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An Intelligent Archive Testbed Incorporating Data Mining

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Standard-Based Data and Information Systems for Earth Observation

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

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Abstract

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by US and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA’s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a “value chain” for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival.

This work was performed by the first author (Danks) as part of his official duties as an employee of the US government. It was supported by the NASA’s Science Mission Directorate. The remaining authors were supported under Cooperative Agreement NCC5-645 between NASA and George Mason University. The opinions expressed are those of the authors and do not necessarily reflect the official position of NASA.

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Abbreviations

ACCESS:

Advancing Collaborative Connections for Earth System Science

AUC:

Area under the ROC curve

AVHRR:

Advanced Very High-Resolution Radiometer

CRS:

Coordinate Reference Systems

DAAC:

Distributed Active Archive Center

DCE:

Distributed Communication Environment

DDR:

Double Data Rate

ECHO:

EOS ClearingHOuse

EDC:

EROS Data Center

EDS:

Electronic Data Systems

EOS:

Earth Observing System

EOSDIS:

Earth Observing System Data and Information System

FPAR:

Fractional Photosynthetically Active Radiation

FTP:

File Transfer Protocol

GFLOPS:

Giga (10**9) Floating Point Operations Per Second

GMU:

George Mason University

GSFC:

Goddard Space Flight Center

GSSD:

Global Surface Summary of the Day

HDF:

Hierarchical Data Format

IA:

Intelligent Archive

IA-KBS:

Intelligent Archive in the Context of a Knowledge Building System

IDU:

Intelligent Data Understanding

IHMC:

Institute for Human and Machine Cognition

LAI:

Leaf Area Index

LP DAAC:

Land Processes DAAC

LSDM:

Large-Scale Data Mining

MCAT:

Metadata Catalog

MDCE:

MatLab Distributed Computing Engine

MEaSUREs:

Making Earth Science Data Records for Use in Research Environ- ments

MODIS:

Moderate-Resolution Imaging Spectroradiometer

NASA:

National Aeronautics and Space Administration

NCDC:

National Climate Data Center

NDVI:

Normalized Difference Vegetation Index

NOAA:

National Oceanic and Atmospheric Administration

PRECIP:

precipitation

RAID:

Redundant Array of Independent Disks

REASoN:

Research, Education and Applications Solutions Network

RDS:

Remote Data Storage

ROC:

Receiver Operating Characteristic

SDRAM:

Synchronous Dynamic Random Access Memory

SRB:

Storage Resources Broker

TB:

Terabyte

TCP/IP:

Transmission Control Protocol/Internet Protocol

TMIN:

Temperature, Minimum

TMAX:

Temperature, Maximum

TOPS:

Terrestrial Observation and Prediction System

US:

United States

USFS:

United States Forest Service

VPD:

Vapor Pressure Deficit

References

  • Bonnlander BV (2005) “Statistical Forecasts of Wildfire: A Baseline Approach”; (2005) CMU Laboratory for Symbolic Computation Technical Report #172

    Google Scholar 

  • Burgan R, Klaver R, Klaver J (1998) Fuel models and fire potential from satellite and surface observation. International Journal of Wildland Fire 8:159–170

    Article  Google Scholar 

  • Clausen M, Lynnes C (July 2003) Virtual Data Products in an Intelligent Archive, White Paper prepared for the Intelligent Data Understanding program, http://daac.gsfc.nasa.gov/intelligent_archive/presentations.shtml

  • Hopley L, Van Schalkwyk J (March 3, 2007) “The magnificent ROC (Receiver Operating Characteristic curve)”, http://anaesthetist.com/mnm/stats/roc/Findex.htm

  • Isaac D, Lynnes C (January 2003) Automated Data Quality Assessment in the Intelligent Archive, White Paper prepared for the Intelligent Data Understanding program, http://disc.gsfc.nasa.gov/IDA/

  • Isaac D, McConaughy G (September 2004) “Intelligent Archives in the Context of Knowledge Building Systems: Data Volume Considerations”, White Paper prepared for the Intelligent Data Understanding program, http://disc.gsfc.nasa.gov/IDA/

  • Jolly W, Graham J, Michaelis A, Nemani R, Running S (2005) A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales. Environmental Modeling & Software, 20:873–882

    Article  Google Scholar 

  • Loveland T, Merchant J, Ohlen D, Brown J (1991) Development of a land-cover characteristics database for the conterminous US. Photogrammetric Engineering and Remote Sensing, 57:1453–1463

    Google Scholar 

  • McConaughy G, McDonald K (September 2003) “Moving from Data and Information Systems to Knowledge Building Systems: Issues of Scale and Other Research Challenges,” White Paper prepared for the Intelligent Data Understanding program, http://disc.gsfc.nasa.gov/IDA/

  • Morse S, Isaac D, Lynnes C (January 2003) “Optimizing Performance in Intelligent Archives,” White Paper prepared for the Intelligent Data Understanding program, http://disc.gsfc.nasa.gov/IDA/

  • Morse S, Yang W (October 2004) “A Conceptual Specimen Architecture for an Intelligent Archive in a Knowledge Building System,” White Paper prepared for the Intelligent Data Understanding program, http://disc.gsfc.nasa.gov/IDA/

  • NASA (February 3, 2005) “Evolution of EOSDIS Elements,” Study Team Briefing to NASA, http://eosdis-evolution.gsfc.nasa.gov/

  • Ramapriyan H, McConaughy G, Morse S, Isaac D (August 2004) “Intelligent Systems Technologies to Assist in Utilization of Earth Observation Data,” presented at Earth Observing Systems IX, SPIE Meeting, http://disc.gsfc.nasa.gov/IDA/

  • Ramapriyan H, Isaac D, Yang W, Morse S, (2005) “Large Scale Data Mining to Improve Usability of Data – an Intelligent Archive Testbed,” IGARSS, Seoul, Korea

    Google Scholar 

  • Ramapriyan H, Behnke J, Sofinowski E, Lowe D, Esfandiari M (2009) “Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS),” Chapter 7, Standards Based Data and Information Systems for Earth Observations (Eds) Di L and Ramapriyan H, Springer-Verlag, New York.

    Google Scholar 

  • USDA Forest Service (1993) National Interagency Fire Management Integrated Database (NIFMID) reference manual; US Department of Agriculture, Forest Service, Fire and Aviation Management. Washington, DC, USA, p 43

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank the following individuals for their assistance and contributions of ideas to this work: G. McConaughy and C. Lynnes (NASA Goddard Space Flight Center-GSFC) – IA-KBS concepts, S. Morse (SOSACorp) – Intelligent Data Understanding (IDU) research assessment and testbed conceptual design, L. Di (GMU) – IA-KBS concepts, X. Li (GMU) – modeling and forecasting software execution on the testbed, T. Chu (IHMC) – assessment of software porting to testbed, and P. Smith (MacDonald, Dettwiler & Associates, Ltd.), B. Koenig (Electronic Data Systems Corporation - EDS) and C. Yee (Raytheon) – support for RDS/testbed interfaces. They would also like to thank C. Bock, D. Lowe and M. Esfandiari (Earth Science Data and Information System ESDIS Project, NASA GSFC) for their encouragement and support.

The data used in this study were obtained from several sources as indicated in Sect. 10.4.1. The sources include NOAA’s National Climate Data Center, US Forest Service, NASA’s Ames Research Center, and three of NASA’s EOSDIS Data Centers – Land Processes DAAC, National Snow and Ice Data Center and the Goddard Earth Science Data and Information Services Center.

Author information

Authors and Affiliations

  1. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    H.K. Ramapriyan

  2. BPS Consulting, Inc., Bethesda, MD, USA

    D. Isaac

  3. Center for Spatial Information Science and Systems (CSISS), George Mason University, 20770, Greenbelt, MD, USA

    W. Yang

  4. Institute for Human and Machine Cognition, Pensacola, FL, USA

    B. Bonnlander

  5. Carnegie Melon University, Pittsburgh, PA, USA

    D. Danks

Authors
  1. H.K. Ramapriyan
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  2. D. Isaac
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  3. W. Yang
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  4. B. Bonnlander
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  5. D. Danks
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Corresponding author

Correspondence to H.K. Ramapriyan .

Editor information

Editors and Affiliations

  1. Science & System (CSISS), Center for Spatial Information, Greenbelt, 20770, U.S.A.

    Liping Di

  2. Science & System (CSISS), Center for Spatial Information, Greenbelt, 20770, U.S.A.

    H. K. Ramapriyan

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Ramapriyan, H., Isaac, D., Yang, W., Bonnlander, B., Danks, D. (2010). An Intelligent Archive Testbed Incorporating Data Mining. In: Di, L., Ramapriyan, H. (eds) Standard-Based Data and Information Systems for Earth Observation. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88264-0_10

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