Introduction
Accurate topographic data from the riverbed and floodplain areas are crucial
elements in hydrodynamic models. Detailed bathymetry maps of inland water
bodies are essential for simulating flow dynamics and forecasting flood
hazard (Conner and Tonina, 2014; Gichamo et al., 2012; Schäppi et al.,
2010), predicting sediment transport and streambed morphological evolution
(Manley and Singer, 2008; Nitsche et al., 2007; Rovira et al., 2005; Snellen
et al., 2011), and monitoring instream habitats (Brown and Blondel, 2009;
Powers et al., 2015; Strayer et al., 2006; Walker and Alford, 2016). Whereas
exposed floodplain areas can be directly monitored from aerial surveys,
riverbed topography is not directly observable from airborne or space-borne
methods (Alsdorf et al., 2007). Thus, there is a widespread global deficiency
in bathymetry measurements of rivers and lakes.
Within the electromagnetic spectrum, visible wavelengths have the greatest
atmospheric transmittance and the smallest attenuation in water. Therefore,
remote sensing imagery from satellites, such as Landsat (Liceaga-Correa and
Euan-Avila, 2002), QuickBird (Lyons et al., 2011), IKONOS (Stumpf et al.,
2003), WorldView-2 (Hamylton et al., 2015; Lee et al., 2011), and aircrafts
(Carbonneau et al., 2006; Marcus et al., 2003), has been used to monitor the
bathymetry of inland water bodies. However, bathymetry can only be derived
from optical imagery when water is very clear and shallow, the sediment is
comparatively homogeneous, and atmospheric conditions are favorable
(Legleiter et al., 2009; Lyzenga, 1981; Lyzenga et al., 2006; Overstreet and
Legleiter, 2017). Thus, passive remote sensing applications are limited to shallow gravel-bed
rivers, in which water depth is on the order of the Secchi depth (depth at
which a Secchi disk is no longer visible from the surface).
Similarly, airborne lidars operating with a green wavelength can be applied
to retrieve bathymetry maps (Bailly et al., 2010; Hilldale and Raff, 2008;
Legleiter, 2012), but also this method is limited by water turbidity, which
severely restricts the maximum depth to generally 2–3 times the Secchi depth
(Guenther, 2001; Guenther et al., 2000).
Because of satellite or aircraft remote sensing limitations, field surveys,
which are expensive and labor intensive, are normally required to obtain
accurate bathymetric cross sections of river channels. Some preliminary tests
using a green wavelength (λ= 532 nm) terrestrial laser scanning
(TLS) for surveying submerged areas were performed (Smith et al., 2012; Smith
and Vericat, 2014). However, TLS suffers from similar limitations as lidar.
Furthermore, the highly oblique scan angles of TLS make refraction effects
more problematic (Woodget et al., 2015) and decrease returns from the bottom
while increasing returns from the water surface (Bangen et al., 2014).
Therefore, field surveys are normally performed using single-beam or
multi-beam swath sonars transported on manned boats or more recently on
unmanned vessels (e.g., Brown et al., 2010; Ferreira et al., 2009; Giordano et
al., 2015) . However, boats cannot be employed along unnavigable rivers and
require sufficient water depth for navigation.
Unmanned aerial vehicles (UAVs) offer the advantage of enabling a rapid
characterization of water bodies in areas that may be difficult to access by
human operators (Tauro et al., 2015b). Bathymetry studies using UAVs are so
far restricted to (i) spectral signature-depth correlation based on passive
optical imagery (Flener et al., 2013; Lejot et al., 2007) or (ii) DEM
(digital elevation model) generation through stereoscopic techniques from
through-water pictures, correcting for the refractive index of water (Bagheri
et al., 2015; Dietrich, 2016; Tamminga et al., 2014; Woodget et al., 2015).
The high cost, size, and weight of bathymetric lidars severely limit their
implementation on UAVs. An exception is the novel topo-bathymetric laser
profiler, bathymetric depth finder BDF-1 (Mandlburger et al., 2016). This
lidar profiler can retrieve measurements of up to 1–1.5 times the Secchi depth;
thus, it is only suitable for shallow gravel-bed water bodies. The system weighs
∼5.3 kg and requires a large UAV platform (e.g., multi-copters with a
weight of ∼25 kg).
To overcome these limitations, we assess a new operational method to estimate
river bathymetry in deep and turbid rivers. This new technique involves
deploying an off-the-shelf, floating sonar, tethered to and controlled by a
UAV. With this technique we can combine (i) the advantages of UAVs in terms
of the ability to survey remote, dangerous, unnavigable areas
with (ii) the capability of bathymetric sonars to measure bathymetry in deep and turbid
inland water bodies.
UAV measurements of water depth (i.e., elevation of the water surface above
the bed) can enrich the set of available hydrological observations along with
measurements of water surface elevation (WSE), i.e., elevation of the water
surface above sea level (Bandini et al., 2017; Ridolfi and Manciola, 2018;
Woodget et al., 2015), and surface water flow (Detert and Weitbrecht, 2015;
Tauro et al., 2015a, 2016; Virili et al., 2015).
Materials and methods
The UAV used for this study was the off-the-shelf DJI hexa-copter Spreading
Wings S900 equipped with a DJI A-2 flight controller.
UAV payload
The UAV was equipped with a Global Navigation Satellite System (GNSS)
receiver for retrieving accurate position, an inertial measurement unit (IMU)
to retrieve angular and linear motion, and a radar system to measure the
range to water surface. A picture of the UAV and the tethered sonar is shown
in Fig. 1.
Pictures of the UAV and the tethered sonar. These pictures were
retrieved in (a) Marrebæk Kanal, Denmark;
and (b) Furesø
lake, Sjælland, Denmark. In (b) the drone was flown a few
hundred meters from the shore and the picture was retrieved using an
optical camera onboard an auxiliary UAV (DJI Mavic Pro).
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The onboard GNSS system is a NovAtel receiver (OEM628 board) with an Antcom
(3G0XX16A4-XT-1-4-Cert) dual frequency GPS and GLONASS flight antenna. The
UAV horizontal and vertical position is estimated with ∼2–3 cm
accuracy in carrier-phase differential GPS mode. The onboard inertial
measurement unit is an Xsense MTi-10 series. The optical camera is a
SONY RX-100 camera. The radar is an ARS 30X radar developed by Continental.
The radar and GNSS systems are the same instrumentation as described in
Bandini et al. (2017), in which WSE was measured by subtracting the range
measured by the radar (range between the UAV and the water surface) from the
altitude observed by the GNSS instrumentation (i.e., altitude above reference
ellipsoid, convertible into altitude above geoid level). In this research,
the radar and GNSS instrumentation are used to (i) retrieve WSE and
(ii) observe the accurate position of the tethered sonar.
Sonar instrumentation
The sonar used for this study was the Deeper Smart Sensor PRO+
manufactured by the company Deeper, UAB (Vilnius, Lithuania). It costs
∼ USD 240 and weighs ∼100 g.
The sonar is tethered to the UAV with a physical wire connection as shown in
Fig. 2. For specific applications, the sonar can be lowered or raised using a
remotely controlled lightweight wire winch, as shown in Fig. 2. The maximum
extension of the wire was ∼5 m. A remotely controlled emergency hook
can be installed to release the sonar in case of emergency (e.g., if the wire
is caught in obstacles).
Deeper sonar is connected to a UAV with a wire winch.
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This sonar is a single-beam echo sounder with two frequencies:
290 and 90 kHz,
with 15 and 55∘ beam angles, respectively. The 90 kHz
frequency is developed to locate fish with a large scanning angle, while the
narrow field of view of the 290 kHz frequency gives the highest bathymetric accuracy. For this
reason, the 290 kHz frequency is used for observing the bottom structure. The
sonar is capable of measuring depths up to 80 m and has a minimum measuring
depth of 0.3–0.5 m depending on the substrate material. The 15∘
beam angle of the 290 kHz frequency results in a ground footprint of ∼26 cm at 1 m water depth. This footprint is not optimal for resolving
small-scale features at large water depths.
The observations retrieved by the sonar include time, approximate
geographical coordinates of the sonar, sonar depth measurements (including
waveform shape), size and depth of identified fish, and water temperature.
It is essential to analyze multiple echo returns to identify the actual
water depth, especially in shallow water. Indeed, when a sound pulse returns
from the bottom, only a very small part of the echo hits the receiving
transducer. The major portion hits the water surface and is reflected back
to the bottom of the water body. From the bottom, it is reflected upwards
again and hits the receiving transducer a second time. In shallow water,
this double-path reflection is strong enough to generate a second echo that
must be filtered out.
The sonar has a built-in GPS receiver to identify its approximate location.
However, the accuracy of this GPS is several meters (up to 30 m). The large
error of this single frequency GPS receiver is related to many different
factors, including disturbance of the GNSS signal by water beneath the sonar,
the drone, and the topography surrounding the water body. The accuracy of
either GPS option is suboptimal for the generation of bathymetry maps; thus,
more accurate measurements of the sonar position are necessary. The drone
absolute position is accurately known through the differential GNSS system
described in Bandini et al. (2017). In order to estimate the relative
position of the sonar with respect to the drone, the payload system measures
the offset and orientation of the sonar. This concept is described in Fig. 3.
Sonar is the center of the reference system X, Y, Z. The
horizontal displacement between the sonar and the drone is computed along the
X and Y directions, while the vertical displacement is computed along the Z axis (object
distance – OD). The angle α is the azimuth, i.e., the angle between
the Y axis pointing north and the vector between the drone and the sonar,
projected onto the horizontal plane (in green). The azimuth angle is measured
clockwise from north (i.e., α is positive in the figure).
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The displacement between the sonar and the principal point of the onboard
camera sensor is denoted by the variables x and y, in which x measures
the displacement along the east direction and y along the north direction.
As shown in Fig. 3, the azimuth angle is necessary to compute the sonar
displacement in Cartesian coordinates.
The horizontal displacement between the sonar and the onboard camera can be
estimated with the observations from the different sensors comprising the
drone payload: (i) the GNSS system (to measure drone absolute coordinates),
(ii) optical camera (to measure displacement of the sonar with respect to the
drone in pixel units), (iii) radar (to convert the displacement from pixels
to metric units), and (iv) IMU (to project this displacement into the east and
north direction). In this framework, the optical SONY camera continuously
captures pictures (with focus set to infinity) of the underlying water
surface to estimate the sonar position. Lens distortion needs to be corrected
for because the SONY RX-100 camera is not a metric camera. Numerous methods
have been discussed in the literature to correct for lens distortion
(e.g., Brown, 1971; Clarke and Fryer, 1998; Faig, 1975; Weng et
al., 1992). In this research the software PTLens was used to remove lens
radial distortion because the lens parameters of the SONY RX-100 camera are
included in the software database. The displacement of the sonar with respect
to the camera principal point can be measured in pixels along the vertical
and horizontal axis of the image. This displacement in pixels is converted
into metric units through Eqs. (1)–(4). A representation of the variables
contained in these equations is given in Fig. 4. Application of Eqs. (1)
and (2) requires the following input parameters: the sensor width (Wsens) and
sensor height (Hsens), the focal length (F), and the object distance (OD). OD
is the vertical range to the water surface and is measured by the radar.
Equations (1) and (2) compute the width (WFOV) and height (HFOV) of the field
of view.
WFOV=Wsens⋅ODFHFOV=Hsens⋅ODF
Equations (3) and (4) compute the displacement, in metric unit, between the
sonar and the center of the camera sensor along the horizontal (Lw) and
vertical (Lh) axis of the picture. Application of Eqs. (3) and (4) requires
the following input parameters: the width (WFOV) and height (HFOV) of the
field of view, the sensor resolution in pixels along the horizontal
(npixw) and the vertical (npixh) direction, and
the measured distance in pixels between the sonar and the center of the image
along the horizontal (pixw) and vertical (pixh) image
axis.
Lw=WFOVnpixw⋅pixwLh=HFOVnpixh⋅pixh
Relationship between FOV (field of view in degrees), HFOV (height of
the field of view, in metric unit), OD (object distance), F (focal length),
pixh (distance in pixels between center of the image and the object
in the image, along the vertical axis of the image captured by the camera), Hsens
(sensor height), and Lh (distance in metric units between the object and center
of the sensor along the vertical axis of the image). The drawing is valid under
the assumption that the image distance (distance from the rear nodal point of
the lens to the image plane) corresponds to the focal length.
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The magnitude of the displacement vector, L, between the sonar and the camera principal
point and the angle φ (angle between the camera
vertical axis and the displacement vector) are computed through Eqs. (5)
and (6). Figure 5 shows a picture retrieved by the camera. In the current
payload setup, the vertical axis of the camera is aligned with the drone nose
(heading).
L=Lw2+Lh2φ=tan-1LwLh
UAV-borne picture of the tethered sonar. WFOV and HFOV are the width
and height of the field of view. The tethered sonar is located below the
white polyester board floating on the water surface. The red circle indicates
the center of the image, while the red cross indicates the exact position of
the sonar. The north direction is retrieved by the IMU. The vertical axis of
each image captured by the camera coincides with the drone heading. β
is the angle between the drone heading and the north. φ is the angle
measured clockwise from the camera vertical axis to the vector (L), which is the vector on the horizontal
plane connecting the sonar to the image center. Lh and Lw are the vertical
and horizontal components of the vector L. α is the azimuth
angle measured clockwise from the north direction to L. Angles and
vectors highlighted in this figure are on the horizontal plane, i.e., on the
water surface.
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The azimuth angle α of the sonar is computed through Eq. (7), which
requires φ and β as inputs. The symbol β denotes the
drone heading (angle between the drone's nose and the direction of the true
north, measured clockwise from north). This heading angle is measured by the
onboard IMU system.
α=β+φ
Equations (8) and (9) compute the variables x and y, which represent the
displacement of the sonar with respect to the principal point of the onboard
camera sensor along the east and north direction, respectively.
x=cos(90-α)⋅Ly=sin(90-α)⋅L
The absolute position of the drone is simultaneously retrieved by the GNSS
antenna installed on the top of the drone. The offset between the sensor of
the camera onboard the drone and the phase center of the GNSS antenna
position is constant and known a priori. This offset vector also needs to be
converted to spatial real-world coordinates at each time increment accounting
for the drone heading. Using this framework, the absolute sonar position can
be computed in Cartesian coordinates by summing the relative displacement x
and y to the camera absolute position.
Case studies
First, the accuracy of the water depth measured with the sonar was assessed
against measurements obtained by the survey boat. Secondly, UAV surveys were
conducted to evaluate the accuracy of the depth measured by the sonar and
the accuracy of the sonar position.
On boat accuracy evaluation
A bathymetric survey was conducted on a boat in the Furesø lake, Denmark. A
second reference sonar, the Airmar EchoRange SS510 Smart Sensor
(developed by Airmar, Milford, USA), was deployed to assess the accuracy of
the Deeper sonar. According to the technical data sheet, the SS510 Smart
Sensor weighs around 1.3 kg, has a resolution of 3 cm, a 9∘ beam
angle, a measuring range from 0.4 to 200 m, and nominal accuracy 0.25 %
in depth measurements at full range. The horizontal positions of the sonars
during the surveys were acquired with a real-time kinematic (RTK) GNSS rover installed on the boat.
During this survey, ground truth depth measurements were retrieved in
selected locations to validate the observations of the two sonars. Ground
truth measurements were retrieved using a measuring system consisting of a
heavy weight (∼5 kg) attached to an accurate measuring tape. This
reference system has an accuracy of ∼10–15 cm in water depth up to
40 m.
UAV-borne measurements
Flights were conducted in Denmark (DK) above Furesø lake
(Sjælland, DK), above Marrebæk Kanal (Falster, DK), and Åmose Å
(Sjælland, DK).
The flights above Furesø demonstrate the potential of the airborne
technology for retrieving measurements at a line-of-sight distance of a few
hundred meters from the shore. The flight above Marrebæk Kanal
demonstrates the possibility of retrieving accurate river cross sections,
which can potentially be used to inform hydrodynamic river models. The flight
above Åmose Å shows the possibility to retrieve observations with high
spatial resolution, enabling the construction of bathymetric maps of entire
river stretches. The accuracy of the observed river cross sections is
evaluated by comparison with ground truth observations. Ground truth
observations of the river cross sections were obtained by a manual operator
wading into the river and taking measurements with a RTK GNSS rover of
(i) the orthometric height of the river bottom and (ii) the WSE. Ground truth
depth was then computed by subtracting the orthometric height of the bottom
from the WSE measurements.
Results
On boat sonar accuracy
Figure 6 shows the measurements retrieved by the two sonars in the lake. The
background map is from Google Earth. WSE retrieved by the RTK GNSS station
was 20.40±0.05 m a.s.l. (above sea level) during this survey.
Water depth measurements retrieved in Furesø by the two sonars:
(a) observations with Deeper sonar; (b) observations with
SS510 sonar.
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Difference between water depth measured by SS510 sonar and the
Deeper sonar.
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The maximum water depth retrieved during the survey is ∼36 m. In
Fig. 7, we report the difference between the observations retrieved by the
SS510 and the Deeper sonar.
Statistics comparing the Deeper sonar, SS510 sonar, and ground truth
observations.
Statistics
Sample
Root mean
Mean absolute
Mean bias
Relative
size
square error
error
error (MBE)
error
(RMSE) (m)
(m)
(m)
(%)
SS510 sonar - Deeper sonar∗
57 528
0.38
0.32
0.27
3.70 %
Before bias correction
Deeper sonar - ground truth
5
0.58
0.52
0.48
3.80 %
SS510 sonar - ground truth
5
0.675
0.56
0.56
3.65 %
After bias correction
Deeper sonar - ground truth
5
0.12
0.11
5×10-4
2.10 %
SS510 sonar - ground truth
5
0.052
0.047
-0.01
0.57 %
∗ Statistics computed after removing outliers (above the
95th percentile and below the 5th percentile).
Relationship between measurements of two sonars and ground truth.
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Figure 7 shows high consistency between the two sonars. However,
littoral areas with dense submerged vegetation show larger errors. While in
the deepest area (∼30 m deep) the Deeper sonar observed multiple
returns of the sound wave caused by suspended sediments, the analysis of
the waveform was more complicated and subject to errors. In this area, the
Deeper sonar fails to retrieve some water depth observations, where the
waveform analysis does not show a well-defined strong return echo.
The observations retrieved by the two sonars are compared with ground truth
observations in Fig. 8.
Figure 8 depicts a systematic overestimation of water depth by both sensors.
The relationship between the observations of the two sonar sensors (x) and
ground truth (y) can be described with a linear regression of the form
shown in Eq. (10), in which β0 is the offset (y intercept),
β1 is the slope, and ε is a random error term:
y=β0+β1x+ε.
This survey showed an offset of zero. Thus, the bias between the ground truth
observations and the sonar observations can be corrected by multiplying the
sonar observations by β1. Linear regression lines can be fitted to
the observations shown in Fig. 8 with a R2 of ∼0.99. Appendix A
shortly describes how physical variables (such as depth, salinity, and
temperature) can affect water depth observations using sonars.
Table 1 shows comparative statistics between the Deeper, the SS510 sonar, and
the ground truth observations.
Table 1 shows a difference of ∼30 cm between the measurements of the
two sonars, with the Deeper sonar generally underestimating water depth. This
can be due to the wider scanning angle of the Deeper (15∘) compared
to the SS510 sonar (9∘). The Deeper sonar is more affected by steep
slopes, in which the depth tends to represent the most shallow point in the
beam because of the larger scanning angle. The Deeper and SS510 observations
can be corrected multiplying by the slope β1 (∼0.97 for the
Deeper and ∼0.96 for the SS510 sonar). The correction factor is site
specific as it depends on the bed form and material, as well as on the water
properties (temperature, salinity, and pressure). Therefore, the acquisition
of a sample of ground control points is required.
UAV-borne measurements
In Fig. 9 we show the observations of the UAV-borne survey above Furesø.
Water depth (m) observations retrieved in Furesø with the the sonar tethered to the UAV.
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Figure 10 depicts the UAV observations of four different cross sections of
Marrebæk Kanal.
River cross sections retrieved at different locations along
Marrebæk Kanal. The y axis shows the difference between riverbed
elevation and WSE (opposite sign of water depth). Red points are retrieved
with UAV-borne observations and blue lines are the ground truth observations.
The latitude and longitude coordinates of the left bank of the river cross
sections are (a) 54.676300, 11.913296∘; (b) 54.675507,
11.913628∘; (c) 54.682117,
11.911957∘; and (d) 54.681779, 11.910723∘ (WGS84 reference system).
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The accuracy of ground truth observations depends on both (i) the accuracy of
the GNSS observations and (ii) the accuracy in positioning the GNSS pole in
contact with the river bed. A vertical accuracy of ∼5–7 cm and a
horizontal accuracy of ∼2–3 cm are estimated for the RTK GNSS ground
truth observations, while the accuracy of the UAV-borne river-cross-section
observations depends on (i) the error in absolute position of the sonar and (ii) the
sonar's accuracy in measuring depth. The Deeper sonar shows a systematic
overestimation of water depth in Fig. 10, which can be corrected by
multiplying by the slope coefficient (β1∼0.95 for this specific
survey). Figure 11 shows the observations after correction for the measurement
bias.
River-cross-section observations retrieved from Marrebæk Kanal
at the locations shown in Fig. 10 after bias correction of the Deeper sonar
observations. Red points are retrieved with UAV-borne observations and blue
lines are the ground truth observations.
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UAV-borne bathymetric surveys provide high spatial resolution. Surveys can be
interpolated to obtain bathymetric maps of entire river stretches. Figure 12
shows UAV observations in Åmose Å retrieved with the Deeper sonar at
a resolution of ∼0.5 m. These observations were interpolated using the
triangulated irregular network method. Two ground truth cross sections were
retrieved with the RTK GNSS rover. The investigated stretch of Åmose
Å has a length of ∼85 m and a maximum water depth of ∼1.15 m.
Bathymetry observations in Åmose Å. Top panel shows the
surveyed river stretch (north direction pointing towards the left side of the map
as indicated by the north arrow). Background map is an airborne orthophoto
provided by the Danish Styrelsen for Dataforsyning og Effektivisering
(https://kortforsyningen.dk/, last access: 6 September 2017). Raster foreground map shows
UAV-borne observations interpolated with the triangulated irregular network
method. Two ground truth cross sections were retrieved, which are shown in
the bottom panels: (a) upstream and (b) downstream cross section. In the
cross section plots, the x axis shows the distance from left bank (west bank), and the
y axis shows the difference between riverbed elevation and WSE (opposite
sign of water depth).
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Figure 12 shows that the minimum depth restriction is a significant limitation
of the Deeper sonar in small rivers and streams. Water depth values smaller
than ∼0.5 m are generally not measured by the Deeper sonar.
Furthermore, the soft sediment and the submerged vegetation cause significant
errors in the Deeper observations when compared to ground truth cross
sections. In this survey, it was not possible to identify a systematic error
and thus correct for the bias of the UAV-borne observations.
Accuracy of the Deeper sonar position
The accuracy of the absolute position of the Deeper sonar depends on the
accuracy of (i) the drone horizontal position, (ii) the drone heading, and (iii) the
relative position of the sonar with respect to the drone. The
accuracies of these observations are reported in Table 2.
The accuracy of the relative position of the sonar depends on the image
analysis procedure implemented to convert an offset from pixel into metric
units. This procedure is also affected by the accuracy of the radar-derived
WSE, because OD is an input to Eqs. (1) and (2). Tests were conducted in
static mode using a checkerboard, placed at a series of known distances
between 1 and 4 m, to evaluate the accuracy of measuring true distances in
the image. These experiments showed that the offset between the camera and
the sensor could be determined with an accuracy of 3 % of its actual
value. The error in the conversion from image units to true distance units is
mainly due to the (i) uncorrected lens distortion and (ii) assumption, made in
Eqs. (1) and (2), that focal length is precisely known and that the distance
between the rear nodal point of the lens and the image plane is exactly equal
to focal length.
Accuracy of the different sensors used to measure the absolute
position of the sonar.
Sensor
Observation
Accuracy
IMU
β (drone heading)
3∘
GNSS
Drone horizontal position
2 cm at twice the standard deviation (Bandini et al., 2017)
Radar
OD (range to water surface)
0.5 % of the actual range (Bandini et al., 2017)
Camera
Lw, Lh (offset between sonar and camera center along horizontal and vertical axis of the picture)
≈3 % of the actual value
An error propagation study evaluated the overall accuracy of the absolute
position of the sonar in real-world horizontal coordinates. For detailed
information, see the data in the Supplement. The uncertainties of β,
Lw, and Lh have the larger impact on the overall accuracy, compared to other
error sources, such as OD and drone horizontal position. Since the offset (L)
between the center of the camera and the sonar typically assumes values
between 0 and 2 m, the overall accuracy of the Deeper sonar position is
generally better than 20 cm. This accuracy is acceptable for most
bathymetric surveys, particularly in light of the spatial resolution
(15∘ beam divergence) of the Deeper sonar measurements.
Discussion
Bathymetry can be measured with both in situ and remote sensing methods.
In situ methods generally deploy bathymetric sonars installed on vessels.
Remote sensing methods include (i) lidar techniques, (ii) methods evaluating
the relationship between spectral signature and depth, and (iii) through-water
photogrammetry. Remote sensing methods generally allow for larger spatial
coverage than in situ methods, but only shallow and clear water bodies can be
surveyed. Table 3 shows a comparison of the different remote sensing and
in situ techniques. UAV-borne sonar depth measurements bridge the gap between
ground surveys and remote sensing techniques. The deployed Deeper sonar can
measure deep and turbid water, and reach remote and dangerous areas,
including unnavigable streams, when it is tethered to UAV. For depths up to
∼30 m, the 2.1 % accuracy complies with the first accuracy
level established by the International Hydrographic Organization (IHO) for
accurate bathymetric surveys. Indeed, for depths of 30 m, the accuracy of
the tethered sonar is ∼0.630 m, while the first IHO level standard
requires an accuracy better than 0.634 m. Conversely, for depths greater
than 30 m, the UAV-borne sonar measurements comply with the second
IHO level. Because of the large beam angle of the Deeper sonar,
small-scale bathymetric features at greater depth cannot be resolved.
However, a large beam angle (e.g., 8–30∘) is an intrinsic limitation
of single-beam sonar systems. For these reasons, when detection of
small-scale features is required, surveys are generally performed with
vessels equipped with multi-beam swath systems or side-scan imaging sonars.
These systems are significantly more expensive, heavier, and larger than
single-beam sonars, which makes integration with UAV platforms difficult.
Comparison of different approaches for measuring river bathymetry.
Technique
Platform
Spatial resolution (m)
Max. water depth
Typical error
Applicability (e.g., water clarity)
References
Spectral signature
Satellite
High-resolution commercial satellitesa: ≈2 m;
1–1.5 m
0.10–0.20 m
≈1–1.5 times the Secchi depth
Fonstad and Marcus (2005), Legleiter and Overstreet (2012).
medium-resolution satellitesb: typically >30 m
Manned aircraft
Typically 0.5–4 m
Carbonneau et al. (2006), Legleiter and Roberts (2005), Winterbottom and Gilvear (1997).
UAV
0.05–0.20 m
Flener et al. (2013), Lejot et al. (2007).
Through-water photogrammetry
Manned aircraft
Typically 0.1–0.5 m
0.6–1.5 m
0.08–0.2 m
≈ Secchi depth
Feurer et al. (2008), Lane et al. (2010), Westaway et al. (2001).
UAV
Typically 0.01–0.1 m
Bagheri et al. (2015), Dietrich (2016), Tamminga et al. (2014), Woodget et al. (2015).
Lidar
UAV
≈0.020 m at 20 m range
1–1.5 m
≈0.10 m with standard deviation of 0.13 m
≈1–1.5 times the Secchi depth
Mandlburger et al. (2016).
Manned aircraft
Few dm-several m
6 m
0.05–0.3 m
≈2–3 times the Secchi depth
Bailly et al. (2012, 2010), Charlton et al. (2003), Hilldale and Raff (2008), Kinzel et al. (2007).
TLSc
Banks of the water body
Typically ≈0.05 m
0.5 m, but typically ≈0.1 m
0.005–0.1 m
Clear water
Bangen et al. (2014), Heritage and Hetherington (2007), Smith et al. (2012), Smith and Vericat (2014).
Single-or multi- beam swath sonars
Manned/ unmanned vessels
Depending on the instrumentation and water depth
Sonars have minimum depth requirements (min 0.2–1 m)
Variable
Navigable streams
Widely known methodology
Sonar tethered to UAV
UAV
Depending on the water depthd
0.5–80 m
≈3.8 %e ≈2.1 %f of actual depth
All water conditions
Methodology described in this paper
a Multispectral bands: IKONOS, QuickBird, and
WorldView-2.
b Landsat.
c Terrestrial laser scanner (TLS).
d The divergence of the sonar cone beam is 15∘.
e Before bias correction.
f After bias correction.
Table 3 does not include methods requiring the operator to wade into a river,
e.g., measurements taken with a RTK GNSS rover (e.g., Bangen et al., 2014). To
take measurements with a GNSS rover, the operator must submerge the antenna
pole until it reaches the river bed surface. Therefore, this method can only
be used for local observations. Furthermore, innovative approaches such as
using a ground penetrating radar (GPR) are not included because they are
still at the level of local proof-of-concept applications (Costa et al.,
2000; Spicer et al., 1997) and generally require cableways to suspend
instrumentation a few decimeters above the water surface.
In order to obtain reliable measurements and ensure effective post-processing
of the data, the techniques shown in Table 3 require initial expenditure and
expertise from multiple fields, e.g., electric and software engineers (for
technology development and data analysis), pilots (e.g., UAVs and manned
aircrafts), experts in river navigation (for boats), surveyors (e.g., for GNSS rovers, photogrammetry), hydrologists,
and geologists. In Appendix B,
the typical survey expenditures for the different techniques are shown.
Future research
UAV-borne measurements of water depth have the potential to enrich the set
of available hydrological observations. Their advantages compared to
airborne, satellite, and manned boat measurements were demonstrated in this
study. The competitiveness of UAVs in measuring water depth, compared to the
capabilities of unmanned aquatic vessels equipped with sonar and RTK GNSS
systems, is currently limited to water bodies that do not allow navigation
of unmanned aquatic vessels, e.g., because of high water currents, slopes, or
obstacles. The full potential of UAV-borne hydrological observations will be
exploited only with flight operations beyond visual line of sight. The
new generation of waterproof rotary wing UAVs equipped with visual
navigation sensors and automatic pilot systems will make it possible to
collect hyperspatial observations in remote or dangerous locations, without
requiring the operator to access the area.