Mostrar el registro sencillo del ítem

dc.contributor.authorFernandez-Guisuraga, Jose Manuel
dc.contributor.authorSuarez-Seoane, Susana
dc.contributor.authorFernandes, Paolo
dc.contributor.authorFernandez-García, Victor
dc.contributor.authorFernández-Manso, Alfonso
dc.contributor.authorQuintano, Carmen
dc.contributor.authorCalvo, Leonor
dc.date.accessioned2024-05-23T07:56:48Z
dc.date.available2024-05-23T07:56:48Z
dc.date.issued2022
dc.identifier.citationForest Ecosystems, Febrero 2022, 9, 100022.es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/67791
dc.descriptionProducción Científicaes
dc.description.abstractBackground: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.es
dc.format.mimetypeapplication/pdfes
dc.language.isospaes
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.titlePre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystemses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderElsevieres
dc.identifier.doihttps://doi.org/10.1016/j.fecs.2022.100022es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2197562022000227es
dc.peerreviewedSIes
dc.description.projectSpanish Ministry of Economy and Competitiveness, and the European Regional Development Fund (ERDF), GESFIRE project (AGL2013-48189-C2-1-R);es
dc.description.projectSpanish Ministry of Economy and Competitiveness, and the European Regional Development Fund (ERDF), iFIRESEVES project (AGL2017-86075-C2-1-R)es
dc.description.projectRegional Government of Castilla and Leon FIRECYL project (LE033U14)es
dc.description.projectRegional Government of Castilla and Leon SEFIRECYL project (LE001P17)es
dc.description.projectRegional Government of Castilla and Leon, WUIFIRECYL project (LE005P20)es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem