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dc.contributor.authorAlonso Martinez, Laura 
dc.contributor.authorPicos Martín, Juan 
dc.contributor.authorArmesto González, Julia 
dc.date.accessioned2023-10-03T07:54:55Z
dc.date.available2023-10-03T07:54:55Z
dc.date.issued2021-06-17
dc.identifier.citationISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences, V-3-2021, 203-210 (2021)spa
dc.identifier.issn21949050
dc.identifier.urihttp://hdl.handle.net/11093/5209
dc.description.abstractAdvances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%).spa
dc.description.sponsorshipXunta de Galiciaspa
dc.description.sponsorshipAgencia Estatal de Investigación | Ref. PID2019-111581RB-I00spa
dc.description.sponsorshipUniversidade de Vigospa
dc.language.isoengspa
dc.publisherISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciencesspa
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111581RB-I00/ES/PALEOINTERFAZ: ELEMENTO ESTRATEGICO EN LA PREVENCION DE INCENDIOS FORESTALES. DESARROLLO DE METODOLOGIAS DE ANALISIS 3D Y MULTIESPECTRAL PARA LA GESTION INTEGRADA
dc.rightsAttribution 4.0 International
dc.rights© Author(s) 2021
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleForest cover mapping and Pinus species classification using very high-resolution satellite images and random foresteng
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.5194/isprs-annals-V-3-2021-203-2021
dc.identifier.editorhttps://isprs-annals.copernicus.org/articles/V-3-2021/203/2021/spa
dc.publisher.departamentoEnxeñaría dos materiais, mecánica aplicada e construciónspa
dc.publisher.departamentoEnxeñaría dos recursos naturais e medio ambientespa
dc.publisher.grupoinvestigacionXestión Segura e Sostible de Recursos Mineraisspa
dc.publisher.grupoinvestigacionEnxeñería Agroforestalspa
dc.subject.unesco5401.01 Distribución de Recursos Naturalesspa
dc.subject.unesco2506.16 Teledetección (Geología)spa
dc.subject.unesco3106 Ciencia Forestalspa
dc.date.updated2023-10-03T07:52:50Z
dc.computerCitationpub_title=ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences|volume=V-3-2021|journal_number=|start_pag=203|end_pag=210spa


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    Attribution 4.0 International
    Except where otherwise noted, this item's license is described as Attribution 4.0 International