RT info:eu-repo/semantics/article
T1 Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data
A1 Fernández Manso, Alfonso
A1 Quintano Pastor, María del Carmen
A1 Roberts, Dar
K1 Burn severity
K1 EO-1 Hyperion
K1 LiDAR
K1 MaxEnt
AB All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of thedrivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools forforest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensableinstrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structureand topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility ofthe Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to modelthree levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurredon 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperiondata were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction imageswere generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associatedwith 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 generatedas output a suitability surface for each burn severity level. The percentage of contribution of the differentbiophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had agreater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highestcontributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEntas one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-firehyperspectral Hyperion data and pre-fire LiDAR
PB Elsevier
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/67786
UL https://uvadoc.uva.es/handle/10324/67786
LA spa
NO ISPRS Journal of Photogrammetry and Remote Sensing, Septiembre 2019, vol. 155, p. 102–118.
DS UVaDOC
RD 22-dic-2024