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 m, RMSE: ca. 0.03 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.
Introduction
During the past decade, unmanned aerial vehicles (UAVs) or unmanned aerial
systems (UASs) have emerged as a very valuable tool for aerial surveying (Passalacqua
et al., 2015; Tarolli, 2014). An important application in geoscience is the
generation of high-resolution topography (HRT) data (i.e., point clouds,
digital surface models – DSMs – or digital elevation models – DEMs) from 2-D
imagery using structure-from-motion (SfM) and multi-view stereo (MVS)
photogrammetry (Eltner et
al., 2016; James and Robson, 2012). Compared to satellite- or airborne-based
sensing approaches, UAVs provide important advantages; more
specifically, they provide a considerably higher spatial resolution at a
relatively low cost in combination with high versatility in terms of
sensors and data collection. With the capability of detecting topographical
change at a very high resolution and accuracy, the UAV–SfM framework has
become an increasingly used tool for the monitoring of landslides (e.g.,
Clapuyt et al., 2017; Turner et al., 2015), overland flow erosion (e.g.,
Eltner et al., 2017; Pineux et al., 2017), river dynamics (e.g., Hemmelder et al., 2018)
and vegetation dynamics (e.g.,
Candiago et al., 2015).
However, the intercomparison of UAV–SfM photogrammetric products requires
very accurate georeferencing. So far, the use of ground control points
(GCPs) surveyed with precise GPS systems or total stations is generally
employed for accurate positioning. The GCP-based georeferencing method has
been widely proven to be a solid solution for accurate georeferencing (Hawkins,
2016; James et al., 2017; Turner et al., 2016). However, GCPs need to be
placed as a network, and this comes at a cost as it is time-consuming.
Furthermore, the accuracy depends on the quantity and distribution of GCPs (Sanz-Ablanedo et al., 2018). When used in a
monitoring study, additional issues arise from the fact that GCPs can move
(weather impact or surface deformations). Finally, a major limitation arises
from the fact that GCPs cannot be placed in poorly accessible terrain due
to practical or safety reasons (e.g., swamps, landslides or glaciated
areas).
Direct georeferencing based on high-precision GNSS is key to overcoming this
issue, but it requires the accurate geotagging of aerial images at the
exposure time. During the last several years, the development of
high-quality inertial measurement unit (IMU) and global navigation satellite system (GNSS) technology as well as dedicated RTK (real-time kinematic)
and PPK (post-processing kinematic) solutions for UAVs has enabled the
accurate measurement of UAV–camera position and orientation. By double
differencing the phase ambiguities between two GNSS–GPS receivers,
atmosphere propagation delay and receiver clock errors can be eliminated.
RTK positioning requires a stable radio (or internet) link between a base
and the UAV, and this can sometimes be challenging due to radio link outages
and/or GNSS signal blocks. PPK, in contrast, processes the information after
the flight and there is thus no risk of data loss due to link outages. In
addition, precise ephemeris data of GNSS satellites are available during post-processing, which can often provide a more accurate solution. The
utilization of such an approach has the potential to avoid or mitigate the
need for GCPs. Several studies already investigated the application of
RTK–PPK direct georeferencing by the integration of sensor orientation with
onboard RTK GPS (Fazeli
et al., 2016; Forlani et al., 2018; Stöcker et al., 2017). In a study
performed by Gerke and Przybilla (2016), block orientation accuracy was significantly enhanced by using an
onboard RTK GNSS solution. With an enabled RTK GNSS and cross-flight
pattern, the best scenario reached a final horizontal geometric accuracy of
4 cm. Recently, both georeferencing methods have gradually matured and can
deliver centimeter-level accuracy in geomorphological applications (Table 1). However, to our knowledge the accuracy and repeatability of HRT products
derived from RTK–PPK in the context of longer-term 4-D Earth surface
monitoring with time-lapse structure-from-motion photogrammetry has not been
quantified.
Summary of positional accuracy assessments conducted in various
published studies.
AuthorsValidation methodFlight height and GSDMotivation and applicationAccuracyGeoreferencing methodTurner et al. (2012b)Check points50 m, ca. 1 cm px-1Accuracy assessmentMean absolute horizontal accuracy of 0.66 and 1.25 mDirect georeferencingwith single GPSMean absolute horizontal accuracy of 0.10 and 0.13 mGCPHarwin and Lucieer (2012)Check points30–50 m, 1–3 cm px-1 (after down-sampling)Coastal erosionHorizontal RMSE of 0.001–0.083 m Vertical RMSE of 0.04–0.06 mGCPOuédraogo et al. (2014)DEM of differenceMaximum of 100 m,3.3 cm px-1Agricultural soil microto- pographyMean absolute difference of 0.074 mGCPUysal et al. (2015)Check points60 m, 5.2 cm px-1Accuracy assessmentMean vertical accuracy of 0.062 mGCPFazeli et al. (2016)Check points120 m, 2.38 cm px-1Accuracy assessmentMean horizontal accuracy of 0.132 m Mean vertical accuracy of 0.203 mDirect georeferencingwith RTK GPSClapuyt et al. (2016)DEM of difference50 m, 0.43–0.77 cm px-1Reproducibility assessmentMean absolute error of 0.06 mGCPStöcker et al.n(2017)Check points100 m, 2.8 cm px-1Accuracy assessmentMean accuracy on X, Y and Z: 0.217, 0.186 and 0.053 mDirect georeferencingwith RTK GPSGlendell et al. (2017)DEM of difference23–40 m, 0.6–1.1 cm px-1Upland soil erosionRMSE of DoD from 0.05 to 0.35 mGCPForlani et al. (2018)Check points90 m, 2.3 cm px-1Accuracy assessmentMean horizontal accuracy of 0.024 m Mean vertical accuracy of 0.046 mDirect georeferencingwith RTK GPSCheck pointsMean horizontal accuracy of 0.015 m Mean vertical accuracy of 0.023 mGCPDEM of differenceMean absolute difference of 0.125 mDirect georeferencingwith RTK GPSEker et al. (2018)Check points40 m, 0.72–0.89 cm px-1Monitoring landslideRMSE of 0.04 mGCPRossini et al. (2018)Check points110 m, 4.3–4.5 cm px-1Tracking glacial dynamicsTotal RMSE of 0.153 mGCPGrayson et al. (2018)Check points120 m, ca. 3 cm px-1Accuracy assessmentRMSE of 0.025 mGPS precise point positioning (PPP)RMSE of 0.025 mPPKRMSE of 0.022 mGCPDuró et al. (2018)Check points25 m, 2.1 cm px-1Bank erosionMean error of -0.05–0.04 mGCPPadró et al. (2019)Check points80 m, 2.5 cm px-1(RGB sensor) andenvironmental monitoringHorizontal RMSE ≤0.036 m and vertical RMSE ≤0.036 mPPK5 cm px-1 (multispec-tral sensor)Horizontal RMSE ≤0.023 m and vertical RMSE ≤0.030 mGCP
The accuracy and precision of photogrammetry depends on many other factors,
including image quality, camera calibration, flight plan characteristics,
SfM algorithms, and surface texture and albedo. The bundle block adjustment
(BBA) process determines the 3-D positions of key features and points presented
in the overlapping part of multiple images by recognizing and matching
key points (hereafter referred to as tie points, i.e., key points that can be
identified on two or more images). In a next step the relative locations and
orientations of the camera are estimated by performing a fit and minimizing
the error through the tie points (Triggs et al., 2000).
The abovementioned factors affect the identification of the tie points,
which are infrequently reported but important nevertheless. Therefore, the
accuracy of traditional photogrammetric data depends heavily on control
quality, whereas SfM accuracy is also strongly affected by image
characteristics (Mosbrucker et al.,
2017).
The selection and configuration of cameras are of special interest in UAV
photogrammetry. Digital cameras equipped with high-quality sensors (e.g., a
DSLR camera) provide better image quality due to higher resolution and
reduced image noise relative to more portable and smaller sensors (e.g., a
compact or action camera), and this results in high-quality DSMs (Eltner
and Schneider, 2015; Micheletti et al., 2015; Mosbrucker et al., 2017). The
focal length relates to radial distortion and associated calibration of the
camera lens (Rosnell and
Honkavaara, 2012; Sanz-Ablanedo et al., 2012). While small focal length (or
wide angle) leads to a large field of view (FOV), which therefore requires a
less dense flight plan for a given lateral overlap, these images are subject
to increased radial distortion, which can degrade accuracy (James and Robson,
2014; Mosbrucker et al., 2017). Some studies have investigated the impact of
focal length on DEM accuracy (Clapuyt et al.,
2016) but mainly on DEM reproducibility. Furthermore, the distance between
the sensor and the surface also determines ground sample distance (GSD), which
impacts accuracy. Eltner et al. (2016) showed in a review of 54 studies
that the error of SfM-derived DSMs increased nonlinearly with an increasing
surface to camera distance (Eltner et al.,
2016). From an operational point of view, camera weight is a critical
variable as it determines the size and weight of the UAV system. There is a
large difference in weight between DSLR (0.5–1.5 kg) and action cameras
(0.05–0.15 kg), and this has large implications, not only for flight
autonomy (and hence spatial coverage), but also the choice of the UAV
platform. Small action cameras can be mounted on small “micro-drones”, which
are subjected to less stringent UAV flight regulation (e.g., in Belgium, a
UAV operation certificate allows for a maximum flight height of 45 m and a
weight limit of 5 kg, UAV + payload).
The quality of UAV survey output is typically analyzed using the spatial
patterns of errors in DSMs, and this includes both the accuracy and the
reproducibility of DSM generation. Errors propagate when differences of DSMs
(DEM of differences, DoDs) are computed to quantify topographic change.
Given the uncertainty inherent in individual DSMs, how to distinguish real
geomorphic changes from noise and how well these uncertainties are
considered control the reliability of interpretation. In order to isolate
and quantify the uncertainty that is associated with the topographic
reconstructions, reproducibility assessments are critical aspects of
monitoring landform changes over time (Brasington
et al., 2000; Wheaton et al., 2010). However, until now the repeatability of
direct PPK-based georeferencing for SfM-derived point clouds and/or DSMs has
not been thoroughly evaluated. Past research has shown that a RTK-SfM
workflow is repeatable (Forlani et
al., 2018), but the analysis was based on repeated flights conducted over a
very short time frame: i.e., with very similar satellite constellation, base
station setup and light conditions. It remains uncertain to what extent a
PPK–SfM workflow may provide consistent 4-D data when survey conditions are
variable, e.g., when monitoring over longer periods of time (e.g., weeks or
even months). This is particularly relevant for geomorphological
applications that require centimetric precision such as rill erosion or soil
roughness monitoring (d'Oleire-Oltmanns et al., 2012;
Eltner et al., 2015). A second issue is the platform: low-cost,
easily deployable, RTK-enabled micro-UAVs (small form ca. 25×25 cm, weight 1.4 kg) equipped with small cameras have recently become
available, but their accuracy and repeatability, relative to professional
UAV systems (large form ca. 80×80 cm, weight 4.5 kg) equipped with
high-end cameras, remain poorly quantified. In particular, the influence of
the UAV–camera setup on the minimum level of topographical change detection
should be quantified in order to guide geomorphological applications.
The main objective of this study is thereby to quantify the (i) repeatability, (ii) reproducibility and (iii) efficiency of the PPK–SfM
framework in the context of 4-D Earth surface monitoring with time-lapse
structure-from-motion photogrammetry, for which centimetric precision is
required. More specifically, we aim to (i) assess the accuracy and
repeatability of PPK and non-PPK solutions in georeferencing to examine the
capability of using PPK without the need for GCPs, (ii) assess the
reproducibility of surface topography change detection using PPK solutions
for two different UAV–camera setups (i.e., a DSLR camera versus a high-FOV
action camera), and (iii) evaluate different approaches to estimate
uncertainties using PPK solutions and their implications for surface change
detection.
Material and methodsStudy site
The study site is located in an agricultural area (1.7 ha) in the Belgium
loess belt ca. 40 km southeast of Brussels, Belgium (Fig. 1). It is
characterized by a slightly undulated terrain with an altitude range between
207 and 210 m a.s.l. and by very gentle slopes (mean slope: 1∘).
The site is partially cultivated, while other parts are covered by grass. The
surface was classified into five classes: i.e., bare soil, short grass,
shrub, road and haystacks.
We evaluated (i) a high-payload UAV system equipped with a DSLR camera and
(ii) a consumer-grade UAV equipped with a fish-eye action camera. The
high-payload aerial system is a custom-built Hexacopter and is equipped with
a DJI A2 flight controller. The platform has an effective payload of 4 kg
and an autonomy of ca. 15 min. This UAV was equipped with a Canon EOS
550D camera (18 megapixels, 5184×3456 pixels, with Canon EF 28 mm
F/2.8 lens). The consumer-grade UAV was a DJI Phantom 3 Advanced Drone. We
removed the DJI camera–gimbal system and mounted a GoPro Hero 3 camera
(12 megapixels, 4000×3000 pixels, with 2.92 mm F/2.8 123∘
HFOV lens) (Fig. 2). Both platforms are equipped with a compact multi-GNSS
RTK receiver (Reach RTK kit, Emlid Ltd) with RTK–PPK capability as described
below.
Experimental setup. (a) UAV–camera setup: DSLR camera (EOS
550D) mounted on RPAS type Y6, action camera (GoPro Hero 3) mounted on a
Phantom 3 Advanced. (b) Parallel flight lines (top: RPAS type Y6 with DSLR
camera; bottom: Phantom 3 with action camera) and GCP–CP distribution (c).
How GCPs–CPs are displayed in the images and (d) measurement of GCPs–CPs.
PPK GPS module
During the UAV flights, a Reach RS (Emlid Ltd) base station was mounted on a
tripod located in the north of the test area to provide positioning
correction input. The maximal distance between the UAV and the base station
was 220 m. The receiver of the base is configured to log the raw data in a
RINEX file at 5 Hz using the satellite GPS, GLONASS and GALILEO. We did not
use a fixed position for the base station but randomly positioned it in an
area of ca. 10×10 m for each flight. Both UAVs were equipped with
a Reach GNSS receiver to log the raw data as UBX format using GPS and
GLONASS satellites. The antenna model was a Tallysman TW2710, which covers
the GPS L1, GLONASS G1, BeiDou B1, Galileo E1 and SBAS (WAAS, EGNOS and
MSAS) frequency bands. The antenna was mounted on an aluminum plate, with
the center right above the camera lens center to minimize the offset
shift between the antenna phase center and camera projection center. The
antenna height was 22.5 cm, and this difference between the antenna and
camera projection center was considered during the post-processing. No
lever-arm corrections were considered, but the offset between the camera and
the GPS receiver was considered in the camera position assuming a constant
vertical offset (see below). Because of the small magnitude of the physical
offset vector (0, 0 and 22.5 cm in X, Y and Z in the body frame,
respectively), typical tilting during flights would only propagate to a
camera position error of about 1 cm, which is close to the expected GPS
positioning error of about 2–3 cm.
For the high-payload UAV, we used the hot shoe of the camera to time-mark the
pictures with a GPS event logged on a Reach GNSS device mounted on
the UAV. As the action camera has no hot shoe, we built an electronic system
to integrate and synchronize the GPS with the action camera. To this end, a
single-board computer (SBC) is used as a trigger by transmitting an
electrical signal to both the camera and GPS unit. To eliminate the lag
between the shutter opening time of the camera and the GPS recording time,
we quantified the delay between the electrical signal and the shutter
opening by integrating an LED light in the circuit. Several delay times were
tested until the LED light was visible on the images taken by the action
camera. This procedure resulted in a system in which the geotagging was
accurately synchronized with the GPS time. For both UAV–camera systems, we
did not build a link between the UAV–IMU and camera. As a result, the images
only contained positioning information without attitude parameters.
Data collectionFlight planning
Flight missions were planned using the Autopilot app (Hangar Technology,
2018). The side overlap was set to 80 %. The frontal overlap was defined
by the speed of the UAV and the camera trigger interval, which was set
at 2 s for the DSLR camera and 4 s for the action camera; this resulted in a frontal
overlap of ca. 90 % for both systems.
Flight mission arrangements are summarized in Table 2. Three flights
(including repeated flights) were conducted before a part of the study area
was plowed. These flights were conducted at a constant height above the
take-off point, leading to a ground sample distance (GSD) of less than 0.63 and 3.11 cm for the DSLR and the action camera, respectively. It should
be noted that the missions were performed using a simple parallel rather than
cross-hatch flight pattern, as the latter mission setup can mask systematic
bias.
Overview and key parameters of flight missions.
CameraDateMissionFlightSpeedAreaSatelliteGroundNumbernumberheight(m s-1)coveredPDOPsamplingof(m)(ha)valuedistanceimages(cm px-1)Before plowingDSLR29 March 2018F1453.43.751.30.6323camera5 April 2018F2_a453.43.261.20.6360(EOS)F2_b453.43.261.20.6362Action29 March 2018F1_a453.411.331.33.1134cameraF1_b453.413.271.23.1155(GoPro)30 March 2018F2453.412.051.43.1137After plowingDSLR camera6 April 2018F3_a353.00.851.30.5129(EOS)F3_b353.00.81.20.5107Action camera6 April 2018F3_a202.63.231.21.3182(GoPro)F3_b202.63.011.21.3162
Note: repeated flight missions were marked as F_a and
F_b. The missions shown in the list used a parallel
flight plan.
Ground control points
A total of 16 fixed targets were distributed evenly across the study area before
the survey as control points (Fig. 2). Depending on the georeferencing
methods used (see below), the control points were applied as ground control
points (GCPs) or check points (CPs). The targets consisted of a laminated
square board (0.3 m × 0.3 m) painted in yellow and a black cross
marker in center. They were fixed with nails into the ground and remained at
the site for the study period before plowing. For the last flight mission
after plowing, new GCPs were deployed and surveyed. The targets were
surveyed after each flight mission using a Reach RS (RTK solution) with the
EUREF-IP network. The correction stream was provided by BRUS station
(Brussels, Belgium; antenna: ASH701945B_M) via NTRIP
(Networked Transport of RTCM via Internet Protocol), which had a mean
planimetric error of 0.007 m and altimetric error of 0.013 m
(https://emlid.com/, last access: 1 January 2019). Based on repeated measurements of field GCP
coordinates, the planimetric precision was estimated at 0.015 m, while the
altimetric precision was 0.023 m. It should be noted that this assessment
includes minor (G)CP movement induced by rainfall kinetic energy and soil
swelling and shrinking. The coordinate system was referenced to the World Geodetic
Datum of 1984 (WGS84).
Data processingGeoreferencing configuration
The open-source software package RTKLib was used for computing differential
positioning (Takasu and Yasuda, 2009). Raw
GPS data from the UAV-mounted cameras and the base station were then
extracted and corrected by post-processing using RTKLib. We verified the
consistency of the estimated camera positions using PPK by evaluating
different satellite elevation masks (15 and 20∘) and
methods (i.e., “fix-and-hold” versus continuous mode).
We extracted PPK GPS and single GPS solutions for the camera position
estimates. To assess the accuracy of different georeferencing options,
datasets were processed with four configurations, i.e., single GPS,
single GPS+GCPs, PPK only and PPK+1 GCP.
For the conventional methods using GCPs and a single GPS, we used the RTKLib
single GPS solution to acquire the image coordinates and selected half of
the targets as 3-D GCPs during block control processing. The remaining
control points were then used as check points. The setup of GCP–CP is shown
in Fig. 3. In the single GPS+GCPs scenario, the eight selected GCPs were evenly
distributed in the survey area. In the PPK+1 GCP scenario, cross-validation was
used. We selected one point as a GCP, while the remaining targets were then
used as CPs, and this bundle adjustment processing was repeated 16
times. The accuracy assessment was based on the average error of the
cross-validation.
Distribution of GCPs and CPs and illustration of the different georeferencing configurations: single GPS, single GPS+8 GCPs, PPK, PPK+1 GCP. Note: cross-validation was implemented in the PPK+GCP configuration; i.e., one single control point was used as a GCP in each processing.
Point cloud and DSM generation
The geotagged images were processed with the Pix4D Mapper software
(https://www.pix4d.com/, last access: 8 August 2019). The software uses the SfM algorithm to generate 3-D point
clouds, DSMs and orthophoto mosaics of the surveyed area. The procedure
consists of three main steps: (i) initial processing, (ii) point cloud
generation, and (iii) DSM and orthomosaic generation. First, the photographs
are aligned using a point matching algorithm that automatically detects
matching points on overlapping photographs and uses these points to
simultaneously solve for exterior orientation (EO) parameters. With
additional position information that is available for the images or GCPs,
the software then georeferences the model and refines the camera calibration
by minimizing the error between the modeled locations of the points and the
measured locations; meanwhile, nonlinear deformations within the model are
corrected.
Camera accuracy is a key parameter allowing users to set how accurate the
coordinates of images can be, which would affect the determination of
estimated camera positions in the BBA process. Considering the precision of
PPK GPS (ca. 0.02 m) and the antenna angle movement caused by UAV attitude
during flying, we set both the horizontal and vertical accuracy as 0.05 m.
We used the Pix4D 3-D map template for the remaining settings, i.e., a full
key point image scale, an automatic targeted number of key points and a standard
calibration method. In order to maintain the characteristics of the original
data, the clouds were not filtered or smoothed. Gridded DSMs were then
generated based on the mean altitude of these point clouds. The 3-D outputs
(i.e., point clouds and DSMs) used for reproducibility assessment were
georeferenced using the PPK method (and no GCPs were considered). The
corresponding grid resolutions of the DSMs were less than 0.031 m for the
action camera and 0.006 m for the DSLR camera.
Data analysisAccuracy assessment
Absolute accuracy validation was performed using the CPs (which were not
used in the BBA process) by comparing the coordinates of the 16 CPs in the
3-D cloud with the reference values measured in the field by RTK GNSS. The mean
absolute error (MAE), the root mean square error (RMSE) and standard
deviation of the differences were computed for each flight to (i) assess
the accuracy of SfM outputs with different georeferencing configurations,
(ii) assess the precision of PPK–SfM reconstruction considering CPs as
static references during the observation period (i.e., with variable
satellite constellation, light conditions and base station setup), and (iii)
detect whether there are internal systematic shifts and block deformations
in the SfM output.
Precision maps based on Monte Carlo simulation
To demonstrate how tie point uncertainty can vary spatially, we implemented
a Monte Carlo approach that enabled precision maps to be produced when using
SfM-based software. Following the workflow by James et al. (2017), the
processing was implemented using a combination of PhotoScan Professional
(v1.2.4; for image processing and bundle adjustment), Python (integrated
into PhotoScan for Monte Carlo execution) and sfm_georef
(v3.1; James and Robson,
2012, for visualization of results). To construct the image network, images
were automatically matched and oriented in PhotoScan using the “align
images” function. During the alignment process, the georeferencing was
achieved by PPK positioning camera coordinates without GCP reference. The
subsequent Monte Carlo analyses were carried out in PhotoScan using a
Python script to automate repeated bundle adjustments. The simulated
pseudo-random error (camera accuracy) was set as 0.05 m considering the
precision of PPK GPS and the antenna movement caused by drone attitude. The
Monte Carlo processing comprised 1000 iterations for each survey.
Afterwards, the results from all iterations are compiled to give
distributions of determined values for all estimated parameters (e.g.,
coordinate values for each sparse point). To construct 3-D precision maps,
point coordinate standard deviations in X, Y, and Z directions are
calculated for each point and interpolated onto a grid, generating a raster
map representing the spatially variable precision of tie points. For both
camera datasets, we obtained precision maps for each survey and compared
their range with CP observation precisions (i.e., precision of CP residuals)
from the repeated surveys by extracting values from corresponding CP
positions.
Repeatability and reproducibility assessment
To robustly distinguish real changes in DSM–DEM differencing from the
inherent noise (Fuller et al., 2003),
DoD uncertainty must be considered. Regardless of the approach used to
generate DSM–DEMs, the process of accounting for DoD uncertainty follows a
consistent progression via three steps: (i) quantifying the error surface
(δz) of each individual DSM surface, (ii) propagating the identified
uncertainties into the DoD (δuDoD) and (iii) assessing the
significance of the propagated uncertainty (Wheaton et al., 2010). The tie
points differ between each repetition of the survey, and therefore we
analyze the error propagation at the DSM level. There are two primary ways
to build an error surface. The combined error can be calculated as a single
value for the entire DoD based on the average RMSE of each DEM if
spatially explicit estimates of the error do not exist. This method assumes
that the errors in each cell are random and independent. Alternatively, a
spatially variable error can be considered for both DEMs independently
(e.g., Wheaton et al., 2013). The
individual error in the DSMs can be propagated into the DoD as
δuDoD=(δzref)2+(δzcomp)2,
where δuDoD is the propagated error in the DoD as a minimum level
of detection threshold (LoDmin), and δzref and δzcomp are the individual error in the referenced DSM and compared DSM,
respectively.
To define a spatially variable confidence interval associated with each
measurement and combining the uncertainties, a prescribed confidence level
(95 % in the following) is used to locally estimate the measurement
accuracy and precision. The registration error (reg) is considered and assumed
isotropic and spatially uniform, as there may be systematic bias in georeferencing (e.g., Brasington et
al., 2003; Wheaton et al., 2013). Equation (1) can be hereby modified to
LOD95 %=±1.96δzref2+δzcomp2+reg,
where reg is the relative overall registration error between the surveys.
LoD95% is the level of detection at the 95 % confidence interval.
UAV-based monitoring of surface change
As mentioned above, the farmland was plowed on 6 April, leading to surface
roughness and volume change. Surveys implemented before and after the
plowing were compared to detect the change. In this case study, the PPK dataset
was potentially subjected to higher reg error, while the PPK+1 GCP scenario might be able
to substantially mitigate the reg error. Therefore, a reg value was used based on
the CP RMSEs for the PPK dataset, whereas the PPK+1 GCP dataset was regarded with
negligible reg error. In that case, an assessment of the constant and
spatialized LoD was carried out: a DoD using a survey-wide LoD based on the
Z RMSE on CPs and a spatialized LoD based on the Monte Carlo altimetric
precision. A zoomed-in area as well as a transect was sampled to illustrate
the surface change using the LoDmin thresholds (Fig. 6). The sediment
budget was subsequently assessed using the Geomorphic Change Detection (GCD)
software (Wheaton et al., 2010). The
GCD software provides the capability of segregating and quantifying
uncertainties independently in each DEM and propagating them through to the
DEM of difference. For each DEM, we set the two previously mentioned uncertain
surfaces in the change detection between surveys.
ResultsAccuracy and precision of the georeferencing methods
Table 3 summarizes the average (i.e., considering all the flights) check
point accuracy and precision ranges in the X, Y and Z directions for each
block control configuration. For the DSLR surveys, the single GPS configuration
provided an average planimetric and altimetric RMSE of 1.59 and 3.45 m,
respectively, while the RMSEs for the other three georeferencing
configurations were all below 0.036 m. For PPK, the altimetric CP RMSE was
0.036 m, and the average was only slightly (ca. 20 %) improved when adding
one GCP. For the action camera, the CP RMSEs for the GCP solution are better than
1 pixel for the X, Y and Z coordinates. The CP RMSEs for the PPK solutions were
slightly higher than for the GCP solution but in the range of 0.5–1.4 pixels
for the X, Y and Z coordinates. The values reported here are very close to
the estimated error of the PPK solution. When using single GPS+GCP, the accuracy was
substantially enhanced to the centimeter level, and the absolute mean errors were less
than 0.028 m. For both cameras, PPK+1 GCP showed similar planimetric accuracy and
better altimetric accuracy. Adding one GCP improved the accuracy of the
altimetric CP RMSE of the PPK solution by 20 % to 30 %. The standard
deviation for the mapping errors (SD error) is very similar for both
cameras and the PPK and GCP solutions (0.02–0.04 m), while as expected, the precision
was not as good for the single solution (0.24–0.35 m).
Mean absolute error (MAE), standard deviation of error (SDE) and
root mean square error (RMSE) on check points for horizontal
and vertical coordinates for the different configurations (datasets: all
flights listed in Table 2).
Note: we used one survey for different configurations in the case that the errors
were averaged. Standard deviation of error (SDE) is reported to the 95 %
confidence level (1.96σ).
PPK accuracy and repeatability
Figure 4 shows the CP residual distributions for each survey for the PPK
solution. As reported above, the overall accuracies and precisions among the
surveys were robust within a range of 0.10 m, regardless of dates and
missions. The planimetric accuracies of the surveys were robust (with little
bias) and the errors were close to zero. In contrast, the altimetric
accuracy showed a much higher uncertainty among the surveys, with substantial
bias for some flights. Similar results were obtained for both camera setups;
i.e., altimetric errors showed larger variation than planimetric errors. We
can also express the accuracies in pixels to standardize the RMSEs in terms
of the expected error incurred from GSD. The CP XYZ RMSEs for the DSLR
camera correspond to ca. 4–15 pixels. However, it should be noted that the
GSD for the DSLR camera is extremely fine (0.006 m) due to the low flight
height, and this is much finer than the width of the markers used on the CP
(0.02 m) or the precision of the CPs. As a result, the XYZ RMSEs for the
action camera were better and within a range of 1 to 5 pixels.
Distribution of CP residuals on the X, Y and Z directions of
each survey. MAE and RMSE in the legend indicate mean absolute error and
root mean square error of the XYZ direction. Units are given in meters as well
as pixels to standardize results in terms of the expected error incurred
from the GSD at the corresponding flight height.
PPK precision
For soil surface change detection, it is important to quantify the precision
of each surface. Here, we compare different methods to quantify precision.
Figure 5 shows tie point precision maps derived from the Monte Carlo (MC)
simulations. Spatial patterns can be observed from the DSLR precision map,
where shrub areas have higher uncertainties, and non-vegetated areas were
modeled more precisely. The DSLR dataset had a much better precision and
smaller range (0 to 0.05 m) when compared to the MC simulations for the
action camera dataset. For the action camera, the precision ranged between 0
and 0.25 m. In contrast to the results obtained for the DSLR camera, the
precision map for the action camera did not show a clear structured spatial
pattern. The box plots represent the CP-derived precision based on the five
repeated surveys (16 CPs were used in each survey) (Fig. 5c and d). The
DSLR precision maps derived from the MC simulations are in line with the
empirical precision derived from the CPs (i.e., 0.01 to 0.03 m). The
slightly higher mean and range obtained for the empirical precision reflects
the fact that for the MC analysis, only uncertainty in camera position was
considered, while the empirical estimates reflect all sources of variability
(i.e., positioning uncertainties, differences in image quality between
surveys, etc.). In contrast, the action camera MC precision was
substantially higher than the precision derived from the repeated CP
surveys. In other words, the observational precision estimates were smaller
than those estimated from the MC analysis.
Precision maps derived from Monte Carlo simulation. (a) MC
map of DSLR camera, dataset: F3_a. (b) MC map of action
camera, dataset: F2_b. (c) Box plots of MC precision of DSLR
surveys (area of interest). (d) Box plots of MC precision of action camera surveys (area of interest).
Based on the MC precision maps, spatially propagated error estimates can be
generated for the repeated surveys (Fig. A1). The spatially distinct errors
can be quantified: shrubs had a larger error of detection (0.031 m). The
distribution of errors also showed lower precision for shrubs. For the rest
of the surface types, the MC precision was around 0.02 m. For the action camera
dataset, no clear spatial pattern was found, and a spatially uniform
precision is therefore a good approximation.
Soil surface change detection
In order to illustrate the potential of PPK in high-resolution surface
change detection, we evaluate various approaches and camera setups. At the
end of the monitoring period, the surface of the study area changed
substantially as a result of plowing. The DSMs of the plowed area (before
and after plowing) were analyzed (Fig. 6). For the PPK datasets, when the mean
XYZ RMSE was used to estimate the registration error, the threshold was high
and substantially reduced the sensitivity in change detection. For the PPK+1 GCP
datasets, we observed that the bias (particularly in the Z direction) was
removed, and we therefore set the registration error to zero. We then applied
both a spatially uniform DoD threshold (based on the CP RMSEs) and a
spatially variable error surface (based on MC precision). Note that due to
the different flight altitudes (35 and 20 m for the DSLR and action
camera, respectively), the DoD thresholds were similar for both cameras.
Similar change detection can be obtained using the constant DoD for both
cameras (Fig. 6a). As reported above, the MC precision showed smaller
uncertainties for the DSLR dataset than for the action camera dataset,
leading to different levels of detection. The zoomed-in area shows the
detail of the surface changes along a profile and its DoD threshold
(Fig. 6b). The DSLR camera provided much more detail than the profile
generated by the action camera. Nevertheless, a significant surface change
could be detected for both approaches when using the LoDmin threshold.
We assessed the volume changes over the area of interest while considering
the LoD thresholds. Regarding the PPK solution, the volume estimations of
the two camera datasets had significant differences due to the existing bias.
For the PPK+1 GCP datasets, when using a spatially uniform (i.e., constant average)
LoD, the DSLR dataset resulted in a total volume lowering of 8.17±2.70 m3, while a volume increase of 175.50±76.33 m3 was
detected due to changes in bulk density and the construction of ridges. The
action camera dataset evaluated the volume decrease at 6.16±2.36 m3, while 191.77±99.18 m3 accumulated (Fig. 6c). When
using MC LoD, the estimated volume of changes was 155.96±35.05 m3 for the DSLR camera and 92.60±66.5 m3 for the action camera.
Both the DSLR and action camera obtained similar estimations using constant LoD.
The MC LoD for DSLR datasets resulted in more significant surface changes
than those obtained for the action camera dataset.
Change detection based on DoD (datasets: F2,
F3_a of DSLR and action camera surveys). (a) Surface change
map. (b) Height profiles sampled at an identical location from the corresponding
DSMs before and after plowing. Line graph shows height profiles along the
sample transect (X axis: position along the transect, Y axis: surface
height). (c) Volumetric sediment budget.
DiscussionAccuracy and precision of PPK solution in direct georeferencing
The PPK direct georeferencing provided centimeter-level accuracy and
precision during a 14 d monitoring campaign during which light conditions, image
quality and GPS satellite constellation changed. This indicates that
direct georeferencing with accurate positioning is capable of replacing the
conventional ground control method and allows for the acquisition of robust
centimetric HRT data. As already indicated by many studies, a single onboard
GPS provides meter-level accuracy (Turner et al., 2012a).
The quality of GCP-based georeferencing depends on the number and
distribution of GCPs (Sanz-Ablanedo et al.,
2018). The accuracy can be improved by introducing additional and more densely
distributed GCPs, which induces a trade-off between survey time and the quality
of surface reconstruction (Eltner et al.,
2016; Smith et al., 2016). Areas with poor distributions of GCPs or lower
control precision could be vulnerable to systematic errors (James et al., 2017). For
example, in remote glacier studies (Kraaijenbrink et al.,
2016), GCPs can generally only be located at the glacier periphery, which is
unfavorable for internal accuracy. In contrast, precise direct
georeferencing of aerial surveys (kinematic GNSS) provides an evenly
distributed control framework as each image can be regarded as a control
point. Figure A2 exhibits the planimetric image residuals between the
original image positions and the optimized positions after the BBA process. This
shows that the image residuals were evenly distributed and had standard
deviations of only a few centimeters, indicating there was little bias during
the image georeferencing process. The DSLR images had smaller SD of
positional residuals than those of the action camera images, indicating that
the action camera images had higher random error regarding the BBA process.
In this study, our experiments showed that a high-quality GNSS receiver
mounted on an aluminum plate that is positioned as far as possible from the
UAV electronics can provide reliable accuracy and precision in positioning
camera locations. Initial tests showed that the GPS data quality is very
vulnerable to interferences from the UAV motors and electronics, and special
attention should be given to shielding. The PPK positioning (without GCPs)
of camera positions was shown to provide the same level of accuracy and
precision as a GCP solution in our case. Nevertheless, there might be biases
in the PPK GNSS position estimation due to false solutions that can remain
undetected (e.g., false fix in resolving ambiguities). An approach to detecting
this is to check the accordance between fix-and-hold and continuous
resolution in integer ambiguity in RTKLib). Implementing one GCP did
improve the results in our study: on average the addition of a single GCP
slightly reduced the overall RMSEs. Given that it is difficult to assess
the quality of the PPK solution without independent observation, we
recommend that using one GCP (or one single fixed point throughout the
monitoring) provides a robust way to detect perturbations of the GPS signal.
Forlani et al. (2018) balanced
the advantage of an RTK–PPK versus a GCP solution and reported that for the
RTK+1 GCP configuration, the vertical bias was greatly reduced. It should be noted
that applying one GCP only moves the overall project to the approximate
location without internal georeferencing.
DSLR versus action camera
As for the cameras we used in this study, the main differences were related
to the focal length, image resolution and quality. The action camera with
shorter focal length (2.92 mm) provides a larger field of view (diagonal
FOV: 149.2∘) but is characterized by radial lens distortions. The
vertical errors derived from 16 individual check points were all below
0.07 m, indicating that the “doming” effect can be greatly eliminated or
mitigated due to the dense and precise control of camera positions. The DSLR
camera, due to a larger APS-C-sized imager, higher focal length and higher
resolution, together with the complete control of the ISO, shutter speed and
aperture settings, produced much less noise and better overall picture
quality. These differences led to better GSD and image contrast. We observed
that this assisted greatly in recognizing and matching tie points. For
instance, at 45 m of flight height, the DSLR dataset has a higher tie point
density (mean: 213.5 points m-2) than the action camera (mean:
12.1 points m-2), and the detailed images help improve finding and matching tie
points.
To visualize the two camera setup outputs and assess the potential of soil
roughness measurement in different surface types, we derived two
representative transects (Fig. 6b). Due to a higher GSD, the DSLR-derived
data showed abundant and sharp details, while data from the action camera
were relatively smooth. It should be noted that due to the large FOV, the
action camera required a flight plan that was much less dense than for the
DSLR camera (about half), indicating that a much larger area (about double)
could be surveyed in the same time. However, this larger spatial coverage
comes at the cost of ground resolution. A lower distance between the camera
sensor and the surface is required for the action camera to obtain the same
GSD as the DSLR camera (for the GoPro and EOS cameras used in this study, the
flight height ratio to obtain the same GSD equals 1:3.5, and the consumed
time ratio was ca. 1:1.5). For the design and practical implementation of
UAV surveys, it is crucial to take the sensor weight and size into account,
as well as the payload and endurance of the UAV platform. We found that with a
light, small, highly portable and low-cost UAV equipped with a very simple
camera and RTK–PPK GPS system, very good results in terms of accuracy and
precision are possible (RMSE of ca. 1 pixel). In addition, taking advantage
of the large FOV of the compact action camera, it is feasible to cover more
area but at the cost of GSD and accuracy.
Precision estimates
We observed some inconsistencies between the MC-derived precision and
CP-derived precision estimates. The observational precision for the DSLR
dataset was slightly worse than that obtained from the MC estimates. We
attribute this to the fact that CP itself can be regarded as a key feature
that is easy to recognize in the BBA process. In addition, the
observational precision reflects all sources of uncertainty, while the MC
only considered the camera position. In contrast, the MC precision of the action
camera dataset was much lower than the CP precision, which results from the
high radial distortion feature of the high-FOV lens and the lower GSD.
To identify which factor (high GSD or low FOV) controls the precision
estimates, we preprocessed the images using two methods: (i) down-sample the
DSLR images to have the same GSD as action camera images, and (ii) clip the
action camera images to have the same FOV as DSLR images (for that, we
implemented an additional flight mission for the action camera using a
denser flight path). Precision maps were then generated using Monte Carlo
simulation (Fig. A3). With a lower GSD, the precision pattern for the DSLR
dataset remained but showed increased uncertainties. In contrast, the
clipped low-FOV action camera images revealed a clear spatial pattern for
the precision estimates. Based on this analysis, we suggest that a higher
GSD increased the robustness of the tie point matching and hence improved
the precision. The large FOV of the action camera, enabling wide imaging
angles to a single tie point, may to some extent compensate for the difficulties
in the identification of key features due to the lower GSD, at least if
appropriate model calibrations are introduced in the bundle adjustment. It
should be noted that the radial distortion induced by the fish-eye lens is
more severe on the edges of the images. This increases the uncertainties in
tie point orientation and may explain the higher magnitude of tie
point uncertainties (Fig. 5b).
Surface change detection
Using an average RMSE to estimate the registration error resulted in poor
estimates of surface change. This was related to the fact that the PPK
solution provided results with substantial bias in the Z direction for a few
flights. The repeatability assessment showed that the use of a flight-specific registration error based on one GCP could remove the bias.
Furthermore, this study showed that the approach is repeatable as both
UAV–camera setups resulted in a similar estimation of 3-D surface changes. To
obtain a robust change detection, it is crucial to set a proper uncertainty
threshold (LoD). Our results indicated that the approach to estimate the LoD
(i.e., MC-based versus CP-based) substantially affect the results,
particularly for small-scale, high-resolution applications (i.e., that
require centimetric precision). It is also important to understand the
effect of different types of surfaces on the SfM output, particularly in a
region with a “complex” surface, e.g., vegetation area, rough objects and
a surface with few key features. Vegetation has long been recognized as a
source of error in photogrammetry (Lane
et al., 2000; Messinger et al., 2016) due to the clustering of leaves,
wind-caused movement and illumination change, and this increased the
complexity of the imagery, leading to difficulties in isolating tie points (Harwin and Lucieer, 2012). Applying a
spatially explicit error threshold in topographic change detection can help
improve the reliability and sensitivity.
Our study demonstrates that the PPK positioning is a robust solution for
monitoring surface change and estimating sediment budgets at very high
spatial and temporal resolution. This technique can be very advantageous
when it comes to monitoring large areas that are poorly accessible or
require repeated surveying (Clapuyt et
al., 2017; Eltner et al., 2016). A relatively cheap RTK–PPK-enabled micro-UAV (small form 25×25 cm, weight 1.4 kg, autonomy 15 min)
provided similar accuracy and repeatability as a professional multi-rotor UAV
system (large form 80×80 cm, weight 4.5 kg, autonomy 15 min).
Based on our analysis, we suggest that using a micro-drone–action camera
setup is suitable for large-scale monitoring (e.g., gully erosion,
landslides, glaciers, etc.) when a high GSD is not required. When
considering a scene's 3-D geometry, the high FOV also assists in recording
features exposed along vertical facades (e.g., vertical cliff face) from
nadir-view photogrammetry. Furthermore, in countries with strict UAV
regulations and/or inaccessible regions (e.g., mountains) a lightweight
system can be more easily transported in the field than a large UAV system.
The DSLR camera setup can be used when high resolution is needed, for
example for soil roughness assessment, sheet and tillage erosion,
solifluction, and riverbank erosion. Finally, a key step in PPK positioning
is to obtain GPS data from a stationary base station. In this study, we used
an internet-enabled system to geolocate the base station for each flight.
For areas where internet is absent or unreliable and long-term
monitoring is required, we suggest setting up a permanent reference point
that can be used to position a local base station (e.g., a concrete pole).
Conclusions
The UAV–SfM framework is increasingly used in geomorphology to accurately
capture the Earth's surface. Our study showed that the application of PPK
(post-processing kinematic) in direct georeferencing can provide centimeter-level
accuracy and precision, which results in a greatly improved field survey
efficiency. Furthermore, it is a robust method that was demonstrated to be
repeatable among multiple dates and surveys. We investigated the positional
accuracy and the repeatability of DSMs by repeating the same flight plans.
The PPK solution had a similar accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m)
as the traditional approach using georeferencing based on GCPs.
Nevertheless, some flights were characterized by a vertical shift that
could be mitigated using a single GCP. We also evaluated two UAV–camera
setups (with differences in UAV size and weight, portability, camera focal
length, resolution, and sensor quality) and showed that the tie point
uncertainties are very different. Nevertheless, 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. Using low-altitude flights (<45 m) it is possible
to detect surface change using a PPK–SfM workflow with a threshold below 5 cm, even with a low-cost action camera. Precision estimates are critical to
assess significant changes between two surfaces. We evaluated different
methods to estimate precision and registration errors and found that Monte
Carlo simulations (James et al., 2017), in which the camera position uncertainty
is considered, provide a robust way to estimate spatially explicit LoD
thresholds for low-FOV cameras. Overall, the PPK–SfM workflow overcomes some
of the main limitations of GCPs and provides a high-precision and
high-efficiency solution in surveying and geomorphological applications.
Data availability
All data used and produced through this study
are available upon request.
Distribution of the propagated error derived from Monte
Carlo simulation (datasets: F3_a and F3_b of
DSLR surveys; surface classification shown in Fig. 1c).
Residuals on the images and CPs in planimetric view.
Vectors give the horizontal residual component magnified by ×500
for the DSLR survey (a) and ×100 for the action camera survey (b). Inset: mean value and standard deviation of the image residuals.
Monte Carlo Precision maps. (a) Dataset: down-sampled DSLR
images with equal GSD as action camera images. (b) Dataset: clipped action
camera images with equal FOV. Note: the additional action camera flight
mission (right) was conducted 1 year later and the surface had slightly
changed, but there was a spatial pattern.
Author contributions
HZ, EA and KVO designed the study and
contributed to fieldwork. HZ and KVO performed data analysis. All authors
offered advice on data analysis and contributed to paper preparation.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Mike James, Joan-Cristian Padró and anonymous reviewers for their constructive feedback that improved the paper. We are also thankful to Richard Gloaguen and the editors for their constructive comments and careful review.
Financial support
This research has been supported by the China Scholarship Council (grant no. 201706300034) and the BELSPO Stereo Programme (RAPAS Project) (grant no. SR/00/328).
Review statement
This paper was edited by Richard Gloaguen and reviewed by Mike James, Joan-Cristian Padró, and one anonymous referee.
ReferencesBrasington, J., Rumsby, B. T., and McVey, R. A.: Monitoring and modelling
morphological change in a braided gravel-bed river using high resolution
GPS-based survey, Earth Surf. Proc. Land., 25, 973–990,
10.1002/1096-9837(200008)25:9<973::AID-ESP111>3.0.CO;2-Y, 2000.Brasington, J., Langham, J., and Rumsby, B.: Methodological sensitivity of
morphometric estimates of coarse fluvial sediment transport, Geomorphology,
53, 299–316, 10.1016/S0169-555X(02)00320-3, 2003.Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., and Gattelli, M.:
Evaluating multispectral images and vegetation indices for precision farming
applications from UAV images, Remote Sens., 7, 4026–4047,
10.3390/rs70404026, 2015.Clapuyt, F., Vanacker, V., and Van Oost, K.: Reproducibility of UAV-based
earth topography reconstructions based on Structure-from-Motion algorithms,
Geomorphology, 260, 4–15, 10.1016/j.geomorph.2015.05.011, 2016.Clapuyt, F., Vanacker, V., Schlunegger, F., and Van Oost, K.: Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models, Earth Surf. Dynam., 5, 791–806, 10.5194/esurf-5-791-2017, 2017.
d'Oleire-Oltmanns, S., Marzolff, I., Peter, K., and Ries, J.: Unmanned aerial
vehicle (UAV) for monitoring soil erosion in Morocco, Remote Sens., 4,
3390–3416, 2012.Duró, G., Crosato, A., Kleinhans, M. G., and Uijttewaal, W. S. J.: Bank erosion processes measured with UAV-SfM along complex banklines of a straight mid-sized river reach, Earth Surf. Dynam., 6, 933–953, 10.5194/esurf-6-933-2018, 2018.Eker, R., Aydın, A., and Hübl, J.: Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study, Environ. Monit. Assess., 190, 28, 10.1007/s10661-017-6402-8, 2018.Eltner, A. and Schneider, D.: Analysis of Different Methods for 3-D
Reconstruction of Natural Surfaces from Parallel-Axes UAV Images,
Photogramm. Rec., 30, 279–299, 10.1111/phor.12115, 2015.
Eltner, A., Baumgart, P., Maas, H., and Faust, D.: Multi-temporal UAV data
for automatic measurement of rill and interrill erosion on loess soil, Earth Surf. Proc. Land., 40, 741–755, 2015.Eltner, A., Kaiser, A., Castillo, C., Rock, G., Neugirg, F., and Abellán, A.: Image-based surface reconstruction in geomorphometry – merits, limits and developments, Earth Surf. Dynam., 4, 359–389, 10.5194/esurf-4-359-2016, 2016.Eltner, A., Kaiser, A., Abellan, A., and Schindewolf, M.: Time lapse
structure-from-motion photogrammetry for continuous geomorphic monitoring,
Earth Surf. Proc. Land., 42, 2240–2253, 10.1002/esp.4178,
2017.Fazeli, H., Samadzadegan, F., and Dadrasjavan, F.: Evaluating the potential
of RTK-UAV for automatic point cloud generation in 3-D rapid mapping,
Int. Arch. Photogramm., 41,
221–226, 10.5194/isprsarchives-XLI-B6-221-2016, 2016.Forlani, G., Dall'Asta, E., Diotri, F., di Cella, U. M., Roncella, R., and Santise, M.: Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning, Remote Sens., 10, 1–22,
10.3390/rs10020311, 2018.Fuller, I. C., Large, A. R. G., Charlton, M. E., Heritage, G. L., and Milan,
D. J.: Reach-scale sediment transfers: An evaluation of two morphological
budgeting approaches, Earth Surf. Proc. Land., 28, 889–903,
10.1002/esp.1011, 2003.Gerke, M. and Przybilla, H.-J.: Accuracy Analysis of Photogrammetric UAV
Image Blocks: Influence of Onboard RTK-GNSS and Cross Flight Patterns,
Photogramm. Fernerkun., 2016, 17–30,
10.1127/pfg/2016/0284, 2016.Glendell, M., McShane, G., Farrow, L., James, M. R., Quinton, J., Anderson,
K., Evans, M., Benaud, P., Rawlins, B., Morgan, D., Jones, L., Kirkham, M.,
DeBell, L., Quine, T. A., Lark, M., Rickson, J., and Brazier, R. E.: Testing
the utility of structure-from-motion photogrammetry reconstructions using
small unmanned aerial vehicles and ground photography to estimate the extent
of upland soil erosion, Earth Surf. Proc. Land., 42, 1860–1871,
10.1002/esp.4142, 2017.
Grayson, B., Penna, N. T., Mills, J. P., and Grant, D. S.: GPS precise point
positioning for UAV photogrammetry, Photogramm. Rec., 33, 427–447,
2018.Harwin, S. and Lucieer, A.: Assessing the accuracy of georeferenced point
clouds produced via multi-view stereopsis from Unmanned Aerial Vehicle (UAV)
imagery, Remote Sens., 4, 1573–1599, 10.3390/rs4061573, 2012.
Hawkins, B. S.: Using a drone and photogrammetry software to create
orthomosaic images and 3-D models of aircraft accident sites, in: ISASI 2016 Seminar, 17–20 October 2016, Reykjavik, Iceland,
1–26, 2016.Hemmelder, S., Marra, W., Markies, H., and De Jong, S. M.: Monitoring river
morphology & bank erosion using UAV imagery – A case study of the river
Buëch, Hautes-Alpes, France, Int. J. Appl. Earth Obs. Geoinf., 73,
428–437, 10.1016/j.jag.2018.07.016, 2018.James, M. R. and Robson, S.: Straightforward reconstruction of 3-D surfaces
and topography with a camera: Accuracy and geoscience application, J.
Geophys. Res.-Earth, 117, 1–17, 10.1029/2011JF002289, 2012.James, M. R. and Robson, S.: Mitigating systematic error in topographic
models derived from UAV and ground-based image networks, Earth Surf. Proc. Land., 39, 1413–1420, 10.1002/esp.3609, 2014.James, M. R., Robson, S., and Smith, M. W.: 3-D uncertainty-based topographic
change detection with structure-from-motion photogrammetry: precision maps
for ground control and directly georeferenced surveys, Earth Surf. Proc. Land., 42, 1769–1788, 10.1002/esp.4125, 2017.
Kraaijenbrink, P. D. A., Shea, J. M., Pellicciotti, F., De Jong, S. M., and
Immerzeel, W. W.: Object-based analysis of unmanned aerial vehicle imagery
to map and characterise surface features on a debris-covered glacier, Remote
Sens. Environ., 186, 581–595, 2016.Lane, S. N., James, T. D., and Crowell, M. D.: Application of digital
photogrammetry to complex topography for geomorphological research,
Photogramm. Rec., 16, 793–821, 10.1111/0031-868X.00152, 2000.Messinger, M., Asner, G. P., and Silman, M.: Rapid assessments of amazon
forest structure and biomass using small unmanned aerial systems, Remote
Sens., 8, 1–15, 10.3390/rs8080615, 2016.Micheletti, N., Chandler, J. H., and Lane, S. N.: Investigating the
geomorphological potential of freely available and accessible
structure-from-motion photogrammetry using a smartphone, Earth Surf. Proc. Land., 40, 473–486, 10.1002/esp.3648, 2015.Mosbrucker, A. R., Major, J. J., Spicer, K. R., and Pitlick, J.: Camera
system considerations for geomorphic applications of SfM photogrammetry,
Earth Surf. Proc. Land., 42, 969–986, 10.1002/esp.4066, 2017.Ouédraogo, M. M., Degré, A., Debouche, C., and Lisein, J.: The
evaluation of unmanned aerial system-based photogrammetry and terrestrial
laser scanning to generate DEMs of agricultural watersheds, Geomorphology,
214, 339–355, 10.1016/j.geomorph.2014.02.016, 2014.
Padró, J.-C., Muñoz, F.-J., Planas, J., and Pons, X.: Comparison of
four UAV georeferencing methods for environmental monitoring purposes
focusing on the combined use with airborne and satellite remote sensing
platforms, Int. J. Appl. Earth Obs. Geoinf., 75, 130–140, 2019.Passalacqua, P., Belmont, P., Staley, D. M., Simley, J. D., Arrowsmith, J.
R., Bode, C. A., Crosby, C., DeLong, S. B., Glenn, N. F., Kelly, S. A.,
Lague, D., Sangireddy, H., Schaffrath, K., Tarboton, D. G., Wasklewicz, T.,
and Wheaton, J. M.: Analyzing high resolution topography for advancing the
understanding of mass and energy transfer through landscapes: A review,
Earth-Sci. Rev., 148, 174–193, 10.1016/j.earscirev.2015.05.012,
2015.Pineux, N., Lisein, J., Swerts, G., Bielders, C. L., Lejeune, P., Colinet,
G., and Degré, A.: Can DEM time series produced by UAV be
used to quantify diffuse erosion in an agricultural watershed?,
Geomorphology, 280, 122–136, 10.1016/j.geomorph.2016.12.003, 2017.Rosnell, T. and Honkavaara, E.: Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera, Sensors, 12, 453–480, 10.3390/s120100453, 2012.Rossini, M., Di Mauro, B., Garzonio, R., Baccolo, G., Cavallini, G.,
Mattavelli, M., De Amicis, M., and Colombo, R.: Rapid melting dynamics of an
alpine glacier with repeated UAV photogrammetry, Geomorphology, 304,
159–172, 10.1016/j.geomorph.2017.12.039, 2018.Sanz-Ablanedo, E., Chandler, J. H., and Wackrow, R.: Parameterising Internal
Camera Geometry with Focusing Distance, Photogramm. Rec., 27, 210–226,
10.1111/j.1477-9730.2012.00677.x, 2012.Sanz-Ablanedo, E., Chandler, J., Rodríguez-Pérez, J.,
Ordóñez, C., Sanz-Ablanedo, E., Chandler, J. H.,
Rodríguez-Pérez, J. R., and Ordóñez, C.: Accuracy of
Unmanned Aerial Vehicle (UAV) and SfM Photogrammetry Survey as a Function of
the Number and Location of Ground Control Points Used, Remote Sens., 10, 1606, 10.3390/RS10101606, 2018.
Smith, M. W., Carrivick, J. L., and Quincey, D. J.: Structure from motion photogrammetry in physical geography, Prog. Phys. Geogr., 40, 247–275, 2016.Stöcker, C., Nex, F., Koeva, M., and Gerke, M.: Quality assessment of
combined IMU/GNSS data for direct georeferencing in the context of UAV-based
mapping, Int. Arch. Photogramm.,
42, 355–361, 10.5194/isprs-archives-XLII-2-W6-355-2017, 2017.
Takasu, T. and Yasuda, A.: Development of the low-cost RTK-GPS receiver with
an open source program package RTKLIB, in: International Symposium on GPS/GNSS, 4–6 November 2009, Jeju, Korea, 2009.Tarolli, P.: High-resolution topography for understanding Earth surface
processes: Opportunities and challenges, Geomorphology, 216, 295–312,
10.1016/j.geomorph.2014.03.008, 2014.
Triggs, B., McLauchlan, P. F., Hartley, R. I., and Fitzgibbon, A. W.: Bundle
Adjustment – A Modern Synthesis, in Vision Algorithms: Theory and Practice,
edited by: Triggs, B., Zisserman, A., and Szeliski, R., 298–372, Springer, Berlin, Heidelberg, 2000.
Turner, D., Lucieer, A., and Watson, C.: An automated technique for
generating georectified mosaics from ultra-high resolution unmanned aerial
vehicle (UAV) imagery, based on structure from motion (SfM) point clouds,
Remote Sens., 4, 1392–1410, 2012a.Turner, D., Lucieer, A., and Watson, C.: An automated technique for
generating georectified mosaics from ultra-high resolution Unmanned Aerial
Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds,
Remote Sens., 4, 1392–1410, 10.3390/rs4051392, 2012b.Turner, D., Lucieer, A., and de Jong, S. M.: Time series analysis of
landslide dynamics using an Unmanned Aerial Vehicle (UAV), Remote Sens.,
7, 1736–1757, 10.3390/rs70201736, 2015.Turner, I. L., Harley, M. D., and Drummond, C. D.: UAVs for coastal surveying,
Coast. Eng., 114, 19–24, 10.1016/j.coastaleng.2016.03.011, 2016.Uysal, M., Toprak, A. S., and Polat, N.: DEM generation with UAV
Photogrammetry and accuracy analysis in Sahitler hill, Measurement, 73,
539–543, 10.1016/j.measurement.2015.06.010, 2015.Wheaton, J. M., Brasington, J., Darby, S. E., and Sear, D. A.: Accounting for
uncertainty in DEMs from repeat topographic surveys: Improved sediment
budgets, Earth Surf. Proc. Land., 35, 136–156,
10.1002/esp.1886, 2010.Wheaton, J. M., Brasington, J., Darby, S. E., Kasprak, A., Sear, D., and
Vericat, D.: Morphodynamic signatures of braiding mechanisms as expressed
through change in sediment storage in a gravel-bed river, J. Geophys. Res.-Earth, 118, 759–779, 10.1002/jgrf.20060, 2013.