Bathymetric Lidar Monte Carlo Simulation

1. Laser Remote Sensing
REPORT
Bathymetric Lidar Monte Carlo Simulation
Nurahida Laili / P66057042
June 2016
A. Objectives
To run a simulation of processing a bathymetric LiDAR data using Monte Carlo methods
in one given depth with three different water conditions.
B. Methodology
 Data : A package of java codes for bathymetric LiDAR simulation
 Software : Command Prompt (Microsoft Windows) and Microsoft Excel
C. Results and Analysis
In this experiments, a package of java codes for bathymetric LiDAR simulation was
provided to be investigated using Monte Carlo method. It consists of several input
parameters that would be examined to see its contribution respect to the data processing.
Table 1 shows the defined parameters that is given to the simulation.
Table 1. Defined input parameters
Laser wavelength (nm) 532
Photon (n) 1 x 106
Monitor time (ns) 3000
Depth (m) 10
In investigating the influence of the water input parameters, there are three different
sets of input parameters for water optical properties that would be tested to the data. The
input parameters that are changed are the Inherent Optical Properties, such as:
 The spectral absorption (a) and scattering (b) coefficients. Their correlation is shown
in spectral beam attenuation coefficient (c) below:
𝑐(𝜆) = 𝑎(𝜆) + 𝑏(𝜆)
 The spectral single - scattering albedo (wo). The single-scattering albedo is the
probability that a photon will be scattered (rather than absorbed) in any given
interaction. Hence, it is also known as the probability of photon survival.
𝑤0( 𝜆) =
𝑏( 𝜆)
𝑐( 𝜆)
The more dominating scattering (b) is, the bigger value of wo will be. Hence, the value
of wo equal to 1 will be reached when the value of absorption (a) is zero.
 Anisotropy factor (g). It is a parameter that could be adjusted to control the relative
amounts of forward and backward scattering in Henyey-Greenstein (1941) phase
function. The span of g is from −1 to 1. It is defined in Petzold (1972) measurements
to be 0.924, so I decided not to change this parameter.
Three different scenario that will be used to the simulation are define below, the input
parameters are also shown is Table 2.

2. 1) Pure water; In this condition, the water is very clean that the laser can strikes through
the water bodies to the bottom surface. I set the a and b to a very low number, so the c
and wo will also become low.
2) Clear ocean (a > b); In a clear sea water condition, both a and b is bigger than those in
the pure water condition. I set the value of a to be bigger than b, it means the absorption
is dominating. In this setting, the value of c and wo are increasing.
3) Turbid water (b > a); It is a condition where the turbidity is caused by its location that
close to land or a harbor, we may assume that the b is more dominating, but I put the
value of a to be a little bit higher than the other scenario.
Table 2. Changed Input parameters
Settings a b c wo
Pure water 0.05 0.0025 0.0525 0.05
Clear ocean 0.1 0.05 0.15 0.3
Turbid water 0.5 1.75 2.25 0.8
Those parameters then were put into the java codes and then I run the command prompt.
All the results then were being plot in a graphic, as shown in Figure 1.
Figure 1. Return signal from a 10 m deep for three different settings
The shape of the return signal in Figure 1 shows that pure water and clear ocean contain
the surface reflection (the first peak), bottom reflection (second peak), and volume
backscattering signal. Different shape shown by the turbid water as its signal has never
been reflected back to the receiver after it reached the water bodies, so it only has one peak
which is representing the water surface reflection.
As the beam attenuation coefficient c are getting bigger, which means the water is
getting more turbid, the received surface reflection signals are getting stronger. It happened
since more turbid water have more scattering particles, even in the surface layer, so there
would be more photons that are forced to travel back to the receiver right after they reach
the water surface. For the turbid water scenario, as we can see, it was so turbid that the
photons tend to be scattered back to the receiver when it reached the water surface with
even stronger signal compared to the other two water conditions.
Contrarily, the received bottom reflection signals are getting lower as the c value
increases. It happened since a clearer water condition has less c values which means there
were not much scattering or absorption happened. So, there were still many remaining
photons that could travel back to the receiver even after go through the water bodies.

3. It is depicted in Figure 1 that all surface reflection signals were received almost at the
same range of time. For the pure water and clear ocean, the bottom reflection received time
also the same. I think this results are representing a good capability of LiDAR depth
measurement since the length of time range are stable enough for two different water
environments, it is also a good prove for the effectiveness in using bathymetric LiDAR data.
Figure 2. Return signal from a 10 m deep for three different settings (in log scale)
Figure 3. Comparison of different number of photons used in turbid water scenario
The signal decreases with return time, and it exhibits an exponential decaying when the
vertical ordinate is transformed into log scale, as shown in Figure 2. For pure water and
clear ocean, their signal slowly decreased after they reached the water surface. Then they
suddenly increased to the highest peak as they reached the bottom, but then slowly
decreased with a steeper gradient. For turbid water, the signal reached the highest peak
when it hit the water surface, then exponentially decreased to the lowest value compared to
those in other two water scenario. It decreased steeply so that it didn’t even reach the
maximum limit of time (3000 ns).
As we could see in Figure 1, not much noises were found. Each of those pure water and
clear ocean signals have a good converge peak shape. It could be assumed that the number
of photons used in these scenario is appropriate enough in taking the work these 10-meter
depth simulation simulating the scenario that I have made. I also compare the result in using
different number of photons of turbid water as shown in Figure 3, the 1x106
photons has
less noise than other bigger (5x106
) or smaller (1x105
) number. As I have explained before,
I assume that the bathymetry airborne LiDAR is not suggested for shallow (±10 m) and
turbid ocean bathymetric measurements.