EAGER: Scaling Up Plant Demographic Rates with Imagery from Unoccupied Aerial Systems

EAGER：利用空闲航空系统的图像扩大植物人口统计率

• 批准号: 2207158
• 负责人: Trevor Caughlin
• 金额: $ 20.04万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207158&HistoricalAwards=false
• 关键词: EAGER; Scaling; Up; Plant; Demographic

This award is funded in whole under the American Rescue Plan Act of 2021 (Public Law 117-2).Wildfires pose a growing threat to ecosystems in the American West, as climate change and invasive species promote larger and more frequent fires. Understanding how fast, if ever, native plant populations can recover from wildfire, and where recruitment of prior dominants is not occurring will aid efforts to restore degraded ecosystems. Plant population recovery is most often studied by marking and measuring individual plants in field plots or along 50 to 100 m transects. Logistical considerations limit the spatial coverage of these types of field measurements, resulting in a mismatch between multi-kilometer-scale wildfires and plot level responses. The research proposed here will use drones to develop new methods to measure plant population recovery over large areas. These methods will include computer algorithms to detect and distinguish individual plants in aerial imagery, and statistical models to quantify drone-detected plants' growth, survival, and reproduction. This quantitative framework will enable the research team to study how an ecologically important dominant plant species across the inter-mountain west is responding to wide spread fire and to large-scale environmental variation within the vast burned areas. The research will be applied to forecast population recovery of big sagebrush plants, a species with critical ecological importance. The results will aid land management across this vast region by advancing drone technology to monitor post-fire recovery. Novel remote sensing data from unoccupied aerial systems (UAS) could aid spatial models for plant demography by providing imagery with fine enough resolution to detect individual plants across large spatial extents. This project will develop a framework to infer plant demographic rates from UAS imagery by coupling computer vision techniques with hierarchical Bayesian models. The research will focus on spatial population dynamics of big sagebrush, Artemisia tridentata, a species with high conservation value in the American West. As wildfires decimate sagebrush habitat, landscape recovery depends on whether big sagebrush can recolonize disturbed areas. However, like most low-statured plants, our understanding of big sagebrush demography is almost entirely derived from meter-scale field plots, limiting extrapolation to large spatial scales. The research will develop a reproducible workflow to identify individual plants in UAS imagery, quantify demographic rates of plants while accounting for imperfect detection in aerial imagery, and determine the impacts of spatial covariates on sagebrush demography. This research will advance quantitative modeling approaches for spatial plant demography with immediate application to an imperiled ecosystem.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.

该奖项是根据2021年美国救援计划法案（公法117-2）资助的。野火对美国西部的生态系统构成越来越大的威胁，因为气候变化和入侵物种促进了更大和更频繁的火灾。了解本地植物种群从野火中恢复的速度（如果有的话），以及在哪里没有发生先前的优势物种的招募，将有助于恢复退化的生态系统。植物种群恢复最常通过标记和测量田间小区或沿沿着50至100米的样带中的个体植物来研究。对数的考虑限制了这些类型的现场测量的空间覆盖范围，导致多公里规模的野火和地块级别的响应之间的不匹配。这里提出的研究将使用无人机开发新方法来测量大面积的植物种群恢复。这些方法将包括计算机算法来检测和区分航空图像中的单个植物，以及统计模型来量化无人机检测到的植物的生长，存活和繁殖。这个定量框架将使研究团队能够研究西部山间具有生态重要性的优势植物物种如何应对大范围蔓延的火灾和广阔烧毁区域内的大规模环境变化。该研究将用于预测大型山艾树植物的种群恢复，这是一种具有重要生态意义的物种。研究结果将通过推进无人机技术来监测火灾后的恢复情况，从而帮助这一广阔地区的土地管理。来自无人机系统（UAS）的新型遥感数据可以通过提供具有足够精细分辨率的图像来检测大空间范围内的单个植物，从而帮助建立植物种群的空间模型。该项目将开发一个框架，通过将计算机视觉技术与分层贝叶斯模型相结合，从UAS图像中推断植物人口统计率。该研究将侧重于大山艾，蒿，在美国西部具有较高的保护价值的物种的空间种群动态。由于野火摧毁了山艾树的栖息地，景观的恢复取决于大山艾树是否能使受干扰的地区恢复生机。然而，像大多数矮株植物一样，我们对大山艾树种群的理解几乎完全来自米级的野外地块，限制了大空间尺度的外推。该研究将开发一个可重复的工作流程，以识别UAS图像中的单个植物，量化植物的人口统计率，同时考虑航空图像中的不完美检测，并确定空间协变量对山艾树人口统计的影响。这项研究将推进空间植物种群的定量建模方法，并立即应用于受威胁的生态系统。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

• DOI: 10.1002/ecs2.4330
• 发表时间: 2022-12
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: A. Roser;Josh Enterkine;J. M. Requena-Mullor;N. Glenn;Alex R. Boehm;M. de Graaff;P. Clark;F. Pierson;T. T. Caughlin-T.

Demography with drones: detecting growth and survival of shrubs with unoccupied aerial systems

• DOI: 10.1111/rec.14106
• 发表时间: 2024-01-25
• 期刊: RESTORATION ECOLOGY
• 影响因子: 3.2
• 作者: Olsoy，Peter J.;Zaiats，Andrii;Caughlin，T. Trevor
• 通讯作者: Caughlin，T. Trevor

Bayesian models for spatially explicit interactions between neighbouring plants

• DOI: 10.1111/2041-210x.13998
• 发表时间: 2022-10
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Cristina Barber;A. Zaiats;Cara Applestein;Lisa M. Rosenthal;T. T. Caughlin-T.

High‐resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature

高分辨率热图像揭示了树冠结构和遗传学之间的相互作用如何影响植物温度

• DOI: 10.1002/rse2.359
• 发表时间: 2023
• 期刊: Remote Sensing in Ecology and Conservation
• 影响因子: 5.5
• 作者: Olsoy, Peter J.;Zaiats, Andrii;Delparte, Donna M.;Germino, Matthew J.;Richardson, Bryce A.;Roop, Spencer;Roser, Anna V.;Forbey, Jennifer S.;Cattau, Megan E.;Buerki, Sven
• 通讯作者: Buerki, Sven