Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network
Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network
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DOI：
10.1002/ecs2.4206
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发表时间：
2022-08
期刊：
影响因子：
2.7
通讯作者：
M. J. Koontz;Victoria M. Scholl;Anna I. Spiers;M. Cattau;J. Adler;J. McGlinchy;T. Goulden;B. Melbourne;J. Balch
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文献类型：
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作者：
M. J. Koontz;Victoria M. Scholl;Anna I. Spiers;M. Cattau;J. Adler;J. McGlinchy;T. Goulden;B. Melbourne;J. Balch
宏观生态学研究旨在了解生态现象及其在多个数量级的尺度上积累、相互作用和出现的原因和后果。需要广泛的细粒度信息（即大区域的高空间分辨率数据）来充分捕获这些跨尺度现象，但历史上获取和处理这些数据的成本很高。无人占用的航空系统（UAS或携带传感器有效载荷的无人机）和国家生态观测站网络（NEON）通过降低成本和减少对高度专业化设备的需求，使研究人员更容易进入广泛的、细粒度的观测领域。这些工具的整合可以进一步使宏观生态研究民主化，因为它们的优点和缺点是互补的。然而，将这些工具用于宏观生态学可能具有挑战性，因为缺乏心理模型，因此需要大量的时间、精力和创造力的前期投资才能熟练使用。这一挑战激发了由 UAS 使用学术生态学家、NEON 专业人士、成像科学家、遥感专家和航空工程师组成的工作组在科罗拉多州博尔德举行的 2019 年 NEON 科学峰会上综合当前关于如何在心智模型中使用 UAS 和 NEON 的知识，以供不熟悉这些工具的生态学家的目标受众使用。具体来说，我们提供了（1）收集用于 NEON 集成的高质量 UAS 数据的核心原则集合，以及（2）一个案例研究，说明了将 UAS 数据处理为有意义的生态信息并将其与地面观测系统收集的 NEON 数据以及从机载观测平台远程收集的 NEON 数据集成的示例工作流程。借助这种思维模型，我们通过 NEON/UAS 集成更容易访问关键的观测领域（广泛的、细粒度的领域），从而推进宏观生态学的民主化。
Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad‐extent, fine‐grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross‐scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad‐extent, fine‐grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up‐front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS‐using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high‐quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad‐extent, fine‐grain domain—more accessible via NEON/UAS integration.