RT info:eu-repo/semantics/article
T1 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
A1 Fernández Guisuraga, José Manuel
A1 Suárez-Seoane, Susana
A1 Fernandes, Paulo Alexandre Martins
AD  1966-
A1 Fernández García, Víctor
A1 Fernández Manso, Alfonso
A1 Quintano Pastor, Carmen
A1 Calvo Galván, María Leonor
A2 Ecologia
K1 Ecología. Medio ambiente
K1 Ingeniería agrícola
K1 Aboveground biomass
K1 Burn severity
K1 Landsat
K1 LiDAR
K1 Pinus pinaster
K1 2417 Biología Vegetal (Botánica)
K1 31 Ciencias Agrarias
K1 25 Ciencias de la Tierra y del Espacio
AB Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical forunderstanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the jointconsideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim ofthis work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimatedby means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in amegafire 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) fromLiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics forseveral strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearlyto 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 predictburn 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 propertyderived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contributionof the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationshipsbetween burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation ofthreshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.
PB Springer
SN 2197-5620
LK http://hdl.handle.net/10612/14320
UL http://hdl.handle.net/10612/14320
NO 100022
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 30-jun-2024