Combining spectral mixture analysis and object-based classification for fire severity mapping

Abstract

This study shows an accurate and fast methodology in order to evaluate fire severity classes of large forest fires. A single Landsat Enhanced Thematic Mapper multispectral image was utilized with the aim of mapping fire severity classes (high, moderate and low) using a combined-approach based in a spectral mixing model and object-based image analysis. A large wildfire in the Northwest of Spain was used to test the model. Fraction images obtained by Landsat unmixing were used as input data in the object-based image analysis. A multilevel segmentation and a classification were carried out by using membership functions. This method was compared with other simpler in order to evaluate the suitability to distinguish between the three fire severity classes above mentioned. McNemar's test was used to evaluate the statistical significance of the difference between approaches tested in this study. The combined approach achieved the highest accuracy reaching 97.32% and kappa index of agreement of 95.96% and improving accuracy of individual classes.