All 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
