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Northern Research Station
One Gifford Pinchot Drive
Madison, WI 53726
(608) 231-9318
(608) 231-9544 TTY/TDD

You are here: NRS Home / Research Programs /Science Themes / Sustaining Forests / Methods to Conserve and Enhance Forest Resources / Timber Productivity and Wood Quality / External Hardwood Log Scanning and Internal Defect Feature Prediction
Sustaining Forests

External Hardwood Log Scanning and Internal Defect Feature Prediction

Research Issue

[image:] The external defects of a log are evaluated using a laser-based log scanner, which allows optimal product recovery when the log is sawn.

Automatically locating and classifying log defects helps improve the volume and quality of lumber yield.  Traditionally, defect inspection is done visually by the sawyer. While such inspection is done within a matter of seconds, it is easily influenced by the operator’s physical and mental condition, training, physical position relative to the log, lighting, and other environmental factors.  Consequently, there is often a high error rate in identifying defects.  Thus, developing and improving techniques to identify log defects has become an important research area. We are examining a range of computerized defect detection methods and classification systems to assist the sawyer’s decision-making abilities.

Our Research

A test-bed laser log scanning system has been developed and installed at the U.S. Forest Service’s Princeton, WV Methods Testing Laboratory.  The system generates high-resolution surface scans of logs using off-the-shelf industrial components.  A series of statistical models have been developed that accurately predict internal defect features, such as depth, and internal shape, based on measurable external features.  Currently, internal prediction models exist for northern red oak (Quercus rubra), white oak (Quercus alba), sugar maple (Acer saccharum), and yellow-poplar (Liriodendron tulipifera). 

A typical high-resolution laser scan of an average-sized hardwood log consists of approximately 1 million data points; processing the data and determining defect locations and types requires extensive and time consuming operations. However, our new approach uses a parallel processing application which processes most logs in under 2 seconds. While the laser scanning and internal defect prediction methods work well, they are not yet capable of quantifying defects hidden within the log, such as shake, decay, rot, holes, and splits.  Thus, we are working with the U.S. Forest Service’s Forest Products Laboratory to complement laser scanning with acoustical testing to enable detection of hidden log features.

Expected Outcomes

The main goals of this research effort are to provide an economical method of: 1) scanning hardwood logs, 2) locating external defects and determining internal defect features, 3) sorting out low quality/value logs, and 4) determining the best sawing strategy for each log given its shape and defect features as well as potential product markets.  In addition, this research seeks to provide information about the nature and predictability of hardwood log defects, log shape characteristics to foresters, loggers, and other researchers.

Research Results

Thomas, R. Edward; Stanovick, John S.; Conner, Deborah. 2017. The presence and nature of ellipticity in Appalachian hardwood logs. BioResources. 12(4): 8443-8450.

Thomas, Ed. 2016. Equations for predicting internal log defect measurements of common Appalachian hardwoods. USDA Forest Service, Forest Products Laboratory, FPL-RP-687. 2016. 16 p.

Thomas, R. Edward; Bennett, Neal D. 2014. Estimating bark thicknesses of common Appalachian hardwoods. In: Groninger, John W.; Holzmueller, Eric J.; Nielsen, Clayton K.; Dey, Daniel C., eds. Proceedings, 19th Central Hardwood Forest Conference; 2014 March 10-12; Carbondale, IL. General Technical Report NRS-P-142. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 283-294.

Thomas, R. Edward; Bennett, Neal D. 2014. Accurately determining log and bark volumes of saw logs using high-resolution laser scan data. In: Groninger, John W.; Holzmueller, Eric J.; Nielsen, Clayton K.; Dey, Daniel C., eds. Proceedings, 19th Central Hardwood Forest Conference; 2014 March 10-12; Carbondale, IL. General Technical Report NRS-P-142. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 299-310.

Thomas, R. Edward. 2013. RAYSAW: a log sawing simulator for 3D laser-scanned hardwood logs. In: Miller, Gary W.; Schuler, Thomas M.; Gottschalk, Kurt W.; Brooks, John R.; Grushecky, Shawn T.; Spong, Ben D.; Rentch, James S., eds. Proceedings, 18th Central Hardwood Forest Conference; 2012 March 26-28; Morgantown, WV; Gen. Tech. Rep. NRS-P-117. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 325-334.

Thomas, R. Edward. 2013. Predicting internal red oak (Quercus rubra) log defect features using surface defect measurements. In: Miller, Gary W.; Schuler, Thomas M.; Gottschalk, Kurt W.; Brooks, John R.; Grushecky, Shawn T.; Spong, Ben D.; Rentch, James S., eds. Proceedings, 18th Central Hardwood Forest Conference; 2012 March 26-28; Morgantown, WV; Gen. Tech. Rep. NRS-P-117. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 313-324.

Thomas, R. Edward; Thomas, Liya. 2013. Using parallel computing methods to improve log surface defect detection methods. In: Ross, Robert J.; Wang, Xiping, eds. Proceedings, 18th International Nondestructive Testing and Evaluation of Wood Symposium; 2013 September 24-27; Madison, WI. Gen. Tech. Rep. FPL-226. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory: 196-205.

Thomas, Ralph E. 2012. Predicting internal white oak (Quercus alba) log defect features using surface defect indicator measurements. Forest Products Journal. 61(8): 656-663.

Lin, Wenshu; Wang, Jingxin; Thomas, R. Edward. 2011. A three-dimensional optimal sawing system for small sawmills in central Appalachia. In: Fei, Songlin; Lhotka, John M.; Stringer, Jeffrey W.; Gottschalk, Kurt W.; Miller, Gary W., eds. Proceedings, 17th central hardwood forest conference; 2010 April 5-7; Lexington, KY; Gen. Tech. Rep. NRS-P-78. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 67-76.

Thomas, R. Edward. 2011. A simplified hardwood log-sawing program for three-dimensional profile data. In: Fei, Songlin; Lhotka, John M.; Stringer, Jeffrey W.; Gottschalk, Kurt W.; Miller, Gary W., eds. Proceedings, 17th central hardwood forest conference; 2010 April 5-7; Lexington, KY; Gen. Tech. Rep. NRS-P-78. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 644-645.

Thomas, R. Edward. 2011. Validation of an internal hardwood log defect prediction model. In: Fei, Songlin; Lhotka, John M.; Stringer, Jeffrey W.; Gottschalk, Kurt W.; Miller, Gary W., eds. Proceedings, 17th central hardwood forest conference; 2010 April 5-7; Lexington, KY; Gen. Tech. Rep. NRS-P-78. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 77-82.

Thomas, R. Edward. 2009. Modeling the relationships among internal defect features and external Appalachian hardwood log defect indicators. Silva Fennica. Vol. (3). http://www.metla.fi/silvafennica/

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.

Thomas, Edward; Thomas, Liya; Shaffer, Clifford A. 2008. Defect detection in hardwood logs using high resolution laser scan data. In: Proceedings of the 15th international symposium on nondestructive testing of wood; 2007 September 10-12; Duluth, MN. Duluth, MN: Natural Resources Research Institute, University of Minnesota Duluth: 163-167.

Thomas, R. Edward. 2008. Predicting internal yellow-poplar log defect features using surface indicators. Wood and Fiber Science. 40(1): 14-22.

Thomas, R. Edward; Liya Thomas, Clifford A. Shaffer, Lamine Mili. 2007. Using external high-resolution log scanning to determine internal defect characteristics. In Proc, 15th Central Hardwood Forest Conference. Buckley, David S.; Clatterbuck, Wayne K.; [Editors]. e-Gen. Tech. Rep. SRS–101. U.S. Department of Agriculture, Forest Service, Southern Research Station. 770 p. [CD-ROM].

Thomas, L., Shaffer, Mili, E. Thomas. 2007. Algorithm detection of severe surface defects on barked hardwood logs and stems. Forest Prod. J. 57(4):50-56.

Thomas, L.; L. Mili, E. Thomas, C.A. Shaffer. 2006. Defect detection on hardwood logs using laser scanning. Wood and Fiber Sci. 38(4):682-695.

Liya Thomas, Lamine Mili, Clifford Shaffer, Ed Thomas. 2004. Defect detection on hardwood logs using high resolution three-dimensional laser scan data. IEEE Int. Conference on Image Processing, ICIP, Singapore, October 24-27, 2004, 243-246.

Research Participants

Principal Investigator

  • Ed Thomas, USDA Forest Service - Northern Research Station, Research Computer Scientist

Research Partner

  • Robert Ross, USDA Forest Service - Forest Products Laboratory, Madison, WI
  • Xiping Wang, USDA Forest Service - Forest Products Laboratory, Madison, WI

Last Modified: 05/30/2018

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Thomas, R. Edward. 2013. Predicting internal red oak (Quercus rubra) log defect features using surface defect measurements. In: Miller, Gary W.; Schuler, Thomas M.; Gottschalk, Kurt W.; Brooks, John R.; Grushecky, Shawn T.; Spong, Ben D.; Rentch, James S., eds. Proceedings, 18th Central Hardwood Forest Conference; 2012 March 26-28; Morgantown, WV; Gen. Tech. Rep. NRS-P-117. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 313-324.