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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Determining the source region of auroral emissions in the prenoon oval using coordinated Polar BEAR UV-imaging and DMSP particle measurements

The Polar Beacon Experiment and Auroral Research (Polar BEAR) satellite included the capability for imaging the dayside auroral oval in full sunlight at several wavelengths. Particle observations from the DMSP F7 satellite during dayside auroral oval crossings are compared with approximately simultaneous Polar BEAR 1356-A images to determine the magnetospheric source region of the dayside auroral oval. The source region is determined from the DMSP particle data, according to recent work concerning the classification and identification of precipitation source regions. The close DMSP/Polar BEAR coincidences all occur when the former satellite is located between 0945 and 1000 MLT. Instances of auroral arcs mapping to each of several different regions, including the boundary plasma sheet, the low-latitude boundary layer, and the plasma mantle were found. It was determined that about half the time the most prominent auroral arcs are located at the interfaces between distinct plasma regions, at least at the local time studied here.

Newell, Patrick T.↗
NASA NTRS2002

The NASA John C. Stennis Environmental Geographic Information System

Contents include the following: 1. Introduction: Background information. Initial applications of the SSC EGIS. Ongoing projects. 2.Scope of SSC EGIS. 3. Data layers. 4. Onsite operations. 5. Landcover classifications. 6. Current activities. 7. GIS/Key. 8. Infrastructure base map - development. 9. Infrastructure base map - application. 10. Incorrected layer. 11. Corrected layer. 12. Emergency environmental response tool. 13. Future directions. 14. Bridging the gaps. 15. Environmental geographical information system.

Cohan, Tyrus↗
NASA NTRS2005

Kepler Mission Design

The Kepler Mission is in the development phase with launch planned for 2007. The mission goal first off is to reliably detect a significant number of Earth-size planets in the habitable zone of solar-like stars. The mission design allows for exploring the diversity of planetary sizes, orbital periods, stellar spectral types, etc. In this paper we describe the technical approach taken for the mission design; describing the flight and ground system, the detection methodology, the photometer design and capabilities, and the way the data are taken and processed. (For Stellar Classification program. Finally the detection capability in terms of planet size and orbit are presented as a function of mission duration and stellar type.

Koch, David↗
NASA NTRS2001

Ultrasonic Data Display and Analysis System Developed (Including Fuzzy Logic Analysis) for the Windows-Based PC

Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact measurement data obtained at NASA Glenn Research Center's Ultrasonic Nondestructive Evaluation measurement facility. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary waveforms in the time and frequency domains along with the calculation of several frequency-dependent properties including phase velocity and attenuation coefficient and several frequency-independent properties, like the cross correlation velocity. The system allows image generation on all the frequency-dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency-independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of indepth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on an SQL data base. It is ideal for the classification of material properties or the location of microstructure variations in materials. Along with the ultrasonic contact measurement software that it is partnered with, this system is technology ready and can be transferred to users worldwide.

Lovelace, Jeffrey J.↗
NASA NTRS2004

Spaceborne microwave remote sensing of seasonal freeze-thaw processes in theterrestrial high l atitudes : relationships with land-atmosphere CO2 exchange

Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These relatively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, separately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North America and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO2 concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, though both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.

Sensor Microwave Imager (SSM/I)carbon cycle↗
NASA NTRS2004

Spaceborne Microwave Remote Sensing of Seasonal Freeze-Thaw Processes in the Terrestrial High Latitudes: Relationships with Land-Atmosphere CO2 exchange

Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These relatively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, separately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North America and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO2 concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, though both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.

net primary products↗
NASA NTRS2010

Stratifying Tropical Fires by Land Cover: Insights into Amazonian Fires, Aerosol Loading, and Regional Deforestation

This study analyzes changes in the number of fires detected on forest, grass, and transition lands during the 2002-2009 biomass burning seasons using fire detection data and co-located land cover classifications from the Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the total number of detected fires correlates well with MODIS mean aerosol optical depth (AOD) from year to year, in accord with other studies. However, we also show that the ratio of forest to savanna fires varies substantially from year to year. Forest fires have trended downward, on average, since the beginning of 2006 despite a modest increase in 2007. Our study suggests that high particulate matter loading detected in 2007 was likely due to a large number of savanna/agricultural fires that year. Finally, we illustrate that the correlation between annual Brazilian deforestation estimates and MODIS fires is considerably higher when fires are stratified by MODIS-derived land cover classifications.

TenHoeve, J. E.↗
NASA NTRS2010

Development of SMAP Mission Cal/Val Activities

The Soil Moisture Active Passive (SMAP) mission is a NASA directed mission to map global land surface soil moisture and freeze-thaw state. Instrument and mission details are shown. The key SMAP soil moisture product is provided at 10 km resolution with 0.04cubic cm/cubic cm accuracy. The freeze/thaw product is provided at 3 km resolution and 80% frozen-thawed classification accuracy. The full list of SMAP data products is shown.

Colliander, A.↗
NASA NTRS2016

Example of Occupational Surveillance in a Telemedicine Setting: Application of Epidemiologic Methods at NASA Johnson Space Center

In 2010, NASA implemented Lifetime Surveillance of Astronaut Health, a formal occupational surveillance program for the U.S. astronaut corps. Because of the nature of the space environment, space medicine presents unique challenges and opportunities for epidemiologists. One such example is the use of telemedicine while crewmembers are in flight, where the primary source of information about crew health is verbal communication between physicians and their crewmembers. Due to restricted medical capabilities, the available health information is primarily crewmember report of signs and symptoms, rather than diagnoses. As epidemiologists at NASA, Johnson Space Center, we have shifted our paradigm from tracking diagnoses based on traditional terrestrial clinical practice to one in which we also incorporate reported symptomology as potential antecedents of disease. In this presentation we describe how characterization of reported signs and symptoms can be used to establish incidence rates for inflight immunologic events. We describe interdisciplinary data sources of information that are used in combination with medical information to analyze the data. We also delineate criteria for symptom classification inclusion. Finally, we present incidence tables and graphs to illustrate the final outcomes. Using signs and symptoms reported via telemedicine, the epidemiologists provide summary evidence regarding incidence of potential inflight medical conditions. These results inform our NASA physicians and scientists, and support evaluation of the occupational health risks associated with spaceflight.

Babiak-Vazquez, Adriana↗
NASA NTRSDate not supplied

TPSAS-NF1676L-13204-DND

Invasive perennial pepperweed (Lepidium latifolium) has spread rapidly throughout the western United States in the past fifteen years. Pepperweed outcompetes many native species for water and nutrients, disturbing sensitive ecosystems. The purpose of this study is to map the contemporary distribution of pepperweed throughout the wetland ecosystem of the restored South San Francisco Bay Salt Ponds and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with the GER 1500 spectroradiometer along with presence and absence pepperweed GPS data points. A Spectral Angle Mapper (SAM) classification algorithm (Method A) was used to distinguish pepperweed spectra and map its distribution on an EO-1 Hyperion image of the study area. Similarly, a supervised classification was run on an ASTER image of the study area. A GIS multivariate habitat suitability model was created to predict areas most susceptible to pepperweed colonization. Variables incorporated into the model were tidal extent, propensity for disturbance, pond salinity, proximity to levees and channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a suitability map (Method B) and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to disturbance factors, extreme pond salinity and curvature as statistically significant in determining the locations of suitable pepperweed habitats

Andrew Nguyen↗
NASA NTRSDate not supplied

Automated classification of scientific publications linked to GES DISC datasets

The data collections archived and distributedby the GES DISC NASA data center arewidely utilized for various Earth Science studies.As these collections are created, many researchworks are published regarding the collections, algorithms,validations and applications. SinceGES DISC collects these publications and providestheir citations for the users, it is helpful tocategorize them based on how they relate to the datasetsthey are associated with. Specifically,whether the publication that is linked to GES DISCdataset is using it for applicational research,or if it describes the algorithm for dataset creation,or the validation of the dataset, or providesthe general overview of the data collection. Currently,this process requires simple manuallabelling, and as such, may be possible to solve viaautomation. To approach this problem, wedeveloped machine learning classifiers to predictthe category a publication belongs to. We usedmanually labeled publications as training data forsupervised machine learning algorithms:Random Forest and Naive Bayes. We achieved classificationaccuracy that is substantially betterthan the baseline accuracy, thus greatly improvingthe efficiency of the publication internalanalysis.

Rohan Dayal↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS2023

The UCLA Cosmochemistry Database

The UCLA Cosmochemistry Database was initiated as part of a data-rescue and -storage project aimed at archiving a variety of cosmochemical data acquired at University of California, Los Angeles (UCLA). The data collection includes elemental compositions of extraterrestrial materials analyzed by UCLA cosmochemists over the last five decades. The analytical techniques include atomic absorption spectrometry (AAS) and neutron activation analysis (NAA) at UCLA. The data collection is stored on the Astromaterials Data System (Astromat). We provide both interactive tables and downloadable datasheets for users to access all data. The UCLA Cosmochemistry Database archives cosmochemical data that are essential tools for increasing our understanding of the nature and origin of extraterrestrial materials. Future studies can reference the data collection in the examination, analysis, and classification of newly acquired extraterrestrial samples.

Bidong Zhang↗
NASA NTRS1974

Combined spectral and spatial processing of ERTS imagery data

A general procedure is presented for extracting textural properties of blocks of image data. These features are calculated in the spatial domain, taking into account the statistical nature of texture. The procedure is based on the assumption that the texture information in an image is contained in the overall or 'average' spatial relationship which the gray tones in the image have to one another. The spatial gray-tone dependence matrix is discussed together with the spectral features used in the study and the land use classification studies conducted. The results of the study show the usefulness of using both spectral and textural characteristics of ERTS multispectral scanner data for developing classification procedures.

Haralick, R. M.↗
NASA NTRS1985

Unsupervised Classification of Global Radar Units on Venus

Characterization of the Venusian surface in terms of its radar properties was accomplished by application of an unsupervised, linear discriminant algorithm to two Pioneer-Venus (PV) Orbiter radar data sets: the RMS-slope (surface roughness) and reflectivity. Both databases were spatially filtered to the same effective resolution of 100 km prior to classification. A recent supervised classification study using these data was based on presupposed morphologic significance of selected data ranges. The knowledge of both Venusian geology and the geologic significance of the radar data is so limited that the data warrant a more unsupervised approach; for this study a linear discriminant classifier was chosen. This approach is purely statistical, thereby removing any observer bias. Statistical significance of the resulting clusters was evaluated by an ancillary program in which an F test utilizing the Mahalanobis' distance.

Kozak, R. C.↗
NASA NTRS1973

Preparation of urban land use inventories by machine-processing of ERTS MSS data

Spectral classes of urban phenomena identified from ERTS multispectral scanner data in Milwaukee included Surburban, Inner City, Industry, Grassy (open area), Road, Wooded Suburb, Water, Cloud, and Shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use.

Todd, W.↗