Abstract:
Pine flatwoods forests in the southeastern US have experienced severe wildfires
over the past few decades, often attributed to fuel load build-up. These forest communities
are fire dependent and require regular burning for ecosystem maintenance and health.
Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem
integrity, managers are still working to reintroduce fire to long unburned areas. Common
perception holds that reintroduction of fire in long unburned forests will produce severe
fire effects, resulting in a reluctance to prescribe fire without first using expensive
mechanical fuels reduction techniques. To inform prioritization and timing of future fire
use, we apply remote sensing analysis to examine the set of conditions most likely to result
in high burn severity effects, in relation to vegetation, years since the previous fire, and
historical fire frequency. We analyze Landsat imagery-based differenced Normalized Burn
Ratios (dNBR) to model the relationships between previous and future burn severity to
better predict areas of potential high severity. Our results show that remote sensing
techniques are useful for modeling the relationship between elevated risk of high burn
severity and the amount of time between fires, the type of fire (wildfire or prescribed burn),
and the historical frequency of fires in pine flatwoods forests.