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Forest Ecosystems 2022, 9(2): 100022 https://doi.org/10.1016/j.fecs.2022.100022
Research Article | Open Access | Issue | Published: 03 March 2022

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

Show Author's Information José Manuel Fernández-Guisuragaa,b( ),  Susana Suárez-Seoanec,  Paulo M. Fernandesb,  Víctor Fernández-Garcíaa,  Alfonso Fernández-Mansod,  Carmen Quintanoe,f,  Leonor Calvoa
Area of Ecology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, 24071, León, Spain
Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5000-801, Vila Real, Portugal
Department of Organisms and Systems Biology (Ecology Unit) and Research Unit of Biodiversity (IMIB; UO-CSIC-PA), University of Oviedo, Oviedo, Mieres, Spain
Agrarian Science and Engineering Department, School of Agricultural and Forestry Engineering, University of León, 24400, Ponferrada, Spain
Electronic Technology Department, School of Industrial Engineering, University of Valladolid, 47011, Valladolid, Spain
Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004, Palencia, Spain
Keywords:
Aboveground biomass, LiDAR, Landsat, Burn severity, Pinus pinaster
Cite this article:
Fernández-Guisuraga JM, Suárez-Seoane S, Fernandes PM, et al. 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(2): 100022. https://doi.org/10.1016/j.fecs.2022.100022
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Abstract Full text About this article
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 (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).

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.


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About this article

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

Show Author's information José Manuel Fernández-Guisuragaa,b( ), Susana Suárez-Seoanec, Paulo M. Fernandesb, Víctor Fernández-Garcíaa, Alfonso Fernández-Mansod, Carmen Quintanoe,f, Leonor Calvoa
Area of Ecology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, 24071, León, Spain
Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5000-801, Vila Real, Portugal
Department of Organisms and Systems Biology (Ecology Unit) and Research Unit of Biodiversity (IMIB; UO-CSIC-PA), University of Oviedo, Oviedo, Mieres, Spain
Agrarian Science and Engineering Department, School of Agricultural and Forestry Engineering, University of León, 24400, Ponferrada, Spain
Electronic Technology Department, School of Industrial Engineering, University of Valladolid, 47011, Valladolid, Spain
Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004, Palencia, Spain

Abstract

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 (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).

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.

Keywords: Aboveground biomass, LiDAR, Landsat, Burn severity, Pinus pinaster

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Publication history

Received: 24 February 2022
Accepted: 24 February 2022
Published: 03 March 2022
Issue date: April 2022

Copyright

© 2022 Beijing Forestry University.

Acknowledgements

Acknowledgements

The authors would like to thank the Spanish Ministry for the Ecological Transition and the Demographic challenge for providing the Third Spanish National Forest Inventory (SNFI-3) data.

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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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