Unmanned Aerial Vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands

Boon, M. A.; Greenfield, Richard; Tesfamichael, Solomon G.

doi:10.4314/sajg.v5i2.7

Cited by 29 publications

(24 citation statements)

References 22 publications

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“…James and Robson, 2014;Wackrow and Chandler, 2011;Javernick et al, 2014) although the approach, has been largely overlooked in much of the geoscience literature (e.g. Boon et al, 2016;Casella et al, 2016). The results still depend on accurate pre-calibration, as errors will tend to scale with altitude of the UAS.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is little consistency, and in some cases an absence (e.g. Boon et al, 2016;Casella et al, 2016) of an examination of the camera calibration methods. Whilst a number of photogrammetry-focused studies document specifically the mitigation of errors in UAS-derived topographic modelling (Wackrow and Chandler, 2011;James and Robson, 2014;Girod et al, 2017), there is limited evidence of the incorporation of key methods such as inclusion of oblique imagery or precalibration within the broader geoscience literature.…”

Section: Introductionmentioning

confidence: 99%

“…10s -100s m) Terrestrial Laser Scanners (TLS) are increasingly used, offering millimetre range precision; in-combination with instruments such as differential GPS (dGPS) and Total Stations, they can prove to be very effective for detecting and measuring morphology and morphological change (Rosser et al, 2005;Bangen et al, 2014). The expense of airborne techniques, the lack of control over timing of national Lidar-mapping programmes, and possibly the accessibility limitations of some of these approaches has led some scientists to explore the potential of Unmanned Aerial Systems (UASs) for the acquisition of high resolution imagery and elevation mapping through low-altitude aerial photogrammetry (Boon et al, 2016;Clapuyt et al, 2016;Fonstad et al, 2013;Mancini et al, 2013;Ouédraogo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of pre- and self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application

Griffiths

Burningham

2018

Progress in Physical Geography: Earth and Environment

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Structure from Motion (SfM) is a tool being increasingly utilised in geosciences for high-resolution three-dimensional mapping of landscapes. However, a number of authors have demonstrated that broad-scale systematic deformations, in the form of ‘doming’ and ‘bowling’, can occur when applied to linear (low-amplitude, feature-limited) topographies. In such contexts, a more rigorous lens calibration and ground control point acquisition process is required, which means that application of SfM to environments such as tidal flats or desert plains can be challenging. Uncertainties in elevation models generated through SfM were investigated here in the context of the low elevation, micro-topographic environment of saltmarsh. Eight digital surface models (DSMs) were generated for a saltmarsh site in the Deben Estuary (Suffolk, UK) using imagery acquired by a low-cost consumer grade unmanned aerial system (UAS). The results provide clear illustration of the systematic bowling effect following self-calibration during bundle adjustment. This was due to poor estimations of distortion parameters in the camera model. Deformation was most pronounced when UAS-GPS data were used for georeferencing. The use of dGPS-determined ground control points improved the DSM, but did not fully mitigate the deformations. By introducing a pre-calibrated model, derived using a typical checkerboard routine, deformation was significantly mitigated. These results were tested in both the commercial Agisoft PhotoScan® and open-source Micmac software. When self-calibration was used, Micmac generated significantly more accurate DSMs because a more complex lens distortion model could be implemented. The results show that when mapping flat topographies, pre-calibration of the camera model out-performs self-calibration. However, if pre-calibration is not possible, a complex distortion model (such as Micmac’s Four model) can be utilised to limit deformation. The results of the software analysis concluded there is no one-size fits all software solution, and therefore customisable open-source systems offer many potential benefits.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of pre- and self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application

Griffiths

Burningham

2018

Progress in Physical Geography: Earth and Environment

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“…Harwin and Lucieer [45] suggest that a point cloud accurate to 25-40 mm can be obtained from imagery acquired from~50 m flight altitude. Other studies reported RMSEs of residuals of the bundle adjustment transformation on used GCPs as low as a few millimetres [27], or slightly over 1 cm [28], while the flight altitudes were higher-40 m and 120 m. Dandois and Ellis [36] conducted 3D mapping of vegetation dynamics with the use of Ecosynth and UAV imagery. The accuracy was calculated under leaf-on, and also leaf-off, conditions while using six GCPs.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy

Tomaštík

Mokroš

Saloň

et al. 2017

Forests

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This study focuses on the horizontal and vertical accuracy of point-clouds based on unmanned aerial vehicle (UAV) imagery. The DJI Phantom 3 Professional unmanned aerial vehicle and Agisoft PhotoScan Professional software were used for the evaluation. Three test sites with differing conditions (canopy openness, slope, terrain complexity, etc.) were used for comparison. The accuracy evaluation was aimed on positions of points placed on the ground. This is often disregarded under forest conditions as it is not possible to photogrammetrically reconstruct terrain that is covered by a fully-closed forest canopy. Therefore, such a measurement can only be conducted when there are gaps in the canopy or under leaf-off conditions in the case of deciduous forests. The reported sub-decimetre horizontal accuracy and vertical accuracy lower than 20 cm have proven that the method is applicable for survey, inventory, and various other tasks in forests. An analysis of ground control point (GCP) quantity and configuration showed that the quantity had only a minor effect on the accuracy in cases of plots with~1-hectare area when using the aforementioned software. Therefore, methods increasing quality (precision, accuracy) of GCP positions should be preferred over the increase of quantity alone.

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“…Similarly, the method has been embraced by forest engineers because it can provide high-quality forest inventory data, which is otherwise very time-consuming to assess from the ground [12][13][14][15][16]. In addition, SfM has become the standard tool for high-precision mapping (orthophoto generation) of agricultural and natural ecosystems e.g., [10,17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Processing of UAV-Based Thermal Imagery

2017

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Abstract:The current standard procedure for aligning thermal imagery with structure-from-motion (SfM) software uses GPS logger data for the initial image location. As input data, all thermal images of the flight are rescaled to cover the same dynamic scale range, but they are not corrected for changes in meteorological conditions during the flight. This standard procedure can give poor results, particularly in datasets with very low contrast between and within images or when mapping very complex 3D structures. To overcome this, three alignment procedures were introduced and tested: camera pre-calibration, correction of thermal imagery for small changes in air temperature, and improved estimation of the initial image position by making use of the alignment of RGB (visual) images. These improvements were tested and evaluated in an agricultural (low temperature contrast data) and an afforestation (complex 3D structure) dataset. In both datasets, the standard alignment procedure failed to align the images properly, either by resulting in point clouds with several gaps (images that were not aligned) or with unrealistic 3D structure. Using initial thermal camera positions derived from RGB image alignment significantly improved thermal image alignment in all datasets. Air temperature correction had a small yet positive impact on image alignment in the low-contrast agricultural dataset, but a minor effect in the afforestation area. The effect of camera calibration on the alignment was limited in both datasets. Still, in both datasets, the combination of all three procedures significantly improved the alignment, in terms of number of aligned images and of alignment quality.

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Unmanned Aerial Vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands

Abstract: Abstract

Cited by 29 publications

References 22 publications

Comparison of pre- and self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application

Comparison of pre- and self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application

Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy

Optimizing the Processing of UAV-Based Thermal Imagery

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