Forest Mapping Using Classification of Sentinel-2A Imagery for Forest Fire Danger Prediction: a Case Study

Abstract

Timely and accurate effective forest cover mapping is a prerequisite for predicting forest fire danger. Remote sensing data have an undoubted advantage in mapping the forest cover of territories. The paper compares six trained classification algorithms in order to select the best Sentinel 2A image for a typical forestry in the Baikal region. The conducted comparative analysis has included comparison of the classification accuracy by parametric and nonparametric methods with the default parameters set in the ENVI software. The training sample (Samples data) has been created based on forest management materials. The overall accuracy and the Cohen's kappa coefficient have been used to assess general performance of each algorithm. The accuracy of mapping individual vegetation classes has been assessed using the accuracy of the producer and the user and their combination of F-score. The results of the study can be used when choosing a method for classifying forest vegetation in the Baikal zone and other similar areas by satellite imagery in order to predict forest fire danger.