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At least 73 records · Page 4

Path Length Fluctuations Derived from Site Testing Interferometer Data

To evaluate possible sites for NASA's proposed Ka-band antenna array, the NASA Glenn Research Center has constructed atmospheric phase monitors (APM) which directly measure the tropospheric phase stability. These instruments observe an unmodulated 20.2 GHz beacon signal broadcast from a geostationary satellite (Anik F2) and measure the phase difference between the signals received by the two antennas. Two APM's have been deployed, one at the NASA Deep Space Network (DSN) Tracking Complex in Goldstone, California, and the other at the NASA White Sands Complex, in Las Cruces, New Mexico. Two station-years of atmospheric phase fluctuation data have been collected at Goldstone since operations commenced in May 2007 and 0.5 station-years of data have been collected at White Sands since operations began February 2009. With identical instruments operating simultaneously, we can directly compare the phase stability at the two sites. Phase stability is analyzed statistically in terms of the root-mean-square (rms) of the tropospheric path length fluctuations over 10 min blocks. Correlation between surface wind speed and relative humidity with interferometer phase are discussed. For 2 years, the path length fluctuations at the DSN site in Goldstone, California, have been better than 757 micrometer (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. For the 6 months of data collected at White Sands, New Mexico, the path length fluctuations have been better than 830 micrometers (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. This type of data analysis, as well as many other site quality characteristics (e.g., rain attenuation, infrastructure, etc.), will be used to determine the suitability of both sites for NASA s future communication services at Ka-band using an array of antennas.

Acosta, Roberto J.↗

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗

Towards the Development of a Unified Distributed Date System for L1 Spacecraft

The purpose of this grant, 'Towards the Development of a Unified Distributed Data System for L1 Spacecraft', is to take the initial steps towards the development of a data distribution mechanism for making in-situ measurements more easily accessible to the scientific community. Our obligations as subcontractors to this grant are to add our Faraday Cup plasma data to this initial study and to contribute to the design of a general data distribution system. The year 1 objectives of the overall project as stated in the GSFC proposal are: 1) Both the rsync and Perl based data exchange tools will be fully developed and tested in our mixed, Unix, VMS, Windows and Mac OS X data service environment. Based on the performance comparisons, one will be selected and fully deployed. Continuous data exchange between all L1 solar wind monitors initiated. 2) Data version metadata will be agreed upon, fully documented, and deployed on our data sites. 3) The first version of the data description rules, encoded in a XML Schema, will be finalized. 4) Preliminary set of library routines will be collected, documentation standards and formats agreed on, and desirable routines that have not been implemented identified and assigned. 5) ViSBARD test site implemented to independently validate data mirroring procedures. The specific MIT tasks over the duration of this project are the following: a) implement mirroring service for WIND plasma data b) participate in XML Schema development c) contribute toward routine library.

Lazarus, Alan J.↗

GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission. GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration. GeoThermalCloud.jl includes: - site data - simulation scripts - jupyter notebooks - intermediate results - code outputs - summary figures - readme markdown files GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites: - Brady: geothermal exploration of the Brady geothermal site, Nevada - SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region - GreatBasin: geothermal exploration of the Great Basin region, Nevada Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

15 GEOTHERMAL ENERGY↗

Some examples of the utility of HCMM data in geologic remote sensing

Examples of HCMM (Heat Capacity Mapping Mission) data in geologic remote sensing are presented, and the data set is composed of HCMM and aircraft digital scanner data and ground truth data from four western U.S. test sites. Data are used in the thermal model to test thermal data effectiveness, and changes in temperature with depth and time for dry soils are described by the model. It is found that the HCMM thermal inertia image is useful in the separability of bedrock and alluvium in Death Valley, and aa and pahoehoe flows in the Pisgah basalt flow. In a color composite of HCMM day temperature, night temperature, and day visible images of the Pisgah Crater test site, it is possible to distinguish alluvium, playa, aa and pahoehoe basalt flow, rhyolite intrusives, and other elements. Ground checking of units at a few points will extend capabilities to large areas and assist in creating telegeologic maps.

Kahle, A. B.↗

Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities. The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.

15 GEOTHERMAL ENERGY↗

Artificial Intelligence-Assisted Daytime Video Monitoring for Bird, Insect, and Other Wildlife Interactions with Photovoltaic Solar Energy Facilities

Studying bird, insect, and other wildlife interactions with photovoltaic (PV) solar energy facilities is difficult due to limited multi-season, multi-site data. Researchers can address such data gaps by combining passive monitoring and artificial intelligence (AI). As a part of the development of AI-enabled avian–solar monitoring software, we collected over 19,000 h of daytime videos at five PV sites across three U.S. regions between 2019 and 2024. We applied a moving object detection and tracking (MODT Version 1) AI model we developed earlier to 4373 h of the footage to extract moving objects in video frames, and human reviewers interpreted the model output and identified 68,646 bird, 25,968 insect, and 169 other wildlife instances to generate the training/validation dataset. We analyzed the data by site, region, and season, considering ground cover and landscapes. Songbirds were most common, with raptors as the next most frequent group. Most notably, no bird collisions were confirmed in our observations collected from the videos. Birds most often flew over or near panels, with the highest observations in the Midwest and Northeast (approximately 30 observations per hour on average) and fewer in the desert Southwest. Other behaviors included perching, foraging, and nesting. Bird abundance peaked during breeding and migration seasons. AI-assisted video monitoring proved effective for non-invasively studying flying wildlife at solar facilities to inform ecologically mindful energy development.

avian mortality↗

A global view of lander-to-orbiter communications accessibility for a Mars Global Network Mission

Given the mission objective to deploy a number of small landers to the surface of Mars at various latitude/longitude locations, it is of interest to obtain a global perspective of the communications link geometry between the landers and a data relay orbiter. Specifically, the question to be answered is what is the total time interval over one Martian day (1 sol) that a lander at any given latitude and longitude can communicate data to the orbiter. Results should be obtained for more than one elevation angle constraint (lander antenna design issue), and also for several time points into the mission since the orbiter's periapsis location moves under the influence of Mars' oblateness perturbation. Such information is presented in terms of global contour maps of available communications time per sol summed over all orbiter pases on that day. Global data of this type complements more detailed local site data such as communications range and elevation vs time per pass. Communications time contour maps are included here for sol grids of 180, 232, 318, 361, and 404 corresponding to orbiter periapsis latitudes of 35 S, 90 S, equatorial, 45 N, and 90 N. For each of these days, there is a map for both a 15 deg and 45 deg minimum elevation constraint on the lander-to-orbiter line of sight. The jagged appearance of the contour lines is due to computational resolution in interpolating between a finite number of latitude/longitude grid points. Although the contours should really be smooth, the general information content is represented by the lower resolution maps shown here. An example of the tabulated, finite-grid data points is also given. Communication with all sites is possible at the 15 deg elevation constraint, at times only for several minutes per sol but more generally for a much longer time up to 14 hours per sol. Significantly less time is available with a 45 deg elevation constraint, and at certain times in the mission some localized regions of the planet are inaccessible. Still, one may conclude that the reference orbit selection will support a more than adequate communications link through the mission timeline with landers emplaced at any desired location on Mars.

Friedlander, Alan L.↗

LANDSAT data from agricultural sites: Crop signature analysis

The LANDSAT multispectral scanner (MSS) data were analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, was found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites were essentially two dimensional, and that the data from different sites and different acquisition lay on parallel planes in the four dimensional feature space. These results were exploited to gain new insight into the data and to develop alternate models for classification. In particular, it was found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗

Landsat data from agricultural sites - Crop signature analysis

The Landsat multispectral scanner (MSS) data have been analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, has been found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites are essentially two dimensional, and that the data from different sites and different acquisitions lie on parallel planes in the four-dimensional feature space. These results have been exploited to gain new insight into the data and to develop alternate models for classification. In particular, it has been found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗

Dutch X-band SLAR calibration

In August 1989 the NASA/JPL airborne P/L/C-band DC-8 SAR participated in several remote sensing campaigns in Europe. Amongst other test sites, data were obtained of the Flevopolder test site in the Netherlands on August the 16th. The Dutch X-band SLAR was flown on the same date and imaged parts of the same area as the SAR. To calibrate the two imaging radars a set of 33 calibration devices was deployed. 16 trihedrals were used to calibrate a part of the SLAR data. This short paper outlines the X-band SLAR characteristics, the experimental set-up and the calibration method used to calibrate the SLAR data. Finally some preliminary results are given.

Groot, J. S.↗

Phase Fluctuations at Goldstone Derived from 1-Year Site Testing Interferometer Data

A two-element site test interferometer has been deployed at the NASA Deep Space Network (DSN) tracking complex in Goldstone, California, since May 2007. The interferometer system consists of two offset-fed 1.2 m parabolic reflectors which monitor atmospheric-induced amplitude and phase fluctuations on an unmodulated beacon signal (20.199 GHz) broadcast from a geostationary satellite (Anik F2). The geometry of the satellite and the ground-based infrastructure imposes a 48.5 elevation angle with a separation distance of 256 m along an east-west baseline. The interferometer has been recording phase fluctuation data, to date, for 1 yr with an overall system availability of 95 percent. In this paper, we provide the cumulative distribution functions (CDFs) for 1 year of recorded data, including phase rms, spatial structure function exponent, and surface meteorological measurements: surface wind speed, relative humidity, temperature, barometric pressure, and rain rate. Correlation between surface measurements, phase rms, and amplitude rms at different time scales are discussed. For 1 year, phase fluctuations at the DSN site in Goldstone, are better than 23 for 90 percent of the time (at 48.5 elevation). This data will be used to determine the suitability of the Goldstone site as a location for the Next Generation Deep Space Network.

Nessel, James A.↗

BOREAS TGB-1 Soil CH4 and CO2 Profile Data from NSA Tower Sites

The BOREAS TGB-1 team made numerous measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains methane (CH4) and carbon dioxide (CO2) concentrations in soil profiles from the NSA-OJP, NSA-OBS, NSA-YJP, and NSA-BP sites during the period of 23-May to 20-Sep-1994. The soil gas sampling profiles of CH 4 and CO 2 were completed to quantify controls on CO2 and CH4 fluxes in the boreal forest. The data are provided in tabular ASCII files.

Crill, Patrick↗

Characterizing Vegetation Cover in Global Savannas with an Annual Foliage Clumping Index Derived from the MODIS BRDF Product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.

Hill, Michael J.↗