Paper Info
Title
LiDAR DEM Smoothing and the Preservation of Drainage Features.
Abstract
Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11-21 grid cells, threshold angles of 10 degrees-20 degrees, and 3-15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams.
Year
DOI
Venue
2019
10.3390/rs11161926
REMOTE SENSING
Keywords
Field
DocType
DEM,LiDAR,data smoothing,denoise,roughness,micro-topography,hydrology,geomorphometry,streams
Drainage,Remote sensing,Smoothing,Lidar,Geology
Journal
Volume
Issue
Citations 
11
16
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
John B. Lindsay1194.63
Anthony Francioni200.34
Jaclyn M. H. Cockburn300.34