Current field models for wildfire prediction are mostly based on dry or low-moisture fuel combustion research. To better study the live fuel combustion behavior, a laminar flow flat-flame burner was used to provide a convection heating source to ignite an individual live fuel sample. In this research project, four Utah species were studied: Gambel oak (Quercus gambelii), canyon maple (Acer grandidentatum), big sagebrush (Artemisia tridentata), and Utah juniper (Juniperus osteosperma). Leaf geometrical parameters measured included individual leaf total mass, thickness, leaf width, leaf length, and moisture content. Time-stamped images of combustion behavior along with time-dependent mass data were recorded via a LabVIEW system. Combustion characteristics were determined by an automated MATLAB routine modified for analyzing Utah species images of burning fuel samples, including time to ignition, time of flame duration, time to maximum flame height, time to burnout, and maximum flame height. Qualitative results included various combustion phenomena like bursting, brand formation, and bending. Sparks accompanied with leaf material bursting out were observed for Utah juniper sample combustion mostly before ignition, especially for segments cut from the top of the branch. Quantitative results included exploration of the best prediction equations for leaf geometrical properties and combustion characteristics. A beta distribution was used to predict the distribution of dry mass. Multiple linear regressions were performed on other leaf geometrical properties and combustion characteristics. Minimized Bayesian information criterion (BIC) value models were achieved by stepwise regression analysis and compared to the previous empirical prediction models.