Light detection and ranging (lidar) is the premier technology for high-resolution elevation measurements in complex landscapes. Lidar error assessments allow for objective interpretation of Digital Elevation Models (DEMS) and products reliant on these layers. The purpose of this study is to spatially estimate the vertical error of a lidar-derived DEM across seven cover types through modeling of field survey data. We use thirty-four variables and ground-based field survey data in a Random Forest regression to predict elevation error. Four variables captured the variability within the lidar errors, with three variables relevant to the distribution of returns within the vegetation and one relating to the terrain form. Good agreement was observed when comparing the survey against the model predictions (mu = -0.02 m, s = 0.13 m, and RMSE = 0.14 m). With most lidar products reliant upon accurate production of DEMS, providing spatially explicit assessments of uncertainty at the landscape level will increase user confidence in lidar products.