RII Track-2 FEC: Leveraging Intelligent Informatics and Smart Data for Improved Understanding of Northern Forest Ecosystem Resiliency (INSPIRES)

森林是北方农村劳动景观的重要经济组成部分和生态关键组成部分。当地和区域社区依赖这些森林生态系统的健康来支持生物多样性、保护、娱乐和以森林为基础的劳动力。 由于各种复杂的相互作用因素，包括不断变化的环境条件、联邦、州和私人土地所有权的不同管理目标以及自然干扰，森林具有高度动态和多样性。尽管技术和森林相关信息的获取取得了进步，但关键森林数据仍然高度可变、可用性不一致且规模相对粗糙。 INSPIRES 项目将建立一个数字框架，以更好地评估、理解和预测复杂的森林变化。将新兴的计算、监测、遥感和可视化技术集成到数字森林大数据框架中，将为科学家、土地管理者和政策制定者提供全面、近乎实时的森林空间和时间测量，以便于使用。该项目将加强劳动力发展并扩大科学参与，特别是具有不同背景和技能的学生。该项目将通过借鉴广泛的既定项目和学科，包括数据科学、生态学、电气工程、计算机编程和通信来实现其数字和教育目标。这项努力将有助于支持和维持新英格兰北部独特的森林景观，许多农村社区赖以生存。教师和学生将合作建立一个区域复杂系统研究所，该研究所将促进从多个科学角度对森林生态系统完整性和恢复力进行持续分析。最终，这种集成了先进传感和计算技术、环境信息学和分析、生态建模和定量推理技能的大数据框架将适用于其他森林地区和生态系统。新英格兰的森林代表了北部森林生态交错带，这是一个复杂的过渡生态系统组合，具有独特的自然干扰和人类土地利用历史。近几十年来，在土地利用压力、入侵性害虫和极端非生物事件等主要压力因素不断增加的情况下，社会对这些森林及其提供的生态系统服务的需求持续扩大。为了维护北部森林赖以生存的社区的价值和完整性，需要更好地了解这些相互作用的压力源如何影响这个生态系统。 因此，需要一个利用大数据的新数字框架来评估和预测多种未来响应路径下的相互作用。为了应对这一巨大挑战，来自缅因州、新罕布什尔州和佛蒙特州州立大学的教师将合作建立一个区域复杂系统研究所，该研究所将促进从多个科学角度分析森林生态系统的完整性和恢复力。教师和学生将围绕四个研究综合主题开展工作，开发一种新颖、灵活的数字森林大数据框架，有效利用无线传感器、遥感和公民科学等各种来源的复杂数据流，以增强我们对跨多个时空尺度的北部森林生态系统的基本了解。该项目的具体研究主题是：（1）先进传感与计算技术； (2)环境信息学与分析； (3) 综合生态模型； (4) 上下文中的定量推理。特别是，项目参与者将探索如何将 Wabanaki 部落的传统生态知识 (TEK) 和其他可用的定性数据与通常用于分析和建模生态系统的主要定量数据相结合。长期目标是将这一框架扩展到该区域之外，特别是扩展到其他高度关注的生态系统，包括海洋环境。重要的是，这项工作将与正在进行的提高 K-20 数据素养技能的区域努力相结合，同时为支持基于自然资源的经济体和相关产业提供有价值的新方法。区域复杂系统研究所的成立将通过利用先前和正在进行的努力和专业知识，整合、扩展和维持所有三个 EPSCoR 管辖区的优势。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Forests are an economically important and ecologically critical component of Northern rural working landscapes. Local and regional communities depend on the health of these forest ecosystems to support biodiversity, conservation, recreation, and a forest-based workforce. Forests are highly dynamic and diverse due to a wide variety of complex interacting factors, including changing environmental conditions, varying management objectives across federal, state and private land ownership, and natural disturbances. Despite advances in technology and acquisition of forest-related information, critical forest data remains highly variable, inconsistently available, and relatively coarse in scale. The INSPIRES project will build a digital framework to better assess, understand, and forecast complex forest changes. The integration of emerging computational, monitoring, remote sensing, and visualization technologies into a Digital Forest Big Data framework will provide comprehensive, near real-time spatial and temporal measurements of the forest at levels readily usable by scientists, land managers, and policy makers. This project will strengthen workforce development and broaden participation in science, particularly among students with diverse backgrounds and skills. The project will accomplish both its digital and educational aims by drawing from a broad array of established programs and disciplines, including data science, ecology, electrical engineering, computer programming, and communications. This effort will help support and sustain northern New England's unique forested landscape, which many rural communities rely on for their livelihoods. Faculty and students will collaborate on the development of a regional Complex Systems Research Institute that will facilitate ongoing analysis of forest ecosystem integrity and resilience from multiple scientific perspectives. Ultimately this Big Data Framework integrating advanced sensing and computing technologies, environmental informatics and analytics, ecological modeling, and quantitative reasoning skills would be applicable to other forested regions and ecosystems. Forests in New England represent the Northern Forest ecotone, which is a complex assemblage of transitional ecosystems that have a unique history of natural disturbance and human land use. In recent decades, societal demands on these forests and the ecosystem services they provide have continued to expand at a time when key stressors such as land use pressures, invasive pests, and extreme abiotic events are on the rise. Maintaining the value and integrity of the Northern Forest for the communities that depend on them requires a better understanding of how these interactive stressors affect this ecosystem. Thus, a new digital framework for harnessing Big Data is needed to assess and predict interactions under multiple alternative future response pathways. To address this grand challenge, faculty from the state universities of Maine, New Hampshire, and Vermont will collaborate on the development of a regional Complex Systems Research Institute that will facilitate analysis of forest ecosystem integrity and resilience from multiple scientific perspectives. Faculty and students will work across four research-integrated themes to develop a novel and flexible Digital Forest Big Data framework for effectively harnessing complex data streams from a variety of sources such as wireless sensors, remote sensing, and citizen science to enhance our fundamental understanding of Northern Forest ecosystems across multiple spatio-temporal scales. The project?s specific research themes are: (1) Advanced Sensing and Computing Technologies; (2) Environmental Informatics and Analytics; (3) Integrated Ecological Modeling; and (4) Quantitative Reasoning in Context. In particular, project participants will explore how to integrate the traditional ecological knowledge (TEK) of Wabanaki tribes and other available qualitative data with the primarily quantitative data typically employed to analyze and model ecosystems. The long-term goal is to extend this framework beyond the region, particularly to other ecosystems of high interest, including marine environments. Importantly, the effort will link with ongoing regional efforts to improve K-20 data literacy skills, while generating valuable new approaches for supporting natural resources-based economies and associated industries. The formation of a regional Complex Systems Research Institute will incorporate, extend, and sustain the strengths of all three EPSCoR jurisdictions by leveraging prior and ongoing efforts and expertise.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.