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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.



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. View Full-Text

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





Autor: Antonio Luis Montealegre 1,* , María Teresa Lamelas 1,2, Mihai A. Tanase 3 and Juan de la Riva 1

Fuente: http://mdpi.com/



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