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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. Over those parts of the ARNICA test site where ERTS-1 data were available, the search for correspondences between images and ground truth acquired by the vegetation and geology maps was quite positive. The probability of recognition of soil use types can be estimated at: (1) 100% for water plans, rivers, canals, swamplands, and wetlands; (2) 80%-100% for the major types of forestry, farmland zones, moorlands and pasturelands, and urbanization; (3) 20%-50% for communication lines; (4) 60%-80% for forestry species and organization of agricultural areas; (5) 40%-60% for finer discrimination between forest types and more accurate identification of cultivations; (6) 60%-90% for major geological features. These percentages will be improved upon as soon as it is possible to use the repetitive imagery. An early use of automatic cartography using ERTS-1 imagery was made possible for pine forests in the Central Pyrenees, the densitometric signature of which were particularly significant. Important observations were made in related fields of water resources, snow survey, estuary dynamics, and meteorology.

Rey, P.↗
NASA NTRS1973

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. Significant results of the ARNICA program (February - December 1973) were: (1) The quantitative processing of ERTS-1 data was developed along two lines: the study of geological structures and lineaments of Spanish Catalonia, and the phytogeographical study of the forest region of the Landes of Gascony (France). In both cases it is shown that the ERTS-1 imagery can be used in establishing zonings of equal quantitative interpretation value. (2) In keeping with the operational transfer program proposed in previous reports between exploration of the imagery and charting of the object, a precise data processing method was developed, concerning more particularly the selection of digital equidensity samples computer display and rigorous referencing.

Rey, P. A.↗
NASA NTRS1974

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. (1) Accurate recognition of previously known ground features from ERTS-1 imagery has been confirmed and a probable detection range for the major signatures can be given. (2) Unidentified elements, however, must be decoded by means of the equal densitometric value zone method. (3) Determination of these zonings involves an analogical treatment of images using the color equidensity methods (pseudo-color), color composites and especially temporal color composite (repetitive superposition). (4) After this analogical preparation, the digital equidensities can be processed by computer in the four MSS bands, according to a series of transfer operations from imagery and automatic cartography.

Rey, P.↗
NASA NTRS1974

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. Significant correspondence codes relating ERTS imagery to ground truth from vegetation and geology maps have been established. The use of color equidensity and color composite methods for selecting zones of equal densitometric value on ERTS imagery was perfected. Primary interest of temporal color composite is stressed. A chain of transfer operations from ERTS imagery to the automatic mapping of natural resources was developed.

Rey, P. A.↗
NASA NTRS1976

An evaluation of the utilization of remote sensing in resource and environmental management of the Chesapeake Bay region

A nine-month study was conducted to assess the effectiveness of the NASA Wallops Chesapeake Bay Ecological Program in remote sensing. The study consisted of a follow-up investigation and information analysis of actual cases in which remote sensing was utilized by management and research personnel in the Chesapeake Bay region. The study concludes that the NASA Wallops Chesapeake Bay Ecological Program is effective, both in terms of costs and performance.

Fuller, D. B.↗
NASA NTRS1982

District wide water resources investigation and management using LANDSAT data. Phase 1: Lake volume

A technique for estimating available water storage volume using LANDSAT data was developed and applied to Lake Washington and Lake Harris in central Florida. The technique can be applied two ways. First, where the historical stage records are available, the historical LANDSAT data can be used to establish the relationship between lake volume and lake stage. In the second case, where the historical stage records are not available, the historical LANDSAT data can be used to estimate the historical lake stage after the lake volume and stage information become available in the future.

Shih, S. F.↗
NASA NTRS2007

Advanced Systems Map, Monitor, and Manage Earth's Resources

SpecTIR LLC, headquartered in Reno, Nevada, is recognized for innovative sensor design, on-demand hyperspectral data collection, and image-generating products for business, academia, and national and international governments. SpecTIR's current vice president of business development has brought a wealth of NASA-related research experience to the company, as the former principal investigator on a NASA-sponsored hyperspectral crop-imaging project. This project, made possible through a Small Business Technology Transfer (STTR) contract with Goddard Space Flight Center, aimed to enhance airborne hyperspectral sensing and ground-truthing means for crop inspection in the Mid-Atlantic region of the United States. Areas of application for such technology include precision farming and irrigation; oil, gas, and mineral exploration; pollution and contamination monitoring; wetland and forestry characterization; water quality assessment; and submerged aquatic vegetation mapping. Today, SpecTIR maintains its relationship with Goddard through programs at the University of Maryland in College Park, Maryland, and at the U.S. Department of Agriculture campus in Beltsville, Maryland. Additionally, work continues on the integration of hyperspectral data with LIDAR systems and other commercial-off-the-shelf technologies.

Source record↗
NASA NTRS2012

Requirements, Resource Planning and Management for Decrewing/Recrewing Scenarios of the International Space Station

Following the failure of 44P on launch in August 2011, and the subsequent grounding of all Russian Soyuz rocket based launches, the ISS ground teams engaged in an effort to determine how long the ISS could remain crewed, what would be required to safely configure the ISS for decrewing, and what would be required to recrew the ISS upon resumption of Soyuz rocket launches if decrewing became necessary. This White Paper was written to capture the processes and lessons learned from real-time time events and to provide a reference and training document for ISS Program teams in the event decrewing of the ISS is needed. Through coordination meetings and assessments, teams identified six decrewing priorities for ground and crew operations. These priorities were integrated along with preflight priorities through the Increment replanning process. Additionally, the teams reviewed, updated, and implemented changes to the governing documentation for the configuration of the ISS for a contingency decrewing event. Steps were taken to identify critical items for disposal prior to decrewing, as well as identifying the required items to be strategically staged or flown with the astronauts and cosmonauts who would eventually recrew the ISS. After the successful launches and dockings of both 45P and 28S, the decrewing team transitioned to finalizing and publishing the documentation for standardizing the decrewing flight rules. With the continued launching of crews and cargo to the ISS, utilization and science is again a high priority, with the Increment pairs 29 and 30, and 31 and 32 reaching the milestone of at least 35 hours per week average utilization.

Bach, David A.↗
NASA NTRS2013

Requirements, Resource Planning, and Management for Decrewing/Recrewing Scenarios of the International Space Station

Following the failure of 44 Progress (44P) on launch in August 2011, and the subsequent grounding of all Russian Soyuz rocket based launches, the International Space Station (ISS) ground teams engaged in an effort to determine how long the ISS could remain crewed, what would be required to safely configure the ISS for decrewing, and what would be required to recrew the ISS upon resumption of Soyuz rocket launches if decrewing became necessary. This White Paper was written to capture the processes and lessons learned from real-time time events and to provide a reference and training document for ISS Program teams in the event decrewing of the ISS is needed. Through coordination meetings and assessments, teams identified six decrewing priorities for ground and crew operations. These priorities were integrated along with preflight priorities through the Increment re-planning process. Additionally, the teams reviewed, updated, and implemented changes to the governing documentation for the configuration of the ISS for a contingency decrewing event. Steps were taken to identify critical items for disposal prior to decrewing, as well as identifying the required items to be strategically staged or flown with the astronauts and cosmonauts who would eventually recrew the ISS. After the successful launches and dockings of both 45P and 28 Soyuz (28S), the decrewing team transitioned to finalizing and publishing the documentation for standardizing the decrewing flight rules. With the continued launching of crews and cargo to the ISS, utilization and science is again a high priority; both Increment pairs 29 and 30, and Increment 31 and 32 reaching the milestone of at least 35 hours per week average utilization.

Bach, David A.↗
NASA NTRS2014

Peru Water Resources: Integrating NASA Earth Observations into Water Resource Planning and Management in Perus La Libertad Region

Developing countries often struggle with providing water security and sanitation services to their populations. An important aspect of improving security and sanitation is developing a comprehensive understanding of the country's water budget. Water For People, a non-profit organization dedicated to providing clean drinking water, is working with the Peruvian government to develop a water budget for the La Libertad region of Peru which includes the creation of an extensive watershed management plan. Currently, the data archive of the necessary variables to create the water management plan is extremely limited. Implementing NASA Earth observations has bolstered the dataset being used by Water For People, and the METRIC (Mapping EvapoTranspiration at High Resolution and Internalized Calibration) model has allowed for the estimation of the evapotranspiration values for the region. Landsat 8 imagery and the DEM (Digital Elevation Model) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor onboard Terra were used to derive the land cover information, and were used in conjunction with local weather data of Cascas from Peru's National Meteorological and Hydrological Service (SENAMHI). Python was used to combine input variables and METRIC model calculations to approximate the evapotranspiration values for the Ochape sub-basin of the Chicama River watershed. Once calculated, the evapotranspiration values and methodology were shared Water For People to help supplement their decision support tools in the La Libertad region of Peru and potentially apply the methodology in other areas of need.

Padgett-Vasquez, Steve↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

This report illustrates how energy management information systems (EMIS) can enable federal agencies to improve energy performance, reduce costs, and meet energy goals. The guide discusses how to identify and implement needed EMIS functions and capabilities, covering the entire EMIS life cycle including system design, procurement, implementation, and ongoing maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1981

Earth Survey Applications Division

Accomplishments of research and data analysis conducted to study physical parameters and processes inside the Earth and on the Earth's surface, to define techniques and systems for remotely sensing the processes and measuring the parameters of scientific and applications interest, and the transfer of promising operational applications techniques to the user community of Earth resources monitors, managers, and decision makers are described. Research areas covered include: geobotany, magnetic field modeling, crustal studies, crustal dynamics, sea surface topography, land resources, remote sensing of vegetation and soils, and hydrological sciences. Major accomplishments include: production of global maps of magnetic anomalies using Magsat data; computation of the global mean sea surface using GEOS-3 and Seasat altimetry data; delineation of the effects of topography on the interpretation of remotely-sensed data; application of snowmelt runoff models to water resources management; and mapping of snow depth over wheat growing areas using Nimbus microwave data.

Carpenter, L.↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗