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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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NASA NTRSDate not supplied

Recent Progress and Development of Self-Aligned Focusing Schlieren

A broad range of developments and applications of self-aligned focusing schlieren (SAFS)systems is presented. The replacement of the Ronchi ruling (RR) with a𝜇LCD was demonstrated with digital SAFS, which provides the ability to modulate the pattern and orientation without physical intervention. Additionally, replacement of the traditional RR with spectral-spatial filters was demonstrated to yield the ability to simultaneously image horizontal and vertical index of refraction gradients using two separate wavelengths. The application of the Scheimpflug principle to SAFS systems enables off-axis imaging and allowed for extended fields-of-view to be captured using a two-camera system. The addition of a plenoptic camera to a baseline SAFS system enables refocusing capabilities, which is demonstrated on two jets offset along the optical axis. Furthermore, application of an event-based camera to SAFS was shown to enable acquisition of sparse, spatio-temporal data. Beyond the listed developments, SAFS was applied to difficult and constraining facilities, such as the National Full-Scale Aerodynamics Complex(NFAC) 80- by 120-ft Wind Tunnel and the Thermal Acoustic Engines (TAE) rig at NASA Ames Research Center and NASA Glenn Research Center, respectively. Behavior of Ronchi rulings, Rochon prisms, and the use of quarter-wave plates versus quarter-wave films are evaluated to provide insight for future experimental design of SAFS-systems.

Self-Aligned Focusing Schlieren↗
NASA NTRS1977

Pattern recognition of Landsat data based upon temporal trend analysis

The Delta Classifier defined as an agricultural crop classification scheme employing a temporal trend procedure is applied to more than 100 different Landsat data sets collected during the 1974-1975 growing season throughout the major wheat-producing regions of the United States. The classification approach stresses examination of temporal trends of the Landsat mean vectors of crops in the absence of corresponding ground truth information. It is shown that the resulting classifications compare favorably to ground truth estimates for wheat proportion in those cases where ground truth is available, and that the temporal trend procedure yields estimates of the wheat proportion that are comparable to the best results from maximum likelihood classification with photointerpreter-defined training fields.

Engvall, J. L.↗
NASA NTRS2018

Mapping Pine Plantations in the Southeastern U.S. Using Structural, Spectral, and Temporal Remote Sensing Data

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest coverin the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations.Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

Fagan, M. E.↗
NASA NTRS1995

SeaWiFS technical report series. Volume 32: Level-3 SeaWiFS data products. Spatial and temporal binning algorithms

The level-3 data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are statistical data sets derived from level-2 data. Each data set will be based on a fixed global grid of equal-area bins that are approximately 9 x 9 sq km. Statistics available for each bin include the sum and sum of squares of the natural logarithm of derived level-2 geophysical variables where sums are accumulated over a binning period. Operationally, products with binning periods of 1 day, 8 days, 1 month, and 1 year will be produced and archived. From these accumulated values and for each bin, estimates of the mean, standard deviation, median, and mode may be derived for each geophysical variable. This report contains two major parts: the first (Section 2) is intended as a users' guide for level-3 SeaWiFS data products. It contains an overview of level-0 to level-3 data processing, a discussion of important statistical considerations when using level-3 data, and details of how to use the level-3 data. The second part (Section 3) presents a comparative statistical study of several binning algorithms based on CZCS and moored fluorometer data. The operational binning algorithms were selected based on the results of this study.

Hooker, Stanford B.↗
NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗
NASA NTRS1983

Detecting agricultural to urban land use change from multi-temporal MSS digital data

Conversion of agricultural land to a variety of urban uses is a major problem along the Wasatch Front, Utah. Although LANDSAT MSS data is a relatively coarse tool for discriminating categories of change in urban-size plots, its availability prompts a thorough test of its power to detect change. The procedures being applied to a test area in Salt Lake County, Utah, where the land conversion problem is acute are presented. The identity of land uses before and after conversion was determined and digital procedures for doing so were compared. Several algorithms were compared, utilizing both raw data and preprocessed data. Verification of results involved high quality color infrared photography and field observation. Two data sets were digitally registered, specific change categories internally identified in the software, results tabulated by computer, and change maps printed at 1:24,000 scale.

Ridd, M. K.↗
NASA NTRS2000

BOREAS Level-0 ER-2 Navigation Data

The BOREAS Staff Science effort covered those activities that were BOREAS community-level activities or required uniform data collection procedures across sites and time. These activities included the acquisition, processing, and archiving of aircraft navigation/attitude data to complement the digital image data. The level-0 ER-2 navigation data files contain aircraft attitude and position information acquired during the digital image and photographic data collection missions. Temporally, the data were acquired from April to September 1994. Data were recorded at intervals of 5 seconds. The data are stored in tabular ASCII files.

Strub, Richard↗
NASA NTRS2014

Reevaluation of Stratospheric Ozone Trends From SAGE II Data Using a Simultaneous Temporal and Spatial Analysis

This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data is prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

Damadeo, R. P.↗
NASA NTRS2011

Temporal mapping and analysis

A compositing process for selecting spatial data collected over a period of time, creating temporal data cubes from the spatial data, and processing and/or analyzing the data using temporal mapping algebra functions. In some embodiments, the temporal data cube is creating a masked cube using the data cubes, and computing a composite from the masked cube by using temporal mapping algebra.

O'Hara, Charles G.↗
NASA NTRS2024

Temporal Activation of Extravehicular Activity Science and Operations Data: How Rise2's Enablement of a Novel Data Management Prototype Influenced ISS and Artemis Advancements

SSERVI's RIS4E and Rise2 nodes provided early and repeated opportunities to prototype the organization and visualization of science and field operations data together via temporal vs. type-indexing. This data management approach was first explored in our Apollo in Real Time initiative through its meticulous reconciliation and integration of Apollo 11, 13, and 17 mission data, providing an unparalleled temporal and contextual understanding of these historic missions. The positive results from these prototypes served as pre-cursor examples of what eventually became the Collaborative Operations Data Activation (CODA) application at NASA Johnson Space Center (JSC).

B Feist↗
NASA NTRS2013

Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data

High temporal resolution information on burned area is a prerequisite for incorporating bottom-up estimates of wildland fire emissions in regional air transport models and for improving models of fire behavior. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the evolution of nine large wildland fires. For each fire, local input parameters for the kriging model were defined using variogram analysis. The accuracy of the kriging model was assessed using high resolution daily fire perimeter data available from the U.S. Forest Service. We also assessed the temporal reporting accuracy of the MODIS burned area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared to 33% for MCD45A1 and 53% for MCD64A1.

Aqua↗
NASA NTRS1976

Comparison of periodic and other characteristics of geomagnetic and meterological rocket data

The temporal variations in stratospheric winds and temperatures with the geomagnetic field elements were compared. From a periodic analysis of the geomagnetic field elements the amplitude and phase of the quasibiennial, annual, and semiannual waves are given for stations from 1 degree S to 89 degree N. These results are then compared with corresponding waves reported in rocketsonde wind and temperature data. The annual waves are found to be coupled as a result of the annual variation in the dynamo effect of the wind in the lower ionosphere. The semiannual waves are also found to be coupled and three possible causes for the extra tropical stratospheric semiannual wind wave are discussed. Time variance spectra for the interval from 4 days to 44 days in both zonal winds and horizontal geomagnetic field intensity are compared for years when major midwinter warmings occur and years when only minor warmings occur. The noted differences are suggested to arise from upward propagating planetary waves which are absorbed or refracted in varying amounts depending on the prevailing circulation.

Nastrom, G. D.↗
NASA NTRS2006

Using Satellite Data to Characterize the Temporal Thermal Behavior of an Active Volcano: Mount St. Helens, WA

ASTER thermal infrared data over Mt. St Helens were used to characterize its thermal behavior from Jun 2000 to Feb 2006. Prior to the Oct 2004 eruption, the average crater temperature varied seasonally between -12 and 6 C. After the eruption, maximum single-pixel temperature increased from 10 C (Oct 2004) to 96 C (Aug 2005), then showed a decrease to Feb 2006. The initial increase in temperature was correlated with dome morphology and growth rate and the subsequent decrease was interpreted to relate to both seasonal trends and a decreased growth rate/increased cooling rate, possibly suggesting a significant change in the volcanic system. A single-pixel ASTER thermal anomaly first appeared on Oct 1, 2004, eleven hours after the first eruption - 10 days before new lava was exposed at the surface. By contrast, an automated algorithm for detecting thermal anomalies in MODIS data did not trigger an alert until Dec 18. However, a single-pixel thermal anomaly first appeared in MODIS channel 23 (4 um) on Oct 13, 12 days after the first eruption - 2 days after lava was exposed. The earlier thermal anomaly detected with ASTER data is attributed to the higher spatial resolution (90 m) compared with MODIS (1 m) and the earlier visual observation of anomalous pixels compared to the automated detection method suggests that local spatial statistics and background radiance data could improve automated detection methods.

Advanced Spaceborne Thermal Emission and Reflectio↗
NASA NTRS2001

Techniques of Validation of Aerosol and Water Vapor Retrievals From MODIS

Aerosols are extremely important for global climate studies and modeling in the quest to characterize the global radiation budget and forcing. The physical characteristics, composition, abundance, and spatial distribution and dynamics of aerosols are still very poorly known. Aerosol column optical thickness and other parameters as well as column precipitable water vapor amount are some of the main atmospheric parameters retrieved from the MODIS instrument on board the Terra satellite. To ensure the reliability of these parameters, we have embarked on a very massive validation effort. This involves cross correlation between the retrievals from the satellite data and those obtained from sunphotometer measurements at a large number of ground stations spread throughout the globe. Notable among these ground stations is a large network of over 100 stations coordinated under the Aerosol Robotic Network (AERONET) project. Whereas MODIS retrieves the aerosol parameters throughout the globe once or twice a day during the daytime, the ground measurements cover only discrete locations of the earth, though the retrievals are done several times a day. We have devised a method to. match the MODIS and ground retrievals through spatial statistics for the MODIS data and temporal statistics for the ground data. This has produced good comparisons and has enabled the validation of MODIS aerosol and water vapor retrievals at over 100 discrete locations in various parts of the earth both over the land and over the ocean. Currently, the validation statistical data is produced routinely by the MODIS aerosol group and is even available not only for validation but also for use by the science community for short and long term studies at various parts of the earth. One important advantage is that the system can be expanded to incorporate more locations where ground measurements and other studies may be conducted at any time during the lifetime of MODIS.

Ichoku, Charles↗
NASA NTRS2005

Compression Techniques for Improved Algorithm Computational Performance

Analysis of thermal data requires the processing of large amounts of temporal image data. The processing of the data for quantitative information can be time intensive especially out in the field where large areas are inspected resulting in numerous data sets. By applying a temporal compression technique, improved algorithm performance can be obtained. In this study, analysis techniques are applied to compressed and non-compressed thermal data. A comparison is made based on computational speed and defect signal to noise.

Zalameda, Joseph N.↗
NASA NTRS1988

Temporal filters for isolating steady photospheric flows

A variety of temporal filters are tested on artificial data with 60 and 75 s sampling intervals to determine their accuracy in separating the nearly-steady photospheric flows from the p-mode oscillations in Doppler velocity data. Longer temporal averages are better at reducing the residual signal due to p-modes but they introduce additional errors from the rotation of the supergranule pattern across the solar disk. Unweighted filters (boxcar averages) leave residual rms errors of about 6 m/s from the p-modes after 60 min of averaging. Weighted filters, with nearly Gaussian shapes, leave similar residual errors after only 20 min of averaging and introduce smaller errors from the rotation of the supergranule pattern. The best filters found are weighted filters that use data separated by 150 or 120 s so that the p-modes are sampled at opposite phases.

Hathaway, David H.↗
NASA NTRS2010

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

Tang, Ling↗