IDENTIFICATION OF FLOODED AREAS DUE TO SEVERE STORM USING ENVISAT ASAR DATA AND NEURAL NETWORKS
Abstract
The specific objective of the present study is to identify flooded areas due to cyclonic storm using Envisat ASAR VV polarized data and Artificial Neural Network (ANN). On October 30, 2006, the Ogni storm crossed the Indian coast. It impacted three coastal districts in Andhra Pradesh, including Guntur, Prakasam, and Krishna. The present study considers only nine mandals of Guntur district of Andhra Pradesh for identification of flooded areas. For this purpose, pre and post event images of study area were procured of Envisat satellite (April 23, 2006 and November 4, 2006). Field visit to the affected district after the disaster was carried out to gather landcover information. In all, 564 pixels landcover information was collected during the visit (These were corresponding to pre event Envisat image of April 23, 2006). Out of the 564 pixels, randomly 406 pixels (91 were water and the remaining 315 were non-water pixels) were used for training the Neural Network and the remaining for testing. Using the trained ANN model, the total water area in the nine mandals of Guntur using Envisat ASAR satellite imagery of April 23, 2006 was found to be 2.344 thousand hectares. The trained model was applied to the post event Envisat ASAR image of November 4, 2006 to obtain completely submerged and partial/non submerged areas under water. The completely submerged landcover under water in nine mandals of Guntur district on November 4, 2006 was found to be 13.2705 thousand hectares. Results suggest a high accuracy of classification and indicate that this may be a rapid tool for damage estimation and post disaster relief and recovery efforts.
References
Abhyankar, A. A., Singh, A., Sharma, U., Patwardhan, A. and Inamdar, A. (2004). Constructing a tropical cyclone hazard index for coastal India, International Symposium on Natural Hazards, Hyderabad, February 24-28.
Hess, L. L., Melack, J. M., Filoso S. and Wang, Y. (1995). Delineation of Inundated Area and Vegetation along the Amazon Floodplain with the SIR-C Synthetic Aperture Radar. IEEE Transactions of Geoscience and Remote Sensing, 33 (4), 896-904.
https://doi.org/10.1109/36.406675
Toan, T. L, Ribbes, F., Wang, L., Floury, N. and Ding, K. (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions of Geoscience and Remote Sensing, 35(1), 41-55.
https://doi.org/10.1109/36.551933
Ormbsy, J. P., Blanchard, J. P. and Blanchard, A. J. (1985). Detection of lowland flooding using active microwave systems, Photogrammetric Engineering and Remote Sensing, 51, 317-328.
Ribbes, F. and Toan, T. L. (1998). Mapping and Monitoring Rice crop with Radarsat Data, Geoscience and Remote Sensing Symposium, IGARSS '1998 Proceedings, IEEE International, 2749-2751.
https://doi.org/10.1109/IGARSS.1998.702339
Imhoff, M. L., Verimillion, C., Story, M., Choudhury, A. and Gafoor, A. (1987). Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data, Photogrammetric Engineering and Remote Sensing, 53, 405-413.
Schumann, G., Hostache, R., Puech, C., Hoffmann, L., Matgen, P., Pappenberger, F. and Pfister, L. (2007). High-resolution 3-D flood information from Radar imagery for flood hazard management, IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1715-1725.
https://doi.org/10.1109/TGRS.2006.888103
Chokmani, K., Bernier, M., Poulin, J. and Raymond S. (2017). River flood mapping in urban areas combining Radarsat-2 data and flood return period data, Remote Sensing of Environment, 198(1), 442-459
https://doi.org/10.1016/j.rse.2017.06.042
Jiren, L. and Shifeng, H. (2005). Application of ERS/ENVISAT to flood monitoring and assessment in China, IEEE International Geoscience and Remote Sensing Symposium, 8, 5674-5677.
Ramsey III, E., Rangoonwala, A. and Bannister, T. (2013). Coastal flood inundation monitoring with satellite C-band and L-band synthetic aperture radar data, Journal of the American Water Resources Association, 49(6), 1239-1260.
https://doi.org/10.1111/jawr.12082
Yamada, Y., Sakurai-Amano, T. and Takagi, M. (2002). Detection of flood damaged areas in the entire Chao Phraya River Basin from JERS-1/SAR images with a help of spatial information, IEEE International Geoscience and Remote Sensing Symposium, 5, 2877-2879.
Badji, M. and Dautrebande, S. (1997). Characterization of flood inundated areas and delineation of poor drainage soil using ERS-1 SAR imagery, Hydrological Processes, 11(10), 1441-1450.
https://doi.org/10.1002/(SICI)1099-1085(199708)11:10<1441::AID-HYP527>3.0.CO;2-Y
Shao, Y., Fan, X., Liu, H., Xiao, J., Ross, S., Brisco, B., Brown, R. and Saples, G. (2001). Rice Monitoring and production estimation using multi-temporal Radarsat, Remote Sensing of Environment, 76, 310-325.
https://doi.org/10.1016/S0034-4257(00)00212-1
Lee, K. S. and Lee, S. I. (2003). Assessment of post-flooding conditions of rice fields with multi-temporal SAR data. International Journal of Remote Sensing, 24 (17), 3457-3465.
https://doi.org/10.1080/0143116021000021206
Henry, J. B., Chastanet, P., Fellah, K. and Desnos, Y.L. (2003). Envisat multi-polarized ASAR data for flood mapping, IEEE International Geoscience and Remote Sensing Symposium, 2, 1136-1138.
Yang, C., Huang, H., Wei, Y., Zhu, H. and Zhuo, J. (2002). Rapidly Assessing the flood Disaster by using Remote sensing and GIS, Geoscience and Remote Sensing Symposium, IGARSS '02, IEEE International, 5, 2880-2882.
Malnes, E., Guneriussen, T. and Hogda, K. A. (2002). Mapping of flood area by Radarsat in Vansjo, Norway http://projects.itek.norut.no/snowman/Publications/Malnes1_ISRSE_2002.pdf (last accessed on July 31, 2013).
Abhyankar, A. A., Patwardhan, A. and Inamdar, A. B. (2012). Estimation of Flooded Areas due to Supercyclone using Radarsat-1 SAR Data and Discriminant Approach-An Indian Case Study, paper presented at the 33rd Asian Conference on Remote Sensing, Pattaya, Thailand, November 26-30.
Notes on Cyclone Damages and Relief Measures, Guntur district {Submitted to Inter Ministerial Team (09-11-2006 and 10-11-2006); Collector and District Magistrate, Guntur; unpublished (2006)}.
Agriculture Production Plan, Agriculture Department, Guntur District, 2008-2009.
Director of Economics and Statistics, 2006, Government of India, Andhra Pradesh, Hyderabad
Rosich, B. and Meadows, P. (2004). Absolute Calibration of ASAR Level 1 Products Generated with PF-ASAR, ENVICLVL-EOPG-TN-03-0010, Issue 1, date of issue: October 7, 2004, revision 5, Technical note http://earth.esa.int/pub/ESA_DOC/ENVISAT/ASAR/ASAR_products_absolute_calibration_v1.5.pdf (last accessed on September 15, 2009).
Zhenghao, S. and Fung, K. B. (1994). A Comparison of Digital Speckle Filters, IEEE International Geoscience and Remote Sensing Symposium, 4, 2129-2133.
Haykins, S. (2005). Neural Networks: A Comprehensive Foundation, Second Edition, Pearson Prentice Hall.
Paliwal, M. and Kumar, A. K. (2009). Neural Network and Statistical Techniques: A Review of Applications. Expert Systems with Application, 36, 2-17.
https://doi.org/10.1016/j.eswa.2007.10.005
Hair, J., Anderson, R., Tatham, R. and Black, W. (1998). Multivariate Data Analysis, Fifth edition, Prentice-Hall.
Abhyankar, A. A. (2010). Rapid Assessment of Tropical Cyclone Impacts Using Remote Sensing with Application to Disaster Management, PhD. Thesis, IIT Bombay (unpublished).
Copyright (c) 2019 Journal of Civil Engineering, Science and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Upon acceptance of an article, the corresponding author on behalf of all authors will be asked to complete and upload the Copyright Transfer Form (refer to Copyright Issues for more information on this) alongside the electronic proof file.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer the publication in the Journal.
2) For open-access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds the copyright or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subjected to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) are responsible to ensure their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
5) In signing this statement, the author(s) declare that the researches which they have conducted comply with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.
