000060637 001__ 60637
000060637 005__ 20171221090011.0
000060637 0247_ $$2doi$$a10.3390/rs6054240
000060637 0248_ $$2sideral$$a85700
000060637 037__ $$aART-2014-85700
000060637 041__ $$aeng
000060637 100__ $$aMontealegre, A.L.
000060637 245__ $$aForest fire severity assessment using LiDAR data in a Mediterranean environment
000060637 260__ $$c2014
000060637 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060637 5203_ $$aMediterranean 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.
000060637 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060637 590__ $$a3.18$$b2014
000060637 591__ $$aREMOTE SENSING$$b5 / 28 = 0.179$$c2014$$dQ1$$eT1
000060637 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060637 700__ $$aLamelas, M.T.
000060637 700__ $$aTanase, M.
000060637 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva, J.$$uUniversidad de Zaragoza
000060637 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDepartamento de Geografía y Ordenación del Territorio$$cAnálisis Geográfico Regional
000060637 773__ $$g6, 5 (2014), 4240-4265$$pRemote sens. (Basel)$$tRemote sensing (Basel)$$x2072-4292
000060637 8564_ $$s5046049$$uhttps://zaguan.unizar.es/record/60637/files/texto_completo.pdf$$yVersión publicada
000060637 8564_ $$s83564$$uhttps://zaguan.unizar.es/record/60637/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060637 909CO $$ooai:zaguan.unizar.es:60637$$particulos$$pdriver
000060637 951__ $$a2017-12-21-08:59:49
000060637 980__ $$aARTICLE