Forest fire severity estimation based on the LiDAR-PNOA data and the values of the Composite Burn Index


  • A.L. Montealegre Universidad de Zaragoza
  • M.T. Lamelas Universidad de Zaragoza; Centro Universitario de la Defensa de Zaragoza
  • M.A. Tanase Universidad de Alcalá
  • J. de la Riva Universidad de Zaragoza



Fire severity, CBI, LiDAR, Mediterranean forest, logistic regression


Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities. An estimation of the fire severity as accurate as possible is required by forest managers to decide which strategy is most appropriate to mitigate the effect of fire. The aim of this research is to estimate the post-fire severity, relating a pool of independent variables derived from the LiDAR (Light Detection And Ranging) points clouds delivered by the National Plan for Aerial Orthophotography (PNOA) to field data based on Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables. In addition, the obtained results are compared to different spectral indices derived from Landsat Thematic Mapper.


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Author Biographies

A.L. Montealegre, Universidad de Zaragoza

GEOFOREST-IUCA,Departamento de Geografía y Ordenación del Territorio

M.T. Lamelas, Universidad de Zaragoza; Centro Universitario de la Defensa de Zaragoza

GEOFOREST-IUCA,Departamento de Geografía y Ordenación del Territorio

M.A. Tanase, Universidad de Alcalá

Departamento de Geología, Geografía y Medio Ambiente,

J. de la Riva, Universidad de Zaragoza

GEOFOREST-IUCA,Departamento de Geografía y Ordenación del Territorio


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