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Scientific methods for taxonomic and origin identification of timber



USERS OF THIS GUIDE: Authorities, traders, importers and all others interested in the current capacity of timber tracking methods for the taxonomy and geographical origin of timber (products). AIM OF THIS GUIDE: Inform about the scientific methods available for timber tracking (taxonomy and origin) and on the laboratories offering these identification services. This guide is a concise version of the scientifically more detailed Timber Tracking Tool Infogram.
Scientific methods for
taxonomic and origin
identification of timber
June 2020
Editor: Nele Schmitz
Recommended citation:
GTTN (2020). Schmitz, N. (ed.). Scientific methods for taxonomic and origin
identification of timber. Global Timber Tracking Network, GTTN Secretariat, European
Forest Institute and Thünen Institute.
Pictures: Victor Deklerck, Volker Haag, Justyna A. Nowakowska, Charlie Watkinson.
Acknowledgements: we thank Jez W.B. Braga, Victor Deklerck, Ed Espinoza, Manfred
Groening, Gerald Koch, Tereza C.M. Pastore, Tahiana Ramananantoandro, Hilke
Schröder, Charlie Watkinson and Alex C. Wiedenhoeft for their valuable comments
during the development of this guide and Jo Van Brusselen and José Bolaños from the
GTTN secretariat.
Authorities, traders, importers and all others interested in the current capacity of timber
tracking methods for the taxonomy and geographical origin of timber (products).
Inform about the scientific methods available for timber tracking (taxonomy and origin)
and on the laboratories offering these identification services. This guide is a concise
version of the scientifically more detailed Timber Tracking Tool Infogram.
Scientific methods for the verification of the taxonomy and/or the origin of the
timber based on anatomical, chemical or genetic characteristics of the wood.
Taxonomic identification Origin identification Both
Genetics Genetics: The DNA, present in the wood, can be
investigated to identify the species, as well as the
geographic origin and the individual.
Anatomy: The cellular structure of wood varies between
families, genera and sometimes even species. It can be
investigated at macroscopic or microscopic level using
standard sets of anatomical features or by digital image
Scientific methods for timber tracking
Chemistry: The chemical contents of wood can be studied using Direct Analysis in Real
Time (DART) Time-Of-Flight Mass Spectrometry (TOFMS), Near Infra-Red (NIR)
Spectroscopy or stable isotopes. DART TOFMS looks at wood chemicals. NIR
Spectroscopy looks at the surface of the wood, studying both wood chemicals and
physical characteristics of the wood, which can vary both between species and origins.
At this moment, however, DART TOFMS is only reliable for species identifications.
Stable isotopes are linked to environmental conditions and hence only vary between
origins of samples but not between species.
All timber identifications rely on having
reference samples, and these specimens
are the basis of determining if the trade
documents list the correct taxonomic
name(s) and geographic origin(s).
Essentials for a timber identification:
Reference sample
Unknown sample
Trade documents
The factors that determine which method is most suitable for your case are:
The question that needs to be answered (taxonomic identification at the family, genus or
species level, geographic origin at the level of region or individual tree).
The type of wood product (raw wood, veneer, plywood, other manufactured solid wood,
charcoal, particle board, pulp, paper or fibreboard).
The size of the sample that can be taken (smaller or bigger than 1 cm³).
The online service provider directory guides you through the above questions
and offers you a list of the possible laboratories that can perform the analysis.
You can contact the lab(s) of your choice and get an estimate of the costs and
time that will be needed for your specific case.
To get a geographic overview of the laboratories offering authentication analyses
for timber, you can consult the service provider map.
species group
area of harvest
identification Individual tree
Pulp, paper,
* Including: raw wood, veneer, plywood, other manufacturedsolid wood
Taxonomic identification Origin identification
Anatomy Genetics
Particle board
Solid wood*
The objective of the Global Timber Tracking Network (GTTN) is to promote the operationalization of
innovative tools for wood identification and origin determination, to assist the fight against illegal logging
and related trade around the globe. GTTN is an open alliance that cooperates along a joint vision and the
network activities are financed through an open multi-donor approach. GTTN phase 2 coordination (2017-
2019) is financed by the German Federal Ministry of Food and Agriculture (BMEL). GTTN phase 2 (2017-
2019) is coordinated by the European Forest Institute with the technical support from the Thünen Institute.
Coordinating partners
... Scientific techniques for timber tracking based on inherent characteristics (as opposed to externally introduced markers) include structural (wood anatomy, manual or machine vision), chemical (mass spectrometry and near-infrared spectroscopy), and genetic methods (DNA barcoding, population genetics, and phylogeographic studies and DNA profiling). These techniques vary in (i) what they can identify (i.e., species, genus, geographical origin, individual), (ii) their potential to be used for screening on the front-line (in the field or at customs) or as diagnostic laboratory methods, and (iii) logistics such as cost, processing speed, equipment, and technical expertise required and have been reviewed and compared by Dormontt et al. (2015), Schmitz et al. (2020) and Schmitz (2020). The identification of species and geographical origin may be used to verify compliance documentation and the integrity of timber supply chains or, if the impetus exists, as forensic evidence in a court of law. ...
Full-text available
Illegal logging and illegal timber trade is a global problem. Anatomical, genetic, and chemical techniques support illegal logging legislation by verifying the species and geographic origin of timber. In principle, these methods can be used to identify timber species and the origin of harvest, however, the availability of specific tests for important timber species is unclear. We review the status of these methods for the top 322 global priority timber taxa. Our results show that for species identification, reference data exist for 100% of taxa using wood anatomy, 86% using genetics, 41% for using DART TOFMS, and 6% using NIRS. For origin identification, data exist for 24% of taxa, with most studies applying genetic approaches (23%). No studies have developed forensic-ready tests for the global priority timber taxa. The review highlights that the current potential for identifying species is greater than for geographic origin and more research focused on determining the geographical origin of timber is required. Based on the current rate, it will take approx. 27 years to generate geographic data for all 322 priority taxa. Finally, we identify research opportunities to improve global timber tracing efforts. Our findings indicate more research is needed, and quickly so that scientific verification can support regulators to combat illegal logging.
... The paucity of wood identification expertise [1,2] has spurred interest in automated wood identification technologies, especially in the context of combating illegal logging. Among the technologies considered [3], computer vision-based wood identification (CVWID) has been widely studied [4][5][6][7][8][9][10][11][12][13] and is highly effective [14], field-deployable [9,15], and in rare cases, field-tested [16]. Additionally, the democratization of CVWID technologies through affordable, open-source, do-it-yourself, hardware (e.g., the XyloTron (XT) platform [17]; the XyloPhone [18]) and robust, efficient software implementations of standard computer vision (CV) and machine learning (ML) techniques (e.g., [19][20][21][22]) can enable robust, multi-point monitoring of the wood and wood products value chain. ...
Full-text available
Computer vision wood identification (CVWID) has focused on laboratory studies reporting consistently high model accuracies with greatly varying input data quality, data hygiene, and wood identification expertise. Employing examples from published literature, we demonstrate that the highly optimistic model performance in prior works may be attributed to evaluating the wrong functionality—wood specimen identification rather than the desired wood species or genus identification—using limited datasets with data hygiene practices that violate the requirement of clear separation between training and evaluation data. Given the lack of a rigorous framework for a valid methodology and its objective evaluation, we present a set of minimal baseline quality standards for performing and reporting CVWID research and development that can enable valid, objective, and fair evaluation of current and future developments in this rapidly developing field. To elucidate the quality standards, we present a critical revisitation of a prior CVWID study of North American ring-porous woods and an exemplar study incorporating best practices on a new dataset covering the same set of woods. The proposed baseline quality standards can help translate models with high in silico performance to field-operational CVWID systems and allow stakeholders in research, industry, and government to make informed, evidence-based modality-agnostic decisions.
The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions.
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