Analysis of the shape of sedimentary particles can provide information about their transport history and aid facies differentiation and the characterization of depositional environments. Triangular (Sneed and Folk) diagrams, employing ratios of the three orthogonal particle axes, have been advocated as the most appropriate method for unbiased presentation of primary particle shape data. A spreadsheet method for the production of these diagrams is described. Clast data-sets from a range of environments are presented using this method. An alternative use of the spreadsheet for the presentation of sedimentary fabric shape is suggested.

[1] The spatial and temporal resolution of surface grain-size characterization is constrained by the limitations of traditional measurement techniques. In this paper we present an extremely rapid image-processing-based procedure for the measurement of exposed fluvial gravels and other coarse-grained sediments, defining the steps required to minimize the errors in the derived grain-size distribution. This procedure differs significantly from those used previously. It is based around a robust object-detection algorithm that produces excellent results on images exhibiting a wide range of sedimentary conditions, crucially, without any user intervention or site-specific parameterization. The procedure is tested using a data set comprising 39 images from three rivers with contrasting grain lithology, shape, roundness, and packing configuration and representing a very wide range of textures. It is shown to perform more consistently than the best existing automated method, achieving a precision equivalent to that obtainable by Wolman sampling, but taking between one sixth and one twentieth of the time. The error in area-by-number grain-size distribution percentiles is typically less than 0.05 ψ.Citation: Graham, D. J., S. P. Rice, and I. Reid (2005), A transferable method for the automated grain sizing of river gravels, Water Resour. Res., 41, W07020,

This is the first in a pair of papers in which we present image-processing based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on imageprocessing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially-deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.

14Novel topographic survey methods that integrate both structure-from-motion (SfM) 15 photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving