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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

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Forest Ecosystems 2022年第2期9卷 234-246页

作者： Jose Manuel Fernandez-Guisuraga Susana Suarez-Seoane Paulo MFernandes Victor Fernandez-Garcia Alfonso Fernandez-Manso Carmen Quintano Leonor Calvo Area of Ecology Department of Biodiversity and Environmental ManagementFaculty of Biological and Environmental SciencesUniversity of Leon24071LeonSpain Centro de Investigacao e de Tecnologias Agroambientais e Biologicas Universidade de Tras-os-Montes e Alto Douro5000-801Vila RealPortugal Department of Organisms and Systems Biology(Ecology Unit)and Research Unit of Biodiversity(IMIB UO-CSIC-PA)University of OviedoOviedoMieresSpain Agrarian Science and Engineering Department School of Agricultural and Forestry EngineeringUniversity of Leon24400PonferradaSpain Electronic Technology Department School of Industrial EngineeringUniversity of Valladolid47011ValladolidSpain Sustainable Forest Management Research Institute University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology(INIA)34004PalenciaSpain

Background: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(R^(2) equal to 0.72 and 0.68,respectively)from LiDAR and spectral data than understory AGB(R^(2)=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(R^(2)=0.60)and overstory(R ^(2)=0.53)AGB,whereas the relationship with understory AGB was weaker(R ^(2)=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).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 contri-bution 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.

关键词： Aboveground biomass burn severity Landsat LiDAR Pinus pinaster

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Spatial pattern analysis of post-fire damages in the Menderes District of Turkey

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Frontiers of Earth Science 2020年第2期14卷 446-461页

作者： Emre COLAK Filiz SUNAR Civil Engineering Faculty Istanbul Technical UniversityIstanbul 80626Turkey

Forest fires,whether caused naturally or by human activity can have disastrous effects on the environment.Turkey,located in the Mediterranean climate zone,experiences hundreds of forest fires every year.Over the past two decades,these fires have destroyed approximately 308000 ha of forest area,threatening the sustainability of its ecosystem.This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1,2017,by using pre-and post-fire Sentinel 2(10 m and 20 m)and Landsat 8(30 m)satellite images,MODIS and VIIRS fire radiative power(FRP)data(1000 m and 375 m,respectively),and reference data obtained from a field study.Hence,image processing techniques integrated with the Geographic Information System(GIS)database were applied to a satellite image data set to monitor,analyze,and map the effects of the forest fire.The results show that the land surface temperature(LST)of the burned forest area increased from 1 to 11°C.A high correlation(R=0.81)between LST and burn severity was also determined.The burned areas were calculated using two different classification methods,and their accuracy was compared with the reference data.According to the accuracy assessment,the Sentinel(10 m)image classification gave the best result(96.43%for Maximum Likelihood,and 99.56%for Support Vector Machine).The relationship between topographical/forest parameters,burn severity and disturbance index was evaluated for spatial pattern distribution.According to the results,the areas having canopy closure between 71%–100%and slope above 35%had the highest burn incidence.As a final step,a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the postfire analysis.A high correlation was found between FRPslope,and FRP-burn severity(0.96 and 0.88,respectively).

关键词： remote sensing GIS spectral indices disturbance index land surface temperature burn severity

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