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.
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.
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
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.
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.
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
Description of the study sites.
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,
Experimental setup.
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.
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.
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.
Note: repeated flight missions were marked as F_a and F_b. The missions shown in the list used a parallel flight plan.
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
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
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.,
Distribution of GCPs and CPs and illustration of the different georeferencing configurations:
The geotagged images were processed with the Pix4D Mapper software
(
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.
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.
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
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
(
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 (
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
Table 3 summarizes the average (i.e., considering all the flights) check
point accuracy and precision ranges in the
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 (1.96
Figure 4 shows the CP residual distributions for each survey for the
Distribution of CP residuals on the
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.
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.
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
Change detection based on DoD (datasets: F2,
F3_a of DSLR and action camera surveys).
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
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
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
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).
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
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
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 (
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
Monte Carlo Precision maps.
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.
The authors declare that they have no conflict of interest.
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.
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).
This paper was edited by Richard Gloaguen and reviewed by Mike James, Joan-Cristian Padró, and one anonymous referee.