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

Samira Nourbakhshbeidokhti,Anne Chin,Alicia Kinoshita,Joan Florsheim

doi:10.3390/rs11050586

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.

Highlights

Understanding the impacts of natural and anthropogenic disturbances on ecosystem processes requires reliable and efficient methods to survey and detect topographic and geomorphologic variability in affected regions
This paper: (1) reviews techniques and challenges for processing Terrestrial Laser Scanning (TLS) data for analyzing topographic changes in stream channels in terrains after disturbance; (2) reviews and compares methods for estimating changes between multi-temporal TLS datasets; (3) develops an error analysis workflow for analysis of point clouds and Digital Elevation Model (DEM) derived from TLS datasets; and (4) tests the proposed workflow to compare four methods to analyze multi-temporal TLS point clouds collected for a burned stream channel following the 2012 Waldo Canyon Fire (Colorado, USA)
This review focuses on TLS, a Light Detection and Ranging (LiDAR) method useful in the context of quantifying topographic and volumetric changes in channel sedimentation related to topographic variability after disturbances

Summary

Introduction

Understanding the impacts of natural and anthropogenic disturbances on ecosystem processes requires reliable and efficient methods to survey and detect topographic and geomorphologic variability in affected regions. Many methods are available to analyze and compare point cloud data and derivative DEMs [6], which are often accompanied by inherent uncertainty and error [7] These spatially distributed errors occur during data collection and processing [7,8,9,10,11,12], which can be enhanced in fluvial systems with large topographic variations [13] or propagated during analysis of multiple scans [14,15,16] discussed . TLS data acquired following a wildfire disturbance in Colorado, USA [5] provided a test case to compare the analysis methods reviewed in this paper and illustrate the workflow presented in Section 3 (Figure 1 and Table 1). Waneddeermroorns.stWraetedtehme ostnesptrsaoteuttlhineesdtefposr othuetlwinoedrkffloorwthien worodrekrf:lo(w1) dinatoardaceqr:u(i1s)itidoant,a(a2c)qpuriespitairoant,io(2n), (p3r)ecphaarnagtieones, t(i3m) acthiaonngaenedstainmaalytisoins, aanndd a(4n)aelyrrsoisr, aannadly(4si)se. rror analysis

Methods

Findings

Discussion

Conclusion