Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2024-11-08 DOI:10.1002/rse2.421

Ethan P. White, Lindsey Garner, Ben G. Weinstein, Henry Senyondo, Andrew Ortega, Ashley Steinkraus, Glenda M. Yenni, Peter Frederick, S. K. Morgan Ernest

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引用次数: 0

Abstract

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.

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利用无人驾驶飞机系统和计算机视觉对涉水鸟类进行近实时监测

由于航空调查方法的发展，以及使用计算机视觉模型对生物个体进行识别和分类，在大面积地理区域进行野生动物种群监测变得越来越可行。然而，航空调查仍然不经常进行，而且从获取航空图像到将其转换为种群监测数据之间往往会有很长时间的延迟。近实时监测对于积极的管理决策和生态预测越来越重要。要在大范围内实现这一目标，需要结合机载图像、将图像处理成生物个体信息的计算机视觉模型，以及确保图像在获取后迅速处理成数据的自动化工作流程。在此，我们介绍了在美国佛罗里达州大沼泽地对涉禽进行近实时监测的端到端工作流程。我们使用无人驾驶飞机系统（又称无人机）以每周一次的频率采集图像，处理成正交合成图（使用 Agisoft metashape），使用 Retinanet-50 物体检测器转换成个体级物种数据，进行后处理、存档，并在基于网络的可视化平台上展示（使用 Shiny）。工作流程的主要组成部分是使用 Snakemake 自动完成的。底层计算机视觉模型能够准确地检测物体、进行物种分类，并对六个目标物种（白鹮、大白鹭、大蓝鹭、鹳和鹭琵鹭）中的五个物种进行总计数和物种计数。该模型在雪鹭方面的表现较差，原因是标签数量较少，且难以将雪鹭与白朱鹭（数量最多的物种）区分开来。通过将调查后处理自动化，这些物种的种群数据几乎可以实时获得（调查日期后 1 周），为生态预测和积极管理提供了所需的时间尺度信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.