scholarly journals Applying CLSM to increment core surfaces for histometric analyses: A novel advance in quantitative wood anatomy

2013 ◽  
Vol 31 (2) ◽  
pp. 140-145 ◽  
Author(s):  
Wei Liang ◽  
Ingo Heinrich ◽  
Gerhard Helle ◽  
Isabel Dorado Liñán ◽  
Thilo Heinken
Keyword(s):  
Wood Anatomy ◽  
Increment Core ◽  
Quantitative Wood Anatomy

scholarly journals Quantitative Wood Anatomy—Practical Guidelines

2016 ◽  
Vol 7 ◽  
Author(s):  
Georg von Arx ◽  
Alan Crivellaro ◽  
Angela L. Prendin ◽  
Katarina Čufar ◽  
Marco Carrer
Keyword(s):  
Wood Anatomy ◽  
Quantitative Wood Anatomy ◽  
Practical Guidelines

scholarly journals Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 36 ◽  
Author(s):  
Tuo He ◽  
João Marco ◽  
Richard Soares ◽  
Yafang Yin ◽  
Alex Wiedenhoeft
Keyword(s):  
Machine Learning ◽  
Wood Anatomy ◽  
Support Vector ◽  
Correct Identification ◽  
Similar Species ◽  
Learning Models ◽  
Important Species ◽  
Quantitative Wood Anatomy ◽  
Depth Analysis ◽  
Machine Learning Models

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers.

scholarly journals Q-NET – a new scholarly network on quantitative wood anatomy

2021 ◽  
pp. 125890
Author(s):  
Georg von Arx ◽  
Marco Carrer ◽  
Alan Crivellaro ◽  
Veronica De Micco ◽  
Patrick Fonti ◽  
...  
Keyword(s):  
Wood Anatomy ◽  
Quantitative Wood Anatomy

Simulation study to determine necessary sample sizes for image analysis-based quantitative wood anatomy of vessels of beech (Fagus sylvatica)

2017 ◽  
Vol 45 ◽  
pp. 35-38 ◽  
Author(s):  
Daniela Diaconu ◽  
Jan Hackenberg ◽  
Dominik Florian Stangler ◽  
Hans-Peter Kahle ◽  
Heinrich Spiecker
Keyword(s):  
Image Analysis ◽  
Fagus Sylvatica ◽  
Simulation Study ◽  
Wood Anatomy ◽  
Sample Sizes ◽  
Quantitative Wood Anatomy

scholarly journals Tree-Ring Anomalies in Toona Ciliata

IAWA Journal ◽  
2006 ◽  
Vol 27 (2) ◽  
pp. 213-231 ◽  
Author(s):  
Ingo Heinrich ◽  
John Charles Gripper Banks
Keyword(s):  
Tree Ring ◽  
Wood Anatomy ◽  
Ring Width ◽  
Increment Core ◽  
Growth Anomalies ◽  
False Rings ◽  
Ring Structures ◽  
Toona Ciliata ◽  
Ring Analysis ◽  
Growth Experiments

New increment core samples of Toona ciliata collected in the Australian tropics and subtropics compared to already existing material from the Upper Kangaroo Valley, near Sydney exhibit distinct differences in tree-ring structures. This necessitated a closer examination of the wood anatomy, possible false rings and the speciesʼ crossdating capacity in northeast Australia. During tree-ring analysis two growth anomalies (extensive zones of narrow and indistinct rings) and three types of false rings were discovered which complicated crossdating. However, in growth experiments only one type of false ring could be induced artificially by totally defoliating young trees. It was possible to alter their phenological performance by artificially changing the environmental conditions. Visual crossdating of samples originating from northeast Australia was feasible within and between trees. For selected years a positive relationship between ring width and precipitation data was found.

Wood anatomy of two South Indian species ofAvicennia With 3 plates

1987 ◽  
Vol 98 (9-10) ◽  
pp. 537-542
Author(s):  
K. V. Krishnamurthy ◽  
K. Sigamani
Keyword(s):  
Wood Anatomy ◽  
Indian Species ◽  
South Indian
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