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°–20°, 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.
Highlights
Modern terrain mapping techniques that are based on Light Detection and Ranging (LiDAR) have allowed for accurate fine-resolution digital elevation model (DEM) production [1,2,3], with the ability to represent small-scale topographic variation [4]
This study aims to explore the potential for feature-preserving smoothing for the removal of roughness in fine-resolution LiDAR DEMs in a way that can preserve small drainage features
The 11 × 11 test reduced the topographic complexity for a slightly smaller range of scales (Figure 4a), and there was a very small increase in Circular variance of aspect (CVA), relative to the untreated DEM, at scales within the hillslope trough (Figure 3). While this would suggest that feature-preserving DEM smoothing (FPDEMS) adds complexity at these intermediate scales, visual examination of the 11 × 11 test DEM (Figure 5) did not reveal any obvious evidence of this
Summary
Modern terrain mapping techniques that are based on Light Detection and Ranging (LiDAR) have allowed for accurate fine-resolution digital elevation model (DEM) production [1,2,3], with the ability to represent small-scale topographic variation [4]. Low-pass filters are frequently applied to smooth DEMs to remove error and lessen roughness [9,20,21] These filters suppress the short-scale signal corresponding to error/roughness, reduce local variation, and preserve the longer-range signal representing spatially autocorrelated information. Small drainage features often control modelled surface drainage patterns Very often, it is the ability of LiDAR DEMs to represent these small-scale drainage features that provides the justification for the acquisition of these data in projects. It is the ability of LiDAR DEMs to represent these small-scale drainage features that provides the justification for the acquisition of these data in projects It is counter-productive to smooth fine-resolution DEMs if the outcome is the removal of salient topographic information
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