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Architecture for a Colombian Data Cube Using Satellite Imagery for Environmental Applications

  • Germán Bravo
  • Harold Castro
  • Andrés Moreno
  • Christian Ariza-Porras
  • Gustavo Galindo
  • Edersson Cabrera
  • Saralux Valbuena
  • Pilar Lozano-Rivera
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 735)

Abstract

SOLAP data cubes are a main tool to help on the processing of satellite imagery. This article presents the work developed for adapting an existing SOLAP Data Cube to the current Colombian Protocol for processing satellite images to analyze deforestation. We studied different technological alternatives to support such protocol and extend the capabilities of the Australian data cube to include the whole analysis process. In this way, it is possible for different institutions to produce and consume information to/from the data cube allowing the standardization of such information. In consequence, an institution can generate new results based on previous information generated for another institution, keeping the associated metadata along the whole process. This paper introduces the defined architecture, a first implementation and the first results obtained by IDEAM, the Colombian official institution responsible for the monitoring of deforestation in the country.

Keywords

SOLAP data cubes Deforestation Data cubes architecture 

References

  1. 1.
    Ariza-Porras, C., et al.: CDCol: a geoscience data cube that meets Colombian needs. In: Solano, A., Ordoñez, H. (eds.) CCC 2017, CCIS, vol. 735, pp. 87–99, Springer, Cham (2017)CrossRefGoogle Scholar
  2. 2.
    Bolton, D.K.: Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using landsat time-series and airborne lidar data. Remote Sens. of Environ. 163, 48–60 (2015). doi: 10.1016/j.rse.2015.03.004 CrossRefGoogle Scholar
  3. 3.
    Cabrera, E.V.: Protocolo de procesamiento digital de imágenes para la cuantificación de la deforestación en Colombia, Nivel Subnacional Escala gruesa y fina. IDEAM, Bogota, D.C. (2011)Google Scholar
  4. 4.
    Campbell, J.B.: Introduction to Remote Sensing. CRC Press, Boca Raton (2002)Google Scholar
  5. 5.
    CDCol.: Memorias II Taller Inter institucional del Cubo de Datos de Colombia, Bogotá (2015)Google Scholar
  6. 6.
    Colliat, G.: OLAP, relational, and multidimensional database systems. ACM Sigmod Rec. 25(3), 64–69 (1996)CrossRefGoogle Scholar
  7. 7.
    Feddema, J.J.: The importance of land-cover change in simulating future climates. Science 310, 5754 (2005)CrossRefGoogle Scholar
  8. 8.
    Galindo, G.E.: Protocolo de procesamiento digital de imágenes para la cuantificación de la deforestación en Colombia. V 2.0. IDEAM, Bogota (2014)Google Scholar
  9. 9.
    Google Earth Engine Team: Google Earth Engine: A planetary-scale geospatial analysis platform (2015). https://earthengine.google.com
  10. 10.
    Guo, H.W.: Building up national Earth observing system in China. Int. J. Appl. Earth Obs. Geoinf. 6, 167–176 (2005). doi: 10.1016/j.jag.2004.10.007 CrossRefGoogle Scholar
  11. 11.
    IDEAM, IAvH, Invemar, SINCHI e IIAP: Estado de la Biodiversidad, de los ecosistemas continentales, marinos, costeros y avances en el conocimiento. Informe del Estado del Medio Ambiente y los Recursos Naturales Renovables (Vol. Tomo 2), Bogotá, D.C (2011)Google Scholar
  12. 12.
    Innes, J.L.: Forests in environmental protection. In: John, A.H., Owens, N. (eds.) Forests and Forest Plants in Encyclopedia of Life Support Systems (EOLSS). Eolss Publishers, Oxford, UK (2004)Google Scholar
  13. 13.
    Ip, A.: Generalized Data Framework Solution Architecture (Draft). Australian Goverment - Geoscience Australia (2015)Google Scholar
  14. 14.
    Khorram, S.N.: Remote Sensing. Springer, US, Boston, MA (2012)CrossRefGoogle Scholar
  15. 15.
    Ma, Y.W.: Towards building a data-intensive index for big data computing – A case study of remote sensing data processing. Inf. Sci. 319, 171–188 (2015). doi: 10.1016/j.ins.2014.10.006 CrossRefGoogle Scholar
  16. 16.
    Andina, O.N.F.: Agosto - Septiembre, p. 15. Bosques y Cambio Climático -, Boletín Técnico N (2014)Google Scholar
  17. 17.
    Rivest, S.E.: SOLAP technology: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. ISPRS J. Photogramm. Remote sens. 60(1), 17–33 (2005)CrossRefGoogle Scholar
  18. 18.
    U.S. Geological Survey: Landsat 7 science data users handbook (1998)Google Scholar
  19. 19.
    UNESCO: Application of satellite remote sensing to support water resources management in Africa: Results from the TIGER initiative. technical documents in hydrology, 85 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Germán Bravo
    • 1
  • Harold Castro
    • 1
  • Andrés Moreno
    • 1
  • Christian Ariza-Porras
    • 1
  • Gustavo Galindo
    • 2
  • Edersson Cabrera
    • 2
  • Saralux Valbuena
    • 2
  • Pilar Lozano-Rivera
    • 2
  1. 1.Systems and Computer Engineering Department, School of EngineeringUniversidad de Los AndesBogotáColombia
  2. 2.Subdirección de Ecosistemas e Información AmbientalInstituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM)BogotáColombia

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