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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Applications of remote sensing to hydrologic planning

The transfer of LANDSAT remote sensing technology from the research sector to user operational applications requires demonstration of the utility and accuracy of LANDSAT data in solving real problems. This report describes such a demonstration project in the area of water resources, specifically the estimation of non-point source pollutant loads. Non-point source pollutants were estimated from land cover data from LANDSAT images. Classification accuracies for three small watersheds were above 95%. Land cover was converted to pollutant loads for a fourth watershed through the use of coefficients relating significant pollutants to land use and storm runoff volume. These data were input into a simulator model which simulated runoff from average rainfall. The result was the estimation of monthly expected pollutant loads for the 17 subbasins comprising the Magothy watershed.

Loats, H., Jr.↗
NASA NTRS1990

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

Passive microwave data collected by Nimbus 7 were used to classify and monitor the Arctic multilayer sea ice cover. Sea ice concentration maps during several summer minima are analyzed to obtain estimates of ice floes that survived summer, and the results are compared with multiyear-ice concentrations derived from these data by using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data was found to be about 25 to 40 percent less than the summer ice-cover minimum, indicating that the multiyear ice cover in winter is inadequately represented by the passive microwave winter data and that a significant fraction of the Arctic multiyear ice floes exhibits a first-year ice signature.

Comiso, J. C.↗
NASA NTRS1992

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.

Chien, Steve↗
NASA NTRS1993

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks.

Chien, Steve↗
NASA NTRS2018

ICESat-2 Photon Classification: Finding Signal Photons in the ATL03 Geolocated Photon Data Product

ICESat-2 carries NASA's next-generation laser altimeter, ATLAS, (Advanced Topographic Laser Altimeter System), designed to measure changes in ice sheet height, sea ice freeboard, and vegetation canopy height. ATLAS contains a photon-counting lidar which transmits green (532-nm) pulses at 10kHz. Each pulse is split into 3 pairs of beams (one strong and one weak). Approximately 1014 photons per pulse travel from ATLAS through the atmosphere to reflect off the Earth's surface. Some return back into the ATLAS telescope where they are recorded. Photons from sunlight and instrument noise at the same wavelength are also recorded. The flight software time tags all photons within a 500m to 6 km range window and generates histograms. Using the histograms, it selects a telemetry window which varies from 20m over flat surfaces to hundreds of meters over rougher terrain. ATL03 contains the time, height (relative to the WGS-84 ellipsoid), latitude and longitude of every photon within the telemetry window. The basic challenge is to determine which of these photons were reflected off the surface. We have developed an algorithm that identifies these signal photons and assigns a confidence level (low, medium, or high) to each signal photon based on the signal to noise ratio. We present an overview of the signal identification algorithm and show the results on actual ICESat-2 data over ice sheet, sea ice, vegetated, and water surfaces. Higher level ATLAS products work with aggregations of the photons in order to determine the ellipsoidal height of the Earth, canopy height and structure, and other quantities of geophysical interest.

Remote Sensing↗
NASA NTRS1981

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with remote sensing data

The applicability of digital LANDSAT data in updating the Minnesota Lake Land Management Information System and in assessing the trophic status of lakes was investigated. Data from various combinations of training, classification, and geometric correction techniques were compared to a photointerpreted reference data set consisting of ground-verified samples and six randomly selected photographs covering approximately one quarter of the study area. For a reconnaissance inventory, the training via polygons selected from aerial photographs with a canonical analysis minimum distance classifier is the most accurate and efficient analysis technique. The applications test project resulted in disappointing thematic classification results per se, but did contribute to the development of mechanisms for implementing a LANDSAT data processing capability in the State Planning Agency on a permanent basis.

Lillesand, T. M.↗
DOE OSTI2020

Square Kilometre Array Science Data Challenge 1: analysis and results

ABSTRACT As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

Bonaldi, A.↗
NASA NTRS1984

Vegetation, land-use and seasonal albedo data sets: Documentation of archived data tape

Global data bases of vegetation, land use, and land cover were compiled at a 1 deg latitude x 1 deg longitude resolution, drawing on approximately 100 published sources complemented by a large collection of satellite imagery. Six datasets prepared and archived at NCAR are described: a vegetation data set (VEGTYPE) representing natural (pre-agricultural) vegetation based on the UNESCO classification system; a cultivation intensity data set (CULTINT) defining the areal extent (expressed as %) of presently cultivated land in the 1 x 1 cells; and four integrated surface-albedo data sets (January, April, July, October) for snow-free conditions except for permanently snow-covered continental ice, incorporating natural vegetation and cultivation characteristics from the vegetation and cultivation-intensity data sets. Non-zero data are included for permanent land only, including continental ice. Documentation of the data-tape format as well as descriptions and regional maps of the individual data sets are presented.

Matthews, E.↗
NASA NTRS1979

Trophic classification of selected Colorado lakes

Multispectral scanner data, acquired over several Colorado lakes using LANDSAT-1 and aircraft, were used in conjunction with contact-sensed water quality data to determine the feasibility of assessing lacustrine trophic levels. A trophic state index was developed using contact-sensed data for several trophic indicators. Relationships between the digitally processed multispectral scanner data, several trophic indicators, and the trophic index were examined using a supervised multispectral classification technique and regression techniques. Statistically significant correlations exist between spectral bands, several of the trophic indicators and the trophic state index. Color-coded photomaps were generated which depict the spectral aspects of trophic state.

Blackwell, R. J.↗
NASA NTRS1980

Trophic classification of Tennessee Valley area reservoirs derived from LANDSAT multispectral scanner data

LANDSAT MSS data from four different dates were extracted from computer tapes using a semiautomated digital data handling and analysis system. Reservoirs were extracted from the surrounding land matrix by using a Band 7 density level slice of 3; and descriptive statistics to include mean, variance, and ratio between bands for each of the four bands were calculated. Significant correlations ( 0.80) were identified between the MSS statistics and many trophic indicators from ground truth water quality data collected at 35 reservoirs in the greater Tennessee Valley region. Regression models were developed which gave significant estimates of each reservoir's trophic state as defined by its trophic state index and explained in all four LANDSAT frames at least 85 percent of the variability in the data. To illustrate the spatial variations within reservoirs as well as the relative variations between reservoirs, a table look up elliptical classification was used in conjunction with each reservoir's trophic state index to classify each reservoir on a pixel by pixel basis and produce color coded thematic representations.

Meinert, D. L.↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1973

Remote sensing techniques for support of coastal zone resource management.

Description of remote sensing studies carried out for the purpose of developing and/or demonstrating techniques which can be employed for land use inventory, marsh vegetation classification, and water characteristics surveys. Attention is given to results obtained with (1) photo interpretation techniques and procedures for the development of land use information from high-altitude aircraft and satellite imagery, (2) computer based pattern recognition techniques utilizing multispectral scanner data for marsh vegetation classification, and (3) infrared and microwave techniques for the monitoring and surveying of coastal water temperature and salinity characteristics.

Piland, R. O.↗
NASA NTRS1982

Appendix B: Principles of computer processing of LANDSAT data

Computer processing facilitates extraction of information from every pixel by executing a variety of functional operations, called processed algorithms, in general or specialized routines. The best results are obtained when data from more than one multispectral band are used together. Multivariate tatistical analysis, computer tape characteristics, processing modes, and a choice of systems (batch or interactive) are discussed. The major operations in computer processing elaborated include: preprocessing, enhancement, effects of rationing, and classification. Techniques for multisource data correlation are considered with emphasis on geobased systems.

Source record↗
NASA NTRS1993

Parallel Consensual Neural Networks

A new neural network architecture is proposed and applied in classification of remote sensing/geographic data from multiple sources. The new architecture is called the parallel consensual neural network and its relation to hierarchical and ensemble neural networks is discussed. The parallel consensual neural network architecture is based on statistical consensus theory. The input data are transformed several times and the different transformed data are applied as if they were independent inputs and are classified using stage neural networks. Finally, the outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote sensing data and geographic data are given. The performance of the consensual neural network architecture is compared to that of a two-layer (one hidden layer) conjugate-gradient backpropagation neural network. The results with the proposed neural network architecture compare favorably in terms of classification accuracy to the backpropagation method.

Benediktsson, J. A.↗