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Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9158)

Abstract

Within the ESA CCI “Fire Disturbance” project a dynamic self-learning water masking approach originally developed for AATSR data was modified for MERIS-FR(S) and MERIS-RR data and now for SPOT VEGETATION (VGT) data. The primary goal of the development was to apply for all sensors the same generic principles by combining static water masks on a global scale with a self-learning algorithm. Our approach results in the generation of a dynamic water mask which helps to distinguish dark burned area objects from other different types of dark areas (e.g. cloud or topographic shadows, coniferous forests). The use of static land-water masks includes the disadvantage that land-water masks represent only a temporal snapshot of the water bodies. Regional results demonstrate the quality of the dynamic water mask. In addition the advantages to conventional water masking algorithms are shown. Furthermore, the dynamic water masks of AATSR, MERIS and VGT for the same region are presented and discussed together with the use of more detailed static water masks.

Keywords

Self-learning algorithm Land-water mask Interpretation Remote sensing VGT data Cloud cover 

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References

  1. 1.
    Borg, E., Fichtelmann, B.: Determination of the usability of remote sensing data. EP 1591961 B1 (2005)Google Scholar
  2. 2.
    Carroll, M.L., Townshend, J.R., DiMiceli, C.M., Noojipady, P., Sohlberg, R.A.: A new global raster water mask at 250m resolution. Int. J. of Digital Earth 2, 291–308 (2009)CrossRefGoogle Scholar
  3. 3.
    ESA CCI ECV Fire Disturbance (fire_cci), N° 4000101779/10/I-LGGoogle Scholar
  4. 4.
    Fichtelmann, B., Borg, E., Kriegel, M.: Verfahren zur operationellen Bereitstellung von Zusatzdaten für die automatische Fernerkundungsdatenverarbeitung. In: Angewandte Geoinformatik 2011 (Strobl, Blaschke, Griesebner), 23. AGIT Symposium, Salzburg, pp. 12–20 (2011)Google Scholar
  5. 5.
    Fichtelmann, B., Borg, E.: A New Self-Learning Algorithm for Dynamic Classification of Water Bodies. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part III. LNCS, vol. 7335, pp. 457–470. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Fichtelmann, B., Borg, E., Guenther, K.P.: Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to MERIS Data. In: Murgante, B., Misra, S., Rocha, A.M.A., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014, Part I. LNCS, vol. 8579, pp. 376–392. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Google Earth. http://www.google.de/intl/de/earth/ (last access: January 21, 2014)
  8. 8.
    Haas, E.M., Bartholomé, E., Combal, B.: Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa. J. Hydrology 370, 52–63 (2009)CrossRefGoogle Scholar
  9. 9.
    Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.: High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–53 (2013). http://earthenginepartners.appspot.com/science-2013-global-forest (last access: April 23, 2014)
  10. 10.
    Klein, I., Dietz, A., Gessner, U., Dech, S., Kuenzer, C.: Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. Remote Sensing Lett. (6), 78–87 (2015). http://www.dlr.de/eoc/desktopdefault.aspx/tabid-5258/15811_read-41169/ (last access: January 22, 2015)
  11. 11.
    Justice, C., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F., Kaufman, Y.: The MODIS fire products. Remote Sensing of Environment 83(1&2), 244–262 (2002)CrossRefGoogle Scholar
  12. 12.
    Lehner, B., Doll, P.: Development and validation of a global database of lakes, reservoirs, and wetlands. Journal of Hydrology 296, 1–22 (2004)CrossRefGoogle Scholar
  13. 13.
    VITO, Terms of use (last update: July 8, 2014). http://www.spot-vegetation.com/userguide/book_1/1/13/133/e133.htm (last access: January 22, 2015)
  14. 14.
    USGS (U.S. Geological Survey): Documentation for the Shuttle Radar Topography Mission (SRTM) Water Body Data Files. http://dds.cr.usgs.gov/srtm/version2_1/SWBD/SWBD_Documentation/Readme_SRTM_Water_Body_Data.pdf (last access: January 21, 2014)
  15. 15.
    Wessel, P., Smith, W.H.F.: A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101(B4), 8741–8743 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.German Aerospace CenterGerman Remote Sensing Data CenterNeustrelitzGermany
  2. 2.German Aerospace CenterGerman Remote Sensing Data CenterWesslingGermany

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