D-ISN: TRACK 1: Supply Chain Analysis to Thwart Illegal Logging: Machine Learning-based Monitoring and Strategic Network Inspection

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This Disrupting Operations of Illicit Supply Networks (D-ISN) award will contribute to the Nation's security by investigating strategies for disrupting illicit supply networks, with specific focus on illegal logging. Illegal logging refers to breaking local or international laws at any point along the supply chain of timber, including illegal activities such as illegal harvesting or over-logging at export nodes, misreporting of raw or processed product volume at intermediate nodes, and misclassification of origin or species of timber at import nodes. Despite several existing laws to combat illegal logging, these activities continue to thrive at both local and global scale. Illegal logging has been implicated as a leading cause of the destruction of major forest ecosystems that play a significant role in carbon sequestration and in supporting biodiversity. The effectiveness of laws and mechanisms for combating illegal logging is limited by poor understanding of where, how much, and what type of timber is harvested and where it goes in the global trade of wood products. To address this systemic challenge, the project will develop quantitative models and methods to monitor the rates of harvest and conduct strategic inspection to detect illegally harvested or traded timber in the global supply chain. The project will demonstrate how current monitoring and inspection technology can be leveraged to: (1) quantify the flow of illegal timber in the global supply chain; and (2) improve the compliance with and enforcement of laws for legal trade of timber. The project will train graduate students to work at the interface of operations research and environment sustainability and will develop methods that can contribute to further advance the sustainable development policies in the United States. The project develops spatiotemporal network models and analysis tools to identify and thwart the flow of illegal timber throughout the global supply chain. The project will investigate: (i) estimation of illegal timber harvest rates in both protected and concession forest regions based on remote sensing data, botanical characteristics, and ecology of timber species; (ii) supply chain network analysis to evaluate the movement of illegal timber and estimate illegal trade volume; (iii) strategic network inspection to improve the detection of fraud and increase the forensic capacity for wood identification at critical network locations. The project will build on advances in machine learning, remote sensing, network optimization, and game theory. The datasets and models from this project will be useful to multiple communities: agencies responsible for tracking movement of illegal timber, researchers seeking to advance scientific methods for analysis and certification of natural resources supply chains, and ecologists interested in using machine learning to assess the impact of human-induced changes on forest dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.