Measuring tree-ring widths using the CooRecorder software application

Maxwell, Richard; Larsson, Lars-Åke

doi:10.1016/j.dendro.2021.125841

Cited by 62 publications

(27 citation statements)

References 2 publications

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“…The trunks were pulled to the shore, sawn into disks, and returned to the lake. The samples were airdried for 6 to 12 months and their cross-sectional surfaces were sanded down and scanned for digital images from which the series of tree-ring widths were measured using CooRecorder 7.7 software (Figure 2) (Rydval et al, 2014;Maxwell and Larsson, 2021). In addition to visual dating, the process of cross-dating was carried out using statistical tests (Holmes, 1983) using the treering width data.…”

Section: Tree-ring Datamentioning

confidence: 99%

“…In addition to visual dating, the process of cross-dating was carried out using statistical tests (Holmes, 1983) using the treering width data. Moreover, the surface reflectance of the wood was determined from the scanned images using the CooRecorder (Rydval et al, 2014;Maxwell and Larsson, 2021), and the minimum blue channel light intensity (BI) of the latewood portion of each annual ring was obtained. The BI data have been shown to provide a highly skilled surrogate for the MXD of P. sylvestris tree rings (Campbell et al, 2007).…”

Section: Tree-ring Datamentioning

confidence: 99%

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Disentangling the Evidence of Milankovitch Forcing From Tree-Ring and Sedimentary Records

et al. 2022

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Tree-ring records constitute excellent high-resolution data and provide valuable information for climate science and paleoclimatology. Tree-ring reconstructions of past temperature variations agree to show evidence for annual-to-centennial anomalies in past climate and place the industrial-era warming in the context of the late Holocene climate patterns and regimes. Despite their wide use in paleoclimate research, however, tree rings have also been deemed unsuitable as low-frequency indicators of past climate. The arising debate concerns whether the millennia-long tree-ring records show signals of orbital forcing due to the Milankovitch cycles. Here, we produce a summer-temperature reconstruction from tree-ring chronology running through mid- and late-Holocene times (since 5486 BCE) comprising minimum blue channel light intensity (BI). The BI reconstruction correlates with existing and new tree-ring chronologies built from maximum latewood density (MXD) and, unlike the MXD data, shows temperature trends on Milankovitch scales comparable to various types of sedimentary proxy across the circumpolar Arctic. Our results demonstrate an unrevealed potential of novel, unconventional tree-ring variables to contribute to geoscience and climate research by their capability to provide paleoclimate estimates from inter-annual scales up to those relevant to orbital forcing.

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Section: Tree-ring Datamentioning

confidence: 99%

Section: Tree-ring Datamentioning

confidence: 99%

Disentangling the Evidence of Milankovitch Forcing From Tree-Ring and Sedimentary Records

et al. 2022

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“…This is a powerful tool that can greatly cut down on the processing time to generate ring-width measurements while maintaining the benefits of image based measurements (Griffin et al, 2021;Maxwell & Larsson, 2021). Though trained only on Picea abies, the consistent structure of conifer wood means it should be easily deployable across other conifer species (Fabijańska et al, 2017;Martinez-Garcia et al, 2021;Resente et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Our tree-ring CNN performed well on both the annotated validation data set and the real world test of full length cores.The CNN successfully identified rings spanning much of the variation in conifer ring appearance including relatively wide rings with reaction wood and relatively narrow rings with latewood density fluctuations (Figure 2C and D). This is a powerful tool that can greatly cut down on the processing time to generate ring-width measurements while maintaining the benefits of image based measurements (Griffin et al, 2021; Maxwell & Larsson, 2021). Though trained only on Picea abies , the consistent structure of conifer wood means it should be easily deployable across other conifer species (Fabijańska et al, 2017; Martinez-Garcia et al, 2021; Resente et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…As a test of the real world application we analyzed 34 full-length increment core samples for a total of 4,355 rings, from trees growing in Romania using the CNN with the weights derived from the Slovakian samples. Prior to this analysis we cross-dated and measured the Romanian samples using Coo Recorder (Larsson, 2003; Maxwell & Larsson, 2021), dating and measurement quality was verified in COFECHA (Holmes et al, 1986). Only cross-dated samples with good image quality were used to verify CNN results.…”

Section: Methodsmentioning

confidence: 99%

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Automation of tree-ring detection and measurements using deep learning

Poláček

Arizpe

Hüther

et al. 2022

Preprint

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We present an implementable neural network-based automated detection and measurement of tree-ring boundaries from coniferous species.We trained our Mask R-CNN extensively on over 8,000 manually annotated rings. We assessed the performance of the trained model from our core processing pipeline on real world data.The CNN performed well, recognizing over 99% of ring boundaries (precision) and a recall value of 95% when tested on real world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were a 99% match with human measurements of the same samples.Our CNN is readily deployable through a Docker container and requires only basic command line skills. Application outputs include editable annotations which facilitate the efficient generation of ring-width measurements from tree-ring samples, an important source of environmental data.

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Tree‐ring‐based reconstruction of the precipitation variability in the middle Taihang Mountains (1608–2021)

Hou,

Zhao,

Chen

et al. 2024

Intl Journal of Climatology

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Understanding historical droughts in northern China is challenging due to the limited time records of meteorological stations. In this study, we reconstructed the precipitation variability in the middle Taihang Mountains over 414 years by using the tree‐ring width of Pinus tabulaeformis. Based on our climate response analysis, Pinus tabulaeformis growth at the sampling sites was mainly restricted by the total precipitation from August of the previous year to June of the current year (P8‐C6), which was used as the dependent variable for precipitation reconstruction. Spatial analysis demonstrated that the reconstructed precipitation contained obvious spatial representativeness in the Taihang Mountains. We compared the precipitation sequence with other dryness/wetness sequences around the study area, revealing common drought events in most of northern China. Furthermore, the multi‐taper method (MTM) demonstrated a significant period of 2–4 years of the reconstructed precipitation. The precipitation variability in the Taihang Mountains may be influenced by the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Variability (AMV) and has exhibited a negative correlation in recent decades. This study has reconstructed the historical changes in wet and dry conditions to offer a reference for the utilization of modern water resources, especially by linking the decrease in precipitation in the study area with the downfall of the Ming Dynasty.

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Measuring tree-ring widths using the CooRecorder software application

Cited by 62 publications

References 2 publications

Disentangling the Evidence of Milankovitch Forcing From Tree-Ring and Sedimentary Records

Disentangling the Evidence of Milankovitch Forcing From Tree-Ring and Sedimentary Records

Automation of tree-ring detection and measurements using deep learning

Tree‐ring‐based reconstruction of the precipitation variability in the middle Taihang Mountains (1608–2021)

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