A 32-year species-specific live fuel moisture content dataset for southern California chaparral

Live fuel moisture content (LFMC) strongly affects the behavior of
wildland fire, resulting in its incorporation into wildfire spread models
and danger ratings. In this dataset, over ten thousand LFMC observations
were combined with predictor variables from Landsat imagery and the
Weather Research and Forecasting model to train species-specific random
forest models that predict the LFMC of four fuel types—chamise, old growth
chamise, black sage, and bigpod ceanothus. These models are then utilized
to create a historical, 32-year long, LFMC dataset in southern California
chaparral. Additionally, the high spatial and temporal sampling frequency
of chamise allowed for quantile mapping bias correction to be applied. The
final chamise output, which is the most robust, has a mean absolute error
of 9.68% and an R2 value of 0.76. The LFMC dataset successfully captures
the variability in the annual cycle, the spatial heterogeneity, and the
interspecies differences, which makes it applicable for better
understanding varying fire season characteristics and landscape level
flammability.