75
1.25
0.00
1.50
2.00 2.00
2.00
0.00
0.50
1.00 2.25
2.50 2.00
3.50
1.00
1.50 1.00
1.00 1.25
0.00
0.50
2.25 2.50
2.75 3.00
0.00
0.50
1.25 1.50
1.25
0.00
0.00
1.00 1.25
1.50 1.25
0.00
0.75
1.75 1.75
1.50
0.00
2.25
2.75 3.00
2.75
0.00
2.75
3.25
0.00
0.50
0.75 1.25
0.00
0.25
1.25 2.00
2.50
1.25
1.50
1.75 2.00
2.25 2.25
0.50
1.50
1.72 2.00
2.25 2.00
1.00
2.00
2.50 2.25
2.75
0.75
1.25
2.00 2.50
2.50
0.00
0.75
1.25 1.50
1.50
0.00
0.75
1.25 1.50
0.75
1.50
1.50 2.50
2.50
2.00
2.25
2.25 2.50
1.25
2.00
1.75 2.00
1.75
0.00
0.75
1.25 1.75
1.50
0.00
0.00
1.00 1.25
1.00
0.00
0.75
1.25 1.00
0.75
1.25
2.00 2.25
2.00
0.00
0.50
0.50 0.75
0.00
0.50
0.75 0.50
2.75
2.50
3.00 2.50
2.50 2.75
2.75
0.00
0.75
1.75 3.00
2.75
1.00
1.25
1.25 1.25
1.50
0.25
0.50
0.75 0.50
0.75
0.00
0.75
1.50 2.25
2.75 2.00
1.75
2.50
1.50 2.75
3.00 3.00
0.00
0.00
0.25 0.50
0.50
0.00
0.25
1.50 1.00
1.25
Green
Density
(kg/m3)
9
801.90
875.20
778.00
789.00
885.70
731.20
788.20
766.30
731.30
836.30
716.50
160
Table A.2. Continued…
1
2
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
1
2.25
0.00
0.25
0.00
0.00
0.25
0.25
0.25
0.00
0.75
0.00
0.75
0.00
0.25
0.00
0.00
0.00
0.00
0.00
1.25
1.25
2.00
1.00
0.00
3.00
1.00
0.00
0.75
0.75
0.75
2.25
0.50
0.50
2.50
0.75
0.75
1.00
1.25
1.25
1.25
0.75
1.75
0.00
0.00
1.25
3
1.75
2.75
0.75
1.25
0.00
0.75
1.25
0.50
0.25
0.00
1.25
0.25
1.25
0.00
1.00
0.25
0.50
0.50
0.00
0.25
1.75
2.50
2.25
1.50
0.50
3.50
1.25
0.75
1.25
0.75
0.75
2.00
0.75
0.75
1.75
1.25
2.25
2.00
1.75
1.75
1.75
1.75
2.25
0.50
0.25
1.75
4
2.50
2.50
1.50
1.25
0.75
0.75
2.50
1.00
0.75
0.25
1.50
0.75
1.50
0.25
1.50
0.50
0.75
1.75
0.25
0.75
2.25
2.75
2.75
2.25
0.75
4.00
1.75
1.00
1.50
0.75
0.50
1.75
0.75
1.50
2.75
1.25
2.25
1.50
1.75
2.25
1.50
2.00
2.75
1.25
0.50
1.75
5
2.25
3.00
1.75
0.75
1.25
6
2.25
7
1.75
2.00
1.00
1.25
2.50
2.75
1.25
1.50
0.50
1.75
1.25
1.75
0.75
2.25
2.75
2.25
2.25
0.25
1.50
2.75
2.75
2.50
0.25
1.75
1.75
0.75
2.50
8
9
1.00
2.25
798.10
2.50
1.50
2.25
1.25
1.00
2.50
2.50
2.75
2.50
1.00
3.50
1.50
1.00
1.25
1.25
0.75
2.00
1.00
1.50
3.00
1.75
2.50
1.25
1.50
2.75
2.00
3.00
1.75
1.50
2.25
2.25
2.50
2.25
1.25
3.50
2.00
1.25
1.75
1.50
1.25
2.25
1.25
1.25
3.75
2.25
2.50
2.25
2.75
2.50
2.75
1.75
1.00
1.25
1.25
2.50
2.25
1.25
2.25
2.25
2.25
754.10
801.90
2.25
4.00
2.00
2.50
2.50
3.50
3.00
815.90
2.00
819.20
794.30
161
Table A.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
1.00
0.00
0.00
0.50
0.50
0.00
0.00
1.00
0.50
0.75
0.75
1.25
0.50
0.50
0.50
0.50
1.75
0.50
0.75
1.25
1.00
0.00
0.75
1.00
0.25
0.00
0.50
0.50
0.00
0.50
0.00
0.00
0.00
0.00
1.00
0.00
1.50
0.25
0.75
0.50
1.25
0.25
0.00
0.00
0.50
3
1.25
1.00
0.75
0.75
1.00
0.75
1.75
1.25
1.25
0.75
1.00
1.50
0.75
0.75
0.75
1.50
2.00
1.00
1.25
1.25
1.25
0.00
1.00
1.50
1.25
0.25
1.00
1.00
0.50
0.75
0.25
0.50
0.25
0.25
1.00
0.75
2.50
0.75
1.25
1.25
1.75
0.50
0.25
1.25
1.25
4
1.25
0.00
1.00
1.00
1.75
1.50
3.00
1.75
2.00
1.00
1.25
1.50
1.50
1.00
1.00
1.75
5
1.50
1.25
1.25
1.50
2.00
1.75
2.75
2.00
2.75
0.75
1.50
1.50
2.00
1.25
1.25
2.00
6
2.25
2.00
1.25
1.25
2.50
2.25
3.25
1.75
2.25
0.75
1.25
2.25
2.75
1.50
0.50
2.25
1.25
1.50
1.50
1.50
0.50
1.50
1.50
1.25
0.50
0.75
1.00
0.50
1.00
0.50
0.75
0.50
0.50
1.25
1.00
2.25
1.25
1.75
1.50
2.25
0.75
1.50
1.50
1.25
1.50
1.25
1.75
1.75
0.75
1.25
1.50
1.50
0.75
1.00
1.25
1.25
1.75
1.00
1.50
2.25
2.00
1.00
1.50
1.50
1.75
1.00
1.00
0.75
0.75
2.25
1.00
0.50
0.50
1.25
1.75
2.25
1.00
2.75
2.00
2.50
1.00
1.75
2.00
1.75
1.25
0.25
0.25
1.25
1.25
2.50
1.25
3.00
1.25
2.00
1.25
1.00
1.75
2.00
7
8
9
786.90
2.00
2.00
3.50
2.75
2.75
2.00
1.75
3.25
1.50
3.00
3.00
3.00
771.00
712.40
709.90
0.50
2.50
812.30
1.25
1.25
0.75
1.00
2.00
1.25
3.25
896.50
753.60
1.25
1.00
2.25
1.25
162
Table A.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
0.00
0.00
0.75
0.00
0.25
0.50
0.00
0.00
0.75
0.25
0.00
0.75
1.00
1.00
0.00
0.00
0.00
1.25
0.50
0.00
0.50
0.00
0.00
0.25
0.00
0.50
0.00
0.00
0.00
0.00
0.25
0.50
0.00
0.00
0.00
2.00
1.00
3.00
0.50
0.00
0.00
1.00
0.00
0.00
3
0.75
0.25
1.25
0.00
0.75
0.75
0.25
0.00
1.25
0.50
0.75
1.00
1.00
1.25
0.50
1.25
1.25
2.00
1.25
0.00
1.75
0.75
0.75
0.50
0.75
0.75
0.50
0.50
0.25
0.75
1.25
0.75
0.75
0.25
0.25
2.75
1.25
2.50
0.75
1.50
0.00
1.75
0.75
0.00
4
1.75
0.75
2.50
1.75
1.00
1.25
0.75
0.25
2.00
0.75
1.50
1.75
1.50
2.25
0.75
1.75
2.25
2.50
1.75
0.75
2.00
1.25
1.50
1.00
1.75
1.00
0.75
1.25
0.75
1.25
1.50
1.75
1.75
0.75
1.25
3.25
1.75
2.50
1.75
2.25
0.50
1.75
1.75
0.50
5
1.75
0.75
2.50
2.00
1.75
1.75
1.25
1.00
2.25
1.50
2.25
2.25
1.25
1.75
1.25
2.50
2.75
2.75
2.50
1.00
2.25
1.50
1.50
1.25
2.25
1.25
0.75
1.50
1.00
1.50
1.75
2.00
2.00
1.25
1.75
3.50
1.75
2.75
2.00
2.75
1.50
2.00
2.00
0.75
6
2.00
7
1.50
2.50
3.75
2.25
2.00
1.00
1.25
2.00
2.00
2.00
2.75
1.75
2.00
1.50
2.75
2.50
2.75
2.50
0.75
2.50
1.25
1.75
1.00
2.00
2.25
0.50
1.25
1.00
1.25
1.25
2.25
1.75
1.00
2.25
3.00
2.00
2.75
2.25
3.00
2.00
1.75
1.75
8
9
2.25
741.30
1.75
2.50
2.75
1.00
3.00
1.75
826.00
3.25
2.25
738.80
693.90
2.00
747.30
696.30
815.70
651.60
1.75
2.00
1.00
1.00
0.50
821.00
834.10
819.10
2.50
2.50
826.40
857.10
163
Table A.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
0.00
0.00
0.00
1.25
0.75
0.25
0.75
1.25
0.00
0.00
0.50
0.00
0.00
0.25
1.25
0.00
0.00
1.00
0.75
0.25
0.00
0.00
0.00
0.00
0.75
3
0.75
0.00
0.50
1.25
1.50
2.25
1.50
2.00
0.75
0.50
0.75
4
2.25
0.25
1.75
1.50
2.00
2.50
1.75
2.25
0.75
0.75
1.00
5
2.75
0.75
1.50
6
2.50
1.25
2.25
7
2.50
0.75
2.25
2.00
2.00
2.75
1.50
1.00
1.00
2.00
1.75
2.25
2.50
2.00
0.25
0.75
1.75
0.50
0.75
1.25
1.75
0.75
0.25
0.00
0.50
0.00
1.50
0.75
1.00
2.25
1.00
1.50
0.75
2.50
1.00
1.00
0.25
0.75
0.50
2.00
0.75
1.25
2.00
1.50
1.75
0.50
3.00
1.25
1.25
0.75
1.25
0.75
2.50
1.00
1.25
2.50
1.50
1.50
8
2.50
9
890.60
2.75
1.75
0.75
814.30
831.50
0.75
0.50
1.00
3.25
3.00
164
Table A.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log, 4 = fourth log); HM200
acoustic velocity readings in the grapples (up) and on the ground (down) on the
unprocessed portion of the stem once a log is produced (1 = the stem portion after
log 1 is cut, 2 – stem portion after log 2 is cut, 3 = stem portion after log 3 is cut –
only one selected tree for “in-grapple” measurements - #156 - produced 4 logs and
had an HM200 reading up (11614 ft/sec) and down (11450 ft/sec) on the stem
portion after log 3 was cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
1
ft
2
35
18
18
35
35
35
35
35
35
35
35
35
35
27
27
35
35
35
35
35
35
35
35
18
35
35
Log length
2
3
ft ft
3
4
27 27
35 35
35 35
27 27
35 18
27 27
27 27
35 27
35 18
35 35
35 18
35 35
35
35 35
18
27
27 18
35 35
35 35
27 27
27 27
27 18
41
35 27
35
27
4
ft
5
27
35
27
18
Log HM200 readings
1
2
3
4
ft/s
ft/s
ft/s
ft/s
6
7
8
9
13484 13091 13073
13025 12894 12008 10597
13222 14370 12959 11253
13708 12795 12566
13287 12959 12303
12894 12828 11942
13287 12795 11909
13714 13648 12326
13550 13222 12467 11319
13714 13058 11844
13976 13156 11942
13320 12730 11024
13222 12566
12205 12008 11253
12205 12205
13878 12828
12664 12566 11877
12500 11516 12730
12238 12008 10925
12467 12828 12073
13287 12959 12336
13648 13222 12795
12894 12730
13386 13878 13091 12533
12828 12336
12402 12336
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
10
11
12
13
13714
13714
13615
12326
12697
12664
12041
11942
12730
12566
12467
12205
13287
13025
12697
12795
165
Table A.3. Continued…
1
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
2
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
18
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
3
35
35
35
35
35
35
35
27
27
27
35
35
35
35
35
35
35
35
35
4
27
18
35
35
35
35
35
27
35
35
35
18
35
35
27
27
35
35
35
35
35
35
35
27
35
35
35
35
18
18
5
39
35
18
27
27
35
18
35
35
27
27
35
18
27
35
35
18
27
27
27
18
18
35
18
35
27
18
27
18
18
18
18
18
6
13058
13091
13484
13156
13550
13976
12894
13222
13714
13714
13386
12402
14206
12664
13156
12894
13058
13583
12992
13123
13320
13878
13386
15026
13878
13320
13714
13320
13550
14042
13222
13648
13058
13648
13156
12730
12238
12566
13222
12402
13222
14272
13550
13812
13878
7
12730
13058
13386
12894
13386
13878
13156
13091
13320
13320
13156
12730
13878
12238
12730
12566
12894
13714
11909
8
11581
12631
13058
13386
13058
14436
13550
12467
13550
12992
13058
13878
12828
12664
13320
13091
12894
12402
11844
12730
13058
12238
12894
13976
13222
13484
13714
12008
12631
12861
9
10
11
12
13
14042
12598
12434
13878
12631
12598
12402
12795
12336
12369
12336
11745
12795
12238
12795
12205
12467
12238
12336
11745
11385
11745
11975
11909
11385
11385
13419
13353
13287
13025
12598
12533
12041
11942
12172
12270
12106
11778
11713
12336
12369
12697
11745
12467
10433
12171
13025
12205
11581
11745
11942
12697
12205
11811
11778
11808
11844
12106
11188
11516
13123
12566
12205
13025
10696
10531
11942
166
Table A.3. Continued…
1
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
2
35
35
35
35
35
25
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
35
35
35
35
18
35
35
35
35
35
35
35
3
35
27
35
35
35
35
18
35
35
18
35
35
27
35
18
27
35
27
35
35
35
35
35
35
35
35
35
35
35
27
35
27
35
35
35
35
35
35
35
27
35
35
18
35
4
35
18
27
27
27
5
18
18
27
27
18
18
27
18
18
18
35
27
35
18
35
27
35
27
27
18
35
18
35
27
18
18
35
27
27
35
27
27
27
18
27
6
12500
13058
13156
12992
14206
13386
14337
12730
12894
12894
12664
13714
13058
14370
13156
13976
13550
13386
13222
14206
13320
13058
13812
13714
13648
13648
13714
12730
14206
13156
13780
11713
14042
13550
13386
13058
13648
13747
13320
13386
13714
13058
13714
10696
13714
7
13156
12697
12992
12566
13878
13058
14206
12500
12402
13025
13402
13386
13091
13484
13222
13845
9941
13583
12336
13714
13058
13058
13386
13386
13156
13550
13222
12894
13976
12828
12894
13051
13550
13484
12828
12992
13878
13550
12664
13451
12894
13386
13780
13386
8
12172
12467
11811
12828
12073
9
10
11
12
13
11811
11680
11811
11713
12992
13550
12795
13484
12533
12303
12336
12562
12631
12205
12631
12533
12467
12073
13156
12992
12697
12533
13517
13386
14469
12828
12533
12500
12795
12467
11778
11385
11713
12697
12303
12467
12697
11778
12795
12205
12894
12073
12073
12533
11844
12467
12073
12205
12828
12041
12172
10564
10269
10827
12631
12238
11715
12467
13222
12336
12205
12697
11745
12697
11811
167
Table A.3. Continued…
1
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
2
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
27
35
35
35
3
4
35
18
27
35
35
35
35
35
35
35
35
35
35
35
35
27
18
35
27
27
27
27
35
27
27
35
35
35
27
35
35
35
27
35
35
27
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
27
35
27
35
35
27
27
27
27
27
27
35
27
35
18
35
35
18
27
35
35
18
27
27
18
27
27
35
35
18
18
5
18
18
18
18
18
6
13911
14108
14206
13845
13812
13058
12828
13484
12008
12730
12894
13156
13714
13222
14206
13714
13812
12566
13714
13714
13156
14534
14242
12566
13976
13484
13058
13714
13058
13058
14206
13812
12894
13878
13386
13484
14436
13812
13320
13648
13386
13451
13320
13878
14764
7
8
13386
12861
13845
13714
12730
12566
13058
12238
12566
12664
13156
13386
13550
14042
13714
12828
12041
11909
12336
11811
11450
12073
12172
12467
12467
13058
12894
12402
13091
13648
12730
13976
13451
12500
13878
13091
12500
13058
12566
12664
13156
13320
12730
13550
13320
13058
13812
13222
13714
13222
12992
13714
12730
13451
14534
11253
12467
12828
11811
12959
12697
11581
12894
12073
11909
12205
11581
11844
12533
12205
12402
12828
12697
12073
12697
12795
12697
12467
12008
11024
12697
13287
9
10
11
12
13
13123
12172
12795
11909
12828
11811
12336
11811
12894
13381
13091
12697
12139
12139
23917
11844
13287
13123
13287
13053
12730
12336
12828
12697
12927
12566
12598
13845
12730
12566
12467
13714
13255
12172
12073
12697
12073
12008
13123
14140
13123
13976
12861
14469
12697
13287
11778
12205
10860
11450
9941
168
Table A.3. Continued…
1
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
35
18
35
35
35
35
35
27
35
35
35
35
27
35
35
3
35
35
27
27
27
35
27
35
35
35
35
35
35
18
35
35
27
18
35
27
27
35
27
35
35
4
35
18
27
18
18
35
27
27
35
27
35
18
35
35
35
18
27
27
18
18
27
27
35
18
18
27
5
18
18
18
27
27
27
35
18
27
27
18
27
18
18
18
18
18
18
6
13812
13058
14206
14534
12828
13386
12828
11417
12730
13714
13386
13550
13222
13222
13386
13878
13156
12336
13156
13648
12664
12172
13484
13878
13550
13615
13878
12894
12730
13714
13386
13320
12894
13714
13878
13714
13583
13386
13222
7
13648
12828
13451
14206
13091
12894
12697
11909
12828
13648
13484
13550
12730
13222
13058
13550
13320
12303
13386
13320
12205
12008
12959
13222
13222
8
12402
12106
12566
12533
12303
11581
13091
12467
12730
13222
13058
12467
12402
13386
13222
12205
12205
11942
12467
12697
13222
13091
12992
12467
12303
11188
9
12407
12106
12631
12467
12566
11942
11581
11
13255
12
12402
13
12402
15518
15486
12795
12631
12566
12336
11942
11942
12861
12992
12697
12566
13091
12861
12303
12205
12402
12402
10269
12566
12697
12631
11581
11024
12041
10
13550
11614
10203
11778
10630
169
Table A.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
A
A
A
A
A
A
A
A
Test Truck
#
T1
T2
T3
T4
T5
T6
T7
T8
Number of
Logs
47
55
42
55
43
55
39
21
Volume
Gross BF
4110
4340
4630
4520
4270
4460
4350
5010
Average
Small End Diam
in
9.26
8.98
9.79
8.76
9.60
9.16
10.13
13.90
Length
ft
26.60
26.16
30.31
27.65
29.05
26.15
30.31
32.43
170
Table A.5. Green veneer produced from processed logs delivered exclusively from
site A: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Green Veneer Volume
Pieces
Grade
3/8's
2832
G1
30204.98
1768
G2
18856.78
493
G3
5258.141
43
AB
458.6208
551
C+
5876.746
2195
C
23410.99
756
D
8063.194
290
X
3093.024
39
XX
415.9584
3050
In-Process
32530.08
8967
NET
95638.44
12017
TOTAL
128168.5
171
Appendix B. Data from Stand B (“Brohme Flats”, located near Sutherlin,
OR).
Details: Loader – tracked knuckle boom; cut and measured in July 17 - July 21,
2006; ST300 tool borrowed from Forest Products Laboratory in Wisconsin.
Table B.1. Measurements from 200 trees sampled: diameter at breast height
(DBH), ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable length (with no
limbs) and the respective HM200 acoustic velocity measurements on the ground
(down) or in the grapples of the loader (up), R - remarks (1 = dead, 2 = double
leader, 3 = broken top, 4 = missing data). Disks from the bottom and top of each
produced log as well as “in-grapple” measurements were taken from the 40 trees
highlighted.
ST300
R
Tree
DBH
#1
1
#
2
(in)
3
18.7
19.7
19.8
15.1
15.5
12.8
23.6
9.3
20.2
7.7
10.3
10.4
23.7
13.2
8.5
25.6
11.2
14.9
10.9
19.0
9.3
16.7
13.0
16.4
9.3
ft/s
4
12336
13707
13402
12548
12575
12567
13462
12502
13178
13461
12449
12668
11685
13169
14237
11998
14866
13344
13552
12450
13813
14011
13268
13079
13811
2
2
2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Limbs on
#2
Tree
length
ft/s
5
12937
13640
13053
13563
13553
11534
13180
12251
13897
12806
14569
14498
12564
15604
13324
12567
14850
14107
11786
13497
14286
12494
13825
12222
14410
ft
6
102.0
92.0
68.0
75.0
72.0
83.0
89.0
67.0
87.0
61.0
78.0
70.0
73.0
78.0
70.0
92.0
69.0
85.0
54.0
81.0
90.0
85.0
96.0
92.0
77.0
HM200
(down)
(up)
ft/s
ft/s
7
8
12041 12106
12270 12238
12467
12041
11680
11975 12172
11647
11647 11778
11778
11056
11713
12631
10663
10564
10007
10958
13386
12041
11713
12139 12139
11745
12328
12270
12238
12992
Limbs off
Merch
length
ft
9
97.0
88.0
62.0
70.0
70.0
70.0
70.0
35.0
70.0
27.0
35.0
54.0
62.0
62.0
27.0
53.0
35.0
70.0
27.0
71.0
53.0
70.0
53.0
62.0
35.0
HM200
(down)
(up)
ft/s
ft/s
10
11
12073 11549
12205 12369
12336
12238
11647
12650 12795
11975
12402 12566
11811
12205
12959
12894
10663
11319
11942
11155
13714
12238
12073
12303 10598
12664
12631
12959
12894
13812
172
Table B.1. Continued…
1
2
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
3
10.0
18.1
11.4
12.9
25.2
8.0
8.6
15.2
8.0
8.0
20.1
10.7
11.1
16.5
9.0
6.5
6.9
16.2
13.1
15.7
14.1
17.2
16.2
6.7
6.7
10.3
12.6
17.1
10.2
12.7
13.0
10.5
8.5
11.8
8.3
14.3
14.1
18.0
11.2
13.0
16.6
13.5
15.2
12.4
10.2
10.5
4
15491
13267
14119
13470
13140
13920
13733
13237
13793
13584
14416
14176
14196
14754
13490
14248
13807
13280
13927
10769
13149
11698
13704
13389
11959
14687
13369
12435
13250
15012
13368
13432
13582
12702
13530
13543
13812
13935
13583
13567
12307
14454
12272
14911
14380
14529
5
14130
13385
14174
15589
13492
12868
14731
12275
13289
14002
15345
14162
14478
13589
13536
13458
13494
12795
13640
12238
13514
13427
13426
12498
12764
13142
13859
12912
14667
15225
12937
13651
13587
12832
13353
13746
13553
13063
13882
13569
13971
13293
12862
13563
14082
13067
6
84.0
89.0
97.0
55.0
62.0
61.0
66.0
94.0
55.0
55.0
80.0
74.0
67.0
68.0
70.0
67.0
53.0
76.0
52.0
79.0
59.0
82.0
77.0
55.0
51.0
61.0
64.0
66.0
72.0
70.0
85.0
79.0
63.0
63.0
73.0
89.0
79.0
69.0
83.0
70.0
94.0
85.0
98.0
82.0
84.0
70.0
7
12894
12041
12205
13911
11286
11549
12500
11549
12205
11549
11680
10597
11450
11647
11024
11778
13386
11188
12041
11024
11581
10400
11188
10696
10663
11220
11975
10860
11647
11516
10958
10302
11219
12238
12336
11286
12795
12402
12500
11909
11909
11778
10660
12500
12664
13255
8
13878
11778
11877
11286
12664
9
35.0
88.0
62.0
45.0
53.0
27.0
35.0
70.0
27.0
35.0
70.0
35.0
62.0
62.0
35.0
18.0
35.0
70.0
35.0
70.0
53.0
53.0
70.0
27.0
27.0
53.0
62.0
62.0
62.0
62.0
62.0
62.0
35.0
53.0
35.0
70.0
70.0
62.0
62.0
62.0
80.0
70.0
70.0
70.0
62.0
35.0
10
13550
12303
12894
13976
11155
12139
12894
11483
12566
12073
11811
13058
11909
11450
12073
12795
11713
10860
12238
11417
11877
10663
11450
11713
11319
11745
12041
10761
11909
11877
11919
11024
11909
12270
12894
12238
13156
12500
13025
11909
12369
13123
12205
12795
13189
13648
11
13878
11647
12238
11877
12155
173
Table B.1. Continued…
1
3
2
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
3
15.8
18.7
10.9
9.8
11.8
16.9
15.2
14.8
10.4
16.5
21.1
16.5
20.0
14.0
12.6
18.5
17.3
11.7
27.4
18.9
15.8
15.0
12.8
12.8
13.4
20.5
15.7
9.6
10.3
12.8
10.7
11.0
15.6
17.5
16.4
13.3
9.1
7.5
17.0
16.4
16.8
15.5
18.8
8.4
18.5
4
14289
5
12467
14767
12911
13317
13331
14255
14069
13912
14518
14173
14051
14557
14259
14461
13175
15739
13974
13889
13238
13953
12876
15151
14859
6
90.0
75.0
89.0
103.0
96.0
91.0
101.0
105.0
81.0
87.0
101.0
107.0
70.0
104.0
60.0
80.0
94.0
65.0
89.0
98.0
82.0
98.0
101.0
85.0
81.0
88.0
81.0
24.0
83.0
86.0
71.0
77.0
92.0
102.0
86.0
70.0
88.0
80.0
85.0
77.0
86.0
80.0
88.0
71.0
84.0
7
12172
12992
14839
12336
12336
11219
12621
12173
12336
12733
11745
11286
11617
12336
12566
12402
11811
11778
11286
11253
12959
11385
11877
12106
12730
12041
11056
13615
12500
11417
9678
11483
12664
11647
10564
12861
12828
13189
12172
12434
12664
12402
10761
13451
12434
8
14239
11286
12500
12762
11975
13615
13353
12238
12697
12566
13451
9
88.0
70.0
62.0
53.0
62.0
70.0
70.0
70.0
62.0
70.0
97.0
97.0
62.0
88.0
56.0
70.0
88.0
53.0
62.0
70.0
70.0
70.0
70.0
53.0
70.0
80.0
70.0
18.0
53.0
70.0
62.0
62.0
53.0
88.0
70.0
53.0
35.0
35.0
80.0
70.0
70.0
70.0
70.0
35.0
70.0
10
12041
12631
13320
13419
12959
12664
13123
12795
15715
12992
11745
11745
12566
12424
12795
12467
11647
12008
11614
11811
12828
12631
12861
13353
12959
11811
12467
13222
12927
12041
12205
12008
12598
12041
12992
12927
14206
14042
12172
12631
12828
12785
12139
14108
12631
11
11286
11778
13045
12959
12172
13222
13648
12172
12959
12467
14042
174
Table B.1. Continued…
1
4
2
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
3
13.6
13.4
15.0
10.9
18.1
24.2
18.6
16.2
12.9
9.4
9.4
11.7
16.2
22.3
17.6
8.9
15.2
16.5
16.3
10.3
10.4
11.6
12.4
8.8
8.9
12.4
18.5
10.2
19.0
18.0
14.5
18.1
16.1
17.0
12.0
20.7
21.8
19.6
10.4
12.4
19.6
15.8
8.8
14.8
17.4
15.8
4
14629
5
12093
15854
13369
14734
13222
13852
15387
14105
15159
15292
14154
13971
14680
15160
15030
13864
13506
13769
12489
13877
13002
13519
14559
6
90.0
86.0
86.0
96.0
85.0
53.0
56.0
97.0
85.0
81.0
62.0
74.0
103.0
92.0
92.0
88.0
103.0
90.0
95.0
98.0
78.0
77.0
81.0
90.0
87.0
72.0
83.0
81.0
81.0
75.0
97.0
90.0
72.0
98.0
90.0
70.0
94.0
100.0
88.0
74.0
78.0
111.0
48.0
55.0
85.0
76.0
7
12336
12205
13222
12933
12073
12041
12205
11647
12566
13419
13812
12927
12369
11581
11745
13222
12566
12894
11680
13156
13353
12664
12566
12172
12500
13419
12270
12664
11450
12992
11909
12402
13320
11253
12106
12467
11319
11549
12008
12236
10827
11647
13123
12106
12041
12172
8
12205
12238
12106
13123
11614
12927
14567
12828
12270
12402
13681
12959
9
70.0
70.0
70.0
53.0
80.0
45.0
54.0
88.0
70.0
62.0
53.0
62.0
88.0
88.0
88.0
53.0
88.0
88.0
88.0
62.0
62.0
70.0
62.0
35.0
53.0
70.0
80.0
53.0
70.0
62.0
70.0
70.0
53.0
70.0
70.0
62.0
70.0
88.0
35.0
53.0
70.0
88.0
35.0
35.0
70.0
71.0
10
12631
12303
13123
13287
12041
12073
12664
11647
12664
13681
13845
13123
12927
11188
11844
14009
12697
12664
11877
14239
13780
13291
13878
13550
13419
12041
13255
11483
11778
12467
12467
13419
11680
12795
12467
12467
11877
13058
12664
10597
12041
13386
12402
12369
12434
11
12795
12238
12238
12762
12041
14436
11778
12172
12533
13648
13320
175
Table B.1. Continued…
1
2
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3
14.6
9.9
7.6
13.9
22.1
13.1
14.6
12.6
17.5
12.5
10.7
16.3
18.2
12.7
18.1
20.9
10.8
16.5
15.7
14.8
13.6
10.9
21.3
11.9
11.2
18.9
18.1
9.5
23.7
21.0
15.3
9.7
14.1
14.7
14.3
10.6
12.7
15.6
4
5
13899
13518
13972
14366
13735
13728
12592
12388
13583
13522
12206
14369
12071
14088
6
102.0
44.0
75.0
74.0
84.0
71.0
76.0
88.0
85.0
66.0
58.0
86.0
96.0
76.0
85.0
96.0
77.0
105.0
73.0
95.0
88.0
78.0
102.0
76.0
77.0
72.0
93.0
58.0
78.0
67.0
82.0
75.0
80.0
78.0
72.0
70.0
85.0
95.0
7
12730
13812
11942
12500
12434
13484
12992
13681
12631
11089
13222
12402
12073
13648
12959
11778
13189
12238
12500
11909
12424
13156
11549
12992
12828
12303
11778
13812
12238
11909
12303
13253
12598
12631
12500
12992
11319
12664
8
13353
12303
13058
12270
12106
12205
12631
9
70.0
35.0
27.0
53.0
80.0
62.0
70.0
70.0
70.0
53.0
35.0
70.0
88.0
70.0
70.0
88.0
70.0
88.0
70.0
88.0
70.0
70.0
97.0
62.0
62.0
62.0
88.0
53.0
70.0
53.0
70.0
45.0
70.0
70.0
70.0
53.0
70.0
70.0
10
12894
13714
13714
12828
12402
13615
13058
13911
12664
10991
13320
12467
12172
13451
13123
11353
13287
11385
12336
12041
12664
12959
11450
12762
13123
12205
11877
13618
12303
12008
12467
13780
12631
12631
12467
13259
12828
12828
11
12927
11459
12927
12467
12041
12631
12795
176
Table B.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
0.75 1.50
1.75 1.75
1.50 1.00
0.75 1.75
2.50 2.25
3.00
0.00 1.00
1.25 1.00
0.25 0.75
1.00 1.25
0.00 0.75
1.25 1.00
0.25 0.75
1.25 1.25
0.75
0.50 1.25
2.50 2.75
2.75
0.25 0.50
0.75 0.25
0.50 1.25
1.75 2.00
1.75
0.25 0.25
0.50
0.25 0.50
1.00 0.75
0.25 0.50
0.25
1.25 2.50
2.75 2.50
0.25 1.00
1.50 1.00
0.00 0.50
0.75 0.50
1.75 2.00
2.00 2.25
2.25
0.25 0.25
0.50 0.75
0.00 0.25
1.50 2.00
2.25
1.25 1.50
1.00
0.50 1.25
1.00 1.25
1.25
0.00 0.50
0.75 0.75
0.25
0.25 0.75
1.25 1.00
1.00
0.50 0.75
0.75 1.00
0.75
0.00 0.50
0.75 0.75
0.50
0.00 0.50
0.75 0.75
0.25
0.00 0.25
0.50 0.25
0.25
0.25 1.00
1.25 0.75
1.00
0.00 0.25
0.50 0.75
0.25
0.00 0.50
0.75
1.75 2.50
2.75 2.00
0.25 0.50
0.50
0.25 0.50
0.50 0.25
0.50 0.75
1.25 1.00
0.75
0.25 0.50
0.25
0.25 0.25
0.50
0.50 1.25
1.50 1.50
0.25 0.50
0.75 0.50
0.00 0.25
0.75 0.50
1.00 1.75
2.25 2.25
0.00 0.25
0.50 0.25
Green
Density
(kg/m3)
9
837.20
821.20
839.50
730.30
887.50
795.90
800.30
177
Table B.2. Continued…
1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
2
0.25
0.25
0.25
1.25
0.50
0.75
0.75
1.00
0.25
0.25
0.25
0.00
0.25
0.25
0.50
0.00
0.25
0.25
0.50
0.25
0.25
0.00
0.75
0.00
0.25
0.50
0.25
0.75
0.25
0.00
0.25
0.00
0.50
0.50
0.00
0.00
0.25
0.25
0.00
0.50
0.25
0.00
0.00
0.50
1.00
0.00
3
0.50
0.50
1.25
1.75
1.25
1.25
1.75
1.50
0.50
0.50
0.75
0.50
0.75
0.75
1.25
0.50
0.75
0.50
1.25
0.50
0.75
0.50
1.25
0.50
0.75
0.75
0.75
1.00
0.75
5.00
0.50
0.25
0.75
0.75
0.50
0.50
0.75
0.75
0.75
1.00
0.00
0.50
1.25
0.75
1.50
0.50
4
0.25
0.50
1.50
1.50
1.50
1.50
3.25
1.75
0.25
0.25
1.00
0.75
1.50
1.00
1.50
0.75
1.00
0.50
1.25
0.50
1.25
1.25
1.00
0.75
1.00
1.00
0.50
0.75
0.50
0.75
0.75
1.00
1.25
1.00
0.75
0.75
1.00
1.25
1.00
1.50
0.50
1.25
1.75
1.75
1.50
0.75
5
0.25
6
7
8
9
818.10
842.40
1.50
1.75
1.50
1.00
1.75
0.75
0.50
2.00
0.75
1.00
1.00
0.75
0.25
0.75
0.25
1.25
0.75
1.50
1.00
0.75
1.25
0.50
0.50
0.75
0.50
0.25
1.25
1.00
1.00
0.50
1.25
2.25
1.00
1.25
1.25
0.75
1.50
2.00
2.50
1.75
0.25
0.75
1.50
0.75
1.00
0.25
0.25
0.25
0.25
785.40
1.00
0.75
0.50
1.00
1.75
1.25
1.50
0.25
1.00
1.00
1.75
0.75
1.75
2.50
1.75
1.00
750.90
792.60
752.90
178
Table B.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
0.25
0.00
0.25
3.00
0.00
0.50
0.50
0.25
0.00
0.75
0.75
0.25
0.25
0.00
0.00
0.00
0.25
0.25
0.00
0.50
0.00
0.00
0.50
0.50
0.00
0.75
0.75
0.50
0.25
0.25
0.50
0.00
0.00
0.00
0.50
1.75
0.00
0.25
0.00
0.00
0.50
0.00
0.50
0.25
0.50
3
0.50
0.75
0.50
3.50
1.00
1.25
1.25
0.50
0.25
1.75
1.75
0.50
0.50
0.50
0.50
0.25
0.25
0.75
0.75
1.00
0.25
0.25
1.00
1.25
0.50
1.50
1.25
0.75
0.50
1.00
1.25
0.25
0.25
0.25
1.25
2.75
0.50
0.75
0.25
0.25
0.75
0.75
1.00
1.00
1.00
4
0.75
1.25
0.75
3.00
1.50
1.50
1.50
1.25
0.75
1.50
2.50
0.75
5
1.25
1.00
0.75
2.75
1.75
1.25
1.25
1.00
0.50
1.50
2.00
1.25
0.50
0.75
0.25
0.50
1.00
1.25
1.25
0.50
0.50
1.25
1.75
1.25
1.75
1.75
1.25
0.25
1.50
1.50
1.00
1.25
0.75
1.75
3.25
1.25
1.00
1.25
0.50
0.75
1.00
1.25
1.75
1.25
0.25
1.25
0.50
0.50
1.50
1.00
1.50
0.25
0.25
1.50
1.75
0.75
1.75
1.75
1.00
0.50
0.75
1.00
1.25
1.00
0.50
1.50
6
7
8
9
0.75
2.50
1.50
1.00
1.25
1.00
0.25
1.75
1.75
0.75
0.25
741.50
835.50
938.30
1.25
0.75
0.25
0.25
0.75
1.75
1.50
1.50
0.25
1.00
1.25
1.50
0.75
0.75
0.25
674.00
750.90
1.50
1.50
787.10
733.40
0.75
743.80
1.25
0.50
0.25
0.50
0.25
1.50
850.40
864.80
833.70
0.75
0.50
0.25
1.50
1.75
1.00
1.00
179
Table B.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
0.25
0.00
0.00
0.25
0.00
0.25
0.00
0.00
0.00
0.00
0.00
0.50
0.00
0.50
0.75
0.25
1.00
0.75
0.75
0.25
0.75
0.75
0.75
0.25
0.25
0.50
0.25
0.00
0.00
0.75
0.50
0.00
0.25
0.25
0.50
0.50
0.25
0.50
0.00
0.25
0.75
0.25
0.25
0.00
3
0.75
0.50
0.50
0.75
0.50
0.75
0.25
0.50
0.25
0.25
0.50
1.25
0.50
1.00
1.25
0.50
1.50
1.25
1.50
0.50
1.50
1.25
1.50
0.25
0.75
1.25
0.75
0.50
0.25
1.25
0.75
0.50
0.50
0.50
1.00
1.25
0.75
1.00
0.50
0.50
1.50
0.75
0.75
0.75
4
0.50
1.00
1.25
1.00
0.50
1.00
0.75
0.75
0.50
0.50
0.75
1.75
0.75
1.25
1.25
0.75
2.00
1.50
1.75
1.25
1.00
1.50
1.75
0.75
0.75
1.00
1.50
0.25
0.75
1.00
0.75
1.00
0.25
0.50
1.25
1.75
0.50
1.25
1.25
1.25
2.25
0.75
1.25
1.75
5
0.25
1.25
1.50
1.50
0.25
0.75
0.75
0.75
0.25
0.25
0.25
2.75
0.50
1.00
1.00
1.25
1.75
1.00
1.25
0.75
1.25
1.50
1.25
1.00
0.50
1.00
1.50
6
0.25
0.50
1.50
1.50
0.25
7
8
9
722.80
0.50
847.50
750.10
749.80
790.90
0.50
0.25
0.25
2.50
0.25
0.75
816.20
1.00
1.25
800.50
742.60
1.50
0.50
1.25
1.00
0.50
1.25
1.00
748.10
1.50
0.50
1.25
0.25
1.00
1.50
0.50
1.00
0.75
1.50
1.00
1.25
2.25
1.25
0.75
0.50
740.40
1.50
0.25
1.00
1.00
1.50
180
Table B.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
0.25
0.25
0.50
0.00
0.50
0.50
0.00
0.25
0.25
0.75
0.00
0.50
0.75
0.75
0.00
0.00
0.25
0.00
0.00
0.00
0.25
0.00
0.25
0.00
0.50
3
0.75
1.25
0.75
0.50
1.00
0.75
0.75
0.75
0.75
1.25
0.75
0.75
1.25
1.25
0.50
0.75
0.75
0.25
0.50
0.75
0.75
0.25
0.25
0.50
0.75
4
1.25
2.00
1.50
1.00
1.25
1.25
1.25
1.25
1.00
1.50
1.00
1.00
2.50
1.75
0.75
2.00
1.25
1.25
0.75
0.75
1.00
0.50
0.50
0.75
1.25
5
1.00
2.50
2.00
1.25
2.25
1.50
1.50
0.75
0.75
1.00
1.25
0.75
3.25
2.25
2.25
1.00
1.00
0.25
0.50
1.25
0.75
0.25
1.25
1.25
6
7
8
9
2.25
1.75
2.25
2.00
813.40
1.25
0.50
1.25
1.25
706.40
2.00
0.75
745.60
765.40
756.70
743.90
0.50
1.25
181
Table B.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Log length
1
2
3
ft
ft
ft
2
3
4
35 35 27
35 35 18
35 27
35 35
35 35
35 35
35 35
35
35 35
27
35
27 27
35 27
35 27
27
35 18
35
35 35
27
18 35 18
35 18
35 35
18 35
35 27
35
35
35 35 18
35 27
27 18
35 18
27
35
35 35
27
35
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
13058 12073 10827
12992 12238 10860
13058 11942
12500 11745
12073 11089
13156 11909
12139 11516
12402
11909 11745
12205
12959
13222 12467
10860 10564
11745 10696
11942
11188 11122
13714
12500 11909
12073
12369 12500 11713
12566 12467
12730 12336
13222 13386
12402 12828
13812
13550
13058 12402 10860
13222 12467
13976 13222
11089 11188
12139
12894
11745 11188
12566
12073
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
11581 11450 10827 10827
11745 11778 11122 10860
12008
11909
12336
12303
13222
13222
11713
11713
182
Table B.3. Continued…
1
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
2
35
35
35
35
35
18
35
35
35
35
35
18
35
27
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
3
35
4
27
27
35
35
18
35
35
18
27
27
27
27
27
27
18
35
35
27
35
27
35
35
35
35
27
35
35
27
18
27
35
35
35
18
18
5
12073
13058
12566
11745
12073
12795
11713
12008
12238
11745
12073
10203
12073
11713
11319
12238
12402
11089
12730
12008
12336
11417
11909
12402
12894
12566
13484
12500
13484
12238
11811
10433
12402
12894
13648
13648
13156
12500
13648
13222
13058
12894
13386
13222
13550
6
11516
7
11188
11319
8
9
11118
11319
13320
12828
13386
13386
11089
11024
11286
10597
10925
11450
11450
10696
11056
11713
11450
10433
11877
11909
12894
12467
12566
11450
12828
9810
9318
12402
12828
12073
12730
12828
13123
12566
12566
12730
12238
13386
10630
10
11
183
Table B.3. Continued…
1
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
2
35
35
35
35
18
35
35
35
35
27
35
35
35
35
18
35
35
35
18
18
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
27
27
35
35
3
35
35
35
27
35
18
35
35
18
35
35
35
35
35
35
35
27
35
35
35
27
27
18
35
35
18
27
35
35
35
35
35
35
35
35
35
27
18
27
35
35
4
27
27
35
18
18
18
18
18
18
5
12730
12172
12566
12500
12795
12894
12664
12238
12238
11450
12073
12992
13222
13222
12697
13058
12402
12566
13222
12795
12566
12238
12238
12730
12828
13550
13058
14206
14042
12730
12894
13222
12894
12336
14108
12828
12730
12730
13550
13123
12566
12336
13091
12500
13058
6
12894
12172
11909
12467
13058
12631
12073
11844
11450
11680
11581
12894
12073
12730
12894
12730
11581
12336
13058
11516
11811
11713
12041
11909
12073
12533
12205
12238
12566
12402
11844
12238
12336
11909
12664
13320
12073
12041
12697
11252
12402
7
8
9
10
11
10696
10696
11155
11319
10696
10696
12303
12631
12730
11385
12730
11089
10860
11122
11647
11811
11188
11188
12566
12402
12566
12402
12336
12336
11811
12041
11811
12041
11188
11024
11745
10630
11122
10794
11188
11024
10696
184
Table B.3. Continued…
1
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
2
35
35
35
35
35
35
18
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
35
35
35
35
35
35
35
27
18
35
35
35
35
3
27
18
27
35
35
35
35
35
35
35
27
27
35
27
18
35
27
18
35
27
35
35
18
35
35
27
35
35
35
35
35
18
35
18
35
35
27
27
35
35
4
18
18
18
18
18
18
18
18
18
18
18
5
13878
14042
13812
13714
12073
12566
13878
13845
13484
12894
14436
14272
13386
13386
13878
13619
13878
12566
13550
11745
11745
12730
12894
13648
12238
13222
12566
12730
12172
13058
12369
10597
13058
13386
12566
12566
12992
13058
13714
13714
12631
12566
13648
13484
14206
6
13320
13386
12566
12894
11089
11745
14108
12730
12500
11844
13845
13222
12073
12205
7
11385
11385
10958
10630
12730
10597
12172
10531
12992
12336
13714
12730
13550
9
10
11
21129
14108
11483
13845
13222
12238
11516
13845
13222
12073
10993
10958
12008
11877
11549
11385
12238
13222
12238
13222
13386
12992
11877
10039
10531
12861
12730
12205
12959
11089
11581
12172
12238
13222
11253
12238
12205
12238
12008
11877
12073
11122
12566
8
11385
11713
11877
185
Table B.3. Continued…
1
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
35
35
35
18
35
35
35
35
35
35
3
35
18
35
35
35
35
35
35
35
35
35
35
35
35
27
27
27
35
18
35
18
35
27
35
35
35
18
35
35
4
18
18
35
18
27
35
5
12894
11089
13320
12992
12730
13156
13222
12238
13648
13386
12566
12730
13058
13156
11745
13058
13550
12336
12467
13878
12730
12238
12402
13550
13156
12992
12664
13386
13484
13058
6
12336
10860
12238
12232
13648
12894
11745
12894
12566
12073
12008
12073
12664
11352
12336
12566
11942
12566
13222
12073
12041
12664
13845
12172
12402
12073
12861
12402
12664
7
8
9
10
11
10269
10302
15387
15387
18602
12336
12172
12041
12664
12073
12041
12664
12402
12402
11024
9186
15387
10696
10827
11089
186
Table B.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
B
B
B
B
B
B
B
B
B
Test Truck
#
S1
S2
S3
S4
S5
S6
S7
S8
S9
Number of
Logs
49
55
35
43
26
55
36
46
52
Volume
Gross BF
4320
4390
4310
4510
4130
4310
4020
4520
3920
Average
Small End Diam
in
8.49
8.18
9.86
8.98
10.96
8.33
9.14
8.70
7.85
Length
ft
29.67
26.31
32.31
30.51
32.73
28.05
33.56
32.39
30.25
187
Table B.5. Green veneer produced from processed logs delivered exclusively from
site B: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
2292
2450
658
2
996
2381
434
358
73
1897
9644
11541
Green Veneer Volume
Grade
3/8's
G1
24445.56
G2
26130.72
G3
7017.965
AB
21.3312
C+
10622.94
C
25394.79
D
4628.87
X
3818.285
XX
778.5888
In-Process
20232.64
NET
102859
TOTAL
123091.7
188
Appendix C. Data from Stand C (“Collins Flats”, located near Tiller, OR).
Details: Loader – tracked knuckle boom; cut and measured in July 24 - July 28,
2006; ST300 tool borrowed from Forest Products Laboratory in Wisconsin.
Table C.1. Measurements from 200 trees sampled: diameter at breast height
(DBH), ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable length (with no
limbs) and the respective HM200 acoustic velocity measurements on the ground
(down) or in the grapples of the loader (up), R - remarks (1 = dead, 2 = double
leader, 3 = broken top, 4 = missing data, 5 = butt rot). Disks from the bottom and
top of each produced log as well as “in-grapple” measurements were taken from the
40 trees highlighted.
ST300
R
Tree
DBH
#1
1
#
2
(in)
3
21.3
18.2
14.3
11.6
12.1
8.0
22.6
9.4
25.7
8.6
16.0
21.1
7.4
9.6
19.9
25.1
26.4
23.4
26.1
25.8
22.2
21.2
25.3
14.6
6.9
ft/s
4
13090
13200
13392
13644
14382
12863
13167
14054
13181
13893
14638
13492
13214
14320
13464
13595
13695
13563
11697
12510
11083
13785
12857
13037
14362
2
5
5
5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Limbs on
#2
Tree
length
ft/s
5
13254
12982
13564
13394
12596
13871
13048
13912
12111
13842
13693
13102
13085
13791
13845
11778
11730
13336
11715
11585
10357
13861
13370
13271
12704
ft
6
87.0
99.0
53.0
65.0
74.0
31.0
48.0
56.0
94.0
54.0
86.0
86.0
27.0
53.0
74.0
101.0
75.0
82.0
64.0
95.0
81.0
90.0
79.0
64.0
32.0
HM200
(down)
(up)
ft/s
ft/s
7
8
10335
10269
11352
11877 11745
11811
13353
12139
11549
10991
11614
11614
11220
11319
11745 11713
11614
10794 10794
11483
11385
10892
10696
9843 10105
11975
11024 11188
10892
11253
Limbs off
Merch
length
ft
9
70.0
80.0
53.0
53.0
70.0
27.0
45.0
35.0
70.0
27.0
70.0
80.0
27.0
35.0
70.0
88.0
70.0
80.0
63.0
88.0
80.0
88.0
70.0
62.0
18.0
HM200
(down)
(up)
ft/s
ft/s
10
11
11155
11089
11909
12434 12369
12205
13091
12238
12730
11155
12928
11647
11516
13287
12336 12336
11745
11220 11155
11909
11811
10630
11253
10269 10367
12041
11516 11713
11188
12106
189
Table C.1. Continued…
1
5
2
5
5
2
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
3
13.2
22.7
22.3
8.6
29.3
20.1
12.3
20.0
14.6
10.3
22.5
12.2
26.7
21.6
30.8
8.4
29.6
25.0
28.3
24.0
21.7
25.1
18.7
28.9
26.2
9.2
21.2
18.1
16.1
23.8
14.8
18.2
25.2
30.9
28.3
22.4
8.2
18.9
21.6
26.7
26.5
18.1
18.6
30.6
22.3
19.6
4
13939
13230
12307
14821
12298
13244
13894
11963
13948
14521
13994
13817
12597
12206
14933
12854
12192
13604
12298
12048
13259
12525
13520
11953
13399
15311
13180
13018
12834
12221
13401
13184
12968
11546
12638
13716
13167
13077
12692
12405
12584
13870
12933
12716
13847
13640
5
13796
12914
12196
13927
12171
13361
12903
11417
14718
14858
13979
13163
13327
12223
12695
11815
13551
11792
12223
13407
15248
13162
13496
11994
12506
13561
14022
13063
12892
12559
13649
12559
10997
12502
13193
12253
12848
14233
12269
12863
13424
14378
13464
13241
13268
13682
6
52.0
52.0
37.0
45.0
93.0
57.0
72.0
68.0
97.0
54.0
105.0
78.0
86.0
70.0
66.0
45.0
88.0
71.0
69.0
86.0
84.0
78.0
107.0
94.0
101.0
76.0
112.0
90.0
72.0
80.0
92.0
71.0
59.0
109.0
96.0
101.0
37.0
75.0
92.0
46.0
69.0
97.0
72.0
60.0
64.0
79.0
7
11680
10696
10663
12566
10892
12270
11188
10367
11253
13681
10794
10860
10991
10991
11352
8465
11155
10696
11220
11549
11877
11286
10531
9908
10072
12205
10925
11549
11647
11483
10892
9383
10925
9449
10892
10598
12041
12139
11188
11647
11975
11647
12369
11516
11975
11680
8
10827
12697
12270
11122
11352
11056
11417
11844
9383
10958
12139
11581
9
45.0
45.0
35.0
35.0
88.0
53.0
53.0
63.0
70.0
35.0
97.0
62.0
80.0
62.0
62.0
27.0
80.0
62.0
62.0
80.0
70.0
70.0
88.0
88.0
97.0
35.0
80.0
80.0
70.0
70.0
80.0
62.0
53.0
88.0
88.0
88.0
18.0
70.0
88.0
45.0
62.0
88.0
70.0
53.0
62.0
70.0
10
12172
11155
10466
11156
11155
12434
11942
10794
12139
13878
11220
12467
11253
11056
11614
11319
11188
10466
11188
11581
12041
11549
11809
10072
10367
13386
11877
11844
12402
11319
11516
11188
11286
9908
10892
11647
11778
12467
11811
11811
12172
11877
12467
11680
11778
11811
11
11188
13320
12730
11253
11614
11253
11614
12139
11188
11122
12697
11811
190
Table C.1. Continued…
1
5
2
2
5
5
2
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
3
23.3
23.6
25.3
15.8
15.4
22.9
28.4
7.7
28.5
24.9
18.4
22.8
19.3
24.6
24.8
27.8
18.9
25.2
13.2
21.8
29.4
19.5
22.0
31.1
30.4
29.5
26.9
28.1
22.7
13.0
16.2
13.1
29.6
20.7
23.5
24.1
28.5
23.9
21.1
22.0
26.6
28.4
18.4
14.4
28.5
4
13694
11483
11982
12989
13977
13253
12602
5
12693
12408
11838
12955
13915
14112
11538
12670
13123
12818
11858
13229
12670
12133
11030
12263
13142
14172
13916
13871
12865
11071
13507
12262
13724
6
112.0
102.0
83.0
40.0
77.0
58.0
101.0
58.0
88.0
74.0
78.0
101.0
97.0
87.0
65.0
115.0
82.0
105.0
80.0
79.0
89.0
82.0
101.0
115.0
98.0
91.0
69.0
110.0
76.0
64.0
87.0
72.0
70.0
82.0
75.0
110.0
90.0
83.0
84.0
53.0
102.0
90.0
85.0
68.0
108.0
7
11319
10663
10696
12205
11778
11680
10925
13222
11089
11089
11581
10794
10433
10236
11155
10728
11581
11024
12008
10531
10696
10564
11319
10696
11319
10663
11647
10761
11614
11680
11352
11680
11909
10794
11155
11293
11385
11713
11811
11286
11024
10531
11647
11549
11056
8
11352
11253
11581
10892
10630
10302
10696
11975
10630
11680
9
105.0
97.0
70.0
36.0
53.0
53.0
97.0
35.0
80.0
70.0
70.0
97.0
97.0
80.0
62.0
97.0
70.0
80.0
70.0
70.0
88.0
62.0
88.0
97.0
89.0
53.0
62.0
88.0
70.0
62.0
70.0
70.0
70.0
70.0
70.0
105.0
80.0
80.0
80.0
45.0
97.0
88.0
80.0
53.0
105.0
10
11680
10925
10860
12303
12369
11811
11056
13550
11319
11253
11811
10925
10696
10269
11155
10991
11975
11844
12238
10827
10663
11319
11647
10696
11483
11024
11778
11122
11647
11778
11975
11811
10860
11385
11220
11516
11286
11680
12041
11614
10794
10663
12238
12139
10958
11
11811
11385
11811
11056
10761
12139
11056
12205
10663
12500
191
Table C.1. Continued…
1
5
5
2
2
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
3
24.9
26.3
18.5
23.2
25.7
30.0
29.5
18.2
18.5
7.3
18.9
8.3
20.2
16.1
23.0
27.1
17.4
20.8
10.4
10.5
13.2
13.4
14.5
30.7
23.6
10.8
26.7
28.3
23.2
25.8
21.0
14.5
23.8
17.6
20.6
22.5
17.7
20.1
24.5
10.6
9.4
20.0
16.7
18.6
12.8
22.2
4
5
12770
12762
13622
13713
13280
12064
13550
13544
13823
13123
13728
13682
13967
13278
13412
13450
6
108.0
107.0
70.0
71.0
100.0
93.0
75.0
71.0
98.0
45.0
81.0
52.0
75.0
73.0
95.0
106.0
82.0
76.0
44.0
54.0
69.0
84.0
81.0
97.0
97.0
70.0
82.0
105.0
84.0
97.0
112.0
80.0
79.0
83.0
96.0
102.0
78.0
95.0
94.0
61.0
75.0
76.0
96.0
86.0
84.0
87.0
7
11516
10564
11811
11713
10302
10630
10466
11975
11220
11056
10203
11745
10925
11483
10203
9875
12500
10958
10433
12598
11647
11155
10269
10696
11286
12467
10992
10531
10269
10892
10433
11483
11188
10007
11024
11089
11713
10696
11385
11877
12369
10531
10827
11483
10355
11353
8
10499
11975
11319
11778
11680
12598
15354
11253
9
97.0
105.0
53.0
70.0
88.0
88.0
70.0
62.0
88.0
18.0
80.0
18.0
70.0
62.0
70.0
80.0
80.0
70.0
27.0
27.0
62.0
62.0
70.0
80.0
97.0
53.0
80.0
88.0
80.0
88.0
88.0
70.0
70.0
80.0
88.0
97.0
70.0
88.0
88.0
53.0
53.0
70.0
88.0
80.0
45.0
80.0
10
11745
10925
11811
11811
10564
10794
10531
12041
11253
12795
10302
12041
11024
11778
10531
10466
12533
11155
12828
13091
11745
11909
12631
11089
11581
12992
11089
10860
10466
11056
11647
11680
11319
11680
11155
11352
11811
10892
11253
12106
12697
10696
11089
11877
12795
10958
11
11056
12139
11286
11942
11909
12927
11713
11319
192
Table C.1. Continued…
2
2,5
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
18.5
19.1
26.8
13.5
22.5
14.3
23.4
24.8
24.0
17.1
13.2
14.8
17.7
15.2
28.1
19.1
20.8
22.9
9.8
20.3
18.1
8.0
15.9
19.0
24.8
10.6
11.2
18.1
12.6
17.2
23.3
24.2
10.2
10.4
17.1
17.5
8.5
9.7
12195
11525
13681
12927
13861
13905
12293
13232
12846
12253
90.0
109.0
80.0
74.0
96.0
73.0
105.0
94.0
100.0
84.0
67.0
67.0
88.0
76.0
89.0
79.0
69.0
101.0
55.0
74.0
79.0
70.0
84.0
73.0
73.0
52.0
42.0
86.0
45.0
97.0
108.0
102.0
67.0
79.0
71.0
68.0
50.0
56.0
10663
10957
10728
10466
10466
11253
9875
12664
10678
10466
11319
11942
10794
12303
10991
11352
11844
10728
9744
11647
11385
11680
12270
11516
11483
11549
11319
10696
11024
10236
10171
10171
12205
10597
10663
10761
11385
11319
10728
12106
12303
10663
10860
70.0
88.0
70.0
62.0
80.0
70.0
88.0
80.0
62.0
80.0
53.0
62.0
70.0
70.0
70.0
70.0
63.0
97.0
27.0
70.0
72.0
35.0
80.0
70.0
70.0
35.0
35.0
80.0
18.0
70.0
88.0
97.0
35.0
53.0
63.0
62.0
35.0
35.0
10663
10794
10630
11189
10794
11319
10367
11319
11056
10911
11778
11778
11483
12303
11220
11188
11811
10794
11319
11713
11647
12894
12500
11516
11385
12238
11844
10991
11286
10860
10827
10367
12336
11581
10531
10794
12336
12073
10630
11581
12697
13287
10761
193
Table C.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
1.50 2.25 2.75 3.00
2.25
1.25 1.75 2.00 1.50
1.25
0.50 0.75 1.25
0.50 1.25 1.25 0.75
0.25 1.25 1.50 1.00
0.75 2.25
0.50 1.25 1.50
0.50 0.75 0.75
1.25 2.75 3.25 3.00
2.75
0.00 0.25 0.50
0.25 1.25 1.50 1.25
0.75
0.75 1.50 2.00 2.25
1.75
0.25 0.50
0.25 0.75 0.25
1.00 1.50 1.75 2.25
0.75 1.50 2.25 2.50
2.00
1.75
2.75 2.50 2.75 2.00
1.75 2.50 2.75 2.50
1.50
0.75 2.25 2.75 1.75
1.25 2.25 2.50 3.25
2.75
1.25 2.75 2.50 2.25
1.75
1.00 1.50 2.25 2.75
2.50
1.25 2.25 2.75 2.50
0.50 1.25 1.50 1.25
0.50 0.75
1.25 2.25 2.25
1.25 2.25 2.75
1.50 3.50
0.25 0.50 1.25
2.75 3.25 3.50 3.25
3.00
0.75 1.75 2.25
0.75 1.50 1.75 1.00
0.75 1.50 2.00 1.25
1.00 1.50 2.25 2.00
1.75
0.50 1.75 1.50 1.00
0.75 1.75 2.50 2.25
2.00
0.75 1.50 2.75 2.50
0.75 2.25 3.00 2.25
2.00
1.25 2.00 2.25 2.50
1.75 2.75 3.00 3.25
Green
Density
(kg/m3)
9
773.70
867.70
833.60
852.50
942.70
821.40
859.60
832.20
814.60
798.70
194
Table C.2. Continued…
1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
2
0.25
1.75
2.25
1.25
1.25
1.00
1.25
1.25
1.75
1.75
0.25
0.75
1.00
0.25
1.00
0.00
0.75
0.75
0.75
1.75
1.25
0.25
0.50
1.00
1.75
1.75
0.75
0.75
1.75
0.75
0.25
0.75
1.50
1.25
1.50
0.75
1.25
0.00
0.25
1.25
1.25
0.50
1.50
1.50
1.25
2.25
3
0.75
2.50
3.50
2.75
1.75
1.75
2.50
1.75
2.50
2.50
0.75
1.50
1.50
1.25
1.50
0.75
1.75
2.25
2.25
2.50
2.50
0.50
1.50
1.75
1.50
2.50
1.50
1.50
2.50
1.25
0.75
1.25
2.75
1.75
2.75
1.75
2.00
3.00
0.50
2.75
1.50
1.25
2.25
2.25
1.75
2.75
4
0.50
3.00
3.25
2.25
2.75
2.25
2.75
2.00
3.00
2.75
0.50
1.75
2.00
2.00
2.00
1.25
2.50
2.75
3.25
3.25
2.75
5
6
3.25
2.75
2.00
2.75
2.50
2.50
1.50
2.25
2.50
0.25
2.25
2.25
3.25
1.50
1.50
2.25
2.75
814.60
2.25
2.00
854.70
851.20
3.00
3.00
3.00
2.75
1.75
2.50
2.00
2.75
2.25
2.25
2.75
1.50
1.25
2.25
2.75
2.50
1.00
2.25
2.50
2.50
3.25
0.25
3.00
2.25
2.00
2.75
2.75
2.25
3.50
2.75
2.50
2.50
2.00
1.25
1.50
2.00
3.00
2.75
7
1.00
2.25
2.50
0.75
2.00
2.00
1.25
8
9
1.75
1.50
854.90
2.75
834.80
2.00
827.20
1.75
1.75
814.30
1.25
1.50
2.25
2.00
2.75
2.25
2.50
2.00
1.75
3.25
2.50
2.00
3.00
2.25
2.25
2.00
1.75
1.25
833.00
1.75
795.40
813.80
813.90
195
Table C.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
1.25
0.00
0.50
0.25
1.00
0.75
0.00
1.25
0.75
1.50
1.75
1.75
1.50
1.25
0.50
0.75
0.25
1.25
0.75
0.75
1.25
0.75
1.25
0.75
2.50
3.50
2.25
0.25
0.75
1.25
0.75
0.75
0.75
1.25
0.25
1.25
1.75
1.50
0.75
0.25
1.00
0.25
0.75
0.75
0.75
3
2.25
1.25
1.75
1.25
2.25
1.75
1.25
2.50
2.25
2.75
2.50
2.25
3.00
2.25
1.50
1.50
1.25
2.75
1.50
1.50
2.00
1.25
2.25
1.50
3.00
3.25
2.50
0.75
1.50
2.50
1.75
1.50
1.50
2.75
1.50
2.00
3.00
1.75
1.50
0.50
2.50
0.50
1.50
1.25
1.50
4
3.00
1.75
2.25
1.50
2.75
3.25
2.50
2.75
2.50
3.25
2.75
2.75
2.75
2.75
1.75
2.00
1.50
3.25
2.75
2.25
2.50
2.75
3.00
2.50
3.50
3.00
2.75
1.50
1.75
2.75
2.75
2.25
2.00
3.50
2.25
3.00
3.25
2.25
2.25
0.25
3.00
0.25
2.50
1.50
2.50
5
3.25
1.75
2.75
0.75
2.75
3.00
3.25
2.75
2.75
3.50
2.75
3.25
3.00
3.00
1.25
1.75
1.75
3.00
2.50
2.50
3.00
2.50
3.25
2.75
6
3.25
1.00
2.50
7
2.25
2.75
2.25
2.75
2.50
2.25
2.75
2.25
1.75
1.00
3.25
3.00
2.50
1.25
3.25
2.50
3.50
3.25
1.75
2.00
2.75
2.50
1.25
2.25
3.00
3.00
2.00
2.50
2.25
1.25
2.75
2.50
8
9
820.60
2.00
885.90
2.75
2.25
2.50
2.50
3.25
2.75
1.50
2.25
2.50
2.25
832.60
1.50
0.75
857.50
829.20
800.80
901.20
2.00
2.75
1.50
760.30
827.40
765.20
2.25
1.25
2.75
810.80
2.00
196
Table C.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
2.50
0.25
0.75
0.25
0.75
0.50
0.50
0.00
1.50
0.75
0.50
0.25
0.75
1.00
1.25
0.75
0.75
1.25
0.75
0.75
0.00
0.25
0.75
1.00
0.25
0.25
0.25
1.00
0.75
0.25
1.25
0.75
0.75
1.75
0.25
1.25
1.00
1.00
1.75
2.25
0.50
0.25
0.75
0.75
3
3.00
0.50
1.25
0.75
1.50
1.25
1.25
1.00
2.75
1.75
0.50
3.50
1.50
1.75
2.25
1.75
1.50
2.50
1.50
1.75
1.75
0.75
1.00
1.50
0.75
0.75
1.25
1.50
1.25
1.25
1.50
1.50
1.50
3.00
1.25
1.75
1.75
1.75
2.50
2.75
1.25
1.25
1.25
1.75
4
3.00
1.25
1.75
0.25
1.00
2.00
1.50
1.50
3.25
2.50
0.75
3.00
2.25
2.50
3.00
2.50
1.75
2.75
2.25
2.50
2.50
1.25
2.75
2.50
1.00
1.25
2.00
2.25
1.50
1.50
2.00
2.25
2.25
3.50
1.50
2.50
2.25
2.50
3.25
3.25
1.50
1.50
1.50
2.50
5
3.25
1.50
2.00
6
2.75
1.50
1.50
1.25
1.25
2.75
2.75
0.75
2.75
2.75
2.50
2.75
2.25
1.50
2.75
2.00
2.75
2.75
2.25
2.50
2.75
0.25
1.00
1.75
2.00
1.25
1.00
2.50
2.50
2.50
3.25
1.25
2.50
1.75
2.25
2.75
2.75
1.25
1.25
1.25
2.75
0.75
0.25
1.00
2.75
2.50
7
2.25
8
9
766.70
2.75
2.50
2.75
2.50
2.00
2.25
1.50
899.60
787.20
2.25
2.00
2.25
1.25
2.50
2.50
1.75
1.00
0.50
2.25
2.25
2.00
1.25
747.60
2.00
1.00
1.75
2.75
2.00
1.25
1.25
1.25
806.50
2.50
197
Table C.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
0.75
1.25
0.75
1.00
1.25
0.50
0.75
0.00
0.25
0.50
0.50
2.50
1.00
1.25
0.25
0.75
0.75
1.00
1.25
0.00
0.25
1.25
1.25
0.25
0.25
3
1.25
2.75
1.25
1.50
2.50
2.25
2.00
1.75
0.75
1.25
1.50
3.25
2.75
2.50
1.75
1.75
1.50
1.75
2.50
0.75
0.75
1.75
2.50
0.75
1.25
4
1.75
3.25
1.75
2.50
3.00
2.00
2.25
2.25
0.50
1.75
1.75
3.25
2.25
0.75
2.25
0.25
2.25
2.25
2.75
1.25
1.00
3.00
2.25
0.50
0.75
5
1.75
3.25
2.25
2.75
3.25
1.75
2.25
0.25
1.50
2.00
3.00
6
7
8
9
2.75
2.50
2.25
1.25
1.75
1.25
2.50
2.50
3.25
1.00
0.75
2.25
2.25
1.75
2.00
3.00
726.80
1.75
2.25
832.20
797.70
198
Table C.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Log length
1
2
3
ft
ft
ft
2
3
4
35 35
35 27 18
35 18
35 18
35 35
27
27 18
35
35 35
27
35 35
35 27 18
35
35
35 35
35 35 18
35 35
35 27 18
18 27 18
35 35 18
35 27 18
35 35 18
35 35
35 27
18
27 18
27 18
35
35
35 35 18
18 35
35 18
18 27 18
35 35
35
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
11581 10594
11680 10696
9777
12073 11385
12500 11877
12828 11745
13091
12205 11877
12730
11352 10925
12928
12500 11745
12073 12073 10696
13287
12336
12073 11581
12073 11188
9875
11909 11680
12238 11581 10958
10433 11188 10105
12172 10597
9941
11089 10302
8957
12500 12172 11286
11909 11188
11581 10827
12106
11566 11549
11450 10794
10466
11156
11844 11089
9875
12795 12500
12073 11122
10630 11056 10203
12566 11745
13878
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
11942
11877
10925
10794
8793
9875
9777
9744
8957
8957
11352
11188
10860
10794
12664
12500
199
Table C.3. Continued…
1
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
2
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
18
35
35
27
35
35
35
35
35
35
35
35
35
18
35
35
35
35
35
3
35
27
27
27
27
4
27
27
27
27
27
35
35
35
35
35
18
27
27
35
23
27
27
18
35
35
35
35
35
18
27
35
35
18
27
35
35
35
35
18
18
18
35
27
18
18
18
18
27
18
18
18
18
18
35
18
18
35
27
27
18
5
11909
12664
11581
11516
12008
11319
11909
10302
11581
12238
12238
11909
11844
10597
11417
13386
12402
12238
12894
11581
12336
11745
11516
10597
11680
11877
11778
12894
12500
11942
12402
12402
13058
11417
12238
12073
12566
11745
11417
12369
12730
11844
11581
13550
11745
6
11253
12205
11319
10433
11581
7
10302
8
9
10
11
10696
11811
11024
10860
10696
11713
11581
11056
10564
11319
11450
11909
11089
11089
9974
10367
10531
10958
10827
10531
10531
10630
11188
12402
11056
11909
10696
10630
11942
11713
12238
11188
11056
10827
11188
9974
11253
11909
10958
10696
10827
10827
11188
10794
10367
10269
12828
12172
11713
11450
11352
11352
10860
10696
12172
11745
11549
11877
12008
12238
12041
11450
11352
12008
10923
10696
12303
11877
11614
10860
11188
9875
9121
9449
9941
8957
10269
11253
10696
10958
10696
10072
10302
10696
200
Table C.3. Continued…
1
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
2
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
18
35
35
35
35
35
35
35
35
35
35
3
35
35
35
35
27
27
35
35
27
35
35
35
27
35
27
27
18
27
35
35
27
35
35
35
35
35
35
27
27
27
18
35
35
27
35
35
35
35
18
35
35
35
35
27
35
4
27
27
18
27
18
18
18
18
27
18
35
18
18
18
27
18
18
35
27
35
18
18
18
5
11844
12336
11909
10925
10925
11089
11680
12500
12238
12500
11188
11188
11581
12238
11909
11417
10925
11745
11417
12073
12073
12566
12566
10925
11844
11909
12238
11414
12238
12894
11450
11253
10925
12828
12303
11581
12894
11516
11188
12172
10925
11417
10860
12402
12500
6
10761
11581
10925
10925
10564
10958
10761
11417
11713
12008
10957
10696
10958
11417
11319
11713
11024
11713
11188
11352
11319
11417
11352
10433
11024
10761
11680
11450
11942
12205
11450
11253
10860
12205
12238
11188
11680
11024
12336
11417
10761
10597
10302
11811
11253
7
8
11089
11516
10597
9
10761
11581
10466
10
11
9941
9941
10466
10466
10203
10072
12172
12008
11188
10991
11024
10860
10761
10433
10860
11122
11811
11811
11122
11122
10203
10105
11188
10531
12286
10531
11811
10203
11286
10105
11188
10138
10958
10466
10597
10663
10466
10466
12073
10630
11811
10728
9875
10039
9941
9678
9186
10072
10860
9711
10860
10860
11056
10531
9514
9875
10039
201
Table C.3. Continued…
1
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
2
18
18
18
35
35
35
35
35
35
27
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
3
4
35
27
35
27
35
27
27
35
27
27
35
27
35
18
27
35
27
35
35
35
35
27
35
35
35
35
35
18
18
35
35
27
18
27
35
35
35
27
27
35
35
27
18
18
18
27
18
18
18
18
18
18
18
27
18
18
18
18
18
18
18
27
18
5
12795
10696
12041
11581
12500
10925
10860
13222
11417
12828
13091
12073
12238
13058
11417
12238
13058
11352
11089
10860
11680
12173
12008
11909
12172
12073
11909
12172
11745
12238
12238
12795
11024
12402
12238
12828
11516
11417
11516
11024
11352
11024
11745
10925
11909
6
7
10597
9810
10531
11188
10039
10433
12336
10761
11581
11581
12238
11581
11417
12697
11188
11089
10302
11089
11745
11417
10925
11581
11089
11253
11417
10764
11188
11778
11942
10466
11024
11942
12369
11056
9974
10597
10531
10827
10564
10925
10531
11188
8
9
11581
11188
12369
12697
11089
11089
11417
10925
10531
10531
9711
11549
10531
10433
10039
10039
9777
10039
10433
10531
9186
10433
9711
10531
9285
10958
10105
9613
9777
9547
10039
10
11
202
Table C.3. Continued…
1
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
27
35
35
35
35
35
35
35
18
35
27
35
27
35
18
35
35
35
35
35
18
35
35
35
35
35
18
35
35
35
3
35
27
18
27
35
35
35
35
27
35
35
27
4
18
18
27
18
35
35
35
27
27
18
35
35
35
18
27
18
27
27
18
5
10958
11417
11909
12172
12073
12730
11516
11745
11877
11516
11319
12073
12697
12894
12106
11745
11745
12238
11844
11680
11286
11253
11253
11089
13156
11844
11286
11417
12336
12073
6
10925
10696
11385
11056
10597
12172
10925
10761
12073
10860
7
11286
9810
11188
11319
10696
9
10
11
10958
11056
12631
12500
12336
12073
10531
10433
10400
10302
9613
9613
9711
12992
11253
11089
12073
10827
10039
10696
10925
10530
9711
9055
11024
11188
10302
8
9613
203
Table C.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
C
C
C
C
C
C
C
C
Test Truck
#
T1
T2
T3
T4
T5
T6
T7
T8
Number of
Logs
48
61
32
39
35
34
27
34
Volume
Gross BF
3750
4260
3830
3730
3440
3900
3830
3980
Average
Small End Diam
in
8.71
9.41
10.22
9.00
10.03
9.85
10.48
9.50
Length
ft
25.98
20.92
28.75
29.49
25.43
30.50
31.48
31.29
204
Table C.5. Green veneer produced from processed logs delivered exclusively from
site C: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
775
1544
829
1
481
2953
1603
417
47
2640
8650
11290
Green Veneer Volume
Grade
3/8's
G1
8265.84
G2
16467.69
G3
8841.782
AB
10.6656
C+
5130.154
C
31495.52
D
17096.96
X
4447.555
XX
501.2832
In-Process
28157.18
NET
92257.44
TOTAL
120414.6
205
Appendix D. Data from Stand D (“Weatherly Ridge”, located near Elkton,
OR).
Details: Loader – rubber-tire truck-mounted knuckle boom; cut and measured in
July 31 – August 08, 2006; ST300 tool borrowed from Forest Products Laboratory
in Wisconsin and worked through tree 75; second ST300 tool borrowed from
Roseburg Forest Products for the rest of the trees.
Table D.1. Measurements from 200 trees sampled: diameter at breast height
(DBH), ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable length (with no
limbs) and the respective HM200 acoustic velocity measurements on the ground
(down) or in the grapples of the loader (up), R - remarks (1 = dead, 2 = double
leader, 3 = broken top, 4 = missing data, 5 = butt rot). Disks from the bottom and
top of each produced log as well as “in-grapple” measurements were taken from the
40 trees highlighted.
ST300
R
Tree
DBH
#1
1
#
2
(in)
3
17.1
18.1
12.6
13.7
18.8
21.6
10.7
18.5
7.8
24.3
11.5
26.3
16.0
20.7
13.4
11.8
8.2
9.9
17.0
9.2
15.0
7.4
18.0
8.0
ft/s
4
12386
13253
12254
14273
13245
13776
13175
13555
13775
12436
13382
11727
12462
13337
13599
14412
14212
13938
12505
14633
12875
14667
13429
14073
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Limbs on
#2
Tree
length
ft/s
5
13416
12899
12538
13386
13595
12253
14209
12773
14178
12925
13336
12745
13800
14211
14199
14144
14582
13956
13397
14785
13732
14632
12861
13324
ft
6
80.0
67.0
106.0
77.0
85.0
75.0
71.0
64.0
69.0
79.0
64.0
105.0
98.0
93.0
99.0
98.0
77.0
92.0
83.0
83.0
79.0
69.0
97.0
77.0
HM200
(down)
(up)
ft/s
ft/s
7
8
12927
12106
11450
12992
11253
11942
13451
12795
13058
12402
10400
11680 11811
12402
12238
12533
12828 12959
13517
12861
12106
13287
13353 13353
13419
11713
12367
Limbs off
Merch
length
ft
9
70.0
62.0
70.0
70.0
80.0
70.0
62.0
62.0
45.0
40.0
62.0
97.0
88.0
88.0
89.0
70.0
35.0
62.0
70.0
53.0
70.0
35.0
88.0
70.0
HM200
(down)
(up)
ft/s
ft/s
10
11
12795
12041
11975
12992
11483
11975
13484
12631
13419
12303
12861
11647 11680
12664
12303
13615
13583 13747
14534
13222
12336
14042
13287 13287
14370
11581
12795
206
Table D.1. Continued…
1
2
2
2
2
2
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
3
13.7
14.7
22.5
22.9
19.2
9.9
18.0
11.0
9.0
10.0
14.6
7.9
9.4
14.0
10.0
9.1
8.8
10.1
23.1
18.6
18.4
11.4
11.7
19.1
15.7
24.5
15.7
19.8
19.0
23.4
17.8
15.0
9.4
16.0
18.6
17.8
22.2
9.4
24.0
16.8
23.0
12.8
22.5
13.6
18.2
16.2
4
14338
13077
12471
12536
14354
13447
13710
13281
13952
13903
14141
13148
14144
13832
13365
14502
14325
14273
13007
13873
13675
13625
13720
12972
12442
13976
13685
13470
13561
14044
12982
14312
14201
12927
13900
13828
14146
14118
12628
14265
12839
14224
12632
13775
14248
14859
5
13930
12773
12381
12681
13693
14608
12437
13260
14519
14545
13670
14097
14715
12392
13383
13735
13901
13862
11888
14238
14121
13637
13944
13123
12750
13008
13780
13063
13855
12965
12864
13018
13987
13227
12952
14332
13381
13926
13316
14096
14217
13470
12782
13454
12741
14077
6
100.0
91.0
89.0
90.0
73.0
74.0
82.0
84.0
74.0
86.0
77.0
77.0
68.0
86.0
74.0
81.0
73.0
77.0
86.0
92.0
96.0
75.0
86.0
100.0
89.0
96.0
82.0
47.0
82.0
109.0
104.0
96.0
71.0
81.0
91.0
105.0
96.0
74.0
98.0
86.0
82.0
76.0
85.0
70.0
92.0
87.0
7
12139
11614
12434
11549
12500
11975
12139
12041
12828
11811
12402
12270
12730
11844
11877
12303
12041
12172
12008
11319
11778
12303
14173
10991
11122
11516
11385
11516
11614
11713
11286
12041
12795
11549
12697
12762
11745
12500
11417
12566
11745
12828
11909
11909
12270
12533
8
12139
11614
11647
12008
12238
12041
12172
12336
11352
11877
11516
11811
9
80.0
88.0
70.0
80.0
70.0
53.0
74.0
62.0
62.0
70.0
70.0
54.0
53.0
70.0
53.0
45.0
35.0
53.0
80.0
88.0
88.0
62.0
62.0
88.0
70.0
88.0
70.0
45.0
80.0
97.0
82.0
70.0
62.0
45.0
88.0
70.0
94.0
35.0
97.0
80.0
80.0
53.0
80.0
70.0
80.0
70.0
10
12795
11549
12664
11319
12467
12270
12172
12467
12927
12795
12467
13025
12927
12303
12139
13353
13484
12795
12041
12106
12041
12336
13451
11483
11222
11581
12139
11188
11581
11909
11417
12795
13222
12008
12664
12959
11909
12894
11647
12958
11713
12992
11844
12139
12566
12959
11
12795
11647
11647
12467
13353
12894
12762
12336
11483
11745
11253
12008
207
Table D.1. Continued…
1
5
1
2
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
3
11.9
24.8
17.5
21.9
18.9
20.3
18.3
8.8
18.3
14.7
11.0
8.3
7.7
17.0
8.8
13.9
17.1
17.2
11.4
10.4
17.7
15.0
8.4
17.7
16.2
14.7
17.4
18.0
16.8
16.5
15.3
15.3
20.7
13.8
15.7
13.0
19.4
23.9
13.0
13.2
12.9
18.7
8.8
13.6
13.3
4
13819
12651
14144
13619
12848
16231
5
14897
13800
13813
13286
12929
14435
15104
15900
16477
17516
16521
16642
15255
15914
16088
15903
16276
16464
16602
17308
15339
15390
6
73.0
86.0
83.0
85.0
74.0
82.0
89.0
65.0
104.0
92.0
60.0
78.0
64.0
104.0
77.0
90.0
76.0
89.0
83.0
72.0
88.0
91.0
80.0
86.0
107.0
96.0
98.0
78.0
75.0
93.0
79.0
97.0
74.0
92.0
92.0
89.0
94.0
87.0
77.0
75.0
94.0
85.0
64.0
84.0
82.0
7
10007
11417
12533
12172
11811
11975
10072
13287
11385
11713
12598
10958
12238
11417
12533
12959
12533
11647
13648
13681
12467
12369
15125
11778
11122
12566
12500
12697
12434
11745
12270
11352
11386
12172
12533
11909
11647
11516
12828
12424
12073
12172
10433
12336
12927
8
12631
11647
11942
12566
10958
11417
12533
12205
12598
12664
11745
9
70.0
80.0
70.0
80.0
70.0
70.0
70.0
35.0
88.0
80.0
53.0
35.0
18.0
88.0
35.0
70.0
70.0
70.0
62.0
54.0
80.0
70.0
35.0
80.0
88.0
70.0
88.0
70.0
70.0
88.0
70.0
70.0
70.0
70.0
80.0
70.0
70.0
80.0
70.0
70.0
70.0
80.0
35.0
70.0
70.0
10
12959
11680
12795
12241
11647
12041
13123
13714
11909
12238
12500
12172
13123
11877
13812
13123
12139
12467
13878
13714
12795
12631
15190
12041
11745
12992
12861
12631
12467
12041
12303
11975
11319
12631
12598
12303
12139
11581
12795
12467
12795
12205
13222
12467
12959
11
13255
11647
12041
12730
12238
11877
13878
11289
13123
12631
12205
208
Table D.1. Continued…
1
2
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
3
15.0
14.1
23.5
22.1
9.3
14.8
11.7
8.8
14.7
13.3
15.1
14.2
15.2
13.0
20.1
15.7
12.6
10.9
10.9
16.4
14.3
19.2
10.2
17.8
16.3
18.4
10.9
9.2
13.6
22.2
13.8
14.7
21.7
16.6
14.5
6.8
14.1
24.8
24.2
10.0
18.3
15.0
17.5
25.9
12.2
24.0
4
5
15784
14844
16861
15642
16646
16165
16040
15722
6
101.0
85.0
100.0
105.0
77.0
88.0
84.0
83.0
78.0
80.0
102.0
89.0
74.0
78.0
61.0
84.0
90.0
87.0
90.0
83.0
83.0
76.0
79.0
85.0
89.0
94.0
87.0
83.0
75.0
109.0
65.0
56.0
74.0
84.0
105.0
67.0
90.0
81.0
64.0
80.0
90.0
89.0
86.0
88.0
72.0
69.0
7
11745
12106
12238
12500
10630
10499
10663
12664
10203
12598
11581
12697
12336
13156
11680
12467
12172
13386
12500
13255
13320
12467
13353
11877
11877
11581
13156
12992
12697
11385
12762
12828
11450
12467
11909
12041
11514
11089
13225
12533
11549
11909
10367
11745
12500
12270
8
12598
13451
13451
11516
12828
12927
12894
12500
9
88.0
80.0
97.0
88.0
35.0
62.0
62.0
35.0
70.0
70.0
80.0
80.0
70.0
70.0
53.0
70.0
70.0
70.0
53.0
70.0
62.0
70.0
45.0
80.0
70.0
70.0
53.0
35.0
70.0
97.0
62.0
54.0
71.0
80.0
70.0
18.0
88.0
80.0
53.0
35.0
88.0
54.0
80.0
80.0
62.0
62.0
10
12270
12370
11844
12041
13222
12894
13386
13386
11877
13123
12238
12828
12041
13419
11680
12467
12795
13911
13222
13287
13353
12139
13550
12041
12927
11811
13911
13648
12927
11647
12795
12664
11385
12402
12959
13386
11647
11089
12795
13222
11680
12467
10367
11745
12467
12270
11
12631
13287
13517
12139
12959
12795
12828
12697
209
Table D.1. Continued…
1
2
5
5
4
2,5
2
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3
22.1
15.0
20.0
13.4
17.0
23.0
19.3
13.8
14.7
11.6
17.7
11.5
17.2
16.6
14.2
20.6
8.1
22.1
14.8
15.8
17.3
22.6
8.6
7.8
10.1
13.7
18.9
23.5
26.2
14.6
14.3
18.0
5.6
6.6
8.5
19.5
17.4
16.5
26.3
4
14639
14571
5
14927
14756
6
77.0
75.0
81.0
91.0
78.0
92.0
97.0
53.0
108.0
91.0
98.0
58.0
85.0
89.0
108.0
69.0
73.0
87.0
78.0
112.0
59.0
62.0
68.0
55.0
82.0
90.0
75.0
109.0
107.0
78.0
43.0
70.0
49.0
62.0
63.0
85.0
96.0
86.0
80.0
7
12434
12828
11909
12336
12270
11647
12467
13615
12073
13091
12139
13320
12598
11778
11188
12927
12664
11909
13353
11731
12434
12336
12467
12500
13258
12566
10433
10400
11286
11450
12106
12008
12238
12566
11844
12336
10630
10860
10828
8
12631
13386
12172
10521
12139
12139
9
45.0
62.0
80.0
80.0
70.0
88.0
70.0
45.0
89.0
88.0
88.0
53.0
80.0
70.0
88.0
62.0
27.0
80.0
70.0
88.0
53.0
62.0
35.0
54.0
53.0
70.0
70.0
98.0
105.0
70.0
35.0
62.0
18.0
35.0
35.0
80.0
88.0
80.0
71.0
10
12434
12828
11909
12369
12303
11811
12959
13648
13156
12730
12730
12500
12566
12041
12041
12795
13320
11942
13451
11877
12434
12041
12500
12500
13550
13091
11975
10761
11647
12139
12402
12239
12861
13648
12894
12369
10827
11024
11483
11
12566
13451
12205
10761
12566
12205
210
Table D.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
0.75 1.25 1.50 1.75
1.50 2.25 2.50 2.75
0.75 2.25 3.50 3.75
3.25
2.25
0.75 1.25 1.50 1.25
0.75 1.25 1.50 1.50
1.25
0.50 0.75 1.75 2.25
0.25 0.25 0.75 1.25
1.25 1.25 1.50 1.50
0.25 0.50 0.50 1.25
1.25 1.50 2.25 2.75
2.25
0.25 0.50 0.75 0.50
2.00 2.25 2.75 2.75
2.50
2.50
0.75 1.25 1.25 1.50
1.25
0.75 1.25 1.50 1.75
1.75
0.25 0.50 0.75 1.00
0.75
0.00 0.25 0.75 1.25
1.25
0.00 0.25 0.25 0.25
0.25 0.25 0.25 0.50
0.50
0.50 0.75 1.25 1.75
1.50
0.25 0.50 0.75 0.75
0.50
0.50 1.25 1.75 2.00
0.00 0.25 0.50 0.25
0.25 0.75 1.00 1.25
1.25
0.25 0.75 1.25 1.00
0.75 1.25 1.50 1.25
0.75
1.75 2.25 2.50 2.75
2.50
0.00 0.25 0.50 0.75
0.50
1.25 1.50 2.50 2.75
2.75
0.75 1.25 1.75 2.00
0.25 0.50 0.50 0.50
0.50 1.25 1.50 1.50
1.25
0.00 0.25 0.50 0.25
0.25
0.25 0.25 0.50 0.25
0.25 0.50 0.75 0.50
0.50
0.25 0.50 0.75 0.50
0.00 0.25 0.50 0.25
0.00 0.25 0.50 0.25
0.25 0.75 1.50 1.50
0.75
0.25 0.25 0.50 0.25
0.25 0.75 1.00 0.75
Green
Density
(kg/m3)
9
827.30
790.70
849.50
893.40
797.00
876.30
875.00
768.60
211
Table D.2. Continued…
1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
2
0.25
0.00
0.50
0.25
0.25
0.50
0.25
0.75
0.75
1.75
0.25
0.75
1.25
2.25
0.75
0.25
0.25
0.75
0.75
0.00
0.75
0.25
0.75
0.75
2.25
1.25
0.00
0.25
0.75
0.50
0.25
1.25
1.25
0.75
0.50
1.00
0.75
0.25
0.50
0.25
0.50
0.25
0.00
0.75
0.25
0.25
3
0.25
0.50
1.00
1.50
0.75
1.00
0.75
1.25
1.50
3.00
0.75
2.00
2.50
2.50
1.75
0.75
0.50
1.25
1.25
0.75
1.25
0.75
1.50
1.25
2.75
2.25
0.75
0.75
1.50
1.25
0.50
2.25
2.50
1.50
1.25
1.75
1.25
0.25
1.25
0.50
1.00
0.75
0.25
1.75
0.50
0.75
4
0.50
0.75
1.25
2.25
1.50
1.25
0.75
1.50
1.50
3.25
1.25
2.00
2.50
2.75
2.50
1.25
0.50
1.25
1.50
1.25
1.75
0.50
2.25
1.50
3.00
2.25
1.50
1.00
2.25
1.75
0.50
2.75
2.25
2.25
2.25
2.25
1.50
0.25
1.75
1.25
1.00
0.75
0.25
2.25
1.00
1.25
5
0.25
0.75
1.50
1.50
1.75
1.00
0.50
1.50
1.25
2.75
1.25
6
7
8
9
757.00
807.90
1.25
1.25
1.25
801.70
0.25
1.25
1.00
2.50
1.00
908.60
775.00
785.90
2.50
2.75
2.25
1.25
0.25
1.00
1.75
1.00
1.75
0.25
2.75
2.25
3.00
1.75
2.50
1.25
2.25
1.75
0.25
2.50
2.25
2.50
2.50
2.50
1.25
0.25
2.25
1.50
2.25
2.25
1.25
1.00
0.25
1.25
0.50
1.25
1.25
0.50
0.25
2.50
1.50
2.50
2.25
2.00
1.50
2.25
1.50
1.50
777.40
780.80
759.00
2.50
1.25
1.50
1.00
1.25
756.60
758.90
1.00
2.50
0.75
1.25
820.80
2.00
1.25
1.25
873.30
795.40
212
Table D.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
1.00
0.25
0.00
0.25
2.00
0.50
0.25
2.00
0.00
1.25
0.25
1.25
0.75
1.50
0.50
0.75
0.75
0.75
0.50
0.25
1.25
0.50
0.25
0.25
0.50
0.25
0.25
0.00
0.25
0.00
0.25
0.75
0.25
0.25
0.50
0.25
0.25
0.25
0.25
0.50
0.50
0.25
0.25
1.00
0.50
3
1.25
1.00
0.50
0.50
2.50
1.00
1.00
2.25
0.75
1.75
1.25
1.75
1.25
1.50
1.00
1.25
1.50
1.25
1.00
0.75
2.25
1.50
1.25
0.75
1.25
0.75
0.50
0.75
0.50
0.75
1.25
1.25
0.75
0.25
1.00
1.00
0.50
0.75
0.75
1.25
0.75
0.75
0.75
1.25
1.75
4
1.50
1.75
0.25
0.75
2.75
1.25
1.25
2.75
1.00
2.25
1.75
2.50
1.50
1.75
1.25
1.50
2.25
1.50
1.75
1.00
2.75
2.50
1.25
1.25
1.50
1.50
0.50
1.25
0.75
1.25
1.50
1.50
1.50
0.50
1.25
1.25
0.25
1.50
1.25
1.50
1.25
1.25
1.00
1.75
2.00
5
1.75
1.25
0.25
0.25
3.00
1.50
0.75
3.00
1.25
2.50
1.75
2.75
2.25
2.00
1.50
1.50
1.75
1.25
1.50
1.25
2.50
2.50
1.25
1.00
1.25
1.25
6
7
8
9
1.50
0.25
1.25
1.25
2.25
1.50
788.70
0.75
798.40
844.00
2.25
1.25
1.00
1.25
0.75
1.50
2.00
715.20
1.00
1.25
1.25
1.00
1.00
1.25
1.75
1.50
0.25
1.25
1.25
0.25
1.50
1.25
1.50
1.25
1.25
1.25
1.00
0.75
0.75
2.00
1.25
1.25
1.25
0.75
1.25
0.75
0.25
1.25
836.50
213
Table D.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
0.00
0.25
0.25
1.25
0.50
0.50
0.25
1.25
0.25
1.75
0.25
0.25
0.50
0.75
0.75
1.00
0.75
0.50
0.25
0.25
0.25
1.25
0.75
0.75
0.25
0.00
0.75
1.00
0.75
0.75
0.75
0.25
0.75
0.00
0.25
0.75
0.50
0.50
0.00
0.25
0.50
0.50
0.75
0.50
3
0.25
0.50
0.75
1.75
1.25
1.00
0.25
1.50
0.75
1.50
0.50
0.50
1.25
1.25
1.50
1.50
1.25
1.25
0.75
0.25
0.75
2.25
1.50
1.25
0.75
0.75
1.50
1.75
1.50
1.50
1.75
0.75
1.25
0.75
0.75
1.50
1.25
0.75
0.25
0.75
1.25
1.25
1.50
1.25
4
0.75
0.75
0.75
1.75
1.50
1.25
0.50
2.25
1.50
2.25
0.50
0.50
1.50
1.50
1.75
1.75
1.75
1.50
1.25
0.25
1.25
2.75
2.25
1.25
1.50
1.25
1.75
2.50
1.75
2.50
2.50
1.00
2.25
1.25
1.25
2.25
2.25
1.00
0.75
1.25
1.50
1.25
2.25
1.50
5
1.00
0.50
0.50
1.50
1.00
1.50
0.25
1.75
2.00
1.75
0.25
0.25
1.25
2.25
1.75
2.25
2.25
1.00
6
0.75
0.25
0.25
1.25
1.00
7
8
9
791.90
700.20
1.50
1.50
1.50
0.25
763.10
815.10
2.25
2.00
786.30
813.00
2.25
0.75
1.25
2.50
1.75
0.75
1.75
1.50
1.75
2.50
1.50
2.75
2.25
1.25
2.50
1.50
1.25
2.75
2.25
0.75
1.00
1.00
2.25
1.50
0.75
2.00
1.25
2.25
2.00
1.50
1.50
0.25
1.50
1.25
1.25
2.25
801.60
2.25
1.00
2.50
2.00
0.25
847.70
214
Table D.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
0.75
0.75
0.25
0.75
1.00
0.25
0.75
1.25
0.25
0.25
0.25
0.25
0.50
1.25
2.75
0.25
0.75
1.00
0.25
0.00
0.25
1.00
0.25
0.50
1.75
3
1.25
1.75
0.25
1.50
1.75
0.75
1.25
1.75
0.25
0.75
0.75
0.75
1.25
1.75
3.50
0.75
1.25
2.50
0.25
0.25
0.25
1.75
1.50
1.25
2.75
4
1.50
2.25
0.25
2.25
2.25
1.25
1.50
2.50
0.50
1.25
1.00
1.25
1.50
2.25
3.25
1.00
1.75
2.75
0.25
0.50
0.50
2.25
2.25
2.25
2.75
5
1.25
2.25
0.25
2.25
2.00
1.50
6
1.25
7
0.75
840.50
1.00
0.50
815.40
0.25
1.00
1.50
2.50
897.30
2.50
761.30
788.90
2.75
1.25
0.25
2.75
2.25
2.00
2.50
9
2.00
2.25
0.25
0.50
1.25
1.25
2.75
2.75
1.25
8
2.25
1.50
1.75
1.25
215
Table D.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Log length
1
2
3
ft
ft
ft
2
3
4
35 35
35 27
35 35
35 35
35 27 18
35 35
35 27
35 27
27 18
35 35
35 27
35 35 27
35 35 18
35 35 18
35 27 27
35 35
35
35 27
35 35
35 18
35 35
35
35 35 18
35 35
35 27 18
35 35 18
35 35
35 27 18
35 35
35 18
35 35
35 27
35 27
35 35
35 35
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
12894 12566
12073 11811
11909 11909
13222 12500
11745 11581 10531
12238 11581
13878 13091
12992 12205
13583 13287
12828 11844
13156 12566
12664 12008 10564
13484 12894 11024
12730 12402 10958
14534 13976 12205
14206 13386
14534
13320 13091
12894 11909
14206 13287
13550 13058
14370
12828 12073 10269
13320 12566
13222 12959 11877
12664 11581 10269
13058 12402
11844 11581 10367
12730 12073
12402 12041
12894 11745
12730 12073
13222 12828
13058 12238
13058 12172
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
11385
11319
10564
10564
13484
13386
13058
13058
12500
11056
12467
11024
11713
10269
11877
10269
11122
11024
10860
10367
216
Table D.3. Continued…
1
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
2
27
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
3
27
18
35
18
18
18
27
35
35
27
27
35
35
35
35
18
27
35
18
35
27
18
35
35
35
4
18
18
18
18
18
18
27
27
18
18
35
27
27
18
27
35
27
35
35
27
35
27
35
35
35
27
18
18
35
27
18
18
18
18
18
18
5
13222
13222
12730
12336
13451
12894
12664
12073
12730
12566
12664
13320
12336
11680
12172
12500
11450
12336
12566
11909
13058
13386
11942
13222
12992
12238
12894
12172
13058
12073
13222
12500
12566
13222
13156
13058
11909
13222
12500
11909
12664
13156
13714
12664
12730
6
12697
12303
12008
11713
13222
12467
12205
12172
11844
11942
13222
11253
11024
11745
11581
11122
11713
12172
11549
11844
12828
11942
12730
12402
12073
7
8
9
11909
12008
13222
13222
12467
12467
12303
11942
11024
11319
11024
11220
11122
11122
11745
11581
12500
12402
11352
11581
11253
11581
10
11
10696
10531
11056
11319
11450
10696
10794
10039
10531
10531
11319
11056
11385
11024
12172
12566
12073
12697
11942
12008
12697
12730
12402
11811
12402
12073
11253
11581
12894
10827
11549
11024
12073
12336
10433
10794
10696
11385
10958
11122
217
Table D.3. Continued…
1
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
2
35
35
18
35
35
35
35
35
35
27
35
35
35
35
17
35
18
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
3
18
4
35
18
35
35
35
27
27
27
35
27
35
35
35
35
35
35
35
35
35
35
27
35
35
27
35
35
35
27
35
35
35
27
35
35
27
27
35
35
18
18
35
35
18
18
18
18
18
18
18
18
5
12664
12172
13123
12894
13812
13484
12566
12894
14370
13845
13320
12894
15190
12730
12369
13550
12959
12992
12730
12894
12172
12402
11745
13222
13320
12730
12500
11909
13058
12664
13222
12730
13222
12664
13550
13156
13156
12008
12402
13222
13156
13550
13386
12073
13320
6
12205
7
8
12500
9
12205
10
11
11745
10794
11417
11385
10794
10794
12238
12238
12730
12730
12631
12172
12073
11745
12828
11745
12073
13714
13714
12828
12238
11811
12402
12730
13484
12172
12073
12008
12008
11417
10761
12073
12336
11844
11745
11581
12402
12238
12336
12205
12238
12500
12336
12336
11581
12402
12566
13222
11417
12992
11713
10794
10925
12172
10696
11549
10860
11188
10541
10958
10696
10696
218
Table D.3. Continued…
1
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
2
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
18
35
35
35
35
35
27
27
35
35
18
35
35
35
35
35
27
35
35
35
35
27
35
35
35
35
35
35
27
35
3
27
27
35
35
18
35
35
35
18
35
27
35
18
27
35
35
18
35
35
27
27
18
27
35
4
18
18
18
27
27
18
35
27
18
18
18
35
27
27
27
27
27
18
27
27
27
35
35
35
18
27
18
18
18
18
18
18
27
5
12894
13058
12238
13878
12073
12730
13058
14042
13222
13550
13386
12500
13583
12402
13058
12631
13976
13648
12894
12730
12894
12697
11946
12894
13222
13386
12402
11680
12894
13222
12730
12336
12402
12336
12730
12500
12566
12992
12073
12828
12566
12730
13058
13714
13648
6
12467
12697
12008
12828
10860
12238
12566
13878
13287
12894
13222
11253
13386
12073
12828
12336
13714
12992
11680
12566
12697
11713
12467
12238
7
11024
12205
8
9
12566
12238
12992
13320
12894
13222
12238
12336
13222
12992
12697
12828
12566
12697
12336
12336
11122
10564
10958
11549
11516
11188
12467
10531
9777
11680
12467
12073
12205
12336
12205
12533
12697
12073
12467
12238
11745
12730
13386
13091
10531
11122
10958
11305
11450
10630
11942
10
11
219
Table D.3. Continued…
1
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
35
35
35
35
35
35
35
27
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
18
35
35
35
35
18
27
3
35
35
18
27
35
35
27
4
18
18
27
35
35
18
27
18
27
18
35
35
35
35
35
18
18
18
27
35
27
27
35
35
18
18
18
27
18
5
13058
12992
13484
12730
12402
12664
12894
13320
12402
13550
12566
12336
12238
12500
12566
13714
13320
12402
11188
11844
12073
12402
12402
12861
13648
12894
12828
11745
11778
11319
6
12566
12664
11024
12828
11745
12073
12697
7
11286
11450
8
9
10
11
12041
12631
12533
12205
12041
11942
13222
12008
12303
12336
11122
13550
13222
12336
12336
10302
10335
9810
10072
11811
11713
12336
13386
12730
11680
11089
11417
11417
10860
10860
10072
10925
11713
12566
10761
11745
11450
11286
10203
10433
220
Table D.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
D
D
D
D
D
D
D
D
D
D
D
D
Test Truck
#
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
Number of
Logs
61
52
57
56
52
52
18
22
21
19
21
20
Volume
Gross BF
3670
4140
3850
3990
3770
4080
4070
4930
4290
4380
5040
2910
Average
Small End Diam
in
7.02
8.40
7.96
8.04
8.19
8.19
13.17
13.50
13.19
13.42
14.62
10.80
Length
ft
29.92
29.12
26.65
28.32
27.60
30.33
33.06
32.18
31.62
32.26
28.43
30.65
221
Table D.5. Green veneer produced from processed logs delivered exclusively from
site D: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
1552
1811
1018
199
90
720
732
629
63
1299
6814
8113
Green Veneer Volume
Grade
3/8's
G1
16553.01
G2
19315.4
G3
10857.58
AB
2122.454
C+
959.904
C
7679.232
D
7807.219
X
6708.662
XX
671.9328
In-Process
13854.61
NET
72675.4
TOTAL
86530.01
222
Appendix E. Data from Stand E (“Burchard Creek”, located near Elkton,
OR).
Details: Loader – rubber-tire truck-mounted knuckle boom; cut and measured in
August 09 – August 16, 2006; ST300 tool borrowed from Forest Products
Laboratory in Wisconsin and worked through tree 47; second ST300 tool borrowed
from Roseburg Forest Products for the rest of the trees.
Table E.1. Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides of each
tree measured, tree length (with limbs on) and merchantable length (with no limbs)
and the respective HM200 acoustic velocity measurements on the ground (down) or
in the grapples of the loader (up), R - remarks (1 = dead, 2 = double leader, 3 =
broken top, 4 = missing data, 5 = butt rot). Disks from the bottom and top of each
produced log as well as “in-grapple” measurements were taken from the 40 trees
highlighted.
ST300
R
Tree
DBH
#1
1
#
2
(in)
3
17.6
21.2
11.4
10.0
12.9
11.7
9.3
7.3
12.4
12.2
8.2
11.9
7.7
7.8
13.8
10.1
22.0
9.3
12.4
8.2
10.9
9.3
14.6
14.0
ft/s
4
13142
12589
13536
13369
13858
14719
13930
15043
14416
15596
13712
13337
15478
15168
14619
15812
13568
13352
14752
14753
14557
14568
13655
13967
2
4
4
4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Limbs on
#2
Tree
length
ft/s
5
13544
12953
12839
12930
13457
15343
14546
14900
14647
14043
14209
14053
14936
14022
13931
15286
12262
14252
14451
14255
14543
14929
14596
14145
ft
6
98.0
101.0
75.0
79.0
99.0
87.0
71.0
81.0
89.0
91.0
70.0
72.0
75.0
77.0
81.0
67.0
81.0
70.0
87.0
67.0
74.0
59.0
59.0
80.0
Limbs off
HM200
(down)
(up)
ft/s
ft/s
7
8
12008
11909
11877
12270
11286
12336
12927
14370
12041
12631
13484
13484
13353
12402
12861
13353
12369
13451
13353
13091
12992
12795
13336
13451
12992
Merch
length
ft
9
88.0
88.0
53.0
45.0
88.0
70.0
18.0
35.0
70.0
70.0
35.0
62.0
35.0
35.0
70.0
62.0
70.0
53.0
53.0
53.0
62.0
53.0
53.0
58.0
HM200
(down)
(up)
ft/s
ft/s
10
11
12106
12106
12303
12566
11680
12664
12861
14698
12828
13451
13058
13386
14040
13550 13648
12795
12861
11319
13550
13091 13156
13353 13320
13189
13091
12927 13058
14042
223
Table E.1. Continued…
1
4
4
4
2
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
3
11.7
9.3
20.4
10.3
8.4
14.7
16.7
14.6
14.1
19.9
9.4
18.6
12.9
13.9
14.5
6.9
8.1
10.3
15.0
11.0
7.0
6.9
9.3
12.4
8.4
8.4
9.9
9.4
15.0
8.8
12.3
8.6
10.5
14.7
10.1
10.3
8.5
11.4
7.2
15.9
8.6
11.1
8.2
12.3
12.6
9.8
4
14680
13189
13906
14459
14613
14234
14764
14436
13979
13048
14579
15436
14280
13976
11676
14334
14529
14412
14448
14687
14790
15112
14674
14629
14285
17082
16766
15990
15207
16057
16809
20538
16210
16228
16305
17103
17506
17225
17770
15403
14948
15589
15190
16738
15938
15880
5
14716
14804
14104
14875
15014
14501
11690
14314
12943
13075
14822
12836
14448
13708
14217
14988
13104
14068
13687
14809
14536
12973
15200
16256
17218
16409
15402
16685
16198
15120
16649
15999
16800
16980
17256
17298
16478
15481
16084
15188
15580
15772
18186
16008
15795
14324
6
89.0
65.0
91.0
72.0
68.0
95.0
94.0
100.0
78.0
97.0
85.0
90.0
78.0
75.0
95.0
80.0
66.0
80.0
80.0
79.0
73.0
67.0
75.0
91.0
72.0
81.0
78.0
89.0
107.0
76.0
99.0
79.0
87.0
73.0
95.0
78.0
82.0
78.0
71.0
92.0
84.0
90.0
81.0
92.0
92.0
72.0
7
11975
12894
11581
13615
12894
11483
12402
11909
11483
13419
12172
12467
11286
12402
12762
12533
12592
12634
12598
13911
12369
12697
14108
12631
14272
13189
12894
12992
13156
13320
12664
12697
12336
11942
12959
12959
13255
12631
12861
12073
11877
12008
11877
12697
8
12041
12894
11352
12500
12402
12533
12631
13189
12992
13123
12697
12434
9
70.0
53.0
80.0
62.0
35.0
80.0
88.0
88.0
70.0
88.0
53.0
80.0
70.0
70.0
80.0
35.0
35.0
53.0
70.0
62.0
35.0
35.0
53.0
70.0
53.0
53.0
62.0
62.0
70.0
53.0
70.0
53.0
62.0
70.0
70.0
53.0
35.0
70.0
18.0
80.0
35.0
62.0
35.0
70.0
62.0
53.0
10
12664
12927
12041
13747
13320
12959
11516
12762
11909
11516
13911
12041
12631
13419
12238
13648
13222
13189
12467
13189
13222
12992
13419
13123
12937
13058
13681
13058
12631
13419
12467
13123
12959
12303
12631
13737
13648
12467
13451
12238
13714
12894
13386
12795
12992
12762
11
12664
12927
13386
13156
12598
13419
12894
13780
12959
13156
13550
224
Table E.1. Continued…
1
1
2
2
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
3
8.6
11.3
8.9
11.3
7.4
8.6
8.3
18.1
9.7
13.5
17.1
13.4
19.5
14.7
9.6
14.4
10.4
6.8
11.3
13.5
13.8
18.0
16.6
15.1
19.0
13.4
8.7
13.3
9.9
12.8
16.9
7.0
11.9
11.6
11.2
11.5
12.8
12.8
9.7
11.3
21.5
6.1
10.1
9.0
19.7
4
17303
16417
15424
16403
15982
15924
15410
14360
16150
5
15824
17120
15656
15606
16221
17339
15579
13501
16401
17495
16602
17175
14098
15334
14648
15989
16900
16410
15782
6
81.0
86.0
65.0
83.0
80.0
77.0
93.0
104.0
68.0
89.0
101.0
97.0
95.0
99.0
82.0
80.0
80.0
71.0
76.0
74.0
91.0
76.0
76.0
77.0
94.0
85.0
74.0
83.0
79.0
76.0
92.0
62.0
78.0
80.0
81.0
87.0
76.0
87.0
72.0
82.0
95.0
50.0
67.0
73.0
102.0
7
12631
12008
13156
11745
12828
12762
12795
11581
15420
12730
12041
12631
11647
11483
12828
12238
12664
13615
12664
13255
12303
12303
12139
12303
11647
11647
13615
12523
12959
13353
12272
12139
12172
12467
11778
12008
12402
12041
13419
12238
11417
12172
13123
13156
11352
8
13189
13123
12762
13747
11778
13189
13550
9
35.0
53.0
54.0
62.0
35.0
53.0
62.0
88.0
35.0
62.0
88.0
88.0
70.0
70.0
62.0
67.0
62.0
27.0
62.0
62.0
62.0
70.0
70.0
70.0
88.0
70.0
35.0
70.0
53.0
70.0
88.0
27.0
62.0
70.0
62.0
62.0
70.0
62.0
35.0
62.0
88.0
18.0
53.0
62.0
88.0
10
13386
13222
13681
13845
14042
14436
13353
11877
14698
13222
12369
12894
12139
12631
12762
12828
13123
14206
13123
13451
12598
12795
12238
12467
11647
12139
13878
12795
13419
13451
12303
13091
12730
12795
12139
12467
12467
12894
14042
12664
11647
12861
13419
13484
11877
11
13681
13386
12894
14075
12303
13615
14020
225
Table E.1. Continued…
1
2
5
2
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
3
20.1
13.1
10.3
10.1
11.3
12.8
15.4
19.5
7.4
20.0
11.7
10.3
6.8
12.7
9.7
13.2
10.8
8.3
16.8
17.9
9.8
15.4
14.2
18.2
15.1
16.5
11.8
11.7
9.5
16.5
14.5
10.1
13.5
9.8
11.9
16.0
14.7
20.4
10.2
13.2
8.5
12.4
13.0
11.1
20.2
8.4
4
5
16479
17316
15530
16224
15024
16373
15165
16760
16011
15458
15584
14751
16616
17911
6
90.0
64.0
43.0
80.0
77.0
89.0
104.0
82.0
64.0
75.0
84.0
72.0
67.0
90.0
83.0
91.0
72.0
73.0
67.0
94.0
71.0
80.0
68.0
87.0
86.0
77.0
83.0
64.0
65.0
82.0
86.0
74.0
87.0
66.0
80.0
73.0
77.0
91.0
50.0
84.0
76.0
82.0
68.0
89.0
62.0
68.0
7
10531
12172
11417
11680
11713
13287
11647
11516
11909
11319
11516
13156
12336
11745
12992
10663
13091
12172
12369
11909
13386
11549
11647
12369
12041
10696
12402
11680
13091
12762
12402
12205
11519
12467
12631
12336
12598
11745
12894
11417
12631
11089
11319
12270
11483
12172
8
13387
11614
11942
13484
12336
12336
12434
9
84.0
62.0
35.0
53.0
53.0
70.0
88.0
70.0
35.0
70.0
62.0
62.0
27.0
70.0
35.0
70.0
35.0
35.0
53.0
70.0
53.0
70.0
53.0
70.0
70.0
70.0
70.0
62.0
35.0
70.0
62.0
53.0
70.0
35.0
53.0
70.0
70.0
88.0
45.0
70.0
35.0
53.0
62.0
62.0
53.0
45.0
10
11056
12303
13058
12828
12270
13287
12139
11647
12828
11647
12467
13189
13320
12631
13878
12008
13878
13058
12861
11975
14042
11975
11909
12795
12303
12139
13222
12041
13058
12795
12730
13287
11975
13320
12927
12139
12631
11581
13091
11909
13714
11877
11319
12927
11319
13123
11
13255
12467
12008
13156
12955
12139
13025
226
Table E.1. Continued…
1
2
4
2
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3
21.3
17.8
19.4
18.2
12.3
12.5
18.4
12.7
9.8
13.2
16.1
10.3
11.2
17.8
12.9
17.6
8.4
14.7
23.4
6.8
18.8
9.1
7.5
10.3
12.1
13.5
18.3
15.4
14.7
10.3
9.6
8.9
17.2
14.5
16.6
12.6
11.0
12.5
20.3
4
5
15526
15265
15813
14857
15399
15170
16604
17834
15498
16426
15848
14708
15742
14054
16549
16427
16202
15485
6
67.0
81.0
99.0
98.0
91.0
91.0
84.0
91.0
75.0
84.0
89.0
93.0
77.0
96.0
99.0
103.0
82.0
98.0
69.0
60.0
102.0
60.0
71.0
69.0
84.0
87.0
92.0
95.0
91.0
74.0
67.0
70.0
59.0
95.0
64.0
65.0
78.0
92.0
87.0
7
12106
11975
12172
11909
13025
11877
13058
12270
12369
12139
11811
11745
12303
12172
11877
11680
11450
11909
11975
12927
12467
13320
12762
12894
12270
12566
11286
12041
11188
11877
11714
12664
12041
12041
12205
12238
12828
11745
11319
8
11909
12664
13419
12369
11745
13681
12533
12957
12238
12664
9
62.0
70.0
88.0
88.0
62.0
62.0
62.0
62.0
35.0
80.0
62.0
53.0
70.0
88.0
88.0
97.0
53.0
88.0
62.0
27.0
88.0
45.0
35.0
62.0
70.0
70.0
88.0
62.0
70.0
53.0
35.0
53.0
54.0
70.0
62.0
62.0
62.0
70.0
70.0
10
12008
11975
12598
12303
12894
12467
13320
13353
14108
12172
12369
13123
12533
12424
12106
11811
12730
12402
11909
13583
12631
13484
13552
12730
12566
12959
11352
12434
11975
12664
12172
13255
12073
12992
12303
12533
13189
12303
11811
11
12270
12894
13353
14108
12336
12402
12631
12762
12467
12894
227
Table E.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
1.50 1.75 1.75 2.00 1.50
1.25 1.75 2.00 2.00 2.50
2.00
1.00 1.00 1.00 0.75
0.75 0.75 0.75 0.75
0.75 1.00 1.50 1.75 1.50
0.75 0.75 1.00 0.75 1.00
1.00 0.75 0.75 0.50
0.50 0.50 0.50 0.50
0.75 1.00 1.25 1.50 1.50
0.50 0.50 0.50 0.50 0.50
1.25 0.75 1.25 1.50
0.75 0.75 0.75 1.00
0.50 0.50 0.50 0.75
0.75 1.00 0.75 0.75
1.00 1.00 1.00 1.00 1.00
0.75 0.75 0.75 0.75
1.00 1.75 2.00 2.00
0.75 0.75 0.75 0.75
0.75 1.00 1.50 1.25 1.00
0.50 0.75 0.75 0.75
0.75 0.75 0.75 1.00
0.50 0.75 0.75
1.25 0.50 2.00
1.25 1.25 1.25 1.25
0.75 1.00 1.50 1.50 1.25
0.50 0.75 0.75 1.00
1.25 1.50 2.00 2.25 2.00
0.75 1.00 1.00 0.75
1.00 1.00 1.00 1.00
1.50 1.50 1.50 1.50 1.25
1.00 1.50 1.50 1.75 1.50
1.50 2.50 2.75 1.75 1.75
1.00 1.25 1.75 1.50
1.50 1.50 1.50 1.50 1.75
1.00 0.25 1.00 1.00 0.75
1.25 1.50 1.50 1.75 1.50
1.00 1.00 1.00 1.00
0.75 1.00 1.25 1.50
1.00 1.00 1.00 1.50 1.25
0.50 0.50 0.50 0.50
Green
Density
(kg/m3)
9
752.70
794.80
667.60
781.00
832.60
783.10
830.80
228
Table E.2. Continued…
1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
2
0.50
0.50
1.25
0.75
0.50
0.50
0.75
0.50
1.00
0.50
0.75
0.75
1.25
0.75
0.50
0.50
0.75
0.75
0.75
0.50
0.50
0.50
0.50
0.75
0.50
0.25
0.50
0.25
0.50
0.25
0.25
0.50
1.00
0.50
0.25
0.00
1.00
1.25
1.25
1.00
1.00
1.00
0.50
1.00
0.50
1.00
3
0.50
1.25
1.25
0.75
0.75
0.50
0.75
0.50
1.00
0.75
0.75
0.75
1.25
0.75
0.50
0.75
0.75
1.00
0.75
0.75
0.50
0.75
0.50
1.00
0.75
0.75
0.50
0.75
1.25
1.00
0.75
1.25
1.00
1.00
0.50
0.50
0.75
1.50
0.75
1.75
1.50
1.00
1.25
1.75
0.75
2.25
4
0.50
0.50
1.50
0.75
0.50
0.50
0.75
0.50
1.00
0.50
0.75
1.00
1.00
1.25
0.75
0.75
0.75
1.00
0.75
0.50
0.50
0.75
0.50
1.50
0.75
1.50
0.50
1.25
1.50
1.25
1.25
1.50
1.00
1.25
0.75
0.75
0.75
2.25
0.75
2.25
1.25
1.00
1.75
2.00
1.50
2.50
5
0.50
0.75
1.25
0.75
0.25
0.50
1.00
0.75
0.75
0.75
1.00
1.00
1.25
1.25
0.75
0.50
1.00
1.25
0.50
0.50
0.50
0.75
0.50
1.25
0.75
1.25
0.50
1.25
1.25
0.75
0.75
1.00
0.50
1.25
0.75
1.00
0.75
2.00
0.75
1.50
1.50
1.00
1.75
1.50
1.25
1.25
6
7
8
9
785.80
742.70
724.40
0.50
718.30
0.75
0.50
1.00
807.90
884.60
1.00
785.90
0.50
728.40
0.75
0.50
0.50
1.25
741.30
1.00
0.50
1.00
1.25
0.75
673.40
1.00
0.75
2.00
1.25
1.25
1.25
1.75
1.25
0.75
678.10
1.00
0.75
899.60
229
Table E.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
0.25
0.50
0.25
1.00
0.50
1.25
0.75
0.50
1.75
1.25
0.25
0.50
1.00
0.75
0.50
0.25
0.75
0.00
1.25
0.25
0.50
0.25
0.75
0.50
1.50
0.25
0.50
0.00
1.25
0.75
0.75
0.75
0.00
0.25
0.75
0.75
0.75
0.25
1.75
0.75
0.25
0.25
0.75
0.50
0.25
3
0.75
0.75
0.75
1.75
1.25
2.50
2.00
1.50
2.25
1.25
0.75
1.25
1.00
1.50
1.75
0.50
1.25
1.00
1.75
0.75
1.25
1.25
0.75
1.25
1.75
0.50
1.00
0.75
2.25
1.75
1.25
1.25
0.50
0.75
1.00
1.50
1.75
0.50
2.25
0.75
1.00
0.75
1.50
1.25
1.50
4
1.00
0.75
1.25
1.75
1.75
2.50
2.25
1.75
2.75
1.25
0.50
1.50
1.00
1.25
2.25
0.75
1.25
1.50
2.25
1.25
1.50
1.75
0.50
1.50
2.50
0.25
1.25
1.00
2.75
2.50
2.25
1.50
1.25
0.75
1.50
2.25
2.50
0.50
2.50
1.00
1.25
0.50
1.75
1.50
2.25
5
0.75
0.75
0.75
1.75
1.50
2.75
1.75
1.75
2.75
1.25
0.25
1.50
0.75
1.25
0.25
0.25
1.25
1.75
2.50
1.00
1.25
1.25
0.50
1.25
2.75
0.75
0.75
2.50
2.75
1.75
0.75
0.50
1.50
1.75
2.75
0.25
2.50
0.75
0.75
0.50
1.25
0.75
1.75
6
7
8
9
755.00
2.25
1.25
804.20
1.00
819.90
2.00
1.00
1.00
648.00
0.75
2.50
2.00
2.25
1.00
1.50
1.50
0.75
0.75
0.50
1.00
1.25
759.40
0.75
876.40
230
Table E.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
0.50
0.25
1.25
0.75
0.50
0.75
0.75
1.25
1.00
1.75
0.00
0.75
0.75
0.75
0.25
0.25
0.75
0.75
0.75
1.25
0.75
2.00
1.00
0.00
0.00
0.75
0.50
0.50
1.50
0.25
1.25
1.25
1.25
1.50
0.75
0.50
1.00
0.75
0.75
0.50
1.00
0.50
0.25
0.75
3
1.00
0.75
2.00
1.50
1.00
1.50
1.25
2.00
1.75
2.25
0.75
0.75
0.75
1.50
1.50
1.00
1.50
1.50
1.00
1.25
1.25
2.75
1.25
0.50
0.25
1.25
1.25
0.50
2.75
0.75
2.50
2.25
1.75
2.00
0.75
1.00
1.50
1.25
0.50
1.25
1.25
0.75
0.75
1.50
4
1.25
0.75
2.25
2.25
1.00
2.00
2.00
2.50
2.00
2.00
1.50
0.75
0.75
2.25
2.00
1.50
2.00
1.25
1.00
1.50
1.50
2.25
1.25
1.00
0.50
1.75
1.50
0.75
3.00
0.75
2.75
2.75
2.50
1.50
0.50
1.25
1.25
1.50
0.50
1.50
1.50
1.25
1.00
2.00
5
0.75
0.50
2.75
2.25
0.50
1.75
1.75
2.25
1.75
2.00
1.50
0.75
0.75
2.25
1.50
1.25
1.75
0.75
1.00
1.50
1.25
1.75
1.75
0.75
1.25
1.50
1.00
2.75
0.50
2.25
2.50
2.50
1.50
0.75
1.25
1.00
1.50
0.50
1.25
1.25
0.75
1.25
2.25
6
7
8
9
1.50
1.75
1.25
1.00
785.90
810.20
1.50
1.00
1.25
741.70
876.30
1.75
1.25
0.75
1.75
1.25
0.50
0.50
1.00
1.25
783.40
820.80
741.50
740.60
667.90
1.00
1.00
0.50
1.75
798.80
231
Table E.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
0.75
1.50
0.25
1.00
1.50
0.25
1.75
0.50
0.50
0.75
0.75
1.25
1.00
1.00
0.75
0.75
0.50
0.50
1.25
0.75
0.75
0.75
0.75
0.50
1.00
3
1.50
2.25
0.50
1.00
2.25
0.75
1.50
1.00
0.75
1.00
0.75
1.25
1.75
1.75
1.75
1.25
1.00
0.75
2.25
1.75
1.50
1.50
1.00
1.00
1.75
4
1.75
2.50
0.75
1.00
2.75
0.75
1.50
1.25
0.50
1.00
1.00
1.25
2.75
2.50
2.50
1.25
1.25
0.75
2.75
2.50
2.25
1.75
1.50
1.50
2.50
5
1.25
2.50
0.75
1.00
2.75
6
1.25
2.25
0.25
1.00
1.75
1.50
1.50
1.25
0.25
1.00
0.75
1.25
2.75
2.25
2.25
1.25
0.75
0.50
2.50
2.50
1.75
1.75
1.75
2.75
0.75
1.00
2.50
1.25
1.50
2.25
1.50
2.25
7
8
9
826.80
793.20
820.90
849.60
719.40
232
Table E.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Log length
1
2
3
ft
ft
ft
2
3
4
35 35 18
35 35 18
35 18
27 18
35 35 18
35 35
18
35
35 35
35 35
35
35 27
35
35
35 35
35 27
35 35
35 18
35 18
35 18
35 27
35 18
35 18
18 35
35 35
35 18
35 27 18
35 27
35
35 27 18
35 35 18
35 35 18
35 35
35 35 18
35 18
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
12402 12502 10958
12402 12402 10860
12336 12041
12336 12303
13156 11581
9711
13222 12238
12861
14698
13058 12500
14042 12894
13058
13648 13222
14040
13550
13058 12703
13320 12703
11745 11089
13550 13451
13058 13025
13550 13022
13386 12959
12992 12533
13058 12694
13025 13386
13484 12073
13222 12369
12500 12336 11188
13878 13583
13320
13550 12959 11942
12402 11844
9449
13386 12894 11549
12402 11417
12336 11680 10105
13878 13714
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
13025
13287
13025
13022
12795
12697
12073
12467
12073
12369
233
Table E.3. Continued…
1
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
2
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
35
3
27
35
35
27
4
18
18
18
35
27
18
35
18
18
27
27
35
18
35
18
27
35
35
18
35
27
18
27
35
27
18
18
27
27
18
27
35
27
18
5
12336
12894
13878
12730
13648
13222
13222
13156
13550
13222
12992
13976
13320
13222
13648
14108
13058
13058
13386
12992
13386
13156
12828
12992
13976
13648
13058
13451
12730
13714
13058
13386
13550
13222
12894
13386
13222
13714
14042
14042
14534
13648
12402
14698
13058
6
12205
12336
13058
12336
7
11188
8
9
13222
13058
12959
12008
12566
12959
12073
12959
12008
12861
12664
12697
12861
13222
11942
12172
12795
12008
12795
12566
11909
12172
13386
12861
12861
12795
12697
13222
12467
13222
11942
12795
12795
12795
12795
13583
13451
13222
13222
11024
12008
12467
11024
12566
12238
12336
12697
13222
13451
13320
14370
13222
12073
12828
10433
10
11
234
Table E.3. Continued…
1
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
2
35
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
27
35
35
35
35
35
35
18
35
35
35
27
35
35
35
35
35
35
35
35
35
3
35
35
35
35
27
27
27
27
27
27
35
35
35
35
35
35
18
35
35
4
18
18
18
18
27
35
35
27
35
27
27
35
18
18
35
18
27
18
18
35
35
35
35
18
18
18
18
5
12894
13550
12566
12992
13058
12664
13550
14075
13386
13714
12894
12730
12730
12992
12566
12500
13878
13058
13648
13714
12894
13091
12894
13156
12467
13058
13058
13222
14042
13386
12008
12861
13386
13714
12566
11811
12336
13058
13058
12402
13714
13058
12008
12828
11909
6
12664
12992
11581
12238
12205
12828
12828
12959
12959
12073
11909
11680
12073
11680
12008
12500
13615
13386
12402
7
10860
11549
8
9
10
11
12139
12041
11188
11549
12008
12008
13615
13615
13058
13058
10433
10958
12172
12073
11909
12336
11745
12467
12697
12008
13025
13387
12238
11713
12073
12369
12041
13058
12172
11352
11417
10630
10269
9941
10367
235
Table E.3. Continued…
1
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
2
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
3
27
27
4
35
35
18
35
18
35
18
35
35
35
35
27
35
27
18
35
27
35
35
35
18
35
18
27
27
18
18
27
35
35
35
27
27
27
27
18
18
18
5
12894
13484
13320
13484
13878
12730
13878
13058
13222
12238
14370
12566
12073
12992
12828
12238
13714
12402
13058
13156
12992
13648
12664
13320
12894
12828
13058
12566
13091
12402
13714
12073
11417
13058
11417
13222
12172
12402
12894
12828
13320
12664
13386
13714
14108
6
12073
12697
7
8
12073
9
12073
11713
11811
12369
11516
12369
11516
12697
12697
11844
12697
12008
12566
13222
13222
10
11
11188
11122
11745
11417
12106
11417
13451
11253
11286
12500
11909
11909
12566
11811
12566
12467
12533
11352
12369
11516
12402
11680
12861
11417
11286
11056
12697
10860
12631
11713
11745
12730
12402
12566
12073
13222
12828
10039
11549
11122
236
Table E.3. Continued…
1
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
35
35
35
35
35
35
35
35
35
35
27
35
27
35
35
35
35
35
35
35
35
35
35
27
35
35
35
35
35
35
3
27
27
18
35
35
35
35
18
27
27
4
18
35
18
18
27
35
35
35
27
35
18
18
27
35
27
18
27
35
35
18
18
27
18
18
5
12730
12894
13222
13386
12894
12992
12828
12992
13058
12172
13583
12664
13714
13552
13058
13058
13222
12402
12566
12402
12664
12172
13550
12205
13222
12500
12730
13550
12730
11909
6
12073
11713
12795
11745
12566
11909
12238
12205
12566
11581
7
11286
12730
12959
12336
12238
12730
11417
12205
11516
12697
12959
11811
12664
11942
12861
12828
11745
11581
8
9
10
11
12205
11713
11188
11877
11909
11188
11188
12106
12500
12500
12106
12106
12336
12172
12730
12336
12238
12730
11188
10696
10564
9777
237
Table E.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
E
E
E
E
E
E
E
Test Truck
#
R1
R2
R3
R4
R5
R6
R7
Number of
Logs
60
42
56
55
75
55
46
Volume
Gross BF
3630
4200
4150
4170
3830
4100
3630
Average
Small End Diam
in
7.02
8.71
7.80
7.65
6.36
7.87
7.85
Length
ft
30.10
30.40
29.41
29.47
29.97
28.67
30.02
238
Table E.5. Green veneer produced from processed logs delivered exclusively from
site E: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
1936
1554
878
17
513
1441
463
272
65
1087
7139
8226
Green Veneer Volume
Grade
3/8's
G1
20648.6
G2
16574.34
G3
9364.397
AB
181.3152
C+
5471.453
C
15369.13
D
4938.173
X
2901.043
XX
693.264
In-Process
11593.51
NET
76141.72
TOTAL
87735.23
239
Appendix F. Data from Stand F (“Letz Go”, located near Lorane, OR).
Details: Loader/Processor – Tracked 1238 B Thunderbird with Waratah Processor
Head; cut and measured in August 24 – August 29, 2006; ST300 tool borrowed
from Roseburg Forest Products.
Table F.1. Measurements from 200 trees sampled: diameter at breast height (DBH),
ST300 acoustic velocity measurements on two (#1 and #2) opposite sides of each
tree measured, tree length (with limbs on) and merchantable length (with no limbs)
and the respective HM200 acoustic velocity measurements on the ground (down) or
in the grapples of the loader (up), R - remarks (1 = dead, 2 = double leader, 3 =
broken top, 4 = missing data, 5 = butt rot). Disks from the bottom and top of each
produced log as well as “in-grapple” measurements were taken from the 40 trees
highlighted.
ST300
R
Tree
DBH
#1
1
#
2
(in)
3
24.5
20.5
10.6
11.6
11.0
19.9
20.6
11.0
14.2
18.2
15.5
11.6
8.6
12.6
11.6
16.5
27.9
13.7
18.3
19.0
23.9
18.5
15.4
16.8
9.8
ft/s
4
15827
13766
15265
16011
15571
ft/s
5
14445
15414
14011
14604
17112
15797
16520
2
4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
#2
Limbs on
Tree
length
ft
6
95.0
89.0
75.0
86.0
82.0
92.0
92.0
86.0
83.0
103.0
68.0
85.0
81.0
75.0
80.0
64.0
105.0
90.0
73.0
76.0
74.0
73.0
83.0
65.0
79.0
HM200
(down)
(up)
ft/s
ft/s
7
8
11909 11745
12041
13320
12008
12500
11647
11056
12762
12697
11483
12402
11778
12336
11811
12795
13320
11680
12762 12795
11483
11647
11056
12008
11975
13123
12336
Limbs off
Merch
length
ft
9
62.0
85.0
35.0
53.0
35.0
80.0
80.0
62.0
80.0
97.0
62.0
62.0
53.0
62.0
62.0
54.0
72.0
62.0
72.0
62.0
35.0
72.0
70.0
62.0
45.0
HM200
(down)
(up)
ft/s
ft/s
10
11
13189 13189
12008
13714
12664
12894
12073
11385
13287
12927
11877
12467
12631
13255
12172
13484
13156
12139
13615 13583
11647
12073
11909
12500
12467
12861
13320
240
Table F.1. Continued…
1
2
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
3
19.6
18.3
10.3
11.3
15.6
12.8
15.4
7.8
9.6
21.9
17.2
18.3
19.2
11.3
8.1
23.5
8.4
15.2
13.5
17.5
15.1
19.3
6.8
10.6
7.7
19.0
19.9
15.4
21.4
17.3
9.1
6.4
15.0
14.8
7.4
13.7
17.8
18.8
12.5
21.4
15.2
18.6
12.9
15.8
16.7
13.6
4
15198
5
14066
15167
14906
14712
16014
14538
14368
15570
15516
15397
14787
15778
16513
16100
16119
12390
14920
15083
15345
14781
14645
6
89.0
60.0
55.0
93.0
85.0
99.0
82.0
53.0
69.0
75.0
83.0
88.0
74.0
83.0
38.0
78.0
80.0
71.0
97.0
84.0
81.0
81.0
77.0
79.0
68.0
95.0
90.0
95.0
90.0
97.0
77.0
42.0
81.0
81.0
67.0
78.0
116.0
88.0
78.0
92.0
92.0
93.0
78.0
73.0
87.0
97.0
7
12106
12762
12730
11614
11647
11450
11253
11909
12238
11975
11516
11877
11680
11713
13255
11024
12008
11549
11680
13484
12434
12894
13353
13517
13189
11483
12106
11877
11778
13255
12697
13255
12369
12369
12238
13091
11680
11942
12992
11909
12566
11942
12270
12762
11942
11909
8
12073
12172
12139
13222
11909
14239
11745
11877
11122
11877
13156
9
81.0
54.0
53.0
70.0
80.0
80.0
80.0
18.0
45.0
71.0
70.0
80.0
72.0
54.0
18.0
72.0
45.0
62.0
81.0
72.0
72.0
72.0
18.0
45.0
18.0
89.0
88.0
81.0
80.0
80.0
27.0
18.0
80.0
72.0
18.0
73.0
89.0
80.0
62.0
88.0
80.0
89.0
72.0
62.0
80.0
80.0
10
12369
12303
12927
12664
11909
11975
11417
12041
12730
12139
11614
12434
11942
12598
12697
11516
12369
11909
12402
12959
12828
12566
13780
13845
12369
11581
12270
12598
11877
12566
13451
13714
12795
12992
12795
12927
12402
12598
13451
12172
12959
12631
12500
12959
12402
12253
11
12369
13419
12139
11811
12762
12861
12041
12434
11647
12484
13091
241
Table F.1. Continued…
1
2
1
2
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
3
12.6
12.4
8.8
15.1
17.9
20.6
17.1
23.9
8.8
15.3
28.0
16.8
20.8
18.4
20.0
10.1
15.6
10.2
11.4
13.7
14.7
9.8
25.3
9.4
15.5
12.9
13.1
20.7
19.8
17.1
6.7
7.2
13.2
19.1
15.8
15.9
17.6
12.6
8.3
11.1
20.7
16.3
16.9
21.2
10.3
4
15135
5
15153
15319
15537
15172
14136
14757
15644
13102
15554
12748
16629
16349
13029
14900
13208
15241
15636
16290
15301
14801
14199
14888
15436
15065
17270
14546
14815
15098
15125
14052
14057
16690
15688
17716
14519
6
94.0
77.0
66.0
85.0
73.0
79.0
81.0
109.0
59.0
103.0
107.0
89.0
84.0
76.0
79.0
81.0
81.0
77.0
65.0
85.0
69.0
79.0
78.0
70.0
80.0
78.0
90.0
90.0
58.0
88.0
62.0
65.0
81.0
76.0
89.0
96.0
75.0
73.0
81.0
92.0
94.0
76.0
99.0
76.0
68.0
7
12270
11647
12598
12238
11811
12270
12303
11056
12894
12664
10367
12533
12106
12434
12959
11975
11844
12106
10171
12106
12533
11188
10892
11680
12598
12927
12959
12894
12861
12894
12730
12369
12073
12139
11647
11778
12205
13320
12336
12697
11713
11778
11385
11942
13287
8
12402
12631
12566
13648
12598
12106
11220
12992
12303
12336
12861
11778
9
71.0
62.0
27.0
72.0
70.0
70.0
62.0
88.0
45.0
72.0
70.0
81.0
80.0
72.0
70.0
54.0
54.0
45.0
53.0
73.0
62.0
45.0
70.0
45.0
74.0
63.0
80.0
80.0
54.0
62.0
18.0
18.0
62.0
72.0
62.0
80.0
70.0
70.0
45.0
62.0
70.0
72.0
80.0
72.0
35.0
10
12697
12762
12566
12664
12598
11647
12500
12008
12467
13780
11024
12927
12402
12500
11811
12828
12139
12959
12073
12500
12861
11909
11188
12467
12598
13123
12959
13123
12992
13484
12369
12697
12467
11975
12205
12500
12664
13156
12664
13255
11975
12008
12205
12369
13320
11
12861
12566
12336
12566
13291
12500
10335
13156
12402
10105
12664
11975
242
Table F.1. Continued…
1
2
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
3
18.6
12.0
16.4
18.9
12.1
8.3
19.8
12.8
22.3
14.0
15.8
20.6
12.7
19.5
20.6
7.4
16.0
22.0
21.6
13.5
21.7
18.4
14.1
17.6
15.1
7.1
12.1
13.8
19.2
9.6
12.1
9.9
8.4
10.0
18.9
21.2
19.2
18.5
19.1
15.6
10.0
8.0
21.4
30.4
23.8
9.0
4
5
13557
13739
16216
15714
13970
14548
14131
14423
15294
16041
13687
15333
14313
13984
15053
14960
14839
15555
14994
13779
14880
15350
15766
16353
14771
14723
13457
13171
15150
14403
15853
13412
14897
13124
13473
13527
15913
14751
14865
15020
12576
15276
12698
14545
14350
15244
14871
16286
14774
16349
15844
14758
14565
14697
15203
14787
14796
15286
14411
14767
15485
13225
12904
16649
6
87.0
78.0
80.0
93.0
79.0
80.0
75.0
85.0
87.0
99.0
59.0
108.0
49.0
95.0
105.0
75.0
104.0
92.0
97.0
73.0
64.0
68.0
44.0
71.0
92.0
60.0
97.0
90.0
72.0
57.0
66.0
62.0
78.0
74.0
54.0
96.0
84.0
91.0
79.0
83.0
57.0
68.0
74.0
114.0
100.0
68.0
7
11450
12500
11877
12533
12828
14239
11319
11417
11909
12008
12566
10958
12631
12008
12303
12303
12041
11614
11647
12139
13353
11778
12730
12959
12467
12566
11975
11778
12270
13091
11877
12992
11877
11942
12172
11024
10433
11745
12172
12336
12566
12927
13681
10597
11188
13123
8
13353
12041
12500
12795
13419
12500
12894
14108
10630
11188
9
72.0
45.0
80.0
70.0
54.0
35.0
62.0
54.0
81.0
80.0
54.0
89.0
35.0
88.0
89.0
35.0
54.0
89.0
89.0
62.0
53.0
62.0
35.0
62.0
88.0
18.0
72.0
88.0
72.0
54.0
45.0
54.0
45.0
35.0
53.0
62.0
80.0
80.0
70.0
80.0
35.0
35.0
70.0
105.0
73.0
54.0
10
12008
13156
12402
13287
13287
13386
11614
12500
12566
12828
12861
12041
12894
12041
12664
13320
12992
11877
12008
12205
13156
11844
12894
13156
12500
12861
12992
12008
12336
12730
12533
12598
12795
12992
12303
11909
12008
11942
12533
12402
13320
13550
13123
10892
12139
13091
11
14206
12762
12533
14239
11745
12402
13812
13156
10958
12106
243
Table F.1. Continued…
1
4
2
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3
14.0
14.4
11.2
14.2
22.2
19.5
8.2
11.0
8.2
16.5
23.3
18.0
19.8
16.8
22.5
11.2
9.2
12.9
7.8
17.8
18.0
15.8
21.0
20.5
12.5
13.3
18.4
11.0
18.8
12.8
6.4
11.9
8.9
22.5
10.3
12.5
25.1
9.8
4
15152
16647
15971
16622
13212
14345
16293
15269
16048
15650
15969
14798
13923
15109
15465
13963
16023
15065
16140
14104
14006
14412
14604
14024
16730
13602
15729
16087
15831
15591
17697
15880
17665
15605
16511
15609
13187
18058
5
14868
15954
16331
14898
13532
13750
14277
15484
16272
15833
14723
14182
14952
15079
15373
14683
15399
15801
13758
15889
15365
14060
14903
13803
16895
14499
15224
15715
14261
16169
16551
14625
17170
13653
15729
18175
13316
18041
6
66.0
70.0
84.0
87.0
78.0
102.0
69.0
61.0
78.0
90.0
99.0
86.0
79.0
65.0
80.0
85.0
71.0
81.0
60.0
72.0
76.0
77.0
72.0
93.0
86.0
52.0
89.0
86.0
73.0
78.0
78.0
84.0
86.0
105.0
89.0
80.0
89.0
70.0
7
12500
12927
11811
11909
11516
11220
13287
12467
12434
10663
11745
12303
12238
12402
12172
12041
12762
12598
12894
12533
12467
12631
12336
10892
12467
12861
12434
11975
12762
12467
13189
11942
12828
11155
13615
13123
12172
13451
8
11942
11089
11056
11778
12992
9
62.0
62.0
70.0
72.0
72.0
89.0
53.0
54.0
45.0
80.0
70.0
80.0
62.0
62.0
80.0
54.0
45.0
62.0
27.0
70.0
45.0
72.0
70.0
90.0
70.0
45.0
80.0
62.0
62.0
72.0
18.0
62.0
54.0
70.0
62.0
72.0
88.0
54.0
10
12631
13058
12631
12828
11811
11811
13419
12631
13287
12566
12631
12402
12697
12631
12402
12664
13747
13025
13222
12664
13287
12828
12467
11516
12828
13484
13156
12861
13320
12664
13714
12631
12106
12203
14272
13484
12533
14173
11
11545
11909
11286
12992
12959
244
Table F.2. Measurements from 200 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
2.00 2.75 3.25 3.00 3.00
0.75 1.50 1.75 2.25 2.25
0.75 1.00 0.75 0.50
0.00 0.50 0.75 1.00 0.50
0.00 0.50 0.75 0.50 0.50
1.50 2.25 2.50 2.50 1.75
0.75 2.00 2.50 2.75 2.00
0.75 0.75 1.25 1.25 0.75
0.75 1.25 1.50 1.25 1.00
0.75 1.50 2.00 2.25 2.00
0.25 0.75 1.25 1.00
0.75 1.00 1.25 0.75 0.50
0.00 0.75 1.00 0.75 0.50
0.75 1.25 1.50 1.50
0.75 1.25 1.50 1.25 1.00
0.75 1.50 1.50 0.75
2.25 3.50 3.50 0.75 2.50
1.50
0.50 1.25 1.50 1.50 1.25
1.50 1.75 2.50 2.25
1.75 2.50 2.75 3.00
1.75 2.50 2.75 2.75
1.25 2.50 2.50 2.75
1.00 1.75 2.25 2.25 1.50
1.25 1.75 2.25 2.50
0.75 1.25 1.25 0.75
1.25 2.00 2.25 2.00 1.50
1.00 1.75 2.25
0.00 0.25 1.50 0.50
1.00 1.50 2.25 2.25 1.25
0.50 1.25 1.75 2.00 1.75
0.75 1.25 1.75 1.75 1.25
1.00 1.75 1.75 1.50 0.75
3.25 1.00 0.75
0.75 1.25 1.25 1.00
1.25 2.50 3.25 3.00
1.25 1.75 2.00 1.75 1.50
1.25 1.75 2.00 1.75 1.25
1.75 2.25 2.75 2.75
0.75 1.25 1.50 1.50 0.50
0.25 0.50
Green
Density
(kg/m3)
9
865.10
815.20
882.10
872.10
850.00
872.40
825.60
787.90
245
Table F.2. Continued…
1
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
2
1.50
0.50
0.75
0.25
1.25
0.00
1.25
0.00
0.00
0.00
1.25
1.50
0.50
0.75
0.50
0.00
0.50
0.75
0.75
0.25
0.75
0.75
0.75
0.75
1.50
0.75
0.75
0.50
0.75
1.00
0.75
0.00
0.75
0.50
0.75
0.75
0.75
1.00
1.50
0.75
1.25
2.25
0.75
1.50
0.75
1.25
3
2.25
0.75
1.25
0.75
1.50
0.75
2.25
0.25
0.50
0.50
2.50
1.75
1.25
1.50
1.50
0.75
0.75
1.25
1.25
0.50
1.25
1.50
1.50
1.50
2.25
1.50
1.75
1.25
1.25
1.50
1.25
0.75
1.25
0.75
1.75
1.75
1.50
1.50
2.25
1.25
1.50
3.00
2.00
2.75
1.75
2.50
4
2.75
0.75
1.50
1.75
1.75
1.50
2.50
0.50
0.75
0.50
2.75
2.50
1.75
2.25
1.75
0.75
0.50
1.75
1.75
0.25
1.50
2.00
2.25
2.25
3.25
1.75
2.50
1.50
1.25
1.50
1.50
1.25
1.50
1.25
2.00
2.25
1.50
1.75
3.00
1.25
1.25
3.25
2.50
3.25
2.25
2.75
5
2.50
0.25
1.50
1.50
1.25
1.75
1.75
0.50
0.50
0.25
2.75
3.00
1.50
2.25
1.75
0.50
1.50
1.50
0.25
1.50
2.50
2.50
1.25
3.50
1.75
2.75
1.50
1.50
1.25
1.25
1.50
1.25
1.00
1.50
2.00
1.75
1.50
2.75
6
7
8
9
869.80
888.00
1.00
1.00
0.75
2.50
3.25
1.25
2.00
1.75
908.70
890.00
1.50
1.50
789.20
2.50
2.25
1.25
3.00
1.50
2.50
1.00
1.25
1.25
778.20
763.80
1.25
910.10
2.50
1.75
801.40
1.75
2.50
2.50
3.00
2.50
2.75
1.50
2.25
2.00
2.25
0.75
1.75
824.40
246
Table F.2. Continued…
1
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
2
0.50
0.75
0.50
0.25
0.75
0.75
0.50
1.50
0.75
0.75
0.75
0.50
1.25
0.75
1.25
0.50
0.50
0.75
0.75
1.50
1.00
0.75
0.75
0.50
0.75
0.75
0.50
1.00
0.75
0.50
0.75
0.25
1.25
0.75
0.75
0.25
1.75
0.75
1.25
0.75
0.75
0.75
0.50
0.75
1.50
3
0.75
1.50
1.00
0.50
1.25
1.50
1.25
2.75
1.00
1.75
1.50
1.00
1.50
2.25
1.75
0.50
0.75
1.50
1.50
1.75
1.50
1.00
1.50
0.75
1.00
2.00
1.25
1.75
1.50
1.00
1.50
1.25
1.75
1.25
1.25
0.75
2.25
1.25
2.25
1.50
1.25
1.75
0.75
1.50
2.25
4
1.25
2.00
1.25
1.25
1.75
1.75
1.25
2.50
1.25
2.00
1.75
1.25
2.25
2.50
2.00
0.50
0.75
1.50
2.25
2.25
2.50
1.50
1.50
1.25
1.25
2.50
1.50
1.75
2.25
0.75
1.75
1.75
2.25
2.00
1.50
1.00
2.00
1.75
2.75
1.75
1.75
2.50
1.25
2.25
2.75
5
1.25
1.75
1.00
1.25
1.50
2.25
1.00
2.25
0.50
2.25
1.50
1.00
2.75
6
0.25
1.50
0.25
0.50
1.25
2.75
2.25
2.50
1.75
1.00
1.00
1.00
1.75
1.50
2.25
2.50
0.50
1.75
2.00
2.00
2.50
1.25
1.00
0.75
1.25
2.75
1.75
1.50
7
8
9
881.80
787.80
823.10
0.75
2.75
1.50
2.25
818.80
786.40
837.60
844.20
0.75
1.50
2.00
1.50
2.50
791.90
0.50
2.50
1.25
834.20
773.20
2.75
2.50
1.75
2.50
2.50
2.50
2.25
1.25
247
Table F.2. Continued…
1
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
2
0.50
0.75
1.25
1.00
1.00
1.50
0.75
0.75
1.75
0.76
0.25
0.75
0.75
1.75
0.25
0.50
0.75
0.50
0.75
1.25
1.00
0.75
0.75
1.25
0.75
0.50
0.50
1.25
2.75
1.25
0.00
0.50
0.75
0.75
0.75
1.25
1.00
0.75
0.75
0.50
0.75
1.75
0.75
0.75
3
0.75
1.50
2.25
1.75
1.50
2.00
1.25
0.75
2.50
1.50
1.50
1.50
1.50
2.50
0.75
1.25
1.50
1.00
1.25
2.25
2.75
1.50
1.50
2.00
1.75
1.00
1.00
2.25
3.25
2.00
0.75
1.25
1.50
1.25
1.50
2.25
1.75
1.00
1.25
1.25
1.75
2.50
1.50
2.25
4
0.75
2.50
2.75
2.25
1.50
2.25
1.50
1.00
2.75
1.75
0.50
2.25
1.75
2.50
1.25
1.50
1.75
1.25
1.25
2.50
3.25
1.75
2.25
2.75
2.25
1.00
0.75
2.75
3.75
2.50
0.75
1.50
1.50
1.50
2.00
3.25
2.25
0.75
1.50
0.75
2.25
3.00
2.25
2.50
5
0.50
1.25
2.25
3.25
1.75
6
7
8
9
757.50
0.50
2.25
2.50
790.20
2.00
2.75
1.75
1.50
2.00
2.00
2.50
1.00
0.75
1.50
1.00
0.75
1.50
1.50
2.25
1.50
2.50
3.00
1.50
1.25
0.25
2.25
4.00
2.00
1.00
1.50
1.25
1.50
2.25
2.50
2.50
0.50
1.25
0.50
1.50
2.75
2.25
2.50
1.50
1.25
2.25
1.50
839.50
3.25
1.50
751.20
823.90
835.90
869.50
2.00
1.00
1.50
1.75
1.25
2.75
1.50
860.50
0.75
831.40
248
Table F.2. Continued…
1
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
1.00
1.25
0.75
0.75
1.00
0.75
0.75
0.75
0.75
1.00
1.25
0.50
3
1.75
2.00
1.25
1.50
1.75
1.25
1.50
1.25
1.50
1.50
2.25
1.25
4
2.25
2.50
1.50
1.75
2.25
1.50
2.50
2.00
2.00
1.25
2.75
1.50
5
1.50
2.50
1.25
0.75
1.25
2.50
1.75
2.25
1.25
2.75
1.25
0.75
0.75
1.25
0.50
0.50
0.75
0.50
0.75
0.75
0.75
1.00
0.50
1.50
1.50
1.50
1.75
0.75
1.50
1.25
1.75
1.25
1.50
2.25
1.00
2.25
1.75
2.50
1.75
1.25
1.75
1.00
2.50
1.50
2.25
2.50
1.25
2.25
1.50
2.25
1.50
0.50
2.25
0.75
3.00
1.25
2.00
2.75
2.75
6
7
2.50
1.00
8
9
842.70
817.90
1.50
1.25
1.25
0.50
3.00
0.75
2.50
837.70
2.00
249
Table F.3. Measurements from 200 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Log length
1
2
3
ft
ft
ft
2
3
4
35 27
27 35 18
35
35 18
35
35 27 18
35 27 18
35 27
35 27 18
35 35 27
35 27
35 27
35 18
35 27
35 27
27 27
35 35
35 27
27 27 18
35 27
35
27 27 18
35 35
35 27
27 18
35 27 18
27 27
35 18
35 35
35 27 18
35 27 18
35 27 18
18
27 18
35 35
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
13320
13091
12828
12402 11188
13714
12894
12106
12894
12402
12073 11614
12172
11713 10794
13484
12828
13484
12697 11778
12664
11909 11581
12894
11942
12664
12467
13484
12795
12336
11942
13484
13222
13320
12959
12566
11909
14042
11811
11811 11122
12402
11811
11909
12697
12697 12369
12730
12238
12992
12828
13583
13123
12894
12467 11713
12566
12073
13222
12467
13222
12073
12730
11942 10860
12828
11942 10860
11745
11581 10367
12041
12959
12369
12566
11909
Unprocessed stem portion HM200
readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
13091
13091
11581
12008
11909
11188
12402
12369
11909
12203
11713
250
Table F.3. Continued…
1
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
2
35
35
27
27
18
27
27
35
35
35
18
27
18
27
18
35
35
35
35
35
27
18
35
35
18
27
35
35
35
35
35
35
27
35
35
35
35
35
27
27
35
35
35
35
27
3
35
27
27
27
27
18
27
27
35
27
27
4
18
18
18
18
27
18
18
35
35
27
27
27
18
18
18
18
18
27
35
18
27
27
27
27
35
27
27
27
27
27
27
35
18
18
27
18
27
35
35
27
27
18
18
18
18
27
18
18
18
18
5
12500
13156
12467
13222
12697
11581
11811
12566
13484
13484
13222
13222
13780
13976
12369
12664
12566
13386
12992
13222
13451
13714
13058
13320
12795
13845
12992
13058
13714
12828
13648
13156
12828
13320
12894
12992
13058
12992
12566
13583
12730
12008
12566
12073
13222
6
11188
12205
12073
12205
11811
13123
11581
12336
12828
13451
12959
7
8
11516
9
11188
10
11
12205
12205
11811
12073
11581
11778
12631
11614
10892
11188
10039
10630
12664
11975
11549
11877
13091
12861
12041
12795
10597
12402
12467
12467
10696
12467
11188
11877
11188
11614
12697
11614
13780
11516
11680
12467
11942
12697
10630
12369
11877
9711
12303
12959
12664
11713
13222
12697
12697
13320
12238
13091
12828
12566
12697
12336
12467
12402
12369
12795
11942
11286
12959
12402
11581
12467
12205
12467
11549
11286
11942
11811
11877
11286
11450
11778
251
Table F.3. Continued…
1
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
2
27
35
35
35
27
35
27
27
27
35
27
35
18
35
27
27
35
35
35
27
35
18
18
35
27
35
35
35
35
27
35
27
27
35
27
35
27
27
35
35
27
35
35
27
35
3
27
35
27
27
27
35
27
27
18
18
27
27
27
35
18
18
27
27
27
27
27
27
27
27
27
35
35
18
27
35
27
27
27
27
18
27
35
27
27
27
27
4
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
5
14337
12008
13484
12894
13091
12402
12959
12566
13091
12730
12828
13058
10696
11352
12566
12959
13320
13668
13648
13222
13812
12369
12697
12730
12697
12500
12997
12894
13648
12828
13550
12467
12566
12894
12959
13320
12073
13091
12664
13714
13845
13386
11909
13091
13222
6
13976
11516
12828
12697
12697
11581
12697
11713
12795
11450
12566
12697
13091
11188
12303
13025
12566
13091
13091
12959
13320
12336
11450
11942
12958
12073
12992
11549
12959
12073
12205
12073
12697
12205
13287
12467
12828
12697
11450
11811
12959
7
13222
8
9
10
11
11942
11713
11549
12500
12434
10931
11942
11549
11778
12073
11549
11549
14206
13091
11811
12566
12073
12073
13978
13714
12008
11549
12959
12073
12106
12073
10958
10794
12106
11877
12533
11188
11778
11188
11122
11549
11450
11450
10794
252
Table F.3. Continued…
1
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
2
35
27
35
35
35
35
35
27
35
35
35
35
35
35
35
35
18
27
35
18
27
27
27
27
35
35
35
35
35
35
35
35
35
35
35
18
27
35
35
35
27
27
35
35
27
3
27
27
27
4
18
35
27
18
27
27
27
27
27
18
27
27
35
27
35
27
27
18
27
18
18
27
27
27
35
27
35
35
27
27
27
27
35
27
27
35
18
27
27
27
27
18
18
18
27
18
18
18
35
27
18
18
18
5
13550
13091
12566
12894
13156
12894
13320
13091
12238
12664
12238
13550
12172
12894
13222
12894
12861
13485
13156
12631
13222
12697
13091
12205
12992
12238
12073
12500
12500
13156
12697
13320
13550
13484
11581
11713
13714
12664
13484
13320
13320
12205
12500
13648
12828
6
12697
12959
12073
7
11614
11909
12697
10696
11811
12959
12336
11942
11811
12369
11581
12959
12500
12895
11844
12697
12566
12533
12336
13780
12369
11811
12205
12073
12008
12467
12894
11089
12336
12828
12697
12697
12073
12828
12336
12073
12861
12467
8
9
10
11
14206
12959
13550
12369
11549
12139
12402
12041
11549
10138
11713
13878
10433
11975
12894
10466
11975
10203
11713
10138
11713
12172
12073
11056
11581
11188
10958
11056
11056
11188
11549
12041
10630
11713
11188
10958
12106
10958
10958
253
Table F.3. Continued…
1
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2
27
35
35
35
35
35
35
35
27
35
27
35
27
27
35
35
35
27
35
35
35
27
18
35
27
35
35
27
35
27
3
18
27
35
27
27
27
27
27
18
27
35
18
27
35
27
35
18
27
27
27
27
27
27
35
27
27
35
27
4
18
18
18
18
27
18
18
18
18
5
13222
13484
12992
12402
12828
12730
12664
12828
13714
13484
13222
13222
13222
13222
12402
11844
13878
13222
13714
13222
14042
13320
13714
13156
13845
12664
14660
13976
13058
14337
6
12959
12566
12402
12336
12336
12467
12566
12566
13386
12697
12402
13386
13091
12566
11713
12664
13714
12959
12566
13320
12959
11811
12467
12172
13583
13583
12828
13815
7
8
9
10
10991
12992
11122
12664
10467
12336
12467
11
11644
12106
11450
12205
10827
11877
11713
12697
11549
10827
254
Table F.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs in each truck, gross volume (Scribner long log board feet (BF), average small
end diameter and average length of logs delivered.
Site
F
F
F
F
F
F
F
F
Test Truck
#
T1
T2
T3
T4
T5
T6
T7
T8
Number of
Logs
55
40
42
47
63
43
50
39
Volume
Gross BF
4530
5150
4590
4070
4830
5140
4360
4740
Average
Small End Diam
in
8.76
10.30
9.55
8.68
8.08
10.14
8.84
10.10
Length
ft
26.85
29.20
28.55
26.19
25.22
26.98
26.92
27.23
255
Table F.5. Green veneer produced from processed logs delivered exclusively from
site F: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
814
2396
1317
3
532
2171
816
445
89
1150
8583
9733
Green Veneer Volume
Grade
3/8's
G1
8681.798
G2
25554.78
G3
14046.6
AB
31.9968
C+
5674.099
C
23155.02
D
8703.13
X
4746.192
XX
949.2384
In-Process
12265.44
NET
91542.84
TOTAL
103808.3
256
Appendix G. Data from Stand G (“B&M Block – Dunn Forest”, located near
Corvallis, OR).
Details: Loader/Processor – tracked Linkbelt 240LX with Waratah HTH622B; cut
and measured in July 9 – July 13, 2007; ST300 tool borrowed from Roseburg
Forest Products.
Table G.1. Measurements from 182 trees sampled: diameter at breast height
(DBH), ST300 acoustic velocity measurements on two (#1 and #2) opposite sides
of each tree measured, tree length (with limbs on) and merchantable length (with no
limbs) and the respective HM200 acoustic velocity measurements on the ground
(down) or in the grapples of the loader (up), R - remarks (1 = dead, 2 = double
leader, 3 = broken top, 4 = missing data, 5 = butt rot). Disks from the bottom and
top of each produced log as well as “in-grapple” measurements were taken from the
40 trees highlighted.
ST300
R
1
4
4
Tree
DBH
#1
#
2
500
501
625
626
627
628
629
631
636
635
637
640
638
642
641
643
646
816
817
818
819
826
827
828
829
(in)
3
9.6
17.8
19.5
10.2
27.3
23.0
20.4
8.2
10.1
10.3
10.6
10.7
15.1
ft/s
4
26.6
12.5
20.3
19.2
12.7
30.9
22.5
13.7
12.2
8.9
25.0
#2
ft/s
5
Limbs on
Tree
length
ft
6
68.0
94.0
74.0
71.0
83.0
82.0
83.0
52.0
68.0
71.0
71.0
68.0
35.0
81.0
109.0
79.0
86.0
77.0
83.0
113.0
71.0
91.0
75.0
39.0
112.0
HM200
(down)
(up)
ft/s
ft/s
7
8
11056 11089
10172 10794
11516
11650
10991
11352
11319
11253 11089
10564 10564
12008 11844
12303
10892
11581
11286 11483
11516
11516 11680
11680
11385
11942
10892 10761
12139
10958 10794
10860 10925
12073
11352
Limbs off
Merch
length
ft
9
68.0
72.0
74.0
71.0
83.0
82.0
83.0
52.0
68.0
48.0
71.0
68.0
35.0
81.0
93.0
79.0
86.0
77.0
73.0
113.0
71.0
78.0
75.0
39.0
106.0
HM200
(down)
(up)
ft/s
ft/s
10
11
13255 13255
10564 10773
11811
11730
10991
11452
11450
10991
9416
10892 10892
12500 13700
12467
11188
11581
11778 13319
11844
11975 12041
12172
11713
12500
11385 11385
12303
10892 11188
11253 11253
12073
12008
257
Table G.1. Continued…
1
4
4
2
830
832
833
835
836
837
936
939
940
941
942
943
946
947
949
950
951
952
953
954
955
1062
1063
1064
1065
1066
1068
1071
1072
1073
1076
1078
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1092
3
9.8
27.4
27.4
12.8
19.0
22.7
15.9
15.2
17.9
15.2
20.2
18.7
14.3
10.7
21.6
16.1
9.8
23.2
17.8
16.0
13.9
7.0
17.1
9.7
17.7
19.2
10.2
20.9
10.7
22.6
7.5
19.3
24.7
13.6
8.2
26.4
14.6
22.6
17.8
17.9
26.4
15.4
24.8
19.4
4
15727
15491
5
16698
15398
6
76.0
109.0
73.0
73.0
92.0
96.0
79.0
100.0
76.0
68.0
94.0
86.0
57.0
69.0
103.0
74.0
81.0
85.0
78.0
70.0
82.0
40.0
94.0
86.0
98.0
78.0
77.0
90.0
96.0
104.0
50.0
99.0
98.0
82.0
77.0
111.0
86.0
116.0
85.0
118.0
114.0
106.0
122.0
116.0
7
11417
10367
11975
12008
10367
11089
11450
11417
12270
12730
11155
11614
10958
11385
10531
11647
11778
11516
12467
12139
11975
12172
11417
12106
11909
10630
10860
10367
10827
10531
12664
11155
11034
11516
11713
10564
11089
10794
12762
11122
10957
11220
10925
12205
8
11417
12139
12172
11516
11778
12139
12336
12139
10138
10761
11647
11942
10564
11155
12762
9
76.0
98.0
73.0
73.0
92.0
96.0
79.0
86.0
76.0
68.0
78.0
86.0
57.0
69.0
103.0
74.0
81.0
85.0
78.0
70.0
82.0
40.0
79.0
59.0
98.0
78.0
77.0
90.0
96.0
104.0
50.0
92.0
98.0
82.0
77.0
111.0
75.0
116.0
85.0
118.0
114.0
84.0
122.0
116.0
10
11975
10499
12008
12664
10630
11450
12795
11909
12730
12762
11549
11778
11188
11811
10827
11909
11745
11942
12664
12303
12303
12927
12073
13025
12008
12238
11417
10860
10958
11089
12664
11385
11319
11844
11877
11024
11385
10923
12959
12303
10728
12434
11877
12598
11
11286
12664
12331
11811
11811
12172
12762
12270
10794
10892
11909
12041
11253
11516
12959
258
Table G.1. Continued…
1
4
4
4
4
4
2
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1247
1248
1249
1251
1252
1253
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1277
1278
1279
1280
1281
3
23.7
24.0
19.8
24.0
21.8
21.9
16.6
18.7
22.3
17.4
22.0
13.2
10.6
21.5
10.7
18.7
16.6
9.3
6.5
5.9
16.5
11.7
13.2
18.7
8.5
17.4
9.1
15.4
23.8
13.4
13.0
19.3
11.7
10.0
13.2
14.6
16.9
13.7
12.8
16.9
15.8
6.7
18.7
17.0
10.5
4
5
13810
16936
15752
14904
14684
15615
16877
14166
16379
15008
17130
13579
15429
14774
17116
15905
14413
16118
15106
14492
15847
15906
13724
15639
13573
16776
12787
16495
16684
15409
15435
15903
15255
16279
14793
16087
14979
16682
16378
15464
16523
16920
15607
16657
15577
15354
14748
15981
16354
12936
16967
15001
18636
16458
12634
14450
15656
16033
17532
15092
15825
15004
16501
17207
15414
13430
17023
17748
14754
15574
14551
15184
15270
15800
16512
17115
16648
15872
14572
15477
6
118.0
101.0
110.0
99.0
96.0
115.0
95.0
93.0
93.0
98.0
94.0
70.0
71.0
119.0
52.0
102.0
81.0
69.0
43.0
40.0
80.0
38.0
67.0
84.0
54.0
98.0
59.0
89.0
97.0
91.0
95.0
93.0
75.0
87.0
89.0
80.0
103.0
99.0
83.0
84.0
104.0
56.0
81.0
101.0
87.0
7
11778
12270
11811
12041
12172
11247
11713
12664
11745
12402
10663
12533
12336
11253
12500
11450
10269
11253
11745
12106
10892
11844
11975
10335
11385
10302
13714
11089
9843
10761
11352
11024
11647
13091
10663
10794
11778
11713
12369
12270
11286
10958
10531
11352
8
12402
13091
11647
10663
12566
9
118.0
101.0
110.0
88.0
96.0
115.0
95.0
93.0
93.0
98.0
94.0
70.0
71.0
97.0
52.0
102.0
81.0
69.0
43.0
40.0
68.0
38.0
67.0
84.0
54.0
89.0
38.0
89.0
97.0
59.0
95.0
82.0
75.0
87.0
56.0
78.0
103.0
65.0
69.0
69.0
94.0
10
12106
12270
11909
12008
12369
11647
11713
12795
11914
12402
10925
12828
12927
12467
12664
11811
11909
11843
11745
11909
11183
12008
12205
10564
11811
10653
14075
11483
10302
11778
11844
11155
11516
81.0
101.0
50.0
10958
10958
12828
11909
11450
11975
13091
13517
12927
12270
11
12402
12402
12073
11877
13583
259
Table G.1. Continued…
1
4
4
4
4
4
4
2
1282
1283
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1296
1297
1298
1299
1300
1301
1465
1466
1467
1468
1469
1470
1471
1472
1473
1486
1482
1487
1488
1490
1492
1474
1475
1476
1477
1478
1479
1480
1481
1493
1497
1495
1496
3
13.6
9.4
14.4
16.0
17.0
8.9
15.9
22.8
23.0
21.6
19.8
21.9
11.5
20.6
14.5
21.0
15.5
20.6
18.0
17.5
11.6
21.7
17.8
20.2
7.5
16.5
16.3
13.9
8.4
19.8
18.9
14.0
20.0
15.3
8.9
12.8
19.2
12.4
23.2
14.2
11.7
19.4
10.0
16.2
14.8
4
15020
16968
5
16339
16663
14676
16099
14141
16696
15502
14594
15543
16834
16660
17429
15685
15649
14995
15149
15444
15191
17394
14630
17080
16429
15925
16945
15031
17285
15387
16529
15279
14501
15647
16250
15680
16665
15729
14431
14486
15509
15560
16234
12970
13966
16328
16267
16148
16330
14962
15753
13295
14720
15183
12528
15905
15629
16210
16967
15602
14958
15618
15991
15792
15866
16187
17101
15148
15031
15109
13454
16766
13953
16663
16072
15698
15936
6
85.0
80.0
76.0
75.0
83.0
56.0
82.0
102.0
116.0
114.0
96.0
86.0
91.0
96.0
88.0
107.0
77.0
96.0
89.0
106.0
94.0
94.0
86.0
85.0
60.0
89.0
86.0
73.0
34.0
89.0
102.0
80.0
92.0
85.0
74.0
75.0
87.0
56.0
95.0
72.0
38.0
90.0
75.0
80.0
96.0
7
11024
11942
10728
12336
11975
12041
12205
11778
10925
11089
11385
12336
12533
11471
12139
11549
11942
11450
12434
11450
11713
12303
11614
11647
11549
12402
10892
10433
12047
11188
10794
11056
11253
11942
11483
10892
11188
10433
9482
9121
10958
11352
12041
11745
11450
8
10958
11417
11581
11877
12139
12041
11188
11188
11680
9
85.0
80.0
62.0
75.0
83.0
44.0
76.0
93.0
105.0
91.0
96.0
86.0
82.0
96.0
79.0
77.0
77.0
96.0
89.0
106.0
86.0
94.0
86.0
60.0
60.0
89.0
71.0
73.0
34.0
89.0
81.0
80.0
92.0
74.0
74.0
75.0
76.0
56.0
95.0
55.0
38.0
90.0
75.0
80.0
90.0
10
11188
12303
11909
13189
12336
12238
12762
12467
12205
12664
11450
12402
13287
11778
12172
12664
12106
11745
12631
11844
11942
12303
11614
11385
11581
12270
11647
11024
12598
11417
11549
11253
11811
11778
11909
11188
11450
10761
9646
10269
11220
11188
12828
11877
11713
11
11909
11778
12336
11385
12270
12598
11385
11417
12073
260
Table G.1. Continued…
1
4
4
4
4
2
1498
1499
1500
1501
1502
1503
1505
1506
1508
1509
1510
1511
1512
1513
1514
1515
1516
1689
1692
1693
1694
1695
1696
1697
3
17.8
21.9
14.7
19.2
22.1
10.5
18.0
19.3
17.3
15.1
16.2
16.0
17.0
10.3
17.0
20.1
21.7
13.0
16.8
10.5
7.8
17.2
15.3
10.9
4
15867
15345
15163
17677
16404
17576
16083
15434
5
15236
15839
15976
16940
15431
16967
15768
14793
16452
15827
16586
16233
16000
16564
15939
14494
16192
17426
15854
16918
17831
13780
12238
16913
16451
16533
16563
15120
11076
14414
14993
17060
6
95.0
99.0
90.0
100.0
84.0
76.0
91.0
87.0
99.0
98.0
83.0
98.0
88.0
52.0
86.0
98.0
102.0
71.0
91.0
72.0
62.0
98.0
79.0
79.0
7
10860
11089
10958
11549
11220
13490
11877
10138
11483
11909
11745
11713
11680
12336
12697
11319
11220
10892
11844
12336
10728
10991
10433
11844
8
12697
11352
9
95.0
66.0
90.0
100.0
84.0
76.0
91.0
87.0
91.0
98.0
83.0
98.0
88.0
49.0
86.0
98.0
84.0
71.0
91.0
72.0
62.0
98.0
79.0
79.0
10
10991
12369
11417
11942
11680
13812
12172
10499
12467
12008
11909
12894
11877
12172
12631
11614
12106
11056
12795
12631
11319
11352
10499
12828
11
12697
12106
261
Table G.2. Measurements from 182 trees sampled: largest limb diameter measured
at its base every 20 ft along the stem measured. Disks from the bottom and top of
each produced log were taken from the 40 trees highlighted and green density was
measured.
Tree #
1
500
501
625
626
627
628
629
631
636
635
637
640
638
642
641
643
646
816
817
818
819
826
827
828
829
830
832
833
835
836
837
936
939
940
941
942
943
946
947
949
Largest branch size every 20 ft along the stem (in)
02040608010012020
40
60
80
100
120
140
2
3
4
5
6
7
8
1.00 1.25 1.75 0.75
1.25 1.75 2.25 2.50
1.50
1.50 2.75 3.50 3.50
0.75 1.25 1.50 1.00
2.50 3.25 3.50 3.50
2.75
1.75 2.75 3.25 3.25
2.50
1.00 2.25 2.50 2.25
2.50
1.25
1.25
1.25
1.00
0.00
1.25
1.75
1.25
1.75
1.75
1.25
2.50
1.75
1.25
1.00
0.50
2.25
0.00
3.00
2.75
0.00
1.75
2.25
0.00
1.25
0.00
0.75
1.00
0.75
0.00
0.75
0.00
1.25
1.75
2.00
1.25
1.50
1.75
2.75
1.50
3.25
2.50
1.75
3.00
2.75
2.00
1.50
1.25
3.50
0.25
3.75
3.25
1.25
2.50
3.00
1.75
1.75
0.00
1.25
1.75
1.75
0.75
1.25
2.00
1.00
1.75
1.75
1.25
0.75
1.00
1.00
1.50
1.75
3.75
1.75
3.00
2.75
2.00
3.25
3.75
1.75
1.75
1.50
3.50
1.25
2.75
2.75
1.75
3.75
3.25
1.25
1.00
3.75
0.50
3.75
3.75
1.50
2.75
3.50
1.75
2.00
1.25
2.25
2.50
2.25
1.75
1.50
2.75
3.75
0.75
3.50
3.50
1.50
2.75
3.25
2.00
1.75
2.25
2.50
2.50
2.50
0.75
2.75
Green
Density
(kg/m3)
9
860.00
782.20
760.00
805.70
784.40
1.00
832.50
2.00
0.50
721.30
2.25
3.50
2.50
860.70
841.30
788.80
2.25
2.00
2.75
1.25
711.80
731.30
2.50
1.50
1.50
0.75
686.80
2.25
1.75
781.70
2.25
1.25
262
Table G.2. Continued…
1
950
951
952
953
954
955
1062
1063
1064
1065
1066
1068
1071
1072
1073
1076
1078
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1092
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1247
1248
1249
2
1
0.25
0.00
0.00
0.00
0.00
0.50
0.00
0.00
0.75
0.75
1.25
1.75
0.00
0.00
0.75
0.00
2.00
1.50
0.75
1.75
1.25
1.75
0.00
0.00
1.50
0.00
1.50
0.75
1.75
2.50
1.00
1.75
1.00
1.50
0.75
0.00
0.75
0.00
1.25
0.75
0.50
2.25
0.75
1.75
3
1.75
1.00
2.25
1.75
1.50
1.25
2.00
1.50
0.50
1.75
1.75
1.50
2.50
2.00
2.00
1.25
0.00
2.25
2.25
1.25
3.00
1.75
2.50
1.50
1.75
2.75
1.50
2.25
2.25
2.50
3.50
2.50
2.00
2.25
2.50
1.75
0.75
2.50
1.50
2.50
2.00
1.25
2.50
2.50
3.25
4
2.50
1.25
3.25
2.50
2.25
1.25
5
2.50
0.75
3.50
2.50
2.50
2.00
2.25
0.75
2.25
2.50
1.25
3.00
2.75
2.75
1.75
2.00
3.00
2.00
1.25
3.50
2.00
3.00
2.25
2.25
3.25
2.25
2.75
2.50
3.00
3.75
3.00
2.50
2.50
3.25
2.25
2.25
2.75
2.25
3.00
2.25
1.50
2.50
3.25
3.50
2.75
0.50
2.25
2.75
0.75
2.75
3.00
2.75
2.75
3.25
1.75
0.75
3.75
1.75
3.25
2.25
2.50
3.00
2.00
3.50
2.50
3.25
3.75
3.50
2.50
2.25
3.75
2.50
3.00
2.75
2.50
3.25
2.25
1.00
2.50
3.00
6
7
8
9
763.40
781.70
776.00
0.50
809.40
775.50
839.60
2.00
2.25
1.75
2.25
3.50
1.00
2.75
1.50
2.50
2.75
2.00
3.75
2.00
2.75
3.50
2.25
2.25
1.75
3.25
2.00
2.75
2.75
2.25
2.75
3.25
1.75
1.50
2.75
893.20
770.30
845.70
723.80
2.50
822.20
2.25
1.25
3.00
1.25
2.00
1.75
2.00
2.00
2.00
718.90
2.00
718.70
867.40
263
Table G.2. Continued…
1
1251
1252
1253
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1277
1278
1279
1280
1281
1282
1283
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
2
1.25
0.75
0.50
0.25
1.25
1.00
1.75
1.50
0.25
1.75
1.00
1.75
2.50
1.25
0.75
2.25
0.75
0.75
0.75
0.75
1.25
0.00
1.50
0.75
1.25
0.75
0.75
0.75
0.75
0.75
1.00
1.00
1.25
1.00
0.75
1.25
1.75
1.75
0.75
0.75
1.75
3
1.50
0.75
0.50
0.50
1.75
2.50
2.25
2.25
0.75
2.25
1.50
2.25
3.50
2.00
1.50
3.50
1.75
1.50
1.25
1.50
2.00
1.25
1.75
1.75
1.75
1.50
2.50
1.50
1.50
1.50
1.00
1.75
1.75
2.00
1.50
1.50
2.75
2.50
1.75
1.50
2.25
4
2.75
0.75
0.25
1.00
2.00
5
2.50
6
2.00
1.75
1.00
2.50
2.75
0.75
2.75
1.25
2.25
3.50
2.25
1.75
3.25
1.75
1.75
1.50
1.75
2.25
1.75
2.00
2.50
2.50
2.75
2.50
1.75
1.75
2.00
1.25
1.75
2.00
2.25
1.25
2.00
3.25
2.50
2.50
1.75
2.50
1.25
2.50
1.25
7
8
9
2.50
1.75
3.25
1.25
1.50
2.75
1.50
1.50
1.25
1.25
2.25
1.50
1.25
2.50
2.50
2.75
1.75
1.25
1.75
0.75
1.50
1.50
2.00
1.75
2.75
2.75
2.00
1.50
2.50
1.00
0.75
1.25
904.80
0.75
1.75
0.75
790.20
1.50
1.75
2.00
1.25
0.75
1.25
2.00
0.75
817.60
2.00
2.50
1.75
2.25
1.50
1.25
264
Table G.2. Continued…
1
1296
1297
1298
1299
1300
1301
1465
1466
1467
1468
1469
1470
1471
1472
1473
1486
1482
1487
1488
1490
1492
1474
1475
1476
1477
1478
1479
1480
1481
1493
1497
1495
1496
1498
1499
1500
1501
1502
1503
1505
1506
1508
2
0.75
1.00
0.00
0.75
1.00
0.50
0.75
0.75
1.00
0.00
1.00
0.00
0.75
0.00
1.25
1.50
1.75
1.25
1.25
1.25
1.75
1.00
0.50
0.50
1.75
0.75
2.00
1.25
1.25
1.75
0.75
1.00
1.25
1.25
1.50
0.50
1.75
2.00
0.75
1.00
1.25
1.50
3
1.25
2.50
1.75
1.75
2.00
2.00
1.75
1.75
1.25
2.00
2.00
1.75
1.25
1.75
1.75
1.75
1.25
2.25
2.50
2.00
2.50
2.25
0.75
1.25
2.50
1.50
2.50
1.00
1.75
2.25
1.25
2.25
1.50
1.75
1.50
1.00
2.00
2.75
0.75
1.75
1.75
2.50
4
1.50
2.75
2.50
2.75
2.50
2.75
2.25
2.00
1.50
2.50
2.25
2.50
1.50
2.50
1.75
1.00
5
1.25
3.00
1.79
2.75
2.25
2.50
2.00
2.00
1.00
2.75
1.75
2.75
6
0.50
2.75
1.25
2.50
2.75
2.00
0.75
2.75
1.75
2.50
2.75
2.00
2.75
2.75
0.75
1.50
3.00
2.75
3.00
0.75
2.50
2.75
1.50
2.00
2.00
0.25
1.25
2.75
1.50
2.25
2.00
1.00
2.00
1.75
2.50
2.00
1.50
2.75
3.25
0.50
2.50
2.00
2.75
2.00
0.50
1.75
1.00
2.00
2.75
1.50
2.50
3.00
0.50
1.00
1.75
2.00
2.00
1.75
2.25
0.75
2.75
1.25
7
8
9
753.60
733.40
2.00
1.25
796.20
738.00
736.80
822.70
1.25
739.80
1.50
1.25
2.75
1.50
744.40
0.75
1.50
2.75
2.50
1.00
1.50
265
Table G.2. Continued…
1
1509
1510
1511
1512
1513
1514
1515
1516
1689
1692
1693
1694
1695
1696
1697
2
0.75
1.25
0.75
1.50
0.75
1.00
1.75
1.25
1.25
1.00
1.25
1.25
1.50
0.25
1.25
3
1.75
1.75
1.25
1.75
1.50
2.25
2.25
2.00
1.50
2.00
1.25
1.00
1.25
1.50
1.00
4
2.00
1.75
1.50
1.75
1.25
2.50
2.75
2.75
1.00
1.25
1.00
0.50
1.50
1.25
0.75
5
1.50
2.50
1.25
2.00
1.00
2.25
2.50
2.50
0.50
0.75
0.75
1.00
1.00
6
1.25
2.00
1.00
2.00
1.75
2.25
1.75
0.50
7
8
9
730.10
835.20
266
Table G.3. Measurements from 182 trees sampled: lengths and HM200 acoustic
velocity measurements for each log produced along the height of a tree starting at
the butt log (1 = base, 2 = second log, 3 = third log); HM200 acoustic velocity
readings in the grapples (up) and on the ground (down) on the unprocessed portion
of the stem once a log is produced (1 = the stem portion after log 1 is cut, 2 – stem
portion after log 2 is cut). Disks from the bottom and top of each produced log as
well as “in-grapple” measurements were taken from the 40 trees highlighted.
Tree
#
1
500
501
625
626
627
628
629
631
636
635
637
640
638
642
641
643
646
816
817
818
819
826
827
828
829
830
832
833
835
836
837
936
939
940
941
Log length
1
2
3
ft
ft
ft
2
3
4
27
35 27
35 35
35
35 35
40 35
35 35
27
27 18
27
35
35
27
35 18
35 35
18
35 18
35 35
35 35
35 18
35 35
18
35 27
35
35
18
35 35
27 18
35 35
18
35 35
35 27
35 35
35 35
18
35 27
35 27
35 35
35 27
Log HM200 readings
1
2
3
ft/s
ft/s
ft/s
5
6
7
13156
12073 11122
12402 11188
12467
11647 11155
12467 11745
11909 11647
13025
12270 11056
13156
13058
12566
11778
13550 12664
12467 12566 12139
12730 12402
12238 11483
12238 11417
12959 12303
11647 11908 10892
12467 11909
11975
12467
12500
12566 12073
12566 13714
10935 11253 10531
13058 12467
12894 12795
12139 10925
12402 11319 10728
13386 13287
12730 12533
13123 12467
13451 12664
Unprocessed stem portion
HM200 readings
1
2
up
down
up
down
ft/s
ft/s
ft/s
ft/s
8
9
10
11
10991
10991
10138
10138
11385
11352
11122
10564
11319
11155
10892
10663
12073
11680
13189
12303
12172
12238
11253
11352
267
Table G.3. Continued…
1
942
943
946
947
949
950
951
952
953
954
955
1062
1063
1064
1065
1066
1068
1071
1072
1073
1076
1078
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1092
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
2
35
35
35
27
35
35
18
35
35
35
35
18
35
27
35
35
27
18
35
35
18
35
35
35
18
35
18
35
35
35
35
40
35
35
35
35
35
35
35
35
35
35
35
35
35
3
35
35
18
18
35
35
18
35
35
27
35
4
35
35
35
18
35
35
35
35
35
18
18
35
35
35
35
35
35
27
35
35
35
35
35
35
35
35
35
35
35
35
35
18
18
18
18
18
18
18
18
18
18
27
22
18
18
18
18
5
12730
12795
11745
12894
11581
12795
12828
13287
13550
12959
13123
13255
12959
13550
13222
12795
12894
10860
12631
11925
13320
12467
12467
13058
13681
12959
11220
12073
13780
13287
11811
13878
13123
13714
12467
12959
12566
12402
13123
12959
12795
13944
12467
12959
12467
6
11811
12139
10892
12236
10991
12073
12303
12303
12538
12270
12467
7
8
9
11467
11056
11581
11713
12730
12073
11844
12172
10171
11811
10958
10171
11647
11745
10335
11844
11516
11614
10335
11844
12664
12664
12713
11834
10
11
10794
9987
10794
9987
12073
12466
12073
11811
11979
11975
11909
11483
11647
12073
11483
12303
13320
10302
12467
11417
12894
12631
11483
12139
11975
12959
12139
12566
12139
12467
12959
12730
12402
12894
11975
12631
11188
10531
9121
10728
10302
10435
10203
11220
10039
11122
10794
10892
11450
11647
10892
11549
9777
268
Table G.3. Continued…
1
1244
1245
1247
1248
1249
1251
1252
1253
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1277
1278
1279
1280
1281
1282
1283
1285
1286
1287
1288
1289
1290
1291
1292
1293
2
35
27
35
35
35
18
27
18
18
35
27
35
35
18
35
18
27
35
35
35
35
35
35
35
35
35
35
35
35
35
3
18
4
35
18
35
35
18
18
35
35
35
35
27
35
35
35
18
35
35
35
35
35
35
35
18
35
27
35
35
18
27
18
18
27
35
18
18
27
35
27
18
35
27
27
35
35
35
35
18
18
5
13780
13780
13058
13615
14014
12927
13156
12894
12992
12303
13156
12894
11811
13255
11975
13320
12434
11811
12795
13550
12073
12238
12631
12467
12568
13714
13058
14108
13878
13451
11975
12631
13123
12795
14173
12303
13944
13078
13320
13123
13058
12392
12959
12713
6
13320
7
12730
11647
12631
13058
11122
11385
8
9
11155
10925
13511
13550
11549
10663
10630
12073
10761
12631
11253
11483
12402
11647
12631
12828
13681
12664
12795
11089
11975
11516
13156
13287
12795
13287
12730
12139
12467
12139
11319
11056
10
11
269
Table G.3. Continued…
1
1294
1296
1297
1298
1299
1300
1301
1465
1466
1467
1468
1469
1470
1471
1472
1473
1486
1482
1487
1488
1490
1492
1474
1475
1476
1477
1478
1479
1480
1481
1493
1497
1495
1496
1498
1499
1500
1501
1502
1503
1505
1506
1508
1509
1510
2
35
35
35
35
35
35
35
35
35
35
35
35
35
18
35
35
35
18
35
35
35
35
35
18
35
35
18
35
35
27
35
27
35
35
35
35
35
35
35
27
35
35
35
35
35
3
35
35
27
35
35
35
35
35
27
35
35
18
4
18
18
18
35
27
18
35
35
35
35
35
27
35
35
27
27
35
27
27
35
35
35
35
35
35
35
18
5
12959
13944
12959
14128
12959
12795
13123
13156
13451
13058
13780
12303
12467
12303
12730
12467
11909
11549
12139
12566
11647
12730
12546
12992
11811
11319
11811
11483
11319
11385
12566
13156
12730
13287
12730
12959
12894
13386
13058
13780
13550
13714
13222
13615
12467
6
12631
8
9
11220
11024
11286
11024
10892
10892
12467
11385
11286
11942
11811
11516
11417
12402
11811
11647
11089
11155
11288
11024
14042
13780
11647
14042
12566
12139
12238
12595
13058
12523
12631
12073
11647
7
10958
11483
11483
11385
11089
12073
12139
12533
11155
11909
11286
12566
12139
12631
12467
12631
12894
11975
10531
10
11
270
Table G.3. Continued…
1
1511
1512
1513
1514
1515
1516
1689
1692
1693
1694
1695
1696
1697
2
35
35
27
35
35
35
35
35
27
27
35
35
27
3
35
35
35
35
35
18
27
27
18
18
4
18
5
13451
13222
12664
13615
12894
12795
12139
13878
13550
12533
13123
11745
12402
6
12795
12139
13615
12303
12139
10794
13156
12402
10825
12303
7
8
9
12664
12205
12762
12336
11056
10
11
271
Table G.4. Truck/Mill ticket data for sampled logs delivered to the mill: number of
logs, gross volume (Scribner long log board feet (BF), average small end diameter
and average length of logs delivered.
Site
G
Test Truck
#
Yard
Number of
Logs
168
Volume
Gross BF
34060
Average
Small End Diam
in
12.30
Length
ft
34.51
272
Table G.5. Green veneer produced from processed logs delivered exclusively from
site G: number of sheets (pieces), grade and the square footage in 3/8-inch
equivalent.
Pieces
1018.883
4303.649
2074.614
350.6601
436.1686
2415.804
1741.955
1786.397
254.4629
0
14382.59
14382.59
Green Veneer Volume
Grade
3/8's
G1
10867
G2
45901
G3
22127
AB
3740
C+
4652
C
25766
D
18579
X
19053
XX
2714
In-Process
0
NET
153399
TOTAL
153399