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NASA NTRS1993

Compression for an effective management of telemetry data

A Technological DataBase (T.D.B.) records all the values taken by the physical on-board parameters of a satellite since launch time. The amount of temporal data is very large (about 15 Gbytes for the satellite TDF1) and an efficient system must allow users to have a fast access to any value. This paper presents a new solution for T.D.B. management. The main feature of our new approach is the use of lossless data compression methods. Several parametrizable data compression algorithms based on substitution, relative difference and run-length encoding are available. Each of them is dedicated to a specific type of variation of the parameters' values. For each parameter, an analysis of stability is performed at decommutation time, and then the best method is chosen and run. A prototype intended to process different sorts of satellites has been developed. Its performances are well beyond the requirements and prove that data compression is both time and space efficient. For instance, the amount of data for TDF1 has been reduced to 1.05 Gbytes (compression ratio is 1/13) and access time for a typical query has been reduced from 975 seconds to 14 seconds.

Arcangeli, J.-P.↗

NASA NTRS2011

A New Global Mascon Solution Tuned for High-Latitude Ice Studies

A new global mascon solution has been developed with I-arc-degree spatial and IO-day temporal sampling. The global mas cons are estimated from the reduction of nearly 8 years of GRACE K-band range-rate data. Temporal and anisotropic spatial constraints have been applied for land, ocean and ice regions. The solution construction and tuning is focused towards the Greenland and Antarctic ice sheets (GIS and AIS) as well as the Gulf of Alaska mountain glaciers (GoA). Details of the solution development will be discussed, including the mascon parameter definitions, constraints, and the tuning of the constraint damping factor. Results will be presented, exploring the spatial and temporal variability of the ice sheets and GoA regions. A detailed error analysis will be discussed, including solution dependence on iteration, damping factor, forward modeling, and multitechnique comparisons. We also investigate the fundamental resolution of the solution and the spatial correlation of ice sheet inter-annual change. Finally, we discuss future improvements, including specific constraint application for the rest of the major land ice regions and improvements in solution regularization.

Luthcke, S. B.↗

NASA NTRS1971

ADP of multispectral scanner data for land use mapping

The advantages and disadvantages of various remote sensing instrumentation and analysis techniques are reviewed. The use of multispectral scanner data and the automatic data processing techniques are considered. A computer-aided analysis system for remote sensor data is described with emphasis on the image display, statistics processor, wavelength band selection, classification processor, and results display. Advanced techniques in using spectral and temporal data are also considered.

Hoffer, R. M.↗

NASA NTRS1977

Evaluation of change detection techniques for monitoring coastal zone environments

Development of satisfactory techniques for detecting change in coastal zone environments is required before operational monitoring procedures can be established. In an effort to meet this need a study was directed toward developing and evaluating different types of change detection techniques, based upon computer aided analysis of LANDSAT multispectral scanner (MSS) data, to monitor these environments. The Matagorda Bay estuarine system along the Texas coast was selected as the study area. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. Each of the four techniques was used to analyze a LANDSAT MSS temporal data set to detect areas of change of the Matagorda Bay region.

Weismiller, R. A.↗

NASA NTRS1974

Applications of ERTS data to coastal wetland ecology with special reference to plant community mapping and typing and impact of man

Complete seasonal ERTS-1 coverage of Atlantic coastal wetlands from Delaware Bay to Georgia provides a basis for assessment of temporal data for wetland mapping, evaluation, and monitoring. Both MSS imagery and digital data have proved useful for gross wetland species delineation and determination of the upper wetland boundary. Tidal effects and (band to band or seasonal) spectral reflectance differences make it possible to type vegetatively coastal wetlands in salinity related categories. Management areas, spoil disposal sites, drainage ditches, lagoon-type developments and highway construction can be detected indicating a monitoring potential for the future. A northern test site (Maryland-Virginia) and a southern test site (Georgia-South Carolina), representing a range of coastal marshes from saline to fresh, were chosen for intensive study. Wetland maps were produced at various scales using both ERTS imagery (bands 5 and 7) and digital data (bands 4, 5 and 7).

Anderson, R. R.↗

NASA NTRS2002

Thermal Orbital Environmental Parameter Study on the Propulsive Small Expendable Deployer System (ProSEDS) Using Earth Radiation Budget Experiment (ERBE) Data

The natural thermal environmental parameters used on the Space Station Program (SSP 30425) were generated by the Space Environmental Effects Branch at NASA's Marshall Space Flight Center (MSFC) utilizing extensive data from the Earth Radiation Budget Experiment (ERBE), a series of satellites which measured low earth orbit (LEO) albedo and outgoing long-wave radiation. Later, this temporal data was presented as a function of averaging times and orbital inclination for use by thermal engineers in NASA Technical Memorandum TM 4527. The data was not presented in a fashion readily usable by thermal engineering modeling tools and required knowledge of the thermal time constants and infrared versus solar spectrum sensitivity of the hardware being analyzed to be used properly. Another TM was recently issued as a guideline for utilizing these environments (NASA/TM-2001-211221) with more insight into the utilization by thermal analysts. This paper gives a top-level overview of the environmental parameters presented in the TM and a study of the effects of implementing these environments on an ongoing MSFC project, the Propulsive Small Expendable Deployer System (ProSEDS), compared to conventional orbital parameters that had been historically used.

Sharp, John R.↗

NASA NTRS2020

Amazonia Disasters: Assessing Methods for Gold Mining-related Deforestation Detection in Amazonia Using NASA Earth Observations

Artisanal and small-scale gold mining (ASGM) is responsible for a large fraction of deforestation and disturbance in Amazonia. These activities cause severe impacts on the rainforest ecosystem and socioeconomic state of the region. NASA DEVELOP partnered with the Asociación para la Conservación de la Cuenca Amazónica (ACCA), NASA SERVIR Science Coordination Office, and the Spatial Informatics Group to enhance ASGM-related deforestation detection methods. ACCA currently uses the Omnibus Q-test Change Point Detection Algorithm to identify changes in Synthetic Aperture Radar (SAR) monthly-aggregated temporal data from the Sentinel-1 satellite. The team determined the algorithm's accuracy by comparing a stratified random sample of change points against data from January 2019 to June 2020 identified using PlanetScope and Landsat 8 Operational Land Imager (OLI) Earth observations through Collect Earth Online. Our results indicated a users' accuracy of 55% for temporal change detection and producer's and user's accuracies of 99% and 97%, respectively, for detecting when change did not occur. Of the labeled change points, only 19% were due to mining activity. This research can help our partners have a more accurate understanding of where illegal gold mining may be taking place and inform decisions to remediate this activity.

DEVELOP Project Summary↗

NASA NTRS2020

Amazonia Disasters: Assessing Methods for Gold Mining-Related Deforestation Detection in Amazonia Using NASA Earth Observations

DEVELOP Tech Paper↗

NASA NTRS2009

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Cook, Bruce D.↗

NASA NTRS2009

Observations from Space: A Unique Vantage Point for the Study of the Environment and Possible Associations with Disease Occurrence

Health providers/researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. The field of geospatial health remains in its infancy, and this program will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential. NASA will discuss the Public Health Projects developed to work with Grantees and the CDC while providing them with information on opportunities for future collaborations with NASA for future research.

Estes, S.↗

NASA NTRS2010

NASA Satellite Observations: A Unique Asset for the Study of the Environment and Implications for Public

Health providers/researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. The field of geospatial health remains in its infancy, and this program will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential. NASA will discuss some of their Public Health Projects and also providing the audience with information on opportunities for future collaborations with NASA for future research.

Estes, Sue↗

NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗

NASA NTRS1978

Nationwide forestry applications program: Ten-Ecosystem Study (TES) site 5 report, Kershaw County, South Carolina, report 4

The author has identified the following significant results. The Kershaw County site, South Carolina, was selected to be representative of both the oak-pine ecosystem and the southeastern pine ecosystem. The following processing results have concluded that: (1) early spring LANDSAT data provide the best contrast between forest features; (2) level 2 forest features (softwood, hardwood, grassland, and water) can be classified with an accuracy of 70% + or - 5.7% at the 90% confidence level; (3) level 3 species classification was inconclusive; (4) temporal data did not provide a significant increase in classification accuracy of level 2 features, over single date classification to warrant the additional processing; and (5) training fields from only 10% of the site can be used to classify the entire site.

Dillman, R. D.↗

NASA NTRS1973

A study of the utilization of ERTS-1 data from the Wabash River Basin

The author has identified the following significant results. In soil association mapping, computerized analysis of ERTS-1 MSS data has yielded images which will prove useful in the ongoing Cooperative Soil Survey program, involving the Soil Conservation Service of USDA and other state and local agencies. In the present mode of operation, a soil survey for a county may take up to 5 years to be completed. Results indicate that a great deal of soils information can be extracted from ERTS-1 data by computer analysis. This information is expected to be very valuable in the premapping conference phase of a soil survey, resulting in more efficient field operations during the actual mapping. In the earth surface features mapping effort it was found that temporal data improved the classification accuracy of forest classification in Tippecanoe County, Indiana. In water resources study a severe scanner look angle effect was observed in the aircraft scanner data of a test lake which was not present in ERTS-1 data of the same site. This effect was greatly accentuated by surface roughness caused by strong winds. Quantitative evaluation of urban features classification in ERTS-1 data was obtained. An 87.1% test accuracy was obtained for eight categories in Marion County, Indiana.

Landgrebe, D. A.↗

NASA NTRS2008

Validation of SO2 Retrievals from the Ozone Monitoring Instrument over NE China

The Dutch-Finnish Ozone Monitoring Instrument (OMI) launched on the NASA Aura satellite in July 2004 offers unprecedented spatial resolution, coupled with contiguous daily global coverage, for space-based UV measurements of sulfur dioxide (SO2). We present a first validation of the OMI SO2 data with in situ aircraft measurements in NE China in April 2005. The study demonstrates that OMI can distinguish between background SO2 conditions and heavy pollution on a daily basis. The noise (expressed as the standard deviation,sigma) is approximately 1.5 DU (Dobson units; 1 DU = 2.69 10 (exp 16) molecules/cm (exp 2)) for instantaneous field of view boundary layer (PBL) SO2 data. Temporal and spatial averaging can reduce the noise to sigma approximetly 0.3 DU over a remote region of the South Pacific; the long-term average over this remote location was within 0.1 DU of zero. Under polluted conditions collection 2 OMI data are higher than aircraft measurements by a factor of two. Improved calibrations of the radiance and irradiance data (collection 3) result in better agreement with aircraft measurements on polluted days. The air mass corrected collection 3 data still show positive bias and sensitivity to UV absorbing aerosols. The difference between the in situ data and the OMI SO2 measurements within 30 km of the aircraft profiles was about 1 DU, equivalent to approximately 5 ppb from 0 to 3000 m altitude. Quantifying the SO2 and aerosol profiles and spectral dependence of aerosol absorption between 310 and 330 nm are critical for an accurate estimate of SO2 from satellite UV measurements.

Krotkov, Nickolay A.↗

NASA NTRS2024

Northern Colorado Water Resources: Using Earth Observations to Assess Relationships between Snowpack and Wildfires for Water Utility Management

In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.

airborne LiDAR↗

NASA NTRS2012

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

Ong, James C.↗

NASA NTRS2009

Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

Zavodsky, Bradley↗