A STREAMLINED ANALYSIS TOOLSET TO PROCESS LIDAR POINT CLOUD FOR TERRAIN EXTRACTION

LiDAR (Light Detection and Ranging) has become an emerging technology for fine scale analyses of earth surface processes with the advantages of rapid data acquisition and feature-rich dataset. LiDAR data are typically represented as a point cloud with millions of points that require intensive computation processing to extract surface features. Although various data processing tools have been available, most of them are expensive and lack of user-friendly interfaces. In this presentation, we introduce a streamlined toolset established to clean and segment ground points from LiDAR point cloud to generate high resolution digital elevation models (DEMs). This toolset integrates a set of available functions developed in the open-source Point Cloud Library and other freeware using python and ArcGIS Model-builder. It can also integrate other functions provided in ArcGIS for advanced terrain analysis. We will demonstrate the interface of this toolset with several examples and compare the DEMs generated using this toolset with the DEMs generated using commercial software packages (such as Quick Terrain Modeler).