Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929761185431552 |
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| author | Sarkar, Shailik Yousuf, Raquib Bin Wang, Linhan Mayer, Brian Mortier, Thomas Deklerck, Victor Truszkowski, Jakub Simeone, John C. Norman, Marigold Saunders, Jade Lu, Chang-Tien Ramakrishnan, Naren |
| author_facet | Sarkar, Shailik Yousuf, Raquib Bin Wang, Linhan Mayer, Brian Mortier, Thomas Deklerck, Victor Truszkowski, Jakub Simeone, John C. Norman, Marigold Saunders, Jade Lu, Chang-Tien Ramakrishnan, Naren |
| contents | Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14115 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation Sarkar, Shailik Yousuf, Raquib Bin Wang, Linhan Mayer, Brian Mortier, Thomas Deklerck, Victor Truszkowski, Jakub Simeone, John C. Norman, Marigold Saunders, Jade Lu, Chang-Tien Ramakrishnan, Naren Machine Learning Computational Engineering, Finance, and Science Computers and Society J.m; K.4.1; I.2.0; J.2 Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain. |
| title | Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation |
| topic | Machine Learning Computational Engineering, Finance, and Science Computers and Society J.m; K.4.1; I.2.0; J.2 |
| url | https://arxiv.org/abs/2502.14115 |