LiDAR (Light Distance And Ranging, a.k.a. ALSM (Airborne Laser Swath Mapping)) data is quickly becoming one of the hottest tools in the Geosciences for studying the earth's surface. Capable of generating digital elevation models (DEMs) more than an order of magnitude more accurate than those currently available, LiDAR data offers geologists the opportunity to study the processes the shape the earth's surface at resolutions not previously possible. Unfortunately, access to these datasets for the average geoscience user is currently difficult because of the massive volumes of data generated by LiDAR. The distribution, interpolation and analysis of large LiDAR datasets, which frequently exceed a billion data-points, push the computational limits of typical internet-based data distribution and processing systems. The high point-density of LiDAR datasets makes grid interpolation difficult for geoscience users who lack the computing and software resources necessary to handle these enormous data volumes. We are using a geoinformatics approach to the distribution, interpolation and analysis of LiDAR data that capitalizes on cyberinfrastructure being developed as part of the GEON project (www.geongrid.org). Our approach utilizes a comprehensive workflow-based solution, the GEON LiDAR Workflow (GLW) (http://www.geongrid.org/science/lidar.html), which begins with user-defined selection of a subset of raw point data and ends with download and visualization of interpolated surfaces and derived products. The workflow environment allows us to modularize and generalize the procedure. It provides the freedom to easily plug-in new processes, to utilize existing sub workflows within an analysis, and easily extend or modify the analysis using drag-and-drop functionality through the Kepler workflow management system (http://kepler-project.org/). In this GEON-based workflow, the billions of points within a LiDAR dataset point cloud are hosted in an IBM DB2 spatially indexed database running on the DataStar terascale supercomputer at San Diego Supercomputer Center; a machine designed specifically for data intensive computations. Data selection is performed via a WMS map-based interface that allows users to execute spatial and attribute subset queries on the larger dataset. The subset of data is then passed to a GRASS Open Source GIS-based web service that handles interpolation to grid and analysis of the data. The interpolation and analysis portion of the workflow was developed entirely within the open source domain and offers spline with regularized smoothing and tension (rst) interpolation to grid with user-defined grid (DEM) resolution as well as control over the spline parameters. We also compute geomorphic metrics such as slope, curvature, and aspect as derived products from the DEM that the GLW generates. In order to serve as broad a community as possible, users may choose to download their results in ESRI or ascii grid formats as well as geo tiff. Additionally, the GLW feeds into GEON web services in development that allow visualization of outputs in a web browser window or in 3D through Fledermaus' free viewer iView3D or our own OpenGL-based tool, LViz (http://activetectonics.la.asu.edu/GEONatASU/LViz.html). Although the GLW was conceived for LiDAR data distribution and processing, most of the functions within this workflow are not limited to LiDAR data and may be used for distributing, interpolating and visualizing any computationally intensive point dataset (such as gravity). Currently, the GLW hosts two pilot data sets: 1) Northern San Andreas Fault and associated marine terraces and 2) Western Rainier Seismic Zone. In addition, we have a number of datasets that we anticipate bringing online in the coming months. Efforts are currently underway to improve GLW interpolation performance on large datasets and to recruit additional GEON computing resources for utilization by the GLW. Finally, we are pursuing implementation of additional interpolation algorithms and analysis tools within the GLW. Ultimately, we believe that the GEON LiDAR Workflow could be adopted as a valuable infrastructure resource for democratizing access to future LiDAR point cloud datasets for the geoscience community.