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Fluvial and aquatic applications of Structure from Motion photogrammetry and unmanned aerial vehicle/drone technology

机译:运动摄影测量和无人机/无人机技术的结构的河流和水生应用

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Structure from motion (SfM) has seen rapid uptake recently in the fluvial and aquatic sciences. This uptake is not least due to the widespread availability of cheap unmanned aerial vehicles/drones, which help mitigate the challenging terrain and deliver efficient and reproducible and high‐accuracy images and topographical data. These data can have unprecedented spatio‐temporal coverage and includes measurements of fluvial and aquatic topography, hydraulics, geomorphology and habitat quality. SfM data also offer novel quantification of underwater archeology, structures and aquatic organisms. Studies are shifting from proof‐of‐concepts in topographic survey to genuine applications including grain‐size mapping, bathymetric surveys, geomorphological mapping, vegetation mapping, restoration monitoring, habitat classification, geomorphological change detection and sediment transport path delineation. Integrating point cloud analyses and orthophoto mosaics with digital elevation models has been shown to be effective in providing novel process understanding of fluvial and aquatic systems. Underwater and through‐water studies are beginning to overcome problems of accessibility, visibility and image distortion. Archival photographs and video (both above‐ and under‐water) are being reprocessed using a SfM workflow to generate three‐dimensional surfaces and objects from historical surveys, thereby extending the time period over which change can be detected. Recently, a SfM workflow has been developed to model free water surfaces with clear potential for future exploitation in hydraulics, sediment transport and river bed evolution studies. Future applications of SfM could seek to exploit the daily repeat coverage of high‐resolution satellite images but must be mindful of the necessary investment in this development versus the increasing availability and coverage of spaceborne light detection and ranging.
机译:在河流和水生科学中,运动(SFM)产生的结构(SFM)迅速吸收。这种吸收尤其是由于廉价无人驾驶汽车/无人机的广泛可用性,这有助于减轻具有挑战性的地形,并提供高效,可再现和高智能图像和地形数据。这些数据可以具有前所未有的时空覆盖范围,包括河流和水生地形,液压,地貌和栖息地质量的测量。 SFM数据还提供了水下考古,结构和水生生物的新量化。研究正在从地形调查中的验证概念转变为真正的应用,包括谷物尺寸的映射,测深度调查,地貌图,植被映射,植被映射,恢复监测,栖息地分类,地貌变化检测和沉积物传输路径。已证明将点云分析和原始镶嵌模型与数字高度模型进行了有效的方式,可有效地提供对河流和水生系统的新过程。水下和透水研究开始克服可及性,可见性和图像失真的问题。档案照片和视频(无论是在水下和水下)正在使用SFM工作流程重新处理,以从历史调查中生成三维表面和对象,从而延长了可以检测到变化的时间段。最近,已经开发了SFM工作流程,以模拟游离水面,具有明显的液压,沉积物运输和河床进化研究的潜力。 SFM的未来应用可能会试图利用高分辨率卫星图像的每日重复覆盖范围,但必须注意这种开发的必要投资,而不是增加太空传播光检测和范围的供应和覆盖率。

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