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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Data from: Bouncing Back: plant-associated soil microbes respond rapidly to prairie establishment

It is well established that soil microbial communities change in response to altered land use and land cover, but less is known about the timing of these changes. Understanding temporal patterns in recovering microbial communities is an important part of improving how we assess and manage reconstructed ecosystems. We assessed patterns of community-level microbial diversity and abundance in corn and prairie plots 2 to 4 years after establishment in agricultural fields, using phospholipid fatty acid biomarkers. Principal components analysis of the lipid biomarkers revealed differing composition between corn and prairie soil microbial communities. Despite no changes to the biomass of Gram-positive bacteria and actinomycetes, total biomass, arbuscular mycorrhizal fungi biomass, and Gram-negative bacteria biomass were significantly higher in restored prairie plots, approaching levels found in long-established prairies. These results indicate that plant-associated soil microbes in agricultural soils can shift in less than 2 years after establishment of perennial grasslands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Balancing aggregation and smoothing errors in inverse models

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

Turner, A. J.↗
DOE OSTI2022

Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. The ACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vapor environment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal component analysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the vertical component of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects on ACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lower and higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained by the fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties and the below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensational growth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increased ACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraints to future model evaluations of aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The development of rainfall retrievals from radar at Darwin

Abstract. The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1 10mmh-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 10mmh-1 (deeper convective rain).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Distinction of saffron cod ( Eleginus gracilis ) from several other gadid species by using microsatellite markers

Nine microsatellite loci isolated in saffron cod ( Eleginus gracilis ) have potential applications for population genetics. Polymerase chain reaction products of samples of E. gracilis from northwestern Alaska amplified reliably, produced only one or two microsatellite bands, and had no apparent homozygote excess. A collection of E. gracilis sampled in the Gulf of Alaska (GOA) near Kodiak Island did not amplify reliably at one locus, and allele frequency profiles clustered distinctly (with principal component analysis [PCA]) from the northwestern Alaska collection. Northwestern Alaska and GOA E. gracilis collections were genetically different (on the basis of a standardized genetic differentiation measure [ G ' ST ]=0.313, chord distance [ D chord ]=0.078, P<0.0001) and differed in expected aver-age heterozygosities at shared loci (0.859 and 0.689, respectively). We tested the microsatellite primers on other gadid species endemic to the northern Pacific Ocean, Bering Sea, and Arctic Ocean for cross-species amplification. Not all loci amplified reliably in navaga ( E. nawaga ), Pacific tomcod ( Microgadus proximus ), Arctic cod ( Boreogadus saida ), Pacific cod ( Gadus macrocephalus ), or walleye pollock ( G. chalcogrammus ). Reliable loci varied in microsatellite size profiles and produced distinct PCA clusters and accurate genotype assignments that allowed accurate species identification. Furthermore the identifications support previous morphological and genetically determined systematic classifications and distinguished the geographically separated collections of E. gracilis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1973

A unified development of several techniques for the representation of random vectors and data sets

Linear vector space theory is used to develop a general representation of a set of data vectors or random vectors by linear combinations of orthonormal vectors such that the mean squared error of the representation is minimized. The orthonormal vectors are shown to be the eigenvectors of an operator. The general representation is applied to several specific problems involving the use of the Karhunen-Loeve expansion, principal component analysis, and empirical orthogonal functions; and the common properties of these representations are developed.

Bundick, W. T.↗
NASA NTRS1974

A statistical-chemical and thermodynamic approach to the study of lunar mineralogy

Principal components analysis is used to study the chemical compositions of pyroxenes of five Apollo 12 specimens. Important correlations are recognized in the variation of oxide weight per cent. These correlations indicating substitutional relationships can be interpreted as representative of stable and metastable trends of crystallization by using crystal-chemical and thermodynamic information. The per cent variance of pyroxene groups with characteristic trends in each specimen can be evaluated and interpreted in terms of history of crystallization. Distribution of Fe and Mg in certain pairs of olivine and pyroxene, which are found in contact in the rock and which may have crystallized simultaneously, is useful in recognizing the tendency towards chemical equilibrium in Fe-Mg distribution during a limited interval in the liquidus or subsolidus stages.

Saxena, S. K.↗
NASA NTRS1978

Application of remote sensing to reconnaissance geologic mapping and mineral exploration

A method of mapping geology at a reconnaissance scale and locating zones of possible hydrothermal alteration has been developed. This method is based on principal component analysis of Landsat digital data and is applied to the desert area of the Chagai Hills, Baluchistan, Pakistan. A method for airborne spectrometric detection of geobotanical anomalies associated with prophyry Cu-Mo mineralization at Heddleston, Montana has also been developed. This method is based on discriminants in the 0.67 micron and 0.79 micron region of the spectrum.

Birnie, R. W.↗
NASA NTRS1981

Drift and dispersion studies of ocean-dumped waste using Landsat imagery and current drogues

The drift and dispersion of industrial acid wastes dumped 64 km off the Delaware coast were investigated using 16 Landsat images. Waste plume drift velocities and spread rates were obtained, and principal component analysis was performed to discriminate the acid waste from other pollutants. Waste plumes averaged drifting rates from 0.59 km/hr to 3.39 km/hr into the southwest quadrant, and remained above the thermocline, which formed at depths ranging from 13 m to 24 m. Rapid waste movement toward shore occurred primarily during storms, although plumes were rapidly dispersed and diluted at spread rates in excess of 4 cm/sec. Landsat data analysis also indicates plume width increase of about 1.5 cm/sec during calm sea conditions, which is also in agreement with Falk's (1974) estimates of plume dilution.

Klemas, V.↗
NASA NTRS1981

Lunar volcanic glasses and their constraints on mare petrogenesis

The compositional properties of volcanic glasses from the Apollo 11, 14, 15 and 16 landing sites are examined and implications of the results for mare basalt petrogenesis and deep lunar structures are discussed. Major-element and nickel analyses were performed on the glasses using electron probe techniques, and R-mode principal component analysis was performed on the 19 different compositions of glass distinguished. The glasses are found to form two distinct chemical arrays based on the major elements and Ni. The presence of two chemically isolated cumulate systems in the mantle at different depths is thus inferred, and a model is developed for mare petrogenesis in which each system was itself composed of two lithologic components that underwent hybridization, assimilation or mixing to generate the large compositional range of magmas represented by the lunar volcanic glasses. The surface-correlated elements associated with the volcanic glasses are attributed to another reservoir in the deep interior which may be responsible for gas emissions causing lunar transient phenomena. The model developed allows predictions to be made concerning the liquidus phase relations, trace and radiogenic element distributions, nonradiogenic isotope compositions and sample ages.

Delano, J. W.↗
NASA NTRS1984

Preliminary Evaluation of Thematic Mapper Image Data Quality

Thematic Mapper (TM) data from Mississippi County, Arkansas, and Webster County, Iowa, were examined for the purpose of evaluating the image data quality of the TM which was launched on board the LANDSAT-4 spacecraft. Preliminary clustering and principal component analysis indicates that the middle infrared and thermal infrared data of TM appear to add significant information over that of the near IR and visible bands of the multispectral scanner data. Moreover, the higher spatial resolution of TM appears to provide better definition of the edges and the within variability of agricultural fields. The geometric performance of TM data, without ground control correction, was found to exceed expectations. The modulation transfer function for the 1.65 m band was found to agree with prelaunch specifications when the effects of the GSFC cubic convolution and the atmosphere were removed. The band to band registration for the bands within the noncooled focal plane was found to be better than specified. However, the middle infrared and thermal infrared, which are on a separate cooled focal plane were found to be misregistered and were significantly worse than prelaunch specifications.

Macdonald, R. B.↗
NASA NTRS1984

The Use of Thematic Mapper Data for Land Cover Discrimination: Preliminary Results from the UK Satmap Programme

In assessing the accuracy of classification techniques for Thematic Mapper data the consistency of the detector-to-detector response is critical. Preliminary studies were undertaken, therefore, to assess the significance of this factor for the TM. The overall structure of the band relationships can be examined by principal component analysis. In order to examine the utility of the Thematic Mapper data more carefully, six different land cover classes approximately Anderson level 1 were selected. These included an area of water from the sediment-laden Mississippi, woodland, agricultural land and urban land. A plume class was also selected which includes the plume of smoke emanating from the power station and drifting over the Mississippi river.

Jackson, M. J.↗
NASA NTRS1984

LANDSAT instruments characterization

Twelve subscenes were selected from LANDSAT 5, scene 50129-17075 over White Sands, NM. Data set WHITE covers the entire White Sands desert area. Black and white films and hard copy prints of each band in this area were produced. A new command file was written for the interactive digital image manipulation system (IDIMS) to generate pseudocolor pixel prints of LANDSAT Thematic Mapper bands for data from B and A tapes. The program PCMAIN.FOR of principal component analysis and a supplemental program HEDIT.FOR for generating sampled data sets were developed. The procedures for performing component analysis on a subscene by using these programs are described. Modifications to various subroutines are listed. Bright target saturation internal calibration lamp state dependence was examined using calibration data of night scene 50052-02182, WRS 111/112 over Harrisburg, PA.

Lee, Y.↗
NASA NTRS1985

LANDSAT 4 investigations of Thematic Mapper and Multispectral Scanner applications

Structural and stratigraphic characteristics of the Drum Mountains in Utah were obtained using LANDSAT 5 TM data that were digitally enhanced using band ratioing, principal components analysis, and spatial filtering techniques. Color ratioing composite images proved most useful for distinguishing between and identifying exposure of hyroxly bearing hydrothermally altered inclusive rocks and bleached contact metamorphic rocks dominated by calc-silicate mineral assemblages. The amount and detail of geologic information interpreted from TM images is more significant than that from MSS and in geologic maps. Enhanced LANDSAT 5 TM data also identified and mapped hydrothermal alteration and distinguished between altered and unaltered limonitic assemblages in the Tonopath, Nevada quadrangle.

Lauer, D. T.↗
NASA NTRS1985

Determining Mineral Types and Abundances from Reflectance Measurements

Mineral types and their abundances were quantitatively determined from laboratory reflectance spectra using principal components analysis (PCA). PCA reduced the measured spectral dimensionality and allowed testing the uniqueness and validity of spectral mixing models. In addition to interpreting absorption bands, in this new approach we interpreted variations in the overall spectral curves in terms of physical processes, namely changes in mixtures of minerals, in particle size and in illumination geometry. Application of this approach to reflectances of planetary surfaces allows interpretation to be extended to quantitative determinations of mineral types and abundances.

Smith, M. O.↗
NASA NTRS1984

Feature selection and the information content of Thematic Mapper simulator data for forest structural assessment

An assessment is made of the information content of Thematic Mapper Simulator (TMS) data for the case of a forested region, in order to determine the sensitivity of such data to forest crown closure and tree size class. Principal components analysis and Monte Carlo simulation indicated that channels 4, 7, 5 and 3 were optimal for four-channel forest structure analysis. As the number of channels supplied to the Monte Carlo feature selection routine increased, classification accuracy increased. The greatest sensitivity to the forest structural parameters, which included succession within clearcuts as well as crown closure and size class, was obtained from the 7-channel TMS data.

Spanner, M. A.↗