In adaptive management of rangelands, monitoring is the vital link that connects management actions with on-the-ground changes. Traditional field monitoring methods can provide detailed information for assessing the health of rangelands, but cost often limits monitoring locations to a few key areas or random plots. Remotely sensed imagery, and drone-based imagery in particular, can observe larger areas than field methods while retaining high enough spatial resolution to estimate many rangeland indicators of interest. However, the geographic extent of drone imagery products is often limited to a few hectares (for resolution ≤1 cm) due to image collection and processing constraints. Overcoming these limitations would allow for more extensive observations and more frequent monitoring. We developed a workflow to increase the extent and speed of acquiring, processing, and analyzing drone imagery for repeated monitoring of two common indicators of interest to rangeland managers: vegetation cover and vegetation heights. By incorporating a suite of existing technologies in drones (real-time kinematic GPS), data processing (automation with Python scripts, high performance computing), and cloud-based analysis (Google Earth Engine), we greatly increased the efficiency of collecting, analyzing, and interpreting high volumes of drone imagery for rangeland monitoring. End-to-end, our workflow took 30 d, while a workflow without these innovations was estimated to require 141 d to complete. The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits.

中文翻译：

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扩大无人机数据收集和分析以进行牧场监测的创新

在牧场的适应性管理中，监测是将管理行动与实地变化联系起来的重要环节。传统的现场监测方法可以提供用于评估牧场健康状况的详细信息，但成本通常将监测位置限制在几个关键区域或随机地块。遥感影像，尤其是基于无人机的影像，可以观察到比现场方法更大的区域，同时保持足够高的空间分辨率来估计许多感兴趣的牧场指标。然而，由于图像采集和处理的限制，无人机图像产品的地理范围通常仅限于几公顷（分辨率≤1 cm）。克服这些限制将允许进行更广泛的观察和更频繁的监测。我们开发了一个工作流程来提高获取、处理和分析无人机图像的范围和速度，以重复监测牧场管理者感兴趣的两个常见指标：植被覆盖度和植被高度。通过在无人机（实时运动学 GPS）、数据处理（Python 脚本自动化、高性能计算）和基于云的分析（谷歌地球引擎）中结合一套现有技术，我们大大提高了收集、分析，并解释大量无人机图像以进行牧场监测。端到端，我们的工作流程需要 30 天，而没有这些创新的工作流程估计需要 141 天才能完成。围绕无人机和图像分析的技术正在迅速发展，这使得大容量工作流程更容易实施。