In previous work, researchers compared three sampling techniques for estimating the biomass of surface fine woody fuels by using them on known distributions. An important result was that precise estimates of fuel biomass required intensive sampling for both planar intercept and fixed-area methods. This study explores Bayesian statistical methods as a means to reduce the sampling effort needed to obtain a desired precision. We examined how initial estimates of the minimum and maximum fuel loading at a site could be used as prior information in a Bayesian framework. We found that, under certain scenarios, Bayesian techniques dramatically increased the precision of the estimator compared to using no prior information from the site.Faculty Sponsor: Kathy Gray

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