Global coverage of oyster reefs has dramatically declined due to environmental changes driven by climate change and human activities, leading to substantial losses in their ecological functions. Quantifying oyster reef morphology is essential for devising restoration strategies and understanding their ecomorphodynamics. This study focused on the Liyashan oyster reef conservation area in the intertidal zone of Haimen, Jiangsu, China. We employed Unmanned Aerial Vehicle (UAV) photogrammetry to obtain RGB orthoimages and Digital Elevation Models (DEMs) with centimeter-level resolution and accuracy. By integrating UAV photogrammetry and Deep Learning techniques, we efficiently and accurately identified reef footprints and generated pixel-level reef height maps. Based on the reef height data, we introduced the Volume Balance Index (VBI) to evaluate reef fragmentation (degree of pitting), where a lower VBI value indicates higher relative fragmentation. Quantitative results at the reef block scale demonstrate a significant negative correlation between reef size (area and height) and overall fragmentation, with a strong logarithmic relationship between reef height and VBI. Generally, less degraded reefs are primarily distributed in areas close to open water, a pattern potentially related to local hydrodynamic conditions. This research presents a cost-effective and efficient method for monitoring intertidal oyster reefs and provides the foundation for further research on their ecomorphodynamics.

 

oyster reef, UAV, SfM, deep learning, morphology mapping