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dc.creatorRodríguez Galiano, Víctor Franciscoes
dc.creatorSánchez Castillo, Manueles
dc.creatorDash, Jadunandanes
dc.creatorAtkinson, Peteres
dc.creatorOjeda Zújar, Josées
dc.date.accessioned2018-05-08T11:45:02Z
dc.date.available2018-05-08T11:45:02Z
dc.date.issued2016
dc.identifier.citationRodríguez Galiano, V.F., Sánchez Castillo, M., Dash, J., Atkinson, P. y Ojeda Zujar, J. (2016). Modelling interannual variation in the spring and autumn land surface phenology of the European forest. Biogeosciences, 13, 3305-3317.
dc.identifier.urihttps://hdl.handle.net/11441/74294
dc.description.abstractThis research reveals new insights into the weather drivers of interannual variation in land surface phenology (LSP) across the entire European forest, while at the same time establishes a new conceptual framework for predictive modelling of LSP. Specifically, the random-forest (RF) method, a multivariate, spatially non-stationary and nonlinear machine learning approach, was introduced for phenological modelling across very large areas and across multiple years simultaneously: the typical case for satellite-observed LSP. The RF model was fitted to the relation between LSP interannual variation and numerous climate predictor variables computed at biologically relevant rather than humanimposed temporal scales. In addition, the legacy effect of an advanced or delayed spring on autumn phenology was explored. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation, with relative errors of 10 and 20 %, respectively: a level of precision that has until now been unobtainable at the continental scale. Multivariate linear regression models explained only 36 and 25 %, respectively. It also allowed identification of the main drivers of the interannual variation in LSP through its estimation of variable importance. This research, thus, shows an alternative to the hitherto applied linear regression approaches for modelling LSP and paves the way for further scientific investigation based on machine learning methods.es
dc.formatapplication/pdfes
dc.language.isospaes
dc.publisherCopernicus GmbHes
dc.relation.ispartofBiogeosciences, 13, 3305-3317.
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleModelling interannual variation in the spring and autumn land surface phenology of the European forestes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Geografía Física y Análisis Geográfico Regionales
dc.relation.publisherversionhttps://www.biogeosciences.net/13/3305/2016/bg-13-3305-2016.pdfes
dc.identifier.doi10.5194/bg-13-3305-2016es
idus.format.extent13 p.es
dc.journaltitleBiogeoscienceses
dc.publication.issue13es
dc.publication.initialPage3305es
dc.publication.endPage3317es
dc.identifier.sisius20947030es

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