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

Authors:
Nele Schmitz
Nele Schmitz
Hans Beeckman at Royal Museum for Central Africa
Hans Beeckman
Jose Antonio Cabezas at Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
Jose Antonio Cabezas
Maria Teresa Cervera at Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Consejo Superior de Investigaciones Cientificas (INIA-CSIC)
Maria Teresa Cervera
  • Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Consejo Superior de Investigaciones Cientificas (INIA-CSIC)
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Abstract

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.
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The Timber Tracking Tool Infogram
Overview of wood identification methods’ capacity
April 2019
www.globaltimbertrackingnetwork.org
Editor: Nele Schmitz
Contributors*:
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)
Affiliations:
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 www.flaticon.com
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.
www.globaltimbertrackingnetwork.org
*
Test specimen characteristics
Type of test specimen Minimal test specimen size
Smaller
than 1 cm³
Solid wood Pulp, paper,
fibreboard
Charcoal
Veneer,
plywood Manufactured
or raw wood
Heartwood
Bigger
than 1 cm³
1
1 2
3 4
1 5
Authentication question
Taxonomic identity
of a specimen Geographic origin of a specimen
Country
or region Forest concession Individual
tree
Species
level Genus
level
Natural forest Planted forest
2 3 4 5 2* 3 5*
2*
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
100-
200 €
1 3
4 5
200-
600 €
1 2
3
: see next page for extended legend
anatomy
1
DNA
2
isotopes
3
NIR
5
DART
4
MV
6
1 2 4
5 6
1 2 4
5 6
1 m-1 h
1 5
6
1-
100 €
Treatment unknown
5 1 2 3 4 6
Without resins and
untreated
5 1 2 3 4 6
Sapwood Sap- or
heartwood
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
6
6
1 5 2
3 4
1 5
6
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)
WHERE TO FIND AN EXPERT?
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.
GROWING POSSIBILITIES
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
tree.
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
case.
*
www.globaltimbertrackingnetwork.org
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).
... Wood-based products such as pulp, paper and particle board tend to be more difficult to identify than solid wood (Sieburg-Rockel and Koch 2020). The selection of the most appropriate technique depends on the specific identification inquiry in question Schmitz et al. 2019Schmitz et al. , 2020. Notably, for the genus-level identification of pulp and paper products, exclusively anatomical and chemotaxonomic approaches prove applicable. ...
Chemotaxonomic and anatomic wood species identification in bleached pulp: blind test and method validation
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This paper presents a comparative analysis of the blind test outcomes of two independent methods for the identification of tropical wood species in pulp and paper products. Both, the established anatomical and the relatively new chemotaxonomic method support the European Deforestation Regulation 2023/1115 (EUDR), which aims to ensure that only legally harvested timber that has not contributed to deforestation is traded in the EU. The blind test involved 570 decisions on 15 test sheets of 37 self-manufactured mixed tropical hardwood pulps and an industrial beech pulp, used as a matrix. Both detection techniques demonstrated robust performance with over 80 % hit rates. The results show that the synergies and combination of the strengths of both methods can be utilized and lead to even better combined performance. In order to establish the chemotaxonomic identification method as a complement to the conventional anatomy-based method, statistical analyses were performed to assess its intermediate precision between three different GC-MS systems. In most cases, the method gave consistent results independent of the instrument used.
View
... Among the regions of the world 42 where this type of crime is predominantly high are Africa (central), Asia (southeast) and Europe (Russia) (May 2017, EU and EC 2022, European Union et al. 2022. Although measures to stop illegal deforestation have been put in place (Dormontt et al. 2015, United Nations and International Consortium on Combating Wildlife 2016, Schmitz et al. 2019, EU 2022, UN 2022, there is a need for stronger measures , Interpol 2022, ITTO 2022, Schmitz et al. 2022, UNDER et al. 2022. ...
WOOD SPECIES IDENTIFICATION USING MASK RCNN-RESIDUAL NETWORK APPROACH
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  • Mar 2023
Wood identification plays a significant role in forestry and cultural heritage. Its impact and application are more pronounced in the guide against illegal logging and ecological issues. Moreover, it is extremely difficult to identify individual wood species using the existing wood identification methods. Due to the above-mentioned reasons, there is a need to develop an accurate wood identification method that is based on computer vision. Computers react to what they see, just as human eyes react, the insights gained from computer vision as a field of artificial intelligence (AI) enable computers to extract meaningful data from visual inputs such as images and videos, and to take automated actions. Just like AI allows computers to possess thinking ability, computer vision gives them the ability to see. Computer vision, through the region-based convolutional neural network has been able to solve various visual issues that are related to videos and images accurately. However, its application to wood technology and wood related fields still remains uninvestigated. Therefore, in this study, a computer vision based mask region-based convolutional neural network (Mask R-CNN) associated with a modified residual network (ResNet) was employed as a hybrid method (Mask RCNN-ResNet) for a robust and accurate wood identification at species level. The technique of Mask R-CNN involves detecting, extracting the relevant features of visual data from a processed image, then, segmenting the target object using region of interest alignment, and mask generation to make a decision. A modified ResNet network (modified model of residual network) reduced the number of training parameters, thereby leading to an increased efficiency during computation. Mask RCNN-ResNet method is able to identify individual wood at species level with 92% identification accuracy higher than the results obtained in the existing work that used other computer-based wood identification methods on the same wood species datasets.
View
... Several institutional and international legal measures have been put in place to prevent overexploitation and irreversible loss of species and habitats [5][6][7]. Innovative programmes are emerging [8,9], solid research is under way [10][11][12][13][14], and research with significant impact and news items are being shared worldwide [15]. ...
Computer Vision-Based Wood Identification: A Review
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  • Nov 2022
Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.
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... This is particularly true for geographic assignment techniques, which are specific to the species, resulting in a resource-intensive validation process (Ogden & Linacre 2015). Financial costs associated with scientific verification techniques for identifying timber have been addressed by Dormontt et al. (2015) and Schmitz (2019). ...
Tracing the world`s timber: The status of scientific verification technologies for species and origin identification
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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.
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... 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. ...
Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA
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  • Sep 2021
  • MICROCHEM J
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View
... 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. ...
The XyloPhone: toward democratizing access to high-quality macroscopic imaging for wood and other substrates
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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.
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... 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. ...
WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber
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Societal Impact Statement Forest products are the most used inedible renewable resource, but supplies are finite. It is difficult to know which tree species are in wood products or where they come from. Scientific evidence is needed to support or refute origin and species claims in traded products. We describe the building of a geo‐referenced wood reference collection (xylarium) supported by herbarium voucher specimens. The WorldForest ID program, hereinafter referred to as WFID (www.worldforestid.org), is embarking on large‐scale field collections of wood samples suitable for science‐based authentication technologies. By coordinating with regulatory and enforcement authorities in both producer and consumer countries the WFID xylarium is legally robust and commercially relevant. Summary We describe a program called WorldForestID which is being developed to monitor and support authentication and compliance in international trade of timber products. 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 forest 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‐locations 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 identity 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.
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... 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. ...
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry
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  • Sep 2020
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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.
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The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products
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