Fanny Ville, Damià Vericat, Ramon J. Batalla, Colin Rennie

Bed mobility and stability are spatially and temporally variable, making it a complex phenomenon to study. This paper is the second of a pair, in which we present an automated image processing procedure for monitoring the mobility/stability of gravel river beds. The method is based on local comparison of the shape of the grains identified at the same coordinates between successive photos to identify coincident and new grains. From this categorisation in a given study area, several variables can be extracted, such as: the general proportion of mobile or immobile grains (number or area), the maximum mobile or immobile diameters, the proportion per grain fraction of grains that remained immobile (stable) and grains newly identified. Additionally, percentiles of the surface Grain Size Distribution (GSD) before and after a target hydrological event, as well as the immobile and mobilized GSD (which could be used as a proxy for bedload GSD) can be computed. In this part 2 paper, we present the entire GIS-based procedure for identifying the shape of each grain in digital images of bed patches to then classify their dynamic status (mobile/immobile), and derive a reliable result compatible with different forms of sampling (Area-by-number, Abn, and Grid-by-number, Gbn) and types of measurements (continuous and discrete square holes grain size reading). The performance of the GIS procedure is evaluated for the mentioned above variables over a control set composed of ten 1×1m paired before/after image samples representing different field conditions. The automatic classification applied on a perfect (manual) grain delineation yields Mean Absolute Errors (MAE) lower than 3% in both Abn and Gbn, while the automatic classification applied on an automated delineation with 10 min of manual boundary revision shows MAE around 8% and presents a larger MAE of 29% for only the estimation of the mobile percentile.