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Nearshore Bathymetry Retrieval from Wave-Based Inversion for Video Imagery

2022Journal article Open access
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dc.contributor.authorSantos, Diogo
dc.contributor.authorAbreu, Tiago
dc.contributor.authorSilva, Paulo A.
dc.contributor.authorSantos, Fábio
dc.contributor.authorBaptista, Paulo
dc.date.accessioned2023-01-24T09:37:50Z
dc.date.available2023-01-24T09:37:50Z
dc.date.issued2022
dc.description.abstractA wavelet-based method for bathymetry retrieval using a sequence of static images of the surface wave field, as obtained from video imagery, is proposed. Synthetic images of the water surface are generated from a numerical Boussinesq type model simulating the propagation of irregular waves. The spectral analysis is used to retrieve both wave periods and wavelengths by evaluating the spectral peaks in the time and spatial domains, respectively. The water depths are estimated using the linear dispersion relation and the results are validated with the model’s bathymetry. To verify the proposed methodology, 2D and 3D simulations considering effects of wave shoaling and refraction were performed for different sea conditions over different seafloors. The method’s ability to reproduce the original bathymetry is shown to be robust in intermediate and shallow waters, being also validated with a real case with images obtained with a shore-based video station. The main improvements of the new method compared to the consideration of a single image, as often used in Satellite Derived Bathymetry, is that the use of successive images enables the consideration of different wave periods, improving depth estimations and not requiring the use of subdomains or filters. This image processing methodology shows very positive results to provide bathymetry maps for shallow marine environments and can be useful to monitor the nearshore with high time- and space-resolution at low cost.pt_PT
dc.description.sponsorshipThis research was funded by Direção Geral da Política do Mar, through projectNAVSAFETY of the Fundo Azul program. Thanks are due to FCT/MCTES (PT) for the financial support to CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020), through national funds and to the project Space for Shore funded through EOEP 5 Coastal Erosion Program (ESA/AO/1-9219/18/I-LG).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/rs14092155pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/21805
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relationLA/P/0094/2020pt_PT
dc.relationCentre for Environmental and Marine Studies
dc.relationCentre for Environmental and Marine Studies
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/14/9/2155pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBathymetry retrievalpt_PT
dc.subjectWavelengthpt_PT
dc.subjectWavelet analysispt_PT
dc.subjectImage sequencespt_PT
dc.titleNearshore Bathymetry Retrieval from Wave-Based Inversion for Video Imagerypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Environmental and Marine Studies
oaire.awardTitleCentre for Environmental and Marine Studies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50017%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50017%2F2020/PT
oaire.citation.issue9pt_PT
oaire.citation.startPage2155pt_PT
oaire.citation.titleRemote Sensingpt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAbreu
person.givenNameTiago
person.identifier1092691
person.identifier.ciencia-id6312-F126-2D6D
person.identifier.orcid0000-0003-3438-3479
person.identifier.ridC-9326-2019
person.identifier.scopus-author-id35745774800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isProjectOfPublicationda57e4e8-cd82-4b95-bdc6-0524b7efb2f9
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