Comparison on Geospatial Interpolation Methods for Generating Coastal Marsh Substrates from Lidar Point Clouds

Sea levels are rising globally. To better assess the impacts of sea level rise and coastal inundation on Texas gulf coast, we need to generate coastal marsh substrate as accurate as possible for the coupled hydrodynamic-marsh model (Hydro-MEM) to the region. After marsh is identified and removed from lidar point clouds, we need generate marsh substrate from neighboring points. The experiment is to find robust and fast algorithms for interpolating underlying surface of coastal marsh. First, we evaluated four commonly used geospatial interpolation methods for lidar points. Then we put forward a multiple-scale raster approach which adopts the principle of Voronoi diagram and inverse distance weights interpolation, the experiments show that it can run much fast than point methods with satisfactory accuracy.