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K -meter S urvey S ystem Lidar Data - PowerPoint PPT Presentation

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Mike Contarino. K -meter S urvey S ystem Lidar Data . Go forth and collect data. Jennifer Prentice. Dave Allocca Tom Curran. EO and Special Mission Sensors Div AIR-4.5.6 Bldg. 2185 Suite 1100 22347 Cedar Point Road Patuxent River, MD 20670-1161 301-342-2025

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Presentation Transcript

Mike Contarino

K-meter Survey System

Lidar Data

Go forth and collect data

Jennifer Prentice

Dave Allocca

Tom Curran

EO and Special Mission Sensors Div

AIR-4.5.6 Bldg. 2185 Suite 1100

22347 Cedar Point Road

Patuxent River, MD 20670-1161


Brian Concannon

Alan Laux








P(d) =




h + d)

What is ksys?

  • System Attenuation Coefficient
  • An apparent optical property
  • Water Clarity



Po, FOV, Div

Returned Optical Signal


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

a, b, c, b, n


Returned optical power vs.

depth is a function of :

- system parameters

- water IOP’s


Three Simple Lidar Cases

Wide Div

Narrow FOV

Wide Div

Wide FOV

Pencil Beam

Wide FOV


Loss is due to a


Loss is due to a and b




CH 2

CH 1

K-Meter Survey System (KSS)

Shipboard KSS Optical Layout

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: 100mJ @ 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


Comparison of Lidar and In-situ Profile Data

Shallow Mixed Layer at Station 10


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)


KSS Transect Data, Stations 2 to 10

>500 Ksys Samples

CH 2

Narrow FOV

CH 1

Wide FOV


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


  • Krumboltz REFERENCE
  • H. Krumboltz, “Experimental Investigation of System Attenuation Coefficient for HALS”,,Report No NADCS0035-30 prepared for Defense Mapping Agency, August 1979.
  • 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, 1338-1346, (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, 345-353, (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).