Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem

Abstract

Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level.

Keywords: Fire management; Fire severity; Mediterranean; Pine forests; Random forest algorithm; Remote sensing.