Evaluating Performance of Support Vector Machine and Random Forest Classifiers in Monitoring Wildfire from pre- and post-event Landsat8 satellite Images

Occurrence of wildfires in forests is one of the important environmental hazards. Remote sensing is one of the useful sources for detecting and monitoring wildfires. The purpose of this paper is to evaluate "during fire" image and "before and after fire" images from Landsat8 satellite in identifying fire areas using Support Vector Machine (SVM) and Random Forest (RF) classifiers. Based on the analysis of the output from the images of the Sacramento area in the state of California, it was found that RF classification method with an overall accuracy of 99.83%, compared to the SVM method with an overall accuracy of 99.53%, has a better ability to distinguish fire from non-fire areas. It should be noted that in both methods, the overall accuracy was considerable and indicated their desirability to wildfire detection. Moreover, the classification results with a “single image” input during a fire were better than the “difference image” input.