38: Functional Data Analysis of Wildland Fire Intensity

Thomas M. Menino Convention & Exhibition Center 

Wildland fire behavior is determined by fuels and environmental variables. We analyze fire intensity data gathered during managed burns at the Fort Stewart-Hunter Army Airfield in Georgia in 2022. Because fire intensity changes continually throughout the burn, we treat intensity as a smooth functional response measured across time, capturing the full temporal trajectory of fire intensity. Response functions were expressed using a B-spline basis expansion to provide a flexible functional representation. The independent variables are scalar-valued vegetation characteristics measured at each site, e.g., live and dead biomass components including live biomass and litter weights; 1-hour and 10-hour fuels; pine needles; pine cones; conifer, fragmented, and cypress litter; and coarse and fine chars. These provided the fuel for prescribed fires. A functional regression approach is used to estimate the time-varying effects of fuel variables on fire intensity, with covariate effects changing over time. This approach detects early-stage impacts that scalar regression models may not capture and shows how fuel variables influence the temporal dynamics of fire intensity.

Functional data analysis

Basis expansion

Prescribed fire behavior

Wildland fire intensity

Temporal fire dynamics

Functional response 

Section on Statistics and the Environment