基于无人机的地下数据采集采用低技术含量的地面真实载荷系统增强浅水监测

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2023-10-25 DOI:10.3390/drones7110647
Aris Thomasberger, Mette Møller Nielsen
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引用次数: 0

摘要

无人驾驶飞行器(uav)是一种广泛应用于浅水栖息地监测的工具。在进行基于无人机的水下栖息地监测时,一个反复出现的问题是收集地面真实数据,作为航空图像分类所需的训练和验证样本,以及识别生态相关信息,如植被深度限制。为了解决这些限制,开发了一种有效载荷系统,以视频和深度测量的形式收集地下数据。在一个7ha的大研究区,收集了136个观测点,并利用这些观测点对航拍影像进行了分类训练和验证,基于观测点插值建立了类分布图,识别了额外的生态相关信息,并绘制了研究区测深图。基于地面真实样本的分类总体准确率达到98%,与基于点插值的类分布图的准确率达到84%。此外,还记录了植被深度限制等生态相关信息,并绘制了研究地点的测深图。研究结果表明,应用该工具可以改善基于无人机的浅水监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring
Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool.
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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