Braided rivers are difficult to monitor because of unstable mainstream migration, complex planform morphology, and intense channel adjustment. To address this challenge, this study develops an integrated remote-sensing framework that links cross-sensor surface-water extraction, geometry-reliable boundary reconstruction, and river-geometry metric derivation for channel dynamics monitoring. Using the braided reach of the Lower Yellow River (LYR) as the study area, the framework was applied to investigate abnormal channel dynamics during 1986–2025. Results show that the improved deep learning model achieved robust and consistent surface-water extraction across Landsat-8, Landsat-7, and Sentinel-2 imagery, while the boundary reconstruction procedure effectively reduced raster-induced jagged artefacts and improved the geometric reliability of extracted channel boundaries. Based on the reconstructed boundaries, water-surface width, river centerline, sinuosity, and the Deviation Degree from Regulated River Alignments were derived and used to identify abnormal channel-dynamics reaches. In the braided reach of the LYR, the results revealed clear spatial concentration, temporal intermittency, and an upstream shift in abnormal-reach occurrence after 2000. Overall, the proposed framework extends remote sensing from surface-water mapping to long-term, geometry-reliable monitoring of braided-river channel dynamics and provides practical support for potentially unstable reach screening and warning-oriented river management.
Qin et al. (Wed,) studied this question.