Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

Producción Científica

Detalhes bibliográficos
Autores: Fernández Guisuraga, José Manuel, Suárez Seoane, Susana, Fernandes, Paolo, Fernández García, Victor, Fernández Manso, Alfonso, Quintano Pastor, María del Carmen, Calvo, Leonor
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/67791
Acesso em linha:https://doi.org/10.1016/j.fecs.2022.100022
https://uvadoc.uva.es/handle/10324/67791
Access Level:acceso abierto
Palavra-chave:Aboveground biomass
Burn severity
Landsat
LiDAR
Pinus pinaster
id ES_312b5bc93edba93eeb2d2d5e985d4c59
oai_identifier_str oai:uvadoc.uva.es:10324/67791
network_acronym_str ES
network_name_str España
repository_id_str
spelling Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystemsFernández Guisuraga, José ManuelSuárez Seoane, SusanaFernandes, PaoloFernández García, VictorFernández Manso, AlfonsoQuintano Pastor, María del CarmenCalvo, LeonorAboveground biomassBurn severityLandsatLiDARPinus pinasterProducción CientíficaBackground: 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.Spanish Ministry of Economy and Competitiveness, and the European Regional Development Fund (ERDF), GESFIRE project (AGL2013-48189-C2-1-R);Spanish Ministry of Economy and Competitiveness, and the European Regional Development Fund (ERDF), iFIRESEVES project (AGL2017-86075-C2-1-R)Regional Government of Castilla and Leon FIRECYL project (LE033U14)Regional Government of Castilla and Leon SEFIRECYL project (LE001P17)Regional Government of Castilla and Leon, WUIFIRECYL project (LE005P20)Elsevier2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.fecs.2022.100022https://uvadoc.uva.es/handle/10324/67791reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidEspañolhttps://www.sciencedirect.com/science/article/pii/S2197562022000227info:eu-repo/semantics/openAccessoai:uvadoc.uva.es:10324/677912026-06-13T12:44:47Z
dc.title.none.fl_str_mv Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
title Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
spellingShingle Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
Fernández Guisuraga, José Manuel
Aboveground biomass
Burn severity
Landsat
LiDAR
Pinus pinaster
title_short Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
title_full Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
title_fullStr Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
title_full_unstemmed Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
title_sort Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems
dc.creator.none.fl_str_mv Fernández Guisuraga, José Manuel
Suárez Seoane, Susana
Fernandes, Paolo
Fernández García, Victor
Fernández Manso, Alfonso
Quintano Pastor, María del Carmen
Calvo, Leonor
author Fernández Guisuraga, José Manuel
author_facet Fernández Guisuraga, José Manuel
Suárez Seoane, Susana
Fernandes, Paolo
Fernández García, Victor
Fernández Manso, Alfonso
Quintano Pastor, María del Carmen
Calvo, Leonor
author_role author
author2 Suárez Seoane, Susana
Fernandes, Paolo
Fernández García, Victor
Fernández Manso, Alfonso
Quintano Pastor, María del Carmen
Calvo, Leonor
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Aboveground biomass
Burn severity
Landsat
LiDAR
Pinus pinaster
topic Aboveground biomass
Burn severity
Landsat
LiDAR
Pinus pinaster
description Producción Científica
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.fecs.2022.100022
https://uvadoc.uva.es/handle/10324/67791
url https://doi.org/10.1016/j.fecs.2022.100022
https://uvadoc.uva.es/handle/10324/67791
dc.language.none.fl_str_mv Español
language_invalid_str_mv Español
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2197562022000227
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405593343623168
score 15,300719

Registos relacionados