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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Seismicity and the subduction process

There is considerable variation between subduction zones in the largest characteristic earthquake within each zone. Assuming that coupling between downgoing and upper plates is directly related to characteristic earthquake size, tests for correlations between variation in coupling and other physical features of subduction zones are conducted: the lateral extent and penetration depth of Benioff zones, age of subducting lithosphere, convergence rate, and back-arc spreading. Using linear multivariate regression, coupling is correlated with two variables: convergence rate and lithosphere age. Secondary correlations within the data set are penetration depth versus lithosphere age, and lateral extent versus convergence rate. Taken together, the observed correlations suggest a simple qualitative model where convergence rate and lithosphere age determine the horizontal and sinking rates, respectively, of slabs: these parameters influence the seismic coupling in the subduction zone. In the limit of a fast sinking rate and slow convergence rate, back-arc spreading occurs and thereby appears to be a passive process.

Ruff, L.↗
NASA NTRS1983

Longitudinal flying qualities criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Stengel, R. F.↗
NASA NTRS1985

Assessing the capability of EOS sensors in measuring ocean-atmosphere moisture exchange

As part of the Science Synergism Studies to identify interdisciplinary Scientific studies, which could be addressed by the Environmental Observing System (EOS), the techniques being developed to measure ocean-atmosphere moisture exchanges using satellite sensors were evaluated. Studies required to use sensors proposed for EOS were examined. A method has been developed to compute the moisture flux using the wind speed, sea surface temperature, and preciptable water measured by satellite sensors. It relies on a statistical model which predicts surface-level humidity from precipitable water. The Scanning Multichannel Microwave Radiometer (SMMR) measures all 3 parameters and was found to be sensitive to the annual cycle and large interannual variations such as the 1982 to 1983 El Nino. There are systematic differences between geophysical parameters measured by Nimbus/SMMR and in situ measurements. After quadratic trends and crosstalks were removed from the parameters through multivariate regressions, the latent heat fluxes computed from SMMR agree with those computed from ship reports to within 30 W/sq m. The poor quality of ship reports may be the cause of a portion of this scatter. Similar results are found using SEASAT/SMMR data. When the scatterometer winds were used instead of the SMMR winds, the difference between the satellite fluxes and the ship fluxes was reduced.

Liu, W. T.↗
NASA NTRS1986

Longitudinal flying qualitites criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems, the relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Bar-Gill, A.↗
NASA NTRS1987

Diet and the role of lipoproteins, lipases, and thyroid hormones in coronary lesion growth

The relationships between the coronary lesion growth and the blood contents of lipoprotein fractions, thyroic hormones, and the lipoprotein lipase activity were investigated in male patients with severe coronary atherosclerosis, who participated in a lipid-lowering dietary intervention program. A quantitative computer-assisted image-processing technique was used to assess the severity of coronary obstructions at the beginning of the program and at its termination two years later. Based on absolute coronary scores, patients were divided into a no-lesion growth group (14 patients) and a progression group (21 paients). At the end of the trial, the very-low-density lipoprotein cholesterol and triglycerides were found to be significantly higher, while the high-density lipoprotein cholesterol and hepatic lipase (HL) were lower in the progression group. Multivariate regression analysis showed HL to be the most important determinant of changes in coronary atherosclerotic lesions.

Barth, Jacques D.↗
NASA NTRS1991

A case study demonstration of the soil temperature extrema recovery rates after precipitation cooling at 10-cm soil depth

Since the invention of maximum and minimum thermometers in the 18th century, diurnal temperature extrema have been taken for air worldwide. At some stations, these extrema temperatures were collected at various soil depths also, and the behavior of these temperatures at a 10-cm depth at the Tifton Experimental Station in Georgia is presented. After a precipitation cooling event, the diurnal temperature maxima drop to a minimum value and then start a recovery to higher values (similar to thermal inertia). This recovery represents a measure of response to heating as a function of soil moisture and soil property. Eight different curves were fitted to a wide variety of data sets for different stations and years, and both power and exponential curves were fitted to a wide variety of data sets for different stations and years. Both power and exponential curve fits were consistently found to be statistically accurate least-square fit representations of the raw data recovery values. The predictive procedures used here were multivariate regression analyses, which are applicable to soils at a variety of depths besides the 10-cm depth presented.

Welker, Jean Edward↗
NASA NTRS1990

Early experiences building a software quality prediction model

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Agresti, W. W.↗
NASA NTRS1999

Strain Gauge Balance Uncertainty Analysis at NASA Langley: A Technical Review

This paper describes a method to determine the uncertainties of measured forces and moments from multi-component force balances used in wind tunnel tests. A multivariate regression technique is first employed to estimate the uncertainties of the six balance sensitivities and 156 interaction coefficients derived from established balance calibration procedures. These uncertainties are then employed to calculate the uncertainties of force-moment values computed from observed balance output readings obtained during tests. Confidence and prediction intervals are obtained for each computed force and moment as functions of the actual measurands. Techniques are discussed for separate estimation of balance bias and precision uncertainties.

John S Tripp↗
NASA NTRS1997

Analysis of Forest Foliage Using a Multivariate Mixture Model

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Hlavka, C. A.↗
NASA NTRS2005

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Sullivan, Dana↗
NASA NTRS2007

[Space Weather Impact on the Electricity Market]

Forbes & St. Cyr (2004, hereafter "FISC") have provided evidence that the electricity market can be impacted by space weather. Our analysis indicated that the estimated market impact for PJM was 3.7 % or approximately $500 million dollars over the 19 month sample period. Kappenman has taken exception to this estimate and contends that we have exaggerated the magnitude of the problem that space weather poses to PJM. There are four specific issues: (1) he claims that we have ignored relevant literature; (2) he asserts that Dst is not an appropriate proxy for GICs in PJM; (3) he charges that our findings are inconsistent with the impact of the 17 September 2000 storm; and (4) he alleges that our discussion of October 2003 storms is misleading. In our article, we have explained our methodology, multivariate regression analysis, with a particular focus on how it compares to correlation analysis. We have also explained the limitations of our analysis. We noted that "...While the Dstlprice relationship was found to be robust, the precise estimate should be treated with a relatively high degree of caution given that econometric modeling is not an exact science as well as the fact that the measure of space weather may be a poor proxy for GICs" (paragraph 96). We have also noted that additional research using local magnetometer data are needed (paragraph 97). We did not claim that that our findings for PJM are representative of the impact of space weather on other power grids. On the contrary, we noted that ... "Only analysis of other power grids will tell. " (paragraph 97). Kappenman inaccurately asserts that we have indicated that our findings . . . "imply much higher total costs are likely across the US and elsewhere in the world." He also inaccurately asserts that we have claimed that " . . . Dst is the most suited proxy for GIC in the PJM grid.. ." Moreover, he inaccurately refers to our analysis as a correlation study that uses Dst as quasi-binary indicator.

SaintCyr, O. Chris↗
NASA NTRS2009

Physiological Factors Contributing to Postflight Changes in Functional Performance

Astronauts experience alterations in multiple physiological systems due to exposure to the microgravity conditions of space flight. These physiological changes include sensorimotor disturbances, cardiovascular deconditioning and loss of muscle mass and strength. These changes might affect the ability of crewmembers to perform critical mission tasks immediately after landing on lunar and Martian surfaces. To date, changes in functional performance have not been systematically studied or correlated with physiological changes. To understand how changes in physiological function impact functional performance an interdisciplinary pre/postflight testing regimen (Functional Task Test, FTT) has been developed that systematically evaluates both astronaut postflight functional performance and related physiological changes. The overall objectives of the FTT are to: Develop a set of functional tasks that represent critical mission tasks for Constellation. Determine the ability to perform these tasks after flight. Identify the key physiological factors that contribute to functional decrements. Use this information to develop targeted countermeasures. The functional test battery was designed to address high priority tasks identified by the Constellation program as critical for mission success. The set of functional tests making up the FTT include the: 1) Seat Egress and Walk Test, 2) Ladder Climb Test, 3) Recovery from Fall/Stand Test, 4) Rock Translation Test, 5) Jump Down Test, 6) Torque Generation Test, and 7) Construction Activity Board Test. Corresponding physiological measures include assessments of postural and gait control, dynamic visual acuity, fine motor control, plasma volume, orthostatic intolerance, upper and lower body muscle strength, power, fatigue, control and neuromuscular drive. Crewmembers will perform both functional and physiological tests before and after short (Shuttle) and long-duration (ISS) space flight. Data will be collected on R+0 (Shuttle only), R+1, R+6 and R+30. Using a multivariate regression model we will identify which physiological systems contribute the most to impaired performance on each functional test. This will allow us to identify the physiological systems that play the largest role in decrement in functional performance. Using this information we can then design and implement countermeasures that specifically target the physiological systems most responsible for the altered functional performance associated with space flight.

Bloomberg, J. J.↗
NASA NTRS2011

Analysis of Association Between Remotely Sensed (RS) Data and Soil Transmitted Helminthes Infection Using Geographical Information Systems (GIS): Boaco, Nicaragua

Soil-transmitted helminths are intestinal nematodes that can infect all members of a population but specially school-age children living in poverty. Infection can be significantly reversed with anthelmintic drug treatments and sanitation improvement. Implementation of effective public health programs requires reliable and updated information to identify areas at higher risk and to calculate amount of drug required. Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from RS data using ARC Map 9.3 (ESRI). Prevalence data and RS data matching at the same geographical location will be analyzed for correlation and those variables from RS data that better correlate with prevalence will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat TM and ETM+. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections are determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). This study will demonstrate the importance of an integrated GIS/RS approach to define sampling clusters without the need for any ground-based survey. Such information is invaluable to identify areas of high risk and to geographically target control programs that maximize cost-effectiveness and sanitation efforts.

MorenoMadrinan, Max J.↗
NASA NTRS2011

Use of Remote Sensing/Geographical Information Systems (RS/GIS) to Identify the Distributional Limits of Soil-Transmitted Helminths (STHs) and Their Association to Prevalence of Intestinal Infection in School-Age Children in Four Rural Communities in Boaco, Nicaragua

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

Moreno, Max J.↗
NASA NTRS2013

Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson􀂶s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

Coroneos, Rula↗
NASA NTRS2014

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

Robertson, Franklin R.↗
NASA NTRS2014

Tropospheric Ozone Increases over the Southern Africa Region: Bellwether for Rapid Growth in Southern Hemisphere Pollution?

Increases in free-tropospheric (FT) ozone based on ozonesonde records from the early 1990s through 2008 over two subtropical stations, Irene (near Pretoria, South Africa) and Réunion (21 deg. S, 55 deg. E; approx. 2800 km NE of Irene in the Indian Ocean), have been reported. Over Irene a large increase in the urban-influenced boundary layer (BL, 1.5-4 km) was also observed during the 18-year period, equivalent to 30%decade−1. Here we show that the Irene BL trend is at least partly due to a gradual change in the sonde launch times from early morning to the midday period. The FT ozone profiles over Irene in 1990-2007 are re-examined, filling in a 1995-1999 gap with ozone profiles taken during the Measurements of Ozone by Airbus In-service Aircraft (MOZAIC) project over nearby Johannesburg. A multivariate regression model that accounts for the annual ozone cycle, El Niño-Southern Oscillation (ENSO) and possible tropopause changes was applied to monthly averaged Irene data from 4 to 11 km and to 1992-2011 Réunion sonde data from 4 to 15 km. Statistically significant trends appear predominantly in the middle and upper troposphere (UT; 4-11 km over Irene, 4-15 km over Réunion) in winter (June-August), with increases 1 ppbv yr(exp. −1) over Irene and approx. 2 ppbv yr(exp. −1) over Réunion. These changes are equivalent to approx. 25 and 35-45%decade( exp. −1), respectively. Both stations also display smaller positive trends in summer, with a 45%decade(exp. −1) ozone increase near the tropopause over Réunion in December. To explain the ozone increases, we investigated a time series of dynamical markers, e.g., potential vorticity (PV) at 330-350 K. PV affects UT ozone over Irene in November-December but displays little relationship with ozone over Réunion. A more likely reason for wintertime FT ozone increases over Irene and Réunion appears to be long-range transport of growing pollution in the Southern Hemisphere. The ozone increases are consistent with trajectory origins of air parcels sampled by the sondes and with recent NOx emissions trends estimated for Africa, South America and Madagascar. For Réunion trajectories also point to sources from the eastern Indian Ocean and Asia.

Trends↗
NASA NTRS2017

Biomass Retrieval from L-Band Polarimetric UAVSAR Backscatter and PRISM Stereo Imagery

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m x 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t-and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R(exp. 2), Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R(exp. 2) and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R(exp. 2) increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R(exp. 2) of 0.77. With the canopy height from PRISM, R(exp. 2) increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above approximately 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation.Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R(exp. 2) increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) wave-form data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant

Zhang, Zhiyu↗