DOI QR코드

DOI QR Code

A Statistical Analysis of JERS L-band SAR Backscatter and Coherence Data for Forest Type Discrimination

  • Zhu Cheng (Program in Environmental and Resource Engineering SUNY College of Environmental Science and Forestry) ;
  • Myeong Soo-Jeong (Program in Environmental and Resource Engineering SUNY College of Environmental Science and Forestry)
  • Published : 2006.02.01

Abstract

Synthetic aperture radar (SAR) from satellites provides the opportunity to regularly incorporate microwave information into forest classification. Radar backscatter can improve classification accuracy, and SAR interferometry could provide improved thematic information through the use of coherence. This research examined the potential of using multi-temporal JERS-l SAR (L band) backscatter information and interferometry in distinguishing forest classes of mountainous areas in the Northeastern U.S. for future forest mapping and monitoring. Raw image data from a pair of images were processed to produce coherence and backscatter data. To improve the geometric characteristics of both the coherence and the backscatter images, this study used the interferometric techniques. It was necessary to radiometrically correct radar backscatter to account for the effect of topography. This study developed a simplified method of radiometric correction for SAR imagery over the hilly terrain, and compared the forest-type discriminatory powers of the radar backscatter, the multi-temporal backscatter, the coherence, and the backscatter combined with the coherence. Statistical analysis showed that the method of radiometric correction has a substantial potential in separating forest types, and the coherence produced from an interferometric pair of images also showed a potential for distinguishing forest classes even though heavily forested conditions and long time separation of the images had limitations in the ability to get a high quality coherence. The method of combining the backscatter images from two different dates and the coherence in a multivariate approach in identifying forest types showed some potential. However, multi-temporal analysis of the backscatter was inconclusive because leaves were not the primary scatterers of a forest canopy at the L-band wavelengths. Further research in forest classification is suggested using diverse band width SAR imagery and fusing with other imagery source.

Keywords

References

  1. Angelis, C. F., C. C. Freitas, D. M. Valetriano, and L. V. Dutra, 2002. Multitemporal analysis of land use/land cover JERS-1 backscatter in the Brazilian tropical rainforest. Interntion Journal of Remote Sensing, 23(7): 1231- 1240 https://doi.org/10.1080/01431160110092876
  2. Askne, J. I. H., P. B. G. Dammert, L. M. H. Ulander, and G. Smith, 1997. C-band repeat-pass interferometric SAR observations of the Forest. IEEE Transactions on Geoscience and Remote Sensing, 35(1): 25-35 https://doi.org/10.1109/36.551931
  3. Atlantis Scientific Inc., 1999a. EarthView APP User's Guide, Version 1.5, Nepean, Ontario, Canada, 149p
  4. Atlantis Scientific Inc., 1999b. EVInSAR User's Guide, Version 1.2, Nepean, Ontario, Canada, 318p
  5. Bayer, T., R. Winter, and G. Schreier, 1991. Terrain influences in SAR backscatter and attempts to their correction. IEEE Transactions on Geoscience and Remote Sensing, 29(3): 451-462 https://doi.org/10.1109/36.79436
  6. Beaudoin, A., N. Stussi, D. Troufleau, N. Desbois, L. Piet, and M. Deshayes, 1995. On the use of ERS-1 SAR data over hilly terrain: necessity of radiometric corrections for thematic applications. Proceedings of International Geoscience and Remote Sensing Symposium, Vol. 3, 10-14 July 1995, Firenze, Italy, pp. 2179-2182
  7. Curlander J. and R. McDonough, 1991. Synthetic Aperture Radar: Systems and Signal Processing, John Wiley and Sons, Inc., New York, 647p
  8. Elachi C., 1998. Spaceborne Radar Remote Sensing: Applications and Techniques, IEEE Press, New York, 255p
  9. European Forests Observation by Radars (EUFORA), 1998, URL: http://www.rss. chalmers.se/WWW_rsg/Research/Projects/E UFORA/EUFORA_FinalReport.pdf
  10. Gens, R. and J. L. Van Genderen, 1996. SAR interferometry-Issues, techniques, applications. International Journal of Remote Sensing, 17: 1803-1835 https://doi.org/10.1080/01431169608948741
  11. Henderson, F. M. and A. J. Lewis, 1998. Principles and Applications of Imaging Radar, Manual of Remote Sensing, Third Edition, Vol. 2, John Wiley & Sons, Inc., New York, 866p
  12. Huurneman, G., R. Gens and L. Broekema, 1996. Thematic information extraction in a neural network classification of multi-sensor data including microwave phase information. International Archives of Photogrammetry and Remote sensing, Vol. XXXI, Part B2, pp. 170-175
  13. Johnson, Richard. A. and D. W. Wichern, 1998. Applied Multivariate Statistical Analysis. 4th Ed. Prentice Hall
  14. Kinn, Gerald, 2002. Direct Georeferencing In Digital Imaging Practice. Photogrammetric Engineering and Remote Sensing, 68(5): 399-402
  15. Kuehl, Robert, 1994. Design of Experiments: Statistical Principles of Research Design and Analysis. 2nd ed. Duxbury Press
  16. Manly, Bryan. F. J., 1994. Multivariate Statistical Methods: A Primer, 2nd Ed. Chapman & Hall/CRC
  17. Raney, R. K., T. Freeman, R. W. Hawhins, and R. Bamler, 1994. A plea for radar brightness Proceedings of International Geoscience and Remote Sensing Symposium, Vol. 2, 8-12 Aug. 1994, Pasadena, CA, U.S.A., pp.1090-1092
  18. Rignot, E. J. M., C. L. Williams, J. Way, and L. A. Viereck, 1994. Mapping of Forest Types in Alaskan Boreal Forests Using SAR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 32(5): 1051-1059 https://doi.org/10.1109/36.312893
  19. Rodes, E. and S. Saatchi, 2002. Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. Interntion Journal of Remote Sensing, 23(7): 1487-1506 https://doi.org/10.1080/01431160110093000
  20. Ruck, G. T., D. E. Barrick, W. D. Stuart, and C. K. Krichbaum, 1970. Radar Cross Section Handbook, Editor: George T. Ruck, Volume 2, Plenum Press, New York-London, 949p
  21. Salas, W. A., M. J. Ducey, E. Rignot, and D. Skole, 2002a. Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: I. Spatial and temporal variability in backscatter across a chrono-sequence of secondary vegetation stands in Rondonia. Interntion Journal of Remote Sensing, 23(7): 1357-1379 https://doi.org/10.1080/01431160110092939
  22. Salas, W. A., M. J. Ducey, E. Rignot, and D. Skole, 2002b. Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and radiometric considerations for operational monitoring. Interntion Journal of Remote Sensing, 23(7): 1381-1399 https://doi.org/10.1080/01431160110092948
  23. SAS Institute, 1999. SAS/STAT User's Guide, Version 8, STATS Publishing Inc
  24. Schreier, G., 1993. SAR Geocoding: Data and Systems, Chapter 14, Karlsruhe: Wichmann, 435p
  25. Sgrenzaroli, M., G. F. De Grandi, H. Eva, and F. Achard, 2002. Tropical forest cover monitoring: estimates from the GRFM JERS-1 radar mosaics using wavelet zooming techniques and validataion. 23(7): 1329-1355 https://doi.org/10.1080/01431160110092920
  26. Shimada, M. and Isoguchi, O., 2002. JERS-1 SAR mosaic of Southeast Asia using calibrated path images. Interntion Journal of Remote Sensing, 23(7):1507-1526 https://doi.org/10.1080/01431160110092678
  27. Shimada, M., 1996. Radiometric and Geometric calibration of JERS-1 SAR. Advances in space research, 17(1): 79-88
  28. Simard, M. G. D. Grandi, S. Saatchi, and P. Mayaux, 2002. Mapping tropical coastal vegetation using JERS-1 and ERS-1 radar data with a decision tree classifier. Interntion Journal of Remote Sensing, 23(7): 1461-1474 https://doi.org/10.1080/01431160110092984
  29. Smith, J. A., 1980. The Lambertian assumption and Landsat data. Photogrammetric Engineering and Remote Sensing, 46(9): 1183-1189
  30. Stofan, E. R., D. L. Evans, C. Schmullius, B. Holt, J. J. Plaut, J. van Zyl, S. D. Wall, and J. Way, 1995. Overview of results of spaceborne imaging radar-C, X-band synthetic aperture radar (SIR-C/X-SAR). IEEE Transactions on Geoscience and Remote Sensing, 33(4): 817-828 https://doi.org/10.1109/36.406668
  31. Strozzi, T., P. B. G. Dammert, U. Wegmuller, J. M. Marinex, J. I. H. Askne, A. Beaudoin, and M. T. Hallikainen, 2000. Landuse mapping with ERS SAR interferometry. IEEE Transcations on Geoscience and Remote Sensing, 38(2): 766-744 https://doi.org/10.1109/36.842005
  32. Torma, M. and J. Koskinen, 1997. Land-use classification using temporal SAR-images, Proceedings of International Geoscience and Remote Sensing Symposium, Vol. 1, Singapore, pp. 44-46
  33. Ulaby, F. T., R. K. Moore, A. K. Fung, 1982. Microwave Remote Sensing: Active and Passive, Chapter 7, Vol.2, Addison-Wesley Publishing Company: Massachusetts, 2162p
  34. Ulander, L. M. H., 1996. Radiometric slope correction of synthetic-aperture radar images. IEEE Transactions on Geoscience and Remote Sensing, 34(5): 1115-1122 https://doi.org/10.1109/36.536527
  35. U.S. Geological Survey, 1998. URL: http://www.apa.state.ny.us/gis/shared/htmlpages/metadata/10mDEM.html
  36. Van Zyl, J. J., B. D. Chapman, P. Dubois, and J. Shi, 1993. The effect of topography on SAR calibration. IEEE Transactions on Geoscience and Remote Sensing, 30(5): 1036-1043
  37. Wegmuller, U. and C. L. Werner, 1995. SAR interferometric signatures of forest. IEEE Transactions on Geoscience and Remote Sensing, 33(5): 950-959 https://doi.org/10.1109/36.406681
  38. Wegmuller, U. and C. L. Werner, 1997. Retrieval of vegetation parameters with SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 35(1): 18-24 https://doi.org/10.1109/36.551930
  39. Wegmuller, U., T. Strozzi, and C. Werner, 1997. Forest applications of ERS, JERS, and SIR-C SAR interferometry. Proceedings of International Geoscience and Remote Sensing Symposium, Vol. 2, Singapore, pp. 790-792