Real-Time Integration of Geospatial Raster and Point Data Streams

  • Carlos Rueda
  • Michael Gertz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5069)

Abstract

Sensor and network technology advances are increasingly placing an immense amount of real-time geospatial data streams at the scientist’s disposal. The effective integration and assimilation of such datasets, however, is still a challenging goal. In this paper, we describe a computational framework that simplifies the design, execution, and visualization of processing workflows involving the integration of satellite raster and ground point data streams. The framework is enabled for interoperability by adhering to open sensor data standards, and demonstrated with the evaluation of key environmental inputs needed for the estimation of reference evapotranspiration over California.

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References

  1. 1.
    Hart, Q., Brugnach, M., Temesgen, B., Rueda, C., Ustin, S., Frame, K.: Daily reference evapotranspiration for California using satellite imagery and weather station measurement interpolation. Civil Eng. and Environ. Systems (to appear)Google Scholar
  2. 2.
    Ziegler, P., Dittrich, K.: Data Integration – Problems, Approaches, and Perspectives. In: Conceptual Modelling in Information Systems Engineering, pp. 39–58 (2007)Google Scholar
  3. 3.
    Peng, R., Hua, K.A., Hamza-Lup, G.L.: A Web services environment for Internet-scale sensor computing. In: Proc. IEEE International Conference on Services Computing, pp. 101–108 (2004)Google Scholar
  4. 4.
    Moodley, D., Terhorst, A., Simonis, I., McFerren, G., van den Bergh, F.: Using the Sensor Web to detect and monitor the spread of wild fires. In: 2nd International Symposium on Geo-information for Disaster Management (2006)Google Scholar
  5. 5.
    Chu, X., Buyya, R.: Service Oriented Sensor Web. In: Sensor Networks and Configuration, pp. 51–74 (2007)Google Scholar
  6. 6.
    Chaudhry, N., Shaw, K., Abdelguerfi, M.: Stream Data Management (Advances in Database Systems). Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Rueda, C., Gertz, M.: Modeling satellite image streams for change analysis. In: Proceedings of the 15th annual ACM international Symposium on Advances in Geographic Information Systems (ACMGIS), pp. 43–50. ACM Press, New York (2007)Google Scholar
  8. 8.
    Gertz, M., Hart, Q., Rueda, C., Singhal, S., Zhang, J.: A data and query model for streaming geospatial image data. In: Grust, T., Höpfner, H., Illarramendi, A., Jablonski, S., Mesiti, M., Müller, S., Patranjan, P.-L., Sattler, K.-U., Spiliopoulou, M., Wijsen, J. (eds.) EDBT 2006. LNCS, vol. 4254, pp. 687–699. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., Lee, E.A., Tao, J., Zhao, Y.: Scientific Workflow Management and the Kepler System. In: Concurrency and Computation: Practice & Experience (2005)Google Scholar
  10. 10.
    Open Geospatial Consortium. OpenGIS Sensor Web Enablement: Architecture Document, www.opengeospatial.org/pt/14140
  11. 11.
    California Irrigation Management Information System, http://wwwcimis.water.ca.gov
  12. 12.
    Geostationary Operational Environmental Satellite, www.goes.noaa.gov.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Carlos Rueda
    • 1
  • Michael Gertz
    • 1
  1. 1.Dept. of Computer ScienceUniversity of CaliforniaDavisU.S.A.

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