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当前位置: 首页 > 文章 > 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 森林生态系统(英文版) 2022,9
Position: Home > Articles > 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 Forest Ecosystems 2022,9

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

作  者：
José Manuel Fernández‐Guisuraga;Susana Suárez‐Seoane;Paulo M. Fernandes;Víctor Fernández-García;Alfonso Fernández–Manso;Carmen Quintano;Leonor Calv
关键词：
Aboveground biomass;Burn severity;Landsat;LiDAR;Pinus pinaster;LANDSAT TIME-SERIES;AIRBORNE LIDAR;FOREST STRUCTURE;POSTFIRE VEGETATION;MEDITERRANEAN BASIN;HYPERSPECTRAL DATA;SHRUB BIOMASS;CANOPY COVER;NW SPAIN;FUE
摘  要：
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. 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 ​= ​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 ​= ​0.60) and overstory (R2 ​= ​0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ​= ​0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ​= ​122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ​= ​158.41). 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.

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