Automated grain sizing from uncrewed aerial vehicles imagery of a gravel-bed river: Benchmarking of three object-based methods

Abstract

Measuring grain sizes in gravel-bed rivers is crucial when studying river dynamics and sediment transport. Automated methodologies have been developed in recent years for detecting individual grains and measuring their size on digital imagery. These object-based methodologies have mainly been applied to handheld imagery. Low-cost and high-resolution orthoimages covering long river reaches are nowadays accessible with the improvements in uncrewed aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry. Applying object-based grain-sizing methodologies to such orthoimages may provide wide-scale information about the grain size spatial distribution along streambeds. We examined how accurate three object-based models (BASEGRAIN, PebbleCountsAuto and GALET) were, by comparing their outcomes to in-field manual measurements of grain sizes and manual grain labelling. We found that BASEGRAIN and PebbleCountsAuto underestimated grain sizes on average, whereas GALET generally overestimated grain size percentiles. Grain size measurements obtained by manually labelling grain features were consistent with in-field measurements.