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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Development of three-dimensional spatial displays using a geographically based information system

Procedures necessary for the development of a generalized three-dimensional perspective software capability in support of graphic, topographic, and color mapping of Landsat data are reviewed. The NASA Earth Resources Laboratory developed the procedures in order to facilitate the processing and analysis of disparate, geographically oriented base maps from aircraft and satellite sensors. Perspective displays are obtained through a translation of the space-viewed object to a vantage point coordinate system, followed by a rotation through two angles for alignment along the vantage line of sight, and finally a perspective transformation to yield two-dimensional displays with no hidden lines. Matrix equations for the transformations are reviewed, including scaling, and block diagrams are provided of the data and perspective software systems. The classification data plane may be mapped onto a topographic elevation data plane.

Junkin, B. G.↗
NASA NTRS2004

Exploring the Moon and Mars Using an Orbiting Superconducting Gravity Gradiometer

Gravity measurement is fundamental to understanding the interior structure, dynamics, and evolution of planets. High-resolution gravity maps will also help locating natural resources, including subsurface water, and underground cavities for astronaut habitation on the Moon and Mars. Detecting the second spatial derivative of the potential, a gravity gradiometer mission tends to give the highest spatial resolution and has the advantage of requiring only a single satellite. We discuss gravity missions to the Moon and Mars using an orbiting Superconducting Gravity Gradiometer and discuss the instrument and spacecraft control requirements.

Paik, Ho Jung↗
NASA NTRS1982

An inventory of undiscovered Canadian mineral resources

Unit regional value (URV) and unit regional weight are area standardized measures of the expected value and quantity, respectively, of the mineral resources of a region. Estimation and manipulation of the URV statistic is the basis of an approach to mineral resource evaluation. Estimates of the kind and value of exploitable mineral resources yet to be discovered in the provinces of Canada are used as an illustration of the procedure. The URV statistic is set within a previously developed model wherein geology, as measured by point counting geologic maps, is related to the historical record of mineral resource production of well-developed regions of the world, such as the 50 states of the U.S.A.; these may be considered the training set. The Canadian provinces are related to this training set using geological information obtained in the same way from geologic maps of the provinces. The desired predictions of yet to be discovered mineral resources in the Canadian provinces arise as a consequence. The implicit assumption is that regions of similar geology, if equally well developed, will produce similar weights and values of mineral resources.

Labovitz, M. L.↗
NASA NTRS1974

Computer and photogrammetric general land use study of central north Alabama

The object of this report is to acquaint potential users with two computer programs, developed at NASA, Marshall Space Flight Center. They were used in producing a land use survey and maps of central north Alabama from Earth Resources Technology Satellite (ERTS) digital data. The report describes in detail the thought processes and analysis procedures used from the initiation of the land use study to its completion, as well as a photogrammetric study that was used in conjunction with the computer analysis to produce similar land use maps. The results of the land use demonstration indicate that, with respect to computer time and cost, such a study may be economically and realistically feasible on a statewide basis.

Jayroe, R. R.↗
DOE OSTI2022

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Time series mapping of snow volume in the mountains at global scales and at resolutions needed for water resource management is an unsolved challenge to date. Snow depth mapping by differencing surface elevations from airborne lidar is a mature measurement approach filling the observation gap operationally in a few regions, primarily in mountain headwaters in the Western United States. The same concept for snow depth retrieval from stereo- or multi-view photogrammetry has been demonstrated, but these previous studies had limited ability to determine the uncertainties of photogrammetric snow depth at the basin scale. For example, assessments used non-coincident or discrete points for reference, masked out vegetation, or compared a subset of the fully snow-covered study domain. Here, using a unique data set with simultaneously collected airborne data, we compare snow depth mapped from multi-view Structure from Motion photogrammetry to that mapped by lidar at multiple resolutions over an entire mountain basin (300 km 2 ). After excluding reconstruction errors (negative depths), SfM had lower snow-covered area (~27%) and snow volume (~16%) compared to lidar. The reconstruction errors were primarily in areas with vegetation, shallow snow (< 1 m), and steep slopes (> 60°C). Across the overlapping snow extent, snow depths compared well to lidar with similar mean values (< 0.03 m difference) and snow volume (± 5%) for output resolutions of 3 m and 50 m, and with a normalized median absolute deviation of 0.19 m. Our results indicate that photogrammetry from aerial images can be applied in the mountains but would perform best for deeper snowpacks above tree line.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
NASA NTRS1977

Statistical mapping of sheet aiquile SE-20-9 (national map) making use of ERTS images

New possibilities of remote sensing by means of satellites to do research on natural resources are reported. These images make it possible to carry out integrated studies of natural resources in the shortest time possible and with small investments. Various maps and a complete description of each are included. With the use of these satellites, scientists can hopefully plan development projects at the national level.

Torrez, J. G.↗
NASA NTRS1977

Reindeer range inventory in western Alaska from computer-aided digital classification of LANDSAT data

An inventory of reindeer-range resources was conducted for the USDA Soil Conservation Service of 1.6 million hectares of wildlands in western Alaska using clustering techniques with digital Landsat data. Computer-aided digital analysis produced a provisional map of rangeland types which was used to design the field collection of vegetation and soil types data. This field data facilitated refinement of the inventory map and was used to describe the map units. The informational classes important to range resources were wet, moist and alpine tundra, tidal marsh, brush and open spruce forest. A significant feature of the study was the extraction of acreage figures by administrative boundaries within the study area. In addition to soil and vegetation association map products (at scales of 1:250,000 and 1:63,360) acreage values were tallied from the digital data for each of the four grazing permit areas established by the Bureau of Land Management.

George, T. H.↗
NASA NTRS1982

Geographic Information System in Bolivia: a Case Study for Latin America

Bolivia's Geological Service is concluding a successful project designed to give the Department of Oruro the capability to evaluate its natural resources using data generated by three United States satellites. A permanent integrated geographic information system was created for preparing base maps of soil characteristics, land use, geomorphology, geology, water resources and hydrology. The information compiled through the project was stored on magnetic disks and tapes to permit periodic updating, retrieval of data on specific aspects of development projects, and obtaining various data mixes to analyze aspects of prospective development projects. This is the first digital information system developed in Latin America.

Adrien, P. M.↗
NASA NTRS2016

Extreme Mapping: Looking for Water on the Moon

There are many challenges when exploring extreme environments. Gathering accurate data to build maps about places that you cannot go is incredibly complex. NASA supports scientists by remotely operating robotic rovers to explore uncharted territories. One potential upcoming mission is to look for water near a lunar pole (the Resource Prospector mission). Learn about the technical hurdles and research steps that NASA takes before the mission. NASA practices on Earth with Mission Analogs which simulate the proposed mission. This includes going to lunar-type landscapes, building field networks, testing out rovers, instruments and operational procedures. NASA sets up remote science back rooms just as there are for actual missions. NASA develops custom Ground Data Systems software to support scientific mission planning and monitoring over variable time delays, and separate commanding software and infrastructure to operate the rovers.

MOON↗
NASA NTRS1973

Mapping of soil banks using ERTS-1 pictures

Earth Resources Technology Satellite (ERTS-1) pictures of different wavelengths (MSS 4,5,6,7) were used in the study of two strip mine areas in southeastern Ohio. The first area was near Piedmont Lake and the second area was near New Lexington. Prints were examined under a binocular microscope and the gray tone was correlated with the actual ground conditions at several sites. For the New Lexington area, color infrared pictures taken at an elevation of 18,000 feet were also used for correlation with the ERTS-1 imagery. The results indicate that MSS 5 and 7 are most useful in defining the stripped land and show that the hydrological and soil characteristics are remarkably different than the surrounding lands.

Ahmad, M. U.↗
DOE OSTI2022

Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection

The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1973

Mapping Atlantic coastal marshlands, Maryland, Georgia, using ERTS-1 imagery

Eastern coastal marshes are the most extensive and productive in the United States. A relatively low cost, moderately accurate method is needed to map these areas for management and protection. Groundbased and low-altitude aircraft methods for mapping are time-consuming and quite expensive. The launch of NASA's Earth Resources Technology Satellite has provided an opportunity to test the feasibility of mapping wetlands using small scale imagery. The test sites selected were in Chesapeake Bay, Maryland, and Ossabaw Island, Georgia. Results of the investigation indicate that the following may be ascertained from ERTS imagery, enlarged to 1:250,000: (1) upper wetland boundary; (2) drainage pattern in the wetland; (3) plant communities; (4) ditching activities associated with agriculture; and (5) lagooning for water-side home development. Conclusions are that ERTS will be an excellent tool for many types of coastal wetland mapping.

Anderson, R. R.↗
NASA NTRS1992

Model-driven mapping onto distributed memory parallel computers

The author addresses the problem of exploiting the parallelism available in a program to efficiently employ the resources of the target machine in the context of building a mapping compiler for a distributed memory parallel machine. He demonstrates the effectiveness of using execution models to select the best mapping technique from among those available for a given program segment on a particular machine. Through analysis of the execution models for several mapping techniques for one class of programs on a linear processor array, it is shown that selecting the best technique for a particular program instance can make a significant difference in performance. On the other hand, the results of benchmarks from a mapping compiler for the Warp systolic array machine show that the execution models considered are accurate enough to select the best mapping technique for a given program.

Sussman, Alan↗
NASA NTRS2014

The Development of a Long-Term, Continually Updated Global Solar Resource at 10 km Resolution: Preliminary Results From Test Processing and Continuing Plans

Background: Considering the likelihood of global climatic weather pattern changes and the global competition for energy resources, there is an increasing need to provide improved and continuously updated global Earth surface solar resource information. Toward this end, a project was funded under the NASA Applied Science program involving the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), National Renewable Energy Laboratory (NREL), the State University of New York/Albany (SUNY) and the NOAA National Climatic Data Center (NCDC) to provide NREL with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and variability and to provide a mechanism for continual updates of solar resource information. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983. The old version of the ISCCP data provided this information for all the world TMs available geosynchronous satellite systems and NOAA TMs AVHRR data sets at a 30 km effective resolution. This new version aims to provide a new and improved satellite calibration at an effective 10 km resolution. Thus, working with SUNY, NASA will develop and test an improved production system that will enable NREL to continually update the Earth TM solar resource. Objective and Methods: In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. An assessment of the solar resource values relative to calibration uncertainty and assumptions are presented. Errors resulting assumptions in snow cover and background aerosol amount are described. These uncertainties and the statistics of the agreement between the measurements and new satellite estimates are also reviewed and compared to other solar data sets. Findings and Conclusions: Preliminary results show that insolation values show an overall small bias (less than 1%) with a RMS of 25% relative to surface measurements. Exceptions at certain locations were found and will be discussed relative to the uncertainties identified above. Lastly, we will identify the next steps in the development and improvement of this production system including some accuracy goals in preparation for ultimate delivery to NREL.

Stackhouse, P.↗
NASA NTRS2001

Lightning Detection in a Flash

In a joint project with NASA's Kennedy Space Center, Global Atmospherics, Inc. (GAI), participated in the upgrade and commercialization of the Lightning Detection and Ranging (LDAR) System. Under a Space Act Agreement, GAI and Kennedy agreed to the joint development of a new LDAR system that meets the needs of both NASA and private industry. The resulting development was a volumetric lightning mapping system. NASA operates a three- dimensional LDAR system capable of determining the exact location and altitude of in-cloud and cloud-to-cloud lightning. Under the Space Act Agreement, GAI contributed its wealth of experience and resources to update and improve the current lightning mapping system used by NASA. Previously, commercial systems were only capable of locating cloud-to-ground lightning. The resulting innovations allowed GAI to position the LDAR system for commercial applications. The upgraded product has the ability to measure in-cloud and cloud-to-cloud lightning. Notable improvements have also been made in the system's location accuracy and signal detection. The new product, known as LDAR II, is targeted for use by utility providers, aviation companies, airports, and commercial space vehicle launch facilities. Presently, forecasting services, research facilities, and a utility company are using the system.

Source record↗
NASA NTRS2019

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827-2807 km2 for Senegal and 245-1271 km2 for The Gambia with one outlier for each country. We further report "Places of Agreement" (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

Mondal, Pinki↗
DOE OSTI2023

National Critical Minerals Data Dashboard

The Critical Minerals (CM) Data Dashboard provides data-driven insights to help unlock the potential of conventional and unconventional CM resources in the United States. The dashboard focuses on 12 CM essential for producing zero-emission transportation and clean power technology. More information about these CM and potential resources is available in our CM Story Map. Dashboard data sets can be filtered geographically using the options provided along the top of the dashboard. Data sets can also be filtered by various attributes using the options in the expandable side panel on the left side of the screen. The data sets in this dashboard were compiled from multiple sources including the USGS, USEPA, OSMRE, and DHS., Users are encouraged to provide feedback/suggestions on the function and content of the dashboard, as well as any issues that may arise.

Conventional Resources↗