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At least 721 records · Page 40

Remote Thermal IR Spectroscopy of our Solar System

Indirect methods to detect extrasolar planets have been successful in identifying a number of stars with companion planets. No direct detection of an extrasolar planet has yet been reported. Spectroscopy in the thermal infrared region provides a potentially powerful approach to detection and characterization of planets and planetary systems. We can use knowledge of our own solar system, its planets and their atmospheres to model spectral characteristics of planets around other stars. Spectra derived from modeling our own solar system seen from an extrasolar perspective can be used to constrain detection strategies, identification of planetary class (terrestrial vs. gaseous) and retrieval of chemical, thermal and dynamical information. Emission from planets in our solar system peaks in the thermal infrared region, approximately 10 - 30 microns, substantially displaced from the maximum of the much brighter solar emission in the visible near 0.5 microns. This fact provides a relatively good contrast ratio to discriminate between stellar (solar) and planetary emission and optimize the delectability of planetary spectra. Important molecular constituents in planetary atmospheres have rotational-vibrational spectra in the thermal infrared region. Spectra from these molecules have been well characterized in the laboratory and studied in the atmospheres of solar system planets from ground-based and space platforms. The best example of such measurements are the studies with Fourier transform spectrometers, the Infrared Interferometer Spectrometers (IRIS), from spacecraft: Earth observed from NIMBUS 8, Mars observed from Mariner 9, and the outer planets observed from Voyager spacecraft. An Earth-like planet is characterized by atmospheric spectra of ozone, carbon dioxide, and water. Terrestrial planets have oxidizing atmospheres which are easily distinguished from reducing atmospheres of gaseous giant planets which lack oxygen-bearing species and are characterized by spectra containing hydrocarbons such as methane and ethane. Spectroscopic information on extrasolar planets thus can permit their classification. Spectra and spectral lines contain information on the temperature structure of the atmosphere. Line and band spectra can be used to identify the molecular constituents and retrieve species abundances, thereby classifying and characterizing the planet. At high enough spectral resolution characteristic planetary atmospheric dynamics and unique phenomena such as failure of local thermodynamic equilibrium can be identified. Dynamically induced effects such as planetary rotation and orbital velocity shift and change the shape of spectral features and must be modeled in detailed spectral studies. We will use our knowledge of the compositional, thermal and dynamical characteristics of planetary atmospheres in our own solar system to model spectra observed remotely on similar planets in extrasolar planetary systems. We will use a detailed radiative transfer and beam integration program developed for the modeling and interpretation of thermal infrared spectra measured from nearby planet planets to generate models of an extra-solar "Earth" and "Jupiter". From these models we will show how key spectral features distinguish between terrestrial and gaseous planets, what information can be obtained with different spectral resolution, what spectral features can be used to search for conditions for biogenic activity, and how dynamics and distance modify the observed spectra. We also will look at unique planetary phenomena such as atmospheric lasing and discuss their utility as probes for detection and identification of planets. Results of such studies will provide information to constrain design for instrumentation needed to directly detect extrasolar planets.

Kostiuk, Theodor↗

On the algebraic structure of bilinear systems.

It is shown that a particular bilinear model is both quite general and easy to work with. A basic structure theory is developed with the aid of previous results. Some preliminary ideas are discussed together with the system interconnection, the canonical form, questions of controllability, aspects of observability, and equivalent realizations. It is pointed out that in actually determining equivalent realizations for systems and in the classification of systems, the results available in the study of Lie algebras are of fundamental importance.

Brockett, R. W.↗

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.

Chien, Steve↗

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks.

Chien, Steve↗

A Framework for Inferring Taxonomic Class of Asteroids.

Introduction: Taxonomic classification of asteroids based on their visible / near-infrared spectra or multi band photometry has proven to be a useful tool to infer other properties about asteroids. Meteorite analogs have been identified for several taxonomic classes, permitting detailed inference about asteroid composition. Trends have been identified between taxonomy and measured asteroid density. Thanks to NEOWise (Near-Earth-Object Wide-field Infrared Survey Explorer) and Spitzer (Spitzer Space Telescope), approximately twice as many asteroids have measured albedos than the number with taxonomic classifications. (If one only considers spectroscopically determined classifications, the ratio is greater than 40.) We present a Bayesian framework that provides probabilistic estimates of the taxonomic class of an asteroid based on its albedo. Although probabilistic estimates of taxonomic classes are not a replacement for spectroscopic or photometric determinations, they can be a useful tool for identifying objects for further study or for asteroid threat assessment models. Inputs and Framework: The framework relies upon two inputs: the expected fraction of each taxonomic class in the population and the albedo distribution of each class. Luckily, numerous authors have addressed both of these questions. For example, the taxonomic distribution by number, surface area and mass of the main belt has been estimated and a diameter limited estimate of fractional abundances of the near earth asteroid population was made. Similarly, the albedo distributions for taxonomic classes have been estimated for the combined main belt and NEA (Near Earth Asteroid) populations in different taxonomic systems and for the NEA population specifically. The framework utilizes a Bayesian inference appropriate for categorical data. The population fractions provide the prior while the albedo distributions allow calculation of the likelihood an albedo measurement is consistent with a given taxonomic class. These inputs allows calculation of the probability an asteroid with a specified albedo belongs to any given taxonomic class.

Asteroids↗

Maldives Climate II: Evaluating the Potential Impacts of Sea Level Rise on Human Development and Coastal Infrastructure

The Republic of the Maldives is a low-lying island nation in the Indian Ocean which has experienced rapid urbanization, landcover changes, and sea level rise over recent years. The growth of tourism, coastal erosion, and urbanization have all driven land reclamation efforts across many islands. As in-situ landcover change monitoring has proven difficult across the vast archipelago, the NASA DEVELOP team collaborated with the Maldives Ministry of Environment, Climate Change, and Technology; USAID; and the U.S. Department of State, to utilize Earth observations to predict sea level rise impacts on coastal infrastructure. The team used a supervised classification algorithm within Google Earth Engine to create land use maps and time series analyses of nine islands and atolls using imagery from Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, Sentinel-2 Multispectral Instrument, and PlanetScope, covering a combined period of 2000 through 2023. Additionally, the team projected coastal inundation with a modified deterministic Bathtub model utilizing elevation data from CoastalDEM and 2050-2100 Shared Socioeconomic Pathway scenarios identified in the NASA Sea Level Rise Projection Tool. The team found that islands undergoing urban growth experienced a 23% decrease in vegetation between 2014 and 2022. Furthermore, the model predicted that 51–57% of the study area’s-built environment has a chance of inundation by 2100 under the low and high sea level scenarios. These analyses demonstrate how remote sensing can be used to both track land use changes over time and project how coastlines will be affected by sea level rise.

remote sensing↗

Wake Vortex Tracking Using a 35 GHz Pulsed Doppler Radar

A 35 GHz, pulsed-Doppler radar system has been designed and assembled for wake vortex detection and tracking in low visibility conditions. Aircraft wake vortices continue to be an important factor in determining safe following distances or spacings for aircraft in the terminal area. Currently, under instrument meteorological conditions (IMC), aircraft adhere to conservative, fixed following-distance guidelines based primarily on aircraft weight classifications. When ambient conditions are such that vortices will either drift or dissipate, leaving the flight corridor clear, the prescribed spacings are unnecessarily long and result in decreased airport throughput. There is a potential for significant airport efficiency improvement, if a system can be employed to aid regulators and pilots in setting safe and efficient following distances based on airport conditions. The National Aeronautics and Space Administration (NASA), the Federal Aviation Agency, and Volpe National Transportation Systems Center have promoted and worked to develop systems that would increase airport capacity and provide for safe reductions in aircraft separation. The NASA Aircraft Vortex Spacing System (AVOSS), a wake vortex spacing system that can provide dynamic adjustment of spacings based on real-time airport weather conditions, has demonstrated that Lidar systems can be successfully used to detect and track vortices in clear air conditions. To fill the need for detection capability in low-visibility conditions, a 35 GHz, pulsed-Doppler radar system is being investigated for use as a complimentary, low-visibility sensor for wake vortices. The radar sensor provides spatial and temporal information similar to that provided by Lidar, but under weather conditions that a Lidar cannot penetrate. Currently, we are analyzing the radar design based upon the data and experience gained during the wake vortex Lidar deployment with AVOSS at Dallas/Fort Worth International Airport. As part of this study, two numerical models were utilized in system simulations. The results of this study improve our understanding of the method of detection, resolution requirements for range and azimuth, pulse compression, and performance prediction. Simulations applying pulse compression techniques show that detection is good in heavy fog to greater than 2000 m. Both compressed and uncompressed short pulses show the vortex structure. To explore operational challenges, siting and scanning strategies were also analyzed. Simulation results indicate that excellent wake vortex detection, tracking and classification is possible in drizzle (+15 dBZ) and heavy fog (- 13 dBZ) using short pulse techniques (<99ns) at ranges on the order of 900 m, with a modest power of 500 W output. At 1600 m, detection can be expected at reflectivities as low as -13 dBZ (heavy fog). The radar system, as designed and built, has the potential to support field studies of a wake vortex spacing system in low-visibility conditions ranging from heavy fog to rain, when sited within 2000m of the flight path.

Neece, Robert T.↗

Preliminary evaluation of spectral, normal and meteorological crop stage estimation approaches

Several of the projects in the AgRISTARS program require crop phenology information, including classification, acreage and yield estimation, and detection of episodal events. This study evaluates several crop calendar estimation techniques for their potential use in the program. The techniques, although generic in approach, were developed and tested on spring wheat data collected in 1978. There are three basic approaches to crop stage estimation: historical averages for an area (normal crop calendars), agrometeorological modeling of known crop-weather relationships agrometeorological (agromet) crop calendars, and interpretation of spectral signatures (spectral crop calendars). In all, 10 combinations of planting and biostage estimation models were evaluated. Dates of stage occurrence are estimated with biases between -4 and +4 days while root mean square errors range from 10 to 15 days. Results are inconclusive as to the superiority of any of the models and further evaluation of the models with the 1979 data set is recommended.

Cate, R. B.↗

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Stutz, John↗

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.

Hanson, Robin↗

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Srivastava, Ashok N.↗

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Srivastava, Ashok N.↗

Multi-Wavelength Observations of Asteroid 2100 Ra-Shalom: Visible, Infrared, and Thermal Spectroscopy Results

The August 2003 apparition of asteroid 2100 Ra-Shalom brought together a collaboration of observers with the goal of obtaining rotationally resolved multiwavelength spectra at each of 5 facilities: infrared spectra at the NASA Infrared Telescope Facility (Clark and Shepard), radar images at Arecibo (Shepard and Clark), thermal infrared spectra at Palomar (Lim, McConnochie and Bell), visible spectra at McDonald Observatory (Vilas, Lederer and Jarvis), and visible lightcurves at Ondrojev Observatory (Pravec). The radar data was to be used to develop a high spatial resolution physical model to be used in conjunction with spectral data to investigate compositional and textural properties on the near surface of Ra Shalom as a function of rotation phase. This was the first coordinated multi-wavelength investigation of any Aten asteroid. There are many reasons to study near-Earth asteroid (NEA) 2100 Ra-Shalom: 1) It has a controversial classification (is it a C- or K-type object)? 2) There would be interesting dynamical ramifications if Ra-Shalom is a K-type because most K-types come from the Eos family and there are no known dynamical pathways from Eos to the Aten population. 3) The best available spectra obtained previously may indicate a heterogeneous surface (most asteroids appear to be fairly homogeneous). 4) Ra-Shalom thermal observations obtained previously indicated a lack of regolith, minimizing the worry of space weathering effects in the spectra. 5) Radar observations obtained previously hinted at interesting surface structures. 6) Ra-Shalom is one of the largest Aten objects. And 7) Ra-Shalom is on a short list of proposed NEAs for spacecraft encounters and possible sample returns. Preliminary results from the visible, infrared, and thermal spectroscopy measurements will be presented here.

Clark, Beth Ellen↗

Characterizing K2 Candidate Planetary Systems Orbiting Low-Mass Stars. I. Classifying Low-Mass Host Stars Observed During Campaigns 1-7

We present near-infrared spectra for 144 candidate planetary systems identified during Campaigns 1-7 of the NASA K2 Mission. The goal of the survey was to characterize planets orbiting low-mass stars, but our Infrared Telescope Facility/SpeX and Palomar/TripleSpec spectroscopic observations revealed that 49% of our targets were actually giant stars or hotter dwarfs reddened by interstellar extinction. For the 72 stars with spectra consistent with classification as cool dwarfs (spectral types K3-M4), we refined their stellar properties by applying empirical relations based on stars with interferometric radius measurements. Although our revised temperatures are generally consistent with those reported in the Ecliptic Plane Input Catalog (EPIC), our revised stellar radii are typically 0.13 solar radius (39%) larger than the EPIC values, which were based on model isochrones that have been shown to underestimate the radii of cool dwarfs. Our improved stellar characterizations will enable more efficient prioritization of K2 targets for follow-up studies.

Campaigns17 of the NASA K2 Mission↗

Context dependent anti-aliasing image reconstruction

Image Reconstruction has been mostly confined to context free linear processes; the traditional continuum interpretation of digital array data uses a linear interpolator with or without an enhancement filter. Here, anti-aliasing context dependent interpretation techniques are investigated for image reconstruction. Pattern classification is applied to each neighborhood to assign it a context class; a different interpolation/filter is applied to neighborhoods of differing context. It is shown how the context dependent interpolation is computed through ensemble average statistics using high resolution training imagery from which the lower resolution image array data is obtained (simulation). A quadratic least squares (LS) context-free image quality model is described from which the context dependent interpolation coefficients are derived. It is shown how ensembles of high-resolution images can be used to capture the a priori special character of different context classes. As a consequence, a priori information such as the translational invariance of edges along the edge direction, edge discontinuity, and the character of corners is captured and can be used to interpret image array data with greater spatial resolution than would be expected by the Nyquist limit. A Gibb-like artifact associated with this super-resolution is discussed. More realistic context dependent image quality models are needed and a suggestion is made for using a quality model which now is finding application in data compression.

Beaudet, Paul R.↗

X-ray spectral classification of supernova remnants in the Large Magellanic Cloud

The solid state spectrometer on the Einstein Observatory was used to measure the 0.6-4.5 keV X-ray spectra of six prominent supernova remnants in the Large Magellanic Cloud, namely, N132D, N63A, N49, 0525-66.0, N157B, and 0540-69.3. Thermal emission is detected from the first four remnants and is similar in nature to that observed from young galactic SNRs. In contrast, N157B and 0540-69.3 have featureless X-ray spectra which are well described by power-law models. The present data support a synchrotron origin for the X-rays from N157B and 0540-69.3, although an alternative emission mechanism is considered for the latter object.

Clark, D. H.↗

The relative importance of aerosol scattering and absorption in remote sensing

The relative importance of aerosol optical thickness and absorption is illustrated through computing radiances for radiative transfer models. The radiance of sunlight reflected from models of the earth-atmosphere system is computed as a function of the aerosol optical thickness and its albedo of single scattering; it is noted that the albedo varies from 0.6 in urban environment to nearly 1 in areas with low graphitic carbon content. The calculations are applied to the example of satellite measurements of biomass. It is found that when surface classifications are made by means of clustering techniques the presence of gradients in the aerosol optical properties results in the dispersion of points in the plot correlating radiances viewed in two different directions. Finally, though such a remote sensing parameter as contrast is weakly affected by aerosol absorption, it is highly dependent on its optical thickness.

Fraser, R. S.↗

Estimating biogeochemical fluxes across sagebrush-steppe landscapes with Thematic Mapper imagery

Thematic Mapper (TM) satellite data were coupled to an ecosystem simulation model to simulate variation in nitrogen mineralization over time and space in a sagebrush steppe. This system of data inputs and calculations provides estimates of ecosystem properties including rates of biogeochemical processes over extensive and complex landscapes, and under changing management and climatic conditions. The landscape surface was divided into three sagebrush ecosystem types plus one other class consisting of nonsagebrush vegetation. This classification presented a complex mosaic of ecosystem types that shifted markedly in composition from one end of the 933-sq km study area to the other. Annual N-mineralization rates ranged from 5 to 25 kg N/ha among the three sagebrush types. The most active type comprised 42 percent of the entire area but contributed 60 percent to the nitrogen mineralization throughout the landscape.

Reiners, W. A.↗