Abstract
In many global applications of remote sensing land-water masks can improve the interpretation results. Their use can be of advantage to distinguish between different types of dark areas (e.g. cloud or topographic shadows, burned areas, coniferous forests, water areas). On one hand, water bodies cannot always be classified exactly on basis of available remote sensing data. On the other hand static land-water masks of different quality are available. But the main deficiencies are caused by the fact that land-water masks represent only a temporal snapshot of the water bodies. A dynamic self-learning water masking approach was developed at first for AATSR data to combine the advantages of static mask with results of pre-classifications. This paper presents the adaption of this procedure for MERIS remote sensing data. As before with AATSR data the aim consists in integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. The results for some regional examples demonstrate the quality of masks and the advantages to conventional water masking algorithms. Furthermore, it will be discussed, that it is useful for a global water mask of high quality to integrate further special masks as cloud or in particular terrain shadow masks. At least, the land-water mask plays not only an important role in complex processing chains itself is the result of a complex procedure. Beside the results have shown successful transfer of a developed processing scheme for operational deriving of actual land-water masks to data of a second sensor, the adaption to further sensors or the adaption of the processor to other object types as e.g. forest will be possible in future as part of operational monitoring systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Borg, E., Fichtelmann, B.: Determination of the usability of remote sensing data. EP 1591961 B1 (2005)
Carroll, M.L., Townshend, J.R., DiMiceli, C.M., Noojipady, P., Sohlberg, R.A.: A new global raster water mask at 250 m resolution. Int. J. of Digital Earth 2, 291–308 (2009)
ESA CCI ECV Fire Disturbance (fire_cci), N° 4000101779/10/I-LG
ESA: MERIS Algorithm Theoretical Basis Document (ATBD), 2-17 - Pixel Classification, Issue 5.0, (2011), https://earth.esa.int/instruments/meris/atbd/atbd_2.17.pdf (last access: January 29, 2014)
Fichtelmann, B., Borg, E., Kriegel, M.: Verfahren zur operationellen Bereitstellung von Zusatzdaten für die automatische Fernerkundungsdatenverarbeitung. In: 23rd AGIT Symposium Angewandte Geoinformatik (Strobl, Blaschke, Griesebner), Salzburg, pp. 12–20 (2011)
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.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part III. LNCS, vol. 7335, pp. 457–470. Springer, Heidelberg (2012)
Frey, C.M., Kuenzer, C., Dech, S.: Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005. Int. J. of Remote Sensing 33, 7165–7183 (2012)
Global Climate Observing System (GCOS): Guidelines for the generation of satellite-based datasets and products meeting GCOS requirements. Geneva: World Meteorological Organization (2009)
Google Earth, available software at http://www.google.de/intl/de/earth/ (last access: January 21, 2014)
Google Maps, http://www.evl.uic.edu/pape/data/WDB/ , http://www.evl.uic.edu/pape/data/WDB/ (last access: January 21, 2014)
Guenther, K.P., Maier, S.W.: AVHRR compatible vegetation index derived from MERIS data. Int. J. of Remote Sensing 28(3-4), 693–708 (2007)
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, 850–853 (2013), http://earthenginepartners.appspot.com/science-2013-global-forest
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)
Knaur, T.: Knaurs Grosser Weltatlas (Orig.: The Times Atlas of the World by Bartholomew and Times Book), Droemersche Verlagsanstalt Th. Knaur Nachf. Munich (1993)
Lehner, B., Doll, P.: Development and validation of a global database of lakes, reservoirs, and wetlands. Journal of Hydrology 296, 1–22 (2004)
MERIS Product Handbook Issue 2.1 (2006), https://earth.esa.int/handbooks/meris/index30082011.html (last access: January 21, 2014)
Plummer, S.: The ESA Climate Change Initiative. Description. ESA-ESRIN, Frascati (2009)
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)
Wan, Z., Zhang, Y., Zhang, Q., Li, Z.: Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment 83(1&2), 163–180 (2002)
Wessel, P., Smith, W.H.F.: A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101(B4), 8741–8743 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Fichtelmann, B., Borg, E., Guenther, K.P. (2014). Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to MERIS Data. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8579. Springer, Cham. https://doi.org/10.1007/978-3-319-09144-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-09144-0_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09143-3
Online ISBN: 978-3-319-09144-0
eBook Packages: Computer ScienceComputer Science (R0)