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Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy

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Institute of Oceanography, University of Gdansk, al. Marszalka Pilsudskiego 46, 81-378 Gdynia, Poland
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Maritime Institute, Gdynia Maritime University, Morska 81-87 Str., 81-225 Gdynia, Poland
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CNR-National Research Council, ISMAR- Institute of Marine Sciences in Venice, Castello 2737/f, 30122 Venice, Italy
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NORBIT-Poland Sp. z o.o., al. Niepodleglosci 813-815/24, 81-810 Sopot, Poland
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Department of Environmental Sciences, Informatics and Statistics (DAIS), University Ca’ Foscari of Venice, Campus Scientifico, Via Torino 155, 30170 Mestre, Italy
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2117; https://doi.org/10.3390/rs12132117
Received: 20 May 2020 / Revised: 27 June 2020 / Accepted: 30 June 2020 / Published: 1 July 2020
Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world. View Full-Text
Keywords: multibeam echosounder; image processing; marine geology; backscatter change detection; tidal inlets multibeam echosounder; image processing; marine geology; backscatter change detection; tidal inlets
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MDPI and ACS Style

Janowski, L.; Madricardo, F.; Fogarin, S.; Kruss, A.; Molinaroli, E.; Kubowicz-Grajewska, A.; Tegowski, J. Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy. Remote Sens. 2020, 12, 2117. https://doi.org/10.3390/rs12132117

AMA Style

Janowski L, Madricardo F, Fogarin S, Kruss A, Molinaroli E, Kubowicz-Grajewska A, Tegowski J. Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy. Remote Sensing. 2020; 12(13):2117. https://doi.org/10.3390/rs12132117

Chicago/Turabian Style

Janowski, Lukasz, Fantina Madricardo, Stefano Fogarin, Aleksandra Kruss, Emanuela Molinaroli, Agnieszka Kubowicz-Grajewska, and Jaroslaw Tegowski. 2020. "Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy" Remote Sensing 12, no. 13: 2117. https://doi.org/10.3390/rs12132117

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