A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis

Mil, Tom De; Vannoppen, Astrid; Beeckman, Hans; Acker, Joris Van; Bulcke, Jan Van den

doi:10.1093/aob/mcw063

Cited by 35 publications

(33 citation statements)

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“…The use of the Nanowood system for the purpose of tree‐ring analysis was first reported in De Ridder et al (), where the original principles of 3D densitometry are highlighted and the proof of concept is validated in terms of accuracy compared to conventional density measurements, that is, gravimetric/volumetric measurements. This was further elaborated on in van den Bulcke et al (), introducing a software toolbox for processing of dendrochronological helical XCT (DHXCT) images, and scaled up to high‐throughput analyses as presented in De Mil et al (). To date, there are no publications that have used this system to derive MXD for dendroclimatological studies, but the functionality is available, evidenced by the successful production of data for the comparison experiment in section of this review, and several other studies are underway.…”

Section: Primary and Currently Applied Microdensitometric Techniquesmentioning

confidence: 99%

Scientific Merits and Analytical Challenges of Tree‐Ring Densitometry

Björklund

Arx

Nievergelt

et al. 2019

Reviews of Geophysics

115

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X-ray microdensitometry on annually resolved tree-ring samples has gained an exceptional position in last-millennium paleoclimatology through the maximum latewood density (MXD) parameter, but also increasingly through other density parameters. For 50 years, X-ray based measurement techniques have been the de facto standard. However, studies report offsets in the mean levels for MXD measurements derived from different laboratories, indicating challenges of accuracy and precision. Moreover, reflected visible light-based techniques are becoming increasingly popular, and wood anatomical techniques are emerging as a potentially powerful pathway to extract density information at the highest resolution. Here we review the current understanding and merits of wood density for tree-ring research, associated microdensitometric techniques, and analytical measurement challenges. The review is further complemented with a careful comparison of new measurements derived at 17 laboratories, using several different techniques. The new experiment allowed us to corroborate and refresh "long-standing wisdom" but also provide new insights. Key outcomes include (i) a demonstration of the need for mass/volume-based recalibration to accurately estimate average ring density; (ii) a substantiation of systematic differences in MXD measurements that cautions for great care when combining density data sets for climate reconstructions; and (iii) insights into the relevance of analytical measurement resolution in signals derived from tree-ring density data. Finally, we provide recommendations expected to facilitate future inter-comparability and interpretations for global change research.Plain Language Summary Paleoclimatology, the study of how the climate has changed throughout earth history, is an important component of climate change research. The wood density of tree

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Section: Primary and Currently Applied Microdensitometric Techniquesmentioning

confidence: 99%

Scientific Merits and Analytical Challenges of Tree‐Ring Densitometry

Björklund

Arx

Nievergelt

et al. 2019

Reviews of Geophysics

115

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“…From each tree, we took two perpendicular cores at breast height (Buchanan & Hart, 2011;Woodall, 2008). After drying the samples (24 hr at 103°C) to obtain correct wood density estimates, they were scanned with the X-ray Computed Tomography scanner (XCT, 110 µm resolution) Mil, Vannoppen, Beeckman, Acker, & Bulcke, 2016;Van den Bulcke et al, 2014). After drying the samples (24 hr at 103°C) to obtain correct wood density estimates, they were scanned with the X-ray Computed Tomography scanner (XCT, 110 µm resolution) Mil, Vannoppen, Beeckman, Acker, & Bulcke, 2016;Van den Bulcke et al, 2014).…”

Section: Data Collectionmentioning

confidence: 99%

“…were measured with the MATLAB-program DHXCT (De Mil et al, 2016). We used the automatic detection procedure based on wood density and visually inspected for missing and/or falsely indicated rings.…”

Section: Data Collectionmentioning

confidence: 99%

Environmental drivers interactively affect individual tree growth across temperate European forests

Maes

Perring

Vanhellemont

et al. 2018

Global Change Biology

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Forecasting the growth of tree species to future environmental changes requires a better understanding of its determinants. Tree growth is known to respond to global‐change drivers such as climate change or atmospheric deposition, as well as to local land‐use drivers such as forest management. Yet, large geographical scale studies examining interactive growth responses to multiple global‐change drivers are relatively scarce and rarely consider management effects. Here, we assessed the interactive effects of three global‐change drivers (temperature, precipitation and nitrogen deposition) on individual tree growth of three study species (Quercus robur/petraea, Fagus sylvatica and Fraxinus excelsior). We sampled trees along spatial environmental gradients across Europe and accounted for the effects of management for Quercus. We collected increment cores from 267 trees distributed over 151 plots in 19 forest regions and characterized their neighbouring environment to take into account potentially confounding factors such as tree size, competition, soil conditions and elevation. We demonstrate that growth responds interactively to global‐change drivers, with species‐specific sensitivities to the combined factors. Simultaneously high levels of precipitation and deposition benefited Fraxinus, but negatively affected Quercus’ growth, highlighting species‐specific interactive tree growth responses to combined drivers. For Fagus, a stronger growth response to higher temperatures was found when precipitation was also higher, illustrating the potential negative effects of drought stress under warming for this species. Furthermore, we show that past forest management can modulate the effects of changing temperatures on Quercus’ growth; individuals in plots with a coppicing history showed stronger growth responses to higher temperatures. Overall, our findings highlight how tree growth can be interactively determined by global‐change drivers, and how these growth responses might be modulated by past forest management. By showing future growth changes for scenarios of environmental change, we stress the importance of considering multiple drivers, including past management and their interactions, when predicting tree growth.

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“…Wood cores were inserted in paper straws and oven dried for 24 hours before being scanned at 110 µm using the NanoWood CT facility [28], developed in collaboration with XRE (X-ray Engineering, www.XRE.be). The scanned images were reconstructed (GPU GeForce GPX 770 4 GB) with the Octopus reconstruction software package ( [29,30], X-ray Engineering, www.XRE.be) and further processed (extraction of volumes, tilt correction, tangential correction) through a tailored toolchain [13]. X-ray CT wood density profiles result from correcting structure direction of fibre and ring deviations, calibrating with a reference material, and averaging in tangential and axial direction [15], based on 30-40 voxels at a given radial plane.…”

Section: X-ray Ct Densitometrymentioning

confidence: 99%

“…High-resolution densitometry can be obtained with blue intensity [7] or X-ray densitometry [8] systems that combine densitometry with anatomical observations [9,10], and many other devices exist as well [11]. X-ray Computed Tomography (CT) microdensitometry is a technique that allows obtaining 3D density volumes, that can be converted to large datasets of density profiles [12,13], with resolution ranging from submicron level [14] to coarser resolutions to perform tree-ring analysis [15,16]. Maximum latewood density of conifers is next to tree-ring width one of the parameters used to, for instance, reconstruct summer temperature [17], and earlywood is being explored as well [18].…”

Section: Introductionmentioning

confidence: 99%

Wood Density Profiles and Their Corresponding Tissue Fractions in Tropical Angiosperm Trees

Mil

Tarelkin

Hahn

et al. 2018

Forests

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Wood density profiles reveal a tree’s life strategy and growth. Density profiles are, however, rarely defined in terms of tissue fractions for wood of tropical angiosperm trees. Here, we aim at linking these fractions to corresponding density profiles of tropical trees from the Congo Basin. Cores of 8 tree species were scanned with X-ray Computed Tomography to calculate density profiles. Then, cores were sanded and the outermost 3 cm were used to semi-automatically measure vessel lumen, parenchyma and fibre fractions using the Weka segmentation tool in ImageJ. Fibre wall and lumen widths were measured using a newly developed semi-automated method. An assessment of density variation in function of growth ring boundary detection is done. A mixed regression model estimated the relative contribution of each trait to the density, with a species effect on slope and intercept of the regression. Position-dependent correlations were made between the fractions and the corresponding wood density profile. On average, density profile variation mostly reflects variations in fibre lumen and wall fractions, but these are species- and position-dependent: on some positions, parenchyma and vessels have a more pronounced effect on density. The model linking density to traits explains 92% of the variation, with 65% of the density profile variation attributed to the three measured traits. The remaining 27% is explained by species as a random effect. There is a clear variation between trees and within trees that have implications for interpreting density profiles in angiosperm trees: the exact driving anatomical fraction behind every density value will depend on the position within the core. The underlying function of density will thus vary accordingly.

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A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis

Cited by 35 publications

References 50 publications

Scientific Merits and Analytical Challenges of Tree‐Ring Densitometry

Scientific Merits and Analytical Challenges of Tree‐Ring Densitometry

Environmental drivers interactively affect individual tree growth across temperate European forests

Wood Density Profiles and Their Corresponding Tissue Fractions in Tropical Angiosperm Trees

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