Data Mining In Earth Sciences

Rahul Ramachandran, Sara Graves, and Ken Keiser (University of Alabama in Huntsville) (I)

The volume of raw data being stored by different Earth Science archives today
defies even the partial manual examination by scientists. The Earth Observing
System data volumes will reach a terabyte per day by the time all the planned
satellites are flown. The ADaM (Algorithm Development and Mining) system,
developed at the Information Technology and Systems Center at the University of
Alabama in Huntsville, is a powerful tool that can be used to mine these large
volumes of Earth Science data. This system contains algorithms for detecting a
variety of geophysical phenomena to address the needs of the earth science
community. It has been utilized in a variety of earth science applications.
Short summaries on a couple of these applications are given below:

  • Cumulus Cloud Detection: Boundary layer cumulus clouds over land
    are difficult to detect in satellite data, owing to low contrast in both
    visible and infrared channels. For Geostationary Operational Environmental
    Satellite (GOES) data the problem becomes severe, as the infrared channel
    resolution is 4km, compared to 1km in the visible channel. A study was
    conducted analyzing three of the different image processing and pattern
    recognition techniques available in ADaM for cumulus cloud detection. These
    were classifiers based on 1) texture and spectral features, 2) edge
    detection and spectral features, and 3) purely spectral features.

  • Phenomena Detection: The ability of ADaM to mine for particular
    data values or geophysical phenomena within a specified data product has
    been actively utilized on several projects. Special Sensor Microwave/Imager
    (SSM/I) data is being mined during ingest to detect and locate Mesoscale
    Convective Systems. In a different project, Advanced Microwave Sounding Unit
    (AMSU) data is mined in real time to locate tropical storms and estimate
    their maximum wind speeds. Operational forecasters at National Hurricane
    Center are utilizing this information to aid in their analysis and tracking
    of hurricanes.

This presentation will describe these and other earth science mining


Rahul Ramachandran is a Research Scientist at the Information Technology and Systems
Center. His academic background is in Atmospheric Science and Computer Science. His
research interests include application of information technology for scientific
research, data mining, automatic pattern recognition and image classification problems.

Presentation (PowerPoint File)

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