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dc.contributor.authorAngelats Company, Eduard
dc.contributor.authorSoriano González, Jesús
dc.contributor.authorAlcaraz, Carles
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Tecnologia Agroalimentària i Biotecnologia
dc.date.accessioned2021-05-11T16:46:17Z
dc.date.available2021-05-11T16:46:17Z
dc.date.issued2019
dc.identifier.citationAngelats, E.; Soriano-González, J.; Alcaraz, C. Automatic mapping of seagrass beds in alfacs bay using Sentinel-2 imagery. A: X Jornadas de Geomorfología Litoral. "X Jornadas de Geomorfología Litoral : Libro de ponencias: 1-292 (2019)". 2019, p. 209-212. ISBN 978-84-09-12002-4. DOI 10.5281/zenodo.3629244.
dc.identifier.isbn978-84-09-12002-4
dc.identifier.otherhttps://zenodo.org/record/3629245#.YJqw92ZKg1I
dc.identifier.urihttp://hdl.handle.net/2117/345472
dc.description.abstractSeagrass are marine flowering plants that form extensive meadows in shallow coastal waters. They play a critical role in coastal ecosystems by providing food and shelter for animals, recycling nutrients, and stabilizing sediments. Therefore, they are widely used as an ideal biological indicator for assessing the health status and quality of coastal ecosystems. In the Alfacs Bay (Ebro Delta), seagrasses are located in the shores, showing an annual variation with a peak in summer. The decreasing of averaged salinity and increasing of nutrients concentration and turbidity, has led to a notable reduction of the seagrass beds. Thus, a cartography to monitor spatiotemporal changes of meadows and to forecast the evolution of the environmental characteristics of the system, is needed. Nowadays, the standard methodology is a combination of photointerpretation and field prospection with significant workload resources. In contrast, an automatic methodology relying on multispectral moderate resolution Sentinel 2 (S2) satellite imagery is proposed. The methodology consists of: atmospheric correction of Level-1C images, application of Green Normalized Difference Vegetation Index, statistic thresholding to tell apart possible seagrass areas and a supervised learning method to refine this classification and to identify habitats. The methodology has been applied and calibrated using S2 satellite imagery and reference data comprising several patches distributed along the Alfacs Bay. In these patches, seagrass areas were identified (visually and location with GNSS). The results showed that seagrass meadows can be automatically delineated using S2 imagery.
dc.description.sponsorshipThis work was supported by the early stage researcher grant ‘2018 FI_B00705’.018 FI_B00705.
dc.format.extent4 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Física
dc.subject.lcshSeagrasses
dc.subject.lcshArtificial satellites in remote sensing
dc.subject.lcshRemote-sensing images
dc.subject.otherSeagrass
dc.subject.otherMapping
dc.subject.otherRemote sensing
dc.subject.otherSentinel 2
dc.titleAutomatic mapping of seagrass beds in alfacs bay using Sentinel-2 imagery
dc.typeConference lecture
dc.subject.lemacPraderies -- Catalunya
dc.subject.lemacImatges satel·litàries
dc.identifier.doi10.5281/zenodo.3629244
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://digital.csic.es/handle/10261/189642
dc.rights.accessOpen Access
local.identifier.drac28837886
dc.description.versionPostprint (published version)
local.citation.authorAngelats, E.; Soriano-González, J.; Alcaraz, C.
local.citation.contributorX Jornadas de Geomorfología Litoral
local.citation.publicationNameX Jornadas de Geomorfología Litoral : Libro de ponencias: 1-292 (2019)
local.citation.startingPage209
local.citation.endingPage212


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain