RT info:eu-repo/semantics/article
T1 Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature
A1 Quintano Pastor, Carmen
A1 Fernández Manso, Alfonso
A1 Roberts, Dar A.
A2 Ecologia
K1 Ecología. Medio ambiente
K1 Incendios forestales
K1 Landsat
K1 Ecosistemas
K1 MESMA
AB Forest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitablefor evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planningpost-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 pinasterAit. Burn severity degree (low, moderate, and high) was measured 2–3 months after fire in 111 field plots using the CompositeBurn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fireLandsat 7 Enhanced Thematic Mapper (ETM +) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetationand 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 MESMAfraction images and LST. Finally, a burn severity map was generated from the probability images and independentlyvalidated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fractionimage and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burnseverity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, wereached a higher accuracy (κ = 0.79) than using the original three burn severity levels (κ = 0.66). Our study demonstratesthe validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterraneancountries
PB Elsevier
YR 2017
FD 2017-10-02
LK http://hdl.handle.net/10612/6822
UL http://hdl.handle.net/10612/6822
NO Remote Sensing of Environment, 2017
NO 14 p.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 24-abr-2024