Response to comments on egusphere-2022-75

Abstract

The grain-scale morphology of sediments and their size distribution inform on their transport history, are important factors controlling the efficiency of erosion and transport and control the quality of aquatic ecosystems. In turn, constraining the spatial evolution of the size and shape of grains can offer deep insights on the dynamics of erosion and sediment transport in coastal, hillslope and fluvial environments. However, the size distribution of sediments is generally assessed using insufficiently representative field measurements and determining the grain-scale shape of sediments remains a real challenge in geomorphology. Here, we determine the size distribution and grain-scale shape of sediments located in coastal and river environments with a new methodological approach based on the segmentation and geomorphological fitting of 3D point clouds. Point cloud segmentation into individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are individualized into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be conceived and tested, including cuboids and ellipsoids, this study focuses mostly on ellipsoids. A phase of results checking is then performed to remove grains showing a best-fitting model with a low level of confidence. The main benefits of this automatic and non-destructive method are that it provides access to 1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; 2) a very large number of data, only limited by the number of grains in the point-cloud dataset; 3) the 3D morphology of grains, in turn allowing to develop new metrics characterizing the size and shape of grains; and 4) the in-situ orientation and organization of grains and grain clusters. The main limit of this method is that it is only able to detect grains with a characteristic size greater than the resolution of the point cloud.