Ultra-High-Resolution Mapping of Posidonia oceanica (L.) Delile Meadows through Acoustic, Optical Data and Object-based Image Classification

Clicks: 370
ID: 112220
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #10 of 115 articles by views in journal of marine science and engineering

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In this study, we present a framework for seagrass habitat mapping in shallow (5–50 m) and very shallow water (0–5 m) by combining acoustic, optical data and Object-based Image classification. The combination of satellite multispectral images-acquired from 2017 to 2019, together with Unmanned Aerial Vehicle (UAV) photomosaic maps, high-resolution multibeam bathymetry/backscatter and underwater photogrammetry data, provided insights on the short-term characterization and distribution of Posidonia oceanica (L.) Delile, 1813 meadows in the Calabrian Tyrrhenian Sea. We used a supervised Object-based Image Analysis (OBIA) processing and classification technique to create a high-resolution thematic distribution map of P. oceanica meadows from multibeam bathymetry, backscatter data, drone photogrammetry and multispectral images that can be used as a model for classification of marine and coastal areas. As a part of this work, within the SIC CARLIT project, a field application was carried out in a Site of Community Importance (SCI) on Cirella Island in Calabria (Italy); different multiscale mapping techniques have been performed and integrated: the optical and acoustic data were processed and classified by different OBIA algorithms, i.e., k-Nearest Neighbors’ algorithm (k-NN), Random Tree algorithm (RT) and Decision Tree algorithm (DT). These acoustic and optical data combinations were shown to be a reliable tool to obtain high-resolution thematic maps for the preliminary characterization of seagrass habitats. These thematic maps can be used for time-lapse comparisons aimed to quantify changes in seabed coverage, such as those caused by anthropogenic impacts (e.g., trawl fishing activities and boat anchoring) to assess the blue carbon sinks and might be useful for future seagrass habitats conservation strategies.
Reference Key
rende2020journalultra-high-resolution Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sante Francesco Rende;Alessandro Bosman;Rossella Di Mento;Fabio Bruno;Antonio Lagudi;Andrew D. Irving;Luigi Dattola;Luca Di Giambattista;Pasquale Lanera;Raffaele Proietti;Luca Parlagreco;Mascha Stroobant;Emilio Cellini;Rende, Sante Francesco;Bosman, Alessandro;Di Mento, Rossella;Bruno, Fabio;Lagudi, Antonio;Irving, Andrew D.;Dattola, Luigi;Giambattista, Luca Di;Lanera, Pasquale;Proietti, Raffaele;Parlagreco, Luca;Stroobant, Mascha;Cellini, Emilio;
Journal journal of marine science and engineering
Year 2020
DOI
10.3390/jmse8090647
URL
https://doi.org/10.3390/jmse8090647
Keywords
uav mediterranean sea seagrass object-based image analysis (obia) pléiades-hr satellite images multibeam bathymetry seafloor mapping habitat mapping tyrrhenian sea

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.