Observing the Middle Elbe Biosphere in Germany by Means of TerraSAR-X Images


The Lower Saxonian Elbe Valley Biosphere Reserve is part of the UNESCO Biosphere Reserve Elbe River Landscape, and used mainly for agriculture. One of tasks of the Biosphere Reserve Administration is to develop sustainable forms of land use which requires comprehensive updated land cover maps. Land use maps are hard to produce because of surveying costs and time. Nevertheless, these large areas need to be monitored. TerraSAR-X images are used to establish agricultural land use maps. In this study, two areas are selected within the Elbe Biosphere Reserve situated around the oxbows Wehninger Werder and Walmsburger Werder. Multi temporal classification methods were used to identify the different crops using maximum likelihood classifier for the years 2010 and 2011. The crop classifications were used to evaluate the effect of the number of images, the necessity of polarizations, and the consequences of some missing images within the crop calendar. These classifications were analyzed to estimate producer accuracy and Kappa index for each crop besides the overall accuracy for each agricultural land use map. The study shows that using dual polarization imagery enhances producer accuracies for many crops over the single polarization imagery, and demonstrates the importance of using frequent images during the cultivation period.

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Farghaly, D. , Elba, E. and Urban, B. (2014) Observing the Middle Elbe Biosphere in Germany by Means of TerraSAR-X Images. International Journal of Geosciences, 5, 196-205. doi: 10.4236/ijg.2014.52021.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] U. Bramick, F. Fladung and P. Doering-Arjes, “Aalmanagementplan—Flussgebietsgemeinschaft Elbe,” Institut für Binnenfischerei, 2008.
[2] G. Puhlmann and S. Reinhardt, “Partnerships between River Biosphere Reserves,” UNESCO Publications, 2007, pp. 75-77.
[3] W. Hardtle, B. Redecker, T. Assmann and H. Meyer, “Vegetation Responses to Environmental Conditions in Floodplain Grasslands: Prerequisites for Preserving Plant Species Diversity,” Basic and Applied Ecology, Vol. 7, No. 3, 2006, pp. 280-288.
[4] R. N. Lubowski, S. Bucholtz, R. Claassen, M. J. Roberts, J. C. Cooper, A. Gueorguieva and R. Johansson, “Environmental Effects of Agricultural Land-Use Change,” United States Department of Agriculture (USDA), Economic Research Service, Economic Research Report No. 25, 2006.
[5] F. Krüger and A. Grongroft, “The Difficult Assessment of Heavy Metal Contamination of Soils and Plants in Elbe River Floodplains,” Acta Hydrochimica et Hydrobiologic, Vol. 31, No. 4-5, 2003, pp. 436-443.
[6] B. Urban, “River Elbe Ecology—Contributions to a Large Scale Environmental Project,” In: R. Ramesh and S. Ramachandran, Eds., Coastal Urban Environments, Capital Publishing Company, New Delhi, 2003, pp. 67-88.
[7] G. Tevi and A. Tevi, “Remote Sensing and GIS Techniques for Assessment of the Soil Water Content in Order to Improve Agricultural Practice and Reduce the Negative Impact on Groundwater,” Water Science & Technology Journal, Vol. 66, No. 3, 2012, pp. 580-587.
[8] B. Urban, F. Krüger, T. Weniger, J. Prüter, T. Keienburg, F. Lang and M. Graf, “Auenboden der Elbe als Archiv für die Stoffdynamik im Einzugsgebiet,” Proceedings Deutsche Bodenkundliche Gesellschaft DBG Exkursionsführer, Oldenburg/Berlin, 2011, pp. 42-59.
[9] C. Sehnert, S. Huang and K. Lindenschmidt, “Quantifying Structural Uncertainty due to Discretisation Resolution and Dimensionality in a Hydrodynamic Polder Model,” Journal of Hydroinformatics, Vol. 11, No. 1, pp. 19-30.
[10] A. E. Mynett and Z. Vojinovic, “Hydroinformatics in Multi-Colours-Part Red: Urban Flood and Disaster Management,” Journal of Hydroinformatics, Vol. 11, No. 3-4, 2009, pp. 166-179.
[11] B. Koppe, B. Llacay and G. Peffer, “RAMWASS Decision Support System (DSS) for the Risk Assessment of Water-Sediment-Soil Systems—Application of a DSS Prototype to a Test Site in the Lower Part of the Elbe River Valley, Germany,” Proceedings of the European Conference on Flood Risk Management Research into Practice (FLOODrisk), CRC Press, London, 2008.
[12] E. M. Makhanya, S. E. Piper and M. Townsend, “Mapping Rural Land Use in Selected Subsistence Farming Areas of South Africa Using Remote Sensing Products,” Proceedings IntArchPhRS. Band XXIX, Part B 7, Washington, D.C., 1992, pp. 675-682.
[13] S. J. Purkis and V. V. Klemas, “Remote Sensing and Global Environmental Change,” Wiley-Blackwell, Chichester, West Sussex, 2011.
[14] D. Farghaly, B. Urban, P. Lohmann and E. Elba, “Differentiation and Extend of Aquatic Weeds over Lake Kyoga, Uganda by Multiple Remote Sensing Technology,” Proceedings 4th TerraSAR-X Science Team Meeting, DLR-Oberpfaffenhofen, 2011.
[15] P. Lohmann, U. Soergel, M. Tavakkoli and D. Farghaly, “Multi-Temporal Classification for Crop Discrimination Using TerraSAR-X Spotlight Images,” Proceedings IntArchPhRS (38), Part 1-4-7/WS, Hannover, 6 S., CD, 2009.
[16] M. Tavakkoli, “Multi-Temporal Classification of Crops Using ENVISAT ASAR Data,” Ph.D. Dissertation, Leibniz University of Hannover, 2011.
[17] M. Tavakkoli, P. Lohmann and U. Soergel, “Monitoring Agricultural Activities Using Multi-Temporal ASAR ENVISAT Data,” Proceedings IntArchPhRS, Band XXXVII, Teil B 7-2, Peking, 2008, pp. 735-742
[18] J. A. Richards, “Remote Sensing with Imaging Radar,” Springer, Australia, 2009.
[19] Landwirtschaftskammer Niedersachsen, “Endbericht— Machbarkeitsuntersuchung zur Monovergarung von Grassilagen schadstoffkontaminierter Standorte aus Deichvorland der Elbe,” 2011.
[20] I. Leyer, “Auengrünland der Mittelelbe-Niederung,” Vegetationskundliche und-okologische Untersuchungen in der rezenten Aue, der Altaue und am Auenrand der Elbe, J. Cramer, Borntraeger, Berlin, 2002.
[21] P. Lohmann, U. Soergel and D. Farghaly, “Classification of Agricultural Sites Using Time-Series of High Resolution Dual-Polarisation TerraSAR-X Spotlight Images,” Proceedings 29th EARSeL Symposium—Imaging Europe, Chania, 2009.
[22] M. Mansourpour, M. A. Rajabi and J. A. R. Blais, “Effects and Performance of Speckle Noise Reduction Filters on Active Radar and SAR Images,” Proceedings Int-ArchPhRS, Band XXXVI 1/ W41, Ankara, 2006.
[23] L. Gagnon, “Wavelet Filtering of Speckle Noise—Some Numerical Results,” Proceedings 12th Conference on Vision Interface (VI’99) Trois-Rivières, Québec, 1999, pp. 336-343.
[24] J. Lee, T. L. Ainsworth and K. Chen, “Speckle Filtering of Dual-Polarization and Polarimetric Sar Data Based on Improved Sigma Filter,” Proceedings IGARSS, 2008.
[25] G. F. De Grandi, M. Leysen, J. S. Lee and D. Schuler, “Radar Reflectivity Estimation Using Multiplicative SAR Scenes of the Same Target: Technique and Applications,” Proceedings IGARSS, 1997.
[26] D. G. Rossiter, “Statistical Methods for Accuracy Assessment of Classified Thematic Maps,” 2004.

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