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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Potential applications of digital, visible, and infrared data from geostationary environmental satellites

An hourly, digital data base from the Visible/Infrared Spin-Scan Radiometer (VISSR) instrument on the GOES-1 and SMS-2 geostationary satellites is described. Several examples of developmental applications of these quantitative digital data are presented. These include a review of recent attempts to develop products that are of use to meteorologists who provide services to aviation, agriculture, forestry, hydrology, oceanography, and climatology. The sample products include high resolution thermal gradients of land and ocean surfaces, thermal change analyses, fruit frost/freeze application, cloud-top altitude analysis, analysis of hurricane characteristics, and analyses of solar insolation.

Miller, D. B.↗
DOE OSTI2022

Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Spatial Analysis and Modeling Systems (SAMS)

The objective is to develop a uniform environmental data gathering and distribution system to support (1) emergency management for environmental disasters, and (2) the calibration and validation of remotely sensed data. Initial activities will be to select a data test site and to demonstrate multi-discipline applications using simulated or satellite data in a non real-time mode. Rainfall and flooding are chosen as the testbeds for the SAMS concept because of the abundance of data and the availability of models. The capability to display and process GOES data and analyze GOES generated rain-rate maps will be integrated into SAMS.

Vermillion, Charles↗
NASA NTRS1997

On-Orbit and Ground Performance of the PGBA Plant Growth Facility

PGBA, a plant growth facility developed for commercial space biotechnology research, successfully grew a total of 30 plants (6 species) for 10 days on board the Space Shuttle Endeavour (STS-77) and is scheduled for reflight on board MSL-1 (STS-83) for a 16 day flight. The PGBA life support systems provide atmospheric, thermal, and humidity control as well as lighting and nutrient supply in a 23.6 liter chamber. Atmosphere treatment includes ethylene and other hydrocarbon removal, CO2 replenishment, and O2 control. The normally closed system uses controlled CO2 replenishment from the crew cabin as required by the plants. Temperature is controlled (1 C) at user-specified setpoints between 20-32 C, using water-filled coolant loops, solid state Peltier thermoelectric devices, and liquid heat exchangers. The thermoelectric cooling systems were optimized for low power consumption and high cooling efficiencies. Relative humidity is maintained between 60-100% using a cooled porous metal plate to remove water vapor from the air stream without cooling the bulk air below the dew point. The lighting system utilizes three compact fluorescent bi-axial lights with variable lighting control and light intensity (PAR) between 220 and 330 micromol/sq m/s at a distance of 20 cm in spaceflight configuration (on orbit power limited to 230 Watt for entire payload). A ground, up to 550 micromol/sq m/s light intensity can be achieved with 330 Watt payload power consumption. Plant water and nutrient support is sustained via the 'Nutrient Pack' system including the passive or active 'Water Replenishable Nutrient Pack.' The root matrix material (soil or Agar) and nutrient formulation of each pack is prepared according to plant species and experimental requirements. These systems were designed by NASA Ames personnel. Data acquisition and control systems provide 32 channels of environmental data as well as digitized or analog video signals for downlink.

Hoehn, A.↗
NASA NTRS2008

Effective Interpolation of Incomplete Satellite-Derived Leaf-Area Index Time Series for the Continental United States

Many earth science modeling applications employ continuous input data fields derived from satellite data. Environmental factors, sensor limitations and algorithmic constraints lead to data products of inherently variable quality. This necessitates interpolation of one form or another in order to produce high quality input fields free of missing data. The present research tests several interpolation techniques as applied to satellite-derived leaf area index, an important quantity in many global climate and ecological models. The study evaluates and applies a variety of interpolation techniques for the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf-Area Index Product over the time period 2001-2006 for a region containing the conterminous United States. Results indicate that the accuracy of an individual interpolation technique depends upon the underlying land cover. Spatial interpolation provides better results in forested areas, while temporal interpolation performs more effectively over non-forest cover types. Combination of spatial and temporal approaches offers superior interpolative capabilities to any single method, and in fact, generation of continuous data fields requires a hybrid approach such as this.

Jasinski, Michael F.↗
NASA NTRS1993

Materials, processes, and environmental engineering network

The Materials, Processes, and Environmental Engineering Network (MPEEN) was developed as a central holding facility for materials testing information generated by the Materials and Processes Laboratory. It contains information from other NASA centers and outside agencies, and also includes the NASA Environmental Information System (NEIS) and Failure Analysis Information System (FAIS) data. Environmental replacement materials information is a newly developed focus of MPEEN. This database is the NASA Environmental Information System, NEIS, which is accessible through MPEEN. Environmental concerns are addressed regarding materials identified by the NASA Operational Environment Team, NOET, to be hazardous to the environment. An environmental replacement technology database is contained within NEIS. Environmental concerns about materials are identified by NOET, and control or replacement strategies are formed. This database also contains the usage and performance characteristics of these hazardous materials. In addition to addressing environmental concerns, MPEEN contains one of the largest materials databases in the world. Over 600 users access this network on a daily basis. There is information available on failure analysis, metals and nonmetals testing, materials properties, standard and commercial parts, foreign alloy cross-reference, Long Duration Exposure Facility (LDEF) data, and Materials and Processes Selection List data.

White, Margo M.↗
NASA NTRS2008

Exploiting Satellite Remote-Sensing Data in Fine Particulate Matter Characterization for Serving the Environmental Public Health Tracking Network (EPHTN): The HELIX-Atlanta Experience and NPOESS Implications

As part of the U.S. National Environmental Public Health Tracking Network (EPHTN), the National Center for Environmental Health (NCEH) at the U.S. Centers for Disease Control and Prevention (CDC) led a project in collaboration with the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) called Health and Environment Linked for Information Exchange (HELIX-Atlanta). Under HELIX-Atlanta, pilot projects were conducted to develop methods to better characterize exposure; link health and environmental datasets; and analyze spatial/temporal relationships. This paper describes and demonstrates different techniques for surfacing daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM(sub 2.5) for the purpose of integrating respiratory health and environmental data for the CDC's pilot study of HELIX-Atlanta. It describes a methodology for estimating ground-level continuous PM(sub 2.5) concentrations using spatial surfacing techniques and leveraging NASA Moderate Resolution Imaging Spectrometer (MODIS) data to complement the U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM(sub 2.5) from the EPA database for the year 2003 as well as PM(sub 2.5) estimates derived from NASA's MODIS data. Hazard data have been processed to derive the surrogate exposure PM(sub 2.5) estimates. The paper has shown that merging MODIS remote sensing data with surface observations of PM(sub 2.5), may provide a more complete daily representation of PM(sub 2.5), than either data set alone would allow, and can reduce the errors in the PM(sub 2.5) estimated surfaces. Future work in this area should focus on combining MODIS column measurements with profile information provided by satellites like the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Visible Infrared Imager/Radiometer Suite (VIIRS) and the Aerosol Polarimeter Sensor (APS) NPOESS sensors will provide first-order information on aerosol particle size and are anticipated to provide information on aerosol products at higher resolution and accuracy than MODIS. Use of the NPOESS remote sensing data should result in more robust remotely sensed data that can be coupled with the methods discussed in this paper to generate surface concentrations of PM(2.5) for linkage with health data in Environmental Public Health Tracking.

Al-Hamdan, Mohammad Z.↗
NASA NTRS1979

Skylab medical data evaluation program (SMEDEP)

A day-by-day summary of selected data collected during the experiment is presented. The clinical and environmental data are presented in a mission-day format along with a tabulation of biomedical measurements whose values exceed three standard deviations from the preflight measurements.

Hoffler, G. W.↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment Outside the International Space Station

Crop seeds may prove to be a vital component of future long-term spaceflight missions, and the impacts of extended exposure on seed degradation is not well understood. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also served as a test of biological materials aboard the Materials International Space Station Experiment (MISSE) platform, and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seed and other biological samples to be preserved upon exposure to the space environment outside the ISS. MISSE-Seed was launched to the ISS on NG-15 in July of 2021 and returned to Earth aboard SpX-24 after approximately 8 months of exposure to the space environment. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. Hardware and experimental design, environmental profiles, and post-flight germination and growth results will be presented.

Alexander D Meyers↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

NASA's Earth Observing System Data and Information System - EOSDIS

This slide presentation reviews the work of NASA's Earth Observing System Data and Information System (EOSDIS), a petabyte-scale archive of environmental data that supports global climate change research. The Earth Science Data Systems provide end-to-end capabilities to deliver data and information products to users in support of understanding the Earth system. The presentation contains photographs from space of recent events, (i.e., the effects of the tsunami in Japan, and the wildfires in Australia.) It also includes details of the Data Centers that provide the data to EOSDIS and Science Investigator-led Processing Systems. Information about the Land, Atmosphere Near-real-time Capability for EOS (LANCE) and some of the uses that the system has made possible are reviewed. Also included is information about how to access the data, and evolutionary plans for the future of the system.

Ramapriyan, Hampapuram K.↗
NASA NTRS1975

Strain gaged struts and data reduction techniques to maximize quality data from spacecraft flight measurements

The flight instrumentation for the Viking project was established to obtain environmental data, and maximize the data obtainable from the limited number of telemetry channels. To achieve these objectives, a set of six struts were strain gaged and calibrated as load cells to obtain a complete force-time history across an interface. The force-time history can be used with an analytical model to calculate the response of any part of the structure above the instrumented interface. Special matched filters were required to reduce the flight data and derive the phase correlated loads. Flight data results from the Viking Dynamic Simulator flown in February 1974 are described in addition to ground test results used to verify the data reduction process.

Day, F. D., III↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment outside the International Space Station

If crop seeds are to be stored on space vehicles for long-duration missions, the effects of the space environment on the seeds must be understood for developing mitigation strategies to prevent seed degradation. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also tested the Materials International Space Station Experiment (MISSE) platform for exposing biological samples to the space environment and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seeds, or other biological samples, to be preserved upon exposure to the space environment outside the ISS. The experiment was launched to the ISS on NG-15and returned to Earth aboard SpX-24. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). The exposure lasted eight months outside the ISS in the MISSE hardware at the Zenith position. Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record the environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. In this presentation, the hardware and experimental design, the environmental profiles, and the results of post-flight germination tests will be presented.

Jeffrey T Richards↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment Outside the International Space Station

If crop seeds are to be stored on space vehicles for long-duration missions, the effects of the space environment on the seeds must be understood for developing mitigation strategies to prevent seed degradation. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also tested the Materials International Space Station Experiment (MISSE) platform for exposing biological samples to the space environment and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seeds, or other biological samples, to be preserved upon exposure to the space environment outside the ISS. The experiment was launched to the ISS on NG-15and returned to Earth aboard SpX-24. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). The exposure lasted eight months outside the ISS in the MISSE hardware at the Zenith position. Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record the environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. In this presentation, the hardware and experimental design, the environmental profiles, and the results of post-flight germination tests will be presented.

Jeffrey T Richards↗