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NASA NTRS2018

Hyperspectral Data Processing: an Opportunity for End-To-End Processing

The evolution and improvements in hyperspectral instrumentation are being matched by information technology improvements in science data processing and analysis. Research has improved techniques in both onboard and ground-based processing to support other high data volume instruments. Algorithms and hardware have evolved, permitting faster access to the observations. Cloud computing is taking the algorithms to the data. Technologies are being specifically designed to address high volume data sets and are an investment in the improvement of hyperspectral data processing.

science data processing↗

NASA NTRS2012

Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.

Spruce, J. P.↗

NASA NTRS2011

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Bhaduri, Kanishka↗

NASA NTRS1975

Snowcover mapping by machine processing of Skylab and LANDSAT MSS data

Skylab and LANDSAT MSS data were analyzed using computer-aided analysis techniques (CAAT). Results indicated that the middle infrared wavelength bands of the Skylab S-192 scanner would allow effective discrimination between snowcover and water-droplet clouds, whereas the limited spectral response of the LANDSAT-1 or 2 scanners do not allow such spectral discrimination. Five spectral classes of snowcover were defined and mapped. These classes were found to be related to differences in the proportion of snow and forest cover in the individual resolution elements.

Bartolucci, L. A.↗

NASA NTRS2013

Spinoff 2012

Topics covered include: Water Treatment Technologies Inspire Healthy Beverages; Dietary Formulas Fortify Antioxidant Supplements; Rovers Pave the Way for Hospital Robots; Dry Electrodes Facilitate Remote Health Monitoring; Telescope Innovations Improve Speed, Accuracy of Eye Surgery; Superconductors Enable Lower Cost MRI Systems; Anti-Icing Formulas Prevent Train Delays; Shuttle Repair Tools Automate Vehicle Maintenance; Pressure-Sensitive Paints Advance Rotorcraft Design Testing; Speech Recognition Interfaces Improve Flight Safety; Polymers Advance Heat Management Materials for Vehicles; Wireless Sensors Pinpoint Rotorcraft Troubles; Ultrasonic Detectors Safely Identify Dangerous, Costly Leaks; Detectors Ensure Function, Safety of Aircraft Wiring; Emergency Systems Save Tens of Thousands of Lives; Oxygen Assessments Ensure Safer Medical Devices; Collaborative Platforms Aid Emergency Decision Making; Space-Inspired Trailers Encourage Exploration on Earth; Ultra-Thin Coatings Beautify Art; Spacesuit Materials Add Comfort to Undergarments; Gigapixel Images Connect Sports Teams with Fans; Satellite Maps Deliver More Realistic Gaming; Elemental Scanning Devices Authenticate Works of Art; Microradiometers Reveal Ocean Health, Climate Change; Sensors Enable Plants to Text Message Farmers; Efficient Cells Cut the Cost of Solar Power; Shuttle Topography Data Inform Solar Power Analysis; Photocatalytic Solutions Create Self-Cleaning Surfaces; Concentrators Enhance Solar Power Systems; Innovative Coatings Potentially Lower Facility Maintenance Costs; Simulation Packages Expand Aircraft Design Options; Web Solutions Inspire Cloud Computing Software; Behavior Prediction Tools Strengthen Nanoelectronics; Power Converters Secure Electronics in Harsh Environments; Diagnostics Tools Identify Faults Prior to Failure; Archiving Innovations Preserve Essential Historical Records; Meter Designs Reduce Operation Costs for Industry; Commercial Platforms Allow Affordable Space Research; Fiber Optics Deliver Real-Time Structural Monitoring; Camera Systems Rapidly Scan Large Structures; Terahertz Lasers Reveal Information for 3D Images; Thin Films Protect Electronics from Heat and Radiation; Interferometers Sharpen Measurements for Better Telescopes; and Vision Systems Illuminate Industrial Processes.

Source record↗

NASA NTRS1990

Determination of cloud optical depth with the multiple field of view sun photometer

An approach to determining cloud optical depth is presented which utilizes a multiple-field-of-view sun photometer, along with modeled behavior of transmittance ratios as a function of the field of view, to infer the optical depth. The data used in the analysis were collected during the FIRE Stratocumulus IFO, which took place from July 15 to 17, 1987. Values which were taken within an hour of local solar noon are presented and compared with the values computed for mu sub 0 = 1.0. The zenith angle for the measurements was between 11 and 18 deg. It is found that this method of inferring the optical depth indicates most clouds with optical depths within these limits. The measured curves have a slightly different shape than do the modeled ratios.

Hein, Paul F.↗

NASA NTRS2009

Comparison of Anomalies and Trends of OLR as Observed by CERES and Computed from Geophysical Parameters Derived from Analysis of AIRS/AMSU Data

Anomalies and trends of outgoing longwave radiation (OLR) serve as important indicators of climate change. Several satellite based instruments currently provide information related to OLR. CERES, on board the EOS Aqua and Terra satellites, contains broad band radiometers that measure total flux and short-wave flux, from which OLR is determined. AIRS is a high spectral resolution IR sounder on EOS Aqua that measures IR radiances covering most of the spectral interval 650 cm-1 to 2670 cm-1. These observations enable the determination of detailed information about atmospheric temperature, moisture, and ozone profiles, as well as surface skin temperatures and cloud parameters. The AIRS OLR product is the total flux over the spectral interval 2 cm-1 to2750 cm-1 computed for the surface and atmospheric state determined from AIRS observations. We compared spatial anomalies and trends of OLR, over the seven year period September 2002 through August 2009, as observed by CERES and computed using Version 5 AIRS products. These two sets of OLR anomalies and trends, obtained in very different ways, agree with each other almost perfectly in essentially every detail. This important finding shows that a very stable high spectral infra-red sounder such as AIRS corroborates the anomalies and trends of OLR obtained from CERES. More significantly, anomalies and trends of the individual geophysical parameters derived from AIRS explain the detailed causes of the anomalies and trends of CERES OLR. Both sets of results show that global mean OLR has been decreasing at a rate of 0.12 W/m2/yr over the seven year time period under study. Both also confirm that the primary cause of this is due to changes in the tropics, in which OLR has been decreasing at a rate of 0.27 W/m2/yr. AIRS products show that the decrease of tropical OLR is a result of increasing tropical atmospheric water vapor and cloud cover over the time period studied, which in turn is responding to a very strong E1 Nino/ La Nina cycle. Equatorial ocean temperatures between 160E and 120W cooled considerably during this time period, with corresponding local decreases in mid-tropospheric humidity and cloud cover, resulting in increases in local OLR. This was more than compensated by substantial increases in water vapor and cloud cover elsewhere in the tropics, resulting in a net decrease in tropical OLR.

Susskind, Joel↗

NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗

NASA NTRS1976

A comparison of low-cloud satellite wind estimates with analyses based on aircraft observations in a disturbed tropical regime

Low-level, ATS-3 satellite wind estimates are compared with values of wind direction and speed interpolated from analyses based on research aircraft observations of a synoptic tropical wave of moderate intensity on 26 July 1969 during BOMEX. The data were stratified according to whether a satellite estimate was positioned in one of three regions; namely, east or west of the wave trough or north of the disturbance center. When cloud and analysis vector magnitude deviations were computed, regional differences became apparent. These differences are attributed to the physical behavior of the cloud targets tracked under the influence of the surrounding large-scale environment.

Smith, C. L.↗

NASA NTRS2018

GC11H _ 0998: A spatial pattern analysis of forest loss in the Madre de Dios region, Peru

Previous studies have quantified the extension of gold mining activities and associated it with forest loss in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map forest loss within and outside key land tenure areas in this region, Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2017 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗

NASA NTRSDate not supplied

NASA Briefing to Unidata

The NASA report to Unidata for October, 2021 covers several topics of mutual interest to Unidata. These include plans for hosting NASA data in the cloud, data analysis-in-place, and NASA plans for Open Source Science. Major collaborations with other organizations are also discussed.

NetCDF↗

NASA NTRS2019

A Spatial Pattern Analysis of Forest Loss in the Madre de Dios Region, Peru

Previous studies have quantified the expansion of gold mining-related forest loss (Espejo et al., 2018; Asner et al., 2017; Swenson et al., 2011) in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map general forest loss within and outside key land tenure areas in this region. Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2018 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗

NASA NTRS1989

The 27-28 October 1986 FIRE IFO cirrus case study: Cirrus parameter relationships derived from satellite and lidar data

Cirrus cloud radiative and physical characteristics are determined using a combination of ground-based, aircraft, and satellite measurements taken as part of the First ISCCP Regional Experiment (FIRE) Cirrus Intensive Field Observations (IFO) during October and November 1986. Lidar backscatter data are used to define cloud base, center, and top heights and the corresponding temperatures. Coincident GOES 4 km visible (0.65 microns) and 8 km infrared window (11.5 microns) radiances are analyzed to determine cloud emittances and reflectances. Infrared optical depth is computed from the emittance results. Visible optical depth is derived from reflectance using a theoretical ice crystal scattering model and an empirical bidirectional reflectance mode. No clouds with visible optical depths greater than 5 or infrared optical depths less than 0.1 were used in the analysis. Average cloud thickness ranged from 0.5 km to 8 km for the 71 scenes. An average visible scattering efficiency of 2.1 was found for this data set. The results reveal a significant dependence of scattering efficiency on cloud temperature.

Minnis, Patrick↗

NASA NTRS1990

The 27-28 October 1986 FIRE IFO cirrus case study - Cirrus parameter relationships derived from satellite and lidar data

Minnis, Patrick↗

NASA NTRS2001

GOES-Microburst Products Performance Analysis In The Cape Canaveral And Kennedy Space Center Areas

In the past few years, the capabilities of NOAA Geostationary Operational Environmental Satellites (GOES) have increased dramatically. Hourly vertical sounder data is now generally available, but may be unavailable depending upon cloud conditions, satellite operations, and computer system problems at NOAA's National Environmental Satellite Display and Information Service (NESDIS). Meteorologists at NESDIS have used vertical sounder data to develop experimental products for forecasting the probability of convective downbursts. The two products of interest are the Microburst Day Predictive Index (MDPI), which provides an indication of microburst potential and the WINDEX which is a forecast of maximum winds assuming a microburst does occur. Data analyses were made for the central Florida convective season, that is, the period beginning May I and ending September 30. The MDPI showed significant potential as an aid in forecasting convective downbursts. MDPI calculated from GOES soundings were well correlated with those calculated from Cape Canaveral RAOBs.

Witiw, Michael R.↗

NASA NTRS2002

GOES-Microburst Products Performance Analysis in the Cape Canaveral and Kennedy Space Center Areas

In the past few years, the capabilities of NOAA Geostationary Operational Environmental Satellites (GOES) have increased dramatically. Hourly vertical sounder data is now generally available, but may be unavailable depending upon cloud conditions, satellite operations, and computer system problems at NOAA#s National Environmental Satellite Display and Information Service (NESDIS). Meteorologists at NESDIS have used vertical sounder data to develop experimental products for forecasting the probability of convective downbursts. The two products of interest are the Microburst Day Predictive Index (MDPI), which provides an indication of microburst potential and the WINDEX which is a forecast of maximum winds assuming a microburst does occur. Data analyses were made for the central Florida convective season, that is, the period beginning May 1 and ending September 30. The MDPI showed significant potential as an aid in forecasting convective downbursts. MDPI calculated from GOES soundings were well correlated with those calculated from Cape Canaveral RAOBs.

Witiw, Michael R.↗

NASA NTRS2001

A 3-Year Climatology of Cloud and Radiative Properties Derived from GOES-8 Data Over the Southern Great Plains

While the various instruments maintained at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) Central Facility (CF) provide detailed cloud and radiation measurements for a small area, satellite cloud property retrievals provide a means of examining the large-scale properties of the surrounding region over an extended period of time. Seasonal and inter-annual climatological trends can be analyzed with such a dataset. For this purpose, monthly datasets of cloud and radiative properties from December 1996 through November 1999 over the SGP region have been derived using the layered bispectral threshold method (LBTM). The properties derived include cloud optical depths (ODs), temperatures and albedos, and are produced on two grids of lower (0.5 deg) and higher resolution (0.3 deg) centered on the ARM SGP CF. The extensive time period and high-resolution of the inner grid of this dataset allows for comparison with the suite of instruments located at the ARM CF. In particular, Whole-Sky Imager (WSI) and the Active Remote Sensing of Clouds (ARSCL) cloud products can be compared to the cloud amounts and heights of the LBTM 0.3 deg grid box encompassing the CF site. The WSI provides cloud fraction and the ARSCL computes cloud fraction, base, and top heights using the algorithms by Clothiaux et al. (2001) with a combination of Belfort Laser Ceilometer (BLC), Millimeter Wave Cloud Radar (MMCR), and Micropulse Lidar (MPL) data. This paper summarizes the results of the LBTM analysis for 3 years of GOES-8 data over the SGP and examines the differences between surface and satellite-based estimates of cloud fraction.

Khaiyer, M. M.↗

NASA NTRSDate not supplied

Icing Physics Studies Using the 3D SIDRM Test Article: Aerodynamic and Supercooled Liquid Icing Analysis

In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation capability. Experimental data is required for development of icing physics models and simulation validation. To that end, this paper presents the analysis of the supercooled liquid icing data subset from tests conducted in 2022 at the NASA Icing Research Tunnel that studied both supercooled water and ice-crystal icing. The test article that was utilized replicated 3D geometrical features of an inter-compressor duct and strut region of a turbofan engine. The surfaces of the Simulated Inter-compressor Duct Research Model (SIDRM) can be heated to simulate the warm surfaces of the turbofan inter-compressor duct. The test article is instrumented with pressure taps, heaters, heat flux gauges, and thermocouples, while a 3D laser scanner, cameras, and a scale to measure ice mass were utilized to characterize the icing behavior. The aim of these tests was to generate ice accretions on the SIDRM test article under well-characterized supercooled liquid icing and ice crystal icing conditions. This paper discusses measurements related to aerodynamic testing and supercooled liquid icing tests that were conducted. Aerodynamic measurements were analyzed and compared to computational simulations and were found to be in good agreement for the range of airspeeds (50 to 230 knots) and angles of attack (0 to 4°) tested. Various parametric sweeps were conducted during the supercooled liquid icing portion of the test entry (cloud median volumetric diameter ranged from 15 to 90 µm, total air temperature from -3 to -17 °C, angle of attack from 0 to 4°, and accretion time from 5 to 20 min). These sweeps were performed to measure that parameter’s impact on ice accretion size, location (icing extent), characteristics (such as glaze/rime ice and shedding behavior), and test article surface temperature. Analysis of the test data showed that clouds composed of larger drops, colder air temperatures, smaller angles of attack, and longer spray times were the primary parameters that resulted in accretions with greater ice mass. Test article angle of attack and cloud droplet size influenced the location of ice accretion as these two parameters directly impact collection efficiency. With respect to icing characteristics, total air temperature dictated icing type, and smaller cloud drop size along with warmer air temperatures resulted in greater amounts of ice shedding. Surface temperature increased during ice accretion from the release of latent (fusion) heat, where total air temperature and cloud drop size impacted the amount of surface temperature change. The icing measurements collected during the SIDRM tests will be used to develop and validate 3D computational engine icing tools, such as GlennICE, that predictively assesses the onset and growth of ice. One of the goals of the sponsoring NASA project is to develop simulation models and tools that can assist in the design and certification of engines for flight in icing conditions in a cost effective way.

supercooled liquid icing↗