Results for “data inference” — Page 78

NASA NTRS2013

The Paradox of a Wet (High H2O) and Dry (Low H2O/Ce) Mantle: High Water Concentrations in Mantle Garnet Pyroxenites from Hawaii

Water dissolved as trace amounts in anhydrous minerals has a large influence on the melting behavior and physical properties of the mantle. The water concentration of the oceanic mantle is inferred from the analyses of Mid-Ocean Ridge Basalt (MORB) and Oceanic Island Basalt (OIB). but there is little data from actual mantle samples. Moreover, enriched mineralogies (pyroxenites, eclogites) are thought as important sources of heterogeneity in the mantle, but their water concentrations and their effect on the water budget and cycling in the mantle are virtually unknown. Here, we analyzed by FTIR water in garnet clinopyroxenite xenoliths from Salt Lake Crater, Oahu, Hawaii. These pyroxenites are high-pressure (>20kb) crystal fractionates from alkalic melts. The clinopyroxenes (cpx) have 260 to 576 ppm wt H2O, with the least differentiated samples (Mg#>0.8) in the 400-500 ppm range. Orthopyroxene (opx) contain 117-265 ppm H2O, about half of that of cpx, consistent with other natural sample studies, but lower than cpx/opx equilibrium from experimental data. The pyroxenite cpx and opx H2O concentrations are at the high-end of on-and off-craton peridotite xenolith concentrations and those of Hawaiian spinel peridotites. In contrast, garnet has extremely low water contents (<5ppm H2O). There is no correlation between H2O in cpx and lithophile element concentrations. Phlogopite is present in some samples, and its modal abundance shows a positive correlation in Mg# with cpx, implying equilibrium. However, there is no correlation between H2O concentrations and or the presence of phlogopite. These data imply that cpx and opx may be at water saturation, far lower than experimental data suggest. Reconstructed bulk rock pyroxenite H2O ranges from 200-460 ppm (average 331 +/- 75 ppm), 2 to 8 times higher than H2O estimates for the MORB source (50-200 ppm), but in the range of E-MORB, OIB and the source of rejuvenated Hawaiian magmas. The average bulk rock pyroxenite H2O/Ce is 69 +/-35, lower than estimates of the MORB source (approx 150) or FOZO, C (200-250) mantle component, but consistent with "dry" EM sources (<100). These data suggest that a metasomatized, refertilized oceanic lithosphere that contains pyroxenitic veins (e.g. the lower part of an oceanic plate, where ascending melts can become trapped and crystallize), will have both higher water concentrations and low H2O/Ce, and may contribute to EM-type OIB sources, like that of Samoa basalts. Therefore, a low H2O/Ce mantle source may not necessarily be "dry".

Peslier, Anne H.↗

NASA NTRS2020

Bayesian Framework For Bioburden Density Calculations To Perform Planetary Protection Probabilistic Risk Assessment

The planetary protection discipline aims to minimize the microbial contamination on spacecraft to prevent the inadvertent contamination of other planetary bodies, known as forward planetary protection (PP). Planetary protection probabilistic risk assessment (PRA) relies on two core methodologies-the contamination probability event tree analysis and statistical parameter estimation. Planetary protection engineers combine several techniques to estimate the bioburden present on spacecraft components. A direct assay to enumerate CFU (colony forming units) is the preferred methodology, but given a similar processing environment the bioburden present on certain components is inferred using: (1) a NASA defined bioburden estimate based upon the biological cleanliness of the manufacturing/assembly environment or (2) sampled data from a similar spacecraft component. The paper presents an empirical Bayesian framework to systematically treat bioburden estimation and its uncertainties on different levels starting with measurement procedures to combining different components to subsystems and whole spacecraft. It is shown that the Bayesian approach can effectively handle estimations and their uncertainties at different levels and produce a reliable estimate for bioburden to be used to evaluate the probability of contamination.

Seuylemezian, Arman↗

NASA NTRSDate not supplied

Introduction to our COSPAR-Sponsored Session: Space-Observation Contributions Supporting Climate Action

• The challenge for us at COSPAR in putting this program together was that we are primarily a scientifically oriented organization. “Climate Action,” as it is often expressed publicly, tends to be the domain of “activists.” As scientists, on the other hand, to retain our credibility – and for the contributions we make to continue carrying the weight of scientific truth – it is critical that we remain even-handed in our analysis and our reporting of results. • COSPAR is the Committee on Space Research, and fortunately, much of what we observe from space bears objectively on the attributes of the Earth System and how it is evolving. We can measure from space changes in the cryosphere, in the oceans, the land surface and the atmosphere, frequently, on a global scale, to a degree that is generally not possible any other way. And we have been making such observations, in increasing detail, scope, and precision, for about 60 years. • These data help us characterize the current state and identify patterns of change in the Earth System that allow us to infer processes and to improve climate models. With modeling we can test and refine our inferences, and make some predictions. The results can then inform policy and decision-makers, the public in general, as well as those taking direct action. • So, for today’s session we have invited 7 15-minute presentations spanning a broad range of climate-related topics, covering observations from space that can inform climate-related decision-making – from sea-ice extent and sea-level rise, to land surface properties and land-based water resources, to interactions between land or water surfaces and the atmosphere, to severe events in the atmosphere itself. • We begin with an overview of the observations contributed by one of the premier space agencies, given by NASA Chief Scientist and Senior Climate Advisor Dr. Kate Calvin, which will be followed by talks on what we are learning about specific attributes of the Earth System from space-based measurements that can inform climate-related policy and action.

Ralph Kahn↗

NASA NTRSDate not supplied

Predicting Lightning Initiation using Deep Learning

Lightning occurrence presents safety challenges to people and property. The main challenge with lightning safety is that the majority of guidance is reactive. In other words, lightning has to have already occurred nearby before a person will respond and take shelter. Further, most injuries or fatalities occur as the storm approaches, or as it's moving away, when rainfall may not be present at the time of the flash. Thus, this project develops a physically-based deep learning model to produce lightning probabilities out to 15 minutes. The deep learning model combines a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network to capture both the spatial and temporal evolution of storms to predict the probability that lightning initiation will occur in the next 15 minutes. The model combines radar reflectivity, correlation coefficient and differential reflectivity to inferred storm hydrometer type and precipitation phase, which aids in the identification of electrification processes. The model is trained with data from the Geostationary Lightning Mapper (GLM), which is a near infrared sensor onboard the GOES-R series of satellites that measures optical brightness from lightning. This presentation will provide an overview of the project.

Andrew T White↗

NASA NTRS1998

Central Plasma Sheet Ion Properties as Inferred from Ionospheric Observations

A method of inferring central plasma sheet (CPS) temperature, density, and pressure from ionospheric observations is developed. The advantage of this method over in situ measurements is that the CPS can be studied in its entirely, rather than only in fragments. As a result, for the first time, comprehensive two-dimensional equatorial maps of CPS pressure, density, and temperature within the isotropic plasma sheet are produced. These particle properties are calculated from data taken by the Special Sensor for Precipitating Particles, version 4 (SSJ4) particle instruments onboard DMSP F8, F9, F10, and F11 satellites during the entire year of 1992. Ion spectra occurring in conjunction with electron acceleration events are specifically excluded. Because of the variability of magnetotail stretching, the mapping to the plasma sheet is done using a modified Tsyganenko [1989] magnetic field model (T89) adjusted to agree with the actual magnetotail stretch at observation time. The latter is inferred with a high degree of accuracy (correlation coefficient -0.9) from the latitude of the DMSP b2i boundary (equivalent to the ion isotropy boundary). The results show that temperature, pressure, and density all exhibit dawn-dusk asymmetries unresolved with previous measurements. The ion temperature peaks near the midnight meridian. This peak, which has been associated with bursty bulk flow events, widens in the Y direction with increased activity. The temperature is higher at dusk than at dawn, and this asymmetry increases with decreasing distance from the Earth. In contrast, the density is higher at dawn than at dusk, and there appears to be a density enhancement in the low-latitude boundary layer regions which increases with decreasing magnetic activity. In the near-Earth regions, the pressure is higher at dusk than at dawn, but this asymmetry weakens with increasing distance from the Earth and may even reverse so that at distances X less than approx. 10 to -12 R(sub E), depending on magnetic activity, the dawn sector has slightly higher pressure. The temperature and density asymmetries in the near-Earth region are consistent with the ion westward gradient/curvature drift as the ions ExB convect earthward. When the solar wind dynamic pressure increases, CPS density and pressure appear to increase, but the temperature remains relatively constant. Comparison with previously published work indicates good agreement between the inferred pressure, temperature, and density and those obtained from in situ data. This new method should provide a continuous mechanism to monitor the pressure, temperature, and density in the magnetotail with unprecedented comprehensiveness.

Wing, Simon↗

NASA NTRS1996

The Properties of the local Interstellar Medium and the Interaction of the Stellar Winds of epsilon Indi and lambda Andromedae with the Interstellar Environment

We present new observations of the Ly alpha lines of Epsilon Indi (K5 5) and A Andromedae (G8 4-3 + ?) These data were obtained by the Goddard High Resolution Spectrograph (GHRS) on the Hubble Space Telescope. Analysis of the interstellar H 1 and D 1 absorption lines reveals that the velocities and temperatures inferred from the H 1 lines are inconsistent with the parameters inferred from the D 1 lines, unless the H 1 absorption is assumed to be produced by two absorption components. One absorption component is produced by interstellar material. For both lines of sight observed, the velocity of this component is consistent with the velocity predicted by the local flow vector. For the Epsilon Indi data, the large velocity separation between the stellar emission and the interstellar absorption allows us to measure the H 1 column density independent of the shape of the intrinsic stellar Ly alpha profile. This approach permits us to quote an accurate column density and to assess its uncertainty with far more confidence than in previous analyses, for which the errors were dominated by uncertainties in the assumed stellar profiles.

Wood, Brian E.↗

NASA NTRS1987

Research relative to angular distribution of snow reflectance/snow cover characterization and microwave emission

Remote sensing has been applied in recent years to monitoring snow cover properties for applications in hydrologic and energy balance modeling. In addition, snow cover has been recently shown to exert a considerable local influence on weather variables. Of particular importance is the potential of sensors to provide data on the physical properties of snow with high spatial and temporal resolution. Visible and near-infrared measurements of upwelling radiance can be used to infer near-surface properties through the calculation of albedo. Microwave signals usually come from deeper within the snow pack and thus provide depth-integrated information, which can be measured through clouds and does not relay on solar illumination.Fundamental studies examining the influence of snow properties on signals from various parts of the electromagnetic spectrum continue in part because of the promise of new remote sensors with higher spectral and spatial accuracy. Information in the visible and near-infrared parts of the spectrum comprise nearly all available data with high spatial resolution. Current passive microwave sensors have poor spatial resolution and the data are problematic where the scenes consist of mixed landscape features, but they offer timely observations that are independent of cloud cover and solar illumination.

Dozier, Jeff↗

NASA NTRS2010

Combined Heat Transfer in High-Porosity High-Temperature Fibrous Insulations: Theory and Experimental Validation

Combined radiation and conduction heat transfer through various high-temperature, high-porosity, unbonded (loose) fibrous insulations was modeled based on first principles. The diffusion approximation was used for modeling the radiation component of heat transfer in the optically thick insulations. The relevant parameters needed for the heat transfer model were derived from experimental data. Semi-empirical formulations were used to model the solid conduction contribution of heat transfer in fibrous insulations with the relevant parameters inferred from thermal conductivity measurements at cryogenic temperatures in a vacuum. The specific extinction coefficient for radiation heat transfer was obtained from high-temperature steady-state thermal measurements with large temperature gradients maintained across the sample thickness in a vacuum. Standard gas conduction modeling was used in the heat transfer formulation. This heat transfer modeling methodology was applied to silica, two types of alumina, and a zirconia-based fibrous insulation, and to a variation of opacified fibrous insulation (OFI). OFI is a class of insulations manufactured by embedding efficient ceramic opacifiers in various unbonded fibrous insulations to significantly attenuate the radiation component of heat transfer. The heat transfer modeling methodology was validated by comparison with more rigorous analytical solutions and with standard thermal conductivity measurements. The validated heat transfer model is applicable to various densities of these high-porosity insulations as long as the fiber properties are the same (index of refraction, size distribution, orientation, and length). Furthermore, the heat transfer data for these insulations can be obtained at any static pressure in any working gas environment without the need to perform tests in various gases at various pressures.

Daryabeigi, Kamran↗

NASA NTRS1980

Einstein X-ray observations of Proxima Centauri and the surrounding region

The first detection of both quiescent and flaring soft X-ray emission from a dMe flare star, Proxima Centauri (dM5e) is reported. The data are analyzed for temporal variability and spectral characteristics. The quiescent state is characterized by a mean X-ray luminosity of 1.5 x 10 to the 27th erg s/s, corresponding to a mean surface flux of 700,000 erg s/sq cm-s, and an inferred temperature of 4-million K. The flare that is detected has a peak flux of 7.4 x 10 to the 27th erg s/s and a peak temperature of 17-million K. The implications of these data for models of the quiescent and flare coronae of dMe stars are discussed.

Haisch, B. M.↗

NASA NTRS2001

Robust Strategy for Rocket Engine Health Monitoring

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

Santi, L. Michael↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS1998

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

Ranson, K. Jon↗

NASA NTRS1990

ISCCP results for the FIRE IFO regions and times

The ISCCP analysis of cloud conditions during and in the vicinity of the FIRE cirrus and marine stratus Intensive Field Observations (IFOs) is presented in two forms: the standard climatology product, which is averaged to a nominal 250 km resolution and a pixel-by-pixel version that samples the original data at about 30 km spacing every three hours. These results are based on NOAA-9 and GOES-6 images covering October to November 1986 and July 1987 for the FIRE Extended Area. Comparisons of these results with other FIRE observations will serve to check the accuracy of the clear sky radiances inferred by the ISCCP analysis, which determines the accuracy of the cloud detections. Of more interest is the exploration of the dependence of the inferred cloud properties for direct evaluation of the role of small scale variability of cloud properties on the radiation field. This is illustrated by preliminary results obtained for the scales contained in the ISCCP data. The ISCCP analysis also sets the larger scale context of the FIRE data: (1) comparisons of the clouds occurring in the FIRE IFOs with nearby locations and times are presented, and (2) comparisons of the cloud conditions for the same month in other years are shown.

Rossow, William B.↗

NASA NTRS1990

Western Eisila Regio, Venus - Radar properties of volcanic deposits

The 1988 Arecibo Observatory dual-polarization radar images are presented for Western Eisila Regio, Venus. The polarization information and Pioneer-Venus Orbiter reflectivity and altimetry data are analyzed for volcanic deposits on two 400-500 km radius constructs, Sif and Gula Montes. Many of the large effusive deposits studied appear to require superposed flows or multiple vents to explain the observed progression of roughness along their length. High Fresnel reflectivity material may be present along the summit region of Gula Mons and in an embayed tessera-like region to the N. Radar-dark units on the flanks of Sif Mons are inferred to be pyroclastic deposits, but radar-dark features near the summits of both edifices are more consistent with very smooth lava flows. Higher spatial resolution Magellan data will be useful in testing these predictions.

Campbell, Bruce A.↗

NASA NTRS1971

Electrostatic Probe Measurements of Plasma Surrounding Three 25,000 Foot Per Second Reentry Flight Experiments

A high-altitude electrostatic probe experiment is described and data are presented for three RAM C reentries at velocities of approximately 25 OOO feet per second. The electron density profiles inferred from the probe measurements are compared with theoretical calculations, and the probable cause of significant disagreement is briefly discussed. Probe measurements during material-addition sequences for radio-blackout alleviation are presented, and comparisons of probe measurements with other pertinent diagnostic measurements are made.

W Linwood Jones, Jr↗

NASA NTRS1977

Radio-emitting electrons and cosmic ray confinement

Evidence from radio data obtained at frequencies of 10 to 8000 MHz for steepening of the observed background-radio-continuum spectra in the directions of the galactic Anticenter (A) and Halo Minimum (H) is used to deduce quantitative information on the variation of the magnetic field with distance from the galactic plane. The radio data are analyzed in the framework of the cosmic-ray diffusion model developed by Bulanov and Dogel (1975), and some inferences are drawn regarding the injection spectrum of cosmic-ray electrons as well as their propagation in the Galaxy. The results indicate that: (1) radio-spectrum steepening is centered around 200 MHz in the direction of H and around 330 MHz in the direction of A, implying a lower mean magnetic field toward H and supporting the existence of a radio halo; (2) an injection electron spectrum with a single power law up to the lowest energies cannot explain the radio observations satisfactorily in terms of the diffusion model; and (3) the observed spectral steepening can be satisfactorily understood as being due partly to the deviation from a power-law injection spectrum below a few GeV and partly to the first break arising from electron energy losses in approximately the same energy region.

Badhwar, G. D.↗

NASA NTRS1977

LANDSAT menhaden and thread herring resources investigation

The author has identified the following significant results. The relationship between the distribution of menhaden and selected oceanographic parameters (water color, turbidity, and possibly chlorophyll concentrations) was established. Similar relationships for thread herring were not established nor were relationships relating to the abundance of either species. Use of aircraft and LANDSAT remote sensing instruments to measure or infer a set of basic oceanographic parameters was evaluated. Parameters which could be accurately inferred included surface water temperature, salinity, and color. Water turbidity (Secchi disk) was evaluated as marginally inferrable from the LANDSAT MSS data and chlorophyll-a concentrations as less than marginal. These evaluations considered the parameters only as experienced in the two test areas using available sensors and statistical techniques.

Kemmerer, A. J.↗