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NASA NTRS1999

Estimating Vegetation Parameters from Interferometric and Polarimetric Radar Using Physical Scattering Models

Radar data from vegetated land surfaces depend on many structural and compositional parameters describing the terrain. Because early, noninterferometric radar systems usually constituted an insufficient observation set from which to estimate parameters of the terrain, statistical regression techniques were used which incorporated some level of apriori knowledge or field measurements. With the advent of radar interferometry and polarimetric interferometry, potentially at multiple baselines, the observation set is now approaching that required to quantitatively estimate the parameters describing a vegetated land surface. Quantitative estimation entails formulating a physical scattering model relating the radar observations to the vegetation and surface parameters on which they depend. This paper describes the physics of candidate scattering models, and shows how the models determine the estimable parameter set. It also indicates the measurement accuracy of parameters such as vegetation height, height-to-base-of-live-crown, and surface topography with multibaseline polarimetric interferometry.

Treuhaft, Robert N.↗

NASA NTRS2011

Software Testing and Verification in Climate Model Development

Over the past 30 years most climate models have grown from relatively simple representations of a few atmospheric processes to a complex multi-disciplinary system. Computer infrastructure over that period has gone from punch card mainframes to modem parallel clusters. Model implementations have become complex, brittle, and increasingly difficult to extend and maintain. Existing verification processes for model implementations rely almost exclusively upon some combination of detailed analysis of output from full climate simulations and system-level regression tests. In additional to being quite costly in terms of developer time and computing resources, these testing methodologies are limited in terms of the types of defects that can be detected, isolated and diagnosed. Mitigating these weaknesses of coarse-grained testing with finer-grained "unit" tests has been perceived as cumbersome and counter-productive. In the commercial software sector, recent advances in tools and methodology have led to a renaissance for systematic fine-grained testing. We discuss the availability of analogous tools for scientific software and examine benefits that similar testing methodologies could bring to climate modeling software. We describe the unique challenges faced when testing complex numerical algorithms and suggest techniques to minimize and/or eliminate the difficulties.

Clune, Thomas L.↗

NASA NTRS1998

Fiber-Supported Droplet Combustion Experiment-2

A major portion of the energy produced in the world today comes from the burning of liquid hydrocarbon fuels in the form of droplets. Understanding the fundamental physical processes involved in droplet combustion is not only important in energy production but also in propulsion, in the mitigation of combustion-generated pollution, and in the control of the fire hazards associated with handling liquid combustibles. Microgravity makes spherically symmetric combustion possible, allowing investigators to easily validate their droplet models without the complicating effects of gravity. The Fiber-Supported Droplet Combustion (FSDC-2) investigation was conducted in the Microgravity Glovebox facility of the shuttles' Spacelab during the reflight of the Microgravity Science Laboratory (MSL- 1R) on STS-94 in July 1997. FSDC-2 studied fundamental phenomena related to liquid fuel droplet combustion in air. Pure fuels and mixtures of fuels were burned as isolated single and duo droplets with and without forced air convection. FSDC-2 is sponsored by the NASA Lewis Research Center, whose researchers are working in cooperation with several investigators from industry and academia. The rate at which a droplet burns is important in many commercial applications. The classical theory of droplet burning assumes that, for an isolated, spherically symmetric, single-fuel droplet, the gas-phase combustion processes are much faster than the droplet surface regression rate and that the liquid phase is at a uniform temperature equal to the boiling point. Recent, more advanced models predict that both the liquid and gas phases are unsteady during a substantial portion of the droplet's burning history, thus affecting the instantaneous and average burning rates, and that flame radiation is a dominant mechanism that can extinguish flames in a microgravity environment. FSDC-2 has provided well-defined, symmetric droplet burning data including radiative emissions to validate these theoretical models for heptane, decane, ethanol, and methanol fuels. Since most commercial combustion systems burn droplets in a convective environment, data were obtained without and with convective flow over the burning droplet (see the following photos).

Colantonio, Renato O.↗

NASA NTRS1993

Confidence bounds on structural reliability

Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.

Mehta, S. R.↗

NASA NTRS1969

A kinetic model for the acid-catalyzed decomposition of Delrin

Kinetic model for acid catalyzed Delrin decomposition, testing for Delrin-citric acid system and predicting thermograms with nonlinear regression analysis

Cagliostro, D. E.↗

NASA NTRS1987

Least squares linear lags and limited memory filters

Pure autoregressive (AR) models which are linear in the short term, that is when a variable can be predicted by linear regression on a limited number of past observations, are discussed. When evenly spaced observations are available, a fixed set of AR coefficients can be calculated independent of the data. For filtering purposes, such a lag structure can be implemented recursively with an efficient algorithm. The method of computing variance recursively is also derived. A complete algorithm is presented in the appendix.

Discenza, Joseph H.↗

NASA NTRS1992

Forward model and CO retrieval algorithm

A progress report on the forward model and CO retrieval algorithm is presented. For the forward model, the fast-transmittance algorithm is discussed including monochromatic calculations, determination of fast-transmittance parameters, and regression errors. Simulations, retrieval technique, and sensitivity of the CO retrieval algorithm are discussed.

Strow, L. L.↗

NASA NTRS1997

Multiwavelength and Statistical Research in Space Astrophysics

The accomplishments in the following three research areas are summarized: multiwavelength study of active galactic nuclei; magnetic activity of young stellar objects; and statistical methodology for astronomical data analysis. The research is largely based on observations of the ROSAT and ASCA X-ray observatories, complemented by ground-based optical and radio studies. Major findings include: discovery of inverse Compton X-ray emission from radio galaxy lobes; creation of the largest and least biased available sample of BL Lac objects; characterization of X-ray and nonthermal radio emission from T Tauri stars; obtaining an improved census of young stars in a star forming region and modeling the star formation history and kinematics; discovery of X-ray emission from protostars; development of linear regression methods and codes for interpreting astronomical data; and organization of the first cross-disciplinary conferences for astronomers and statisticians.

Feigelson, Eric D.↗

NASA NTRS1995

Respiratory sinus arrhythmia: time domain characterization using autoregressive moving average analysis

Fourier-based techniques are mathematically noncausal and are therefore limited in their application to feedback-containing systems, such as the cardiovascular system. In this study, a mathematically causal time domain technique, autoregressive moving average (ARMA) analysis, was used to parameterize the relations of respiration and arterial blood pressure to heart rate in eight humans before and during total cardiac autonomic blockade. Impulse-response curves thus generated showed the relation of respiration to heart rate to be characterized by an immediate increase in heart rate of 9.1 +/- 1.8 beats.min-1.l-1, followed by a transient mild decrease in heart rate to -1.2 +/- 0.5 beats.min-1.l-1 below baseline. The relation of blood pressure to heart rate was characterized by a slower decrease in heart rate of -0.5 +/- 0.1 beats.min-1.mmHg-1, followed by a gradual return to baseline. Both of these relations nearly disappeared after autonomic blockade, indicating autonomic mediation. Maximum values obtained from the respiration to heart rate impulse responses were also well correlated with frequency domain measures of high-frequency "vagal" heart rate control (r = 0.88). ARMA analysis may be useful as a time domain representation of autonomic heart rate control for cardiovascular modeling.

NASA Discipline Regulatory Physiology↗

NASA NTRS2011

Orthostatic Hypotension After Long-Duration Space Flight: NASA's Experiences from the International Space Station

Our laboratory previously reported that the incidence of orthostatic hypotension (OH) was greater after long- than short-duration spaceflight in astronauts who participated in Mir Space Station and Space Shuttle missions. To confirm and extend these findings, we retrospectively examined tilt test data from International Space Station (ISS) and Shuttle astronauts. We anticipated that the proportion of ISS astronauts experiencing OH would be high on landing day and the number of days to recover greater after long- than short-duration missions. Methods: Twenty ISS and 66 Shuttle astronauts participated in 10-min 80? head-up tilt tests 10 d before launch (L-10), on landing day (R+0) or 1 d after landing (R+1). Data from 5 ISS astronauts tested on R+0 or R+1 who used non-standard countermeasures were excluded. Many astronauts repeated the test 3 d (R+3) after landing. Fisher?s Exact Test was used to compare the ability of ISS and Shuttle astronauts to complete the tilt test on R+0. Cox regression was used to identify cardiovascular parameters that were associated with test completion across all tests, and mixed model analysis was used to compare the change and recovery rates between ISS and Shuttle astronauts. In these analyses, ISS data from R+0 and R+1 were pooled to provide sufficient statistical power. Results: The proportion of astronauts who completed the tilt test on R+0 without OH was less in ISS than in Shuttle astronauts (p=0.03). On R+0, only 2 of 6 ISS astronauts completed the test compared to 53 of 66 (80%) Shuttle astronauts. However, 8 of 9 ISS astronauts completed the test on R+1. On R+3, 13 of 15 (87%) of the ISS and 19 of 19 (100%) of the Shuttle astronauts completed the 10-min test. An index comprised of stroke volume and diastolic blood pressure provided a very good prediction of overall tilt survival. This index was altered by spaceflight similarly for both groups soon after landing (pooled R+0 and R+ 1), but ISS astronauts did not recover at the same rate as Shuttle astronauts (p=0.007). Conclusions: The proportion of ISS astronauts who could not complete the tilt test on R+0 due to OH (4 of 6) is similar to that reported in astronauts who flew on Mir (5 of 6). Further, cardiovascular parameters most closely associated with OH recover more slowly after long- compared to short-duration spaceflight.

Lee, Stuart M. C.↗

NASA NTRS2023

Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation

Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.

Bayesian hierarchical modeling↗

NASA NTRSDate not supplied

On the Integration of Loudness Over Time for the Prediction of Single-Event Annoyance

Loudness is the quality of human perception that is most related to acoustic intensity and energy, however there is no agreed-upon way of integrating loudness over time for the prediction of noise-induced annoyance. Most noise metrics used today for aircraft certification and regulation employ the “Equal-Energy Hypothesis” (EEH) and sum the acoustical energy of one or more noise events over time to create a single metric value that represents the entire exposure. Using the EEH creates a natural tradeoff between the peak energy and the duration of a single noise event, the “hypothesis” being that this tradeoff optimally predicts annoyance in a wide variety of situations. This work proposes a strategy for integrating a loudness-like time series which is flexible in two ways: First, it includes a parameter b ∈ [0,1]. When b = 0, the metric will return the peak of the time series. At b = .5, the metric will behave in accordance with the EEH. At b = 1, the metric will penalize the duration of the sound more than the EEH would. Second, transformations are given so that the strategy can be computed from, or generate quantities in units analogous to, decibels (or phon), physical units (acoustic pressure/power), or perceptually-scaled units (sone). This approach is demonstrated on a dataset of annoyance responses to single events of UAV and road vehicle noise that is fit with an augmented linear regression. Analyses based on this integration of A-weighted level and the output of the “Zwicker” loudness model yield similar results: that subjects may have been slightly more sensitive to the durations of the events than the EEH would suppose, but that the EEH cannot be disproven using these data. The time-integrated metrics outperform both time-averaged and centile-based metrics.

Psychoacoustics↗

NASA NTRSDate not supplied

On the Integration of Loudness Over Time for the Prediction of Single-Event Annoyance

Psychoacoustics↗

NASA NTRS2024

Northern Rockies Ecological Conservation: Leveraging Earth Observations to Monitor and Predict Populations of Federally Threatened Whitebark Pine (Pinus albicaulis) across the Intermountain West

Whitebark pine (WBP; Pinus albicaulis) is an ecologically important species in North America. As a federally listed threatened species, an understanding of WBP habitat, distribution, and health is important for the natural resource managers of the National Park Service, United States Forest Service, Bureau of Land Management, Fish and Wildlife Service, and non-profit organizations such as the Whitebark Pine Ecosystem Foundation. Previous attempts to develop models of WBP habitat suitability and distribution lack confidence in their validity and integrity for these organizations. The updated models of habitat suitability and distribution developed by this study would provide managers with a capability to be employed in the conservation and future research direction for WBP. Thus, we developed a habitat suitability model of WBP at a high spatial resolution (Landsat 9 Operational Land Image-2, National Land Cover Database, NASA Shuttle Radar Topography Mission; 30m pixels) using a generalized logistic regression with an area under the curve value of 0.754. We extracted spectral reflectance signatures from overlapped ground sample points and Sentinel-2 Multispectral Instrument. The spectral signature analysis indicates WBP is separable from other tree species. We also utilized a visual validation approach and random forest (RF) modeling to separate WBP from limber pine. Through visual validation the RF classifier successfully identified 8out of 10 WBP trees gathered through ground truth points. Additionally, we achieved an overall accuracy of 91%in our confusion matrix for the distribution model using a dependent validation approach. The derived products from this study allow project partners to assess current suitable habitat and apparent health status in areas of identified WBP occurrence, providing data to aid future research regarding WBP health.

Sentinel-2↗

NASA NTRS2022

Determination of Chlorophyll Content in Selected Grass Communities of Krkonoše Mts. Tundra Based on Laboratory Spectroscopy and Aerial Hyperspectral Data

The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R 2 for all indices was 0.52 for all species together at leaf level and median R 2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R 2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

L. Cervena↗

NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗

NASA NTRS1983

Simplified analysis techniques to support the determination of Shuttle aerodynamics

The use of deterministic and regression methods to supplement the maximum likelihood estimation programs for the determination of the stability and control parameters of the Shuttle vehicle is discussed. Examples taken from STS-2 and STS-3 flight data show the use of deterministic methods to verify the effectiveness of the pitch and roll Reaction Control System (RCS), to verify the model structure used in the maximum likelihood estimator and to indicate regions of the descent trajectory where unmodeled moments dominate the vehicle motion and the application of maximum likelihood estimation would lead to incorrect parameter values. The use of regression methods to determine selected parameters when the more sophisticated maximum likelihood programs are not required, to determine the form of the vehicle mathematical model and to select the data time intervals for most effective parameter identification are also discussed.

Suit, W. T.↗

NASA NTRS1977

Genesis of the Angra dos Reis and other achondritic meteorites

Major, minor, and trace element abundances were determined in seven Angra dos Reis samples, which included whole rocks, fassaite (clinopyroxene), olivine, and whitlockite separates, via sequential instrumental neutron activation analysis. The rare earth element abundance patterns and the trace element abundance patterns of this achondritic meteorite are discussed. A two-stage crystal-liquid fractionation model with source material containing olivine, orthopyroxene, and clinopyroxine is presented for the genesis of Angra dos Reis. Multilinear regression analysis was used to estimate possible composition and properties of the parent magmas. Angra dos Reis was compared with other achondrites, cumulate eucrites and nakhlites in particular. It is thought that the unique Angra dos Reis could not be related to those achondrites by crystal-liquid fractionation of the same parent body.

Ma, M.-S.↗