dc.creator | Rodríguez Galiano, Víctor Francisco | es |
dc.creator | Sánchez Castillo, Manuel | es |
dc.creator | Dash, Jadunandan | es |
dc.creator | Atkinson, Peter | es |
dc.creator | Ojeda Zújar, José | es |
dc.date.accessioned | 2018-05-08T11:45:02Z | |
dc.date.available | 2018-05-08T11:45:02Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Rodríguez 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.uri | https://hdl.handle.net/11441/74294 | |
dc.description.abstract | This 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.format | application/pdf | es |
dc.language.iso | spa | es |
dc.publisher | Copernicus GmbH | es |
dc.relation.ispartof | Biogeosciences, 13, 3305-3317. | |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Modelling interannual variation in the spring and autumn land surface phenology of the European forest | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Geografía Física y Análisis Geográfico Regional | es |
dc.relation.publisherversion | https://www.biogeosciences.net/13/3305/2016/bg-13-3305-2016.pdf | es |
dc.identifier.doi | 10.5194/bg-13-3305-2016 | es |
idus.format.extent | 13 p. | es |
dc.journaltitle | Biogeosciences | es |
dc.publication.issue | 13 | es |
dc.publication.initialPage | 3305 | es |
dc.publication.endPage | 3317 | es |
dc.identifier.sisius | 20947030 | es |