Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

<p>Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but...

Full description

Bibliographic Details
Main Authors: H. Zhang, E. Aldana-Jague, F. Clapuyt, F. Wilken, V. Vanacker, K. Van Oost
Format: Article
Language:English
Published: Copernicus Publications 2019-09-01
Series:Earth Surface Dynamics
Online Access:https://www.earth-surf-dynam.net/7/807/2019/esurf-7-807-2019.pdf
_version_ 1818351836606758912
author H. Zhang
E. Aldana-Jague
F. Clapuyt
F. Wilken
F. Wilken
V. Vanacker
K. Van Oost
K. Van Oost
author_facet H. Zhang
E. Aldana-Jague
F. Clapuyt
F. Wilken
F. Wilken
V. Vanacker
K. Van Oost
K. Van Oost
author_sort H. Zhang
collection DOAJ
description <p>Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02&thinsp;m, RMSE: ca. 0.03&thinsp;m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.</p>
first_indexed 2024-12-13T18:44:04Z
format Article
id doaj.art-86ab6847f9c74982b8cf78617ac9e316
institution Directory Open Access Journal
issn 2196-6311
2196-632X
language English
last_indexed 2024-12-13T18:44:04Z
publishDate 2019-09-01
publisher Copernicus Publications
record_format Article
series Earth Surface Dynamics
spelling doaj.art-86ab6847f9c74982b8cf78617ac9e3162022-12-21T23:35:08ZengCopernicus PublicationsEarth Surface Dynamics2196-63112196-632X2019-09-01780782710.5194/esurf-7-807-2019Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detectionH. Zhang0E. Aldana-Jague1F. Clapuyt2F. Wilken3F. Wilken4V. Vanacker5K. Van Oost6K. Van Oost7Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve, 1348, BelgiumEarth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve, 1348, BelgiumEarth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve, 1348, BelgiumInstitute for Geography, Universität Augsburg, Augsburg, 86159, GermanyDepartment of Environmental Systems Science, ETH Zurich, 8092 Zurich, SwitzerlandEarth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve, 1348, BelgiumEarth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve, 1348, BelgiumFonds de la Recherche Scientifique (FNRS), Brussels, 1000, Belgium<p>Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02&thinsp;m, RMSE: ca. 0.03&thinsp;m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.</p>https://www.earth-surf-dynam.net/7/807/2019/esurf-7-807-2019.pdf
spellingShingle H. Zhang
E. Aldana-Jague
F. Clapuyt
F. Wilken
F. Wilken
V. Vanacker
K. Van Oost
K. Van Oost
Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
Earth Surface Dynamics
title Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
title_full Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
title_fullStr Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
title_full_unstemmed Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
title_short Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection
title_sort evaluating the potential of post processing kinematic ppk georeferencing for uav based structure from motion sfm photogrammetry and surface change detection
url https://www.earth-surf-dynam.net/7/807/2019/esurf-7-807-2019.pdf
work_keys_str_mv AT hzhang evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT ealdanajague evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT fclapuyt evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT fwilken evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT fwilken evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT vvanacker evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT kvanoost evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection
AT kvanoost evaluatingthepotentialofpostprocessingkinematicppkgeoreferencingforuavbasedstructurefrommotionsfmphotogrammetryandsurfacechangedetection