Wildfire spread simulation approximates the dynamic spread of wildfires by modeling fire spread behavior under various fuel, terrain, and weather conditions. To achieve real-time fire spread prediction using wildfire spread simulation models, it is essential to assimilate real-time observation data from active fires into the simulation models. Unmanned Aerial Vehicles (UAVs) has been increasingly used to collect data from wildfires. Data assimilation methods that assimilate UAV-based observation data are widely used for supporting wildfire spread simulation. This dissertation formulates and establishes data assimilation that works with a discrete event wildfire spread model and UAV-based observation data. Particle filter (PF)-based data assimilation algorithms are developed to carry out the data assimilation task. The PF-based data assimilation algorithms are effective when the UAV-based observation data is sparse, partially observed, and noisy. Additionally, an advanced PF approach based on time-space confidence of the UAV observation data is established which can handle more challenging scenarios. Experiment results show that the developed data assimilation method for calibrating UAV-based fire data for wildfire spread simulation is effective and robust.