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

D-ISN：轨道 1：阻止非法采伐的供应链分析：基于机器学习的监控和战略网络检查

基本信息

批准号：
2039771
负责人：
Saurabh Amin
金额：
$ 100万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039771&HistoricalAwards=false
关键词：
ISN TRACK Supply Chain Analysis

项目摘要

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.

非法供应网络（D-ISN）奖的破坏运营将通过调查破坏非法供应网络的策略来促进该国的安全，并特别关注非法伐木。非法伐木是指在供木材供应链的任何时刻违反本地或国际法律，包括非法活动，例如非法收获或出口节点时过度遗产，在中间节点上误会了原始或处理过的产品数量，以及原产地或原产地或原产地的错误分类。进口节点的木材物种。尽管现有的几项法律可以打击非法伐木，但这些活动仍在本地和全球范围内蓬勃发展。非法伐木已被认为是破坏主要森林生态系统的主要原因，这些生态系统在碳固存中起着重要作用和支持生物多样性。法律和机制打击非法伐木的有效性受到对收获何处，多少种木材以及在全球木材产品贸易中进行的何处的了解，限制了哪种类型的木材。为了应对这一系统性挑战，该项目将开发定量模型和方法，以监测收获率并进行战略检查，以检测全球供应链中非法收获或交易的木材。该项目将证明如何利用当前的监测和检查技术到：（1）量化全球供应链中非法木材的流动；（2）提高对木材法律贸易法律的遵守和执行。该项目将培训研究生在运营研究和环境可持续性的界面上工作，并将开发有助于进一步推进美国可持续发展政策的方法。该项目开发了时空网络模型和分析工具，以识别和阻止非法木材在整个全球供应链中的流动。该项目将调查：（i）根据遥感数据，植物特征和木材物种的生态学的保护和特许森林区域中非法木材收获率的估计；（ii）供应链网络分析，以评估非法木材的运动和估计非法贸易量；（iii）战略网络检查，以改善欺诈的检测并提高关键网络位置的木材识别能力。该项目将基于机器学习，遥感，网络优化和游戏理论的进步。该项目的数据集和模型将对多个社区有用：负责跟踪非法木材运动的机构，寻求推进科学方法进行分析和认证自然资源供应链的研究人员，以及有兴趣使用机器学习来评估影响的生态学家该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响评论标准来评估的，这是值得支持的。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A unified explanation for the morphology of raised peatlands

DOI：
10.1038/s41586-023-06807-w
发表时间：
2023-12
期刊：
Nature
影响因子：
64.8
作者：
A. Cobb;R. Dommain;Kimberly Yeap;Hannan Cao;N. Dadap;Bodo Bookhagen;Paul H Glaser;Charles F Harvey
通讯作者：
A. Cobb;R. Dommain;Kimberly Yeap;Hannan Cao;N. Dadap;Bodo Bookhagen;Paul H Glaser;Charles F Harvey

Strategic Monitoring of Networked Systems with Heterogeneous Security Levels