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dc.contributorFacultad de Ciencias Biologicas y Ambientaleses_ES
dc.contributor.authorFernández-Guisuraga, José Manuel
dc.contributor.authorSuárez Seoane, Susana 
dc.contributor.authorCalvo Galván, María Leonor 
dc.contributor.otherEcologiaes_ES
dc.date2020-07
dc.date.accessioned2020-07-06T11:19:35Z
dc.date.available2020-07-06T11:19:35Z
dc.identifier.issn1402-2001
dc.identifier.otherGuisuraga, JM, Suárez‐Seoane, S, Calvo, L. Transferability of vegetation recovery models based on remote sensing across different fire regimes. Appl Veg Sci. 2020; 23: 441– 451. https://doi.org/10.1111/avsc.12500es_ES
dc.identifier.urihttp://hdl.handle.net/10612/12290
dc.descriptionP. 441-451es_ES
dc.description.abstractAim To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location The northwest and east of the Iberian Peninsula. Methods In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability.es_ES
dc.languageenges_ES
dc.publisherWileyes_ES
dc.subjectEcología. Medio ambientees_ES
dc.subject.otherImage texturees_ES
dc.subject.otherMegafirees_ES
dc.subject.otherModel transferabilityes_ES
dc.subject.otherRandom forest regressiones_ES
dc.subject.otherSatellite imageryes_ES
dc.subject.otherSentinel-2es_ES
dc.subject.otherVegetation coveres_ES
dc.subject.otherWorldView-2es_ES
dc.titleTransferability of vegetation recovery models based on remote sensing across different fire regimeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doihttps://doi.org/10.1111/avsc.12500
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.journal.titleApplied Vegetation Sciencees_ES
dc.volume.number23es_ES
dc.issue.number3es_ES
dc.page.initial441es_ES
dc.page.final451es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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