MRA: Resolving the multi-scale drivers of tree mortality from field and remote sensing data on co-located ForestGEO-NEON sites

MRA：通过位于同一地点的 ForestGEO-NEON 站点的实地和遥感数据解决树木死亡的多尺度驱动因素

• 批准号: 2106015
• 负责人: Daniel Johnson
• 金额: $ 100.58万
• 依托单位: University of Florida
• 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: 2021-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106015&HistoricalAwards=false
• 关键词: MRA; Resolving; multi; scale; drivers

Tree mortality is on the rise globally. However, patterns and causes of mortality are poorly understood. Because forests are key locations for terrestrial biodiversity and contribute significantly to terrestrial carbon storage, a better understanding of tree mortality is important. A forest can lose biomass, and thus carbon, quickly through tree death, but typically gain carbon only slowly through growth. Thus, understanding the causes of tree mortality will contribute to a better understanding of carbon cycling. Big disturbances like fires or insect outbreaks can kill many trees at once, but most trees do not die from big disturbances. In contrast, most trees die alone, slowly over years for reasons that are not always obvious. The goal of this work is to determine the rate at which trees are dying annually, and most importantly for what reasons. Using these data tools to better detect future trends in tree mortality and forest dynamics will be built. This information will be conveyed to forest managers, policy makers, and other stakeholders. The data and products from this project will be broadly disseminated and integrated within grade 4-12 education modules. The project will also engage undergraduate researchers in all aspects of the research including introducing them to participants of national and international research networks. Data from two national research networks will be combined to answer questions regarding how, where, and why trees are dying. The researchers will perform annual mortality surveys on tens of thousands of trees at five large-area forest research plots that are part of the Smithsonian Institution’s ForestGEO network (https://forestgeo.si.edu/). All these ForestGEO plots are located on National Ecological Observation Network (NEON) sites (https://www.neonscience.org/) where a suite of ecological data, including air-borne remote sensing, is collected on a regular schedule. Detailed ground-truth data from the ForestGEO plots will be combined with high-resolution hyperspectral and lidar drone-based remote sensing data collected at each site. These data will be used to model causes and factors associated with tree mortality. Using NEON remote sensing data, the model of tree mortality will be upscaled to the NEON domain. Lidar and imagery will be used to identify structural changes in forests that indicate tree death. Models will then be up-scaled further using satellite data collected by NASA to make mortality estimates across the larger landscape. The results of these data collection efforts will advance understanding of tree mortality, the pace of forest change, and the dynamics of terrestrial carbon storage. The project will help advance macrosystems biology by pinpointing how the patterns of tree mortality scale from an individual tree to the entire landscape.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.

全球树木死亡率正在上升。然而，人们对死亡的模式和原因知之甚少。由于森林是陆地生物多样性的关键所在地，并对陆地碳储存作出重大贡献，因此更好地了解树木死亡率非常重要。森林可以通过树木死亡迅速失去生物量，从而失去碳，但通常只能通过生长缓慢获得碳。因此，了解树木死亡的原因将有助于更好地了解碳循环。大的干扰，如火灾或昆虫爆发，可以杀死许多树木一次，但大多数树木不会死于大的干扰。相比之下，大多数树木都是独自死亡，多年来缓慢死亡，原因并不总是显而易见的。这项工作的目标是确定树木每年死亡的速度，最重要的是什么原因。使用这些数据工具，以更好地检测树木死亡率和森林动态的未来趋势将建立。这些信息将传达给森林管理人员、决策者和其他利益攸关方。该项目的数据和产品将广泛传播并整合到4-12年级的教育模块中。 该项目还将吸引本科研究人员参与研究的各个方面，包括将他们介绍给国家和国际研究网络的参与者。来自两个国家研究网络的数据将被结合起来，以回答有关树木如何、在哪里以及为什么死亡的问题。研究人员将对史密森学会ForestGEO网络（https：//forestgeo.si.edu/）的五个大面积森林研究地块的数万棵树木进行年度死亡率调查。所有这些ForestGEO地块都位于国家生态观测网络（氖）网站（https：//www.Escience.org/）上，定期收集一套生态数据，包括空中遥感。来自森林地球观测组织地块的详细地面实况数据将与在每个站点收集的高分辨率超光谱和激光雷达无人机遥感数据相结合。这些数据将被用来模拟与树木死亡率相关的原因和因素。利用氖遥感数据，树木死亡率模型将升级到氖域。激光雷达和图像将用于识别森林中表明树木死亡的结构变化。然后，模型将使用NASA收集的卫星数据进一步升级，以估计更大范围内的死亡率。这些数据收集工作的结果将促进对树木死亡率、森林变化速度和陆地碳储存动态的了解。该项目将通过精确定位树木死亡率的模式从单个树木到整个景观来帮助推进宏观系统生物学。该奖项反映了NSF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

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

• DOI: 10.1111/2041-210x.13982
• 发表时间: 2022-09
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Albert Y. Kim;Valentine Herrmann;Ross Bareto;Brian Calkins;E. Gonzalez-Akre;Daniel J. Johnson;Jennifer A. Jordan;L. Magee;I. McGregor;Nicolle Montero;Karl Novak;Teagan Rogers;J. Shue;K. Anderson‐Teixeira