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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

High-resolution optical imaging of the 'Frosty Leo' nebula

High-resolution optical images have been obtained of IRAS 09371 + 1212 (the 'Frosty Leo' nebula). The images were taken with the Dominion Astrophysical Observatory/Canada-France-Hawaii Telescope (DAO/CFHT) High-Resolution Camera using broadband V and I filters and with an intermediate-band H-alpha filter. The red filters, particularly H-alpha show that the bipolar nebula is surrounded by a nearly spherical envelope of material approximately 30 sec in diameter. The high-resolution images allow a detailed study of changes in the nebular morphology with wavelength. The ansae remain stationary with wavelength, whereas the positions of the lobes and the position angle of the disk change with wavelength from the optical to the near infrared. These results suggest that the ejection process is colliminated by the disk, which has been processing with time. The possibility that the Frosty Leo nebula is not a post-asymptotic giant branch (AGB) star but a pre-main-sequence object formed in isolation from interstellar clouds is discussed.

Langill, Philip P.↗
NASA NTRS2019

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis Along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 sq.m were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Pukar Amatya↗
NASA NTRS1983

A comparison of high resolution optical and radio observations of W3

High resolution maps of the W3 complex for radio continuum and selected optical emission lines are used to derive the visual extinction A(V), the excitation, and the evolutionary state of the component H II regions. A large diffuse H II region in the east, which is the most highly evolved of this type, is found, together with a southern complex of H II regions and a northern complex of young and compact H II regions still embedded in the W3 molecular core. A comparison of the observed dust distribution with ionized and atomic hydrogen and several other molecules in W3 shows the functional relationship between the C-130 column density and A(V) to be in accord with Strohacker's (1978) result for dark clouds.

Dickel, H. R.↗
NASA NTRS1977

High-resolution optical observations of Ca II K in Deneb and Aldebaran

High-spectral-resolution echellograms confirm the existence of an asymmetric emission core in the K line of Deneb that may be due to chromospheric emission from this A-type supergiant or that may be due to scattering from a circumstellar shell. If the core is due to chromospheric emission, its width does not agree with what is predicted using the Wilson-Bappu relation for late-type stars. This would indicate that the chromospheres of hotter stars are qualitatively different from those of cooler stars. A K-emission-core profile for Aldebaran is also presented.

Mcclintock, W.↗
NASA NTRS1982

High-resolution optical measurements of atmospheric winds from space. I - Lower atmosphere molecular absorption

A high-resolution spectroscopic technique, analogous to that used in the thermosphere to measure the vector wind fields in the upper troposphere and stratosphere, is described which uses narrow features in the spectrum of light scattered from the earth's lower atmosphere to provide Doppler information on atmospheric scattering and absorption. It is demonstrated that vector winds can be measured from a satellite throughout the lower atmosphere, using a multiple-etalon Fabry-Perot interferometer of modest aperture. It is found that molecular oxygen and water vapor absorption lines in the spectrum of sunlight scattered by the atmosphere are Doppler-shifted by the line of sight wind, so that they may be used to monitor the global wind systems in the upper troposphere and stratosphere.

Hays, P. B.↗
NASA NTRSDate not supplied

Determining True Sensor Spatial Resolution of Very High Resolution Optical Imagery

Some satellite data is delivered in images with gridded pixels. This gridded pixel size is often assumed to be the spatial resolution of the satellite sensor; however, this is not always the case. An image can be grided to any arbitrary pixel size, but the sensor resolution will remain constant. For example, an image with a pixel grid size much smaller than the sensor resolution will appear blurry along what should be sharp transitions. This discrepancy between an image’s pixel size and true sensor spatial resolution can be the source of much confusion and even misinformation among data users, which may lead them to waste time and resources on using images that do not suit their spatial resolution needs. This presentation will highlight our evaluation of the true spatial resolution of various government and commercial images in the pixel size range of 0.3 m to 60 m. Images evaluated include ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels). Our evaluation of true sensor spatial resolution, or ‘footprint size’ is based on the sensor’s line spread function (LSF). We calculate the width at half the height of the LSF to find the full width at half maximum (FWHM). The FWHM is how we report sensor spatial resolution. Different objects are examined for constructing the LSF depending on the sensor spatial resolution. Coarser resolution sensors in this evaluation such as Sentinel-2 and Landsat 8/9 are examined at bridges over a dark water background. The bright bridge acts as a line impulse, giving a sensor’s line spread function (LSF) in one direction. Additionally, we simulate the impacts of bridge width on the apparent LSF to obtain a true LSF without the effects of bridge width for these sensors. Finer resolution sensors will image the irregularities in bridges such as trusses, sidewalks, and in some cases painted lines, interfering with the LSF construction. Instead, these sensors are evaluated at large (60 m – 140 m) black and white checkerboards known as Cal/Val sites. At these locations, the image’s transition from black to white is extracted as an edge spread function (ESF). We calculate the derivative of this ESF to obtain the sensor’s LSF. From there, we find the FWHM as we do for the coarser resolution images. With the FWHM and pixel size, we determine how over- or under-sampled the images are. When the ratio of a sensor’s spatial resolution and the gridded image’s pixel size is less than 1, the image is considered under-sampled. In this case, each pixel’s information is unique but only a portion of that pixel’s ground area has been measured. On the other side, if the ratio is greater than 1, the image is considered over-sampled. That is, each pixel’s information is sourced from within the ground extent of the pixel and some extent outside additionally. We will show the true spatial resolution and the extent of over-/under-sampling in the imagery from ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels).

Alana Semple↗