A Workflow to Estimate Topographic and Volumetric Changes and Errors in Channel Sedimentation after Disturbance

Abstract

Light Detection and Ranging (LiDAR) methods, such as ground-based Terrestrial Laser Scanning (TLS), have enabled collection of high-resolution point clouds of elevation data to calculate changes in fluvial systems after disturbance, but are often accompanied by uncertainty and errors. This paper reviews and compares TLS analysis methods and develops a workflow to estimate topographic and volumetric changes in channel sedimentation after disturbance. Four analytic methods to estimate topographic and volumetric changes were compared by quantifying the uncertainty in TLS-derived products: Digital Elevation Model (DEM) of difference (DOD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiple Model to Model Cloud Comparison (M3C2). Mean errors across surfaces within each dataset contributed to a propagation error of 0.015–0.016 m and 0.017–0.018 m for the point clouds and derived DEMs, respectively. The estimated error of the total volumetric change implied increased errors in the conversion of point clouds into a surface by C2M and DOD; whereas C2C and M3C2 were generally simpler, efficient, and accurate techniques for evaluating topographic changes. The comparison of methods to analyze TLS data will contribute to applications of remote sensing of hydro-geomorphic processes in stream channels after disturbance. The workflow presented also aids in estimating uncertainties inherent in data collection and analytic methods for topographic and volumetric change analysis.

The Evolution of Sediment Sources Over a Sequence of Postfire Sediment‐Laden Flows Revealed Through Repeat High‐Resolution Change Detection

“…Following Brasington et al (2003) and Lane et al (2003), limits of detection in the z difference were derived using the 95% confidence interval below using the following: where LOD 95% is the limit of detection for the 95% confidence interval and σ DoD is the propagated error from before and after surveys for the change detection period. Spatially distributed LOD 95% ranged from 0.006 to 0.02 m for TLS (Figure 3; Table S1), similar to those reported in other postfire studies (e.g., DeLong et al, 2018; Nourbakhshbeidokhti et al, 2019; Orem & Pelletier, 2015; Staley et al, 2014). Further details on the methods used for spatially distributed uncertainty are outlined in the associated supporting information (Appendix S1).…”

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

“…The M3C2 distance calculation is solely based on point cloud comparison and is therefore chosen over a method interpolating surfaces (e.g. Cloud to Model & Digital Elevation Model of Difference) as M3C2 is more reliable on complex topographies 33 . Furthermore, the M3C2 approach is well adapted to calculate distances between two point clouds on a cliff, as it is suitable for vertical as well as horizontal surfaces and it gives positive and negative values of distance 34 .…”

Investigating the Applicability of Unmanned Aerial Vehicles (UAV) Photogrammetry for the Estimation of the Volume of Stockpiles

“…In addition, Nourbakhshbeidokhti et al (2019) reviewed and compared four methods of TLS analysis by quantifying the uncertainty in TLS-derived products such as DEM of difference (DOD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiple Model to Model Cloud Comparison (M3C2). They also developed a workflow for the estimation of topographic and volumetric changes in channel sedimentation after disturbance.…”