Scope of Wood Scanning Applications in India - A Review

Scope of Wood Scanning Applications in India - A Review

Authors

  •   C Shibu   Department of Forest Products and Utilization, College of Forestry, Kerala Agricultural University
  •   C Arya   Arya
  •   C.P. VaysagP   Utilization, College of Forestry, Kerala Agricultural University
  •   E V AnooV   Utilization, College of Forestry, Kerala Agricultural University

DOI:

https://doi.org/10.36808/if/2022/v148i9/159850

Keywords:

Wood Scanning, NDT, Agar-wood, Urban forest, Genetic tree improvement, Silviculture management, and Wood industries

Abstract

This article describes the opportunities of major wood scanning
technologies in the Indian forestry field. Researchers have developed
various non-destructive technologies to better understand the inner
properties of wood. In the wood industry, the correct identification of wood
species and their physical (in furniture industries) and chemical (in paper
and pulp industries) characteristics is critical for end-use applications. The
measurements of wood properties (Elastic properties, Thermal properties,
Electrical properties, Dielectric properties, and Ionization radiation
properties) with the help of wood scanning technologies are used for
locating and quantifying wood decay and knots in both standing trees and
logs. These measurements have been employed in various perspectives on
forestry applications. For instance, measurement of Modulus of Elasticity
(MoE) is used to assess thinning effects and tree health assessments, and as
a breeding criterion in tree improvement. This article aims to describe major
wood scanning technologies that provide tomography of wood and the
scope of wood scanning technologies, and their current status in the Indian
forestry field. The major wood scanning technologies include Acoustic
Tomography, Electrical Impedance Tomography, Magnetic Induction
Tomography, Radiography Computed Tomography, and Microwave Tomography.

References

Abdullah A.H., Shakaff A.M., Adom A.H., Ahmad M.N., Zakaria A., Ghani S.A., Samsudin N.M., Saad F.S.A., Kamaruddin L.M., Hamid N.H. and Seman I.A. (2012). Exploring MIP sensor of basal stem rot (BSR) disease in palm oil plantation, In: Proceeding: The 14th International Meeting on Chemical Sensors, 20-23 may 2012, Nuremberg (Germany). 1348-1351.

Addis T., A.H. Buchanan and J.C.F. Walker. (1997). Log segregation into stiffness classes. In: Ridoutt, B.G. (ed.)., Managing variability in resource quality, Forest Research Institute, Rotorua. 7-10pp.

Al Hagrey S.A. (2006). Electrical resistivity imaging of tree trunks, Near Surface Geophysics, 4(3): 179-187.

Allison R.B., Wang X. and Senalik C.A. (2020). Methods for Nondestructive Testing of Urban Trees, Forests, 11(12): 1341.

Alves E.E.N., Rodriguez D.R.O., de Azevedo Rocha P., Vergütz L., Junior L.S., Hesterberg D., Pessenda L.C.R., TomazelloFilho M. and da Costa L.M. (2021). Synchrotron-based X-ray microscopy for assessing elements distribution and speciation in mangrove tree-rings, Results in Chemistry, 100-121.

Apiolaza L.A. (2009). Very early selection for solid wood quality: screening for early winners, Annals of Forest Science, 66(6): 1-10.

Arciniegas A., Prieto F., Brancheriau L. and Lasaygues P. (2014). Literature review of acoustic and ultrasonic tomography in standing trees, Trees, 28(6): 1559-1567.

Babst F., Poulter B., Bodesheim P., Mahecha M.D. and Frank D.C. (2017). Improved tree-ring archives will support earthsystem science, Nature Ecology and Evolution, 1(2): 1-2.

Bar A., Hamacher M., Ganthaler A., Losso A. and Mayr S. (2019). Electrical resistivity tomography: patterns in Betula pendula, Fagus sylvatica, Piceaabies and Pinussylvestris, Tree physiology, 39(7): 1262-1271.

Bieker D. and Rust S. (2010). Electric resistivity tomography shows radial variation of electrolytes in Quercusrobur, Canadian Journal of Forest Research. 40(6): 1189-1193.

Bill J., Daly A., Johnsen and Dalen K.S. (2012). Dendro CT–dendrochronology with out damage , Dendrochronologia, 30(3):223-230.

Boero F., Fedeli A., Lanini M., Maffongelli M., Monleone R., Pastorino M., Randazzo A., Salvade A. and Sansalone A. (2018). Microwave tomography for the inspection of wood materials: Imaging system and experimental results, IEEE Transactions on Microwave Theory and Techniques, 66(7): 3497-3510.

Brazee N.J., Marra R.E., Göcke L. and Van Wassenaer P. (2011). Non-destructive assessment of internal decay in three hardwood species of northeastern North America using sonic and electrical impedance tomography, Forestry, 84(1) : 33-39. 5(1): 149-152 pp

Bucur V. (2003b). Techniques for high resolution imaging of wood structure: a review. Measurement Science and Technology, 14(12): 1-8.

Cai C., Javed M.A., Komulainen S., Telkki V.V., Haapala A. and Heräjärvi H. (2020). Effect of natural weathering on water absorption and pore size distribution in thermally modified wood determined by nuclear magnetic resonance, Cellulose,113pp.

Catena A. (2003). Thermography Shows Damaged Tissue and Cavities Present in Trees. In: Nondestructive Characterization of Materials XI (R.E.Green, B.B. Djordjevic and M.P. Hentschel, Eds.) Advances in the statistical sciences, Springer, Berlin, pp 515-522.

Deflorio G., Fink S. and Schwarze F.W. (2008). Detection of incipient decay in tree stems with sonic tomography after wounding and fungal inoculation, Wood Science and Technology, 42(2):117-132.

Divos F. and Daniel I. (2001). Defect detection in timber by stress wave time and amplitude, The e-Journal of nondestructive testing, 6(3): 1-3 Doroshewitz J.J. (2019). A Microwave Tomography System for Forestry Applications, Msc thesis, Michigan State University: 112 pp

Du X., Li J., Feng H. and Chen S. (2018). Image reconstruction of internal defects in wood based on segmented propagation rays of stress waves, Applied Sciences, 8(10): 1778.

Edusei G., Tandoh J.B., Edziah R., Gyampo O. and Ahiamadjie H. (2021). Chronological Study of Metallic Pollution Using Tree Rings at Tema Industrial Area, Pollution, 7(1): 197-204.

Espinosa L., Arciniegas A., Cortes Y., Prieto F. and Brancheriau L. (2017). Automatic segmentation of acoustic tomography images for the measurement of wood decay, Wood Science and Technology, 51(1): 69-84.

Bucur V. (2003a). Nondestructive characterization and imaging of wood. Springer Science and Business Media, Germany, . Espinosa L., Brancheriau L., Cortes Y., Prieto F. and Lasaygues P. (2020). Ultrasound computed tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects, Annals of Forest Science, 77(3): 1-13.

Essien C., Cheng Q., Via B.K., Loewenstein E.F. and Wang X. (2016). An acoustics operations study for Loblolly pine (Pinustaeda) standing saw timber with different thinning history, Bio Resources, 11(3): 7512-7521.

Feng H., Qian Z., Hu M., Zheng Z. and Du X. (2018). The Study of Stress Wave Tomography Algorithm for Internal Defects in RL Plane of Wood, Chinese Automation Congress, 2283-2288pp.

Fundova I., Funda T. and Wu H.X. (2019). Non-destructive assessment of wood stiffness in Scots pine (Pinus sylvestris L.) and its use in forest tree improvement, Forests, 10(6): 491.

Gao S., Wang X., Wiemann M.C., Brashaw B.K., Ross R.J. and Wang L. (2017). A critical analysis of methods for rapid and nondestructive determination of wood density in standing trees, Annals of Forest Science, 74(2): 27.

Gerge T., Bucha T., Gejdos M. and Vyhnalikova Z. (2019). Computed tomography log scanning–high technology for forestry and forest based industry. Central European Forestry Journal, 65(1): 51-59.

Gocke L., Rust S., Weihs U., Gunther T. and Rucker C. (2007). Combining sonic and electrical impedance tomography for the non-destructive testing of trees, Western Arborist, 31-42pp.

Goh C.L., Rahim R.A., Rahiman M.H.F., Talib M.T.M. and Tee Z.C. (2018). Sensing wood decay in standing trees: A review, Sensors and Actuators, 269(3): 276-282.

Guyot A., Ostergaard K.T., Lenkopane M., Fan J. and Lockington D.A. (2013). Using electrical resistivity tomography to differentiate sapwood from heartwood: application to conifers, Tree physiology, 33(2): 187-194.

Hargitai L. and Gergely L. (2002). Practical application of nuclear magnetic tomography in wood processing. Part 1: Introduction, theory, Faipar, 7-9pp.

Indahsuary N., Nandika D., Karlinasari L. and Santoso E. (2014). Reliability of sonic tomography to detect agar-wood in AquilariamicrocarpaBaill, Journal of the Indian Academy of Wood Science, 11(1): 65-71.

ITTO (2020). International Tropical Timber Organisation, Tropical Timber Market

Junior A.F.D., Pincelli A.L.M.S., da Silva A.P.C., da Silva Á.M., de Souza N.D., TommasielloFilho M. and Brito J.O. (2021). Integrating species and successional classes for wood production in a mixed forest restoration system in a neotropical region, Journal of Forestry Research, 1-9pp.

Karlinasari L., Indahsuary N., Kusumo H.T., Santoso E., Turjaman M. and Nandika D. (2015). Sonic and ultrasonic waves in agar-wood trees (Aquilariamicrocarpa) inoculated with Fusarium solani, Journal of Tropical Forest Science, 27(3): 351-356.

Karlinasari L., Putri N., Turjaman M., Wahyudi I. and Nandika D. (2016). Moisture content effect on sound wave velocity and acoustic tomograms in agar-wood trees (AquilariamalaccensisLamk.). Turkish Journal of Agriculture and Forestry, 40(5): 696-704.

Kasal B. and Tannert T. (eds.), (2011). In situ assessment of structural timber, Springer Science and Business Media. London, 5-45pp.

Kasal B. (2010). In situ assessment of structural timber: stateofthe-art, challenges and future directions.Advanced Materials Research, (133): .43-52.

Kiat T.T.W., Rahiman M.H.F. and Jack S.P. (2016). An initial study to investigate the potential of microwave tomography for agar-wood imaging, International Journal of Agriculture, Forestry and Plantation, (4): 26-31.

Kumar C., Psaltis S., Bailleres H., Turner I., Brancheriau L., Hopewell G., Carr E.J., Farrell T. and Lee D.J. (2021). Accurate estimation of log MOE from non-destructive standing tree measurements, Annals of Forest Science, 78(1): 1-15pp

Lasserre J.P., Mason E.G. and Watt M.S. (2008). Influence of the main and interactive effects of site, stand stocking and clone on Pinus radiata D. Don corewood modulus of elasticity, Forest Ecology and Management, 255(8-9): 3455-3459.

Launay J., Ivkovich M., Pâques L., Bastien C., Higelin P. and Rozenberg P. (2002). Rapid measurement of trunk MOE on standing trees using Rigidimeter, Annals of forest science, 59(5-6): 465-469.

Liang S. and Fu F. (2014). Effect of sensor number and distribution on accuracy rate of wood defect detection with stress wave tomography. Wood Research (Bratislava), 59(4): 521-532.

Lin C.J., Kao Y.C., Lin T.T., Tsai M.J., Wang S.Y., Lin L.D., Wang Y.N. and Chan M.H. (2008). Application of an ultrasonic tomographic technique for detecting defects in standing trees, International Biodeterioration and Biodegradation, 62(4): 434441.

Lowell E.C., Todoroki C.L., Dykstra D.P. and Briggs D.G. (2014). Linking acoustic velocity of standing Douglas-fir trees to veneer stiffness: a tree-log-product study across thinning treatments, New Zealand Journal of Forestry Science, 44(1): 116.

Lundahl C.G. and Grönlund A. (2010). Increased yield in sawmills by applying alternate rotation and lateral positioning, Fore. Products J., 60(4): 331-338.

Luo Z., Guan H. and Zhang X. (2019). The temperature effect and correction models for using electrical resistivity to estimate wood moisture variations, Journal of Hydrology, 578(2019): 124022.

Ma T., Inagaki T. and Tsuchikawa S. (2018). Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging, Journal of Near Infrared Spectroscopy, 26(6): 398-405.

Marra R.E., Brazee N.J. and Fraver S. (2018). Estimating carbon loss due to internal decay in living trees using tomography: implications for forest carbon budget. Environmental Research Letters, 13(10): 105004.

McGovern E., Megan E., Senalik A., Chen G., Frank C. and Reis H. (2013). Effect of decay on ultrasonic velocity and attenuation measurements in wood, Materials Evaluation, 71(3): 10-15.

Nicolotti G., Socco L.V., Martinis R., Godio A. and Sambuelli L. (2003). Application and comparison of three tomographic techniques for detection of decay in trees. Journal of arboriculture, 29(2): 66-78.

Nordmark U. (2005). Value recovery and production control in bucking, log sorting, and log breakdown, Forest products journal, 55(6): 73-79.

Oja J., Grundberg S., Fredriksson J. and Berg P. (2004). Automatic grading ofsawlogs: a comparison between X-ray scanning, optical three-dimensional scanning and combinations of both methods, Scandinavian Journal of Forest Research. 19(1): 89-95.

Ondrejka V., Gergeľ T., Bucha T. and Pástor M. (2020). Innovative methods of non-destructive evaluation of log quality, Central European journal, 66(2020): 1-8.

Onoe M., Tsao J.W., Yamada H., Nakamura H., Kogure J., Kawamura H. and Yoshimatsu M., (1984). Computed tomography for measuring the annual rings of a live tree, Nuclear instruments and methods in physics research, 221(1): 213-220.

Putri N., Karlinasari L., Turjaman M., Wahyudi I. and Nandika D. (2017). Evaluation of incense-resinous wood formation in agarwood (Aquilariamalaccensis Lam.) using sonic tomography, Agriculture and Natural Resources, 51(2): 84-90.

Puxeddu M., Cuccuru F., Fais S., Casula G. and Bianchi M.G. (2021). 3D Imaging of CRP and Ultrasonic Tomography to Detect Decay in a Living Adult Holm Oak (Quercus ilex L.) in Sardinia (Italy). Applied Sciences, 11(3): 1199.

Qin R., Qiu Q., Lam J.H., Tang A.M., Leung M.W. and Lau D. (2018). Health assessment of tree trunk by using acoustic-laser technique and sonic tomography, Wood science and technology, 52(4): 1113-1132.

Rahiman M.H.F., Thomas T.W.K., Soh P.J., Rahim R.A., Jamaludin J., Ramli M.F. and Zakaria Z. (2019). Microwave tomography sensing for potential agar-wood trees imaging, Computers and Electronics in Agriculture, 164(2019): 104901.

Raj B., Jayakumar T. and Thavasimuthu M. (2002). Practical nondestructive testing(2nd Ed), Woodhead Publishing, India, 8-87pp.

Rodriguez D.R.O., Alves E.E., Rocha P.A., Costa L.M. and F.M.T. (2017). Dendrochronology application: Potential of the Xray microdensitometric and μ-EDXRF in tree-rings physical and chemical analysis of Pinustaedawood. In: Proceeding: 20th international non-destructive testing and evaluation of wood symposium, 12–15 September 2017, Madison, (USA), 526pp Rodriguez D.R.O.,

de Almeida E., Tomazello-Filho M. and de Carvalho H.W.P. (2020). Space-resolved determination of the mineral nutrient content in tree-rings by X-ray fluorescence, Science of The Total Environment, 708(2020): 134537 Ross R.J. (ed.). (2015). Non-destructive evaluation of wood (2nd Ed). FPL, USA, 21-25pp.

Russo D., Marziliano P.A., Macri G., Proto A.R., Zimbalatti G., and Lombardi F. (2019). Does Thinning Intensity Affect Wood Quality? An Analysis of Calabrian Pine in Southern Italy Using a Non-Destructive Acoustic Method, Forests, 10(4): 303.

Saeidi T., Ismail I., Mahmood S.N., Alani S. and Alhawari A.R. (2020). Microwave imaging of voids in oil palm trunk applying UWB antenna and robust time-reversal algorithm, Journal of Sensors, (2020): 1-17

Sajeev T.V., Swamy G.E., George A., Vimod K.K., Anitha K. and Joseph S. (2019). Avenue Tree Health Assessment in a Tropical City-A Case Study from Thiruvananthapuram Corporation, Kerala, India.Climate Change and Environmental Sustainability, 7(2): 199-207.

Salvade A., Poretti S., Maffongelli M., Monleone R., Lanini M., Pastorino M., Randazzo A. and Fedeli A. (2015). A Microwave Tomographic System for Wood Characterization in the Forest Products Industry, Wood Material Science and Engineering, 10(1): 1-5

Schimleck L., Dahlen J., Apiolaza L.A., Downes G., Emms G., Evans R., Moore J., Pâques L., Van den Bulcke J. and Wang X. (2019). Non-destructive evaluation techniques and what they tell us about wood property variation, Forests, 10(9): 728.

Song J., Brendel O., Bodénès C., Plomion C., Kremer A. and Colin F. (2017). X-ray computed tomography to decipher the genetic architecture of tree branching traits: oak as a case study, Tree Genetics and Genomes, 13(1): 1-15.

Stangle S.M., Bruchert F., Heikkila A., Usenius T., Usenius A. and Sauter U.H. (2015). Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection, 72(1): 57-65.

Tabin T. and Shrivastava K. (2014). Distribution and population status of critically endangered Aquilaria malaccensisLamk. In the forests of Arunachal Pradesh and Assam, India. India, International Journal of Innovative Research in Science, (3): 17595-7604.

Taskhiri M.S., Hafezi M.H., Harle R., Williams D., Kundu T. and Turner P. (2020). Ultrasonic and thermal testing to nondestructively identify internal defects in plantation eucalypts. Computers and Electronics in Agriculture, (173): 105396.

Vanam B. (2019). Timber trade in India-challenges and policies. EPRA International Journal ofMultidisciplinary Research (IJMR), 12(5): 119-122.

Wang P.C. and Chang S.J. (2007a). Nuclear magnetic resonance imaging of wood, Wood and fiber science, 18(2): 308-3: 14.

Wang X., Carter P., Ross R.J. and Brashaw B.K. (2007b). Acoustic assessment of wood quality of raw forest materials: a path to increased profitability, Forest products journal. 5(57): 6-14.

Wang X., Divos F., Pilon C., Brashaw B.K., Ross R.J. and Pellerin R.F. (2004). Assessment of decay in standing timber using stress wave timing nondestructive evaluation tools: A guide for use and interpretation, Gen. Tech. Rep. FPL-GTR-147. Madison, WI: US Department of Agriculture, Forest Service, Forest Products Laboratory, (2004): 2-7.

Wang X., Wiedenbeck J. and Liang S. (2009). Acoustic tomography for decay detection in black cherry trees, USDA Forest Service, Forest Products Laboratory, 127-137pp.

Wei Q., Leblon B. and La Rocque A. (2011). On the use of X-ray computed tomography for determining wood properties: a review, Canadian journal of forest research, 41(11): 2120-2140. Annals of forest science,

Win K.K., Oh J.K., Kim C.K., Hong J.P. and Lee J.J. (2015). Development of stress wave indices for heart-rot detection in teak tree, Wood Science Technology, 49(5): 1021-1035.

Xiao X., Wen J., Xiao Z., and Li W. (2018). Detecting and measuring internal anomalies in tree trunks using radar data for layer identification, Journal Sensors, 3(5): 45-98.

Xu K., Lu J., Gao Y., Wu Y. and Li X. (2017). Determination of moisture content and moisture content profiles in wood during drying by low-field nuclear magnetic resonance, Drying Technology, 35(15) : 1909-1918.

Xu P., Guan C., Zhang H., Li G., Zhao D., Ross R.J. and Shen Y. (2021). Application of Nondestructive Testing Technologies in Preserving Historic Trees and Ancient Timber Structures in China, Forests, 12(3): 318.

Xu Z., Leininger T.D., Williams J.G. and Tainter F.H. (2000). Examination of the Arborsonic Decay Detector for detecting bacterial wetwood in red oaks, Southern Journal of Applied Forestry, 24(1): 6-10.

Zakaria Z., Airiman A.A.H., Talib M.T.M., Ibrahim M., Balkhis I., Mansor M.S.B. and Rahim R.A. (2015). A review of noninvasive imaging: The opportunity of magnetic induction tomography modality in agar-wood industry, Journal of Teknologi., 77(17): 116-117.

Zakaria Z., Mansor M.S.B., Rahim R.A., Balkhis I., Rahiman M.H.F., Rahim S. and Yaacob D. (2013a). Magnetic induction tomography: A review of non-invasive imaging, the opportunity of magnetic induction tomography modality in agar-wood industry. Journal of Technology, (1): 17-118.

Zakaria Z., Mansor M.S.B., Rahim R.A., Balkhis I., Rahiman M.H.F., Rahim H.A. and Yaacob S. (2013b). Magnetic induction tomography: A review on the potential application in agricultural industry of Malaysia, Journal of Forest Research, 5(9): 78.

Internet sources

Microtec, 2019: CT Log Computed Tomography for the sawmill of the future. [Brochure], Available at: https://microtec.eu/assets/products/ctlog/MT-CT-Log2.pdf

Tamilnadu State Disaster Management Authority - NSDMA, 2020 https://tnsdma.tn.gov.in/

https://mumbaimirror.indiatimes.com/mumbai/civic/deathsare-rising-at-the-drop-of-a-tree/articleshow/70684318.cms

Downloads

Download data is not yet available.

Author Biographies

C Shibu, Department of Forest Products and Utilization, College of Forestry, Kerala Agricultural University

MSC forestry,

Department of forest products and utilization,

College of Forestry,

Kerala Agricultural University,

Vellanikkara, Thrissur, Kerala 

C Arya, Arya

BSC forestry,

College of Forestry,

Kerala Agricultural University,

Vellanikkara, Thrissur, Kerala 

C.P. VaysagP, Utilization, College of Forestry, Kerala Agricultural University

BSC forestry

College of Forestry

Kerala Agricultural University

Vellanikkara, Thrissur, Kerala 

E V AnooV, Utilization, College of Forestry, Kerala Agricultural University

Professor & Head

Dept. of Forest Products & Utilization (Wood Science)

College of Forestry

Kerala Agricultural University

Vellanikkara, Thrissur, Kerala 

Published

2022-10-30

How to Cite

Shibu, C., Arya, C., VaysagP, C., & AnooV, E. V. (2022). Scope of Wood Scanning Applications in India - A Review. Indian Forester, 148(9), 915–923. https://doi.org/10.36808/if/2022/v148i9/159850
Loading...