For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for summarization to derive environmental covariates. LiDAR (light detection and ranging) point cloud data were analyzed via SQL to generate rasterized covariates of the digital terrain model (DTM), canopy height model (CHM), and a gap fraction for a boreal study region in Northern Ontario, Canada. These features, along with topographic covariates computed from the DTM, were later ascertained as important for subsequent tree species classification research.