Streak Tube Imaging Lidar (STIL) is today one of the leading technologies for airborne and underwater laser-based imaging systems. There is a vertical blurring effect unique to the STIL sensor which has a simple explanation in the process of the STIL image formation. Although we believe the effect is familiar to many workers in the field, to our knowledge it has not appeared in the literature, and in particular we believe the simple mathematical description required to characterize the effect has not appeared. One reason may be that the effect can mimic or be dominated by transmitter vertical blurring effects such as finite transmitter pulse width, pulse spreading, or other receiver blurring effects such as finite CCD element size and electron-path dispersion within the tube. In addition, the magnitude of the effect depends on the amount of scattering and is small for clear water or short propagation lengths. However, it is not difficult to demonstrate that the effect can be large for a realistic hypothetical system under realistic optical conditions, and, just as importantly, that relatively minor design changes having little effect on other image formation processes can significantly mitigate the STIL effect for the same system.

This paper examines the Navy's current electro-optical systems involved in mine countermeasures to establish a baseline for critical environmental parameters which affect their performance. Having established this baseline of system-related parameters, we examine the current generation of environmental measuring capabilities to determine what further developments are needed to cover the shortfall in capability. A suite of measurements is then proposed, which will allow the Navy a performance-predictive capability to assess the expected operation of the sensors.

The sensitivity of a LIDAR system and the contrast of immersed targets are strongly reduced by the volume backscattering clutter. To overcome this problem it has been suggested to use a radio-frequency modulation in association with an optical carrier at 532 nm; using a pulse laser source will allow one to keep the benefits of the reduction of volume backscattering clutter. Such a technique improves the detection of underwater targets and is justified by the low-pass transfer function shown by sea-water in backscattering configuration.

A modulated light detecting and ranging system has been developed to improve underwater imaging. This system uses the modulation information encoded on an optical signal to distinguish between the backscatter signal and the signal reflected from an underwater target. Through choice of the appropriate modulation frequency, this technique has the ability to improve underwater target contrasts by reducing backscatter noise. Both laboratory tank experiments and in- situ pier measurements have been completed with a modulated lidar prototype. The results show that the target contrast improved as the modulation frequency. Concurrent with the experimental measurements, a theoretical model is being developed for the modulated lidar system. This analytical model incorporates both the Small Angle Diffusion Approximation and the Multi-Component Method developed by Zege et al to solve the radiative transfer equation. The various experimental characteristics are included in the model and the results are compared with relevant experimental data. Preliminary results show good agreement with experimental data, including the reduction of backscatter with increasing modulation frequency.

The real-time simulation of broken and distorted images of a submerged target observed through a random realization of wind roughened sea surface produced by airborne gating imaging lidar is presented. The user is allowed to watch the sequence of images at the computer screen, which the lidar operator vies sat the TC screens of an operating airborne imaging lidar. Th inclusion of all types of noise as well as spatial correlation of receiver noise and noise due to a windy roughened sea surface with detailed treatment of seawater scattering provides a very realistic model. Two image processing techniques to deal with actual images - Matched Filter and Optimal Integration - are compared as to signal-to-noise ratio and probability of detection. Optimal Integration is appropriate for use by an experienced lidar operator.

Collection of airborne dat can be an expensive exercise. Data flights should optimize the quality and quantity of the data collected at minimal cost. Although the site to be surveyed is fixed, a mission planner has some freedom in formulating collection strategies. Choices may include the season, the time of day, the altitude and directions of the data run flights, the spectral bands, and the spectral and spatial resolutions used for the survey. A stochastic model has been developed to simulate and quantitatively estimate the statistical performance of airborne hyper- and multi- spectral systems in imaging a littoral sea bottom through a wavy sea surface. Results include mean and variance of various measures of system performance. Candidate collection plans can be tested with the stochastic model. This paper demonstrates the use of the stochastic model in examining the effect of flight direction on the quality of imagery for a variety of zenith sun angles and surface wave conditions. The calculations show the extreme sensitivity of data quality in terms of image signal to noise ratios to flight direction, sun angle, and sea wave direction.

Contrast transmittance (CT) theory has been used to derive a simple two-parameter visibility model that has been used by the civilian and military sectors for over seventy years. We review this classical theory with special attention to its application to problems in estimating liminal visibility range for in-water divers. Previous work by Duntley of the MIT/SIO Visibility Lab states that calculations based on this simple model give reasonable agreement with observation provided all embedded assumptions hold. Recent dat form divers conducting shallow water observations indicate that these assumptions cause the model to give poor results in this domain. We investigate an enhanced method that eliminates some of these assumptions to provide better estimate of underwater diver visibility.

A new line of 'multicolor' copper vapor lasers developed by SI Vavilov State Optical Institute to provide a set of four simultaneously emitted visible light pulses at 510.6, 539.4, 578.2, and 615.4 nm suggests a promising tool for optical oceanography. Along with application to bathymetry and submerged object detection, multicolor CVL employment offers strong possibilities of estimation of optical characteristics of seawater from elastic-scattering lidar sounding data. An approach to the problem of airborne lidar retrieval of inherent optical properties proposed in this paper is based on reconstruction of depth profiles of the diffuse attenuation coefficient, K_d, for each of the generated wavelength sand subsequent evaluation of IOP's with the use of physical models for light absorption and scattering by ocean water.

Based on a newly-designed serial target, we firstly demonstrate the underwater LIDAR imaging in such a 3m short water tank with highly turbid waters successfully, and show the range-gated phenomenon in water much more clearly. The target-set comprises a series of three bar test targets, which are set at intervals of 22.5cm roughly along the laser illumination direction since the light speed in water is 22.5 cm/ns. We synchronize precisely the UWLI system to range-gate on the targets which we want to capture their images, and to range-gate out the targets which we don't want their images. The attenuation coefficients in waters are 1.0/m and 2.3/m. Compared with non-gated case, the most distinct difference between the gates images and non-gated im ages in turbid water is that the nearer the target is located, the clearer its image is on the non-gated photos, but for the gated case the situation will be inverted completely when the delay time is adjusted suitably; that is, the image of the farther target could be much clearer than the image of the nearer target even in very turbid water.

The high-resolution imaging system ADS40 is a development to fulfill both photogrammetric and remote sensing requirements. The new sensor was introduced in mid 2000 and will close the digital chain for airborne photogrammetric data processing.

Experiments with two laser radar systems were conducted off the coast of Key West Florida in May of 2001. The purpose of the test was to observe the effect of the water optical properties on the Lidar return signal decay rate and compare the performance of the two systems. The first lidar system, the Shipborad K-meter Survey System (KSS) was configured to transmit linearly polarized light and to receive backscattered light in both channels. The second system, the Airborne KSS, is designed to conduct global surveys from patrolling P3-C aircraft. For this test the Airborne KSS was specially configured to operate from the deck of a ship and both systems were operated in conjunction with each other. The shipboard KSS was configured with a remotely controlled mechanical iris in both receiver channels to allow the use of different fields of view in each channel. Several oceanographic in-situ instruments were used to measure such water properties as optical transmission and absorption, backscatter coefficient, diffuse attenuation , temperature, and salinity as functions of depth. This in-situ dat was then compared with the lidar measurements.

The application of shape-from-silhouette methods for 3D imaging of underwater objects is explored in this paper. While there is a significant body of literature on the shape-from-silhouette technique, the reconstruction of an underwater object form a sequence of 2D lidar images present additional difficulties in both the creation and processing of silhouettes from the data, as the water optics and scattering can severely distort the silhouettes. In a recent data collection experiment, a lidar system was used to take images to create silhouettes sufficiently clear to support image reconstruction. Several 3D images were formed by applying the shape-from-silhouette technique to the 2D lidar silhouettes. With the in-air data serving as a benchmark, the 3D images of the underwater object suggest the potential for this approach.

This paper presents result of in-situ measurements of optical hyperspectral parameters, chlorophyll concentration and inherent and apparent optical properties of seawater. As a characteristic of hyperspectral optical measurements a CIE dominant wavelength and different color indices are used. Such important optical parameters as downward diffuse attenuation coefficient, diffuse reflection coefficient, beam attenuation coefficient, and seawater euphoric depth are measured and analyzed. Multiple regression relationships that connect hyperspectral data with biological and optical properties are proposed.

This paper presents experimentally obtained relationships between effective wavelength and a number of important seawater parameters such as dominant wavelength, different color indices, downward diffuse attenuation coefficient, absorption coefficient, seawater effective optical thickness, and a chlorophyll content. A number of regression relationships that connect hyperspectral measurements with listed bio-optical properties are prosed and discussed.

Up to now, no technique was available to perform inventories and surveys of immersed macroalgae. This article presents a method making it possible to discriminate between macroalgae and sea-floor and to identify the various macroalgae groups. To our knowledge, this is the first EEM of macroalgae ever carried out allowing the identification of the spectral characteristics of each macroalgae group. Fluorescence imaging provides the spatial dimension besides spectral properties. An OPO laser excites a test scene of macroalgae in a sea-water aquarium. Laser-induced fluorescence images from 400 nm to 640 nm displayed by steps of 10 nm are filtered at 680 nm. Their analysis, combined to the water depth parameter, leads to an identification method for macroalgae groups in a false-color image: each of the three macroalgae groups is enhanced by one defined threshold on this false-color image.

Airborne lidar provides an effective method for detection and localization of underwater objects, where the transmitted laser beam can penetrate the air-water interface and illuminate the scatterers within the water column, and the optical field generated by this scattering can be collected and processed. Here, we consider the use of lidar data collected form different observation angles of a particular water volume to image objects of interest. Interpretation the lidar returns as tomographic projections of a 3D reflectivity field, we formulate the problem as a 3D tomographic image reconstruction problem. We show tomographic reconstructions from both real and synthetic data sets. The real dat was collected by a Lockheed-Sanders lidar system in a US Navy field test. The synthetic data is produced by using an accurate statistical model that incorprates multiple scattering.

This paper presents the application of neural networks to chlorophyll-a and turbidity estimation using AVHRR data over the Gulf of Finland. Chlorophyll-a and turbidity are two major parameters in surface waters used for monitoring coastal water quality in the study. Since the Gulf of Finland is highly affected by the input from the rivers where have a high concentration of mineral suspended solids and nutrients, the coastal waters of the Gulf are optically dominated by absorption from both dissolved and particulate organic matters. Although AVHRR imagery can provide a synoptic view on surface water information of coastal areas, its quantitative use is still a difficult task in this study.

The estimation of chlorophyll-a is one of the key indices of monitoring the phytoplankton populations. In this paper, an approach for estimating chlorophyll-a concentration using a neural network model is prose. A dat set assembled form various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop containing coincident in-situ chlorophyll and remote sensing reflectance measurements is used to evaluate the efficacy of the proposed neural network model. The data comprises of 919 stations and has chlorophyll-a concentrations ranging between 0.019 and 32.79 (mu) g/l. There are approximately 20 observations form more turbid coastal waters. A feed-forward neural network model with 10 noes in the hidden layer has been constructed to estimate chlorophyll-a concentration. The remote sensing reflectances form five SeaWiFS wavelengths are used as inputs to our model. The network is trained using the Levenberg-Marquardt algorithm. A neural network model can deal with non-linear relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly with the output variable. They are flexible towards the choice of inputs and are tolerant to noise and require no a priori knowledge about the effect of these parameters. This makes them an ideal candidate for estimating chlorophyll-a concentration in coastal waters, where the presence of suspended sediments, detritus, and dissolved organic matter creates an optically complex situation. By allowing the neural network model to include several optical parameters as additional inputs to account for the scattering and absorption phenomena the model can be extended to estimate chlorophyll-a concentration turbid coastal waters.

Concentrations of chlorophyll and suspended sediment in surface water are tow important parameters for monitoring inland water quality. In the open ocean, it is not difficult to derive empirical algorithms relating received radiances at remote sensors to concentrations of water quality parameters. However, in optically complex inland water the task is difficult due to overwhelming of spectral signature of chlorophyll by other organic components present in high concentration. Neural Networks have been successful in modeling various geophysical transfer function. In this study, NN is used to model the transfer function between chlorophyll and sediment concentrations, and above-water upwelling reflectance simulated at three Landsat Thematic Mapper visible bands form spectrometer data. The developed model could estimate chlorophyll better than conventional regression analysis. In estimating surface chlorophyll, Root Mean Square Error (RMSE) for neural network was found to be < 15 percent, while the same for regression was > 30 percent. In estimating suspended sediment, regression performed comparatively better than in chlorophyll estimation with an RMSE of 22 percent. The corresponding RMSE for neural network was 12 percent. Upon validation, the trained model is used to get spatial distribution of the two water quality parameters from the Landsat Thematic Mapper imagery. Prior to this, the LandsatTM digital number values are converted to equivalent spectrometer-derived reflectances with a regression between these two quantities at sampling locations, thus taking into account the atmospheric effects which are often difficult to be satisfactorily quantified in inland waters.

Empirical models have been used to estimate primary production based on phytoplankton biomass and light intensity. In this paper, an alterative approach for estimating primary production using neural networks is proposed. The inputs to the neural network are chlorophyll, surface irradiance, sea surface temperature, and day length. The output of the network is the estimated primary production. The back-propagation learning algorithm is used to train the neural network. A single step learning with random presentation sequence is selected as the learning strategy. The data set used for this experiment is extracted form the Ocean Primary Productivity Working Group database. The results show a significant decrease in the mean squared error of the log transformed primary production compared to the estimation obtained using linear model and the vertically generalized production model. The neural network- based models can deal with non-linear relationships more accurately, can effectively include variables that tend to co-vary non-linearly with the output variable, are flexible towards the choice of inputs, and are tolerant to noise. Hence to improve the estimation of primary production, additional parameters can be easily incorporated in the neural network model, even though no a prior knowledge about het effect of these parameters is available. These important features of neural networks make them an ideal candidate for constructing primary production models for both case 1 and case 2 waters.

This investigation explores how hyperspectral distance metrics may be used as indicators of relative water depth in a coastal region. Spectral reflectance characteristics of near-shore waters imaged by an airborne hyperspectral sensor are examined. Commonly used hyperspectral distance metrics are applied to the data with the goal of distinguishing the spectra derived from various water depths. To improve the separability of the spectra, this study also examines, for one distance metric, the effect of processing only a subset of spectral bands recorded by the sensor. The concept of selecting a subset of bands extends to improving the performance of algorithms that process hyperspectral data for detection, classification, or estimation. An additional benefit is reducing the dimensionality of the dat and, thereby, the computational load. The key to reaching both of these objectives is to understand and match physical processes to appropriate mathematical metrics performance measures in a comprehensive framework. The overall process is driven both by empirical analysis of hyperspectral data and by mathematical examination of the spectra.

The atmospheric correction in ocean color remote sensing ins the most significant technique to retrieve the water leaving radiances, which are less than 10 percent of the satellite radiances, and its main errors are occurred by the estimation errors of the aerosol property and quantity which are highly variable in both space and time. In this study, our main purpose is to develop the advanced atmospheric correction method by using multi-viewing satellite data. POLDER onboard ADEOS can acquire multi-direction reflectance up to 14 viewing angles. In order to evaluate the validity of the multi-angle algorithm, we tired to test the algorithm with POLDER data. Results of comparisons with OCTS algorithm show that this algorithm with 3 or 4 angles POLDER data is available to estimate the aerosol properties because it is not affected form the errors depend on the band ratio.

The regional distribution of atmospheric aerosols over the Indian Ocean, Bay of Bengal and Arabian Sea during the Asian winter monsoon period of November to April, and its inter annual variability during 1996 to 1999 are studjed. During the winter monsoon period, the predominantly northeasterly winds from the Asian continent transport large amount of polluted air mass deep into the otherwise pristine ocean areas. The aerosol optical depth (AOD) is obtained by inverting the observed radiance in channel 1 of NOAA- 14 AVHRR after accounting for multiple scattering and absorption due to aerosols and molecules and the wind dependence of sea surface reflectance. The Scattering phase function and single scattering albedo used for the estimation of AOD are based on the in situ measurements of physical, chemical and optical properties of aerosols in this region during the Indian Ocean experiment (1NDOEX). Transport of aerosols from continental areas into the oceanic environments is studied using a combination of regional atmospheric circulation and the aerosol distribution over the oceanic regions. It is observed that the Bay of Bengal region is significantly affected by large-scale transport of aerosols from the Southeast Asia and the Indian subcontinent. Aerosol distribution over the Arabian Sea is mainly controlled by the transport from the Indian subcontinent and the Arabia. Continental aerosols are transported deep into the Indian Ocean up to the Inter Tropical Convergence Zone (ITCZ). The aerosol distribution shows significant monthly and year-to-year variability at the north of ITCZ.

This work aims to extract optical parameters of coastal sea water with high sediment and chlorophyll content by fitting reflectance spectra to a model. Sea-truth water sampling campaigns were carried out from Dec 1996 to Dec 1999 in coastal waters around Singapore. In-situ reflectance spectra were acquired using a portable spectro radiometer. Laboratory measurements of the total suspended solid (TSS) and chlorophyll-a (Chl-a) were made from the water samples. The Chl-a concentration ranges from 1 to 90 mg/m³, with an average value of about 10 mg/m³. The three component model for sea water was used to model the reflectance spectra. The model also includes chlorophyll fluorescence and surface reflection due to skylight. Each reflectance spectrum is fitted to the model by finding a set of the fitting parameters that best fits the reflectance curve to the mode. The downhill simplex method is employed as the optimization procedure. The chlorophyll absorption coefficient at 440 nm is retrieved and is found to relate to the measured chl-A concentration by a power law relation.

Phytoplankton may exert a warming influence on the planet by decreasing surface albedo. Compared with the case of pure seawater, the globally and annually averaged outgoing radiative flux is decreased by a probable value of 0.25 Wm^-2. This value corresponds to about 20 percent of the net radiative forcing by greenhouse gases and anthropogenic aerosols since pre-industrial times, including indirect effects. The relative importance of phytoplankton is greater on Regional and seasonal scales, with forcing values reaching -1.5 Wm^-2 in coastal zones and high-latitude regions during summer. The effects of space- and time-varying phytoplankton on surface albedo should be taken into account explicitly in the numerical modeling of climate change.

The aim of this paper is to outline the potential of imaging spectrometry and GIS techniques as tools for the monitoring of coastal sea waters and for the management of industrial/agricultural pollution phenomena. A specific flight was realized on September 30, 1999 using a hyperspectral MIVIS sensor that collected data in 102 spectral channels between 0.43 and 12.7 micrometers . Hyperspectral aerial images have been processed in order to obtain updated and accurate water quality maps. Furthermore important information on bathymetry, sea bottom features and hydrodynamic circulation have been derived from image processing and on-site surveys. The obtained information layers have been inserted in a specific GIS database and integrated with further information. The processing of data layers was performed, using a dedicated software, through typical GIS operators like indexing, recording, matrix analysis, proximity analysis. The interactions between sea water, industrial installations, agricultural areas, water resources, urban settlements and natural risks have been analyzed. This allowed the creation and processing of different thematic layers like quality, vulnerability, risk and impact maps.

Simple remote sensors are investigated on the basis on various variants of amalgamation of optical fibers with 'living sensitive element' as they apply to environmental processes. The theory of optical fiber with additional formed cladding is presented, but a profile of the refractive index in this layer lowers from the diffusion dictation. Analysis of a new mechanism for effective operation of such additional liquid cladding of optical fiber with long-period grating is considered.

We use ground-based measurements in order to validate the SeaWiFS calibration as well as the SeaWiFS atmospheric correction scheme over coastal waters. Ground-based extinction measurements provide the required information to compare to the SeaWiFS aerosol products. For most of the cases, the aerosol optical thicknesses at 865 nm agree while large discrepancies occur in the blue. The SeaWiFS algorithm quite systematically selects the maritime model while the actual aerosol models correspond to a broader variety offered by the complexity of the sources near by the European coastal areas. The radiometer also measures at several wavelengths the atmospheric downwelling radiances in the almucantar and in the principal plane (PPL). Knowing by measurements the aerosol optical thickness (tau) _a, a simple scheme was developed in order to derive the aerosol phase function P_a from the PPL radiances. We then have the required inputs to a radiative transfer code to properly account for the aerosols. In a forward mode, it is then applied to simulate the signal at SeaWiFS time of overpass in order to express the TOA radiances for comparison to the SeaWiFS measurements. In a backward mode, it can be used to convert the top of atmosphere radiance into a water leaving radiance. We present here some preliminary results for 3 days over the Venice site in Italy.

The in water penetration of the solar light is described by the attenuation coefficient K_d. K_d depends upon (i) the solar angle, (ii) the state of the sky, and (iii) the water composition. Knowing the above parameters as well as the inherent optical properties of the atmosphere ad of the water, a radiative transfer code can be run to derive K_d at different wavelengths.

A practical implementation of an airborne lidar for depth mapping in the ocean requires an a priori estimation of the system performance in a given condition. A new version of PC program 'Ocean Scientific', OS2001, provides an effective tool to obtain estimates of lidar effectiveness under different operating parameters such as water depth, water clarity, surface roughness, sunlight illumination, electronic system noise, and optical properties of the atmosphere. OS2001 can also be used to optimize the optical design of the lidar for a given application.