R&D and Education
J. Guinan, C. McKeon, E. O'Keeffe, X. Monteys, F. Sacchetti, M. Coughlan and C. Nic Aonghusa
Quarterly Journal of Engineering Geology and Hydrogeology 9 September 2020.
Abstract
The characterization of the seafloor is a fundamental first step in informing resource management, marine spatial planning, conservation, fisheries, industry and research. Integrated Mapping for the Sustainable Development of Ireland's Marine Resource (INFOMAR), Ireland's national seabed mapping programme, delivers freely available, high-resolution seabed imagery derived from multibeam echosounder data in the Irish Exclusive Economic Zone. The European Union established the European Marine Observation and Data Network (EMODnet) Geology data portal, which provides harmonized broad-scale seabed substrate information for all European seas and confidence assessments of the information that underpins the geological interpretations. A multi-scale product has been produced using INFOMAR's high-resolution seabed substrate information at the 1:50 000 scale. As part of the Supporting Implementation of Maritime Spatial Planning in the Celtic Seas project, the EMODnet Geology seabed substrate data portal assisted in addressing the challenges associated with the implementation of the European Union's Marine Spatial Planning Directive. The seabed substrate data in the EMODnet Geology data portal were identified as a valuable tool for guiding the selection of sites for offshore wind farms in the Irish Sea and their subsequent characterization. This paper outlines the approach to delivering a multi-scale seabed substrate dataset for the Irish offshore and its applicability to marine spatial planning and the development of offshore energy resources.
Full article available for download here: https://qjegh.lyellcollection.org/content/early/2020/09/07/qjegh2020-033
EUSeaMap 2019, A European broad-scale seabed habitat map, technical report.
EASME/EMFF/2018/1.3.1.8/Lot2/SI2.810241– EMODnet Thematic Lot n° 2 – Seabed Habitats.
Abstract
EUSeaMap 2019 is the third iteration of EUSeaMap. All versions have been produced as part of the EMODnet Seabed Habitats project, which is one of several thematic lots in EMODnet. The project has brought together a European consortium of specialists in benthic ecology and seabed habitat mapping. The partners first collaborated in EMODnet phase 1 (2009-2012) to deliver a prototype predictive seabed habitat map in four trial basins (Greater North Sea, Celtic Seas, Baltic, Western Mediterranean). This predictive model was named EUSeaMap (Cameron and Askew, 2011). In EMODnet Phase 2 (2012-2016), the consortium extended EUSeaMap coverage to all European regions (Populus et al, 2017).
In the new version, the spatial coverage was extended further North in order to include the Barents Sea. The spatial detail was substantially improved. This was made possible by improvements to the physical predictor variables created by the other EMODnet lots which are the input data to the EUSeaMap model. A substantial revision of the map creation process has also been carried out in order to make it more reproducible. This document describes all these modifications which have led to the elaboration of EUSeaMap 2019.
Authors
Vasquez Mickael, Manca Eleonora, Inghilesi Roberto, Martin Simon, Agnesi Sabrina, Al Hamdani Zyad, Annunziatellis Aldo, Bekkby Trine, Pesch Roland, Askew Natalie, Bentes Luis, Castle Lewis, Doncheva Valentina, Drakopoulou Vivi, Gonçalves Jorge, Laamanen Leena, Lillis Helen, Loukaidi Valia, McGrath Fergal, Mo Giulia, Monteiro Pedro, Muresan Mihaela, O'Keeffe Eimear, Populus Jacques, Pinder Jordan, Ridgeway Amy, Sakellariou Dimitris, Simboura Mika, Teaca Adrian, Tempera Fernando, Todorova Valentina, Tunesi Leonardo, Virtanen Elina (2020).
DOI 10.13155/74782
The Atlantic Seabed Mapping Vision Statement and Roadmap arises from the activities of the Atlantic Seabed Mapping International Work Group and is conducted through the Atlantic Ocean Research Alliance (AORA) between Canada, the European Union and the United States of America. The progress and vision towards achieving a baseline seabed and habitat map of the Atlantic Ocean, was presented at the All Atlantic Ocean Research Forum, 6-7 February 2020, in Brussels, Belgium. The Seabed Mapping Group has, in the last five years, defined and tested all the necessary steps to map the previously uncharted seafloor of the Atlantic Ocean. With the onset of the UN Decade of Ocean Science for Sustainable Development, the Seabed Mapping Group calls on the international leaders to provide the resources and framework necessary to achieve this ambitious goal, in order to deliver on their commitment to the Galway and Belém Statements. Creating an accurate fact based map of the Atlantic seafloor is essential for the sustainable use of our ocean, and will greatly help us to achieve the UN Sustainable Development Goal, SDG 14 – Life Below Water. A diverse group of stakeholders participated in this work and the outcome summarized here in this roadmap is a result of extensive consultation with workshop and meeting participants, as well as others that were invited to comment on the work as it progressed. The editorial team would like to thank all those who contributed with comments and input.
Brochure can be downloaded here.
McCullagh D., Benetti S., Plets R., Sacchetti F., O’Keeffe E. and Lyons K.
https://figshare.com/articles/Geomorphology_and_substrate_of_Galway_Bay_Western_Ireland/11769981
doi = 10.6084/m9.figshare.11769981.v1
A combination of multibeam bathymetry and backscatter, LiDAR altimetry and bathymetry, satellite images, and hydrodynamic model outputs were used to map the seafloor and coastline of Galway Bay (western Ireland). This is the first time these multiple datasets have been integrated into a single combined geomorphological and substrate map. The substrate of the bay is predominantly mud and sand with bedrock outcropping extensively around the coastline. The main depositional features are dunes, while the main erosional features are scours and outcropping bedrock. Hydrodynamic model outputs show good correlation between the direction and intensity of prevailing currents and the location and shape of the features in the bay. This indicates that although Galway Bay was shaped glacially through the passage of the British-Irish Ice Sheet across the bay and ensuing glacial and marine sediment deposition, many of the mapped seafloor landforms are modern and current-induced.
Conor Cahalane, Aidan Magee, Xavier Monteys, Gema Casal, Jennifer A. Hanafin, P. Harris (2019) A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry, Remote Sensing of Environment, DOI: https://doi.org/10.1016/j.rse.2019.111414
Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30 m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used – Pleiades (2 m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.
