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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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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 301-342-2025 PrenticeJE@navair.navy.mil Brian Concannon Alan Laux
-2K d e sys AP 0 P(d) = 2 ( n h + d) What is ksys? • System Attenuation Coefficient • An apparent optical property • Water Clarity Air H2O Po, FOV, Div Returned Optical Signal h 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 d 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 ksysa Loss is due to a ksysc Loss is due to a and b ksyskd Krumboltz
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
A Quick Look at Ksys vs. a(532) and c(532) (Pegau, SLOWDROP Profiler)
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
References • Krumboltz REFERENCE • H. Krumboltz, “Experimental Investigation of System Attenuation Coefficient for HALS”,,Report No NADCS0035-30 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, 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).