United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-40

Hardwood Log Defect 

Photographic Database

Software and User’s Guide

R. Edward Thomas
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March 2009 Fax: (740)368-0152

Abstract

Computer software and user’s guide for Hardwood Log Defect Photographic Database. 

The database contains photographs and information on external hardwood log defects and 

the corresponding internal characteristics. This database allows users to search for specific 

defect types, sizes, and locations by tree species. For every defect, the database contains 

photos of the defect at 1-inch intervals as it penetrates the log and a brief summary of the 

defect’s measurements and features. The defect data presented here was collected from 

three sites in West Virginia.

The Author

ED THOMAS is a research scientist with the Northern Research Station’s Forestry 

Sciences Laboratory at Princeton, WV. He received a B.S. degree in computer science 

from Concord University in 1988 and a M.S. degree in computer science from West Virginia 

University in 1993. He began working for the Forest Service in 1988 and currently is 

engaged in the research and development of log defect detection methods and internal log 

defect characteristics and prediction models.

Manuscript received for publication 30 May 2008

Cover Photos

Top: Complication of photos of overgrown know from yellow-poplar log. 

Photo by U.S. Forest Service.

Bottom: Log sorting in Princeton, WV. Photo by R. Edward Thomas, U.S. Forest Service.
Contents

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

Installing The Hardwood Log Defect Database .................. 4

Using The Hardwood Log Defect Database........................ 9

Advanced Search .............................................................. 9

Research Results Browser............................................. 14

Sample Site Information ................................................. 15

Research Study Information .......................................... 16

Sample Collection ............................................................... 17

Sample Processing ............................................................. 18

Summary .............................................................................. 20

Acknowledgments............................................................... 20

Literature Cited.................................................................... 20
1

Introduction

This report contains the Hardwood Log Defect Photographic Database software and user’s 

guide. It was developed to aid forestry personnel in identifying defects, determining the 

corresponding internal features, and understanding how these defects impact log grades and 

utility. The database consists mainly of yellow-poplar (Liriodendron tulipifera L.) and northern 

red oak (Quercus rubra L.) defect samples. However, the database also contains limited numbers 

of sugar maple (Acer saccharum L.), white oak (Quercus alba L.), black cherry (Prunus serotina

L), and white ash (Fraxinus americana L.) samples.

The Hardwood Log Defect Photographic Database is composed of photos of surface indicators 

and corresponding internal defect features at 1-inch intervals inside a log. The photos of defects 

included in this database were collected as part of a research study that began in 2001. The 

study’s goals are to develop methods of inferring internal defect attributes, such as size, shape, 

and penetration depth, based on surface indicator measurements of width, length, rise, and log 

section diameter at the defect.

Before a hardwood log is processed, it normally undergoes a subjective visual assessment by mill 

personnel. The differences between high and low quality logs are determined by defect type, 

frequency, size, and location. It is difficult to accurately and rapidly detect and measure defects, 

either mechanically or manually (Tian and Murphy 1997). For every surface indicator there is 

usually an associated internal defect. External defect indicators include bumps, splits, holes, and 

circular distortions in the bark pattern.

Bumps usually indicate overgrown knots, branches, or wounds. Some bumps have a cavity 

or hole in the middle, indicating that the overgrown material has decay or is rotten. Circular 

distortions, or rings around a central flattened area, indicate a branch that was overgrown 

years earlier. External defects progress from a pruned or broken branch to an overgrown 

knot characterized by a significant bump and then to a rotten knot or a distortion defect. For 

some classes of defects it is possible to accurately predict internal features based on external 

characteristics.

Previous studies have demonstrated that the use of external or internal defect data improves 

cutting strategies that optimize log recovery or yield, i.e., preserving the largest possible area of 

clear wood on a board face (Steele et al. 1994). The value of the lumber that can be recovered 

depends on the presence and location of defects. This is especially true for hardwood logs. In the 

production of hardwood lumber, boards are sawn to fixed thicknesses and random widths. The 

presence and placement of defects on the boards affect quality and value, so much attention is 

focused on log surface defects during processing.
2

During the past 50 years there has been a significant amount of research on the relationship 

between external hardwood log defect indicators and internal defect features. An excellent 

reference for studying the impact of the external hardwood log defects in this database on 

log quality, value, and grade yield is the “Guide to Hardwood Log Grading” (Rast et al. 

1973). Several guides and pictorial reports have been published for various hardwood species 

illustrating external/internal defect features and their relationships (Marden et al. 1970; Rast et 

al. 1982, 1985 1988, 1989, 1990a, 1990b, 1990c). While these guides are useful references for 

providing insight on the external/internal relationship, only one or two examples of each defect 

type are provided for each species.

Previous studies have concentrated on a single species with specimen collection from a single 

site or from sites in close proximity. Of course, for a defect database to be valuable, it must 

reflect the natural range of the species. It should be expected that there will be variability in 

surface indicators when comparing samples from different geographic regions. Carpenter’s1

examination of surface indicators found that although the frequency and occurrence of surface 

indicators on the same species vary by region, the same internal features usually will be found 

for each external defect type. Thus, although certain defect types may be more prevalent in some 

regions, the internal manifestation of the defect will remain fairly consistent across regions.

Because growth rate varies among regions and sites, the rate of encapsulation for defects will 

also vary. Encapsulation is the process in which an external defect is grown over and absorbed 

into the log. However, the rate at which the encapsulation occurs and the degree to which the 

defect is occluded, or covered over by clear wood, is indicated in the bark pattern. Shigo and 

Larson (1969) discovered that the ratio of defect height to width indicates the depth of the 

defect with respect to the radius of the stem at the defect (Fig. 1). The faster the growth rate, 

the faster the defect is encapsulated, and thus the faster the bark distortion pattern changes. 

Wiemann2 supports Shigo and reports that bark defect patterns are a good indicator of 

encapsulation depth as well as interior defect size and that growth rate mainly impacts how fast 

the ratio changes. In addition, the upper story of the tree is actively growing and defects located 

here are encapsulated more rapidly than in the lower story as the upper story diameter increases 

proportionately faster.3

1

Roswell D. Carpenter. The identification and appraisal of log defects in southern hardwoods from 

surface indicators. Unpublished information presented at annual meeting of Forest Products Research 

Society, Deep South Section; 1950 October 26; Memphis, TN.

2

Mike Weimann, research forest products technologist, U.S. Forest Service, Forest Products Laboratory. 

Personal communication, February 11, 2002.

3

Jeffrey Gove, research forester, U.S. Forest Service, Northern Research Station. 2002. Personal 

communication, February 14, 2002.
3

Figure 1.—Encapsulation depth and stub scar relationship ratio. Figure taken 

from Shigo and Larson (1969), page 16.
4

Installing the Hardwood Log Defect Database

The recommended computer requirements to run the Hardwood Log Defect Database 

(hereafter referred to as the defect database) are:

•	 PC with a 2.0 Ghz Pentium 4 processor or greater with a DVD drive

•	 Microsoft® Windows 2000, XP, or Microsoft Vista

•	 1 GB of memory

•	 4 GB of free hard drive space

The defect database is comprised of more than 10,000 photographs requiring more than four 

gigabytes of disk storage. You must first install the database on your computer before you can 

use it. To do this using Microsoft Windows, open “My Computer” then double click the icon 

for the drive containing the defect database DVD (Fig. 2). Next, find the file named “Setup.

exe” and double click (Fig. 3). The defect database installer will begin expanding the database 

and preparing for the installation. (Fig. 4) will be displayed. When the install file is fully loaded 

the computer will display the main window for the database install application (Fig. 5).

At the main database install window (Fig. 5), you may also customize the location where the 

database will be installed. It is recommended that you create a link to the database application 

on your desktop. Select “Next” to continue the installation, or “Cancel” to halt the install.

Figure 2.—Windows “My Computer” window showing the Hardwood Log Defect Database 

DVD as drive D.
5

Figure 3.—File listing of the Hardwood Log Defect Database DVD.

Figure 4.—Database extraction and preparing to install.
6

Figure 6 displays the copyright and disclaimer notices for the defect database. To continue 

the installation, indicate your agreement and click next. At this point the installer will begin 

installing files to your hard drive (Fig. 7). When the installation is complete you will see the 

window shown in Figure 8. The installer creates links to the application on your desktop (Fig. 

9) and in the “Start” menu (Fig. 10).

Figure 5.—Main database installation window.

Figure 6.—Defect database notification and confirmation window.
7

Figure 7.—Window showing install in progress.

Figure 8.—Window shown when database install is complete.
8

Figure 9.—Windows XP Desktop 

showing the desktop shortcut icon for the 

log defect database.

Figure 10.—Windows “Start” menu item for the log defect database. 
9

Using the Hardwood Log Defect Database

To start the defect database application, click the desktop shortcut shown in Figure 9 or select 

the database application menu item using the start menu (Fig. 10). The database application 

will start and display the main application window (Fig 11). From this window choose which of 

the four sections of the defect database you want to explore: Advanced Search, Research Results 

Browser, Sample Site Information, or Research Study Information (Fig. 11). These options are 

described in detail below.

Figure 11.—Hardwood log defect database main window.

Advanced Search

Advanced Search is accessed from the main page (Fig. 11) and allows the user to search the 

defect database for defects with specific features. The “Search” tab is displayed on the window 

“Search and Browse Log Defects” (Fig. 12).

Using the “Search and Browse” window (Fig. 12), the various criteria can be used to locate 

photos and summaries for defects: tree species, defect type, size of defect, and location of defect. 

The search option buttons use pull-down menus to display the valid search criteria for each 

feature. Figure 13 shows the pull-down menu for defect type. Scroll through the menu items 

and select the item that you want. When you have configured all the search items, click the 

“Find Defects” button at the bottom of the screen (Figures 12 and 13). The program will begin 

searching for defects matching your criteria. When the search is complete, you will be switched 

automatically to the “View Matches” tab (Fig. 14).
10

Figure 12.—Defect database “Search and Browse” main window.

Figure 13.—Defect database “Search and Browse” window showing pulldown menu for defect type selection.
11

The “View Matches” window (Fig. 14) displays thumbnails and a brief summary that reports 

the species, harvest site, and surface indicator size of the defects. Near the top of the window, 

a status bar indicates how may defects were found by the search and which ones are currently 

being displayed. In the sample window, shown in Figure 14, 80 defects were found and the first 

eight are displayed.

Figure 14.—Defects that match search criteria are displayed in a thumbnail view.
12

Figure 15.—View of the surface indicator for a defect.

Figure 16.—Second internal 1-inch slice of the defect shown in Figure 15.

Clicking the thumbnail picture of a defect from “View Matches” window (Fig. 14) will open 

the “Defect Viewer” window (Fig. 15) for that defect. The “Defect Viewer” window shows a 

photo of the surface indicator. Click the tabs across the top of the window, labeled Slice 1, Slice 

2, etc., to see photos of the internal features (Fig. 16) or a summary of the defects information 

(Fig. 17). The photos show the features at 1-inch intervals in the log.
13

Figure 17.—Information summary of the defect shown in Figures 15 and 16.
14

Research Results Browser

The research “Results Browser” window (Fig. 18) presents citations and abstracts of published 

research concerning the detection, modeling, and characteristics of hardwood log defects. For 

publications that are available online, a link for downloading the publication will be shown 

below the abstract.

Figure 18.—Research “Results Browser” window.
15

Sample Site Information

The “Sample Site Information” window describes the harvest sites of the trees used in this study 

(Fig. 19). In addition, a brief summary of the types of defects as well as the number of defects in 

the database by species, type, and harvest site. This information is presented in greater detail in 

the Sample Collection section of this report.

Figure 19.—”Sample Site Information” window showing location of sample sites.
16

Research Study Information

The “Research Study Information” window provides background about the hardwood defect 

database and the processing of the data samples (Fig. 20). This section also discusses historical 

photographic log defect photo guides and provides references to them. An expanded description 

of these and other log defect related publications can be seen by clicking the “Research Results” 

browser button from the main window (Fig. 11).

Figure 20.—“Research Study Information” window describing the processing of the 

wood samples.
17

Sample Collection

Three sites in West Virginia were selected for red oak and yellow-poplar defect sample 

collection: West Virginia University Forest (WVUF) near Morgantown (elevation: 2,300 

feet), Camp Creek State Forest (CCSF) near Princeton (elevation: 2,600 feet), and the 

MeadWestvaco forest (MWVF) near Rupert (elevation 2,400 feet). The CCSF and WVUF 

sites are about 220 miles apart. The MWVF site is approximately 44 miles from CCSF and 180 

miles from the WVUF. Figure 21 shows the locations of the sample sites.

Samples were obtained from 33 yellow-poplar trees randomly selected from the CCSF and 

WVUF sites. Similarly, 33 red oak trees were randomly selected from the WVUF and MWVF 

sites to provide samples for this study. A total of 132 trees were harvested and dissected for the 

red oak and yellow-poplar samples. In addition, approximately 20 trees were sampled for the 

remaining species in the database.

Figure 21.—Wood sample collection sites in West Virginia.
18

The number of defects obtained from each site, by defect type for the red oak and yellow-poplar 

samples are shown in Table 1. Approximately equal numbers of each type of defect on yellowpoplar were obtained from each site with two exceptions: light distortion (LD) and unsound 

knot (UK) defects. We did not collect LD defects from the WVUF. UK defects occurred in 

much fewer numbers at CCSF than at WVUF.

Sample Processing

All defects were identified according to the characteristics defined in Defects in Hardwood 

Timber (Carpenter et al. 1989). Prior to defect selection, every defect on the tree was located, 

classified, and counted by type. Defects then were selected randomly. In some cases, selecting 

one defect would prevent another, nearby defect from being selected. When this occurred, 

one of the defects would be substituted with another of the same type at a different location 

on the log. Next, the section containing the defect was cut from the log. These sections were 

between 12 to 24 inches long with 6 to 8 inches of wood above and below the surface defect 

indicator. An alignment groove was cut into the top of the section in a straight line from the 

pith to the center of the defect (Fig. 22). This provides a common point of reference for each 

slice allowing the position of the defect to be measured as it penetrates the log. At this point 

the growth rings are counted inside the groove. The groove provided a fresh, smooth, uniform 

surface for the ring count. Next, an identifying tag was stapled to the section surface and the 

defect indicator photographed. For each defect sample, the following information was recorded: 

surface indicator width, length and rise; defect type; diameter inside bark; growth rate (rings per 

inch); bark thickness; height above ground; and distance from defect center to bottom center of 

alignment groove. All measurements were recorded to the nearest 1/16 inch, with the exception 

of height above ground, which was measured to the nearest quarter-inch.

Defect type

Yellow-Poplar Red Oak

WVU 

Experimental 

Forest

Camp 

Creek State 

Forest Total

WVU 

Experimental 

Forest

Mead 

Westvaco 

Forest Total

Total 

for both 

species

Adventitious knot 76 76 152 54 51 105 257

Adventitious knot cluster 68 59 127 27 47 74 201

Ant damage 0 0 0 0 8 8 8

Bump 1 3 4 0 4 4 8

Heavy distortion 58 74 132 53 46 99 231

Hole 0 0 0 0 2 2 2

Light distortion 6 96 102 2 37 39 141

Medium distortion 80 86 166 70 62 132 298

Overgrown knot 79 89 168 43 115 158 326

Overgrown knot cluster 1 20 21 0 48 48 69

Sound knot 46 47 93 19 36 55 148

Sound knot cluster 0 2 2 1 12 13 15

Unsound knot 33 7 40 30 31 61 101

Unsound knot cluster 0 0 0 0 12 12 12

Wound 13 13 26 0 43 43 69

Total all types 461 572 1033 299 554 853 1886

Table 1.—Numbers of defects by species, sample site, and defect type
19

After the defect section data was recorded, the section was sawn into 1-inch-thick slices. The 

slices were sawn so that the defect under consideration is perpendicular to the saw lines. A sawn 

defect sample is shown in Figure 23. The slices show the internal features of the defect as well as 

the change in shape, position, and characteristics it experiences before it terminates (usually at 

the pith.) 

Figure 22.—Illustration of routed defect alignment groove with respect to pith 

and external indicator.

Figure 23.—Defect sample after sawing showing labeled surface 

indicator and the series of 1-inch slabs.
20

Summary

I have assembled a database containing photos of representative samples of log defects 

commonly found on hardwood trees in the Central Appalachian forest. Although the 

population percentage of some defect types will vary by region, altitude, and site quality, the 

surface indicator usually will remain constant. However, in better sites the surface indicator 

will grow over more quickly, encapsulating the internal feature faster and deeper compared to 

poorer sites. However, even with site variability, the internal defect manifestation will remain 

relatively constant (Thomas 2008).

It is planned that this database will be continually expanded as defect collection continues for 

the defect modeling and internal prediction study. 

ACKNOWLEDGMENTS

The author would like to acknowledge the assistance of Al Waldron and the West Virginia 

Division of Forestry for assistance in sample collection on the Camp Creek State Forest. I 

would also like to thank Mead Westvaco and West Virginia University for their assistance with 

sample collection. A special thanks is in order to Jeff Palmer for his review of the database and 

help with setting up the installation program.

Literature Cited

Carpenter, Roswell D.; Sonderman, David L.; Rast, Everette D. 1989. Defects in hardwood 

timber. Ag. Hndbk. 678. Washington, DC: U.S. Department of Agriculture. 88 p.

Marden, Richard M.; Stayton, Charles L. 1970. Defect indicators in sugar maple: A 

photographic guide. Res. Pap. NC-37. St. Paul. MN: U.S. Department of Agriculture, 

Forest Service, North Central Experiment Station. 29 p.

Rast, Everette D.; Sonderman, David L.; Gammon, Glenn L. 1973. A guide to hardwood log 

grading. Gen. Tech. Rep. NE-1. Upper Darby, PA: U.S. Department of Agriculture, Forest 

Service, Northeastern Forest Experiment Station. 31p.

Rast, Everette D. 1982. Photographic guide of selected external defect indicators and 

associated internal defects in northern red oak. Res. Pap. NE-511. Broomall, PA: U.S. 

Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 20 p.

Rast, Everette D.; Beaton, John A. 1985. Photographic guide of selected external defect 

indicators and associated internal defects in black cherry. Res. Pap. NE-560. Broomall, 

PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 

22 p.

Rast, Everette D.; Beaton, John A.; Sonderman, David L. 1988. Photographic guide of 

selected external defect indicators and associated internal defects in black walnut. Res. 

Pap. NE-617. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 24 p.
21

Rast, Everette D.; Beaton, John A.; Sonderman, David L. 1989. Photographic guide of 

selected external defect indicators and associated internal defects in white oak. Res. Pap. 

NE-628. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 24 p.

Rast, Everette D.; Beaton, John A.; Sonderman, David L. 1990a. Photographic guide of 

selected external defect indicators and associated internal defects in yellow-poplar. Res. 

Pap. NE-646. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 29 p.

Rast, Everette D.; Beaton, John A.; Sonderman, David L. 1990b. Photographic guide of 

selected external defect indicators and associated internal defects in sugar maple. Res. 

Pap. NE-647. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 35 p.

Rast, Everette D.; Beaton, John A.; Sonderman, David L. 1990c. Photographic guide of 

selected external defect indicators and associated internal defects in yellow birch. Res. 

Pap. NE-648. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 25 p.

Shigo, Alex L.; Larson, Edwin vH. 1969. A photo guide to the patterns of discoloration 

and decay in living northern hardwood trees. Res. Pap. NE-127. Upper Darby, PA: U.S. 

Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 100 p.

 Steele, Phillip H.; Harless, Thomas E.G.; Wagner, Francis G.; Kumar, Lalit; Taylor, Fred W. 

1994. Increased lumber value from optimum orientation of internal defects with respect 

to sawing pattern in hardwood logs. Forest Products Journal. 44(3): 69-72.

Thomas, R. Edward. 2008. Predicting internal yellow-poplar log defect features using 

surface indicators. Wood and Fiber Science. 40(1):14-22.

Tian, X; Murphy, G.E. 1997. Detection of trimmed and occluded branches of harvested tree 

stems using texture analysis. International Journal of Forest Engineering. 8(2): 65-75.
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Thomas, R. Edward. 2009. Hardwood log defect photographic database, 

software and user’s guide. Gen. Tech. Rep. NRS-40. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, Northern Research Station. 21 p.

Computer software and user’s guide for Hardwood Log Defect Photographic 

Database. The database contains photographs and information on external 

hardwood log defects and the corresponding internal characteristics. This database 

allows users to search for specific defect types, sizes, and locations by tree species. 

For every defect, the database contains photos of the defect at 1-inch intervals as it 

penetrates the log and a brief summary of the defect’s measurements and features. 

The defect data presented here was collected from three sites in West Virginia.

KEY WORDS: yellow-poplar, red oak, internal, external, features

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