1 / 26

Global Science Needs Global Data A Case for Data Sharing

The Fifth China - U.S. Roundtable on Scientific Data Cooperation. Global Science Needs Global Data A Case for Data Sharing. E. Lynn Usery. usery@usgs.gov. http://cegis.usgs.gov. Objectives.

elvis
Download Presentation

Global Science Needs Global Data A Case for Data Sharing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The Fifth China - U.S. Roundtable on Scientific Data Cooperation Global Science Needs Global DataA Case for Data Sharing E. Lynn Usery usery@usgs.gov http://cegis.usgs.gov

  2. Objectives • I will focus on the need for complete global geospatial datasets at high resolution to support global science modeling and analysis • Example Science Issues and Global Data Needs • Global Climate and Land Cover Change • Global Ecosystem Modeling • Global Hazards • Earthquakes • Sea Level Rise • Volcanism • Research on Semantic Web; platform for data sharing

  3. USGS Science Strategy http://www.usgs.gov/science_strategy /

  4. USGS Science • Understanding Ecosystems and Predicting Ecosystem Change • Climate Variability and Change • Energy and Minerals for America’s Future • A National Hazards, Risk, and Resilience Assessment Program • The Role of Environment and Wildlife in Human Health • A Water Census of the United States

  5. USGS Science • Data Integration and Beyond • The USGS will use its information resources to create a more integrated and accessible environment for its vast resources of past and future data. It will invest in cyberinfrastructure, nurture and cultivate programs in natural-science informatics, and participate in efforts to build a global integrated science and computing platform.

  6. Global Climate and Land Cover Change • Data Needs – One Example • High resolution (30 m or smaller pixels) satellite images for land cover extraction • U.S. has Landsat archive but does not • include all scenes from non-US-based • receiving stations • Extracted land cover • Classes must match, i.e., same classification • system and same level of detail

  7. Worldwide Usage of Landsat Imagery 1M

  8. Online Data Search, Browse and Order Tools Earth Explorer GLOVIS http://earthexplorer.usgs.gov http://glovis.usgs.gov

  9. Landsat International Data Usage (FY10)

  10. Landsat Global Archive Consolidation (LGAC) • Goal is to consolidate the entire Landsat archive • 5 million scenes held internationally vs. 2 million in the USGS archive • From current stations as well as historical stations • Each station has data that will enhance the USGS archive • Enables scientific analysis of most complete time-series of images for global land change • Facilitates large scale scene selection and data mining capability • Recover data not currently available to users • Some data at risk due to aging media and drive obsolescence • Provides data to global user community as standard product like current Landsat data from US archive

  11. LGAC International Data Holdings

  12. Landsat 8 • Similar requirement for global data from all receiving stations to be archived and made freely available to support global science

  13. Global Ecosystem Modeling – Data Needs • Global species data • Invasive species – cost in U.S. is billions of dollars each year – similar in other countries • Global secession data • Global climate records • As climate changes, how do species adapt; this is a global problem and requires global data sharing

  14. Global Hazards – Earthquakes • Locations, epicenters, seismic wave data, exchanged in real time • Soil effects data • Infrastructure damage • Relief effort and support depends on data availability

  15. Global Hazards – Volcanism • Volcano locations, eruption histories, types, distributed in realtime • Ash cloud distribution and models

  16. Global Hazards – Sea Level Rise • Global elevation high resolution • ASTER Global DEM (15 m resolution) is a start • Need lidar/IfSar along all coasts • Corresponding population data • (current highest resolution is 30 arc-sec) • Corresponding land cover data

  17. Volume – multiple global datasets at high resolution Structure – variety of structures, vector and raster, many different formats Semantics – various attribution and relation schemes, some feature-based, some layers Integration of multiple datasets – for maximum utility all datasets should be able to be integrated to produce new data and information Data Sharing ISSUES

  18. Volunteered Geographic Information/ User Generated Content • USGS “Did You Feel It?” • Open Street Map (OSM) • USGS now researching use of OSM for our transportation and structures data • VGI/UGC rivals traditional geospatial data sources and provides new basis for data sharing

  19. Technical problems • Compatible data models • Resolution, accuracy issues • Attribution issues – need ontology that allows matching across data schema • Data sharing is more than making data available for download over the Web • Requires standards • USGS data meets Federal Geographic Data Committee and Open Geospatial Consortium standards for metadata and packaging

  20. Semantics – Intelligence • USGS is exploring Semantic Web for data sharing; globally linked data • Requirements: • Ontology of features, attributes, and relationships: currently being developed. • Semantic Web triple format: Conversion for selected test areas is in progress. • Uniform Resource Identifiers (URIs) for individual features, i.e., each geographic feature has a unique URI

  21. USGS Semantic Web SPARQL Endpoint for Data Access http://usgs-ybother.srv.mst.edu:8890/sparql

  22. Query – Find the tributaries of West Hunter Creek Default Graph URI http://cegis.usgs.gov/rdf/ontologytest/ PREFIX ogc: <http://www.opengis.net/rdf#> PREFIX fid: <http://cegis.usgs.gov/rdf/nhd/featureID#> SELECT ?feature ?type WHERE { fid:_102217454 ogc:hasGeometry ?geo1. ?geo1 ogc:touches ?geo2. ?feature ogc:hasGeometry ?geo2. ?feature a ?type }

  23. Query Result http://cegis.usgs.gov/rdf/nhd/featureID#_102216432 http://cegis.usgs.gov/rdf/nhd/featureID#_102216448 http://cegis.usgs.gov/rdf/nhd/featureID#_102216340 http://cegis.usgs.gov/rdf/nhd/featureID#_102216320 http://cegis.usgs.gov/rdf/nhd/featureID#_102217454 http://cegis.usgs.gov/rdf/nhd/featureID#_102216276 http://cegis.usgs.gov/rdf/nhd/featureID#_102216358

  24. Major Challenges for Geospatial Data Sharing with Semantics • Semantic spatial data model • Coordinates on the Semantic Web in RDF • Geospatial feature ontologies • Ontology-driven geospatial operators • Moving multi-GB to TB of data to grid/cloud • Implementing spatial operators on Semantic Web and in • grid/cloud environment • Interfacing Semantic Web and grid/cloud capabilities

  25. The Fifth China - U.S. Roundtable on Scientific Data Cooperation Global Science Needs Global DataA Case for Data Sharing E. Lynn Usery usery@usgs.gov http://cegis.usgs.gov

More Related