ABSTRACT Crustose coralline algae (CCA) are key reef‐building organisms, yet their fine structures make traditional visual surveys time‐consuming and limit large‐scale monitoring. This study evaluates the potential of unmanned aerial vehicles (UAVs) for monitoring CCA coverage on intertidal reefs along the Taoyuan coast, Taiwan. Three experiments conducted between 2022 and 2024 were designed to assess the effects of flight altitude, sensor type, classification approach, and the feasibility of large‐scale surveys. A supervised Random Forest classifier was applied using five color indices (R–G, G–B, ExG, ExGR, and NGRDI) to evaluate their effectiveness and identify the optimal combination for CCA classification. Results indicate that classification accuracy was primarily governed by image resolution. Multispectral imagery, under typical UAV configurations, was insufficient to resolve millimeter‐scale CCA features, whereas low‐altitude visible‐light imagery ( 0.4), the derived CCA coverage estimates showed strong agreement with reference observations ( r > 0.87), indicating that low‐altitude visible‐light UAV imagery provides a robust basis for large‐scale ecological assessments. Overall, UAV‐based surveys combined with automated classification provide an efficient and scalable framework for mapping and monitoring CCA and reef habitats, with potential extension to other taxa.
Lin et al. (2026) studied this question.