The objective of this work was to investigate the hypothesis that oceanic internal waves could modulate the production of atmospheric aerosols by modulating sea surface roughness. Airborne profiling lidar was used to measure the displacement of the subsurface scattering layer by internal waves, the distribution of surface roughness, and the aerosol concentration over the internal waves. The direct correlation between internal wave displacement and aerosol density was not statistically significant, primarily due to other processes causing mixing in the atmosphere. However, we found that the magnitude of the aerosol spatial power spectral density at the internal wave wavelength was significantly correlated with the internal wave magnitude. We developed a simple model to describe the interactions, which provided excellent agreement with the measurements for two out of three flights. The disagreement for the third flight is thought to be a result of the deeper thermocline on that day, which put the deeper parts of the internal waves out of the depth range covered by the lidar. The conclusion is that internal waves affect aerosol production over the ocean under certain environmental conditions.
Airborne lidar data for fishery surveys often do not contain physics-based features that can be used to identify fish; consequently, the fish must be manually identified, which is a time-consuming process. To reduce the time required to identify fish, supervised machine learning was successfully applied to lidar data from fishery surveys to automate the process of identifying regions with a high probability of containing fish. Using data from Yellowstone Lake and the Gulf of Mexico, multiple experiments were run to simulate real-world scenarios. Although the human cannot be fully removed from the loop, the amount of data that would require manual inspection was reduced by 61.14% and 26.8% in the Yellowstone Lake and Gulf of Mexico datasets, respectively.
We report the lidar detection of an underwater feature that appears to be a thermal vent in Yellowstone Lake, Yellowstone National Park, USA, with the Montana State University Fish Lidar. The location of the detected vent was 30 m from the closest vent identified in a United States Geological Survey of Yellowstone Lake in 2008. A second possible vent is also presented, and the appearance of both vents in the lidar data is compared to descriptions of underwater thermal vents in Yellowstone Lake from the geological literature.
KEYWORDS: LIDAR, Receivers, Research management, Signal to noise ratio, Water, Signal attenuation, Calibration, Scattering, Pulsed laser operation, Ecosystems
The design of a compact, dual-polarization, nonscanning lidar system intended to fly in a small, single-engine aircraft for airborne study of freshwater marine ecosystems and mapping of fish schools in mountain lakes is discussed. Design trade-offs are presented with special attention paid to selecting the field of view and telescope aperture diameter. Example results and a comparison with a similar existing lidar system are presented.
We use a bio-optical model of the optical properties of natural seawater to investigate the effects of subsurface chlorophyll layers on passive and active remote sensors. A thin layer of enhanced chlorophyll concentration reduces the remote sensing reflectance in the blue, while having little effect in the green. As a result, the chlorophyll concentration inferred from ocean color instruments will fall between the background concentration and the concentration in the layer, depending on the concentrations and the depth of the layer. For lidar, an iterative inversion algorithm is described that can reproduce the chlorophyll profile within the limits of the model. The model is extended to estimate column-integrated primary productivity, demonstrating that layers can contribute significantly to overall productivity. This contribution also depends on the chlorophyll concentrations and the depth of the layer. Using passive remote sensing alone to estimate primary productivity can lead to significant underestimation in the presence of subsurface plankton layers. Active remote sensing is not affected by this bias.
We propose a remote sensing technique to measure sound in the upper ocean. The objective is a system that can be flown on an aircraft. Conventional acoustic sensors are ineffective in this application, because almost none (∼0.1%) of the sound in the ocean is transmitted through the water/air interface. The technique is based on the acoustic modulation of naturally occurring bubbles near the sea surface. It is clear from the ideal gas law that the volume of a bubble will decrease if the pressure is increased, as long as the number of gas molecules and temperature remain constant. The pressure variations associated with the acoustic field will therefore induce proportional volume fluctuations of the insonified bubbles. The lidar return from a collection of bubbles is proportional to the total void fraction, independent of the bubble size distribution. This implies that the lidar return from a collection of insonified bubbles will be modulated at the acoustic frequencies, independent of the bubble size distribution. Moreover, that modulation is linearly related to the sound pressure. A laboratory experiment confirmed the basic principles, and estimates of signal-to-noise ratio suggest that the technique will work in the open ocean.
Several semi-analytic models exist for the inherent optical properties of sea water, at least for Case 1 waters. In these waters, models based on chlorophyll-a concentration seem to be fairly successful. For passive remote sensing, the critical properties are the backscattering coefficient and the zenith diffuse attenuation coefficient. The former describes the total scattering at angles > 0.5π steradians. The diffuse attenuation coefficient is not strictly an inherent optical property, because it depends on the sun angle. The zenith diffuse attenuation coefficient, defined as the attenuation of a diffuse source located at the zenith, depends only on the optical properties of the water. The observed remote sensing reflectance can be estimated from these two parameters and the solar zenith angle. Most of the investigations to date have assumed that the chlorophyll concentration does not vary with depth. This assumption is often quite good, because of the limited penetration of light into sea water. We will consider the case of intense thin plankton layers on a shallow pycnocline, where this assumption might not be valid. For active remote sensing, an additional parameter is important. This parameter is the volume scattering function at a scattering angle of π steradians, which is the sum of contributions from sea water and particles in the water. The sea water contribution is known. The particulate contribution can be modeled as the product of the scattering coefficient, which depends on chlorophyll concentration, and the phase function at π steradians, which does not.
We are proposing a novel remote sensing technique to measure sound in the upper ocean. The objective is a system that can be flown on an aircraft. Conventional acoustic sensors are ineffective in this application, because almost none (~ 0.1 %) of the sound in the ocean is transmitted through the water/air interface. The technique is based on the acoustic modulation of bubbles near the sea surface. It is clear from the ideal gas law that the volume of a bubble will decrease if the pressure is increased, as long as the number of gas molecules and temperature remain constant. The pressure variations associated with the acoustic field will therefore induce proportional volume fluctuations of the insonified bubbles. The lidar return from a collection of bubbles has been shown to be proportional to the total void fraction, independent of the bubble size distribution. This implies that the lidar return from a collection of insonified bubbles will be modulated at the acoustic frequencies, independent of the bubble size distribution. Moreover, that modulation is linearly related to the sound pressure. The basic principles have been demonstrated in the laboratory, and these results will be presented. Estimates of signal-to-noise ratio suggest that the technique should work in the open ocean. Design considerations and signal-to-noise ratios will also be presented.
This paper provides a review of the development of profiling oceanographic lidars. These can provide quantitative profiles of the optical properties of the water column to depths of 20 to 30 m in productive coastal waters and to depths of 100 m for a blue lidar in the open ocean. The properties that can be measured include beam attenuation, diffuse attenuation, absorption, volume scattering at the scattering angle of 180 deg, and total backscattering. Lidar can be used to infer the relative vertical distributions of fish, plankton, bubbles, and other scattering particles. Using scattering as a tracer, lidar can provide information on the dynamics of the upper ocean, including mixed-layer depth, internal waves, and turbulence. Information in the polarization of the lidar return has been critical to the success of many of these investigations. Future progress in the field is likely through a better understanding of the variability of the lidar ratio and the application of high-spectral-resolution lidar to the ocean. Somewhat farther into the future, capabilities are likely to include lidar profiling of temperature in the ocean and an oceanographic lidar in space.
The lidar overlap function is defined as the fraction of the transmitted beam that is within the receiver field of view. For the case of bistatic oceanographic lidar from the deck of a ship, the overlap function can vary from pulse to pulse under the influence of the rough sea surface. This paper considers the overlap function as a function of depth for a bistatic lidar operating from the deck of a ship. The effect is calculated using a Monte-Carlo approach, with a Pierson–Moskowitz spectrum of surface roughness and optical ray tracing through that surface. The results show that a significant decrease in the overlap can result, even at low wind speeds.
A dual-polarization lidar and photography are used to sense internal waves in West Sound, Orcas Island, Washington, from a small aircraft. The airborne lidar detected a thin plankton layer at the bottom of the upper layer of the water, and this signal provides the depth of the upper layer, amplitude of the internal waves, and the propagation speed. The lidar is most effective when the polarization filter on the receiver is orthogonal to the transmitted light, but this does not depend significantly on whether the transmitted light is linearly or circularly polarized. The depolarization is greater with circular polarization, and our results are consistent with a single parameter Mueller scattering matrix. Photographs of the surface manifestation of the internal waves clearly show the propagation direction and width of the phase fronts of the internal waves, even though the contrast is low (2%). Combined with the lidar profile, the total energy of the internal wave packet was estimated to be 9 MJ.
A dual polarization lidar was used to sense internal waves from a small aircraft. Internal waves are gravity waves that
are formed by a vertical displacement of a density gradient in the ocean. If the perturbation is great enough, a nonlinear
wave is produced and the balance between nonlinearity and dispersion can create a soliton-like wave packet. We
observed nonlinear wave packets in West Sound, Orcas Island, Washington. In this region, a density gradient is formed
in the summer by solar heating of the surface water. The perturbation is produced by strong tidal flow through a narrow,
shallow channel at the mouth of the sound. Plankton layers form in association with the density gradients, and these
layers produce an enhanced lidar return that moves up and down with the wave. We observed these internal waves with
a lidar operating at 532 nm. They were much more visible when the receiver was polarized orthogonal to the transmitted
laser pulse. This was the case whether linear or circular polarization was used, with no significant difference between
the two cases. These internal waves were also visible to the naked eye, when the surface currents produced by the waves
modulated the small surface waves that produce the apparent texture of the ocean surface.
Thin layers are water column structures that contain concentrations of organisms (or particles) that occur over very small vertical scales (a few meters or less), but with large horizontal scales (e.g. kilometers). Thin layers are now known to be common phenomenon in a wide variety of environments and can be a critical componant in marine ecosystem dynamics and functioning. While knowledge about their dynamics is important to our basic understanding of oceanic processes, thin layers can have significant impacts on both oceanographic and defense related sensing systems, e.g. thin layers can affect underwater visibility, imaging, vulnerability, communication and remote sensing for both optical and acoustic instrumentation. This paper will review the history of thin layers research, their ecological significance, innovations in oceanographic instrumentation and sampling methodologies used in their study, and the consequences of their occurence to oceanographic sensing systems.
This paper describes the results of a series of airborne observations of sardine schools off the coast of California in the
fall of 2010. The lidar system used a linearly-polarized transmitter and a single receiver that was sensitive to the
backscattered light in the orthogonal polarization. The aircraft was also equipped with a camera to photograph schools.
The camera had a broader swath than the lidar, so was able to see more of the schools at the surface. However, the lidar
detected schools much deeper in the water, was not hampered by waves and sun glare, and could survey at night. The
combination of lidar and photographs proved to be a very powerful survey tool for sardines, since the latter was able to
identify surface targets that appear very similar to fish schools in the lidar return. Examples of these include floating
mats of kelp and ship wakes.
Subsurface optical layers distributed at two different depths were investigated in Monterrey Bay, East Sound, and the Black Sea based on spatial statistics of remote sensing reflectance (Rrs). The main objective of this study was to evaluate the use of Rrs(443)/Rrs(490) (hereafter R1) skewness (ψ) as an indicator of vertical optical structure in different marine regions. Measurements of inherent optical properties were obtained using a remotely operated towed vehicle and R1 was theoretically derived from optical profiles. Although the broad range of trophic status and water stratification, a common statistical pattern consisting of lower ψR1-a deeper optical layer was found in all study cases. This variation was attributed to optical changes above the opticline and related to horizontal variability of particulates and spectral variations with depth. We recommend more comparisons in stratified coastal waters with different phytoplankton communities before the use of ψR1 can be generalized as a noninvasive optical proxy for screening depth changes on subsurface optical layers.
Characterization of 3-D underwater light fields from above the sea surface requires passive and active remote sensing
measurements. In this work, we suggest the use of passive ocean color sensors and lidar (Light Detection and Ranging)
to examine the vertical structure of optical properties in marine waters of the Northern Part of the Gulf of Alaska
(NGOA). We collected simultaneous airborne remote sensing reflectance (Rrs) in the spectral range 443-780 nm
(MicroSAS, Satlantic) and lidar-derived volume backscattering (β) profiles (0-20 m depth, wavelength = 532 nm) during
August 17 2002 in shelf waters situated south of Kodiak Island off Alaska (57.48°-58.04° N, 152.91°-151.67° W). We
evaluated the spectral response of Rrs to perturbations on vertical distribution of β by comparing the spatial variability
between aggregated (250 m horizontal resolution x 1 m vertical resolution) Rrs spectral ratios and different lidar statistics
per bin (Maximum β per bin, mean β per bin, βm, standard deviation of β per bin, βstd, integrated β per bin, βint) or
group of bins (lidar volume extinction coefficient of β between 0 and 5 m depth). Sub-surface changes of βm, βint, and
βstd were mainly correlated with Rrs (490)/Rrs (555) variability along the flight-track (Semi-partial correlation
coefficients = 0.12 to 0.21). Our results evidenced linkages between above and below-sea surface optical properties that
can be used to derive water optical constituents as a function of depth based on combined passive-active data.
At NOAA's Earth System Research Laboratory, lidar systems have been developed and applied to environmental probing for more than three decades. Progressing from early investigations of atmospheric turbidity and winds employing ruby and CO2 lasers, current work is focused on the application of sensors to measure atmospheric properties important for improving air quality understanding and forecasting, and quantifying important climate forcing mechanisms. Additionally, lidar systems are being used for probing the ocean to observe fish schools and marine mammals for research on estuarine health. Here we briefly describe development and applications of lidar systems for characterizing winds and turbulence in the atmosphere, distribution and transport of ozone and aerosol concentrations in urban areas, and inventory of fish stocks in coastal water.
A polarization-sensitive lidar was used to detect honeybees trained to locate buried landmines by smell. Lidar measurements of bee location agree reasonably well with maps of chemical plume strength and bee density determined by visual and video counts, indicating that the bees are preferentially located near the explosives and that the lidar identifies the locations of higher bee concentration. The co-polarized lidar backscatter signal is more effective than the cross-polarized signal for bee detection. Laboratory measurements show that the depolarization ratio of scattered light is near zero for bee wings and up to approximately thirty percent for bee bodies.
NOAA is developing an airborne lidar system for marine fisheries. It measures the direct backscatter of green light from objects, including fish, in the water column. We can, in fact, see fish from an airplane with this lidar. The lidar is described, and examples of fish signatures are presented. Other products are maps of the spatial distribution of fish and vertical profiles of biomass; examples of these are also presented.
This paper calculates signal levels that would be obtained from oceanographic lidar by solving the one-dimensional transient radiative transfer equation for remote sensing. As an example, detection of fish schools is considered. In this technique a pulsed laser is directed into the ocean, and the time-dependent back-scattered flux is measured at different locations. A large number of parameters such as the spatial and temporal variability of optical properties within the ocean, ocean depth, type of ocean water, and presence of biological matters can significantly affect the radiative transport through oceans. But since the emphasis of the work is on the scattering phenomenon, important parameters associated with it, namely the scattering albedo and scattering phase function distribution, are considered in detail.
A new infrared laser-glint sensor is being developed to measure sea-surface roughness statistics, using a CO2 laser to maintain the laser signal above background light levels. This new system allows daylight data collection, whereas our previous HeNe-laser-based system operated only at night. Additionally, the new system incorporates real-time correction of ship motion to allow operation on a moving ship. This paper provides an overview of the instrument design and the scientific objectives of its future deployment. A specific scientific objective discussed here is determining the connection between the fractal dimension of the laser-glint process and surface roughness. Evidence is shown here from previously acquired laser-glint data that the laser-glint- count process is fractal, with a dimension that appears to vary with surface roughness. Plans are outlined for deployment of the new laser-glint sensor with fractal signal processing for remotely sensing surface roughness.
Specular reflections of light, or glints, on the ocean surface can be used to determine surface-roughness statistics. For example, the angular distribution of glints is related to the surface slope distribution. Such statistics are needed for interpreting data from various remote sensors and for studying the physics of the air-sea interface. Laser-glint techniques are convenient because they do not inherently depend on the ambient light conditions, the instruments can be made reasonably compact, and they do not disturb the surface. We deployed a first-generation laser-glint instrument package in the Pacific Ocean near the Oregon coast, during September 1995. This system used laser wavelengths of 633 nm and 830 nm, and was only operable at night. Measurements from this instrument have helped to verify the Cox-Munk model for slope statistics and to quantify the dependence of sea-surface mean- square slope on the air-sea temperature difference. The next- generation laser-glint instrument will use infrared laser light at 10.6 micrometers to enable daytime operation, which previously has not been accomplished with a laser-glint sensor.
Measurements were made of the attenuation coefficient of the National Oceanographic and Atmospheric Administration lidar from a ship in the Southern California Bight in September 1995. The region from about 5 m to about 30 m in depth was covered. The laser was linearly polarized, and the receiver was operated with the same polarization, the orthogonal polarization, and a polarization angle of 45 degrees, so that the first three Stokes parameters of the scattered light can be estimated.
Airborne lidar is being considered as a tool for fish detection and for fisheries surveys. Detection has been demonstrated, and an imaging lidar has been developed to detect and identify fish for commercial fisheries. For survey work, a simpler radiometric lidar is being investigated, and preliminary results suggest that such a lidar can be very useful for biomass estimation.
Measurements of the IR optical thickness and radar reflectivity of cirrus clouds can be used to infer microphysical characteristics of these clouds. This paper presents a technique for estimating the optical thickness from spectral measurements of downwelling radiance of cirrus clouds in the infrared atmospheric transparency `window' near 10 micrometers . To illustrate, results of cirrus particle size retrieval from the FIRE-II experiment are given.
We describe an infrared spectrometer system designed for passive, ground-based remote profiling of atmospheric temperature and humidity. This instrument will be useful in atmospheric science, climate, and global change studies. We plan eventually to demonstrate its potential for unattended remote sensing. If successful, the infrared instrument could become a component of an integrated sounding system being designed for next-generation meteorological observations. At the heart of the infrared instrument is a rugged Michelson interferometer which views thermal atmospheric emission between 550 cm-1 and 2000 cm-1 (5.0 - 18.2 micrometers ), with 1-cm-1 spectral resolution. Calculated weighting functions suggest that we should be able to profile temperature with about 200-m vertical resolution, and humidity with about 500-m vertical resolution, in the lower 2 kilometers of the atmosphere. In order to retrieve accurate profiles, absolute radiometric calibration will be necessary. We have constructed two high-emissivity inner-cone cavities which the interferometer will view between atmospheric measurements in order to establish this calibration. We show measured spectra of a clear atmosphere and of cirrus clouds and point out features of these spectra which will be useful in atmospheric profiling.
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