Decades of fire suppression have led to unnaturally large accumulations of fuel in some forest communities in the western United States, including those found in lower and midelevation forests in Yosemite National Park in California. We employed the Random Forests decision tree algorithm to predict fuel models as well as 1-h live and 1-, 10-, and 100-h dead fuel loads using a suite of climatic, topographic, remotely sensed, and burn history predictor variables. Climate variables and elevation consistently were most useful for predicting all types of fuels, but remotely sensed variables increased the kappa accuracy metric by 5%-12% age points in each case, demonstrating the utility of using disparate data sources in a topographically diverse region dominated by closed-canopy vegetation. Fire history information (time-since-fire) generally only increased kappa by 1% age point, and only for the largest fuel classes. The Random Forests models were applied to the spatial predictor layers to produce maps of fuel models and fuel loads, and these showed that fuel loads are highest in the low-elevation forests that have been most affected by fire suppression impacting the natural fire regime.
ASJC Scopus subject areas
- Global and Planetary Change