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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Mapping soils, crops, and rangelands by machine analysis of multitemporal ERTS-1 data

ERTS-1 data, obtained during the period 25 August 1972 to 5 September 1973 over a range of test sites in the Central United States, have been used for identifying and mapping differences in soil patterns, species and conditions of cultivated crops, and conditions of rangelands. Multispectral scanner data from multiple ERTS passes over certain test sites have provided the opportunity to study temporal changes in the scene. Multispectral classifications delineating soils boundaries in different test sites compared well with existing soil association maps prepared by conventional means. Spectral analysis of ERTS data was used to identify, maps, and make areal measurements of wheat in western Kansas. Multispectral analysis of ERTS-1 data provided patterns in rangelands which can be related to soils differences, range management practices, and the extent of infestation of grasslands by mesquite (prosopis fuliflora) and juniper (juniperus spp.).

Baumgardner, M. F.↗
NASA NTRS1987

Characterizing forest stands with multi-incidence angle and multi-polarized SAR data

The potential for using HH-polarized L-band SAR data obtained at different incidence angles from satellite altitudes to identify and map different forest cover types and stand density classes is studied. Reasonably accurate results are obtained if the speckle characteristics of the data are suppressed by low-pass spatial filters and a contextual classification algorithm. Multipolarized L-band SAR data obtained from aircraft altitudes over the same test site are also analyzed to assess the relationships between polarization and forest stand characteristics. It is found that incidence angle controls, to a very large extent, the characteristics of the data and the type of information that can be obtained from L-band, HH-polarized satellite SAR data. Cross-polarization of L-band SAR data enhances and differentiates various forest stand characteristics which cannot be defined using only the like-polarized data, and vice-versa.

Hoffer, R. M.↗
NASA NTRS2019

Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification

The paper presents the results from a multi-year effort to develop and validate image processing methods forselecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introducea new method based on Linear Programming for matchingclusters of coronal holes. The final matching is then performedusing Random Forests. The methods were carefully validatedusing consensus maps derived from multiple readers, manualclustering, manual map classification, and method validation for50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCNby providing accurate boundary detection. Overall, the methodgave a 95.5% map classification accuracy.

Jatla, Venkatesh↗
NASA NTRS1980

Radar imaging of volcanic fields and sand dune fields: Implications for VOIR

A number of volcanic fields and sand dune fields in the western part of North America were studied using aircraft and Seasat synthetic aperture radar images and LANDSAT images. The capability of radars with different characteristics (i.e., frequency, polarization and look angles was assessed to identify and map different volcanic features, lava flows and sand dune types. It was concluded that: (1) volcanic features which have a relatively large topographic expression (i.e., cinder cones, collapse craters, calderas, etc.) are easily identified; (2) lava flows of different ages can be identified, particularly on the L-band images; and (3) sand dunes are clearly observed and their extent and large scale geometric characteristics determined, provided the proper imaging geometry exists.

Elachi, C.↗
DOE OSTI2026

Meta-analysis of North American Arctic and boreal aboveground biomass datasets: assessing accuracy, dynamics, and similarities

The North American arctic and boreal regions (ABRs) are rapidly warming and experiencing intensifying disturbances. Accurately quantifying aboveground biomass (AGB) is critical for understanding the impacts of these changes on the carbon cycle and for designing climate change mitigation strategies. Several AGB maps have been developed for the North American ABRs, including recent contributions from National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. However, these maps differ widely in training data, methodology, and resulting AGB density estimates. Presently, a comprehensive comparative evaluation is lacking, making it difficult for users to select datasets suited to their research or management needs. Here, in this study, we conducted a comparative analysis of nine AGB density datasets across North American ABRs, specifically for Alaska and Canada. We (1) summarized AGB by ecoregion and Canadian provinces, (2) evaluated their accuracy against field-based measurements, (3) analyzed spatial and temporal similarities among datasets, and (4) assessed their ability to capture disturbance (fire and harvest) impacts on AGB. We found substantial variation in regional and local AGB estimates across datasets, with overall accuracy ranging from R 2 = 0.25–0.62 and Bias% from −47.8% to 69.9% when validated against field plots. Despite these differences, most datasets have comparatively consistent spatial patterns in AGB (r > 0.8 for most cases). In contrast, agreement on the temporal patterns of AGB change is generally low. We found datasets with spatial resolutions ⩽300 m are capable of capturing disturbance impacts on AGB dynamics, though sensitivity varies across products. Our findings and dataset summary provide guidance for selecting appropriate AGB datasets for different applications within our study area. Our analysis also highlights the need to decrease map bias and increase capability to detect temporal change to decrease uncertainty of AGB datasets potentially by using training data which is representative of major plant functional types within the mapped area.

ABoVE↗
NASA NTRS1979

Comparison of theoretical and observed 3.55 cm wavelength brightness temperature maps of the full moon

In the present paper, a map of the moon's 3.33-cm thermal emission, constructed from observation data, is compared with a map calculated from a detailed model for regolith properties. In an 'observed minus predicted' difference map, two distinct anomalies, each approximately 2 K warmer than their surroundings, are detected. The anomalies are smaller in areal extent than the maria in which they are located (Tranquillitatis and Imbrium). A preliminary analysis, based on calculations of the effects of model parameters on observed brightness temperature, indicates that the two 'hotspots' can be explained by postulating that they are regions possessing a 30 to 40 percent higher than moon-average opacity and that their profile of physical temperature with depth is not necessarily anomalous. On a global scale, a pattern of 'maria warmer than highlands' is presented which exhibits a brightness temperature contrast of approximately 5 K.

Keihm, S. J.↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

The Effect of Aerosols and Clouds on the Retrieval of Infrared Sea Surface Temperatures

Comparisons are performed between spatially averaged sea surface temperatures (ASST2) as derived from the second Along-Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) and the NOAA-NASA Advanced Very High Resolution Radiometer (AVHRR) Oceans Pathfinder dataset (MPFSST). Difference maps, MPFSST 2 ASST2, along with the application of a simple statistical regression model to aerosol and cloud data from the Total Ozone Mapping Spectrometer ( TOMS), are used to examine the impact of possible aerosol and cloud contamination. Differences varied regionally, but the largest biases were seen off western Africa. Nighttime and daytime differences off western Africa were reduced from -0.5degrees to -0.2degreesC and from -0.1degrees to 0degreesC, respectively. Significant cloud flagging, based on the model, occurred in the Indian Ocean, the equatorial Pacific, and in the vicinity of the Gulf Stream. Comparisons of the MPFSST and the ASST2 with in situ data from the 2002 version of the World Oceanic Database (WOD02) off western Africa show larger mean differences for the MPFSST. The smallest mean differences occurred for nighttime ASST2 - WOD02 with a value of 0.0degrees +/- 0.4degreesC.

clouds↗
NASA NTRS2004

Effect of aerosols and clouds on the retrieval of infrared sea surface temperatures

Comparisons are performed between spatially averaged sea surface temperatures (ASST2) as derived from the second Along-Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) and the NOAA–NASA Advanced Very High Resolution Radiometer (AVHRR) Oceans Pathfinder dataset (MPFSST). Difference maps, MPFSST 2 ASST2, along with the application of a simple statistical regression model to aerosol and cloud data from the Total Ozone Mapping Spectrometer (TOMS), are used to examine the impact of possible aerosol and cloud contamination. Differences varied regionally, but the largest biases were seen off western Africa. Nighttime and daytime differences off western Africa were reduced from 20.58 to 20.28C and from 20.18 to 08C, respectively. Significant cloud flagging, based on the model, occurred in the Indian Ocean, the equatorial Pacific, and in the vicinity of the Gulf Stream. Comparisons of the MPFSST and the ASST2 with in situ data from the 2002 version of the World Oceanic Database (WOD02) off western Africa show larger mean differences for the MPFSST. The smallest mean differences occurred for nighttime ASST2 2 WOD02 with a value of 0.08 6 0.48C.

Armstrong, Edward M.↗
NASA NTRS1993

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

Sabol, Donald E., Jr.↗
NASA NTRS1999

Tropical Rainfall Distributions Determined Using TRMM Combined with other Satellite and Raingauge Information

Abstract A technique is described to use Tropical Rain Measuring Mission (TRMM) combined radar/radiometer information to adjust geosynchronous infrared satellite data (the TRMM Adjusted GOES Precipitation Index, or TRMM AGPI). The AGPI is then merged with rain gauge information (mostly over land; the TRMM merged product) to provide fine- scale (1 deg latitude/longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37 deg N-S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged-product tropical-mean estimates for 1998 are 3.3 mm/ day over ocean and 3.1 mm/ day over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, very different from the other tropical rainy areas where TRMM merged-product estimates are higher. This regional difference may indicate that TRMM is better at taking in to account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection. Comparisons of these TRMM merged analysis estimates with surface data sets shows varied results; the bias is near zero when compared to western Pacific Ocean atoll raingauge data, but significantly positive compared to Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected. The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the ENSO tropical precipitation pattern from early 1998 (El Nino) through early 1999 (La Nina) and beyond. The El Nino minus La Nina difference map shows the eastern Pacific maximum, the maritime continent minima and other tropical and mid-latitude features. The differences in the Pacific are very similar to those detected by the GPCP analyses. However, summing the El Nino minus La Nina differences over the global tropical oceans yields divergent answers from TRMM, GPCP and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.

Adler, Robert F.↗
NASA NTRS1972

Utilization of ERTS-1 data in North Carolina

The author has identified the following significant results. Initial study and interpretation of ERTS-1 imagery indicates its usefulness for regional land use planning purposes in North Carolina. Quarries, many secondary roads, and the existence of urbanization as well as changes in urban areas may be mapped. Differences in spectral response for various parts of the major cities suggests that the type of activity in urbanized areas may be monitored. Geological mapping at a scale of 1:250,000 may be possible. The major unfavorable comment from various organizations and agencies who are potential users has been to the relatively small scale. The imagery is a powerful illustrative tool for the several state agencies cooperating in the investigation.

Welby, C. W.↗
DOE OSTI2022

Linkage map construction using limited parental genotypic information

Abstract Genetic linkage maps based on single nucleotide polymorphisms (SNPs) represent an essential tool for a variety of genomic analyses. Today, next-generation sequencing (NGS) enables rapid genotyping of different mapping populations based on thousands of SNPs and the construction of highly saturated linkage maps. Nevertheless, missing data in the genotyping of the parental lines creates a bottleneck that determines the number of SNPs that can be used for the linkage map. As a proof of concept, a highly saturated genetic linkage map was constructed using the imputed genotypic data of a recombinant inbred line (RIL) population and the limited genotypic information of its parental lines. Two ABH genotype files were created from a pseudo-parental genotypic data set that includes all the SNPs present in the RIL population. In the first ABH file pseudo-parental 1 was considered parental A, while in the second pseudo-parental 1 was considered parental B. These two duplicate ABH genotype files were merged by chromosome and subjected to linkage map analysis. Since the ABH data were duplicated, two mirrored linkage groups were generated per chromosome. The correct linkage map was identified and selected based on the partial genotypic data of the parental lines. This strategy was effective for constructing a highly saturated linkage map of 33,421 SNPs based on the genotyping of 205 RILs and a limited number of 100 SNPs present in the parental lines. This strategy enables the use of all the NGS SNP data obtained from a low-coverage sequencing experiment in the mapping population.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Comparison of TOPEX/Poseidon Sea Level and Linear Model Results forced by Various Wind Products for the Tropical Pacific

The goal of this paper is to compare TOPEX/Posaidon (T/P) sea level with sea level results from linear ocean model experiments forced by several different wind products for the tropical Pacific. During the period of this study (October 1992 - October 1995), available wind products include satellite winds from the ERS-1 scatterometer product of [HALP 97] and the passive microwave analysis of SSMI winds produced using the variational analysis method (VAM) of [ATLA 91]. In addition, atmospheric GCM winds from the NCEP reanalysis [KALN 96], ECMWF analysis [ECMW94], and the Goddard EOS-1 (GEOS-1) reanalysis experiment [SCHU 93] are available for comparison. The observed ship wind analysis of FSU [STRI 92] is also included in this study. The linear model of [CANE 84] is used as a transfer function to test the quality of each of these wind products for the tropical Pacific. The various wind products are judged by comparing the wind-forced model sea level results against the T/P sea level anomalies. Correlation and RMS difference maps show how well each wind product does in reproducing the T/P sea level signal. These results are summarized in a table showing area average correlations and RMS differences. The large-scale low-frequency temporal signal is reproduced by all of the wind products, However, significant differences exist in both amplitude and phase on regional scales. In general, the model results forced by satellite winds do a better job reproducing the T/P signal (i.e. have a higher average correlation and lower RMS difference) than the results forced by atmospheric model winds.

Hackert, Eric C.↗
NASA NTRS2010

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Tarabalka, Yuliya↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

Giovanni: The Bridge between Data and Science

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a web-based remote sensing and model data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional data sets, covering atmospheric dynamics, atmospheric chemistry, hydrology, oceanographic, and land surface. Data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. Visualization options enable comparisons of multiple variables and easier refinement. Recently, new features have been developed, such as interactive scatter plots and maps. The performance is also being improved, in some cases by an order of magnitude for certain analysis functions with optimized software. We are working toward merging current Giovanni portals into a single omnibus portal with all variables in one (virtual) location to help users find a variable easily and enhance the intercomparison capability

Shen, Suhung↗
NASA NTRS2014

Crosstalk Cancellation for a Simultaneous Phase Shifting Interferometer

A method of minimizing fringe print-through in a phase-shifting interferometer, includes the steps of: (a) determining multiple transfer functions of pixels in the phase-shifting interferometer; (b) computing a crosstalk term for each transfer function; and (c) displaying, to a user, a phase-difference map using the crosstalk terms computed in step (b). Determining a transfer function in step (a) includes measuring intensities of a reference beam and a test beam at the pixels, and measuring an optical path difference between the reference beam and the test beam at the pixels. Computing crosstalk terms in step (b) includes computing an N-dimensional vector, where N corresponds to the number of transfer functions, and the N-dimensional vector is obtained by minimizing a variance of a modulation function in phase shifted images.

Olczak, Eugene↗