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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Information Extraction on an Earth Science Knowledge Graphs with Semantic Parsing

Knowledge graphs are an important tool, both for representing knowledge and for retrieving information. Fundamentally, they are semantic networks that represent entities and relationships in the form of nodes and edges. A large corpus of natural language text can bebroken down into discrete entities and relationships to form a useful knowledge graph. Existing research breaks down text into a subject, object, and verb relationship triple. Although this is a useful first step, it loses much of the original contextual information encoded within the text. Our process uses a novel 7-tuple approach, in which elements of sentences are programmatically parsed into seven categories: initiator, impacted, receiver, beneficiary, result, and context. In this presentation, we show a knowledge graph built using this 7-tupleprocessing of an Earth science corpus. We explain the techniques used to create the graph and analyze its information retrieval capability while assessing the accuracy and limitations of the results.

Carson Davis↗
NASA NTRS2007

NASA ER-2: Flying Laboratory for Earth Science Studies

This viewgraph presentation gives an overview of the NASA ER-2 aircraft. The contents include: 1) ER-2 Specifications; 2) ER-2 Basic Configuration; 3) ER-2 Payload Areas: Nose Area; 4) ER-2 Payload Areas: SuperPod Fore and Aftbody; 5) ER-2 Payload Areas: SuperPod Midbody; 6) ER-2 Payload Areas: Q-Bay; 7) ER-2 Payload Areas: Q-Bay Hatch Designs; 8) ER-2 Payload Areas: External Pods; 9) ER-2 Electrical/Control Interface; 10) ER-2 Typical Flight Profile; 11) Tropical Composition, Cloud and Climate Coupling TC-4; 12) TC-4 Timeline; 13) TC4 Area of Interest; 14) ER-2 TC4 Payload; 15) A/C ready for fuel; 16) ER-2 Pilot being suited; 17) ER-2 Taxing; 18) ER-2 Pilot post flight debrief; and 19) NASA ER-2: Flying Laboratory for Earth Science Studies and Remote Sensing.

Navarro, Robert↗
NASA NTRSDate not supplied

Enhancing Dataset Discovery and Usage Tracking in Earth Sciences: Integrating Knowledge Graphs and Large Language Models

NASA's Data Active Archive Centers (DAACs) have played a crucial role in supporting a wide range of applied research in Earth and Environmental sciences. To date, over 20,000 publications have been collected, citing more than 3,000 NASA Earth science datasets. We present an innovative approach that links datasets and collected publications through a knowledge graph (KG). This KG enables the tracking of dataset citations throughout the dataset's lifecycle, revealing patterns of dataset usage across various applied research areas. We fine-tuned the pre-trained NASA IMPACT INDUS-Base Retriever Large Language Model (LLM) using a set of labeled publication abstracts. Our results indicate that 87% of the publications were classified into one of twenty applied research areas, while the remaining 13% were categorized into non-applied research areas. The classified publications linked to datasets are used to discover datasets by users interested in specific applied research and by dataset providers to determine dataset usage for applications.

open-source↗
NASA NTRSDate not supplied

A Hybrid Approach to Labeling Datasets in Earth Science Publications

NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.

Jacob Atkins↗
NASA NTRS2010

Tracking Provenance of Earth Science Data

Tremendous volumes of data have been captured, archived and analyzed. Sensors, algorithms and processing systems for transforming and analyzing the data are evolving over time. Web Portals and Services can create transient data sets on-demand. Data are transferred from organization to organization with additional transformations at every stage. Provenance in this context refers to the source of data and a record of the process that led to its current state. It encompasses the documentation of a variety of artifacts related to particular data. Provenance is important for understanding and using scientific datasets, and critical for independent confirmation of scientific results. Managing provenance throughout scientific data processing has gained interest lately and there are a variety of approaches. Large scale scientific datasets consisting of thousands to millions of individual data files and processes offer particular challenges. This paper uses the analogy of art history provenance to explore some of the concerns of applying provenance tracking to earth science data. It also illustrates some of the provenance issues with examples drawn from the Ozone Monitoring Instrument (OMI) Data Processing System (OMIDAPS) run at NASA's Goddard Space Flight Center by the first author.

Tilmes, Curt↗
NASA NTRSDate not supplied

OpenNEX: An open collaboration platform for the earth science community

Satellite data from the past several decades provide the most consistent record of land-surface processes that form the basis for scientific assessments of the impacts of climate variations and changes on the environment and human social-economical activities. During this time, scientific research on the characterization and assessment of environmental changes had tended to focus on large-scale land-surface changes with significant social-economic impacts. Increasingly, attention is shifting toward changes that occur more locally and that most directly relate to the everyday life of the majority of the population. In addition, there has been needs to develop management and policy decision support systems that are based on local environmental information. Almost at the same time, the advancement in sensor technology has allowed us to collect an unprecedented volume of environmental data. These data must be curated and analyzed to extract useful information for research and decision support purposes. Established in 2013 and funded by NASA, the Open NASA Earth eXchange (OpenNEX; https://opennex.org/ , Jia et al., 2019 ) project partnered with Amazon Web Services (AWS) to make available a large amount of Earth observing data, modeling results, and analysis tools on the AWS. OpenNEX provides researchers, developers, educators, and ordinary users with easy access to an integrated Earth science computational and data platform, enabling citizen scientists and application developers to realize the full value of NASA data assets and software tools. To encourage the public's engagement in this project, NASA ran virtual workshops and prize competitions. The virtual workshops provided online lectures and tutorials about how prominent scientists used the data in their research and the tutorials gave examples how to use the tools to interrogate the data in the Amazon cloud. Finally, the prize competitions allowed much wider participation in the OpenNEX project and enable testing the non-traditional projects and out-of-box ideas. OpenNEX has continued to evolve and mature. Here, we highlight new features and functionalities available to the community.

Jian Zhang↗
DOE OSTI2023

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗
NASA NTRS2015

Improving the Accessibility and Use of NASA Earth Science Data

Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end‐user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.

Tisdale, Matthew↗
NASA NTRS2019

Evaluation of MODIS and VIIRS Cloud-Gap-Filled Snow-Cover Products for Production of an Earth Science Data Record

MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 sqkm, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

Hall, Dorothy K.↗
NASA NTRSDate not supplied

Localizing Earth Science: The NASA DEVELOP Model of Community Science & Engagement

NASA’s DEVELOP Program conducts rapid feasibility studies that apply Earth observations to address community priorities and needs and enable informed decision making. Part of NASA’s Applied Sciences’ Capacity Building Program, DEVELOP builds skills in both participants (students, recent graduates, and early/transitioning career professionals) who conduct projects on small teams, and partner organizations (state and local governments, federal agencies, non-profit and for-profit organizations, and international organizations) that work closely with DEVELOP to design the project. While only a subset of DEVELOP partner organizations are local municipalities or community-led non-profits, the DEVELOP model is an effective engagement tool and often used by NASA as an introduction for communities to engage in Earth science research and familiarization with satellite remote sensing. This presentation will highlight the DEVELOP model of co-production and community collaboration, lessons learned in partnering with local communities, and evaluation activities that look at the program’s reach and efforts.

Robert Cecil Byles↗
NASA NTRS1986

Lidar remote sensing from space: NASA's plans in the Earth sciences

A multidisciplinary study of the Earth System to provide a better understanding of the complex interrelated processes involved in the system, the Earth Observing System (EOS), is being developed. Capabilities of the Space Station, both the polar orbiting platform and the lower inclination platforms, will be used to accommodate a number of large active and/or passive sensors. Two lidar instruments being considered as part of the Eos payload are the Lidar Atmospheric Sounder and Altimeter (LASA) and the Laser Atmospheric Wind Sounder (LAWS). The LASA instrument is separable into two portions: the atmospheric sounder component and the retroranging component. The LASA atmospheric sounder will sample the spatial distribution of several atmospheric parameters. The retroranging component will be used to determine the precise three-dimensional position of specifically placed retro-reflectors and to sense how these retro-reflectors change position over monthly to yearly time periods. The LAWS utilizes a lidar system capable of measuring the Doppler shift in the backscattered intensity to determine the wind velocity profile.

Curran, R. J.↗
NASA NTRS2003

Space Communications and Data Systems Technologies for Next Generation Earth Science Measurements

The next generation of Earth observing satellites and sensor networks will face challenges in supporting robust high rate communications links from the increasingly sophisticated onboard instruments. Emerging applications will need data rates forecast to be in the 100's to 1000's of Mbps. As mission designers seek smaller spacecraft, challenges exist in reducing the size and power requirements while increasing the capacity of the spacecraft's communications technologies. To meet these challenges, this work looks at three areas of selected space communications and data services technologies, specifically in the development of reflectarray antennas, demonstration of space Internet concepts, and measurement of atmospheric propagation effects on Ka-band signal transmitted from LEO.

Bauer, Robert A.↗
NASA NTRS2007

Data Fusion for Earth Science Remote Sensing

Beginning in 2004, NASA has supported the development of an international network of ground-based remote sensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remote sensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.

data fusion↗