Hydro-morphological mapping of river reaches using videos captured with UAS

Anette Eltner; László Bertalan; Jens Grundmann; Matthew Thomas Perks; Eliisa Lotsari

doi:10.1002/esp.5205

Hydro-morphological mapping of river reaches using videos captured with UAS

Anette Eltner^*
, László Bertalan
, Jens Grundmann
, Matthew Thomas Perks
, Eliisa Lotsari

^*Corresponding author for this work

Not published at Aalto University

Research output: Contribution to journal › Article › Scientific › peer-review

36 Citations (Scopus)

Abstract

Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s⁻¹. The image-based discharge was estimated with deviations to ADCP references of up to 5%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models.

Original language	English
Pages (from-to)	2773-2787
Number of pages	15
Journal	Earth Surface Processes and Landforms
Volume	46
Issue number	14
DOIs	https://doi.org/10.1002/esp.5205
Publication status	Published - Nov 2021
MoE publication type	A1 Journal article-refereed

Funding

We would like to thank Hannes Sardemann, Diana Spieler and Melanie Elias (TU Dresden), Noémi Mária Szopos and Bálint Nagy (University of Debrecen) and Franziska Wolff (University of Eastern Finland) for their support during the field campaigns. Furthermore, we are grateful for the financial support of the DAAD with funds from the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; Project ID 57448822 and 57524996), the Tempus Public Foundation (ID 307670) and the Academy of Finland (ID 332563). The research of LB was supported by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary. We would also like to thank Frank Engel and PNT-hydro for their assistance. We are grateful for the comments of Mike James and two anonymous reviewers. We would like to thank Hannes Sardemann, Diana Spieler and Melanie Elias (TU Dresden), Noémi Mária Szopos and Bálint Nagy (University of Debrecen) and Franziska Wolff (University of Eastern Finland) for their support during the field campaigns. Furthermore, we are grateful for the financial support of the DAAD with funds from the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; Project ID 57448822 and 57524996), the Tempus Public Foundation (ID 307670) and the Academy of Finland (ID 332563). The research of LB was supported by the Thematic Excellence Programme (TKP2020‐NKA‐04) of the Ministry for Innovation and Technology in Hungary. We would also like to thank Frank Engel and PNT‐hydro for their assistance. We are grateful for the comments of Mike James and two anonymous reviewers.

Keywords

bathymetry
discharge
fluvial morphology
image velocimetry
river surface flow velocity pattern
SfM photogrammetry
video frame filtering

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10.1002/esp.5205Licence: CC BY-NC

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@article{12dafcca48a3465cbf85dfe04c30984c,

title = "Hydro-morphological mapping of river reaches using videos captured with UAS",

abstract = "Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21\%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s−1. The image-based discharge was estimated with deviations to ADCP references of up to 5\%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models.",

keywords = "bathymetry, discharge, fluvial morphology, image velocimetry, river surface flow velocity pattern, SfM photogrammetry, video frame filtering",

author = "Anette Eltner and L{\'a}szl{\'o} Bertalan and Jens Grundmann and Perks, \{Matthew Thomas\} and Eliisa Lotsari",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Earth Surface Processes and Landforms published by John Wiley \& Sons Ltd.",

year = "2021",

month = nov,

doi = "10.1002/esp.5205",

language = "English",

volume = "46",

pages = "2773--2787",

journal = "Earth Surface Processes and Landforms",

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TY - JOUR

T1 - Hydro-morphological mapping of river reaches using videos captured with UAS

AU - Eltner, Anette

AU - Bertalan, László

AU - Grundmann, Jens

AU - Perks, Matthew Thomas

AU - Lotsari, Eliisa

PY - 2021/11

Y1 - 2021/11

N2 - Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s−1. The image-based discharge was estimated with deviations to ADCP references of up to 5%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models.

AB - Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s−1. The image-based discharge was estimated with deviations to ADCP references of up to 5%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models.

KW - bathymetry

KW - discharge

KW - fluvial morphology

KW - image velocimetry

KW - river surface flow velocity pattern

KW - SfM photogrammetry

KW - video frame filtering

UR - https://www.scopus.com/pages/publications/85114943308

U2 - 10.1002/esp.5205

DO - 10.1002/esp.5205

M3 - Article

AN - SCOPUS:85114943308

SN - 0197-9337

VL - 46

SP - 2773

EP - 2787

JO - Earth Surface Processes and Landforms

JF - Earth Surface Processes and Landforms

IS - 14

ER -

Hydro-morphological mapping of river reaches using videos captured with UAS

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Keywords

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