无人机和地面实况数据收集、图像标注和机器学习:海岸栖息地绘图和分类协议

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-08-30 DOI:10.1016/j.mex.2024.102935
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引用次数: 0

摘要

无人机航拍成像是以高空间和时间分辨率对沿岸生境进行测绘和监测的有效工具。具体来说,与传统的测绘和监测技术相比,无人机成像可以在更大的范围内进行测绘,既省时又省钱,同时还能提供比飞机和卫星更详细的信息,例如能够区分各种类型的沿岸植被。在此,我们介绍一种基于无人机图像的浅水生境分类系统方法。该方法包括:-采集无人机图像并创建正射影像图;-在现场收集地面实况数据,以指导图像注释并验证最终地图产品;-将无人机图像注释为可能分层的生境类别,并训练用于生境分类的机器学习算法。作为案例研究,我们介绍了一次现场活动,该活动采用这些方法绘制了以海草、海藻和海带为主的沿海地区地图,此外还有沉积物和岩石。这种详细而高效的绘图和分类有助于了解和可持续地管理具有生态价值的海洋生态系统。
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Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:

  • Collection of drone images and creation of orthomosaics.

  • Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.

  • Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.

As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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