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

Sante Francesco Rende,Emilio Cellini,Andrew D Irving,Luigi Dattola,Antonio Lagudi,Luca Parlagreco,Fabio Bruno,Mascha Stroobant,Alessandro Bosman,Pasquale Lanera,Luca Di Giambattista,Raffaele Proietti,Rossella Di Mento

doi:10.3390/jmse8090647

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.

Highlights

The cloud points obtained by Pix4D software were georeferenced by comparing the position of the Ground Control Points detected along the coastline and on the island, while the bathymetric data collected with multibeam were used in order to correct the altitude in the marine area
The Random Tree (RT) algorithm in this study proved to be very effective in generating accurate classification, showing fair performance
The geophysical and optical techniques, if correctly processed, allow generation of high resolution integrated terrestrial and marine digital elevation models that can be used for the analysis of the physical environment both in the geological context and in oceanographic modeling

Summary

Introduction

Seagrass beds are distributed over a near-global extent and play an important role in coastal ecosystems as primary producers, providers of habitat and environmental structure, and shoreline stabilizers [1,2]. In order to protect this seagrass ecosystem, it is important to establish its preservation status and regularly monitor its abundance and distribution. The evaluation of its ecological status should be based on a monitoring strategy design that should be able to record accurately all its different spatial configurations (ranging from highly fragmented to continuous meadows) and arrangements, ranging across scales of meters to kilometers (seagrass landscapes), meters to tens of meters (patches), to tens of centimeters to meters (rhizomes, shoot groups) [6,7]

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