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dc.contributorFacultad de Ciencias Biologicas y Ambientaleses_ES
dc.contributor.authorFernández Manso, Alfonso
dc.contributor.authorQuintano Pastor, Carmen
dc.contributor.authorRoberts, Dar A.
dc.contributor.otherEcologiaes_ES
dc.date2019-09
dc.date.accessioned2019-11-05T23:45:40Z
dc.date.available2019-11-05T23:45:40Z
dc.identifier.citationISPRS Journal of Photogrammetry and Remote Sensing, 2019, vol. 155es_ES
dc.identifier.issn0924-2716
dc.identifier.otherhttps://www.sciencedirect.com/science/article/pii/S0924271619301625es_ES
dc.identifier.urihttp://hdl.handle.net/10612/11311
dc.descriptionP. 102-118es_ES
dc.description.abstractAll ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR.es_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.subjectEcología. Medio ambientees_ES
dc.subject.otherBurn severityes_ES
dc.subject.otherEO-1 Hyperiones_ES
dc.subject.otherLiDARes_ES
dc.subject.otherMaxEntes_ES
dc.titleBurn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doihttps://doi.org/10.1016/j.isprsjprs.2019.07.003
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.journal.titleISPRS Journal of Photogrammetry and Remote Sensinges_ES
dc.volume.number155es_ES
dc.page.initial102es_ES
dc.page.final118es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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