Share This Article:

Separability of Dominant Crop Cultures in Southern Germany Using TerraSAR-X Data

Abstract Full-Text HTML XML Download Download as PDF (Size:1193KB) PP. 97-107
DOI: 10.4236/ars.2015.42009    3,145 Downloads   3,620 Views   Citations

ABSTRACT

The research aims at differentiating dominant crop cultures in two test sites of Baden-Wuert- temberg, Southern Germany by creating crop signatures from radar backscatter values. It seeks to establish whether the crop signatures collected in one test site are comparable or transferable to another test site. The two test sites are located in different agro-ecological zones as described in the climate maps of the “Klimaatlas Baden-Wuerttemberg”. TerraSAR-X images (VV polarization) for the months of July and August 2010 were overlaid with crop fields’ ground truth data. As pre-processing steps, radiometric correction was carried out on the images in order to normalize the topographical effects. Classification of the crops was performed on a field scale, according to the mean and standard deviation of their backscatter values. From the results, potatoes could be uniquely differentiated from the cereals in the two different test sites for both the months of July and August 2010. Cereals (rapes, maize, barley, wheat and oats) had comparable backscatter values and their differentiation varied from one test site to another. The results’ accuracy obtained with a maximum kappa coefficient of 0.82 agrees with results of a similar research carried out in North East Germany.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Thiong’o, K. , Pasternak, R. , Kleusberg, A. , Thonfeld, F. and Menz, G. (2015) Separability of Dominant Crop Cultures in Southern Germany Using TerraSAR-X Data. Advances in Remote Sensing, 4, 97-107. doi: 10.4236/ars.2015.42009.

References

[1] Burini, A., Putignano, C., Del Frate, F., Licciardi, G., Pratola, C., Schiavon, G. and Solimini, D. (2008) TerraSAR- X/SPOT-5 Fused Images for Supervised Land Cover Classification. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, 5, 373-376.
[2] Lohnertz, M., Schlerf, M. and Seeling, S. (2006) Land Cover and Land Use: Description of Vegetation Cover during the Growth Period and Crop Classification with Multitemporal High Resolution SPOT Images. Proceeding of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group, 2, 80-88.
[3] Sabour, S.T., Lohmann, P. and Soergel, U. (2008) Monitoring Agricultural Activities Using Multi-Temporal ASAR ENVISAT Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 735-742.
[4] Bargiel, D. (2013) Capabilities of High Resolution Satellite Radar for the Detection of Semi-Natural Habitat Structures and Grasslands in Agricultural Landscapes. Ecological Informatics, 13, 9-16.
http://dx.doi.org/10.1016/j.ecoinf.2012.10.004
[5] Waske, B. and Braun, M. (2009) Classifier Ensembles for Land Cover Mapping Using Multitemporal SAR Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 450-457.
http://dx.doi.org/10.1016/j.isprsjprs.2009.01.003
[6] Castillejo-González, I.L., López-Granados, F., García-Ferrer, A., Pena-Barragán, J.M., Jurado-Expósito, M., de la Orden, M.S. and González-Audicana, M. (2009) Object-and Pixel-Based Analysis for Mapping Crops and Their Agro- Environmental Associated Measures Using QuickBird Imagery. Computers and Electronics in Agriculture, 68, 207-215.
http://dx.doi.org/10.1016/j.compag.2009.06.004
[7] Devadas, R., Denham, R.J. and Pringle, M. (2012) Support Vector Machine Classification of Object-Based Data for Crop Mapping, Using Multi-Temporal Landsat Imagery. International Society for Photogrammetry and Remote Sensing, 39, 185-190.
http://dx.doi.org/10.5194/isprsarchives-xxxix-b7-185-2012
[8] Woodhouse, H.I. (2006) Introduction to Microwave Remote Sensing. CRC Press, Boca Raton.
[9] Baghdadi, N., Boyer, N., Todoroff, P., El Hajj, M. and Bégué, A. (2009) Potential of SAR Sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for Monitoring Sugarcane Crops on Reunion Island. Remote Sensing of Environment, 113, 1724-1738.
http://dx.doi.org/10.1016/j.rse.2009.04.005
[10] Mróz, M. and Mleczko, M. (2008) Potential of Terrasar-X StripMap Data in Early and Rapid Agricultural Crops Mapping. Proceedings of the 2008 MARS Annual Conference “Geomatics in support of the CAP”, Ljubljana, 3-5 December 2008.
[11] Sonobe, R., Tani, H., Wang, X., Kobayashi, N., Kimura, A. and Shimamura, H. (2014) Application of Multi-Temporal TerraSAR-X Data to Map Winter Wheat Planted Areas in Hokkaido, Japan. Japan Agricultural Research Quarterly, 48, 465-470.
http://dx.doi.org/10.6090/jarq.48.465
[12] Dean, A.M. and Smith, G.M. (2003) An Evaluation of Per-Parcel Land Cover Mapping Using Maximum Likelihood Class Probabilities. International Journal of Remote Sensing, 24, 2905-2920.
http://dx.doi.org/10.1080/01431160210155910
[13] Wu, S., Silvánhyphen Cárdenas, J. and Wang, L. (2007) Per-Field Urban Land Use Classification Based on Tax Parcel Boundaries. International Journal of Remote Sensing, 28, 2777-2801.
http://dx.doi.org/10.1080/01431160600981541
[14] Mahmoud, A., Elbialy, S., Pradhan, B. and Buchroithner, M. (2011) Field-Based Landcover Classification Using TerraSAR-X Texture Analysis. Advances in Space Research, 48, 799-805.
http://dx.doi.org/10.1016/j.asr.2011.04.005
[15] Bargiel, D. and Herrmann, S. (2011) Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data. Remote Sensing, 3, 859-877.
http://dx.doi.org/10.3390/rs3050859
[16] “Google Earth”, 2009.
[17] “Google Earth”, 2015.
[18] (1954) Klima-Atlas von Baden-Württemberg. Bad Kissingen (Deutscher Wetterdienst), 1953. Pp. 37; 9 Figs, 75 Charts. 30 dm. Quarterly Journal of the Royal Meteorological Society, 80, 283.
http://dx.doi.org/10.1002/qj.49708034428
[19] Eineder, M., Fritz, T., Mittermayer, J., Roth, A., Boerner, E. and Breit, H. (2008) TerraSAR-X Ground Segment, Basic Product Specification Document. DTIC Document.
[20] Airbus Defence and Space (2014) Radiometric Calibration of TerraSAR-X Data.
[21] Breit, H., Fritz, T., Balss, U., Lachaise, M., Niedermeier, A. and Vonavka, M. (2010) TerraSAR-X SAR Processing and Products. Institute of Electrical and Electronics Engineers, 48, 727-740.
[22] INFOTERRA (2008) Radiometric Calibration of TerraSAR-X Data.
[23] Uprety, P., Yamazaki, F. and Dell’Acqua, F. (2013) Damage Detection Using High-Resolution SAR Imagery in the 2009 L’Aquila, Italy, Earthquake. Earthquake Spectra, 29, 1521-1535.
http://dx.doi.org/10.1193/060211EQS126M
[24] Fritz, T. and Werninghaus, R. (2007) TerraSAR-X Ground Segment Level 1b Product Format Specification. Clustert Applied Remote Sensing (CAF), German Aerospace Center (DLR). German Aerospace Center (DLR), Technical TX-GS-DD-3307.
[25] McNairn, H., Champagne, C., Shang, J., Holmstrom, D. and Reichert, G. (2009) Integration of Optical and Synthetic Aperture Radar (SAR) Imagery for Delivering Operational Annual Crop Inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 434-449.
http://dx.doi.org/10.1016/j.isprsjprs.2008.07.006
[26] Bargiel, D., Herrmann, S., Sorgel, U. and Lohmann, P. (2010) Land Use Classification with High Resolution Satellite Radar for Estimating the Impacts of Land Use Change on the Quality of Ecosystem Services. Proceedings of the ISPRS TC VII Symposium—100 Years ISPRS, Vienna, 5-7 July 2010, 68-73.
[27] Mirzaee, S., Motagh, M., Arefi, H. and Nooryazdan, M. (2014) Classification of Agricultural Fields Using Time Series of Dual Polarimetry TerraSAR-X Images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL–2/W3, 191-196.
http://dx.doi.org/10.5194/isprsarchives-XL-2-W3-191-2014

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.