MRA: Macroscale Resilience: Assessing the recovery of western U.S. forests following compound disturbance by linking observations from trees to ecoregions

MRA：宏观恢复力：通过将树木观察结果与生态区联系起来，评估美国西部森林在复合干扰后的恢复情况

• 批准号: 2017889
• 负责人: Jennifer Balch
• 金额: $ 168.31万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017889&HistoricalAwards=false
• 关键词: MRA; Macroscale; Resilience; Assessing; recovery

There are an estimated 6.3 billion dead trees currently across western U.S. forests, a legacy from increasing frequency or severity of beetle infestations, wildfires, and droughts against a backdrop of regional warming. A critical question remains: have forests recovered from these disturbances over the last 36 years? Leveraging three core forest sites in the Southern Rockies, Northern Rockies, and Pacific Northwest that are part of the National Ecological Observatory Network (NEON), this work will explore: 1) What combinations and sizes of disturbances create abrupt and persistent forest changes?, and 2) How does forest resilience vary as a function of forest type, drought, and regional warming? This effort will explore these questions by integrating data from individual trees to entire ecoregions to advance understanding of western forest recovery. A novel, core scientific community, the NEON Resilience Network, will be enabled for data-intensive exploration of resilience using an open-source NEON toolkit that contains data, code, methods, and three software packages. In addition, sustained touchpoints will be made with regional and federal land managers developing best-practices for how to resist, adapt, or facilitate ecological transformation. This connection offers a critical co-production pathway, directly connecting NEON-enabled scientific discovery and public lands management decisions.This project will advance theoretical understanding of how resilience varies as a function of compound, large disturbances, within and among ecoregions, and with decadal-scale regional warming. These are essential macrosystems ecological questions to address for western U.S. forests. This research will apply deep-learning techniques to identify individual plant species from NEON’s airborne data collections of hyperspectral, LiDAR (light detection and ranging), and red-green-blue imagery, helping to advance a research frontier. Coupled with extended sampling via unmanned aerial systems (UAS), the research will develop vegetation class spectral signatures to derive annually-resolved plant functional type maps (coniferous forest, deciduous forest, woody shrub, grass/herb, and bare ground) from the Landsat satellite record (1984-present), providing an unprecedented temporal reconstruction of western U.S. forest dynamics. The NEON Resilience Network will also scale core resilience questions across the continental U.S., asking: are there predictable combinations, sequences, or sizes of disturbances that lead to more persistent state changes across vegetation types? This research will help unlock the power of the Landsat record to explore forest resilience in the western U.S., setting an important historical baseline as NEON embarks on monitoring continental-scale forest ecology over the next 30 years.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.

据估计，目前美国西部的森林中有63亿棵死树，这是在地区变暖的背景下，甲虫侵扰、野火和干旱的频率或严重程度日益增加所造成的。一个关键的问题仍然存在：在过去的36年里，森林从这些干扰中恢复过来了吗？利用南落基山脉、北落基山脉和太平洋西北地区的三个核心森林地点，作为国家生态观测网（NEON）的一部分，这项工作将探索：1)干扰的组合和大小造成了突然和持续的森林变化？2)森林恢复力如何随森林类型、干旱和区域变暖而变化？这项工作将通过整合从单个树木到整个生态区域的数据来探索这些问题，以促进对西部森林恢复的理解。一个新颖的核心科学社区，NEON弹性网络，将使用包含数据、代码、方法和三个软件包的开源NEON工具包，对弹性进行数据密集型探索。此外，将与地区和联邦土地管理者建立持续的接触点，制定如何抵制、适应或促进生态转型的最佳实践。这种连接提供了一个关键的合作生产途径，直接将霓虹灯支持的科学发现和公共土地管理决策联系起来。该项目将促进对生态区域内部和区域之间的复合大扰动以及十年尺度的区域变暖如何影响恢复力的理论理解。这些都是美国西部森林需要解决的重要宏观系统生态问题。这项研究将应用深度学习技术，从NEON的高光谱、激光雷达（光探测和测距）和红绿蓝图像的机载数据收集中识别单个植物物种，帮助推进研究前沿。结合无人机系统（UAS）的扩展采样，该研究将开发植被分类光谱特征，从Landsat卫星记录（1984年至今）中获得年度解析的植物功能类型图（针叶林、落叶林、木本灌木、草/草本和裸地），提供美国西部森林动态的前所未有的时间重建。NEON恢复力网络还将对美国大陆的核心恢复力问题进行评估，提出以下问题：是否存在可预测的干扰组合、序列或大小，从而导致植被类型之间更持久的状态变化？这项研究将有助于释放陆地卫星记录的力量，探索美国西部的森林恢复力，为NEON开始监测未来30年大陆尺度的森林生态设定重要的历史基线。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

Cyberinfrastructure deployments on public research clouds enable accessible Environmental Data Science education

公共研究云上的网络基础设施部署可实现环境数据科学教育

• DOI: 10.1145/3569951.3597606
• 发表时间: 2023
• 期刊: ACM
• 作者: McIntosh, Tyler L;Verleye, Erick;Balch, Jennifer K;Cattau, Megan E;Ilangakoon, Nayani T;Korinek, Nathan;Nagy, R. Chelsea;Sanovia, James;Skidmore, Edwin;Swetnam, Tyson L
• 通讯作者: Swetnam, Tyson L

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

• DOI: 10.1002/ecs2.4206
• 发表时间: 2022-08
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: M. J. Koontz;Victoria M. Scholl;Anna I. Spiers;M. Cattau;J. Adler;J. McGlinchy;T. Goulden;B. Melbourne;J. Balch

Warming weakens the night-time barrier to global fire

• DOI: 10.1038/s41586-021-04325-1
• 发表时间: 2022-02-17
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Balch, Jennifer K.;Abatzoglou, John T.;Williams, A. Park
• 通讯作者: Williams, A. Park