Grain size estimation in fluvial gravel bars using uncrewed aerial vehicles: A comparison between methods based on imagery and topography

Grain size assessments are necessary for understanding the various geomorphological, hydrological and ecological processes that occur within rivers. Recent research has shown that the application of Structure-from-Motion (SfM) photogrammetry to imagery from uncrewed aerial vehicles (UAVs) shows promise for rapidly characterising grain sizes along rivers in comparison to traditional field-based methods. Here, we evaluated the applicability of different methods for estimating grain sizes in gravel bars along a study reach in the Olentangy River in Columbus, Ohio. We collected imagery of these gravel bars with a UAV and processed those images with SfM photogrammetry software to produce three-dimensional point clouds and orthomosaics. Our evaluation compared statistical models calibrated on topographic roughness, which was computed from the point clouds, and to those based on image texture, which was computed from the orthomosaics. Our results showed that statistical models calibrated on image texture were more accurate than those based on topographic roughness. This might be because of site-specific patterns of grain size, shape and imbrication. Such patterns would have complicated the detection of topographic signatures associated with individual grains. Our work illustrates that UAV-SfM approaches show potential to be used as an accessible method for characterising surface grain sizes along rivers at higher spatial and temporal resolutions than those provided by traditional methods. Grain size proxies based on image texture and topographic roughness were computed from drone surveys of fluvial gravel bars. Proxies based on image texture better estimated grain sizes than did those based on topographic roughness.image

Automated summary

Grain size assessments are important for understanding geomorphological, hydrological, and ecological processes within rivers. Recent research has shown that utilizing Structure-from-Motion (SfM) photogrammetry with imagery from unmanned aerial vehicles (UAVs) provides a promising method for rapidly characterizing grain sizes along rivers compared to traditional field-based methods. This study evaluated different methods for estimating grain sizes in gravel bars along the Olentangy River in Columbus, Ohio. Findings revealed that statistical models calibrated on image texture were more accurate than those based on topographic roughness, possibly due to site-specific patterns of grain size, shape, and imbrication. The research demonstrates the potential of UAV-SfM approaches as an accessible method for characterizing surface grain sizes along rivers at higher resolutions than traditional methods provide.