Distributed estimation of surface sediment size in paraglacial and periglacial environments using drone photogrammetry

Abstract

Grain-size analysis offers insights into geological processes and landform dynamics. Traditional grain-size sampling methods are labour intensive and offer limited spatial coverage, posing challenges in paraglacial and periglacial environments characterized by large spatial variability in sediment sizes. This study introduces a new workflow that combines structure-from-motion, image segmentation and texture-based optical granulometry techniques to estimate surface grain size in paraglacial and periglacial environments efficiently. Utilizing high-resolution orthomosaics (ground sampling distance $\sim$8 mm) and Cellpose, a deep-learning image segmentation model, the new workflow achieves high-accuracy grain-size distributions (GSDs) with low errors. These GSDs, along with lower resolution orthomosaics (ground sampling distance $\sim$30 mm), are used to train SediNet—a machine-learning framework—to predict GSDs accurately from $340\times 340$ pixel tiles. Tested across six alpine basins in the Canadian Rockies and a rock glacier in Italy, the model demonstrates effectiveness and accuracy, promising advancements in geoscientific research and the understanding of paraglacial and periglacial dynamics.