Accurate stream network delineation in low-gradient wetlands is essential for hydrological modeling, flood risk assessment, and ecological restoration. However, the subtle terrain features and dense vegetation in these environments present significant challenges. This study systematically evaluated 48 UAV-LiDAR processing workflows to identify the optimal approach for mapping fine-scale stream channels in the Kushiro Wetland, Japan, a Ramsar-protected site known for its ecological importance. Workflows combined three ground filtering methods (PMF, CSF, MCC), four interpolation techniques (IDW, TIN, KRG, MBA), two sink-filling algorithms (Wang & Liu, Planchon & Darboux), and two flow direction models (D8, D-infinity). Performance was assessed using the Intersection over Union (IoU) metric to quantify the accuracy of channel network delineation.

The results showed that workflow configuration significantly impacts detection precision, with the optimal workflow—CSF, MBA, Planchon, and D8—achieving a high IoU of 0.85. CSF excelled at preserving complex terrain structures crucial for wetland hydrology. While KRG provided robust interpolation for general terrain representation, MBA was more effective for channel delineation within the optimal workflow. Planchon’s sink-filling algorithm substantially improved hydrological connectivity representation, outperforming Wang & Liu. Minimal differences were observed between D8 and D-infinity flow direction models, suggesting D8’s computational efficiency makes it preferable for similar environments.

These findings provide actionable recommendations for high-resolution wetland mapping and hydrological analysis. The methodological framework developed in this study supports the ongoing Kushiro Wetland Restoration Project and can be applied to other degraded wetland systems globally, contributing to conservation, restoration planning, and ecosystem management efforts.