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Terrestrial structure-from-motion: spatial error analysis of roughness and morphology

Schwendel, Arved; Milan, David

Authors

Arved Schwendel



Abstract

Structure-from-Motion (SfM) photogrammetry is rapidly becoming a key tool for morphological characterisation and change detection of the earth surface. This paper demonstrates the use of Terrestrial Structure-from-Motion (TSfM) photogrammetry to acquire morphology and roughness data at the reach-scale in an upland gravel-bed river. We quantify 1) spatially-distributed error in TSfM derived Digital Elevation Models (DEMs) and 2) identify differences in roughness populations acquired from TSfM photogrammetry versus TLS. We identify an association between local topographic variation and error in the TSfM DEM. On flatter surfaces (e.g. bar and terrace surfaces), the difference between the TSfM and TLS DEMs are generally less than ±0.1 m. However, in areas of high topographic variability (>0.4 m) such as berm or terrace edges, differences between the TSfM and TLS DEMs can be up to ±1 m. Our results suggest that grain roughness estimates from the TSfM point cloud generate values twice those derived from the TLS point cloud on coarse berm areas, and up to four-fold those derived from the TLS point cloud over finer gravel bar surfaces. This finding has implications when using SfM data to derive roughness metrics for hydrodynamic modelling. Despite the use of standard filtering procedures, noise pertains in the SfM DEM and the time required for its reduction might partially outweigh the survey efficiency using SfM. Therefore, caution is needed when SfM surveys are employed for the assessment of surface roughness at a reach-scale.

Citation

Schwendel, A., & Milan, D. (2020). Terrestrial structure-from-motion: spatial error analysis of roughness and morphology. Geomorphology, 350, https://doi.org/10.1016/j.geomorph.2019.106883

Journal Article Type Article
Acceptance Date Sep 27, 2019
Online Publication Date Oct 21, 2019
Publication Date Feb 1, 2020
Deposit Date Nov 18, 2019
Publicly Available Date Oct 22, 2020
Journal Geomorphlogy
Print ISSN 0169-555X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 350
Article Number 106883
DOI https://doi.org/10.1016/j.geomorph.2019.106883
Keywords Digital Elevation Model (DEM); Error; Roughness; SfM photogrammetry; Terrestrial Laser Scanning (TLS)
Public URL https://hull-repository.worktribe.com/output/2970753
Publisher URL https://www.sciencedirect.com/science/article/pii/S0169555X19303745?via%3Dihub

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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
©2019, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/





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