FIRE SEVERITY ASSESSMENT OF AN ALPINE FOREST FIRE WITH SENTINEL-2 IMAGERY
- 1Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton (NB), E3B 5A3, Canada
- 2Department of Land, Environment, Agriculture, and Forestry, University of Padova, Padova, Italy
Keywords: Fire severity, Sentinel-2, Random Forests, Alpine Forest
Abstract. Fire is a common phenomenon in many forests and is considered an important ecological tool. Fire severity mapping presents an effective way to assess post-fire management intervention and is helpful in environmental and climate change research. The objective of this study was to determine the severity of a forest fire event that occurred from 24th to 27th October 2019 at Taibon Agordino using Sentinel-2A satellite images and creating a severity map suitable as a decision-making tool for post-fire management intervention. The Sentinel-2A satellite data was classified into the following five classes: Unburned, Low Severity, Moderate Severity, High Severity, and Shadow with the non-parametric Random Forest (RF) classifier, and the resulting classified image was validated using validation sites. The RF classifier was applied first to the ten original band reflectance of Sentinel-2. In a second step, additional variables were added to the classification, namely the digital elevation model (DEM), the slope, and five vegetation indices (i.e., Differenced Normalized Burn Ratio (dNBR), Relative Differenced Normalized Burn Ratio (RdNBR), Differenced Bare Soil Index (dBSI), Global Environmental Monitoring Index (GEMI) and Burn Area Index (BAI)) The inclusion of vegetation indices and DEM-related variables increased the classification accuracy from 99.26% to 99.61% and the overall accuracy from 70.51% to 83.33%. In the classification with the ten original band reflectance, the variable of importance plot ranked the Red-Edge-3, Red, and SWIR 1 band reflectance as the top three most important input features, while for the classification with 17 variables, RdNBR, DEM and dNBR were the top three most important input features.