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

RII Track-2 FEC：利用智能信息学和智能数据提高对北部森林生态系统恢复力的了解 (INSPIRES)

• 批准号: 1920908
• 负责人: Aaron Weiskittel
• 金额: $ 600万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920908&HistoricalAwards=false
• 关键词: RII; Track; FEC; Leveraging; Intelligent

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.

森林是北方农村劳动景观的重要经济和生态关键组成部分。地方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、娱乐和森林劳动力。由于各种复杂的相互作用因素，包括环境条件的变化、联邦、州和私有土地所有权的不同管理目标以及自然干扰，森林具有高度的活力和多样性。尽管在技术和获取与森林有关的信息方面取得了进展，但关键的森林数据仍然变化无常、不一致，而且规模相对较大。Inspirres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、遥感和可视化技术整合到数字森林大数据框架中，将在科学家、土地管理人员和政策制定者随时可用的水平上提供全面的、接近实时的森林空间和时间测量。该项目将加强劳动力发展，扩大对科学的参与，特别是在具有不同背景和技能的学生中。该项目将通过借鉴包括数据科学、生态学、电气工程、计算机编程和通信在内的一系列既定计划和学科来实现其数字和教育目标。这一努力将有助于支持和维持新英格兰北部独特的森林景观，许多农村社区的生计依赖于这一景观。教师和学生将合作开发一个地区性复杂系统研究所，该研究所将从多个科学角度促进对森林生态系统完整性和复原力的持续分析。最终，这个集成了先进的传感和计算技术、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着北部森林交错带，这是一个复杂的过渡生态系统集合，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、入侵害虫和极端非生物事件等关键压力来源不断增加的情况下，社会对这些森林及其提供的生态系统服务的需求继续扩大。对于依赖北方森林的社区来说，维持北方森林的价值和完整性需要更好地理解这些互动压力如何影响这个生态系统。因此，需要一个新的利用大数据的数字框架，以评估和预测多个替代未来响应路径下的交互作用。为了应对这一重大挑战，缅因州、新罕布夏州和佛蒙特州州立大学的教职员工将合作开发一个地区性复杂系统研究所，该研究所将从多个科学角度促进对森林生态系统完整性和弹性的分析。教师和学生将在四个研究集成主题上合作，开发一个新颖而灵活的数字森林大数据框架，以有效利用来自无线传感器、遥感和公民科学等各种来源的复杂数据流，以增强我们对多个时空尺度上的北方森林生态系统的基本理解。项目？S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)集成生态建模；(4)上下文中的定量推理。特别是，项目参与者将探索如何将瓦巴纳基部落的传统生态知识(TEK)和其他现有的定性数据与通常用于分析和模拟生态系统的主要定量数据结合起来。长期目标是将这一框架扩展到该区域以外，特别是其他高度关注的生态系统，包括海洋环境。重要的是，这一努力将与正在进行的提高K-20数据素养技能的区域努力联系起来，同时产生有价值的新方法，以支持以自然资源为基础的经济和相关产业。地区性复杂系统研究所的成立将结合、扩展和保持所有三个EPSCoR辖区的优势，通过利用先前和正在进行的努力和专业知识。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Ontology Design Pattern for Spatial and Temporal Aggregate Data (STAD)

时空聚合数据（STAD）的本体设计模式

• 发表时间: 2022
• 期刊: 13th Workshop on Ontology Design and Patterns (WOP 2022
• 作者: Kingsley Wiafe-Kwakye, Torsten Hahmann

Sharing Wireless Spectrum in the Forest Ecosystems Using Artificial Intelligence and Machine Learning

• DOI: 10.1007/s10776-022-00572-9
• 发表时间: 2022-08-09
• 期刊: INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS
• 影响因子: 2.5
• 作者: Naderi, Sonia;Bundy, Kenneth;Contosta, Alexandra
• 通讯作者: Contosta, Alexandra

Accounting for Carbon Flux to Mycorrhizal Fungi May Resolve Discrepancies in Forest Carbon Budgets

• DOI: 10.1007/s10021-019-00440-3
• 发表时间: 2019-09
• 期刊: Ecosystems
• 影响因子: 3.7
• 作者: A. Ouimette;S. Ollinger;L. Lepine;Ryan B. Stephens;R. J. Rowe;M. Vadeboncoeur;S. J. Tumber-Dávila;E. Hobbie

Real-time monitoring of deadwood moisture in forests: lessons learned from an intensive case study

• DOI: 10.1139/cjfr-2020-0110
• 发表时间: 2020-11-01
• 期刊: CANADIAN JOURNAL OF FOREST RESEARCH
• 影响因子: 2.2
• 作者: Woodall, C. W.;Evans, D. M.;D'Amato, A. W.
• 通讯作者: D'Amato, A. W.

On studying the patterns of individual-based tree mortality in natural forests: A modelling analysis

• DOI: 10.1016/j.foreco.2020.118369
• 发表时间: 2020-11
• 期刊: Forest Ecology and Management
• 影响因子: 3.7
• 作者: Christian Salas‐Eljatib;A. Weiskittel