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Particle images processed rapidly and automatically into velocity-vector maps. This technique requires no processing of photographic plates, no tedious visual recognition and manual designation of large numbers of image features for subsequent processing, little or no interruption by technician and is order of magnitude faster than any of prior techniques.
This paper presents magnetic field vector (B) maps, electric current vector (curl B) maps, magnetic force (JxB) contour maps, and total field contour maps covering the full tail cross section in the yz plane. The maps are based on 16 years of 5-min averages of Interplanetary Monitoring Platform (IMP) 8 magnetic field data. During this time, IMP 8 traversed the tail between -25R(sub E) and -40R(sub E) in the x direction. Its average x distance was -33R(sub E). For this average distance we show separate maps for low and high dipole tilts, corresponding to equinox and northern hemisphere summer seasons. The low-tilt (equinox) maps show symmetrical field and current patterns; the high-tilt (solstice) maps show the cross-tail current sheet arcing above the equatorial diagonal in the center and dipping below it on the flanks. The shape of warped current sheet fits Fairfield's (1980) displaced ellipse model fairly well. The distance at which the current sheet is hinged to the magnetic equator is found to be 9.88R(sub E) and is independent of Kp. The z profile of current density shows a central peak, 3R(sub E) full width at half maximum, and smaller, flanking shoulders. A Harris sheet profile with a 7R(sub E) thickness fits the B(sub x) profile. Though these are magnetic field data, the JxB maps clearly outline the plasma sheet. This approach also gives 7R(sub E) thickness. Many of the average field and current features inferred and demonstrated in earlier studies are confirmed here; some of them are seen for the first time in full cross-section view. Among new features revealed are a large current vortex in the winter hemisphere lobe, a dawn-dusk asymmetry in the JxB force in the plasma sheet (it is stronger on the duskside), and a separation of the cross-tail current sheet into core and wing parts.
The giant planets in our solar system contain clues to the origin of the planets and the conditions that set up terrestrial planet formation. These will be key to also understanding exoplanet systems Comparative study of Giant Planet composition and structure maps gradients in time and space in our protoplanetary disk Jupiter will be well studied after Galileo/Juno, but which of its features (core size, circulation, etc.) are unique vs universal? Cassini will leave remaining knowledge gaps about Saturn that require in situ sampling and are needed to fit into the puzzle of solar system formation
Searching for earth science data from scratch has, traditionally, been a non-trivial process. Using commercial search engines a starting point, it has not been trivial to locate information and services related to a science users needs. The advent of Google Dataset Search has improved that process. CMR has leveraged the schema.org dataset feature to provide semantic markup on our collection landing pages that are used by Google Dataset Search to provide results that map directly to a scientist's needs and provide an intuitive gateway in to the NASA Earthdata environment.
Searching for earth science data from scratch has, traditionally, been a non-trivial process. Using commercial search engines a starting point, it has not been trivial to locate information and services related to a science users needs. The advent of Google Dataset Search has improved that process. CMR has leveraged the schema.org dataset feature to provide semantic markup on our collection landing pages that are used by Google Dataset Search to provide results that map directly to a scientist's needs and provide an intuitive gateway in to the NASA Earthdata environment.
Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.
The exploration of Venus will soon experience a new golden era thanks to the recently selected NASA Deep Atmosphere of Venus Investigation of Noble gases, Chemistry and Imaging (DAVINCI) mission, NASA Venus Emissivity, Radio Science, InSAR, Topography & Spectroscopy (VERITAS) mission, and ESA EnVision mission. The DAVINCI mission will focus on the analysis of the the atmospheric vertical structure and composition of Earth’s twin planet and on the geologic structure of a tesserae terrain. The VERITAS mission will investigate the geologic features of its surface as well as geodynamic characteristics of the subsurface, providing high-resolution emissivity data, a global radar map at an approximate resolution of 30 meters/pixel, and estimation of the gravity anomaly of the shallow crust of the planet. The ESA EnVision mission will be complementary to the two NASA missions, providing high resolution 0.8-2.5 micron emissivity data, Synthetic Aperture Radar (SAR) data, and Subsurface Radar Sounder (SRS) data. Beyond those, the proposed Roscosmos-NASA Venera-D mission will also be equipped with an orbiter that will investigate the atmospheric composition and circulation, as well as a lander that will analize the in-situ chemical composition and the surface-atmosphere interactions.
We use L1-norm model regularization of |Br| component at the surface on magnetic monopoles bases and along-track magnetic field differences alone (without vector observations) to derive high quality global magnetic field models at the surface of the Moon. The practical advantages to this strategy are the following: monopoles are more stable at closer spacing in comparison to dipoles, improving spatial resolution; L1-norm model regularization leads to sparse models which may be appropriate for the Moon which has regions of localized magnetic field features; and along-track differences reduce the need for ad-hoc external field noise reduction strategies. We examine also the use of Lunar Prospector and SELENE/Kaguya magnetometer data, combined and separately, and find that the Lunar Prospector along-track vector field differences lead to surface field models that require weaker regularization and, hence, result in higher spatial resolution. Significantly higher spatial resolution (wavelengths of roughly 25–30 km) and higher amplitude surface magnetic fields can be derived over localized regions of high amplitude anomalies (due to their higher signal-to-noise ratio). These high-resolution field models are also compared with the results of Surface Vector Mapping approach of Tsunakawa et al. (2015, https://doi.org/10.1002/2014JE004785). Finally, the monopoles- as well as dipoles-based patterns of the Serenitatis high amplitude magnetic feature have characteristic textbook patterns of Br and Bθ component fields from a nearly vertically downwardly magnetized source region and it implies that the principal source of the anomaly was formed when the region was much closer to the north magnetic pole of the Moon.
Surface contours inside the large waste storage tanks typical of the Department of Energy (DOE) complex are, in general, highly irregular. In addition to pipes and other pieces of equipment in the tanks, the surfaces may have features such as mounds, fissures, crystalline structures, and mixed solid and liquid forms. Prior to remediation activities, it will be necessary to characterize the waste to determine the most effective remediation approaches. Surface contour data will be required both prior to and during remediation. The use is described of a structured light source to generate 3-D surface contour maps of the interior of waste storage silos at the Feed Materials Production Center at Fernald, OH. The landscape inside these large waste storage tanks bears a strong resemblance to some of the landscapes that might be encountered during lunar or planetary exploration. Hence, these terrestrial 3-D mapping techniques may be directly applicable to extraterrestrial exploration. In further development, it will be demonstrated that these 3-D data can be used for robotic task planning just as 3-D surface contour data of a satellite could be used to plan maintenance tasks for a space-based servicing robot.
The Fortuna Tessera quadrangle (50-75 N, 0-60 E) is a large region of tessera [1] that includes the major portion of Fortuna and Laima Tesserae [2]. Near the western edge of the map area, Fortuna Tessera is in contact with the highest moun-tain belt on Venus, Maxwell Montes. Deformational belts of Sigrun-Manto Fossae (extensional structures) and Au ra Dorsa (contractional structures) separate the tessera regions. Highly deformed terrains correspond to elevated regions and mildly deformed units are with low-lying areas. The sets of features within the V-2 quadrangle permit us to address the following important questions: (1) the timing and processes of crustal thickening/thinning, (2) the nature and origin of tesserae and deformation belts and their relation to crustal thickening processes, (3) the existence or absence of major evolutionary trends of volcanism and tectonics. The key feature in all of these problems is the regional sequence of events. Here we present description of units that occur in the V-2 quadrangle, their regional correlation chart (Fig. 1), and preliminary geological map of the region (Fig. 2).
We use the method of Tamppari et al. to map water ice clouds in the Martian atmosphere. This technique was originally developed to analyze the broadband Viking IRTM channels and we have now applied it to the TES data. To do this, the TES spectra are convolved to the IRTM bandshapes and spatial resolutions, enabling use of the same processing techniques as were used in Tamppari et al.. This retrieval technique relies on using the temperature difference recorded in the 20 micron and 11 micron IRTM bands (or IRTM convolved TES bands) to map cold water ice clouds above the warmer Martian surface. Careful removal of surface contributions to the observed radiance is therefore necessary, and we have used both older Viking-derived basemaps of the surface emissivity and albedo, and new MGS derived basemaps in order the explore any possible differences on cloud retrieval due to differences in surface contribution removal. These results will be presented in our poster. Our previous work has concentrated primarily on comparing MGS TES to Viking data; that work saw that large-scale cloud features, such as the aphelion cloud belt, are quite repeatable from year to year, though small scale behavior shows some variation. Comparison of Viking and MGS era cloud maps will be presented in our poster. In the current stage of our study, we have concentrated our efforts on close analysis of water ice cloud behavior in the northern summer of the three MGS mapping years on relatively small spatial scales, and present our results below. Additional information is included in the original extended abstract.
Introduction: NASA’s Moon Trek (https://trek.nasa.gov/moon/) is one of a growing number of interactive, browser-based, online portals for planetary data visualization and analysis produced by NASA’s Solar System Treks Project (SSTP). Moon Trek continues to be enhanced with new data and new capabilities enabling it to facilitate the planning and conducting of upcoming lunar missions by NASA, its commercial partners, and its international partners, as well as advancing its role as a valuable outreach tool. A Comprehensive Online Web Portal: Developed at NASA’s Jet Propulsion Laboratory (JPL) and managed as a project of NASA’s Solar System Exploration Research Virtual Institute (SSERVI) at NASA Ames Research Center, Moon Trek is a browser-based web portal. The portal provides easy-to-use tools for browsing, data layering, data product blending, and feature search among thousands of data products covering topography, mineralogy, elemental abundance, geology, and much more. Visualizations are provided in var-ious map projections, interactive 3D viewing, and in virtual reality. Using an in-house stereo workflow, SSTP is able to produce new NAC-based high-resolution mosaics and DEMs. Diverse Applications for Lunar Exploration: Baseline analytic tools available to all users include dis-tance measurement, elevation profiling, sun angle calculation, and 3D print file generation. More advanced account-level tools allow users to perform more computationally intensive analyses. These include ray-traced lighting analysis for user-specified areas over user-specified time/date ranges and time intervals, electro-static surface potential analysis, subsetting of large data products, slope analysis, and Lunar Laser Ranging geometry calculation. Artificial intelligence (AI) and ma-chine learning (ML) based tools have been implemented for crater detection and hazard analysis, boulder detection and hazard analysis, and rockfall detection. New Tools Facilitating Exploration: Additional, new tools have recently been added and others are in development, offering even greater functionality in con-ducting analyses of potential landing sites and areas of surface operations. The new Line-of-Sight tool facilitates communications planning between locations on the lunar surface, between any given site on the lunar surface and a specified ground station on the Earth, and between a site on the lunar surface and a relay asset in lunar orbit, all taking into account local lunar topography. The new Data Plotter tool provides both tabular and graphical representations of pixel values along a user specified path for a growing number of data products. The new NAC Finder tool will identify and pro-vide access to NAC images that intersect a user-defined path or bounded area. The SSTP development team is looking to leverage the capabilities of its existing AI and ML crater, boulder, and rockfall detection and analysis tools, and extend that technology to a generalized feature detector that can be trained on instances of specific types of landforms and then search the lunar surface for more examples of such features. New traverse planning tools are being developed with use cases in generalized concept studies and specific mission planning in mind. These will facilitate finding optimal traverse paths based on constraints such as slope, lighting, hazard avoidance, and communications. These will be complemented by new traverse visualization capabilities. Users will be able to interactively ride along with a rover, examining 3D views of the terrain while adjusting camera height and viewing angle along with selecting different data layer overlays to drape across the terrain. Engaging the Public: The capabilities being developed for mission planning are being leveraged to further enhance Moon Trek’s proven utility as a valuable public outreach resource. This includes providing multiple lev-els of engagement with different points of entry. At its simplest level, promoting understanding through visualization, media and the public will be able to easily visualize and conduct their own exploration of lunar sites targeted by NASA and its partners. For a more in-depth experience, we are working with our stakeholders to promote understanding through interaction by extend-ing our current landing site and traverse analysis capabilities, making simplified access to these tools available to those who want to explore more deeply key factors in planning a mission through interactive and possibly even gamified experiences. The highest degree of public outreach, focusing on engagement through scientific participation, could be achieved through our work with missions and the NASA Office of the Chief Scientist on Moon Trek’s extension as a tool with specialized capabilities for facilitating citizen science. In such scenarios, participants become members of extended mis-sion science teams, using dedicated and integrated interfaces to analyze mission data to help answer questions key to lunar science and exploration. We are work-ing with NASA’s Office of Communications, museums, planetariums, and the media to help them easily integrate accurate, detailed visualizations of NASA’s lunar destinations and exploration into their content/productions and to engage diverse audiences in diverse venues.
The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.
Data from the Earth Radiation Budget Experiment scanning radiometer aboard the NOAA-9 operational meteorological satellite are used to investigate the spatial variability of Outgoing Longwave Radiation (OLR). Daily and monthly radiation maps at 2.5 deg latitude-longitude scale are used as a basis for the study. The regions of greatest variability are in the tropics and subtropics. Storm tracks such as the South Pacific convergence zone appear as regions of high OLR variability. Spatial spectra in longitude show two regimes of OLR. At large scales (wavenumbers less than 6), the spatial spectrum is flat. For wavenumbers greater than 10, the spectra decrease as wavenumber to the -3 power. The spatial spectrum of daily anomalies from the mean is a strong function of latitude and season, with interesting features. Correlations of daily anomalies from the monthly mean decrease exponentially in latitude but have a damped-wave structure in longitude. The spatial variability of the daily maps, as measured by degree variance, have 10 times the power at degree 24 than the monthly maps, but at scales between 1 and 10, the degree variance is practically the same for daily as for monthly.
Dual-polarized, X-band Synthetic Aperture Radar (SAR) imagery was obtained from an altitude of 60,000 feet over a test area near Camden, SC on June 30, 1980. The objective of this study was to determine, qualitatively, the value of the SAR imagery for identifying various forest cover types. In analyzing the HH and HV polarization images, particular attention was given to the tonal and textural characteristics of the cover types involved. The analysis of the dual-polarized SAR imagery has shown that certain forest cover features are more easily identified in one polarization than the other, while some features look very similar in both polarizations. In general, the results for this data set have shown that the overall tonal contrast between features was greater on the HH image. Neither polarization was consistently better for identifying the various forest cover types examined. These results suggest the usefulness of a dual-polarized SAR system for mapping forest cover.
ERTS-1 multispectral images are proving effective for differentiating many kinds of Quaternary surficial deposits and landforms units in Illinois, Iowa, Missouri, Kansas, Nebraska, and South Dakota. Examples of features that have been distinguished are: (1) the more prominent end moraines of the last glaciation; (2) certain possible palimpsests of older moraines mantled by younger deposits; (3) various abandoned river valleys, including suspected ones deeply filled by deposits; (4) river terraces; and (5) some known faults and a few previously unmapped lineaments that may be faults. The ERTS images are being used for systematic mapping of Quaternary landforms and deposits in about 20 potential study areas. Some study areas, already well mapped, provide checks on the reliability of mapping from the images. For other study areas, previously mapped only partly or not at all, our maps will be the first comprehensive, synoptic ones, and should be useful for regional land-use planning and ground-water, engineering-geology, and other environmental applications.
A multispectral scanner data set gathered at a flight altitude of 10,000 ft. over an agricultural area was modified to simulate the spatial resolution of the spacecraft scanners. Signatures were obtained for several major crops and their proportions were estimated over a large area. For each crop, a map was generated to show its approximate proportion in each resolution element, and hence its distribution over the area of interest. A statistical criterion was developed to identify data points that may not represent a mixture of the specified crops. This allows for great reduction in the effect of unknown or alien objects on the estimated proportions. This criterion can be used to locate special features, such as roads or farm houses. Preliminary analysis indicates a high level of consistency between estimated proportions and available ground truth. Large concentrations of major crops show up especially well on the maps.
Global food production in the developing world occurs within sub-hectare fields that are difficult to identify with moderate resolution satellite imagery. Knowledge about the distribution of these fields is critical in food security programs. We developed a semi-automated image segmentation approach using wall-to-wall sub-meter imagery with high-performance computing to map crop area (CA) throughout Tigray, Ethiopia that encompasses over 41,000 km (exp 2). Multiple processing streams were tested to minimize mapping error while applying five unique smoothing kernels to capture differences in land surface texture associated to CA. Typically, very-small fields (mean < 2 ha) have a smooth image roughness compared to natural scrub/shrub woody vegetation at the ~1m scale and these features can be segmented in panchromatic imagery with multi-level histogram thresholding. Multi-temporal very-high resolution (VHR) panchromatic imagery with multi-spectral VHR are sufficient in extracting critical CA information needed in food security programs. A 2011 to 2015 CA map was produced, using over 3000 WorldView-1 panchromatic images wall-to-wall in 1/2 deg mosaics for Tigray, Ethiopia. CA was evaluated with nearly 3000 WorldView-2 2m multispectral 250 X 250 m image subsets by seven expert interpretations, and with in-situ global positioning system photography. CA estimates ranged from 32 to 41% in sub regions of Tigray with median maximum per bin commission and omission errors of 11% and 1% respectively, with most of the error occurring in bins <15%. This empirical, simple, and low direct cost approach via U.S. government license agreement to access commercial VHR data, could be a viable big-data high-performance computing methodology to extract wall-to-wall CA for other regions of the world that have very-small agriculture fields with similar image texture."