Determination of Burnt Scars Using Logistic Regression and Neural Network Techniques from a Single Post-Fire Landsat 7 ETM + Image

Abstract

Using logistic regression (LR) and artificial neural network (NN) algorithms, probabilities of presence/absence (p/a) of burned scars were calculated from post-fire Landsat 7 Enhanced Thematic Mapper plus (ETM+) images in mountainous areas of northern California. The discriminating power of six original TM bands (TM bands 1 through 5 and band 7) and five vegetation indices between burned and unburned areas were analyzed. The LR and NN techniques were applied to two study sites with varied topography. We evaluated the performance of both methods in predicting burned scars based on predictive accuracy, uncertainty index, and computation time. The experimental results indicate that (1) the LR is more efficient than the NN in predicting burned scars, but both techniques can produce similar and acceptable prediction accuracy (overall average accuracy greater than 97 percent for both methods at the two study sites) of p/a of burned areas; (2) among all six original TM bands and five vegetation indices, original TM4 and TM7 and NDVII (TM4, TM7) and NDVI2 (TM4, TM3) exhibit the highest discrimination between burned scars and unburned vegetation areas; and (3) the predictive accuracy produced with samples from the shaded and shadowed areas is lower than that from the sunlit areas.