Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42206
DC FieldValueLanguage
dc.contributor.authorMedina Machín, Anabellaen_US
dc.contributor.authorMarcello, Javieren_US
dc.contributor.authorHernández-Cordero, Antonio I.en_US
dc.contributor.authorMartín Abasolo, Javieren_US
dc.contributor.authorEugenio, Franciscoen_US
dc.date.accessioned2018-10-22T12:04:20Z-
dc.date.available2018-10-22T12:04:20Z-
dc.date.issued2018en_US
dc.identifier.issn1548-1603en_US
dc.identifier.urihttp://hdl.handle.net/10553/42206-
dc.description.abstractVegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.en_US
dc.languageengen_US
dc.publisher1548-1603
dc.relation.ispartofGIScience and Remote Sensingen_US
dc.sourceGIScience and Remote Sensing [ISSN 1548-1603]en_US
dc.subject250616 Teledetección (Geología)en_US
dc.subject.otherAutomatic vegetation classificationen_US
dc.subject.otherHigh resolutionen_US
dc.subject.otherAtmospheric correctionen_US
dc.subject.other6Sen_US
dc.subject.otherSupport Vector Machinesen_US
dc.subject.otherWorldView-2en_US
dc.titleVegetation species mapping in a coastal-dune ecosystem using high resolution satellite imageryen_US
dc.typeinfo:eu-repo/semantics/articlees
dc.typeArticlees
dc.identifier.doi10.1080/15481603.2018.1502910
dc.identifier.scopus85052139090
dc.bustreamingVegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.en_US
dc.contributor.authorscopusid56422204200
dc.contributor.authorscopusid6602158797
dc.contributor.authorscopusid52863616700
dc.contributor.authorscopusid57203524714
dc.contributor.authorscopusid6603605357
dc.investigacionCienciasen_US
dc.type2Artículoen_US
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.orcid0000-0002-9646-1017-
crisitem.author.orcid0000-0002-8373-9235-
crisitem.author.orcid0000-0002-0010-4024-
crisitem.author.fullNameMarcello Ruiz, Francisco Javier-
crisitem.author.fullNameHernández Cordero, Antonio Ignacio-
crisitem.author.fullNameMartín Abasolo, Javier-
crisitem.author.fullNameEugenio González, Francisco-
crisitem.author.departamentoSeñales y Comunicaciones-
crisitem.author.departamentoGeografía-
crisitem.author.departamentoSeñales y Comunicaciones-
Appears in Collections:Artículos
Show simple item record

SCOPUSTM   
Citations

4
checked on Apr 4, 2020

Page view(s)

3
checked on Apr 4, 2020

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.