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Mapping Ocean Waves using LIDAR Technology


Abstract and Figures

The current methods for measuring ocean surface waves include free-floating wave-buoys and radars located on offshore platforms. In this paper, an introduction is made to a new Light Detection and Ranging (LIDAR) scanning system. The system developed by TOTAL A/S is called Wave Mapper, and it allows the possibility of mapping true and breaking ocean waves. Using field measurements from an offshore platform in the Danish sector of the North Sea, the application of the technology is demonstrated. The system provides high-resolution three-dimensional surface mapping in real-time, over an area up to approximately two hundred meters from the operating point on a stationary offshore platform. Radars installed on offshore platforms only make vertical point measurements on the surface of a passing wave which leads to insufficient information for the true characterization and categorization of the wave. This allows for waves of varying profiles and periods to appear comparable, whilst negating the possibility of determining when a wave is breaking. On the Wave Mapper, the lasers are mounted facing downwards and at an angle away from the platform. This allows the wave measurements to be unaffected by the platform. Utilization of light refraction as the measurable quantity allows for the use of light intensity to help categorize the amount of air bubbles in the water, leading to an understanding of when wave breaking is occurring. Wave Mapper measurements are compared with concurrent records from traditional radar equipment located on the opposite side of the offshore platform. The comparison is made by calculating the significant wave height, H_m0 , for each form of measurement. By use of calculations, it is shown that the LIDAR measurements of ocean surface waves match fairly with the measurements from the radar. Preliminary results from full-scale field tests of the Wave Mapper are presented.
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Mapping Ocean Waves using New LIDAR Equipment
Thomas Kabel, Christos Thomas Georgakis
Department of Engineering, Aarhus University, Aarhus, Denmark
Allan Rod Zeeberg
TOTAL E&P Denmark, Esbjerg, Denmark
The current methods for measuring ocean surface waves include free-
floating wave-buoys and radars located on oshore platforms. In this
paper, an introduction is made to a new Light Detection and Ranging
(LIDAR) scanning system. The system developed by Teledyne Optech
in collaboration with TOTAL E&P Denmark is called Wave Mapper, and
it allows the possibility of mapping true breaking ocean waves. Using
field measurements from an oshore platform in the Danish sector of
the North Sea, the application of the technology is demonstrated. The
system provides high-resolution three-dimensional surface mapping in
real-time over an area of up to approximately two hundred meters from
the operating point on a stationary oshore platform. Radars installed on
oshore platforms only make vertical point measurements on the surface
of a passing wave, which leads to insucient information for the true
characterization and categorization of the wave. This allows for waves
of varying profiles and periods to appear comparable, whilst negating the
possibility of determining when a wave is breaking. On the Wave Map-
per, the lasers are mounted facing downwards and at an angle away from
the platform. This allows the wave measurements to be unaected by
the platform. Utilization of light refraction as the measurable quantity
allows for the use of light intensity to help categorize the amount of air
bubbles in the water, leading to an understanding of when wave breaking
is occurring. The Wave Mapper measurements are compared with con-
current records from traditional radar equipment located on the opposite
side of the oshore platform. The comparison is made by calculating the
significant wave height, Hm0, for each form of measurement. By use of
calculations, it is shown that the LIDAR measurements of ocean surface
waves match fairly with the measurements from the radar. Preliminary
results from full-scale field tests of the Wave Mapper are presented.
KEY WORDS: LIDAR; Wave Mapper; Significant Wave Height;
Ocean Waves;
Wave kinematics and hydrodynamic loads are of great importance when
designing oshore structures. Field observations have shown, however,
that the original design basis may be inadequate. Multiple extreme
breaking waves in a single storm have been observed, each exceeding
the 10 000-year return period design crest height (Tychsen and Dixen,
2016; Tychsen et al., 2016). On the basis of this, a prototype of a new
wave scanner has been initiated. Contemporary methods for measuring
ocean waves include free-floating wave buoys (Tucker and Pitt, 2001)
and radars located on oshore platforms (Ewans et al., 2014). The
new prototype is based on the technology of LIDAR (LIght Detection
And Ranging) and is thus based on light instead of radio waves. The
invention of the laser in 1960 started the rapid development of modern
LIDAR technology (Weitkamp, 2005). Since then, a lot of dierent
areas have utilized the method, such as forestry, urban research, and
geoscience (Dong and Chen, 2018). Within oceanic engineering, the
LIDAR was introduced in the 1970s as a method for sea probing (Olsen
and Adams, 1970), others focused on bathymetry measurements from
an Airborne LIDAR (LIDAR measurements from airplane or helicopter)
(Schule et al., 1971). Especially the latter has been researched and
developed throughout the years for coastal engineering (Hwang et al.,
2000, 2002; Irish and Lillycrop, 1999).
For stationary LIDAR equipment, earlier authors have recently had
success, in coastal engineering, to measure the time-varying surface
profile of a swash zone using a fixed industrial LIDAR (Blenkinsopp
et al., 2010; Harry et al., 2018; Martins et al., 2017). Others used
LIDAR in a laboratory to measure the waves in a flume (Allis et al.,
2011) or to measure spatial profiles of propagating sea waves from a
vessel (Belmont et al., 2007, 2008). The basis for these papers is that
they measure a two-dimensional time-varying surface profile. There
are limitations to such two-dimensional measuring systems and to
point measurements for that matter. It is not possible to indicate a true
geometry of the waves from a time elevation signal, nor is it possible
to tell if the elevation misses the true three-dimensional crest peak
(Tychsen and Dixen, 2016). Finally, the breaking of waves, a chapter of
its own when researching methods of measuring ocean waves (Babanin,
2009), cannot possibly be determined with these instruments. For
this reason and others, few researchers have made experiments with
three-dimensional Terrestrial Lidar System (TLS) (Harry et al., 2010;
Park et al., 2011). In Harry et al. (2010) they captured the wave profile
successfully. However, the time spent on scanning the three dimensions
Proceedings of the Twenty-ninth (2019) International Ocean and Polar Engineering Conference
Honolulu, Hawaii, USA, June 16-21, 2019
Copyright © 2019 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1 880653 85-2; ISSN 1098-6189
introduced an alongshore time shift on the wave crest propagation,
especially due to the limited rotational scan rate of 0.16 Hz.
This paper presents a new LIDAR system for mapping time-varying
ocean waves with the possibility of capturing extreme breaking waves.
The paper will describe the new system, its methodology, and show pre-
liminary results from a field measurement campaign showing the capa-
bility of the system along with a crude comparison with a radar.
The experimental work conducted in this paper was undertaken using
a prototype of the new three-dimensional laser measurement system,
developed and manufactured by the Canadian company Teledyne Optech
in collaboration with TOTAL E&P in Denmark. The instrument has
been given the preliminary name Wave Mapper. The dimensions of
this prototype are 1070 mm x 861 mm x 528 mm, making it stationary
experimental equipment. The reasons for the size are the dierent safety
additions needed as the prototype will be placed on an active oshore
platform, along with the additional equipment within the instrument,
which is described later.
The Wave Mapper employs a 1064-nm , with a pulse repetition rate of up
to 500 kHz per beam. For the current setup, the transmitted laser beam is
split into three beams separated by 11.694 mrad, and each with a rate of
190 kHz. This allows the LIDAR to measure a possible 570 000 points
per second. The overall scanning system is constructed of two mirror-
scanners; a polygon scanner and a galvanometer scanner. The polygon
scanner provides the horizontal scan with a Field Of View (FOV) of 60
degrees and a rotational speed of 7 270 Rotations Per Minute (RPM).
The galvanometer mirror rotates back and forth across a FOV of 10 de-
grees, creating a dual vertical scan of the area. For this mirror, the scan
rate is 10 Frames Per Second (FPS) in each direction. This choice of
FOV allows for a resolution of 0.4 m at a 400 m vertical spot distance
from the equipment. The angular accuracy of the system is 10 cm. An
illustrational sketch of the prototype and its scanning area is seen in Fig.
Fig. 1 Principle of the scanning area by the Wave Mapper.
As illustrated in Fig. 1, the Wave Mapper is placed on the side of a plat-
form at an angle of approximately 30 degrees relative to the horizontal
level, thereby measuring down and outward. It is elevated 52 m com-
pared to the Mean Water Level (MWL). This allows for the system to
measure approximately 50-250 m from the platform. In the North Sea,
the LIDAR system is mounted facing North-West, allowing the ocean
waves to primarily travel across the scanning area. This is based on tests
by Teledyne Optech both on land and during field tests conducted in a
coastal region. From field-measurements in the North Sea, however, the
authors have also experienced ocean waves traveling in a direction to-
wards the LIDAR scanner. The equipment has been calibrated during
several tests on land objects.
The Wave Mapper prototype is placed on an oshore platform owned
and operated by TOTAL E&P Denmark. The location of the platform
is within the Danish part of the North Sea, approximately 210 km west
of the coast of Denmark. Field measurements were performed in the
autumn and winter (2017-2018). The reason for this is found in the
delimitations of the LIDAR; it requires a return of light pulses with
sucient intensity to the receiver in order to measure. As given by Irish
and White (1998), a flat-water surface absent of capillary waves will
yield a specular reflection that is insucient for LIDAR detection. A
possible solution is to use suitably turbid water to provide a reflective
surface that will allow for positive LIDAR readings, as seen in Allis
et al. (2011). This is, however, not possible for field measurements
currently. It is only possible in a controlled environment such as a
laboratory. Instead, the field measurements rely on wind-induced waves,
capillary waves, and breaking waves to create a rough ocean surface
that will allow for reflection of the LIDAR pulses. Ocean waves with
these characteristics and with amplitudes large enough to be measured
by the LIDAR are most common in autumn and winter periods. In
that period, three days showed promising warnings and signs of bad
weather and high seas, thereby allowing for good conditions for the
measuring equipment. These days were selected by using the services
of StormGEO. By selecting days with statistically large waves, the
possibility of getting usable data increased greatly. A field measurement
was obtained when the forecast for the significant wave height, Hs, was
6.0 m or more.
In order to provide data against which the LIDAR equipment could be
verified, data from a radar on the opposite side of the platform was uti-
lized. The WaveRadar REX is the contemporary equipment for TOTAL
when measuring ocean wave elevations. From this, it was possible to
extract the significant wave height, Hm0, which would give an indication
of whether the Wave Mapper was measuring correctly. A radar of this
type is a commonly used instrument when measuring ocean waves; for
further details about the REX RADAR as well as a detailed examination
of how well it performs, the reader is referred to Ewans et al. (2014).
Preliminary processing
Prior to the analysis of the data, preliminary processing of the data is
required. This is due to the data being collected as a matrix consisting
of six columns: (x,y,z)-data, time-code, and two columns for the
light intensity factor, while the number of rows equals the number
of data-points collected. The data is sorted in accordance with the
time-stamps which are constant for all the measured points in one frame
at a sampling time, t. Here, one frame is defined as one rotation of the
galvanometer, either towards or away from the platform as described
earlier. In order for comparison and analysis of the data to be possible,
the points are divided into frames in accordance with their time-code.
An example of such a frame of data-points is given in Fig. 2.
Fig. 2 Example of a frame of data points collected
Figure 2 shows indications of the area of measurement that is given
by the LIDAR system. The data points appear around 45 m from the
equipment with a spread of ±40 m perpendicular, however not centered
around y=0. At the same time, it is also given that a negligible amount
of data points is measured when passing a distance of 200 m.
Further the figure demonstrates the random positions of the data points,
due to the principle of point measurement. Because the system measures
at an angle on moving surfaces, rising waves and ripples in the surface
create unique (x,y,z)-values for each laser pulse in each frame. The
solution for this, in order to do further analysis, is to create a grid
system in which the points are interpolated. This allows the analysis
to be conducted in known (x,y)-values. Two grid systems have been
investigated. A simple constant spacing in a polar coordinate system,
hereafter referred to as Standard grid. Another grid was used, where
the distance between gridlines in the y-direction is a summation of
the previous distance of points, thereby allowing for a smaller spacing
between lines at the beginning, where the density of points is the largest,
and a larger distance at the end. This spacing is hereafter referred to as
the Vigsø spacing.
Subsequently, the z-values of the grid system are calculated by interpo-
lation of the data onto the grid using the natural neighboring method. By
doing this, it is possible to have a z-value for each grid point, which can
be used to map the surface of the waves. An example of this is seen in
Fig. 3.
Visualized by the surface plot in Fig 3, a dierence in the A- and B-
movie is clear. The reason for this is due to the galvanometer and its
rotation. The laser diers when measuring outwards, away from the plat-
form, compared to when the galvanometer rotates backward, measuring
towards the LIDAR system.
In continuation of the preliminary processing in the previous section,
the results of the prototype Wave Mapper are shown in this section.
As described earlier, the amount of data collected through the first
testing period was collected in three days. However, problems with
the length of the data-series collected and an undesirable number of
data points being undesirable, allowed for only three data-sets, each of
1-hour, to be treated in the current paper. This disadvantage is due to the
prototype being located and operated from an active operational oshore
platform in the North Sea, thereby having a large return-period on
storms to measure. An example of one of these data-sets is seen in Fig. 4.
The equipment allows for the measuring of 570 000 points per second,
however, as seen in the figure, this result is not given by the collected
data. This is most likely caused by the poor reflective surface of the
ocean waves from which the energy of the backscatter has been too low
for the system to measure. For the data in Fig. 4 the total amount of
points was 9 166 769 over a period of 59 seconds, which gives a mean of
15 540 points per second, which is lower than the maximum of 570 000
points per second. An overview of the three datasets given in this report
is seen in Tab. 1.
Table1 List of datasets
Dataset Frames [-] Duration [s] Points [-]
#1 6525 590 9 166 769
#2 18091 2473 25 315 189
#3 25967 2816 35 646 656
From the table, it is given that an average number of points per frame
for each set is approximately 1300-1400. The duration of datasets 2-3
is approximately 40-50 min, while dataset #1 has a duration of 10
mins. It was described in the Method section how the measurements
are conducted as two dierent movies when the galvanometer measures
outwards and inwards. This changes the measurements, which is why
the datasets are divided into an A-movie and a B-movie. This means a
change in the number of frames as this would now be half that given
in Tab. 1. By further investigating the number of frames given and the
duration of the dataset, it is seen that a duration of 590 seconds does not
compute to 3263 frames for the A-movie with a sampling frequency of
10 Hz. The results show a longer duration than that given by the number
of frames. This also shows when plotting the sampling time, t, as
given for the A-movie (indicated by the subscript: A) in Fig. 5. From
the figure, it is evident that the Wave Mapper has periods during which
the sampling time increases, for dataset #1 it is as much as 50 seconds.
The reasons for these measurement pauses could be found in the safety
equipment also installed in the system. Due to the LIDAR’s placement
on an operational oshore platform, there is a need for control of the
laser pulses. If a person, ship or similar object enters the FOV of the
laser, it lowers the energy power immediately to a value that is safe, thus
not making any measurements. It is also evident, from the middle figure
in Fig. 5, that smaller sampling times can be located along with varying
values. The varying values are seen as an error of the time-stamp when
collected by the data in the software, and not of the equipment itself.
The sampling frequency is controlled by motors, and it is not likely that
these would change in speed. Instead, these changes would be found
in the buering of the time-stamp. These spikes in sampling frequency
or time also change the duration of the entire dataset, as seen in the
last figure. Here, the dashed line shows the true duration should the
sampling be equal to 10 Hz. The collected time is the black line which
shows that fewer points give the same duration, thereby verifying the
large spikes in sampling time, t.
The solution to this change in tis given by dividing the surface elevation
into pieces of time in which the signal is assumed to be sampled at a
Fig. 3 A surface plot with the red dots as the data points. The A- and B-movie relate to the galvanometer’s rotation outwards and inwards,
respectively, creating two separate datasets intertwined with each other.
0 2000 4000 6000
Frames, [-]
Points in Frame, [-]
Dataset #1
1000 1500 2000 2500 3000
Points in Frame, [-]
Normalised histogram, [-]
10-3 Dataset #1
Fig. 4 The data from one dataset. Left: The figure shows the
amount of points in each frame. Right: The figure shows a
normalized histogram of the same points.
constant rate of 10 Hz. This is completed through a threshold of dtT=
2.5 s or 0.4 Hz. Sampling time values above this are deemed too large
to be within a constant value. From this, it is possible to indicate the
piecewise time-signals, see Fig. 6. As seen in Fig. 6, a large part of
the sections only has a small amount of the frames of the entire dataset.
For that reason, another threshold is used, M=400. This threshold
indicates that for further analysis only sections of 40 seconds or more
0 2000
FrameA, [-]
dtA, [s]
0 2000
FrameA, [-]
dtA, [s]
0 5000
FramesA, [-]
Duration TA, [s]
Time Constant time
Fig. 5 Left: the entire sampling time, tA. Middle: a smaller
axis. Right: the accumulation of the duration time, TA.
The dashed line is the duration for 10Hz.
are analyzed. This choice is discussed further in the conclusion. This
results in only four sections being analyzed further for dataset #1. This is
illustrated in Fig. 7. With the piecewise time-signals of the LIDAR data,
the entire duration of collected data is not used, as given in Tab. 1. A
large portion is given as extra time or wasted time, time where dt >2.5 s
or sections with under 40 s (400 consequent frames), see Tab. 2.
Given by Tab. 2, only a smaller portion of the total duration of mea-
0 10 20 30
Sections (dtA < 2.5s (0.4 Hz))
Frames between dt > 2.5s cut-offs
0 10 20 30
Sections (dtA < 2.5s (0.4 Hz))
Frames in section [-]
Threshold, M
Fig. 6 Left: The sections in which dt <dtT. Right: The bar plot
of the section and number of frames in each. The dashed
line is a threshold, M, set by the authors
0 1000 2000 3000
FrameA, [-]
dtA [s]
dtAStart-point End-point
0 1000 2000 3000
FrameA, [-]
dtA [s]
Fig. 7 Left: The figure shows the entire sampling time, tA.
Right: The figure shows a smaller axis, where tAis sup-
posed to be constant around 0.1 s. The start and stop points
are indicated in both figures for the piecewise constant
dt <dtT.
Table2 The duration of good data collected in the dierent datasets
Dataset Total time [s] Data time [s] Sections [-]
#1 590 202 4
#2 2473 464 8
#3 2816 852 7
sured data is able to be analyzed. This is something that needs to be
reconfigured within the software of the system or to be worked around,
as done in this paper. Here, the solution is, as described, to divide the
data into sections of constant t. In the last column in Tab. 2, the num-
ber of sections for each dataset is given. It is evident that the sections
are of small duration, which is not favorable when measuring in deep or
intermediate-depth oceanic areas.
Significant Wave Height
From the sections, it is possible to calculate the significant wave
height, Hm0. The small length of time is a limitation for an analysis
in the frequency domain in which Hm0is calculated. The Hm0value
is, however, the method used by TOTAL E&P and other oshore
companies, which is why the method is chosen.
For the calculation of Hm0, it is given that
fnE(f)d f,for n=..., 1,0,1, ... (1)
where mnis the n’th-order moment of the variance density spectrum
E(f). The above approximation is for deep water. The procedure for
calculating Hm0is done for each section within one dataset.
Due to some of the sections being of greater duration than others, the
values are weighted with their respective duration for each section. This
means that the spectrum is found for each section first, where the mean
of the spectrum then creates a new spectrum that is used to calculate the
final Hm0for the dataset. The spectrums are calculated by resampling
each section for a constant sampling frequency of f=10Hz. Fur-
thermore, the sections of shorter duration are padded with zeros in the
time-domain, to create signals of even lengths. The spectrums and the
Hm0values are given in Fig. 8. From the spectrum, it is also seen that
the peak is given at approximately f=0.1Hz, which coincide well with
a JONSWAP spectrum for the North Sea. All previous figures have been
0 1 2 3 4
Frequency [Hz]
Power/Freq [m2/Hz]
Average Weighted
Sections [-]
Hm0 [m]
Hm0 Average Weighted
Fig. 8 Left: Spectrum of all sections along with the dashed line
showing the averaged weighted spectrum. Right: The Hm0
values calculated and the subsequent averaged weigthed
shown for a single point in the grid system. Since the Vigsø grid, is given
by a 375 x 1575 point grid, these calculations are conducted for multiple
points. The chosen area is given by a subjective choice in which the light
intensity factor from the system is utilized together with a colormap of
the density of data in the (x,y)-plane, see Fig 9. Hereafter, it is possible
to calculate the Hm0values, an example is seen in Fig. 10. This can then
be compared with radar values given by TOTAL E&P from the same pe-
riod. The radar values are also Hm0values. They are calculated as 30 min
blocks each, divided into sections of 225 s. Hereafter, the spectrum and
subsequent mean value are calculated. A comparison of values is given
in Tab. 3.
0 50 100 150 200 250
x [m]
y [m]
Intensity [W/m]
0 2000
Intensity [W/m]
Fig. 9 The colormap shows the density of the (x,y)-points for the
given dataset. The two other sub-figures show the light
intensity factor of the system in each direction.
412040 20
Hm0 [m]
y [m] x [m]
-20 60
-40 -60 40
5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6
Hm0 [m]
Normalised Histogram
Fig. 10 The colormap shows the variety of the Hm0values over the
chosen area. The normalised histogram shows the dier-
ence between LIDAR and Radar-values (red dashed).
Dataset Hm0[m] Hm0Radar [m]
#1 7.19 6.53-6.71
#2 5.34 6.56-6.84
#3 5.01 5.57-5.80
Table 3 The mean Hm0values for the three datasets. The radar
value is given as min/max during the duration of the LI-
DAR signal.
This paper describes the use of a new prototype LIDAR instrument for
mapping the time-varying ocean surface profiles along with detection of
breaking waves. A comparison of data obtained from the Wave Mapper is
made with similar significant wave heights, Hm0, from a radar placed on
the opposite side of the same oshore platform. The data shows LIDAR
to be useful equipment for the study of ocean waves. The new prototype
has certain advantages over the more commonly used wave buoys and
radars. However, it has limitations as well. For the time being, the need
for sectioning of the data signal greatly increases the diculty of signal
processing and data analysis. Furthermore, the length of each section in
all datasets is of such small magnitude that a direct comparison with the
radar values is not truly possible. Howeverm the results do show that
the LIDAR system is capable of mapping waves - true three-dimensional
waves, see Fig. 3. Comparable to industrial LIDARs used in the coastal
region, this prototype shows good results not only when the waves break.
In general, it shows good results for rough surfaces with capillary waves.
In conclusion, this is a great and significant leap forward in the tech-
nology for measuring waves. The prototype allows for true waves to be
measured, and furthermore the mapping and determination of breaking
waves. Finally, it will also allow for area statistics of ocean waves to be
conducted at a later stage. In near future, some of the things that should
be investigated further are the dierent thresholds that have been set up
throughout this report
dtT=2.5s: The threshold for piecewise cutting of sections.
M=400 frames: The sections have dierent lengths.
These thresholds have a strong impact on the significant wave height,
Hm0, when calculated. Currently, they have been chosen by examining
the datasets. A more scientific approach to these values should be
investigated. An overall better way is to estimate the significant wave
height directly from the point clouds, by doing a wavenumber spectrum
and through that calculating the values. This approach is, however, not
seen in this report since this is a comparison with a known equipment
where the other method is used. For future work, the wavenumber
spectrum will be preferred.
Further investigation is underway for the development of LIDAR as a
field measurement tool for breaking ocean waves. Especially the limi-
tation of data collection, caused by the prototype being installed in the
North Sea, is something that needs to be solved for faster and better anal-
ysis of the instrument.
The authors thank Marius Tarpø and Michael Vigsø from Aarhus Univer-
sity for fruitful discussions throughout the entire process. This project is
funded by the Centre for Oil and Gas - DTU/Danish Hydrocarbon Re-
search and Technology Centre (DHRTC).
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Understanding of breaking and broken waves is key for the prediction of nearshore sediment transport and coastal hazards, however the difficulty of obtaining measurements of highly unsteady nearshore waves has limited the availability of field data. This paper reports on a novel field experiment designed to capture the time-varying free-surface throughout the surf and swash zones was conducted on a dissipative sandy beach using an array of 2D LiDAR scanners. Three scanners were deployed from the pier at Saltburn-by-the-Sea, UK for a 6 day period to monitor the surface elevation of nearshore waves from the break point to the runup limit at temporal and spatial resolutions (order of centimetres) rarely achieved in field conditions. The experimental setup and the procedure to obtain a continuous time series of surface elevation and wave geometry is described. A new method to accurately determine the break point location is presented and compared to existing methodologies.
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The SAAB REX WaveRadar sensor is widely used for platform-based wave measurement systems by the offshore oil and gas industry. It offers in situ surface elevation wave measurements at relatively low operational costs. Furthermore, there is adequate flexibility in sampling rates, allowing in principle sampling frequencies from 1 to 10 Hz, but with an angular microwave beam width of 10A degrees and an implied ocean surface footprint in the order of metres, significant limitations on the spatial and temporal resolution might be expected. Indeed there are reports that the accuracy of the measurements from wave radars may not be as good as expected. We review the functionality of a WaveRadar using numerical simulations to better understand how WaveRadar estimates compare with known surface elevations. In addition, we review recent field measurements made with a WaveRadar set at the maximum sampling frequency, in the light of the expected functionality and the numerical simulations, and we include inter-comparisons between SAAB radars and buoy measurements for locations in the North Sea.
The use of LiDAR as an alternative to an array of in-situ instruments for water elevation measurement, specifically in the surf zone, is covered in detail. This paper outlines the advances in remote sensing of the coastal environment and provide both laboratory and field observations obtained through the application of LIDAR scanning devices. The results of this paper show a good correlation between LiDAR and pressure transducer measurements of water elevation in both a wave flume and within the surf zone (mean coefficient of determination of 0.76 and 0.89 respectively). The water surface reflectivity of the study area needs to be maximised in order for the LiDAR to provide suitable measurements, therefore a method of seeding in the wave flume is described. Points to consider for the setup of the LiDAR instrument in both the laboratory and the field are discussed, as well as the influence that wave parameters such as wave height and wave period have on the quality of results. Free surface elevation data across the spatial and temporal domain can be obtained with LiDAR and used for a wide range of wave analyses.
Beach erosion and bedform change in the nearshore can be most commonly attributed to energetic breaking of waves in the surf zone among other physical facings such as winds, tides, river runoff, currents, etc. Typically, beach profile shapes develop in response to incident breaking waves. The in-situ sensors are limited in covering the entire measurement of the spatial wave transformation. In recent years, development of field survey techniques using laser has provoked various applications of 3-D laser scanners (LIDAR) to beach monitoring. Since bubbles and foam induced by wave breaking cause laser beams to be back-scattered, the Terrestrial LIDAR can also detect the beam signals reflected by breaking and broken waves in the nearshore. In this study, the Terrestrial LIDAR was used to survey height of breaking waves across the surf zone on the Mallipo Beach, in the west coast of Korea. In addition, the measurements by the Terrestrial LIDAR were compared with optical records of sea surface oscillations along a vertical staff. The spatial resolution was set to approximately 2 cm at a distance of 100 m from the LIDAR location installed foreshore. The sampling time interval was 0.33 s. As a result, crest elevations of 26 waves incoming in the surf zone were indentified by the Terrestrial LIDAR. Meanwhile, the concurrently recorded optical data showed that only 24 breaking waves passed by the staff. The difference is explained that two of the LIDAR-detected wave crests didn't break but propagated under foam produced by the precedent breaking wave. The variation of the elevation of the breaking wave crests across the surf zone detected by the Terrestrial LIDAR can be further used to investigate the characteristics of coastal processes.
This paper provides an overview of the basic principles of airborne laser altimetry for surveys of coastal topography, and describes the methods used in the acquisition and processing of NASA Airborne Topographic Mapper (ATM) surveys that cover much of the conterminous US coastline. This form of remote sensing, also known as "topographic lidar", has undergone extremely rapid development during the last two decades, and has the potential to contribute within a wide range of coastal scientific investigations. Various airborne laser surveying (ALS) applications that are relevant to coastal studies are being pursued by researchers in a range of Earth science disciplines. Examples include the mapping of "bald earth" land surfaces below even moderately dense vegetation in studies of geologic framework and hydrology, and determination of the vegetation canopy structure, a key variable in mapping wildlife habitats. ALS has also proven to be an excellent method for the regional mapping of geomorphic change along barrier island beaches and other sandy coasts due to storms or long-term sedimentary processes. Coastal scientists are adopting ALS as a basic method in the study of an array of additional coastal topics. ALS can provide useful information in the analysis of shoreline change, the prediction and assessment of landslides along seacliffs and headlands, examination of subsidence causing coastal land loss, and in predicting storm surge and tsunami inundation.
Written by leading experts in the field of Lidar, this book brings all the recent applications and practices up-to-date. With a forward by one of the founding fathers in the area. Its broad cross-disciplinary scope should appeal to scientists ranging from the view of optical sciences to environmental engineers.
An experimental study was completed to investigate the ability of a fixed, two-dimensional LiDAR instrument to obtain detailed measurements of propagating waves within a laboratory wave flume. The results show that this technology can be used to obtain synchronous free-surface measurements at a horizontal spatial resolution O(10 mm), with comparable vertical accuracy to that of more conventional, high-precision capacitance wave probes. The principal advantage of using the LiDAR is that a single, non-intrusive instrument can be utilised to measure the entire wave field at high resolution allowing detailed evaluation of wave transformation throughout the experimental domain.
An airborne laser wave-profiler was used to estimate fetch-limited, one-dimensional wave number spectra by flying seaward of Cape Henlopen, Delaware, to a distance of 95 nautical miles, following passage of a cold front associated with a mean horizontal wind speed of 14 meters sec-1. The equilibrium range constant was estimated at 4.2×10-3, and it is shown that the constant may be overestimated or underestimated depending on assumed angular spreading of the waves. `Overshoot' observed in these one-dimensional wave number spectra is associated with angular spreading. Linear and exponential growth parameters estimated from fetch-limited wave spectra are in reasonable agreement with other field investigations. Observations of exponential wave growth generally support the Miles-Phillips predictions in the range 12≤C/U*≤21 where C is the phase speed of the wave and U* is the friction velocity.
This paper is a functional description of the ARL laser profilometer, a special-purpose laser device built to measure ocean-wave profiles from an airborne platform. Mounted in an aircraft flying at 60 meters above the surface and at 240 km/hr, the profilometer yields data which are processed to produce a vertical-section trace of the ocean surface showing ripples of 2.5 cm or less and waves of any height.