Characterization for spatial derivatives of SRTM data to investigate controls on erosion
Explore the source record for details and available documents.
Shuttle Radar Topography Mission slopes drainage r↗
SEARCH · Search NASA
Results for “Spatial data”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Serving Fisheries and Ocean Metadata to Communities Around the World
NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://qcrnd.nasa.qov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage. The directory also offers data holders a means to post and search their data through customized portals, i.e. online customized subset metadata directories. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1994. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as a coordinating node of the International Directory Network (IDN), has been active at the international, regional and national level for many years through its involvement with the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and partnerships (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP), sharing experience, knowledge related to metadata and/or data management and interoperability.
Meaux, Melanie↗
Globally Gridded Satellite (GridSat) Observations for Climate Studies
Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.
Knapp, Kenneth R.↗
PYTAF: A Python Tool for Spatially Resampling Earth Observation Data
Explore the source record for details and available documents.
Guangyu Zhao↗
Use of spatial information in classification of remotely sensed data
The use is discussed of spatial information for improving classification accuracy of remotely sensed data. In particular a simple example (the unanimous four nearest neighbor rule) is discussed and its results are presented. This algorithm results in improved classifications accuracy (one to five percentage points) and costs little in execution time. Other schemes for improving classification accuracy are also discussed.
Vanroony, D. L.↗
Validation of the Corcos Model for the Space Launch System using Unsteady Pressure Sensitive Paint
During atmospheric ascent launch vehicles (LVs) experience large dynamic loads at transonic conditions where aerodynamic buffet is most critical. To estimate buffet loads, coupled loads analyses typically utilize suitable forcing functions, called buffet forcing functions (BFFs). One of the key buffet environment contributors is the turbulent boundary layer (TBL) on the LV outer skin. The TBL-induced fluctuating pressures can be estimated using the widely-accepted Corcos model. In the context of transonic buffet, the performance of this model is not well established, partly because of lack of data. To fill this gap, NASA recently acquired extremely high-spatial-density data for the Space Launch System (SLS) vehicle, using the unsteady pressure sensitive paint (uPSP) optical measurement technique. A methodology is developed for validation of the Corcos model using these unique data, with a focus on the LV-design application. The model hypotheses are verified and the model parameters are empirically tuned. For selected panels on the vehicle, BFF coherence factors are derived based on the Corcos model and the associated panel BFFs are compared to uPSP data. It is shown that the modeled BFFs are in agreement with direct integration of uPSP data, except for regions where pressure fluctuations are spatially nonuniform. In those regions, the Corcos-based BFFs exhibit inherent limitations of BFF estimation methods that rely on discrete pressure measurements.
buffet↗
Validation of the Corcos Model for the Space Launch System using Unsteady Pressure Sensitive Paint
During atmospheric ascent launch vehicles (LVs) experience large dynamic loads at transonic conditions where aerodynamic buffet is most critical. To estimate buffet loads, coupled loads analyses typically utilize suitable forcing functions, called buffet forcing functions (BFFs). One of the key buffet environment contributors is the turbulent boundary layer (TBL) on the LV outer skin. The TBL-induced fluctuating pressures can be estimated using the widely-accepted Corcos model. In the context of transonic buffet, the performance of this model is not well established, partly because of lack of data. To fill this gap, NASA recently acquired extremely high-spatial-density data for the Space Launch System (SLS) vehicle, using the unsteady pressure sensitive paint (uPSP) optical measurement technique. A methodology is developed for validation of the Corcos model using these unique data, with a focus on the LV-design application. The model hypotheses are verified and the model parameters are empirically tuned. For selected panels on the vehicle, BFF coherence factors are derived based on the Corcos model and the associated panel BFFs are compared to uPSP data. It is shown that the modeled BFFs are in agreement with direct integration of uPSP data, except for regions where pressure fluctuations are spatially nonuniform. In those regions, the Corcos-based BFFs exhibit inherent limitations of BFF estimation methods that rely on discrete pressure measurements.
buffet↗
Space Launch System Unsteady Forces Developed from Unsteady-Pressure-Sensitive-Paint–Based Corcos Model Parameters
During atmospheric ascent, launch vehicles (LVs) experience large dynamic loads at transonic conditions where aerodynamic buffet is most critical. To estimate buffet loads, coupled loads analyses typically utilize suitable forcing functions, called buffet forcing functions (BFFs). One of the key buffet environment contributors is the turbulent boundary layer (TBL) on the LV outer skin. The TBL-induced fluctuating pressures can be estimated using the widely-accepted Corcos model. In the context of transonic buffet, the performance of this model is not well established, partly because of lack of data. To fill this gap, NASA recently acquired extremely high-spatial-density data for the Space Launch System (SLS) vehicle, using the unsteady pressure sensitive paint (uPSP) optical measurement technique. A methodology is developed for validation of the Corcos model using these unique data, with a focus on the LV-design application. The model hypotheses are verified and the model parameters are empirically tuned. For selected panels on the vehicle, BFF coherence factors are derived based on the Corcos model and the associated panel BFFs are compared to uPSP data. It is shown that the modeled BFFs are in agreement with direct integration of uPSP data, except for regions where pressure fluctuations are spatially nonuniform. In those regions, the Corcos-based BFFs exhibit inherent limitations of BFF estimation methods that rely on discrete pressure measurements.
buffet↗
Spatial frequency analysis of multispectral data.
This paper presents the definitions of texture dependent features which can be obtained in terms of the spatial frequencies of small sections of remotely sensed multispectral data. The features are made independent of the direction of view by defining them as symmetric functions of the spatial frequencies sensed with various viewing directions. Several textural features are defined and experimental results indicating existence of signatures in these features are presented. Preliminary experiments have been performed on the classification of 60 samples, 10 from each of the following 6 categories - grass, trees, water, staked tomatoes, treated ground tomatoes, and untreated ground tomatoes. Classifications of the training samples using only one feature at a time indicate that several of the features yield classification efficiencies higher than 65%. The efficiency increases considerably when combinations of these features are used.
Ramapriyan, H. K.↗
Automation of measurement of spatial damping parameters
A traveling wave unit was built to process measurement data on spatial damping parameters and to simplify statistical treatment of the results. Mean values of the loss coefficient, lengths of flexual waves, and wave propagation velocities for a given set of discrete functions were determined. Results are given in a table.
Akolzin, A. N.↗
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.↗
Siting Lab [SWR-24-95]
Siting Lab contains a collection of user-friendly tutorials and guides for working with the Siting Lab (https://data.openei.org/siting_lab) data within the context of the reV model. The python code examples demonstrate the creation and transformation of Siting Lab data into reV compliant format as well as working with the reV model inputs and outputs. Specifically, Siting Lab provides a collection of Jupyter Notebooks that serve as guides for working with data from NREL's spatial analysis portfolio. These notebooks teach users how to create and interact with reV data in order to facilitate external use of the model. The guides in this repository reference NREL's Supply Curve data as well as Siting Lab spatial data available on OEDI.
Lopez, Anthony↗
Potential contribution of MDI to understanding large-scale structures in the corona
In relation to the understanding of the structure and dynamics of the solar atmosphere which requires realistic coronal magnetic field models, a horizontal current-current sheet (HCCS) coronal model was developed. The model includes large-scale, low altitude, horizontal currents and the effect of thin current sheets in the streamer belt of the field above cusp-type neutral points. The effect of the streamer current sheet on the field below the cusp points is accounted for. In order to suggest what can be anticipated from Michelson Doppler imager (MDI) photospheric magnetic field data calculations of the coronal magnetic field using low spatial resolution data, are presented, and results from the calculations of solar eclipses are compared with solar eclipse images.
Zhao, Xuepu↗
Derivation of Acoustic Criteria for the Cassini Spacecraft and Comparison with Flight Data
Acoustic measurements from eight pre-Cassini Titan IV flights, and an acoustic test of a Cassini simulator and Titan payload fairing (PLF), were used to derive acoustic flight and test criteria for the Cassini spacecraft. The flight and ground test data were used or modified to account for the following factors: (a) noise-spike contamination of flight data, (b) spatial and flight-to-flight variations of flight data, (c) application of a thicker banier-blanket to the PLF for the Cassini mission, (d) effects of locating two Cassini assemblies, the Huygens Probe, and the High Gain Antenna (HGA), near the PLF, and (e) higher thrust of upgraded Titan solid rocket motors (SRMS) for the Cassini mission. An overall sound pressure level (OA SPL) of 145 dB was verified for the protoflight acoustic test criteria for the Cassini spacecraft. Cassini flight liftoff data showed an average OA SPL of 133 dB.
Himelblau, Harry↗
Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea
Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.
TROPOMI↗
Spatial and Temporal Scaling of Thermal Infrared Remote Sensing Data
Although remote sensing has a central role to play in the acquisition of synoptic data obtained at multiple spatial and temporal scales to facilitate our understanding of local and regional processes as they influence the global climate, the use of thermal infrared (TIR) remote sensing data in this capacity has received only minimal attention. This results from some fundamental challenges that are associated with employing TIR data collected at different space and time scales, either with the same or different sensing systems, and also from other problems that arise in applying a multiple scaled approach to the measurement of surface temperatures. In this paper, we describe some of the more important problems associated with using TIR remote sensing data obtained at different spatial and temporal scales, examine why these problems appear as impediments to using multiple scaled TIR data, and provide some suggestions for future research activities that may address these problems. We elucidate the fundamental concept of scale as it relates to remote sensing and explore how space and time relationships affect TIR data from a problem-dependency perspective. We also describe how linearity and non-linearity observation versus parameter relationships affect the quantitative analysis of TIR data. Some insight is given on how the atmosphere between target and sensor influences the accurate measurement of surface temperatures and how these effects will be compounded in analyzing multiple scaled TIR data. Last, we describe some of the challenges in modeling TIR data obtained at different space and time scales and discuss how multiple scaled TIR data can be used to provide new and important information for measuring and modeling land-atmosphere energy balance processes.
Quattrochi, Dale A.↗
Distributed Optoelectronic Proximity Sensor
Optoelectronic proximity sensor comprises seven identical units functioning together to provide partially redundant data on spatial relationship between sensor and nearby diffusely reflecting surface. Provides data on distance, relative orientation, and curvature of surface. Sensor called "Hexeye" because its units exhibit hexagonal symmetry when viewed along optical axes and mounted together in hexagonal close packing. Because all six arrays of photodetectors in unit measure essentially identical distributions of light in operating mode, each provides redundant information averaged to increase accuracy or used to verify operation of others. Malfunctioning photodector identified, and output ignored or replaced by interpolated value. Developed for use in such robotic applications as noncontact probing or scanning of surfaces, localization and recognition of objects, and guiding "smart" robot hands in grasping objects.
Lee, Sukhan↗
Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks
Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.
Software↗