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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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NASA NTRS1991

Recovery of reflectance from imaging spectrometer measured radiance using a radiative transfer code

Radiance data were acquired over part of the Clark Mountain Range, California in 1990 by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). From these calibrated radiance data, the lateral distribution of water vapor in precipitable mm was recovered through analysis of the 940 nm water absorption. Surface pressure height was calculated from the 760 nm oxygen absorption band in the AVIRIS data. Surface slopes are estimated from relative elevations derived from the equivalent water vapor elevation for a standard atmosphere. Atmospheric optical depths measured at the Mountain Pass site in these data are extended to the entire region and varied based on the calculated surface pressure elevation. These parameters, derived from every AVIRIS spatial element, are used to constrain the LOWTRAN 7 radiative transfer code for the recovery of surface reflectance. Once transformed to reflectance, the imaging spectrometer data may be analyzed quantitatively for determination of surface composition, and compared with reflectance measurements acquired from different locations, times, and instruments.

Green, Robert O.↗
NASA NTRS1997

Advanced Signal Processing Methods Applied to Digital Mammography

The work reported here is on the extension of the earlier proposal of the same title, August 1994-June 1996. The report for that work is also being submitted. The work reported there forms the foundation for this work from January 1997 to September 1997. After the earlier work was completed there were a few items that needed to be completed prior to submission of a new and more comprehensive proposal for further research. Those tasks have been completed and two new proposals have been submitted, one to NASA, and one to Health & Human Services WS). The main purpose of this extension was to refine some of the techniques that lead to automatic large scale evaluation of full mammograms. Progress on each of the proposed tasks follows. Task 1: A multiresolution segmentation of background from breast has been developed and tested. The method is based on the different noise characteristics of the two different fields. The breast field has more power in the lower octaves and the off-breast field behaves similar to a wideband process, where more power is in the high frequency octaves. After the two fields are separated by lowpass filtering, a region labeling routine is used to find the largest contiguous region, the breast. Task 2: A wavelet expansion that can decompose the image without zero padding has been developed. The method preserves all properties of the power-of-two wavelet transform and does not add appreciably to computation time or storage. This work is essential for analysis of the full mammogram, as opposed to selecting sections from the full mammogram. Task 3: A clustering method has been developed based on a simple counting mechanism. No ROC analysis has been performed (and was not proposed), so we cannot finally evaluate this work without further support. Task 4: Further testing of the filter reveals that different wavelet bases do yield slightly different qualitative results. We cannot provide quantitative conclusions about this for all possible bases without further support. Task 5: Better modeling does indeed make an improvement in the detection output. After the proposal ended, we came up with some new theoretical explanations that helps in understanding when the D4 filter should be better. This work is currently in the review process. Task 6: N/A. This no longer applies in view of Tasks 4-5. Task 7: Comprehensive plans for further work have been completed. These plans are the subject of two proposals, one to NASA and one to HHS. These proposals represent plans for a complete evaluation of the methods for identifying normal mammograms, augmented with significant further theoretical work.

Stauduhar, Richard P.↗
NASA NTRS2013

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

Roberts, Yolanda, L.↗
NASA NTRS2023

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.

Inundation↗
NASA NTRSDate not supplied

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as the optical and SAR data proves high resolution (5-10 m) imagery, and SAR data is unaffected by cloud cover and light availability (day vs. night), which are common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a MODIS snow mask product to mask global snow coverage, which would affect land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd watershed located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management..

Lori Berberian↗
NASA NTRSDate not supplied

Progress on Quantitative Infrared Thermography at the NASA Langley Aerothermodynamic Laboratory

A quantitative infrared thermography technique is being developed for accurate and high-resolution measurements of surface temperature and heat flux in the hypersonic wind tunnels at NASA Langley Research Center. A hemisphere test campaign was carried out in the Langley Aerothermodynamic Laboratory 20-Inch Mach 6 Air Tunnel to assess the current performance of the technique and to identify remaining challenges. Measurements were obtained for three different model materials over a range of freestream Reynolds numbers. The raw infrared images were converted to temperature via a radiometric calibration of the camera sensor. Corrections were made for losses due to test article emissivity and window transmissivity. A preliminary uncertainty analysis yielded an estimated accuracy within 4 K at moderate viewing angles, similar to that of a standard thermocouple. Surface heat flux was obtained from the measured surface temperatures via a finite-difference solution of the one-dimensional heat equation that modeled thermal variation of the material properties. Comparisons of the measured heating to both computational and theoretical predictions demonstrated agreement within the expected uncertainty of the technique. The heat transfer measurement near the stagnation point was found to be highly sensitive to the test article emissivity, which establishes the need for improved characterization of the wind tunnel model materials for high temperature applications.

infrared thermography↗
NASA NTRS1982

Analysis of defect structure in silicon. Silicon sheet growth development for the large area silicon sheet task of the Low-Cost Solar array Project

One hundred ninety-three silicon sheet samples, approximately 880 square centimeters, were analyzed for twin boundary density, dislocation pit density, and grain boundary length. One hundred fifteen of these samples were manufactured by a heat exchanger method, thirty-eight by edge defined film fed growth, twenty-three by the silicon on ceramics process, and ten by the dendritic web process. Seven solar cells were also step-etched to determine the internal defect distribution on these samples. Procedures were developed or the quantitative characterization of structural defects such as dislocation pits, precipitates, twin & grain boundaries using a QTM 720 quantitative image analyzing system interfaced with a PDP 11/03 mini computer. Characterization of the grain boundary length per unit area for polycrystalline samples was done by using the intercept method on an Olympus HBM Microscope.

Natesh, R.↗
NASA NTRS1995

Robust Quantitative Measurement of Flows and Transparent or Highly Reflective Objects

The liquid crystal point diffraction interferometer (LCPDI) is a new instrument that has been developed for the measurement of phase objects. The LCPDI uses the compact, robust design of Linnik's point diffraction interferometer and adds to it phase stepping capability for quantitative interferogram analysis. The result is a compact, simple to align, environmentally insensitive interferometer capable of accurately measuring optical wave-fronts. A solid state camera provides very high data density and automated data reduction. The instrument can measure either transparent objects like fluids and lenses, or highly reflective opaque objects like mirrors. In the former case, the refractive index distribution is measured and then related to various properties like temperature, density, chemical composition, or thickness. In the latter case, the measured phase distribution is related to the object shape. The objects measured must be stationary or quasisteady state because the measurement requires the acquisition of several frames of image data during which time the object's properties must not have changed. The data acquisition time depends on the speed of the frame grabber and the required number of data frames. Typically, three to five frames taking 1 to 2 seconds are required. The potential for faster data acquisition exists.

Mercer, Carolyn R.↗
NASA NTRS1980

The Infrared Astronomical Satellite /IRAS/ Scientific Data Analysis System /SDAS/ sky flux subsystem

The sky flux subsystem of the Infrared Astronomical Satellite Scientific Data Analysis System is described. Its major output capabilities are (1) the all-sky lune maps (8-arcminute pixel size), (2) galactic plane maps (2-arcminute pixel size) and (3) regional maps of small areas such as extended sources greater than 1-degree in extent. The major processing functions are to (1) merge the CRDD and pointing data, (2) phase the detector streams, (3) compress the detector streams in the in-scan and cross-scan directions, and (4) extract data. Functional diagrams of the various capabilities of the subsystem are given. Although this device is inherently nonimaging, various calibrated and geometrically controlled imaging products are created, suitable for quantitative and qualitative scientific interpretation.

Stagner, J. R.↗
NASA NTRS1982

X-ray-imaging observations of clusters of galaxies

Einstein X-ray imaging observations, made to illustrate the variety of phenomena that can be considered through X-ray image analysis, are presented. Attention is given to general cluster properties and intracluster gas. Individual clusters are discussed (considering classification and dynamical evolution), and X-ray images are used to determine cluster mass distribution and to examine distant clusters. X-ray observations have contributed information in regard to processes affecting galaxies, the intracluster medium, and the cluster itself. Analyses have traced massive halos around dominant galaxies in unevolved clusters, and have helped define the cluster gravitational potential. In addition, multi-component double clusters have been discovered, and material which has been ram-pressure stripped from a hot corona around the M86 galaxy in Virgo was observed. Finally, quantitative estimates of the fractions of young and evolved clusters and determinations of total cluster mass are possible using X-ray observations.

Forman, W.↗
NASA NTRS1989

Image-based terrain modeling with thematic mapper applied to resolving the limit of Holocene Lake expansion in the Great Salt Lake Desert, Utah, part 1

The LANDSAT Thematic Mapper (TM) scanner records reflected solar energy from the earth's surface in six wavelength regions, or bands, and one band that records emitted energy in the thermal region, giving a total of seven bands. Useful research was extracted about terrain morphometry from remote sensing measurements and this information is used in an image-based terrain model for selected coastal geomorphic features in the Great Salt Lake Desert (GSLD). Technical developments include the incorporation of Aerial Profiling of Terrain System (APTS) data in satellite image analysis, and the production and use of 3-D surface plots of TM reflectance data. Also included in the technical developments is the analysis of the ground control point spatial distribution and its affects on geometric correction, and the terrain mapping procedure; using satellite data in a way that eliminates the need to degrade the data by resampling. The most common approach for terrain mapping with multispectral scanner data includes the techniques of pattern recognition and image classification, as opposed to direct measurement of radiance for identification of terrain features. The research approach in this investigation was based on an understanding of the characteristics of reflected light resulting from the variations in moisture and geometry related to terrain as described by the physical laws of radiative transfer. The image-based terrain model provides quantitative information about the terrain morphometry based on the physical relationship between TM data, the physical character of the GSLD, and the APTS measurements.

Merola, John A.↗
NASA NTRS2001

Geostationary Imaging FTS (GIFTS) Data Processing: Measurement Simulation and Compression

GIFTS (Geostationary Imaging Fourier Transform Spectrometer), a forerunner of next generation geostationary satellite weather observing systems, will be built to fly on the NASA EO-3 geostationary orbit mission in 2004 to demonstrate the use of large area detector arrays and readouts. Timely high spatial resolution images and quantitative soundings of clouds, water vapor, temperature, and pollutants of the atmosphere for weather prediction and air quality monitoring will be achieved. GIFTS is novel in terms of providing many scientific returns that traditionally can only be achieved by separate advanced imaging and sounding systems. GIFTS' ability to obtain half-hourly high vertical density wind over the full earth disk is revolutionary. However, these new technologies bring forth many challenges for data transmission, archiving, and geophysical data processing. In this paper, we will focus on the aspect of data volume and downlink issues by conducting a GIFTS data compression experiment. We will discuss the scenario of using principal component analysis as a foundation for atmospheric data retrieval and compression of uncalibrated and un-normalized interferograms. The effects of compression on the degradation of the signal and noise reduction in interferogram and spectral domains will be highlighted. A simulation system developed to model the GIFTS instrument measurements is described in detail.

Huang, Hung-Lung↗
NASA NTRS2001

Validation of MODIS Aerosol Optical Depth Retrieval Over Land

Aerosol optical depths are derived operationally for the first time over land in the visible wavelengths by MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the EOSTerra spacecraft. More than 300 Sun photometer data points from more than 30 AERONET (Aerosol Robotic Network) sites globally were used in validating the aerosol optical depths obtained during July - September 2000. Excellent agreement is found with retrieval errors within (Delta)tau=+/- 0.05 +/- 0.20 tau, as predicted, over (partially) vegetated surfaces, consistent with pre-launch theoretical analysis and aircraft field experiments. In coastal and semi-arid regions larger errors are caused predominantly by the uncertainty in evaluating the surface reflectance. The excellent fit was achieved despite the ongoing improvements in instrument characterization and calibration. This results show that MODIS-derived aerosol optical depths can be used quantitatively in many applications with cautions for residual clouds, snow/ice, and water contamination.

Chu, D. A.↗
NASA NTRS2001

Exploring a Black Body Source as an Absolute Radiometric Calibration Standard and Comparison with a NIST Traced Lamp Standard

Radiometric calibration of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is required for the scientific research and application objectives pursued with the spectroscopic measurements. Specifically calibration is required for: inter-comparison of AVIRIS data measured at different locations and at different times; analysis of AVIRIS data with data measured by other instruments; and analysis of AVIRIS data in conjunction with computer models. The primary effect of radiometric calibration is conversion of AVIRIS instrument response values (digitized numbers, or DN) to units of absolute radiance. For example, a figure shows the instrument response spectrum measured by AVIRIS over a portion of Rogers Dry Lake, California, and another figure shows the same spectrum calibrated to radiance. Only the calibrated spectrum may be quantitatively analyzed for science research and application objectives. Since the initial development of the AVIRIS instrument-radiometric calibration has been based upon a 1000-W irradiance lamp with a calibration traced to the National Institute of Standards and Technology (NIST). There are several advantages to this irradiance-lamp calibration approach. First, the considerable effort of NIST backs up the calibration. Second, by changing the distance to the lamp, the output can closely span the radiance levels measured by AVIRIS. Third, this type of standard is widely used. Fourth, these calibrated lamps are comparatively inexpensive. Conversely, there are several disadvantages to this approach as well. First, the lamp is not a primary standard. Second, the lamp output characteristics may change in an unknown manner through time. Third, it is difficult to assess, constrain, or improve the calibration uncertainty delivered with the lamp. In an attempt to explore the effect and potentially address some of these disadvantages a set of analyses and measurements comparing an irradiance lamp with a black-body source have been completed. This research is ongoing, and the current set of measurements, analyses, and results are presented in this paper.

Green, Robert O.↗
NASA NTRS2010

Hypersonic Navier-Stokes Comparisons to Orbiter Flight Data

During the STS-119 flight of Space Shuttle Discovery, two sets of surface temperature measurements were made. Under the HYTHIRM program3 quantitative thermal images of the windward side of the Orbiter with a were taken. In addition, the Boundary Layer Transition Flight Experiment 4 made thermocouple measurements at discrete locations on the Orbiter wind side. Most of these measurements were made downstream of a surface protuberance designed to trip the boundary layer to turbulent flow. In this paper, we use the US3D computational fluid dynamics code to simulate the Orbiter flow field at conditions corresponding to the STS-119 re-entry. We employ a standard two-temperature, five-species finite-rate model for high-temperature air, and the surface catalysis model of Stewart.1 This work is similar to the analysis of Wood et al . 2 except that we use a different approach for modeling turbulent flow. We use the one-equation Spalart-Allmaras turbulence model8 with compressibility corrections 9 and an approach for tripping the boundary layer at discrete locations. In general, the comparison between the simulations and flight data is remarkably good

Candler, Graham V.↗
NASA NTRS2003

Earth Science Imagery Registration

The study of global environmental changes involves the comparison, fusion, and integration of multiple types of remotely-sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, as well as for the validation of new instruments or for new data analysis. Furthermore, future multiple satellite missions will include many different sensors carried on separate platforms, and the amount of remote sensing data to be combined is increasing tremendously. For all of these applications, the first required step is fast and automatic image registration, and as this need for automating registration techniques is being recognized, it becomes necessary to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. In this paper we present one of the first steps toward such an exhaustive quantitative evaluation. First, the different components of image registration algorithms are reviewed, and different choices for each of these components are described. Then, the results of the evaluation of the corresponding algorithms combining these components are presented o n several datasets. The algorithms are based on gray levels or wavelet features and compute rigid transformations (including scale, rotation, and shifts). Test datasets include synthetic data as well as data acquired over several EOS Land Validation Core Sites with the IKONOS and the Landsat-7 sensors.

LeMoigne, Jacqueline↗
NASA NTRS2000

Generational Analysis Reveals that TGF-Beta1 Inhibits the Rate of Angiogenesis in Vivo by Selective Decrease in the Number of New Vessels

Quantitative analysis of vascular generational branching demonstrated that transforming growth factor-beta1 (TGF-beta1), a multifunctional cytokine and angiogenic regulator, strongly inhibited angiogenesis in the arterial tree of the developing quail chorioallantoic membrane (CAM) by inhibition of the normal increase in the number of new, small vessels. The cytokine was applied uniformly in solution at embryonic day 7 (E7) to the CAMs of quail embryos cultured in petri dishes. After 24 h the rate of arterial growth was inhibited by as much as 105% as a function of increasing TGF-beta1 concentration. Inhibition of the rate of angiogenesis in the arterial tree by TGF-beta1 relative to controls was measured in digital images by three well-correlated, computerized methods. The first computerized method, direct measurement by the computer code VESGEN of vascular morphological parameters according to branching generations G(sub 1) through G(sub greater than or equal to 5), revealed that TGF-beta1 selectively inhibited the increase in the number density of small vessels, N(sub v greater than or equal to 5), (382 plus or minus 85 per square centimeter) for specimens treated with 1 microgram TGF-beta1/CAM for 24 h, compared to 583 plus or minus 99 per square centimeter for controls), but did not significantly affect other parameters such as average vessel length or vessel diameter. The second and third methods, the fractal dimension (D(sub f)) and grid intersection (rho (sub v)), are statistical descriptors of spatial pattern and density. According to D(sub f) and rho(sub v), arterial density increased in control specimens from 1.382 plus or minus 0.007 and 662 plus or minus 52 per square centimeters at E7 (0 h) to 1.439 plus or minus 0.013 and 884 plus or minus 55 per square centimeters at E8 (24 h), compared to 1.379 plus or minus 0.039 and 650 plus or minus 111 per square centimeter for specimens treated with 1 microgram TGF-beta1/CAM for 24 h. TGF-beta1 therefore regulates vascular pattern and the rate of angiogenesis in a unique fingerprint manner, as do other major angiogenic regulators that include VEGF, FGF-2 (bFGF), and angiostatin. TGF-beta1 did not stimulate angiogenesis significantly at low cytokine concentrations, which suggests that this quail CAM model of angiogenesis is not associated with an inflammatory response.

Parsons-Wingerter, Patricia↗
NASA NTRS2018

Results of the Aerosol Sampling Experiment on the International Space Station

The indoor air quality of the International Space Station (ISS), in terms of particulate matter, has been assessed through a sampling experiment that collected airborne debris for return to Earth. The Aerosol Sampling Experiment took place on ISS in December 2016 and January 2017, with sample return in February 2017. For redundancy, two different aerosol samplers collected particles in two size ranges spanning from 10 nm to hundreds of micrometers. Multiple analysis techniques have been used to characterize the particles, such as reflective light microscopy, manual scanning electron microscopy (SEM) analysis (secondary and backscattered imaging) with energy dispersive spectroscopy (EDS) x-ray analysis, computer controlled scanning electron microscopy (CCSEM) and Raman spectroscopy. Sampling took place in five locations on ISS, with the greatest deposition of particles in Node 3, followed by Node 1, the US Lab and Node 2. The PMM had very few airborne particles, as expected, owing to the lack of human presence. A large proportion of the sampled material consisted of fibers from clothing. Many particles had complex morphologies or contained multiple metals in a carbonaceous matrix. Metal particles were analyzed for each sampling location in terms of both weight and number percent, with 27 heavy metals detected. Fiberglass and TiO2 particles were present in every location. Quantitative analysis results are presented, and where possible, particle emission sources are identified with the intention to fuel discussions on mitigation.

Meyer, Marit E.↗