Water (Apr 2022)

Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean Sea)

  • Sante Francesco Rende,
  • Alessandro Bosman,
  • Fabio Menna,
  • Antonio Lagudi,
  • Fabio Bruno,
  • Umberto Severino,
  • Monica Montefalcone,
  • Andrew D. Irving,
  • Vincenzo Raimondi,
  • Sebastiano Calvo,
  • Gerard Pergent,
  • Christine Pergent-Martinì,
  • Agostino Tomasello

DOI
https://doi.org/10.3390/w14081285
Journal volume & issue
Vol. 14, no. 8
p. 1285

Abstract

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Underwater photogrammetry provides a means of generating high-resolution products such as dense point clouds, 3D models, and orthomosaics with centimetric scale resolutions. Underwater photogrammetric models can be used to monitor the growth and expansion of benthic communities, including the assessment of the conservation status of seagrass beds and their change over time (time lapse micro-bathymetry) with OBIA classifications (Object-Based Image Analysis). However, one of the most complex aspects of underwater photogrammetry is the accuracy of the 3D models for both the horizontal and vertical components used to estimate the surfaces and volumes of biomass. In this study, a photogrammetry-based micro-bathymetry approach was applied to monitor Posidonia oceanica restoration actions. A procedure for rectifying both the horizontal and vertical elevation data was developed using soundings from high-resolution multibeam bathymetry. Furthermore, a 3D trilateration technique was also tested to collect Ground Control Points (GCPs) together with reference scale bars, both used to estimate the accuracy of the models and orthomosaics. The root mean square error (RMSE) value obtained for the horizontal planimetric measurements was 0.05 m, while the RMSE value for the depth was 0.11 m. Underwater photogrammetry, if properly applied, can provide very high-resolution and accurate models for monitoring seagrass restoration actions for ecological recovery and can be useful for other research purposes in geological and environmental monitoring.

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