1. K-meter Survey System Lidar Data Mike Contarino Jennifer Prentice
Go forth and collect data
Jennifer Prentice
Dave Allocca
Tom Curran
NAVAIR’s HyCODE 2001 Objective simply stated is LiDAR inversion for the K-meter Survey System (KSS). The KSS is a green (532 nm, Nd:YAG) LiDAR (Light Detection and Ranging) system developed at the Naval air Warfare Center Electro Optic Sensors Division in Patuxent River, MD. LiDAR is basically the optical equivalent of the more familiar RADAR). It is an active remote sensing system that provides the parameter Ksys, the system attenuation coefficient for the propagation of laser light through the ocean water column being sampled. Ksys is defined as the slope of the returned power versus depth. By way of introduction this view graph serves to introduce the group assembled by Dr. Michael Contarino to work on this project. Dave Allocca and Tom Curran are the system designers and engineers and continue to work on the optimization of the systems performance. Brian Concannon’s expertise is in receiver engineering and signal processing. Alan Laux contributes his background in physics and optical modeling. I was brought on board just over a year ago to provide the oceanographic research support for the program.
EO and Special Mission Sensors Div
AIR Bldg Suite 1100
22347 Cedar Point Road
Patuxent River, MD
Brian Concannon
Alan Laux

2. What is ksys? System Attenuation Coefficient
An apparent optical property
Water Clarity
P(d) = AP e
-2K
sys d ( n
h + d) 2
Air
H2O
Po, FOV, Div
Returned Optical Signal h
Definition of Ksys – the water column attenuation coefficient (K) observed by LiDAR.
The fundamentals of the system are shown in the panel on the left. It has a well defined optical geometry that includes the fields of view (FOV) of the transmitter and receivers and divergence (Div) of the laser beam at a given height (h) above the surface. The laser light is shot into the medium with a given power (P0) as a narrow, well collimated beam. As the beam traverses several attenuation lengths the photons in the beam undergo single scattering and multiple scattering events and the beam spreads out towards an asymptotic radiance distribution. These events are dependent on the medium and the inherent optical properties (IOP’s) characterizing it: a – the absorption coefficient, b – the scattering coefficient (particularly bb in the backward direction), c – the total attenuation coefficient,  – the volume scattering function (VSF) at (), and  – the refractive index. The receivers collect the backscattered signal as returned optical power (Pd).
The center panel illustrates that the returned optical power reflects the transmission through the atmosphere (TA), air-sea interface (TAS), and the water column (TS). Following the surface flash (reflected and refracted portion of the beam at TAS) we see the exponential decay of the beam with depth. The log transformed slope of the decay is Ksys.
To the right is the simplest case of the LiDAR range equation. The returned optical power (Pd) is a function of the system parameters, air-sea transition, and (). In the equation above, these parameters are all lumped together in the term (A).
 TAS 2 A P0 () e-2Ksysd
P(d) = where  = transmission efficiency of the transmitter/receiver optics
( h + d)2 TAS = transmission efficiency of the air-sea interface
A = receiver aperture
P0 = Transmitted Power
Ksys = system attenuation coefficient
 = index of refraction
h = height above sea surface
d = water depth
Where :
A includes system parameter effects,
air-water transmission and b(p)
Po = transmitted power
h = height above surface
n = water index of refraction
d = water depth
Time = Depth d
a, b, c, b, n
Returned optical power vs.
depth is a function of :
- system parameters
- water IOP’s

3. Three Simple Lidar Cases
Wide Div
Narrow FOV
Wide Div
Wide FOV
Pencil Beam
Wide FOV
As Initially stated Ksys is a system specific parameter and therefore dependent on the optical geometry of the LiDAR. As an example of this, the FOV has a tremendous effect on the returned signal. The panels illustrate three simple LiDAR configurations and how they constrain Ksys.
Case 1: Wide Divergence and Narrow FOV - the narrow FOV blocks most of the scattered light from reaching the detector and being falsely counted. Ksys most closely aligns with the total beam attenuation coefficient with losses due to absorption and scattering out of the beam.
Case 2: Wide Divergence and Wide FOV – the beam appears as a diffuse source to the receiver, similar to the underwater radiance distribution of natural sunlight. Viewing all of the multiply scattered light Ksys aligns with Kd. The reference by Krumboltz at the end of the presentation describes this behavior in detail.
Case 3: Narrow Divergence and Wide FOV – almost all of the scattered light is collected by the receivers, such that losses in light are due to absorption of the photons and Ksys aligns with a.
ksys a
Loss is due to a
ksys c
Loss is due to a and b
ksys kd
Krumboltz

4. K-Meter Survey System (KSS)
CH 2
CH 1
Shipboard KSS Optical Layout
The shipboard KSS in its deployment sampling position on a NAVOCEANO ship in the Yellow Sea. View is the back end of the system where an umbilical cable connects the instrument to the laser power supply and cooler and the remote keyboard and monitor, which are housed below deck. The KSS is shown here attached to its aluminum base frame, it is lifted into position with a hydraulic ram (box 200 lbs). The schematic diagram shows the basic system components. On the transmitter side, the laser is steered into an attenuator and through divergence lens out a telescopic window (8” telescope). On the receiver side the light enters through a window, a 4 nm bandwidth interference filter, and receiver lens to the PMT. Adjustable irises in front of the PMT control the FOV. The shipboard KSS is a bi-static design, with two independent receiver channels (CH 1, CH2). This allows for flexibility in the system to look at two color, Raman, or different FOV’s. During HyCODE 2001 CH 1 was set to a wide FOV of 10 degrees and CH 2 was set to a narrow FOV of 4 degrees. The laser was operating at about half power at 532nm. In this presentation I will be talking to data primarily from CH 1 (wide FOV)
Shipboard System Design Specs:
Div = 3o, CH 1 FOV = 10o, CH 2 FOV = 4o
Dual Independent 4” x 4”, ND of 2.6 to 4
Output Power: 532 nm
8nS Pulse Width
100Mhz Analog Bandwidth
8 bit, 1 GSPS Dual Digitizers
PMT Photo Detectors with 10% QE
Interference Filters with 4 nm Bandwidth

5. Lidar Signals for Different Water Types
Example LiDAR returns for two different water types “Clean”(solid Black line), somewhere enroute between stations 2 and 3 off the shelf, and “Dirty” (solid Blue line), at station 13 within the LEO site test region. Depth is along the X axis from left to right. Clean and Dirty LiDAR waveforms (volt scale along the left side Y axis) show fairly linear, homogeneous decay rates to 27 m and 7 m, respectively. Dashed lines with square symbols represent LiDAR depth profiles. Ksys (m-1) was calculated from waveform data in 2 m binned increments to create the depth profile. Ksys at clean water site was 0.1 m-1 and between 0.45 and 0.5 m-1 at station 13.

6. Comparison of Lidar and In-situ Profile Data
Shallow Mixed Layer at Station 10
The plot in Panel 1 with Depth on the X-axis is presented in this manner to demonstrate the typical view for LiDAR data. The signal is a function of time (temporal measurement) converted to a depth measurement in contrast to the hydrographic data plots in Panels 2 & 3, where the measured parameters are a direct function of depth (spatial measurement).
Panel 1 is similar to the plots in the previous view graph (Slide 5) with waveform voltage on the left hand Y axis and Ksys on the right hand Y axis plotted versus depth (X axis). Note that the waveform is only shown down to a depth of 20 m, which is the deepest depth for this profile that is above what we conservatively consider the current noise floor (50% of the water column, Station 10 recorded bottom depth = 41 m). Compared to the plots in Slide 5, however, the waveform from Station 10 clearly shows three distinct regions of changing slope and a reflection signal (bump) at 15 m depth. Slope transition points are visible at 8, 14, and 17 m.
When these data are converted to a 2 m bin (coarse resolution) Ksys depth profile it appears that the KSS is detecting a shallow surface layer.
Panel 2 and Panel 3 show coincident hydrographic data from the R/V Endeavor ship CTD package, transmissometer, and fluorometer. Note that the depth on these plots is extended to 40 m to better demonstrate the overall hydrographic features of the water column
The KSS depth profile parallels the water column density structure. Inflection points in the KSS profile appear to correspond to changes in the % Transmission and increasing fluorescence signal at 8, 10, 15 and 17 m. These data demonstrate that profile data of the Ksys parameter is detecting shallow optical layer structure at 15 m depth in a coastal region, however identifying the dominating optical influence on the return requires further work towards a more highly resolved Ksys depth profile and analytical comparison with in situ IOP data.

7. HyCODE 2001 – NAVAIR Station Locations
25,000 Ksys Waveforms Collected On Station and In Transit July 22-25
Typical Sample =
40 waveforms
Waveforms processed to yield an average Ksys point measurement for a given depth range
e.g. (1-15 m)
Station Locations for KSS LiDAR. A total of 25K waveforms were collected between July 22 and July 25. Since each Ksys sample represents the average of 40 waveforms, 625 Ksys measurements were made on station and in transit. Stations 2 through 16 comprise a transect beginning off the shelf and extending into the LEO study region (stations West of 15 and 16). Previous viewgraphs have demonstrated waveform data from a site in clear ocean waters between stations 2 and 3, station 13 within the LEO site, and shelf waters exhibiting shallow layered structures at stations 9 and 10. Subsequent viewgraphs are going to show an integrated Ksys value for the upper water column obtained by averaging the value of Ksys in the upper 15 m. We decided to look at a single point measure because of the recent discovery that laser EMI noise appears to be affecting the signal. The noise does not seem to affect the return above 20 meters, but it may to some extent. The effect of this noise source on the return needs to be tested more rigorously in the laboratory. At present we cannot discern if we are seeing structure deeper than 20 m in coastal waters because of interference by this potential noise floor. With lab testing and model analysis, we hope to be able to remove the noise and provide profile data to 40+ m depth. The benefit of generating this type of bulk Ksys parameter is using it as a first look at KSS sensitivity to spatio-temporal changes in ocean optical properties.

8. KSS Transect Data, Stations 2 to 10
>500 Ksys Samples
CH 2
Narrow FOV
CH 1
Wide FOV
The data in the upper panel shows the bulk Ksys (single point measurement) calculated for each sample taken between stations 2 and 10. Black squares represent average Ksys from Channel 1 and green squares are average Ksys from Channel 2. The data patterns track as expected. The focus for this talk is only on the Channel 1 data. However, this plot also serves to illustrate the versatility of the system achieved by having two independent receivers. With the set up used during the HyCODE 2001 cruise we can look at variations in FOV. However, the Channel 2 receiver could also be set up to look at different polarization, spectral (color-e.g.blue, yellow), or Raman scatter returns. Values between 0.1 and 0.2 m-1 were observed for bulk Ksys for CH1 along the cruise track and variations over time were seen at a single station and while in transit between stations. Changing data values between stations 7 and 8 are of particular interest and will be an initial focus to determine the association with in situ optical measurements and hydrographic parameters.
Panel 2 shows data collected with a WetLabs ac-9 at 532 nm on the Pegau SLOWDROP profiler package that match LiDAR profiles between stations 4 and 7. The attenuation coefficient on the left Y axis and the absorption coefficient on the right Y axis. In situ IOP data reflect the same variation trend as Ksys. Ksys values for Channel 1 are between values of a and c as expected (a  K  c), while values from the narrow FOV Channel 2 receiver are very close to value of c measured with the ac-9.

9. A Quick Look at Ksys vs. a(532) and c(532)
(Pegau, SLOWDROP Profiler)
A second look at the relationship between Ksys and a,c is indicating linear correlations. Although the fit is extrapolated to a single point at high values, the five data points at low values are tightly grouped and the trends of the relationship are consistent and proportional. It is emphasized that because of the limited amount of data shown and because no attempt has been made to accurately correlate the in situ data and the LiDAR data in space and time, the equations are not intended to represent a defined relationship between Ksys (m-1) (AOP) and either a or c (IOP’s) at this time.

10. Dr. E. Zege Analytical Lidar Model - KSS-2
Dr. Eleanora Zege from Belarus has developed an Analytical Lidar Model called KSS-2. It is a powerful tool for evaluating the effects of changing ocean and atmospheric conditions as well as system characteristics on real world LiDAR returns (impacted by multiple scattering). It is based on the solution to a set of semi-analytical algorithms, therefore having the advantage of running fast compared to a Monte Carlo Model. The software has a windows based interface and the view graph demonstrates these and some of the variables the model incorporates. Inputs include system geometry: beam diameter, beam orientation, transmit and receiver characteristics; In water optical properties of a Stratified or Homogeneous Ocean: a, c, sea surface aerosol, (); Bottom characteristics; Day versus Night; Weather Conditions input as wind speed at 10 m altitude and meteorological visibility range. Outputs include Total Waveforms: partitioned into atmospheric, surface, water column, bottom and overall waveform returns; Noise sources: standard deviation of the mean for the LiDAR noise signal (shot noise versus fluctuations in environmental conditions; Processed Ksys values. The most important issues that we are looking to use this model towards is evaluating the relative importance and effect of the VSF (()) and, along with laboratory tests, characterizing the effects of various noise sources effecting the measurement.

11. Future Work Science Missions : Validate the model using in-situ data
Solving the forward problem (IOP inputs gives lidar waveform)
Reverse problem
Can a unique set of IOP’s be derived from a single lidar measurement ?
Detect, identify and monitor shallow water column structures
For example: plankton and particle scattering layers
Navy Mission :
Sensor performance predictions (ALMDS = AES1)
Generate global maps of water clarity versus season
Mixed layer detection and mapping
Objectives and Future Work fall into a combination of Science and Naval Missions. One, we want to validate the Zege model using collected in situ data, thereby solving the forward problem: can IOP inputs predict LiDAR waveforms and Ksys. Two, essentially the reverse problem, can a unique set of IOP’s be derived from a single LiDAR measurement? The thrust of the Science mission is to detect, identify an monitor shallow water column structures and transient features such as internal waves. This mission will be accomplished by setting up future deployments of the system to simultaneously compare two FOV’s (as shown in a prior viewgraph), two polarizations, and two wavelengths. The Navy mission for the KSS has been achieved and it is ready to transition into the fleet. As an EO sensor it is a low cost system that can be fielded on a “not to interfere basis” on either a shipboard or an airborne platform. The target objective is sensor performance prediction of ALMDS (the Airborne LiDAR Mine Detection System) which has been transitioned to the fleet as AES1 (Airborne Electro-Optic Sensor 1). For example, the KSS can provide the necessary information to a fleet commander to make the decision whether to use an EO sensor or a sonar in a given environment. The detection and mapping of the mixed layer depth is directly applicable to both the Science and the Naval missions.

12. References Krumboltz REFERENCE
H. Krumboltz, “Experimental Investigation of System Attenuation Coefficient for HALS”,,Report No NADCS prepared for Defense Mapping Agency, August 1979.
Zege REFERENCES
E.P. Zege, A.P. Ivanov, and I.L. Katsev, Image Transfer through a Scattering Medium (Springer- Verlag, Heidelberg, 1991). 349p.
I.L. Katsev, E.P. Zege, A.S. Prikhach, and I.N. Polonsky, “Efficient technique to determine backscattered light power for various atmospheric and oceanic sounding and imaging systems”, JOSA A., 14, , (1997).
E.P. Zege, I.L. Katsev, and I.N. Polonsky, “Analytical solution to LIDAR return signals from clouds with regard to multiple scattering”, Appl. Phys., B60, , (1995).
E.P. Zege, I.L. Katsev, and I.N. Polonsky, “Effects of Multiple Scattering in Laser Sounding of a Stratified Scattering Medium. 1. General Theory”, Izv., Atmos. Oceanic Phys., 34, N1, 36-40, (1998).
References for the behavior of the LiDAR system attenuation coefficient relative to Kd (Krumboltz, 1979)
Zege References – Reference 1 is a text book
References 2, 3, and 4 are papers on the theory and analytical algorithm for determining backscatter signal from an imaging system and LiDAR returns under multiple scattering conditions.