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The Timber Tracking Tool Infogram. Overview of wood identification methods' capacity.

  • Thünen-Institut für Holzforschung
  • Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Consejo Superior de Investigaciones Cientificas (INIA-CSIC)


The short guide gives an overview of the current capacities of the different timber tracking tools. The only way to be sure that a wood (product) at the end of the supply chain is what the documents say it is, is to check the inherent wood characteristics that can reveal species and geographic identity. There is an increasing interest to bring clarity into complexity of the global timber supply chains. Depending on the question, one method will be more suitable than the other. The infogram wants to guide here and inform on the different possibilities offered for the different identification requests. The guide links to a list of experts in timber tracking, that we currently know of in the world.
The Timber Tracking Tool Infogram
Overview of wood identification methods’ capacity
April 2019
Editor: Nele Schmitz
Hans Beeckman1, José Antonio Cabezas2, María Teresa Cervera2, Edgard Espinoza3, Juan Fernandez-
Golfin2, Peter Gasson4, John C. Hermanson5, Marysol Jaime Arteaga6, Gerald Koch7, Frederic Lens8,
Sandra Martínez-Jarquín9, Kathelyn Paredes Villanueva10,11, Tereza C.M. Pastore12, Tahiana
Ramananantoandro13, Rudolf Schraml14, Hilke Schröder15, Alexandre Sebbenn16, Niklas Tysklind17,
Charlie Watkinson18, Alex C. Wiedenhoeft5
José Bolaños, Jo Van Brusselen (The GTTN secretariat)
1-Royal Museum for Central Africa, Tervuren, Belgium; 2-INIA-Centro de Investigación Forestal, Madrid,
Spain; 3-National Fish and Wildlife Forensic Lab, Ashland, OR, USA; 4-Royal Botanic Gardens, Kew, UK;
5-US Forest Service Forest Products Laboratory, Madison, WI, USA; 6-FSC International, Supply Chain
Integrity Program, Lima, Peru; 7-Thünen Institute of Wood Research, Bergedorf, Germany; 8-Naturalis
Biodiversity Center, Leiden, The Netherlands; 9-Department of Biochemistry and Biotechnology,
CINVESTAV Unidad Irapuato, Irapuato, Mexico; 10-Universidad Autónoma Gabriel René Moreno, Santa
Cruz, Bolivia; 11-Wageningen University & Research, Wageningen, The Netherlands; 12-Laboratório de
Produtos Florestais, Serviço Florestal Brasileiro, Brasília, Brazil; 13-Mention Foresterie et Environnement,
Ecole Supérieure des Sciences Agronomiques, Université d'Antananarivo, Antananarivo, Madagascar; 14-
WaveLab, University of Salzburg, Salzburg, Austria; 15-Thünen Institute of Forest Genetics,
Grosshansdorf, Germany; 16-Instituto Floresta de São Paulo, São Paulo, Brazil; 17-Institut National de
Recherche Agricole, Kourou, Guyane Française; 18-Agroisolab UK Ltd, York, UK
Icons by Freepik from
Recommended citation:
Schmitz, N. (ed.), Beeckman,H., Cabezas, J.A., Cervera, M.T., Espinoza, E., Fernandez-Golfin, J.,
Gasson, P., Hermanson J.C., Jaime Arteaga, M., Koch, G., Lens, F., Martínez-Jarquín, S., Paredes-
Villanueva, K., Pastore, T.C.M., Ramananantoandro, T., Schraml, R., Schröder, H., Sebbenn, A.M.,
Tysklind, N., Watkinson, C, Wiedenhoeft, A.C. 2019. The Timber Tracking Tool Infogram. Overview of
wood identification methods’ capacity. Global Timber Tracking Network, GTTN Secretariat, European
Forest Institute and Thünen Institute.
Front cover:
NIRS Wood ID Project, T.C.M. Pastore
* Names are listed in alphabetical order.
Test specimen characteristics
Type of test specimen Minimal test specimen size
than 1 cm³
Solid wood Pulp, paper,
plywood Manufactured
or raw wood
than 1 cm³
1 2
3 4
1 5
Authentication question
Taxonomic identity
of a specimen Geographic origin of a specimen
or region Forest concession Individual
level Genus
Natural forest Planted forest
2 3 4 5 2* 3 5*
LEGEND: Methods conditionally suitable Methods generally suitable
Analysis requirements
1 3
4 5
2 3
On site
identification Time-frame for
identification Resolution of
geographic origin Cost per specimen
1 d-1 w Less than
1 km
Yes No More than
1 km
4-6 w
2 3 2 3
4 6
1 3
4 5
1 2
: see next page for extended legend
1 2 4
5 6
1 2 4
5 6
1 m-1 h
1 5
Treatment unknown
5 1 2 3 4 6
Without resins and
5 1 2 3 4 6
Sapwood Sap- or
1 2 3 1 2 3
5 4 6
With resins or
treated chemically
5 1 2 3 4 6
1 2
5 6
1 3 4
5 6
2 3
4 5
1 5 2
3 4
1 5
The different wood identification methods:
1. Wood anatomy
2. Genetics
3. Stable isotopes
4. Direct Analysis in Real Time Time-of-Flight Mass Spectrometry (DART TOFMS)
5. Near-InfraRed Spectroscopy (NIRS)
6. Machine Vision (MV)
The infogram shows the current wood identification capacities of the different timber
tracking tools. If you have identified one or more suitable methods for your identification
request, you can send an inquiry to the experts. Contact data of the timber tracking
experts can be found via the Find a Partner button on the GTTN website.
Apart from the methodological capacities, whether a timber specimen can be traced
back to its species/genus or its geographic origin also depends on the availability of
reference data. Reference databases are continuously growing as is the science
behind the methods. With time, the capacities of the different wood identification
methods will only increase.
Extended legend
If reference specimens were collected from an individual tree or from a specific
plantation, wood coming from that tree/plantation could be identified as such at
the end of the supply chain. However, if no reference specimens were taken
from the plantation, the DNA or NIRS profile will identify the geographic origin of
the seed material used for that plantation and not the actual geographic
position of the plantation.
Squares of the same colour should be interpreted together. For example,
method 2 has a resolution of less than 1 km only in the case of an individual
Test specimens have to be distinguished from reference specimens, for which
there are different requirements. See the GTTN sampling guide for information
on reference specimen requirements.
For conditionally suitable methods, contact an expert to discuss the specific
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).
... Illegal logging is a real threat to biodiversity, contributing significantly to the continued rise of deforestation rates and posing a threat to already endangered species. The guide published by the Global Timber Tracking Network lists NIRS technology as one of the tools available for species-level wood identification [11]. Such identification is essential for controlling and inspection of timber at the exploration level and in international trade. ...
The NIRS technology, together with multivariate methods, has already proved to be capable of discriminating wood from different forest species that are visually similar and determining the provenance within the same species. However, models developed with dry wood under controlled conditions of humidity and temperature present prediction errors or high outlier exclusion rates when applied to the analysis of samples under different moisture conditions. This work proposes applying external parameter orthogonalization (EPO) to minimize the effect of moisture in the spectra of wood from native Brazilian species under different moisture conditions and enable its analysis by PLS-DA discrimination models. After correction, the models showed high-efficiency rates and a significant reduction in the number of outliers. For the model trained with oven-dried samples and validated with spectra measured on samples under environment conditions, it was observed that the rate of samples with inconclusive results (RIR) was reduced from 87.8% to 18.4% by using EPO. Under these conditions, efficiency rates of 93.5% were obtained for the identification of Carapa guianensis Aubl. (Andiroba), 98.6% for Swietenia macrophylla King (Mogno) and 100.0% for Cedrela odorata L. (Cedro), Erisma uncinatum Warm. (Cedrinho) and Micropholis melinoniana Pierre (Curupixá). For the second validation set, with samples in more extreme moisture conditions, the same model without EPO correction had a RIR of 100.0%. In comparison the model with EPO had a RIR of 43.8%, demonstrating the feasibility of EPO in the correction of moisture interference in samples under moisture conditions different from those used in the model development. Applying more flexible criteria in the identification of samples by PLS-DA also favored the reduction of errors. Thus, EPO and soft-PLS-DA proved to be an effective strategy to enable the application of NIR technology in field conditions, such as in the inspection of illegal timber trade.
... Expert Group UNODC 2016; Lowe et al. 2016;Schmitz et al. 2019Schmitz et al. , 2020Wiedenhoeft et al. 2019). Answers to those calls necessarily focus on the underlying biological variation inherent in wood itself and therefore address questions not of paperwork or permits, but rather of the botanical identification ("species"), the geographic origin, or individualization (log to stump, board to log, etc.) of wood. ...
Full-text available
One rate-limiting factor in the fight against illegal logging is the lack of powerful, affordable, scalable wood identification tools for field screening. Computer vision wood identification using smartphones fitted with customized imaging peripherals offers a potential solution, but to date, such peripherals suffer from one or more weaknesses: low image quality, lack of lighting control, uncontrolled magnification, unknown distortion, and spherical aberration, and/or no access to or publication of the system design. To address cost, optical concerns, and open access to designs and parameters, I present the XyloPhone, a 3D printed research quality macroscopic imaging attachment adaptable to virtually any smartphone. It provides a fixed focal distance, exclusion of ambient light, selection of visible light or UV illumination, uses the lens from a commercially available loupe, is powered by a rechargeable external battery, is fully open-sourced, at a price point of less than USD 110 is a highly affordable tool for the laboratory or the field, and can serve as the foundational hardware for a scalable field-deployable computer vision wood identification system.
... Until recently little use has been made of scientific evidence to support due diligence with respect to traded timber, although over the last 5 years there have been concerted efforts to change this (Dormontt et al., 2015;Koch et al., 2015;Schmitz et al., 2020;Schmitz, Beeckman, et al., 2019;Schmitz, Blanc-Jolivet, et al., 2019;UNODC, 2016b). In this article, we describe the development of the World Forest ID program (WFID), a working prototype for building geo-referenced, wood, and foliage reference collections. ...
Full-text available
We describe a program called WorldForestID which is being developed to monitor and support authentication and compliance in international trade of timber prod-ucts. The program is being run by a consortium of government and non-government organizations: US Forest Service International Programs (USFS IP), Royal Botanic Gardens, Kew, Forest Stewardship Council (FSC), Agroisolab, and World Resources Institute (WRI). Initial funding has come from the US Department of State, USFS IP, US Department of Agriculture Animal and Plant Health Inspection Service, Forest Stewardship Council, and the UK Department for Environment, Food and Rural Affairs (Defra). The aim is to build a comprehensive collection of internationally traded timber species. The collection is used as reference material to validate for-est products. Although there are a large number of xylaria (wood collections, Index Xylariorum IV) around the world, many of the specimens do not provide geo-loca-tions suitable as reference material for pinpointing provenance, many lack-associated herbarium vouchers and some are misidentified. The samples being collected in this program address these issues and include bark, sapwood, and heartwood, ensuring that the material collected is suitable for current and future scientific analysis. We describe the process of collection and validation from field to laboratory and the advantages and disadvantages of the main techniques used to ascertain/verify iden-tity and provenance. Ultimately, we envisage the day that scientific methods will be used routinely and successfully by timber traders, manufacturers, retailers, and law enforcement to accept or reject identity and provenance claims on internationally traded timber and forest products and, where necessary, to support prosecutions when laws such as EU Timber Regulations, Lacey Act and CITES are infringed
... When a program officer is selecting from several forensic wood identification technology options, they should consider more than just prediction accuracy. They must determine where, for what purposes, and at what scale a technology must serve, how the technology performs in the field, how scalable and cost-effective it is, the embodied costs of operator training, system calibration, and other logistical details (Dormontt et al. 2015;Schmitz et al. 2019). Access to these data is critical for making informed deployment decisions, and decision making depends necessarily on the ability to conduct side-by-side comparisons on the same sets of taxa. ...
Full-text available
A wealth of forensic wood identification technologies has been developed or improved in recent years, with many attempts to compare results between technologies. The utility of such comparisons is greatly reduced when the species tested with each technology are different and when performance metrics are not calculated or presented in the same way. Here, a species-level XyloTron computer vision model is presented along with a side-by-side comparison for species- and genus-level identification of the 10 species of Meliaceae studied by Deklerck et al. using mass spectrometry. The species-level accuracies of the XyloTron model and the mass spectrometry models are comparable, while the genus-level accuracy of the XyloTron model is higher than that of the mass spectrometry model. The paper concludes with a call for better practices to compare disparate forensic wood identification technologies from a performance driven perspective.
Full-text available
Forests, estimated to contain two thirds of the world’s biodiversity, face existential threats due to illegal logging and land conversion. Efforts to combat illegal logging and to support sustainable value chains are hampered by a critical lack of affordable and scalable technologies for field-level inspection of wood and wood products. To meet this need we present the XyloTron, a complete, self-contained, multi-illumination, field-deployable, open-source platform for field imaging and identification of forest products at the macroscopic scale. The XyloTron platform integrates an imaging system built with off-the-shelf components, flexible illumination options with visible and UV light sources, software for camera control, and deep learning models for identification. We demonstrate the capabilities of the XyloTron platform with example applications for automatic wood and charcoal identification using visible light and human-mediated wood identification based on ultra-violet illumination and discuss applications in field imaging, metrology, and material characterization of other substrates.
ResearchGate has not been able to resolve any references for this publication.