Assessing spatial patterns and drivers of burn severity in subtropical forests in Southern China based on Landsat 8

Abstract

Burn severity assessment is vital for the development of effective fire management strategies. Subtropical forests in Southern China support rich forest resources but are also disturbed by frequent forest fires, resulting in varying burn severity areas. However, the lack of accuracy assessment of burn severity based on spectral indices has limited our understanding of spatial patterns and drivers of burn severity in this region and the implementation of effective forest fire management. In this study, we compared the accuracy of different spectral indices from Landsat 8 Operational Land Imager (OLI) in assessing burn severity. Moreover, the optimal spectral index was selected to map burn severity and analyze its spatial patterns and drivers by using the random forest model. The results showed that (1) dNBR, RdNBR, and RBR outperformed the others in accuracy of fitting burn severity, and dNBR was slightly higher in accuracy classification; (2) vegetation (pre-fire NDVI) and human activity (distance to the nearest road or settlement) were the most important drivers affecting burn severity patterns; (3) the proportion of moderate and low burn severity was higher, and most burn areas were concentrated near settlements and roads, but areas with higher burn severity tended to be distributed in uphill, sunny slopes, and high altitudes. In view of this, we recommend targeted forest fire management according to the distribution pattern and drivers of burn severity in this region, including setting up fire belts near residential areas and roads, and prioritizing fuel removal plans in areas prone to develop higher burn severity.