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dc.contributorFacultad de Ciencias Biologicas y Ambientaleses_ES
dc.contributor.authorQuintano Pastor, Carmen
dc.contributor.authorFernández Manso, Alfonso 
dc.contributor.authorRoberts, Dar A.
dc.contributor.otherEcologiaes_ES
dc.date2017
dc.date.accessioned2017-10-02T10:12:50Z
dc.date.available2017-10-02T10:12:50Z
dc.date.issued2017-10-02
dc.identifier.citationRemote Sensing of Environment, 2017es_ES
dc.identifier.urihttp://hdl.handle.net/10612/6822
dc.description14 p.es_ES
dc.description.abstractForest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitable for evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planning post-fire rehabilitation. This study assessed the utility of combined Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images and Land Surface Temperature (LST) to accurately map burn severity. We studied a large convection- dominated wildfire, which occurred on 19–21 September 2012 in Spain, in a zone dominated by Pinus pinaster Ait. Burn severity degree (low, moderate, and high) was measured 2–3 months after fire in 111 field plots using the Composite Burn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fire Landsat 7 Enhanced Thematic Mapper (ETM +) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetation and soil (NPVS) and 4.-shade. The thermal band was converted to LST using a single channel algorithm. Next, Multinomial Logistic Regression (MLR) was used to obtain the probability of each burn severity level from MESMA fraction images and LST. Finally, a burn severity map was generated from the probability images and independently validated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fraction image and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burn severity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, we reached a higher accuracy (κ = 0.79) than using the original three burn severity levels (κ = 0.66). Our study demonstrates the validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterranean countrieses_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.subjectEcología. Medio ambientees_ES
dc.subject.otherIncendios forestaleses_ES
dc.subject.otherLandsates_ES
dc.subject.otherEcosistemases_ES
dc.subject.otherMESMAes_ES
dc.titleBurn severity mapping from Landsat MESMA fraction images and Land Surface Temperaturees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.peerreviewedSIes_ES


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