This PDF file contains the front matter associated with SPIE Proceedings Volume 8030, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The Long Island Sound Coastal Observational platform (LISCO) near Northport, New York, has been recently established to support satellite data validation. LISCO has both multispectral and hyperspectral radiometers for ocean color measurements. LISCO offers the potential for improving the calibration and validation activities of current and future Ocean Color satellite missions, as well as for satellite intercomparisons and spectral characterization of coastal waters. The multispectral measurements (SeaPRISM system) are part of the NASA AERONET - Ocean Color Network. In addition, LISCO expand observational capabilities for the continuous monitoring and assessment of the hyperspectral (HyperSAS system) and polarized properties Results of measurements made by both the multi- and hyper-spectral instruments, in operation since October 2009, are presented. Intercomparisons between HyperSAS and SeaPRISM data has been carried out, permitting the quantification of the main sources of uncertainty. The three main OCR satellites, MERIS, MODIS and SeaWiFS, have been evaluated against the LISCO dataset of quality-checked measurements of SeaPRISM and HYPERSAS. A first attempt of validation of the hyperspectral imagery provided by the HICO satellite mission is also presented.

The degree of polarization (DOP) of the underwater light field in oceanic waters is, as follows from the vector radiative transfer equation and has been shown experimentally for a long time [1], related to the single scattering albedo of suspended particles. The single scattering albedo, in turn, is given by the ratio of the scattering coefficient to the attenuation coefficient (or 1 - the ratio of the absorption coefficient to the attenuation coefficient). Knowledge of the single scattering albedo and of the particulate scattering matrix permits solution of the radiative transfer equation for the ocean body. This then opens up the possibility for estimation of attenuation coefficients from measurements of the Stokes components of the upwelling underwater light field which is not possible from unpolarized measurements of the remote sensing reflectance. Results of radiative transfer calculations are presented. We simulated multiple underwater polarized light fields (for the whole visible spectrum) using a vector radiative transfer code with the purpose of obtaining a parameterization of this relationship for different viewing geometries (different solar and zenith/azimuth viewing angles) and in various water conditions (i.e. various IOPs).

Clouds cause a serious problem for optical satellite sensors. Clouds not only conceal the ground, they also cast shadows, which cause either a reduction or total loss of information in an image, by reducing the illumination falling on the shadowed pixels. Ocean color bio-optical inversion algorithms rely on measurements of remote sensing reflectance (R_rs (λ )) at each pixel. If shadows are not removed properly across a scene, erroneous R_rs (λ) values will be calculated for the shadowed pixels, leading to incorrect retrievals of ocean color products such as chlorophyll. The cloud shadow issue becomes significant especially for high-resolution sensors such as the Hyperspectral Imager for the Coastal Ocean (HICO). On the other hand, the contrast of pixels in and outside a shadow provides opportunities to remove atmospheric contributions for ocean color remote sensing. Although identifying cloud is relatively straightforward using simple brightness thresholds, identifying their shadows especially over water is quite challenging because the brightness of the shadows is very close to the brightness of neighboring sunny regions especially in deep waters. In this study, we present automated procedures for our recently proposed cloud shadow detection technique called the Cloud Shadow Algorithm (CSA) and Lee et al. (2007) cloud and shadow atmospheric correction algorithm. We apply both automated procedures to HICO imagery and show examples of the results.

The Hyperspectral Imager for the Coastal Ocean (HICO™) is a hyperspectral sensor which was launched to the International Space Station in September 2009. The Naval Research Laboratory (NRL) has been developing the Coastal Water Signatures Toolkit (CWST) to estimate water depth, bottom type and water column constituents such as chlorophyll, suspended sediments and chromophoric dissolved organic matter from hyperspectral imagery. The CWST uses a look-up table approach, comparing remote sensing reflectance spectra observed in an image to a database of modeled spectra for pre-determined water column constituents, depth and bottom type. In order to successfully use this approach, the remote sensing reflectances must be accurate which implies accurately correcting for the atmospheric contribution to the HICO top of the atmosphere radiances. One tool the NRL is using to atmospherically correct HICO™ imagery is Correction of Coastal Ocean Atmospheres (COCOA), which is based on Tafkaa 6S. One of the user input parameters to COCOA is aerosol optical depth or aerosol visibility, which can vary rapidly over short distances in coastal waters. Changes to the aerosol thickness results in changes to the magnitude of the remote sensing reflectances. As such, the CWST retrievals for water constituents, depth and bottom type can be expected to vary in like fashion. This work is an illustration of the variability in CWST retrievals due to inaccurate aerosol thickness estimation during atmospheric correction of HICO™ images.

Polarimetry has the potential to greatly enhance the capabilities of visible and near-IR remote sensing systems. For remote sensing of the oceans, the passive polarimetric signal is a complicated function of solar angle and viewing geometry, along with the confounding effects of the atmosphere. The atmospheric polarization signal becomes dominant as the sensor altitude increases, and this can lead to complications with ground truth measurements. The purpose of this study is to investigate the combined effects of solar and viewing geometries and sensor altitude to determine a strategy for polarimetric remote sensing of the oceans. A polarimetric radiative transfer code is used to model the nature of polarized light in a coupled atmosphere-ocean system. Viewing geometries are examined to find the look angles and azimuth angles relative to the sun that provide the maximum information about the ocean. The effect of sensor altitude is shown for different aerosol and hydrosol types and concentrations. Finally, the complications of ground truth measurements will be discussed.

The goal of this study is to investigate thermal signatures left at the water surface by waves interacting with a submerged sphere. Two possible mechanisms for the appearance of detectable signatures are identified: 1) turbulence generated by the interaction of the wave's orbital motions and the sphere propagates away from the sphere and disturbs a pre-existing thermal gradient, and 2) the sphere is sufficiently close to the water surface to cause shoaling waves, which break and disturb the thermal gradient. In both cases if the existing thermal gradient and the turbulence intensity are 'strong enough' a detectable thermal signature will be left. In this laboratory study a high-resolution infrared camera was used to observe these temperature fluctuations for a variety of cases. Spheres of 3 different diameters were rigidly mounted at different depths and subjected to mechanically generated waves of varying amplitude. A set of critical conditions for the appearance of detectable signatures is identified and possible scalings are discussed.

The focus of this paper is to describe research being conducted at NAVAIR in Patuxent River, MD to improve optical detection, ranging and imaging in the underwater environment through the use of optical modulation techniques. The modulation provides a way to discriminate against unwanted scattered light that would otherwise reduce detection sensitivity. Another benefit of modulating the transmitted light is that coherent detection of the modulation envelope results in the ability to accurately measure the range to the underwater object. Ways to use the hardware and methods developed for the detection, ranging, and imaging scenario to satisfy other mission requirements are also being investigated. The requirements for the modulation scheme, modulation frequency, and laser characteristics (pulsed, continuous, optical power level) depend on the targeted application. The implementation of this optical modulation technique in a variety of underwater sensors has become possible due to recent advances in laser and receiver technology. A review of the work being done in this area of research will be presented, and results from laboratory experiments will be discussed.

Optical signal transmission underwater is of vital interests to both civilian and military applications. The range and signal to noise during the transmission, as a function of system and water optical properties determines the effectiveness of EO technology. These applications include diver visibility, search and rescue, mine detection and identification, and optical communications. The impact of optical turbulence on underwater imaging has been postulated and observed by many researchers. However, no quantative studies have been done until recently, in terms of both the environmental conditions, and impacts on image quality as a function of range and spatial frequencies. Image data collected from field measurements during SOTEX (Skaneateles Optical Turbulence Exercise, July 22-31, 2010) using the Image Measurement Assembly for Subsurface Turbulence (IMAST) are presented. Optical properties of the water column in the field were measured using WETLab's ac-9 and Laser In Situ Scattering and Transmissometer (LISST, Sequoia Scientific), in coordination with physical properties including CTD (Seabird), dissipation rate of kinetic energy and heat, using both the Vector velocimeter and CT combo (Nortek and PME), and shear probe based Vertical Microstructure Profiler (VMP, Rockland). The strong stratification structure in the water column provides great opportunity to observe various dissipation strengths throughout the water column, which corresponds directly with image quality as shown. Initial results demonstrate general agreement between data collected and model prediction, while discrepancies between measurements and model suggest higher spatial and temporal observations are needed in the future.

Enhancing visibility through scattering media is important in many fields for gaining information from the scattering medium. In the ocean, in particular, enhancement of imaging and visibility is important for divers, navigation, robotics, and target and mine detection and classification. Light scattering from particulates and turbulence in the ocean strongly affects underwater visibility. The magnitude of this degrading effect depends upon the underwater environment, and can rapidly degrade the quality of underwater imaging under certain conditions. To facilitate study of the impact of turbulence upon underwater imaging and to check against our previously developed model, quantified observation of the image degradation concurrent with characterization of the turbulent flow is necessary, spanning a variety of turbulent strengths. Therefore, we present field measurements of turbulence microstructure from the July 2010 Skaneateles Optical Turbulence Exercise (SOTEX), during which images of a target were collected over a 5 m path length at various depths in the water column, concurrent with profiles of the turbulent strength, optical properties, temperature, and conductivity. Turbulence was characterized by the turbulent kinetic energy dissipation (TKED) and thermal dissipation (TD) rates, which were obtained using both a Rockland Scientific Vertical Microstructure Profiler (VMP) and a Nortek Vector velocimeter in combination with a PME CT sensor. While the two instrumental setups demonstrate reasonable agreement, some irregularities highlight the spatial and temporal variability of the turbulence field. Supplementary measurements with the Vector/CT in a controlled laboratory convective tank will shed additional light on the quantitative relationship between image degradation and turbulence strength.

This paper examines imaging performance bounds for undersea electro-optic identification (EOID) sensors that use pulsed laser line scanners to form serial images, typically utilizing one laser pulse for each formed image element. The experimental results presented include the use of two distinct imaging geometries; firstly where the laser source and single element optical detector are nearly co-aligned (near monostatic) and secondly where the laser source is deployed on a separate platform positioned closer to the target (bistatic) to minimize source-to-target beam spread, volumetric scatter and attenuation, with the detector being positioned much further from the target. The former system uses synchronous scanning in order to significantly limit the required instantaneous angular acceptance function of the detector and has the desired intention of acquiring only ballistic photons that have directly interacted with the target element and the undesirable property of acquiring snake photon contributions that indirectly arrive into the detector aperture via multiple forward scattering over the two-way propagation path. The latter system utilizes a staring detector with a much wider angular acceptance function, the objective being to deliver maximum photon density to each target element and acquire diffuse, snake and ballistic photon contributions in order to maximize the signal. The objective of this work was to experimentally investigate pulse-to-pulse detection statistics for both imaging geometries in carefully controlled particle suspensions, with and without artificially generated random uncharacterized scattering inhomogeneities to assess potential image performance in realistic conditions where large biological and mineral particles, aggregates, thin biological scattering layers and turbulence will exist. More specifically, the study investigates received pulse energy variance in clear filtered water, as well as various well-characterized particle suspensions with and without an artificial thin random scattering layer. Efforts were made to keep device noise constant in order to assess the impact of the environment on extrapolated image quality.

Coastal bathymetry near Camp Pendleton, California was measured using wave motion as observed by the WorldView-2 commercial satellite imaging system. The linear finite depth dispersion relation for surface gravity waves was used to determine nearshore ocean depth from successive images acquired of the coastal area. Principal component transformations of co-registered 8-color multispectral images were found to very effectively highlight wave crests in the surf zone. Time sequential principal component images then contain both spatial and temporal information. From these change detection images, wave celerity could be determined and depth inversion could be performed. For waves farther from shore, the principal component transformation no longer highlighted wave crests, but crests could be resolved within a single RGB composite image with equalization enhancement. The wavelength of a wave above a point of known depth was measured. The wave period method was used to determine depth for other waves in the propagation direction of this wave. Depth calculations using these methods compared favorably to reference bathymetry. The spatial resolution for this method of determining depth is higher and perhaps more accurate than the reference bathymetry used in this study, particularly in the surf zone.

Observations taken from DigitalGlobe's WorldView-2 (WV-2) sensor were analyzed for bottom-type and bathymetry for data taken at Guam and Tinian in late February and early March of 2010. Classification of bottom type was done using supervised and unsupervised classification techniques. All eight of the multispectral bands were used. The supervised classification worked well based on ground truth collected on site. Bathymetric analysis was done using LiDAR-derived bathymetry in comparison with the multispectral imagery (MSI) data. The Red Edge (705-745 nm) band was used to correct for glint and general surface reflectance in the Blue (450-510 nm), Green (510-580 nm), and Yellow (585-625 nm) bands. For the Guam beach analyzed here, it was determined that the Green and Yellow bands were most effective for determining depth between 2.5 and 20 m. The Blue band was less effective. Shallow water with coral could also be identified.

Optical Phytoplankton Discriminator (OPD, a.k.a. BreveBuster) determines colored dissolved organic material (CDOM) absorption spectra and particulate light absorbance spectra. The CDOM absorption spectra and correlation coefficients (referred to as 'similarity indexes') between the particulate absorbance spectra and known phytoplankton classes are available in real-time. Post-deployment processing calculates the best fit of multiple absorbance spectra from known phytoplankton taxonomic classes. Through this process the OPD provides an estimate of the phytoplankton community chlorophyll distribution among the classes included in the fit process. The major components of the OPD include: a liquid-waveguide capillary cell (LWCC), a fiber-optic spectrometer, a tungsten-deuterium fiber-optic light and a 0.2 micrometer pore cross-flow filter. In-water operation of the OPD began in May 2003. Since that date 25 of these instruments have been deployed on a variety of autonomous underwater vehicles, buoys, piers, channel markers and boats and ships. It has been utilized in CDOM studies off the New Jersey coast, in HAB monitoring efforts in the Gulf of Mexico and the Great Lakes, and in phytoplankton community structure studies in the Galapagos Islands and the Mediterranean Sea. Most recently, it has been deployed to Veracruz, Mexico for HAB monitoring. Presently, several OPD's operating on Slocum gliders and coastal buoys make up a local HAB observatory south of Tampa Bay, Florida, partially supported by the NOAA/IOOS through GCOOS. This presentation will detail the OPD's capabilities and report results from several of the deployments listed above. The ongoing effort to effectively visualize 4-D phytoplankton community structure will be discussed.

The present work describes the development of two collaborative Autonomous Underwater Vehicles (AUV) for Antarctic exploration to use them in the Ecuadorian Expeditions to the Scientific Base Pedro Vicente Maldonado in Antarctica. One vehicle is an AUV, called TAUV, with classical torpedo architecture, can work as a platform to transport scientific payload in a determined path in open waters. The TAUV length is 2m and diameter of 0.16m and has got three degree of freedom: pitch, yaw and surge. The vehicle achieves stable control with a set of three pairs of control planes. The other vehicle is an AUV, called HAUV, with Hybrid architecture that combines the best characteristics of the ROV and AUV, high stability in the water column, high maneuverability at low velocity without control planes and efficient hydrodynamics. The HAUV length is less than 1.50 m. The propulsion module is formed by four thrusters, three axial and one oriented vertically, this configuration gives to the HAUV three degrees of freedom: heave, surge and yaw. This vehicle can works as a ROV or an AUV. The hybrid configuration features the vehicle to explore dangerous areas near to the glacier wall. Three collaborative behaviors are discussed: formation flying, point inspection near to the glacier wall, replacement of a missing vehicle. Results of some systems of the TAUV and HAUV from laboratory, sea trials in tropical waters and Antarctic environment are show.

A method has been developed which automatically extracts river and river bank locations from arbitrarily sourced high resolution (~1m) visual spectrum imagery without recourse to multi-spectral or even color information. This method relies on quantifying the difference in image texture between the relatively smooth surface of the river water and the rougher surface of the vegetated land or built environment bordering it and then segmenting the image into high and low roughness regions. The edges of the low roughness regions then define the river banks. The method can be coded in any language without recourse to proprietary tools and requires minimal operator intervention. As this sort of imagery is increasingly being made freely available through such services as Google Earth or Worldwind this technique can be used to extract river features when more specialized imagery or software is not available.

To meet the challenge of operating in river environments with denied access and to improve the riverine intelligence available to the warfighter, advanced high resolution river circulation models are combined with remote sensing feature extraction algorithms to produce a predictive capability for currents and water levels in rivers where a priori knowledge of the river environment is limited. A River Simulation Tool (RST) is developed to facilitate the rapid configuration of a river model. River geometry is extracted from the automated processing of available imagery while minimal user input is collected to complete the parameter and forcing specifications necessary to configure a river model. Contingencies within the RST accommodate missing data such as a lack of water depth information and allow for ensemble computations. Successful application of the RST to river environments is demonstrated for the Snohomish River, WA. Modeled currents compare favorably to in-situ currents reinforcing the value of the developed approach.

Remote sensing has been proven as an effective tool for mapping and monitoring water quality in coastal/inland waters during the past two decades. In light of this, it can also be applied to calibrate hydrodynamic models which predict the distribution of river plumes and streams in coastal/inland waters. This research examines the capability of Landsat 7 thermal data to calibrate a 3D hydrodynamic model by simulating a moderate sized river plume discharging into Lake Ontario, USA. The model is provided with a set of input variables and involves modeling material transport using a finite-differencing method to generate profiles of temperature within the water column as well as a surface temperature map. In this way, a Look-Up-Table (LUT) of multiple scenarios of environmental conditions was built by running the hydrodynamic model for several simulation hours. This process resulted in various shapes of the thermal plumes, one of which represented the best output. This was determined by making a comparison with atmospherically compensated Landsat 7 thermal data in the surface temperature domain. The best agreement with the remotely sensed data was found through an optimization in which an error function, calculated between the model outputs and the imagery, was minimized. The root-mean-squared-error (RMSE), computed between the best model output and the observed imagery on a pixel-by-pixel basis, indicated a good fit with less than half of a degree, approximately 0.34° C, on average, over the plume area. This research demonstrates the potential of existing Landsat data and the corresponding method to monitor river plumes of moderate size in inland/coastal environments.

The explosion of the Deepwater Horizon (MC-252) drilling platform on 20 April 2010 began a long response by the United Area Command. Previous responses to oil spills were limited in time due to the amount of oil spilled and were generally confined to the surface. Some of the oil from the Deepwater Horizon wellhead in 1500 meters of water broke into smaller droplets, whose density caused much of the oil to stay within a zone from 1000 to 1300 meters depth. The remainder of the oil rose to the surface. The two primary locations of oil required a broad collection of remote sensing techniques to locate and monitor the oil spill. Surface oil was monitored primarily from the air using aircraft and satellite assets. Satellite visible, infra-red, and radar satellite imagery helped to locate oil in the northern Gulf of Mexico and help predict its movement away from the spill site. Daily over-flights by aircraft provided higher spatial and temporal resolution data that were assimilated into daily products. These remote sensing assets were able to track the surface oil, but the subsurface oil required different techniques. In addition to salinity and temperature profiles to determine the subsurface structure, fluorometry and dissolved oxygen measurements provided information related to oil and its consumption by microorganisms. Water samples collected from CTD casts were analyzed on-board and returned to on-shore laboratories.

The Deepwater Horizon oil spill presented an unprecedented threat to the Gulf of Mexico coastline and living marine resources, and possibly to that of the southeastern USA. Needed for mitigation efforts and to guide scientific investigations was a system for tracking the oil, both at the surface and at depth. We report on such system, implemented immediately upon spill onset, by marshaling numerical model and satellite remote sensing resources available from existing coastal ocean observing activities. Surface oil locations inferred from satellite imagery were used to initialize the positions of the virtual particles in an ensemble of trajectory models, and the particles were tracked using forecast surface currents, with new particles added to simulate the continual release of oil from the well. Three dimensional subsurface tracking were also performed from the well site location at several different depths. Timely trajectory forecasts were used to plan scientific surveys and other spill response activities.

In this publication we present an automated detection method for ocean surface oil, like that which existed in the Gulf of Mexico as a result of the April 20, 2010 Deepwater Horizon drilling rig explosion. Regions of surface oil in airborne imagery are isolated using red, green, and blue bands from multispectral data sets. The oil shape isolation procedure involves a series of image processing functions to draw out the visual phenomenological features of the surface oil. These functions include selective color band combinations, contrast enhancement and histogram warping. An image segmentation process then separates out contiguous regions of oil to provide a raster mask to an analyst. We automate the detection algorithm to allow large volumes of data to be processed in a short time period, which can provide timely oil coverage statistics to response crews. Geo-referenced and mosaicked data sets enable the largest identified oil regions to be mapped to exact geographic coordinates. In our simulation, multispectral imagery came from multiple sources including first-hand data collected from the Gulf. Results of the simulation show the oil spill coverage area as a raster mask, along with histogram statistics of the oil pixels. A rough square footage estimate of the coverage is reported if the image ground sample distance is available.

Observing eddies and other oceanographic patterns in the subtropical and tropical oceans during summer time can be problematic, because sea surface temperature often lacks spatial contrast and satellite altimetry provides coarse resolution data with some time lag. MODIS ocean color observations are supposed to provide timely information, but they suffer from sun glint contamination when the glint reflectance, Lg, is > 0.01 sr^-1. Here, an empirical approach is demonstrated to remove sun glint and clouds using MODIS Rayleigh-corrected reflectance (Rrc) at 469, 555, 645, 859, and 1240-nm. A color index (CI) is derived from the 469-555-645 bands using a baseline subtraction. The CI color patterns appear consistent from adjacent days when different glint and aerosol patterns are present, suggesting the validity of the approach. Applications of the approach over the Gulf of Mexico and other tropical and subtropical regions further validate the approach's general applicability. The new products at 1-km and 500-m resolutions make it possible to observe ocean eddies at both large and small scales. The simple design of the approach also makes it straightforward to implement for other regions when a qualitative MODIS CI is desired to infer circulation patterns and to detect eddies under severe sun glint.

The Conrad Blucher Institute for Surveying and Science (CBI) at Texas A&M University-Corpus Christi operates the Texas Coastal Ocean Observation Network (TCOON.) The network collects near real-time physical oceanographic data at 31 coastal stations along the Texas coast. The data includes water level, wind speed & direction, barometric pressure, water temperature, and air temperature from stations placed in bays and estuaries along the Texas coast. TCOON provides this critical data to many users, including those in the commercial shipping industry, marine construction, legal water-land boundaries, recreational boaters, and those responsible for marine safety and emergency evacuation in the event of a hurricane. Data sets are available in near real time via the Internet and some sets are accessible via voice over the telephone. All data collected since 1991 is available online along with data search tools. TCOON sponsors and developers believe that the more users and uses the system supports, the more valuable the data becomes. The highest scientific standards are used in collection the data as the data often ends up in litigation in the courts. Database software and the online tools used for data downloads are also open source.

The value of surface current data from SeaSonde high frequency radars (HFR), operated by the Central Gulf of Mexico Ocean Observing System (CenGOOS), to NOAA's Deepwater Horizon (DwH) oil spill response is demonstrated. The national integration of HFR data, undertaken as part of the Integrated Ocean Observing System, allowed NOAA to seamlessly utilize the CenGOOS data in giving guidance, throughout the event, on model choice for producing the trajectory forecasts for the spill. Additionally, the value of SeaSondes for response to other maritime emergency events, and to monitor vessels in real-time, under all weather conditions, and beyond-the-horizon, will be discussed. This ability to provide surface current data for a range of response efforts to maritime emergencies demonstrates the importance of extending the HFR coverage in the Gulf .