Indirect Estimation of Seagrass Frontal Area for Coastal Protection: A Mask R-CNN and Dual-Reference Approach

Seagrass constitutes a vital component of coastal ecosystems, providing a wide array of ecosystem services. The accurate measurement of the seagrass frontal area is crucial for assessing its capacity to inhibit water flow and reduce wave energy; however, few effective indirect methods exist. To address this limitation, we developed an indirect method that combines the Mask R-CNN model with a dual-reference approach for detecting seagrass and estimating its frontal area. A laboratory-scale underwater camera experiment generated an experimental dataset, which was partitioned into training, validation, and test sets. Following training, evaluation metrics—including IoU, accuracy, precision, recall, and F1-score—approached their upper limits and remained within acceptable ranges. Validation on real seagrass images confirmed satisfactory performance, albeit with slightly lower metrics than those observed in the experimental dataset. Furthermore, the method estimated seagrass frontal areas with errors below 10% (maximum 7.68% and minimum –0.43%), thereby demonstrating high accuracy by accounting for seagrass bending under flowing water conditions. Additionally, we showed that the indirect measurement significantly influences estimations of the seagrass bending height and wave height reduction capacity, mitigating the overestimation associated with traditional direct methods. Thus, this indirect approach offers a promising, environmentally friendly alternative that overcomes the limitations of conventional techniques.