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Fine-scale automatic mapping of living Posidonia oceanica seagrass beds with underwater photogrammetry

Guilhem Marre*, Julie Deter, Florian Holon, Pierre Boissery, Sandra Luque

*Corresponding author:

ABSTRACT: The Mediterranean seagrass Posidonia oceanica, which provides highly valuable ecosystem services, is subject to increasing anthropogenic pressures, causing habitat loss or fragmentation. Whilst airborne images and acoustic data can be used for monitoring seagrass coverage at a macro-scale and over large periods of time, monitoring its health in the short-term requires precision mapping in order to assess current regression / progression of the meadow. However, currently used fine-scale underwater techniques are imprecise and time-demanding in the field. We propose an automatic classification approach based on underwater photogrammetry for an operational, cost and time-effective fine-scale monitoring method. The method uses a property of the sparse cloud generated during bundle adjustment, the reconstruction uncertainty, to map seagrass patches. The mean precision, recall, and F1 score of the method over 21 study sites with different morphologies were: 0.79, 0.91 and 0.84, respectively. However, the fragmentation level of the meadows had a significant negative effect on classification performances. The temporal monitoring of 3 sites using this method has proven its operability and showed a positive evolution index of the corresponding meadows over a period of 3 yr. This method is generalizable for most encountered configurations and can be integrated in a large monitoring system, as it enables the production of numerous seagrass maps over a short period of time. Moreover, our methodology could be generalized and applied in the study of other submerged aquatic vegetation by adjusting the method’s parameters.