Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA

Fire managers often need data that is spatially explicit at a fine scale (30 m) but is also laborious and time consuming to collect. This study integrates Landsat 5 imagery and topographic information with plot and tree based data to model and map four key canopy fuels variables: Canopy Bulk Density (CBD), Canopy Cover (CC), Canopy Base Height (CBH), and canopy Height (HT). We sampled 223 plots of 500 m2 each in Lassen Volcanic National Park. Within each plot we recorded every tree by species, diameter, condition, and canopy position. Additionally, we measured each tree’s height, height to live crown base and height to dead crown base. Finally, we took three hemispherical photographs of the forest canopy above each plot. We developed five topographic variables—elevation, slope, aspect, and two measures of topographic position—and used Landsat 5 spectral bands 1–5, and 7 as well as the Normalized Difference Vegetation Index (NDVI) and the Tasseled Cap Greenness, Brightness, and Wetness to model and then predict these canopy fuels variables for both 2009 and 2003 across LVNP. RF models relating predictor variables to canopy fuels characteristics had pseudo-r2 values ranging from 0.55 to 0.68. To demonstrate the potential utility of our mapping procedure, we used our 2003 canopy fuels map along with a previously unpublished contemporary surface fuels map and the fire behavior modeling program FlamMap® to relate predicted fire behavior of our fuels maps with fire severity from the Monitoring Trends in Burn Severity (MTBS) dataset for the Bluff (2004) fire.

► We model and map critical wildland fuel variables for fire behavior analysis.
► Canopy and surface fuels are mapped using field plots and LandSat data.
► We predict fire behavior within the perimeter of the historic Bluff fire (2004).
► Compared to standard products, our maps increase fire type prediction accuracy.
► Random Forests produce highly accurate, locally relevant fuel maps.