Seagrass coverage estimation and depth limit analysis from unlabeled underwater videos

Visual coverage estimation of seagrass for ground truth verification is one of the most critical aspects of marine ecosystem monitoring programs worldwide. It has traditionally been an arduous and tedious task. Commonly used tools like a scuba diver and underwater video transects require manual investigation by domain experts to assess seagrass status. Supervised machine learning methods have had a limited role in automating this process due to the lack of labeled seagrass images. This paper proposes two robust algorithms for seagrass coverage estimation from unlabelled underwater videos obtained from scuba divers and investigates their different potentials. Two seagrass-specific features are extracted and modelled for coverage estimation (0%–100%), matching the domain expert’s prediction. We also show that these algorithms detect and rectify rare labeling mistakes from the domain expert. Coverage estimates from one of the methods are then used to estimate the depth limit and its associated uncertainty. • Seagrass coverage is estimated with high accuracy by applying unsupervised methods to analyze underwater videos. • The methods are robust enough to identify rare labeling mistakes made by domain experts. • Coverage estimations obtained from one of the methods have been used to predict depth limits, which align well with existing domain knowledge. • These methods were applied to a case study under varying experimental conditions and were shown to achieve high accuracy. • An end-to-end pipeline for estimating seagrass coverage is proposed and demonstrated, starting from raw, unlabeled video transects and leading to final status predictions that can be reproduced across different experimental scenarios.