A Method for Extracting Stream Channel Flow Paths from LiDAR Point Cloud Data

Danny L. Anderson, Daniel P. Ames

Abstract


Traditional methods of delineating stream channel networks use gridded raster elevation data. Direct use of LiDAR point clouds, without first creating a raster or grid, could improve efficiency and accuracy. This paper reports the development, and demonstration of a method of delineating stream channels directly from LiDAR point cloud data without the intermediary step of interpolation to a raster or grid. This method, termed “mDn”, is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An algorithm was developed and implemented in ArcVew’s Avenue scripting language. Three adjustable parameters allow fine tuning: radial resolution, angular resolution, and maximum course change. A case study area was selected just north of Redfish Lake, Idaho, at the Fishhook Creek inlet. High resolution aerial photography was used to trace the creek for a reference stream. An mDn delineation, a TauDEM delineation, and other common stream delineations were compared with the reference stream, by calculating sinuosity and root mean square error. Although, the TauDEM delineation yielded a higher sinuosity than the mDn delineation, sinuosity of the mDn delineation more closely matched that of the reference stream than either the TauDEM method or the existing published stream delineations. Stream channel delineation using the mDn method yielded the smallest root mean square errors.

Keywords


LiDAR; Point Cloud; Streams; Channels; Delineation

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