MRA: Disentangling cross-scale influences on tree species, traits, and diversity from individual trees to continental scales

MRA：理清从个体树木到大陆尺度对树种、性状和多样性的跨尺度影响

• 批准号: 1926542
• 负责人: Ethan White
• 金额: $ 121.52万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926542&HistoricalAwards=false
• 关键词: MRA; Disentangling; cross; scale; influences

Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees. Together, the remote sensing and field data will be used to understand what influences the number of trees, their size, where different species occur, and how this changes from spatial scales of local parks to the entire United States. This project will also make it easier for other scientists to study trees over large areas by developing software, producing data products, and providing training and collaboration opportunities for working with these novel datasets. This will help drive rapid advances in the cross-scale understanding of tree ecology with broad applications in forestry, management, and fundamental scientific understanding.This project combines National Ecological Observatory Network (NEON) data from airborne remote sensing and field data collection. These data will be used to develop machine learning based approaches to identify, measure, and characterize to species all of the canopy trees located within each forested NEON site. This will yield data on approximately 50 million individual trees at about 40 sites across the United States. These data from NEON will be combined with data from the US Forest Service Forest Inventory and Analysis Project, which samples millions of trees at over 100,000 locations across the United States. These combined data will be used to develop joint models of the distribution, abundance, and structural traits of trees, that explicitly incorporate the concept of scale. These models will be used to understand how the processes influencing tree distribution and traits change across scales by comparing the importance of different factors at scales ranging from a few meters, where individual trees directly interact, to the entire United States, where large gradients in climate and land use are important. This research will address three broad questions in ecology: 1) what processes govern species distribution and abundance at different scales and how do they interact? 2) how are landscape and regional process of species coexistence connected to local biodiversity? 3) how do changes in the processes influencing tree traits across scales impact estimates of biomass and carbon storage?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.

树木对生态系统来说是必不可少的。它们储存碳，减少侵蚀，并作为其他物种的栖息地。影响树木的因素和管理树木的空间尺度，从一棵树到整个大陆都有。由于世界上大约有3万亿棵树，用传统方法收集大面积每棵树的数据是不可能的。因此，有必要使用新技术来测量和描述大范围地理区域上的单株树木。这项研究将通过将高分辨率遥感数据与树木实地数据相结合来解决这一根本挑战。遥感和实地数据将被用来了解是什么影响了树木的数量、大小、不同物种出现的地方，以及这些影响从当地公园的空间尺度到整个美国的变化。该项目还将使其他科学家更容易通过开发软件、生产数据产品以及为使用这些新的数据集提供培训和合作机会来研究大面积的树木。这将有助于推动对树木生态的跨尺度理解的快速进展，在林业、管理和基础科学理解中具有广泛的应用。该项目结合了来自航空遥感的国家生态观测网(NEON)数据和野外数据收集。这些数据将被用来开发基于机器学习的方法，以识别、测量和描述位于每个森林霓虹灯区域内的所有树冠树的物种。这将产生全美约40个地点的约5000万棵树的数据。来自霓虹灯的这些数据将与美国林业局森林调查和分析项目的数据结合起来，该项目对全美10万多个地点的数百万棵树进行了采样。这些合并的数据将被用来开发明确纳入尺度概念的树木分布、丰度和结构特征的联合模型。这些模型将被用来了解影响树木分布和特征的过程如何在不同尺度上发生变化，方法是比较不同因素在几米尺度上的重要性，从个别树木直接相互作用的尺度到整个美国的尺度，在整个美国，气候和土地利用的大梯度是重要的。这项研究将解决生态学中的三个广泛问题：1)什么过程控制着不同尺度上的物种分布和丰度，以及它们是如何相互作用的？2)物种共存的景观和区域过程如何与当地的生物多样性联系在一起？3)不同尺度上影响树木特征的过程的变化如何影响生物量和碳储量的估计？这一奖项反映了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。

项目成果

NEON Tree Crowns Dataset

NEON 树冠数据集

• DOI: 10.5281/zenodo.3765871
• 发表时间: 2020
• 期刊: Zenodo
• 作者: Weinstein, Ben;Marconi, Sergio;Zare, Alina;Bohlman, Stephanie;Graves, Sarah;Singh, Aditya;White, Ethan
• 通讯作者: White, Ethan

Derived estimates of leaf traits and associated models for 1.2 million trees across Eastern US

对美国东部 120 万棵树的叶子特征和相关模型进行了估计

• DOI: 10.5281/zenodo.4647558
• 发表时间: 2021
• 期刊: Zenodo
• 作者: Marconi, Sergio;G., Benjamin Weinstein;W., Jeremy Lichstein;A., Stephanie Bohlman;Singh, Aditya;P., Ethan White
• 通讯作者: P., Ethan White

Cross-site learning in deep learning RGB tree crown detection

• DOI: 10.1016/j.ecoinf.2020.101061
• 发表时间: 2020-03-01
• 期刊: ECOLOGICAL INFORMATICS
• 影响因子: 5.1
• 作者: Weinstein, Ben G.;Marconi, Sergio;White, Ethan P.
• 通讯作者: White, Ethan P.

Estimating individual‐level plant traits at scale

大规模估计个体水平的植物性状

• DOI: 10.1002/eap.2300
• 发表时间: 2021
• 期刊: Ecological Applications
• 影响因子: 5
• 作者: Marconi, Sergio;Graves, Sarah J.;Weinstein, Ben G.;Bohlman, Stephanie;White, Ethan P.
• 通讯作者: White, Ethan P.

Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model

• DOI: 10.1002/rse2.335
• 发表时间: 2023-05
• 期刊: Remote Sensing in Ecology and Conservation
• 影响因子: 5.5
• 作者: Ben. G. Weinstein;S. Marconi;Sarah J. Graves;Alina Zare;Aditya Singh;Stephanie A. Bohlman;L. Magee;Daniel J. Johnson;P. Townsend;E. White