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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

biomass↗
NASA NTRS2008

Mid-Infrared OPO for High Resolution Measurements of Trace Gases in the Mars Atmosphere

The Martian atmosphere is composed primarily (>95%) of CO2 and N2 gas, with CO, O2, CH4, and inert gases such as argon comprising most of the remainder. It is surprisingly dynamic with various processes driving changes in the distribution of CO2, dust, haze, clouds and water vapor on global scales in the meteorology of Mars atmosphere [I]. The trace gases and isotopic ratios in the atmosphere offer important but subtle clues as to the origins of the planet's atmosphere, hydrology, geology, and potential for biology. In the search for life on Mars, an important process is the ability of bacteria to metabolize inorganic substrates (H2, CO2 and rock) to derive energy and produce methane as a by-product of anaerobic metabolism. Trace gases have been measured in the Mars atmosphere from Earth, Mars orbit, and from the Mars surface. The concentration of water vapor and various carbon-based trace gases are observed in variable concentrations. Within the past decade multiple groups have reported detection of CH4, with concentrations in the 10's of ppb, using spectroscopic observations from Earth [2]. Passive spectrometers in the mid-infrared (MIR) are restricted to the sunlit side of the planet, generally in the mid latitudes, and have limited spectral and spatial resolution. To accurately map the global distribution and to locate areas of possibly higher concentrations of these gases such as plumes or vents requires an instrument with high sensitivity and fine spatial resolution that also has global coverage and can measure during both day and night. Our development goal is a new MIR lidar capable of measuring, on global scales, with sensitivity, resolution and precision needed to characterize the trace gases and isotopic ratios of the Martian atmosphere. An optical parametric oscillator operating in the MIR is well suited for this instrument. The sufficient wavelength tuning range of the OPO can extend the measurements to other organic molecules, CO2, atmospheric water vapor, clouds, temperature, dust, and aerosols, as well as possibly polar-cap properties. Our OPO-approach may allow a new capability for active remote sensing of the outer planets and moons, where the weaker sunlight further limit passive instruments. Here we report on the OPO development effort for this lidar instrument.

Yu, Anthony W.↗
NASA NTRS1982

Potential Role of Land Use and Land Cover Information in Powerplant Siting: Example of Three Mile Island

Selecting a site for a nuclear powerplant can be helped by digitizing land use and land cover data, population data, and other pertinent data sets, and then placing them in a geographic information system. Such a system begins with a set of standardized maps for location reference and then provides for retrieval and analysis of spatial data keyed to the maps. This makes possible thematic mapping by computer, or interactive visual display for decisionmaking. It also permits correlating land use area measurements with census and other data (such as fallout dosages), and the updating of all data sets. The system is thus a tool for dealing with resource management problems and for analyzing the interaction between people and their environment. An explanation of a computer-plotted map of land use and cover for Three Mile Island and vicinity is given.

Wray, J. R.↗
NASA NTRSDate not supplied

Mapping Process Model Results to Fracture Model Initial Conditions for Adhesive Bonding

A systematic approach is proposed for mapping adhesive bonding process outcomes to initial conditions for progressive damage analysis (PDA). Herein, two mapping procedures are developed: 1) direct mapping for residual stresses, strains, and deformations obtained from a process model; and 2) functional mapping for quantities such as bondline thickness, degree of cure, and porosity that are assumed to have a functional relationship with fracture toughness but are not discretely modeled in the PDA. The spatial distribution of functionally mapped quantities may be determined by process modeling or inspection. The functional maps between the quantity of interest and fracture toughness may be obtained from independent lower-scale numerical simulations or empirical test campaigns. The proposed mapping procedure links the adhesive bonding process to the structural performance. Therefore, by accounting for the effects of bondline nonuniformities in structural analysis, analytical predictive accuracy can be improved, and off-nominal conditions can be evaluated. The mapping procedures are demonstrated and verified with example problems.

Andrew C Bergan↗
NASA NTRSDate not supplied

Mapping Process Model Results to Fracture Model Initial Conditions for Adhesive Bonding

A systematic approach is proposed for mapping adhesive bonding process outcomes to initial conditions for progressive damage analysis (PDA). Herein, two mapping procedures are developed: 1) direct mapping for residual stresses, strains, and deformations obtained from a process model; and 2) functional mapping for quantities such as bondline thickness, degree of cure, and porosity that are assumed to have a functional relationship with fracture toughness but are not discretely modeled in the PDA. The spatial distribution of functionally mapped quantities may be determined by process modeling or inspection. The functional maps between the quantity of interest and fracture toughness may be obtained from independent lower-scale numerical simulations or empirical test campaigns. The proposed mapping procedure links the adhesive bonding process to the structural performance. Therefore, by accounting for the effects of bondline nonuniformities in structural analysis, analytical predictive accuracy can be improved, and off-nominal conditions can be evaluated. The mapping procedures are demonstrated and verified with example problems.

Andrew Bergan↗
NASA NTRS1998

HST Spatially Resolved Spectra of the Accretion Disc and Gas Stream of the Nova-Like Variable UX Ursae Majoris

Time-resolved eclipse spectroscopy of the nova-like variable UX UMa obtained with the Hubble Space Telescope/Faint Object Spectrograph (HST/FOS) on 1994 August and November is analysed with eclipse mapping techniques to produce spatially resolved spectra of its accretion disk and gas stream as a function of distance from the disk centre. The inner accretion disk is characterized by a blue continuum filled with absorption bands and lines, which cross over to emission with increasing disk radius, similar to that reported at optical wavelengths. The comparison of spatially resolved spectra at different azimuths reveals a significant asymmetry in the disk emission at ultraviolet (UV) wavelengths, with the disk side closest to the secondary star showing pronounced absorption by an 'iron curtain' and a Balmer jump in absorption. These results suggest the existence of an absorbing ring of cold gas whose density and/or vertical scale increase with disk radius. The spectrum of the infalling gas stream is noticeably different from the disc spectrum at the same radius suggesting that gas overflows through the impact point at the disk rim and continues along the stream trajectory, producing distinct emission down to 0.1 R(sub LI). The spectrum of the uneclipsed light shows prominent emission lines of Lyalpha, N v lambda1241, SiIV Lambda 1400, C IV Lambda 1550, HeII Lambda 1640, and MgII Lambda 2800, and a UV continuum rising towards longer wavelengths. The Balmer jump appears clearly in emission indicating that the uneclipsed light has an important contribution from optically thin gas. The lines and optically thin continuum emission are most probably emitted in a vertically extended disk chromosphere + wind. The radial temperature profiles of the continuum maps are well described by a steady-state disc model in the inner and intermediate disk regions (R greater than or equal to 0.3R(sub LI) ). There is evidence of an increase in the mass accretion rate from August to November (from V = 10 (exp -8.3 +/-0.1) to 10(exp -8.1 +/- 0.1 solar mass yr(exp -1)), in accordance with the observed increase in brightness. Since the UX UMA disc seems to be in a high mass accretion, high-viscosity regime in both epochs, this result suggests that the mass transfer rate of UX UMA varies substantially (approximately equal to 50 per cent) on time-scales of a few months. It is suggested that the reason for the discrepancies between the prediction of the standard disk model and observations is not an inadequate treatment of radiative transfer in the disc atmosphere, but rather the presence of addition important sources of light in the system besides the accretion disk (e.g., optically thin contiuum emission from the disk wind and possible absorption by circumstellar cool gas).

Baptista, Raymundo↗
DOE OSTI2023

Spatially Distributed Ramp Reversal Memory in VO 2

Ramp‐reversal memory has recently been discovered in several insulator‐to‐metal transition materials where a non‐volatile resistance change can be set by repeatedly driving the material partway through the transition. This study uses optical microscopy to track the location and internal structure of accumulated memory as a thin film of VO 2 is temperature cycled through multiple training subloops. These measurements reveal that the gain of insulator phase fraction between consecutive subloops occurs primarily through front propagation at the insulator‐metal boundaries. By analyzing transition temperature maps, it is found, surprisingly, that the memory is also stored deep inside both insulating and metallic clusters throughout the entire sample, making the metal‐insulator coexistence landscape more rugged. This non‐volatile memory is reset after heating the sample to higher temperatures, as expected. Diffusion of point defects is proposed to account for the observed memory writing and subsequent erasing over the entire sample surface. By spatially mapping the location and character of non‐volatile memory encoding in VO 2 , this study results enable the targeting of specific local regions in the film where the full insulator‐to‐metal resistivity change can be harnessed in order to maximize the working range of memory elements for conventional and neuromorphic computing applications.

36 MATERIALS SCIENCE↗
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.↗
DOE OSTI2023

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗
NASA NTRS2015

Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Landsat Satellites↗
DOE OSTI2022

Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico

Hurricane Maria left unprecedented impacts on Puerto Rican communities, leaving some without infrastructure services and unable to communicate with family for several months. Here, to understand the forms of community-level resilience that emerged while hard infrastructure systems took time recover, this article (1) abductively explores resilience as an emergent phenomenon of complex adaptive systems; (2) identifies subsequent forms of social capital, local adaptive capacities, and manifestations of quantifiable variables, such as infrastructure performance, in community experiences; and (3) demonstrates a framework to integrate disparate methodologies for resilience assessments via a multiplicity of mappings of space and place. We combine ethnographic and geospatial methods into an interactive GeoApp for analysis using participant-coded narratives and a series of geospatial indicators as a thick map. Thick mapping facilitates quantitative and qualitative data analysis at several scales, while enabling qualitative query of collected narratives. Results highlight local innovation, community bonding and bridging, and nuances in the role of public institutions as emergent elements of resilience. The thick map shows how top-down assessments can be augmented by thick data and how multiple framings can be anchored in the same system or place. These findings are important to inform and integrate community-oriented and technocentric solutions toward resilience-enhancing measures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Surface contour radar observations of the directional wave spectrum during Fasinex

The surface control radar (SCR), a 36-GHz computer-controlled airborne radar which generates a false-color coded elevation map of the sea surface below the aircraft in real time, is described. The SCR turned out to be ideal for documenting the wave spectra during Fasinex (the Frontal Air-Sea Interaction Experiment) due to its high spatial resolution and rapid mapping capability over extensive areas. Synoptic weather maps for February 15-18, 1986 are presented.

Walsh, E. J.↗
DOE OSTI2023

Mapping and imaging ionizing radiation in two and three dimensions

A two-dimensional imaging system and a two-dimensional or three-dimensional optical tomographic mapping system, each employing gas scintillation induced by ionizing radiation, i.e., radioluminescence, and corresponding methods, are disclosed. The systems may employ one or more cameras and corresponding UV filters (potentially solar blind filters) for imaging a radioluminescent scene. For two-dimensional or three-dimensional mapping, the resultant UV images are spatially registered with one another and then reconstructed to form a three-dimensional tomographic map of the ionizing radiation. The two-dimensional map is a plane of the three-dimensional map. The UV images may be spatially registered by using a reference source, optionally, a calibrated reference source allowing dosimetry calculations for the ionizing radiation. Molecular nitrogen is the primary candidate for the radioluminescent gas, though a controlled ambient in a chamber of nitric oxide, argon, krypton, or xenon may be employed. The reconstruction process employs an algebraic reconstruction technique or an Abel inversion.

Bentz, Brian Z.↗