Trails are a tell-tale sign of the movements of both animals and humans through undeveloped landscapes. Mapping these trails can serve as an indicator of the location and volume of this traffic as well as how the traffic changes over time. Semi-automated methods for mapping trails from high-resolution aerial imagery have been investigated, but have proven inadequate to replace manual interpretation methods. The primary objective of this study was to evaluate the utility of aerial LiDAR data, in combination with airborne multispectral imaged data as input to semi-automated image processing routines for delineating trail features. LiDAR-derived products including intensity, surface texture and vegetation height, were generated at both low and high resolutions and then combined with highresolution multi-spectral imagery as inputs for semi-automated feature extraction utilizing the Feature Analyst 5.0 software. The study area was a section of the U.S./Mexico border near San Diego where cross-border trails through undeveloped shrubland areas of varying topography were common. To test the performance of the LiDAR inputs, three study sites were selected that provided examples of the various land cover and terrain types within the region. Semi-automated trail delineation was then conducted for each study site using imagery plus each LiDAR input type alone and in combination. An accuracy assessment based on visually interpreted and manually delineated trails as reference data, revealed an overall increase in agreement by an average of 4% utilizing the LiDAR inputs when compared to imagery alone, with improvements of as much as 23% in some cases. Results of this study demonstrated that the utility of aerial LiDAR data as an input to semi-automated trail delineation varies with the input type and resolution, as well as land cover and terrain conditions.