A novel method for automatic river channel morphology extraction from remote sensing satellite images: A case study of the Golmud fluvial fan

Abstract

Distributive Fluvial Systems (DFS) are critical sedimentary systems governing fluvial dynamics, sediment transport, and ecosystem sustainability in modern and ancient basins. Accurate quantification of DFS channel morphology is essential for advancing sedimentary modeling, optimizing water resource management, and mitigating fluvial hazards. Here, the authors present a novel automated framework that extracts DFS channel networks from remote sensing imagery by integrating multiscale image segmentation, fractal network evolution, and region-merging algorithms. Through hierarchically multiresolution feature processing, this method overcomes limitations of traditional single-scale analysis, enabling adaptive extraction while reducing segmentation heterogeneity. Specifically, the workflow consists of three stages: Image segmentation, feature extraction, and image classification. When applied to the Golmud fluvial fan (Qinghai, China), this approach achieves 90.2% overall channel extraction accuracy using 0.5 m resolution imagery, significantly outperforming traditional DEM-based (81.7%) and water spectral methods (85.4%) in resolving fine-scale channel networks. Crucially, the framework demonstrates robust adaptability to complex sedimentary environments with variable vegetation cover (<30% density) and spectral noise, providing a time-efficient, data-agnostic solution for DFS characterization.