X-ray CT; computed tomography; deep learning; densitometry; earth system sciences; increment cores; multiscale imaging; scanner simulation; tree ring analysis; wood traits; Densitometry; X-Rays; Tomography, X-Ray Computed; Wood; Plant Science
Abstract :
[en] [en] BACKGROUND AND AIMS: Tree rings, as archives of the past and biosensors of the present, offer unique opportunities to study influences of the fluctuating environment over decades to centuries. As such, tree-ring-based wood traits are capital input for global vegetation models. To contribute to earth system sciences, however, sufficient spatial coverage is required of detailed individual-based measurements, necessitating large amounts of data. X-ray computed tomography (CT) scanning is one of the few techniques that can deliver such data sets.
METHODS: Increment cores of four different temperate tree species were scanned with a state-of-the-art X-ray CT system at resolutions ranging from 60 μm down to 4.5 μm, with an additional scan at a resolution of 0.8 μm of a splinter-sized sample using a second X-ray CT system to highlight the potential of cell-level scanning. Calibration-free densitometry, based on full scanner simulation of a third X-ray CT system, is illustrated on increment cores of a tropical tree species.
KEY RESULTS: We show how multiscale scanning offers unprecedented potential for mapping tree rings and wood traits without sample manipulation and with limited operator intervention. Custom-designed sample holders enable simultaneous scanning of multiple increment cores at resolutions sufficient for tree ring analysis and densitometry as well as single core scanning enabling quantitative wood anatomy, thereby approaching the conventional thin section approach. Standardized X-ray CT volumes are, furthermore, ideal input imagery for automated pipelines with neural-based learning for tree ring detection and measurements of wood traits.
CONCLUSIONS: Advanced X-ray CT scanning for high-throughput processing of increment cores is within reach, generating pith-to-bark ring width series, density profiles and wood trait data. This would allow contribution to large-scale monitoring and modelling efforts with sufficient global coverage.
Van den Bulcke, Jan; UGent-Woodlab, Laboratory of Wood Technology, Department of Environment, Ghent University, Gent, Belgium ; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium
Boone, Marijn A; TESCAN XRE, Gent, Belgium
Dhaene, Jelle; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium ; Radiation Physics Research Group, Department of Physics and Astronomy, Ghent University, Gent, Belgium
Van Loo, Denis; TESCAN XRE, Gent, Belgium
Van Hoorebeke, Luc; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium ; Radiation Physics Research Group, Department of Physics and Astronomy, Ghent University, Gent, Belgium
Boone, Matthieu N; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium ; Radiation Physics Research Group, Department of Physics and Astronomy, Ghent University, Gent, Belgium
Wyffels, Francis; ELIS Department, Ghent University - imec, Ghent, Belgium
Beeckman, Hans; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium
Van Acker, Joris; UGent-Woodlab, Laboratory of Wood Technology, Department of Environment, Ghent University, Gent, Belgium ; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium
De Mil, Tom ; Université de Liège - ULiège > TERRA Research Centre > Gestion des ressources forestières ; UGent-Woodlab, Laboratory of Wood Technology, Department of Environment, Ghent University, Gent, Belgium ; Ghent University Centre for X-ray Tomography (UGCT), Gent, Belgium ; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium
Language :
English
Title :
Advanced X-ray CT scanning can boost tree ring research for earth system sciences.
The Special Research Fund of Ghent University (BOF-UGent) is acknowledged for the funding under grant number BOF17-GOA-015, the financial support of the UGCT Center of Expertise (BOF.EXP.2017.0007) and the funding of the XINCAST project.
Arganda-Carreras I, Kaynig V, Rueden C, et al. 2017. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 33: 2424-2426.
von Arx G, Carrer M. 2014. ROXAS - a new tool to build centuries-long tracheid-lumen chronologies in conifers. Dendrochronologia 32: 290-293.
von Arx G, Crivellaro A, Prendin AL, Čufar K, Carrer M. 2016. Quantitative wood anatomy - practical guidelines. Frontiers in Plant Science 7: 781. doi: 10.3389/fpls.2016.00781.
Babst F, Poulter B, Bodesheim P, Mahecha MD, Frank DC. 2017. Improved tree-ring archives will support earth-system science. Nature Ecology & Evolution 1: 8. doi: 10.1038/s41559-016-0008.
Bastin J-F, Fayolle A, Tarelkin Y, Van den Bulcke J, de Haulleville T, Mortier F, et al. 2015. Wood specific gravity variations and biomass of central african tree species: The simple choice of the outer wood. PLoS ONE 10: E0142146. doi:10.1371/journal.pone.0142146
Bednarz T, Wang D, Arzhaeva Y, et al. 2015. Cloud based toolbox for image analysis, processing and reconstruction tasks. In: Sun C, Bednarz T, Pham TD, Vallotton P, Wang D, eds. Signal and image analysis for biomedical and life sciences. Cham: Springer, 191-205.
Beeckman H. 2016. Wood anatomy and trait-based ecology. IAWA Journal 37: 127-151.
Bill J, Daly A, Johnsen Ø, Dalen KS. 2012. DendroCT - dendrochronology without damage. Dendrochronologia 30: 223-230.
Björklund JA, Gunnarson BE, Seftigen K, Esper J, Linderholm HW. 2014. Blue intensity and density from northern Fennoscandian tree rings, exploring the potential to improve summer temperature reconstructions with earlywood information. Climate of the Past 10: 877-885.
Boone MN, Devulder W, Dierick M, Brabant L, Pauwels E, Van Hoorebeke L. 2012. Comparison of two single-image phase-retrieval algorithms for in-line x-ray phase-contrast imaging. Journal of the Optical Society of America A 29: 2667-2672.
Brienen RJ, Helle G, Pons TL, Guyot JL, Gloor M. 2012. Oxygen isotopes in tree rings are a good proxy for Amazon precipitation and El Nino-Southern Oscillation variability. Proceedings of the National Academy of Sciences, USA 109: 16957-16962.
Burvall A, Lundström U, Takman PA, Larsson DH, Hertz HM. 2011. Phase retrieval in X-ray phase-contrast imaging suitable for tomography. Optics Express 19: 10359-10376.
Busse M, Müller M, Kimm MA, et al. 2018. Three-dimensional virtual histology enabled through cytoplasm-specific X-ray stain for microscopic and nanoscopic computed tomography. Proceedings of the National Academy of Sciences, USA 115: 2293-2298.
Carrer M. 2011. Individualistic and time-varying tree-ring growth to climate sensitivity. PLoS One 6: E22813. doi: 10.1371/journal.pone.0022813.
Carrer M, Castagneri D, Prendin AL, Petit G, von Arx G. 2017. Retrospective analysis of wood anatomical traits reveals a recent extension in tree cambial activity in two high-elevation conifers. Frontiers in Plant Science 8: 737. doi: 10.3389/fpls.2017.00737.
Cuny HE, Rathgeber CB, Frank D, et al. 2015. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nature Plants 1: 15160. doi: 10.1038/nplants.2015.160.
Davies TG, Rahman IA, Lautenschlager S, et al. 2017. Open data and digital morphology. Proceedings of the Royal Society B: Biological Sciences 284: 20170194. doi: 10.1098/rspb.2017.0194
De Carlo F, Gürsoy D, Ching DJ, et al. 2018. TomoBank: A tomographic data repository for computational x-ray science. Measurement Science and Technology 29: 034004.
De Gooijer JG, Hyndman RJ. 2006. 25 years of time series forecasting. International Journal of Forecasting 22: 443-473.
De Groote SR, Vanhellemont M, Baeten L, et al. 2018. Competition, tree age and size drive the productivity of mixed forests of pedunculate oak, beech and red oak. Forest Ecology and Management 430: 609-617.
De Mil T, Vannoppen A, Beeckman H, Van Acker J, Van den Bulcke J. 2016. A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis. Annals of Botany 117: 1187-1196.
De Mil T, Tarelkin Y, Hahn S, et al. 2018. Wood density profiles and their corresponding tissue fractions in tropical angiosperm trees. Forests 9: 763. doi: 10.3390/f9120763.
De Ridder M, Van den Bulcke J, Vansteenkiste D, et al. 2010. Highresolution proxies for wood density variations in Terminalia superba. Annals of Botany 107: 293-302.
De Schryver T. 2017. Fast imaging in non-standard X-ray computed tomography geometries. PhD Thesis, Ghent University.
De Witte Y. 2010. Improved and practically feasible reconstruction methods for high resolution X-ray tomography. PhD Thesis, Ghent University.
Dhaene J. 2017. Development and application of a highly accurate polychromatic X-ray microtomography simulator. PhD Thesis, Ghent University.
Dhaene J, Pauwels E, De Schryver T, De Muynck A, Dierick M, Van Hoorebeke L. 2015. A realistic projection simulator for laboratory based X-ray micro-CT. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 342: 170-178.
Dierick M, Van Loo D, Masschaele B, Boone M, Van Hoorebeke L. 2010. A LabVIEW® based generic CT scanner control software platform. Journal of X-ray Science and Technology 18: 451-461.
Dierick M, Van Loo D, Masschaele B, et al. 2014. Recent micro-CT scanner developments at UGCT. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 324: 35-40.
Du Plessis A, Broeckhoven C, Guelpa A, Le Roux SG. 2017. Laboratory x-ray micro-computed tomography: A user guideline for biological samples. GigaScience 6: 1-11.
Fabijańska A, Danek M. 2018. DeepDendro - a tree rings detector based on a deep convolutional neural network. Computers and Electronics in Agriculture 150: 353-363.
Fonti P, García-González I. 2008. Earlywood vessel size of oak as a potential proxy for spring precipitation in mesic sites. Journal of Biogeography 35: 2249-2257.
García-González I, Souto-Herrero M, Campelo F. 2016. Ring-porosity and earlywood vessels: A review on extracting environmental information through time. IAWA Journal 37: 295-314.
Gärtner H, Nievergelt, D. 2010. The core-microtome: A new tool for surface preparation on cores and time series analysis of varying cell parameters. Dendrochronologia 28: 85-92.
Gärtner H, Cherubini P, Fonti P, et al. 2015. A technical perspective in modern tree-ring research - how to overcome dendroecological and wood anatomical challenges. Journal of Visualized Experiments 97: Doi: 10.3791/52337.
Grabner M, Salaberger D, Okochi T. 2009. The need of high resolution μ-Xray CT in dendrochronology and in wood identification. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, 2009. IEEE, 349-352.
Hevia A, Sánchez-Salguero R, Camarero JJ, et al. 2018. Towards a better understanding of long-term wood-chemistry variations in old-growth forests: A case study on ancient Pinus uncinata trees from the Pyrenees. Science of the Total Environment 625: 220-232.
Jacquin P, Longuetaud F, Leban J.M, Mothe F. 2017. X-ray microdensitometry of wood: A review of existing principles and devices. Dendrochronologia 42: 42-50.
Jacquin P, Mothe F, Longuetaud F, Billard A, Kerfriden B, Leban JM. 2019. CarDen: A software for fast measurement of wood density on increment cores by CT scanning. Computers and Electronics in Agriculture 156: 606-617.
Jaeger H, Haas H. 2004. Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304: 78-80.
Katsevich A. 2002. Theoretically exact filtered backprojection-type inversion algorithm for spiral CT. SIAM Journal on Applied Mathematics 62: 2012-2026.
Latham S, Kingston A, Recur B et al. 2018. Reprojection alignment for trajectory perturbation estimation in. IEEE Transactions on Computational Imaging 4: 271-283.
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521: 436-444.
Liang W, Heinrich I, Helle G, Liñán ID, Heinken T. 2013. Applying CLSM to increment core surfaces for histometric analyses: A novel advance in quantitative wood anatomy. Dendrochronologia 31: 140-145.
Maes SL, Vannoppen A, Altman J, et al. 2017. Evaluating the robustness of three ring-width measurement methods for growth release reconstruction. Dendrochronologia 46: 67-76.
Masschaele BC, Cnudde V, Dierick M, Jacobs P, Van Hoorebeke L, Vlassenbroeck J. 2007. UGCT: new X-ray radiography and tomography facility. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 580: 266-269.
Masschaele B, Dierick M, Van Loo D, et al. 2013. HECTOR: A 240kV micro-CT setup optimized for research. Journal of Physics: Conference Series 463: 012012.
Meng B, Pratx G, Xing L. 2011. Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment. Medical Physics 38: 6603-6609.
Nehrbass-Ahles C, Babst F, Klesse S, et al. 2014. The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology 20: 2867-2685.
Okochi T, Hoshino Y, Fujii H, Mitsutani T. 2007. Nondestructive tree-ring measurements for Japanese oak and Japanese beech using micro-focus X-ray computed tomography. Dendrochronologia 24: 155-164.
Onoe M, Tsao JW, Yamada H, et al. 1984. Computed tomography for measuring the annual rings of a live tree. Nuclear Instruments and Methods in Physics Research 221: 213-220.
Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33-40.
Pauwels E, Van Loo D, Cornillie P, Brabant L, d Van Hoorebeke L. 2013. An exploratory study of contrast agents for soft tissue visualization by means of high resolution X-ray computed tomography imaging. Journal of Microscopy 250: 21-31.
Preibisch S, Saalfeld S, Tomancak P. 2009. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25: 1463-1465.
Rowe TB, Luo ZX, Ketcham RA, Maisano JA, Colbert MW. 2016. X-ray computed tomography datasets for forensic analysis of vertebrate fossils. Scientific Data 3: 160040. doi: 10.1038/sdata.2016.40.
Saß U, Eckstein D. 1994. Preparation of large thin sections and surfaces of wood for automatic image analysis. Holzforschung 48: 117-118.
Scharnweber T, Hevia A, Buras A, van der Maaten E, Wilmking M. 2016. Common trends in elements? Within- and between-tree variations of wood-chemistry measured by X-ray fluorescence - a dendrochemical study. Science of the Total Environment 566: 1245-1253.
Scheiter S, Langan L, Higgins SI. 2013. Next-generation dynamic global vegetation models: Learning from community ecology. New Phytologist 198: 957-969.
Schindelin J, Arganda-Carreras I, Frise E, et al. 2012. Fiji: An open-source platform for biological-image analysis. Nature Methods 9: 676-682.
Schuldt B, Leuschner C, Brock N, Horna V. 2013. Changes in wood density, wood anatomy and hydraulic properties of the xylem along the root-toshoot flow path in tropical rainforest trees. Tree Physiology 33: 161-174.
Schweingruber FH, Fritts HC, Bräker OU, Drew LG, Schär E. 1978. The X-ray technique as applied to dendroclimatology. Tree-Ring Bulletin 38.
Staedler YM, Masson D, Schönenberger J. 2013. Plant tissues in 3D via X-ray tomography: Simple contrasting methods allow high resolution imaging. PLoS One 8: E75295. doi: 10.1371/journal.pone.0075295.
Steffenrem A, Kvaalen H, Dalen KS, Høibø OA. 2014. A high-throughput X-ray-based method for measurements of relative wood density from unprepared increment cores from Picea abies. Scandinavian Journal of Forest Research 29: 506-514.
Stelzner J, Million S. 2015. X-ray computed tomography for the anatomical and dendrochronological analysis of archaeological wood. Journal of Archaeological Science 55: 188-196.
Van den Bulcke J, Wernersson EL, Dierick M, et al. 2014. 3D tree-ring analysis using helical X-ray tomography. Dendrochronologia 32: 39-46.
Van Loo D, Bouckaert L, Leroux O, et al. 2014. Contrast agents for soil investigation with X-ray computed tomography. Geoderma 213: 485-491.
Vannoppen A, Maes S, Kint V, et al. 2017. Using X-ray CT based tree-ring width data for tree growth trend analysis. Dendrochronologia 44: 66-75.
Vannoppen A, Boeckx P, De Mil T, et al. 2018. Climate driven trends in tree biomass increment show asynchronous dependence on tree-ring width and wood density variation. Dendrochronologia 48: 40-51.
Vlassenbroeck J, Dierick M, Masschaele B, Cnudde V, Van Hoorebeke L, Jacobs P. 2007. Software tools for quantification of X-ray microtomography at the UGCT. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 580: 442-445.
Voyant C, Notton G, Kalogirou S, et al. 2017. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 105: 569-582.
Wong J, D'Sa D, Foley M, Chan JGY, Chan HK. 2014. NanoXCT: A novel technique to probe the internal architecture of pharmaceutical particles. Pharmaceutical Research 31: 3085-3094.
wyffels F, Schrauwen B. 2010. A comparative study of reservoir computing strategies for monthly time series prediction. Neurocomputing 73: 1958-1964.
Zuidema PA, Poulter B, Frank DC. 2018. A wood biology agenda to support global vegetation modelling. Trends in Plant Science 23: 1006-1015.
