The understory vegetation layer contributes considerably to the physical structure of boreal forests. This research sought to understand the relationships between field- and LiDAR- (light detection and ranging-) derived measures of boreal understory structure. As well as how environmental factors may influence discrepancies that can arise between these derived measures. Five attributes to map and characterize the boreal understory vegetation were selected: mean understory height, percent cover, density, complexity, and volume. Percent understory cover showed limited bias in LiDAR-derived estimates of compared to field measurements, in northeastern Alberta. However, LiDAR was shown to underestimate understory mean height and volume, and to overestimate understory density and complexity. Generalized linear model regression analysis were used to understand the influence of external environmental factors on these error patterns. Explanatory variables for these models included canopy openings, bole density, canopy complexity, and ecosite type. It was found that canopy openings reduced errors in understory mean height, percent cover, and volume. Higher bole density was strongly associated with increased errors in understory mean height and volume, and had weak influence on errors in understory percent cover, complexity, and density. More complex canopies were seen to slightly increase the errors in understory volume and did not influence errors in the remaining attributes. Finally, ecosite had a strong influence on errors in understory mean height, complexity, and volume. In the final phase of this research, a series of predictive maps of understory structure were developed across a 4300-hectare study area in the central mixed-wood subregion of the boreal forest, with independent-validation coefficients of determination ranging from 0.41 - 0.59.