Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 878149, 8 pages
http://dx.doi.org/10.1155/2014/878149
Research Article

Automatic Recognition of Seismic Intensity Based on RS and GIS: A Case Study in Wenchuan Ms8.0 Earthquake of China

Center for Digital Engineering and Simulation, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan 430074, China

Received 30 August 2013; Accepted 11 December 2013; Published 3 February 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Qiuwen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Hao and L. Xie, “The isoseismal of seismic intensity for the 9.21 Taiwan Chi-Chi earthquake of 1999,” Journal of Harbin Institute of Technology, vol. 39, no. 2, pp. 169–172, 2007. View at Google Scholar · View at Scopus
  2. F. Yamazaki, “Applications of remote sensing and GIS for damage assessment,” Structural Safety and Reliability, 2001. View at Google Scholar
  3. D. R. Li, “Development trends and our task of surveying and mapping in the 21st century,” China Surveying and Mapping, vol. 2, pp. 1–5, 2005. View at Google Scholar
  4. P. Li and X. X. Tao, “Quantitative earthquake damage detection from changes in remote sensing images-a case study,” Geoscience and Remote Sensing Symposium, vol. 3, pp. 1–4, 2005. View at Google Scholar
  5. S. G. Li, Science of Disaster, China Coal Industry Press, Zhengzhou, China, 2008.
  6. B. Gutenberg and C. F. Richter, “Magnitude and energy of earthquakes,” Annals of Geophysics, vol. 53, no. 1, pp. 7–12, 2010. View at Google Scholar · View at Scopus
  7. China Earthquake Administration, The Chinese Seismic Intensity Scale, Standards Press of China, Beijing, China, 2009.
  8. Y. Yang, M. X. Deng, and J. H. Xu, “DTM-based isoseismal line computer aided drawing,” Journal of Institute of Disaster Prevention, vol. 9, pp. 21–23, 2007. View at Google Scholar
  9. P. C. Qi, G. M. Guo, and J. H. Pan, “Proposal for teaching models of remote sensing digital image processing course,” Science of Surveying and Mapping, vol. 37, pp. 194–117, 2012. View at Google Scholar
  10. M. J. Versluis, B. P. Sutton, P. W. de Bruin, P. Börnert, A. G. Webb, and M. J. van Osch, “Retrospective image correction in the presence of nonlinear temporal magnetic field changes using multichannel navigator echoes,” Magnetic Resonance in Medicine, vol. 68, no. 6, pp. 1836–1845, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. F. J. Zhao, S. Cai, and X. Chen, “Application of rapid seismic damage assessment based on remote sensing to Wenchuan earthquake,” Journal of Natural Disasters, vol. 19, no. 1, pp. 1–7, 2010. View at Google Scholar · View at Scopus
  12. D. Ehrlich, H. D. Guo, K. Molch, J. W. Ma, and M. Pesaresi, “Identifying damage caused by the 2008 Wenchuan earthquake from VHR remote sensing data,” International Journal of Digital Earth, vol. 2, no. 4, pp. 309–326, 2009. View at Publisher · View at Google Scholar
  13. F. Yamazaki, K. Kouchi, M. Kohiyama, N. Muraoka, and M. Matsuoka, “Earthquake damage detection using high-resolution satellite images,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet (IGARSS '04), vol. 4, pp. 2280–2283, September 2004. View at Scopus
  14. L. P. Zhang and H. Xin, “Advanced processing techniques for remotely sensed imagery,” Journal of Remote Sensing, vol. 13, pp. 559–569, 2009. View at Google Scholar
  15. B. Q. Zhu, “Rapid extraction of aerial remote sensing seismic disaster information,” Journal of Natural Disaster, vol. 7, pp. 34–39, 1998. View at Google Scholar
  16. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, “Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 58, no. 3-4, pp. 239–258, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Li, J. Yin, C. Bai, J. Zhao, and F. Ye, “An object-oriented method for extracting city information based on high spatial resolution remote sensing images,” International Journal of Advancements in Computing Technology, vol. 3, no. 5, pp. 80–88, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. X. G. Tian, Object-Oriented Information Extraction of High-Resolution Remotely Sensed Images, Chinese Academy of Surveying and Mapping, Beijing, China, 2007.
  19. S. Agarwal, L. S. Vailshery, M. Jaganmohan, and H. Nagendra, “Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches,” ISPRS International Journal of Geo-Information, vol. 2, no. 1, pp. 220–236, 2013. View at Publisher · View at Google Scholar
  20. D. P. Ming, J. C. Luo, C. H. Zhou et al., “Information extraction from high resolution remote sensing image and parcel unit extraction based on features,” Journal of Data Acquisition and Processing, vol. 20, no. 1, pp. 34–39, 2005. View at Google Scholar · View at Scopus