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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Ten-Ecosystem Study

Remote sensing methodology developed for the Nationwide Forestry Applications Program utilize computer data processing procedures for performing inventories from satellite imagery. The Ten-Ecosystem Study (TES) was developed to test the processing procedures in an intermediate-sized application study. The results of TES indicate that LANDSAT multispectral imagery and associated automatic data processing techniques can be used to distinguish softwood, hardwood, grassland, and water and make inventory of these classes with an accuracy of 70 percent or better. The technical problems encountered during the TES and the solutions and insights to these problems are discussed. The TES experience is useful in planning subsequent inventories utilizing remote sensing technology.

Mazade, A. V.↗
NASA NTRS1981

Appropriateness in using LANDSAT in development energy related data bases

The use of automated classification systems in the field of resource management and resource inventory is discussed. Applications of LANDSAT classification are outlined and include: energy load forecasting based upon land use inventories and change analysis, impact analysis of activities related to energy extraction, capability/suitability mapping in support of generation and substation location and transmission line routing, and assessment of solar energy potential in a highly urbanized setting where land values are high. It is found that the use of LANDSAT data is adequate for general inventories where few data categories are required, where resolution of data to around 150 acres minimum is required, and where no other complete imagery set can be obtained.

Harnden, E.↗
DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗
DOE OSTI2025

Electricity Baseline 2022 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; data inventory↗
DOE OSTI2025

Electricity Baseline 2021 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗
DOE OSTI2025

Electricity Baseline 2020 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗
NASA NTRS2012

Security Data Warehouse Application

The Security Data Warehouse (SDW) is used to aggregate and correlate all JSC IT security data. This includes IT asset inventory such as operating systems and patch levels, users, user logins, remote access dial-in and VPN, and vulnerability tracking and reporting. The correlation of this data allows for an integrated understanding of current security issues and systems by providing this data in a format that associates it to an individual host. The cornerstone of the SDW is its unique host-mapping algorithm that has undergone extensive field tests, and provides a high degree of accuracy. The algorithm comprises two parts. The first part employs fuzzy logic to derive a best-guess host assignment using incomplete sensor data. The second part is logic to identify and correct errors in the database, based on subsequent, more complete data. Host records are automatically split or merged, as appropriate. The process had to be refined and thoroughly tested before the SDW deployment was feasible. Complexity was increased by adding the dimension of time. The SDW correlates all data with its relationship to time. This lends support to forensic investigations, audits, and overall situational awareness. Another important feature of the SDW architecture is that all of the underlying complexities of the data model and host-mapping algorithm are encapsulated in an easy-to-use and understandable Perl language Application Programming Interface (API). This allows the SDW to be quickly augmented with additional sensors using minimal coding and testing. It also supports rapid generation of ad hoc reports and integration with other information systems.

Vernon, Lynn R.↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advanced Tritium Process Analytics and Optimization via a Digital Twin, SRNL-TR-2023-00550

Improving the process knowledge and understanding of TCAP can be achieved by incorporating advanced tools. This project addresses “Advanced analytics for modeling, forecasting, & optimization of the tritium refinement process” and is being applied to the TCAP process. The value of digitization and advanced analytics for TCAP data are tracking of gas mixtures & inventories is presently a manual effort that is rather cumbersome. Incorporating some automation into the data capture will reduce errors and storing data in an accessible database that can be used for tracking as well as process improvements. In addition, the database approach will enable longer term history to be maintained rather than the current practice of deleting data after six months. The digitized and automated data will allow for models to be developed for the process and will enable the forecasting and optimization.

Korinko, Paul S.↗
DOE OSTI2026

1982 The Twin Cities Metropolitan Area Travel Behavior Inventory

The purpose of the 1982 Travel Behavior Inventory was to update socioeconomic and travel data gathered in a 1970 home interview survey. The area in Minnesota studied included the counties of Ramsey, Hennepin, Dakota, Anoka, Washington, Carver, and Scott. The inventory collected demographic and travel data from September 1, 1982, to February 27, 1983. The survey contains data from 2,640 participating households. From participants' travel records, a total of 21,944 trips were reported by 6,059 household members for a 24-hour period.

1Hz data↗
NASA NTRS2022

MSL MMRTG Power Modeling for Lifetime Performance Predictions

The Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) has been providing power to the Mars Science Laboratory (MSL) rover, Curiosity, for surface operations since 2012. The Jet Propulsion Laboratory (JPL) has employed the Life Performance Prediction Model (LPPM) to generate predictions and outputs based upon flight data from the rover and experimental data from the thermoelectric (TE) couples. In order to make these predictions, LPPM requires various spacecraft inputs relevant to RTG performance such as fin root temperature, load voltage, general purpose heat source (GPHS) inventory, etc., combined with couple data such as thermoelectric properties, interface degradation, and sublimation properties. With tens of thousands of hours of flight data to date, LPPM can not only track performance to date, but also make predictions on the power output of the MMRTG through the 17-year end-of-design-life (EODL). These predictions are useful for mission planners and surface operators who rely on the MMRTG for power in order to perform the critical science necessary during the mission. This paper will showcase the power data extracted from the flight electronics, comparing it to the predictions and outputs generated from LPPM.

Pinkowski, Stanley↗
DOE OSTI2021

BAWLD-CH 4 : a comprehensive dataset of methane fluxes from boreal and arctic ecosystems

Methane (CH 4 ) emissions from the boreal and arctic region are globally significant and highly sensitive to climate change. There is currently a wide range in estimates of high-latitude annual CH 4 fluxes, where estimates based on land cover inventories and empirical CH 4 flux data or process models (bottom-up approaches) generally are greater than atmospheric inversions (top-down approaches). A limitation of bottom-up approaches has been the lack of harmonization between inventories of site-level CH 4 flux data and the land cover classes present in high-latitude spatial datasets. Here we present a comprehensive dataset of small-scale, surface CH 4 flux data from 540 terrestrial sites (wetland and non-wetland) and 1247 aquatic sites (lakes and ponds), compiled from 189 studies. The Boreal–Arctic Wetland and Lake Methane Dataset (BAWLD-CH 4 ) was constructed in parallel with a compatible land cover dataset, sharing the same land cover classes to enable refined bottom-up assessments. BAWLD-CH 4 includes information on site-level CH 4 fluxes but also on study design (measurement method, timing, and frequency) and site characteristics (vegetation, climate, hydrology, soil, and sediment types, permafrost conditions, lake size and depth, and our determination of land cover class). The different land cover classes had distinct CH 4 fluxes, resulting from definitions that were either based on or co-varied with key environmental controls. Fluxes of CH 4 from terrestrial ecosystems were primarily influenced by water table position, soil temperature, and vegetation composition, while CH 4 fluxes from aquatic ecosystems were primarily influenced by water temperature, lake size, and lake genesis. Models could explain more of the between-site variability in CH 4 fluxes for terrestrial than aquatic ecosystems, likely due to both less precise assessments of lake CH 4 fluxes and fewer consistently reported lake site characteristics. Analysis of BAWLD-CH 4 identified both land cover classes and regions within the boreal and arctic domain, where future studies should be focused, alongside methodological approaches. Overall, BAWLD-CH 4 provides a comprehensive dataset of CH 4 emissions from high-latitude ecosystems that are useful for identifying research opportunities, for comparison against new field data, and model parameterization or validation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

AgRISTARS: Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing. Enumerator's manual, 1981 ground data survey

General information and administrative instructions are provided for individuals gathering ground truth data to support research and development techniques for estimating crop acreage and production by remote sensing by satellite. Procedures are given for personal safety with regards to organophosphorus insecticides, for conducting interviews for periodic observations, for coding the crops identified and their growth stages, and for selecting sites for placing rain gages. Forms are included for those citizens agreeing to monitor the gages and record the rainfall. Segment selection is also considered.

Source record↗
DOE OSTI2025

Myco-CORPSE simulations assessing mycorrhizal carbon allocation across U.S. forests and global change scenarios

Plants allocate a substantial portion of their fixed carbon belowground to mycorrhizal fungi in exchange for nutrients and other benefits. However, most current ecosystem models omit mycorrhizal processes, limiting our ability to predict plant–soil carbon dynamics under environmental change. To address this gap, we used a mycorrhiza-explicit soil biogeochemical model, Myco-CORPSE (Mycorrhizal Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment), to simulate tree carbon allocation to arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi in temperate forests.The dataset includes outputs from two sets of model simulations:1. Perturbation experiments: Simulations across gradients of ECM dominance (0–100%), nitrogen deposition, soil temperature, and net primary productivity (NPP) to test how these factors affect mycorrhizal C allocation and nutrient cycling.2. FIA-based simulations: Model applications to over 1,800 U.S. forest sites using site-specific data from the U.S. Forest Inventory and Analysis (FIA) program, including vegetation composition, mycorrhizal type, climate, litter traits, soil properties, and N deposition.Model outputs include simulated mycorrhizal carbon allocation and related biogeochemical variables, such as soil and microbial carbon and nitrogen stocks. Data are provided in CSV format and organized by experiment type (in separate ZIP files). Python scripts for running simulations, plotting, and spatial mapping are also included and organized similarly. No proprietary software is required. These outputs support a peer-reviewed study and were used to generate figures and tables in the associated publication.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

The Role of Remote Sensing in Assessing Forest Biomass in Appalachian South Carolina

Information is presented on the use of color infrared aerial photographs and ground sampling methods to quantify standing forest biomass in Appalachian South Carolina. Local tree biomass equations are given and subsequent evaluation of stand density and size classes using remote sensing methods is presented. Methods of terrain analysis, environmental hazard rating, and subsequent determination of accessibility of forest biomass are discussed. Computer-based statistical analyses are used to expand individual cover-type specific ground sample data to area-wide cover type inventory figures based on aerial photographic interpretation and area measurement. Forest biomass data are presented for the study area in terms of discriminant size classes, merchantability limits, accessibility (as related to terrain and yield/harvest constraints), and potential environmental impact of harvest.

Shain, W.↗