Accuracy and Reproducibility of Acoustic Tomography Significantly Increase with Precision of Sensor Position


Acoustic tomograms are widely used in tree risk assessment. They should be accurate,
repeatable and comparable between consecutive measurements. Previous work has failed to address the effects of different approaches to record sensor positions, operators and models of tomograph on the resulting tomograms.

In this study, three operators used the two most common sonic tomograph
models to measure seven cross-sections of Norway spruce trees, which
were felled after the measurement. We evaluated the effects of model, operator, and different approaches to measure sensor positions on the quality of the tomograms.

The largest source of error was the position of sensors, affecting
estimated stress wave velocity, the shape of the tomogram, and the size
of the defect.

To produce accurate and repeatable tomograms of trees with complex shapes,
it is essential to measure the sensor positions precisely.


Arciniegas, A., Brancheriau, L., and Lasaygues, P. (2015).
Tomography in standing trees: revisiting the determina-
tion of acoustic wave velocity. Annals of Forest Science,
72(6):685–691, doi:10.1007/s13595-014-0416-y.

Gilbert, E. A. and Smiley, E. T. (2004). Picus sonic tomography for the quantification of decay in white oak (quercus
alba) and hickory (carya spp.). Journal of Arboriculture,

Li, L., Wang, X., Wang, L., and Allison, R. B. (2012). Acoustic tomography in relation to 2D ultrasonic velocity and
hardness mappings. Wood Science and Technology, 46(1-
3):551–561, doi:10.1007/s00226-011-0426-y.

Liang, S., Wang, X., Wiedenbeck, J., and Cai, Z. (2008). Evaluation of Acoustic Tomography for Tree Decay Detection.
In 15th International Symposium on Nondestructive Testing
of Wood, pages 49–54, Duluth. Forest Products Society.

Lin, C., Chang, T., Juan, M., Lin, T., Tseng, C., Wang, Y., and
Tsai, M. (2011). Stress Wave Tomography for the Quantification of Artificial Hole Detection in Camphor Trees
(Cinnamomum camphora). Taiwan J For Sci, 26(1):17–32.

Martins, F., Rollo, D. A., Angelo, M., Junior, S., Viana, S. M.,
Cavalcante, L., Rollo, P., Thadeu, H., and Ferreira, D.
(2013). Comparison between resistography readings and
tomographic images for internal assessment in trees trunks.
Cerne, 19(2):331–337.

R Core Team (2016). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing,
Vienna, Austria.

Rabe, C., Ferner, D., Fink, S., and Schwarze, F. W. (2004).
Detection of decay in trees with stress waves and inter-
pretation of acoustic tomograms. Arboricultural Journal,
28(1-2):3–19, doi:10.1080/03071375.2004.9747399.

Ramsey, M. H., Boley, N., Ellison, S. L. R., Hasselbarth,
W., Ischi, H., Wegscheider, W., and Zschunke, A. (2006).
Measurement strategy and quality. In Springer Handbook
of Materials Measurement Methods, chapter A2, pages

Rust, S. (2000). A new tomographic device for the non-
destructive testing of standing trees. In Proceedings of the
12th International Symposium on Nondestructive Testing
of Wood. University of Western Hungary, Sopron, pages

Schneider, C. A., Rasband, W. S., and Eliceiri, K. W. (2012).
NIH Image to ImageJ: 25 years of image analysis. Nature
Methods, 9(7):671–675, doi:10.1038/nmeth.2089.

Wang, X., Allison, R. B., Wang, L., and Ross, R. J. (2007).
Acoustic Tomography for Decay Detection in Red Oak
Trees. Technical report, U.S. Department of Agriculture,
Forest Ser- vice, Forest Products Laboratory, Madison.

Wang, X., Wiedenbeck, J., and Liang, S. (2009). Acoustic
tomography for decay detection in black cherry trees. Wood
and Fiber Science, 41(2):127–137.
Nov 6, 2017
How to Cite
RUST, Steffen. Accuracy and Reproducibility of Acoustic Tomography Significantly Increase with Precision of Sensor Position. Journal of Forest and Landscape Research, [S.l.], v. 2, n. 1, p. 1-6, nov. 2017. ISSN 2366-8164. Available at: <>. Date accessed: 18 jan. 2018. doi:
Short communication


Acoustic tomography; Picea abies; repeatability; reproducibility