14 p.Forest 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
countriesS
