Towards the Use of Sequential Patterns for Detection and Characterization of Natural and Agricultural Areas

  • Fabio Guttler
  • Dino Ienco
  • Maguelonne Teisseire
  • Jordi Nin
  • Pascal Poncelet
Part of the Communications in Computer and Information Science book series (CCIS, volume 442)

Abstract

Nowadays, a huge amount of high resolution satellite images are freely available. Such images allow researchers in environmental sciences to study the different natural habitats and farming practices in a remote way. However, satellite images content strongly depends on the season of the acquisition. Due to the periodicity of natural and agricultural dynamics throughout seasons, sequential patterns arise as a new opportunity to model the behaviour of these environments. In this paper, we describe some preliminary results obtained with a new framework for studying spatiotemporal evolutions over natural and agricultural areas using k-partite graphs and sequential patterns extracted from segmented Landsat images.

Keywords

Temporal Patterns Data Mining and Remote Sensing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, R., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Records 22(2), 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE (1995)Google Scholar
  3. 3.
    Baatz, M., Hoffmann, C., Willhauck, G.: Progressing from object-based to object-oriented image analysis, ch. 2. Lecture Notes in Geoinformation and Cartography, pp. 29–42. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. 65(1), 2–16 (2010)CrossRefGoogle Scholar
  5. 5.
    Vanden Borre, J., Paelinckx, D., Mücher, C.A., Kooistra, L., Haest, B., De Blust, G., Schmidt, A.M.: Integrating remote sensing in natura 2000 habitat monitoring: Prospects on the way forward. J. Nat. Conserv. 19(2), 116–125 (2011)CrossRefGoogle Scholar
  6. 6.
    Hagolle, O., Huc, M.: Séries temporelles de produits landsat de niveau 2a: Manuel de l’utilisateur des données produites au cesbio (2011)Google Scholar
  7. 7.
    Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., Hunt, E.: Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 92(4), 475–482 (2004)CrossRefGoogle Scholar
  8. 8.
    Rouse Jr, J., Haas, R., Schell, J., Deering, D.: Monitoring vegetation systems in the great plains with erts. NASA Special Publication 351, 309 (1974)Google Scholar
  9. 9.
    Lillesand, T.M.: Remote Sens. and Image Interpret. John Wiley & Sons (2006)Google Scholar
  10. 10.
    Nin, J., Laurent, A., Poncelet, P.: Speed up gradual rule mining from stream data! a b-tree and owa-based approach. JIIS 35(3), 447–463 (2010)Google Scholar
  11. 11.
    Nin, J., Torra, V.: Towards the evaluation of time series protection methods. Inform. Sciences 29(11), 1663–1677 (2009)CrossRefGoogle Scholar
  12. 12.
    Petitjean, F., Kurtz, C., Passat, N., Gançarski, P.: Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recogn. Lett. 33(13), 1805–1815 (2012)Google Scholar
  13. 13.
    P. Trojáček and R. Kadlubiec. Detailed mapping of agricultural plots using satellite images and aerial orthophoto maps (2004)Google Scholar
  14. 14.
    Zhang, N., Hong, Y., Qin, Q., Liu, L.: Vsdi: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. Int. J. Remote Sens. 34(13), 4585–4609 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabio Guttler
    • 1
  • Dino Ienco
    • 1
  • Maguelonne Teisseire
    • 1
  • Jordi Nin
    • 2
  • Pascal Poncelet
    • 3
  1. 1.IRSTEA, UMR TETISMontpellierFrance
  2. 2.Barcelona Supercomputing Center (BSC)Universitat Politècnica de Catalunya (BarcelonaTech)BarcelonaSpain
  3. 3.LIRMM, CNRSMontpellierFrance

Personalised recommendations