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Towards Turbid Waters

Optical Variability and Classification in Turbid Waters: HyCODE 2003 College of Marine Science University of South Florida Ken Carder/Dave Costello. Towards Turbid Waters. Optical ID of Red Tides (Chl mg/m 3 >1) [J. Patch ’02 ooXVI]: in prep. (CSR); cruise data ’98-’02

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Towards Turbid Waters

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  1. Optical Variability and ClassificationinTurbid Waters: HyCODE 2003College of Marine ScienceUniversity of South FloridaKen Carder/Dave Costello

  2. Towards Turbid Waters • Optical ID of Red Tides (Chl mg/m3>1) [J. Patch ’02 ooXVI]: in prep.(CSR); cruise data ’98-’02 • Remote depth, albedo, attenuation, and chlorophyll a in Tampa Bay [Lee et al. JGR‘01] • Shallow-water heating: effects on circulation [Warrior ‘02 ooXVI]; in prep.; WFS tests to use AVIRIS/PHILLS data w. Weisberg mod. • PHILLS cal/val: 3 lines completed for cal/val; some bathym. done • Structured light models: • sand-wave effects on H and albedo [Carder, Liu 2003 L&O]: M. Carlo • ridge, channel, ship [Reinersman, Carder ooXVI]: hybrid model; pub in prep. for AO • Predicting system operations in shallow, turbid, shaded waters: new • Bi-Static Laser-Line Cameras for 3-D & to Minimize Path Radiance for c < 4/m • Elastic, 532 nm -measurement results available • Inelastic, 532nm (Ex) : 685nm (Em) -measurement results available

  3. Red Tides Jennifer Patch and Kendall Carder

  4. Florida Gulf Of Mexico Tampa Bay Charlotte Harbor EcoHAB, FSLE, Redtide, and Blackwater cruises (March ‘99 – August ‘02)

  5. HYCODE CRUISES

  6. Measurements: aph(), ad() – quantitative filter technique aCDOM() – Perkin-Elmer lambda 18 spec., 10cm cells [Chl a] and [Phaeo.] – fluorometrically Rrs() – above water; 512-channel spectroradiometer bb() and chlorophyll fluorescence measured using an underway surface flow- through system Drop Package: CTD, 2 ac-9s, FLa, FLg, Ed, c(488), c(660), bottom video: FSLE & R/V Subchaser cruises

  7. K. brevis < 104 cells l-1 K. brevis < 104 cells l-1 K. brevis > 104 cells l-1 K. brevis > 104 cells l-1 aph(443) (upper left) and aCDOM(400) (lower left) are highly correlatedwith [Chl a]. For [Chl a] > 1.0 mg m-3, stations with highK. brevis cell concentrations (upper right) exhibit significantly lower ad(443) per unit [chl a] compared to stations with lowK. brevis concentrations. K. brevis < 104 cells l-1 K. brevis > 104 cells l-1

  8. K. brevis < 104 cells l-1 K. brevis > 104 cells l-1 [Morel, 1988] K. brevis blooms exhibit relatively low particulate backscattering per unit [Chl a]

  9. “Blue water” (extrapolated) Diatoms ( chl,  bbp)  Oligotrophic ( chl) K. brevis ( chl,  bbp)

  10. [Chl a] MODIS image acquired on 22 September 2001 RGB image bbp(551) RGB Composite [Chl a] bbp(551)

  11. Shipboard cell count data collected by the Florida Marine Research Institute (FMRI) between 20-26 September 2001.

  12. False classifications of K. brevis may also occur in shallow, nearshore environments. Such areas where the bottom can reflect a significant amount of light into the water column can mistakenly be interpreted as containing high chlorophyll concentrations. FLH To address this problem, future refinements may include comparing MODIS chlorophyll Fluorescence Line Height (FLH) imagery, which are not contaminated by bottom reflectance, to [chl a] imagery in order to identify these areas of “high [chl a]” that are not due to chlorophyll.

  13. Strategy for Turbid-Water OpticsCharacterize Environment Remotely Model 2-D or 3-D Light Field Determine What Systems Will Work Determine Optimal Operations Plan Perform Operations

  14. Structured Light Models: • Sand waves (Carder, Liu et al. L&O 2003) • Ridges and channels (Reinersman, Carder 02) • Ships and ports (under development)

  15. HYBRID MODEL Monte Carlo calculations for photons entering cube at a given location and direction and exiting at another point and direction. This allows an environment partitioned into cubes to be modeled using relaxation techniques given cubes of various optical properties as building blocks

  16. Rrs(400) viewed @ 57.5o nadir angle with azimuth  from 1 m above ridge (Chl a=0.2)

  17. BARGE BARGE To optimize deployment strategies for optical inspection systems such as ROBOT, we need 2- and 3-D models of light fields around ships and port structures that can be parameterized using remote systems (A/C, buoys), and optical models of sensor performance under various environmental conditions Results from the Reinersman/Carder Hybrid Model

  18. ROSEBUD ROV with ROBOT Laser Line Imaging system

  19. ROBOT Bi-static Laser-Line Imager, Test-Tank Sequence

  20. Individual ROBOT scans are assembled into ortho-geo-rectified 3-D images

  21. ROSEBUD/ROBOT during Submerged Target AGing Experiment (STAGE 4) 10 Jan 03. Speed exaggerated 5x

  22. Normal color video of 110’ USCG Cutter keel, 1-2 m range, c(532)=1.5/m USCG Port Security Technology Demo, Fisher Island, Miami

  23. ROBOT bi-static imagery of 110’ USCG Cutter keel, 1-2 m range, c(532)=1.5/m USCG Port Security Technology Demo, Fisher Island, Miami

  24. ROBOT imagery, prop shaft exiting USCG Cutter hull, 1-2 m range, c(532)=1.3/m USCG Port Security Technology Demo, Fisher Island, Miami

  25. ROBOT Fluorescence-mode imagery of USCG Cutter keel (relatively clean hull), 1-2 m range, c(532)=1.5/m, c(685)=1.3/m. USCG Port Security Technology Demo, Miami

  26. Publications • 19 peer-reviewed pubs • 17 symposium articles • 8 papers in prep.

  27. SPIN-OFFS • Thermal Models: • Remote determination of optical properties of bottom and water column that affect light absorption (heat gain) and reflection (heat loss) for coastal circulation models • Thermohaline flow off shallow platforms to interact at depth with coral (e.g. bleaching) • Coastal and Port Security: • Optimize strategies for optical inspection of hulls, ports, and strategic water ways • Remote observation (A/C, buoys) of optical properties • Prediction of light fields in structured settings (e.g. under hulls, along seawalls, around pilings, in channels) • Optimization of inspection sensors for range, gain, mode (elastic/inelastic) and time of day/night for given environmental conditions

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