Light Detection And Ranging (LiDAR) pointcloud datasets provide immense opportunities for studying 3-D structures in high-resolution detail. The novelty of three- dimensional, heterogeneously spaced, massive datasets creates numerous computational challenges. In this work, we produce software and methods that build on newly released software libraries to process, compute, and visualize lidar pointcloud data. Our scripts streamline processing of raw lidar pointcloud data through various stages: filtering, classification, visualization, and computation of various topographic metrics (slope, aspect, curvature, etc). We demonstrate the value of computing topographic metrics at the pointcloud level over traditional raster-based approaches and apply our tools to calculate slopes and estimate erosion of the California Channel Islands.