Changing climate conditions in northwest Europe present an increasing wildfire risk in the Netherlands. Focus on fuels monitoring in this region is not as extensive as it is in the United States. Accurate estimation of biomass fuel loading is integral to prevention of wildfires which pose a significant risk to both human lives and property. This research project attempted to create predictive models for three major fuel categories (litter/duff, shrub, and downed woody material). Reduction of the number of parameters to measure would streamline the process of fuel load estimation by reducing the number of measurements that need to be taken in the field. The results of this study show that certain parameters contribute more to predicting fuel loads than others in the litter/duff and shrub categories. More parameters need to be collected to determine if a model can be created for the downed woody material category. The findings indicate that the models produced in this study containing these parameters can be used to more quickly and efficiently estimate fuel loading in certain fire-prone communities in the Netherlands. This research can assist land managers in this region in more accurate fuel estimation, therefore creating a more proactive approach to understanding and preventing the risks of destructive wildfire events.