Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation

TL;DR

This paper introduces a machine learning pipeline that combines isotope data and atmospheric variables to accurately identify the geographic origin of timber, aiding in combating illegal logging and product mislabeling.

Contribution

The study presents a novel machine learning approach that integrates isotope ratios and environmental data for precise timber origin identification, outperforming existing models.

Findings

Pipeline outperforms state-of-the-art models

Used by European enforcement agencies

Applicable to identifying origin of falsely labeled products

Abstract

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.