Advertisement

Analysis of Tsunami-Affected and Reconstructed Areas in Nagapattinam Using Remote Sensing

  • G. Wiselin JijiEmail author
  • G. Sumilda Merlin
  • A. Rajesh
Research Article
  • 6 Downloads

Abstract

The recent experiences in the 2004 Indian Ocean tsunami showed the efficiency of remote sensing techniques in quick damage mapping and recovery efforts. This study develops an object-based image analysis method for mapping the tsunami-affected areas. The proposed method is carried out in four steps. In the first step, data preprocessing operations, data registration, and cloud removal are carried out based on the multi-source data. In the second step, object-based classification operation is performed to separate the images into segments. In the third step, infrastructural damages are identified. In the fourth step, developments after the tsunami are found out. The results showed that the proposed method has given a higher accuracy than the earlier methods. The proposed combination of techniques gives a high accuracy of 99% for the detection of changes in vegetation. The change detection result found 39% change in the building in 2005, 32% of soil area and 8.7% of water region had been increased within 2 years. The building elevation method estimated newly developed one story and two story buildings as 11,112 and 142, respectively.

Keywords

Image processing Image segmentation Change detection Building detection 

Notes

Funding

This study was funded by Department of Science and Technology-Earth Science, New Delhi (NRDMS/11/1930/012(G)).

Compliance with Ethical Standards

Conflict of interest

Authors have no conflict of interest.

References

  1. Ahmet, Ġ. (2010). Shadow detection and compensation in aerial images with an application to building height estimation. A Thesis Submitted to the Graduate School of Natural and Applied Sciences of Middle East Technical University.Google Scholar
  2. Al-Khudhairy, D. H. A., Caravaggi, I., & Giada, S. (2005). Structural damage assessments from ikonos data using change detection, object-oriented segmentation, and classification techniques. Brussels: Joint Research Centre, Commission of the European Communities.CrossRefGoogle Scholar
  3. Belaid, L., & Mourou, W. (2011). Image segmentation: A watershed transformation algorithm. Image Analysis & Stereology, 28(2), 93–102.CrossRefGoogle Scholar
  4. Beucher, S. (1990). Segmentation d’image et Morphologie math´ematique. Th`ese de Doctorat, Ecole Nationale Sup´erieure des Mines de Paris.Google Scholar
  5. Beucher, S., & Lantu´ejoul, C. (1979). Use of watersheds in contour detection. In Proceedings of International Workshop Image Process, Real-Time Edge Motion Detection/Estimation, Rennes, France.Google Scholar
  6. Centre for Development and Emergency Practice (CENDEP), The Resource Centre for Participatory Development Studies (RCPDS). (2012). Revisiting Communities after the 2004 Tsunami, Participatory Rapid Appraisal, Nagapattinam, Tamil Nadu, India.Google Scholar
  7. Cheng, H. D., Jiang, X. H., Sun, Y., & Wang, J. (2001). Color image segmentation: advances and prospects. Pattern Recognition, 34(12), 2259–2281.CrossRefGoogle Scholar
  8. Costantini, M. L., et al. (2012). NDVI spatial pattern and the potential fragility of mixed forested areas in volcanic lake watersheds. Forest Ecology and Management, 285, 133–141.CrossRefGoogle Scholar
  9. de Silveira, E. M. O., Acerbi Júnior, F. W., de Mello, J. M., & Bueno, I. T. (2017). Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil. Ciência e Agrotecnologia, 41(5), 554–564.CrossRefGoogle Scholar
  10. Erener, A., & Düzgün, H. S. B. (2009). Prediction of population in urban areas by using high resolution satellite images.Google Scholar
  11. Fonseca, L. M. G., & Manjunath, B. S. (1996). Registration techniques for multisensor remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 62(9), 1049–1056.Google Scholar
  12. Gao, B. C. (1996). NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.CrossRefGoogle Scholar
  13. Garrigues, S., Allard, D., Baret, F., & Weiss, M. (2006). Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105(4), 286–298.CrossRefGoogle Scholar
  14. Griffith, J. A. (2007). Interrelationships among landscapes, NDVI, and stream water quality in the U. S. Central plains. Ecological Applications, 12(6), 1702–1718.CrossRefGoogle Scholar
  15. Han-qiu, X. U. (2005). A study on information extraction of water body with the modified Normalized Difference Water Index (MNDWI). Journal of Remote Sensing, 9(5), 589–595.Google Scholar
  16. Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics and Image Processing, 29(1), 100–132.CrossRefGoogle Scholar
  17. Holme, A Mc R, Burnside, D. G., & Mitchell, A. A. (1987). The development of a system for monitoring trend in range condition in the arid shrublands of Western Australia. Australian Rangeland Journal, 9, 14–20.CrossRefGoogle Scholar
  18. Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., et al. (2004). Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92, 475–482.CrossRefGoogle Scholar
  19. Ji, L., Zhang, L., & Wylie, B. (2009). Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing, 75, 1307.CrossRefGoogle Scholar
  20. Larry, R. (1997). Creating a normalized difference vegetation index (NDVI) image using multi spec, pp. 1–2. The GLOBE Program. University Of New Hampshire, Durham.Google Scholar
  21. Maki, M., Ishiahra, M., & Tamura, M. (2004). Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90, 441–450.CrossRefGoogle Scholar
  22. McFarland, T.M., & van Riper, C., (2013). Use of Normalized Difference Vegetation Index (NDVI) habitat models to predict breeding birds on the san pedro river, Arizona. USGS Science for a Changing World.Google Scholar
  23. McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.CrossRefGoogle Scholar
  24. Nagesh Kumar, D. (2001). Satellite image processing with MATLAB, MathWorks Inc., Image processing tool box users guide.Google Scholar
  25. Nagesh Kumar, D. (2014). Module—6 Lecture Notes—2 image processing using MATLAB. Remote Sensing-Digital Image Processing Software.Google Scholar
  26. Otsu, N. (1979). A threshold selection method from gray level histograms‖. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.CrossRefGoogle Scholar
  27. Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1274–1294.CrossRefGoogle Scholar
  28. Pavlidis, T. (1988). Image analysis. Annual Review of Computer Science, 3, 121–146.CrossRefGoogle Scholar
  29. Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. Journal of Computing, 2(2), 2151–9617.Google Scholar
  30. Ramesh, B., Satheesh, K. S. (2013). Cloud detection and removal algorithm based on mean and hybrid methods. International Journal of Computing Algorithm, 02(1).Google Scholar
  31. Reddy, B. S., & Chatterji, B. N. (1996). An FFT-based technique for translation, rotation and scale-invariant image registration. IEEE Transactions on Image Processing, 5(8), 1266–1271.CrossRefGoogle Scholar
  32. Roerdink, J. B. T. M., & Meijster, A. (2001). The watershed transform: de_nitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae, 41, 187–228.Google Scholar
  33. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third ERTS Symposium, NASA SP-351 I, pp. 309–317.Google Scholar
  34. Sirmacek, B., & Unsalan, C. (2008). Building detection from aerial images using invariant color features and shadow information. In Computer and Information Sciences International Symposium, pp. 1–5.Google Scholar
  35. Soille, P. (1992). Morphologie math´ematique: Du relief `a la dimensionnalit´e, Algorithmes et m´ethodes. Th`ese de Doctorat, Facult´e des Sciences Agronomiques de l’Universit´e Catholique e Louvrain.Google Scholar
  36. Teillet, P. M., Staenz, K., & William, D. J. (1997). Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61(1), 139–149.CrossRefGoogle Scholar
  37. Vanitha1, A., Subashini, P., & Krishnaveni, M. (2013). Sar ice image classification using parallelepiped classifier based on gram-schmidt spectral technique. In Wyld, D.C. (eds) ICCSEA, SPPR, CSIA, WimoA, SCAI. pp. 385–392.Google Scholar
  38. Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., & Sampson, P. H. (1999). Optical indices as bioindicators of forest condition from hyperspectral CASI data. In Proceedings 19th Symposium of the European Association of Remote Sensing Laboratories (EARSeL), Valladolid, Spain.Google Scholar
  39. Zhang, Y. J. (1997). Evaluation and comparison of different segmentation algorithms. Pattern Recognition Letters, 18(10), 963–974.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  • G. Wiselin Jiji
    • 1
    Email author
  • G. Sumilda Merlin
    • 1
  • A. Rajesh
    • 2
  1. 1.Department of Computer Science and EngineeringDr. Sivanthi Aditanar College of EngineeringTiruchendurIndia
  2. 2.Vikram Sarabhai Space CentreIndian Space Research OrganizationThiruvananthapuramIndia

Personalised recommendations