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Remote Sens. 2014, 6(5), 4240-4265; doi:10.3390/rs6054240
Article

Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment

1,* , 1,2
, 3
 and 1
1 GEOFOREST Group, IUCA, Department of Geography, University of Zaragoza, Zaragoza 50009, Spain 2 Centro Universitario de la Defensa de Zaragoza, Zaragoza 50090, Spain 3 Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3052, Australia
* Author to whom correspondence should be addressed.
Received: 2 January 2014 / Revised: 8 April 2014 / Accepted: 15 April 2014 / Published: 8 May 2014
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
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Abstract

Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the 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 and were therefore used to create a continuous map of severity levels.
Keywords: fire severity; composite burn index; Airborne Laser Scanners (ALS); Mediterranean pine forest; logistic regression fire severity; composite burn index; Airborne Laser Scanners (ALS); Mediterranean pine forest; logistic regression
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Montealegre, A.L.; Lamelas, M.T.; Tanase, M.A.; de la Riva, J. Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment. Remote Sens. 2014, 6, 4240-4265.

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