Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

https://doi.org/10.22146/jcef.64497

Pollen Chakma(1), Aysha Akter(2*)

(1) Pollen Chakma Lecturer, Department of Water Resources Engineering Chittagong University of Engineering and Technology Chittagong 4349, Bangladesh
(2) Dr. Aysha Akter Professor, Department of Civil Engineering & Head, Department of Water Resources Engineering Chittagong University of Engineering & Technology (CUET) Chittagong 4349, Bangladesh Cell Phone: +88 01713 018 512 Alternative E-mail: aysha_akter@cuet.ac.bd aysha_akter@yahoo.com Personal Website: http://aakter.weebly.com
(*) Corresponding Author

Abstract


Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165  gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.


Keywords


Super Cyclone Amphan; Storm Surge; Flood Mapping; SAR; Sentinel-1; Google Earth Engine.

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References

Ali, A., 1999. Climate change impacts and adaptation assessment in Bangladesh. Climate Research, 12(2-3 SPEC. ISS. 6), pp.109–116.

Aristizabal, F., Judge, J. and Monsivais-Huertero, A., 2020. High-resolution inundation mapping for heterogeneous land covers with synthetic aperture radar and terrain data. Remote Sensing, 12(6).

Brisco, B., Short, N., Van Der Sanden, J., Landry, R. and Raymond, D., 2009. A semi-automated tool for surface water mapping with RADARSAT-1. Canadian Journal of Remote Sensing, 35(4), pp.336–344.

Brivio, P.A., Colombo, R., Maggi, M. and Tomasoni, R., 2002. Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23(3), pp.429–441.

Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R. and Chokmani, K., 2018. Flood mapping in a complex environment using bistatic TanDEM-X/TerraSAR-X InSAR coherence. Remote Sensing, 10(12), pp.1–20.

Chowdhury, E.H. and Hassan, Q.K., 2017. Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh. Natural Hazards, 88(3), pp.1805–1823.

Cian, F., Marconcini, M. and Ceccato, P., 2018. Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data. Remote Sensing of Environment, 209(February), pp.712–730.

Clement, M.A., Kilsby, C.G. and Moore, P., 2018. Multi-Temporal SAR Flood Mapping using Change Detection.

Dasgupta, S., Huq, M., Khan, Z., Ahmed, M., Mukherjee, N., Khan, M. and Pandey, K., 2010. Vulnerability of Bangladesh to Cyclones in a Changing Climate: Potential Damages and Adaptation Cost. World Bank Policy Research Working Paper, (5280).

Debsarma, S.K., 2009. Simulations of storm surges in the Bay of Bengal. Marine Geodesy, 32(2), pp.178–198.

ESA, 2012. ESA’s radar observatory mission for GMES operational services. [online] ESA Special Publication, Available at: <https://sentinel.esa.int/documents/247904/349449/S1_SP-1322_1.pdf>.

Hassan, M.M., Ash, K., Abedin, J., Paul, B.K. and Southworth, J., 2020. A quantitative framework for Analyzing spatial dynamics of flood events: A case study of super cyclone Amphan. Remote Sensing, 12(20), p.26.

Henry, J.B., Chastanet, P., Fellah, K. and Desnos, Y.L., 2006. Envisat multi-polarized ASAR data for flood mapping. International Journal of Remote Sensing, 27(10), pp.1921–1929.

Hoque, R., Nakayama, D., Matsuyama, H. and Matsumoto, J., 2011. Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Natural Hazards, 57(2), pp.525–548.

Horritt, M.S., 1999. A statistical active contour model for SAR image segmentation. Image and Vision Computing, 17(3–4), pp.213–224.

Hostache, R., Matgen, P. and Wagner, W., 2012. Change detection approaches for flood extent mapping: How to select the most adequate reference image from online archives? International Journal of Applied Earth Observation and Geoinformation, [online] 19(1), pp.205–213. Available at: <http://dx.doi.org/10.1016/j.jag.2012.05.003>.

Huang, C., Chen, Y. and Wu, J., 2014. Mapping spatio-temporal flood inundation dynamics at large riverbasin scale using time-series flow data and MODIS imagery. International Journal of Applied Earth Observation and Geoinformation, [online] 26(1), pp.350–362. Available at: <http://dx.doi.org/10.1016/j.jag.2013.09.002>.

Insom, P., Cao, C., Boonsrimuang, P., Liu, D., Saokarn, A., Yomwan, P. and Xu, Y., 2015. A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification. IEEE Geoscience and Remote Sensing Letters, 12(9), pp.1943–1947.

Islam, A.S., Bala, S.K. and Haque, M.A., 2010. Flood inundation map of Bangladesh using MODIS time-series images. Journal of Flood Risk Management, 3(3), pp.210–222.

Long, S., Fatoyinbo, T.E. and Policelli, F., 2014. Flood extent mapping for Namibia using change detection and thresholding with SAR. Environmental Research Letters, 9(3).

Martinis, S., Kersten, J. and Twele, A., 2015. A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 104, pp.203–212. Available at: <http://dx.doi.org/10.1016/j.isprsjprs.2014.07.014>.

Mason, D.C., Horritt, M.S., Dall’Amico, J.T., Scott, T.R. and Bates, P.D., 2007. Improving river flood extent delineation from synthetic aperture radar using airborne laser altimetry. IEEE Transactions on Geoscience and Remote Sensing, 45(12), pp.3932–3943.

Mason, D.C., Schumann, G.J.P., Neal, J.C., Garcia-Pintado, J. and Bates, P.D., 2012. Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study. Remote Sensing of Environment, [online] 124, pp.705–716. Available at: <http://dx.doi.org/10.1016/j.rse.2012.06.017>.

Mason, D.C., Speck, R., Devereux, B., Schumann, G.J.P., Neal, J.C. and Bates, P.D., 2010. Flood detection in Urban areas using TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing, 48(2), pp.882–894.

Matgen, P., Hostache, R., Schumann, G., Pfister, L., Hoffmann, L. and Savenije, H.H.G., 2011. Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies. Physics and Chemistry of the Earth, [online] 36(7–8), pp.241–252. Available at: <http://dx.doi.org/10.1016/j.pce.2010.12.009>.

Murty, T.S., Flather, R.A. and Henry, R.F., 1986. The storm surge problem in the bay of Bengal. Progress in Oceanography, 16(4), pp.195–233.

Needs Assessment Working Group, 2020. Cyclone Amphan Joint Needs Assessment (JNA).

Nico, G., Pappalepore, M., Pasquariello, G., Refice, A. and Samarelli, S., 2000. Comparison of SAR amplitude vs. coherence flood detection methods - a GIS application. International Journal of Remote Sensing, 21(8), pp.1619–1631.

RSMC-Tropical Cyclones, N.D., 2021. Report on cyclonic disturbances over the north Indian Ocean during 2019.

Sarker, C., Mejias, L., Maire, F. and Woodley, A., 2019. Flood mapping with convolutional neural networks using spatio-contextual pixel information. Remote Sensing, 11(19), pp.1–25.

Schlaffer, S., Chini, M., Giustarini, L. and Matgen, P., 2017. Probabilistic mapping of flood-induced backscatter changes in SAR time series. International Journal of Applied Earth Observation and Geoinformation, [online] 56, pp.77–87. Available at: <http://dx.doi.org/10.1016/j.jag.2016.12.003>.

Schumann, G., Di Baldassarre, G., Alsdorf, D. and Bates, P.D., 2010. Near real-time flood wave approximation on large rivers from space: Application to the River Po, Italy. Water Resources Research, 46(5), pp.1–8.

Singha, M., Dong, J., Sarmah, S., You, N., Zhou, Y., Zhang, G., Doughty, R. and Xiao, X., 2020. Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 166(January), pp.278–293. Available at: <https://doi.org/10.1016/j.isprsjprs.2020.06.011>.

Tehrany, M.S., Pradhan, B., Mansor, S. and Ahmad, N., 2015. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, [online] 125, pp.91–101. Available at: <http://dx.doi.org/10.1016/j.catena.2014.10.017>.

Tien Bui, D., Hoang, N.D., Martínez-Álvarez, F., Ngo, P.T.T., Hoa, P.V., Pham, T.D., Samui, P. and Costache, R., 2020. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, [online] 701, p.134413. Available at: <https://doi.org/10.1016/j.scitotenv.2019.134413>.

Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B.Ö., Floury, N., Brown, M., Traver, I.N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A., Huchler, M. and Rostan, F., 2012. GMES Sentinel-1 mission. Remote Sensing of Environment, [online] 120, pp.9–24. Available at: <http://dx.doi.org/10.1016/j.rse.2011.05.028>.

Twele, A., Cao, W., Plank, S. and Martinis, S., 2016. Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), pp.2990–3004.

Uddin, K., Matin, M.A. and Meyer, F.J., 2019. Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sensing, 11(13).

Woznicki, S.A., Baynes, J., Panlasigui, S., Mehaffey, M. and Neale, A., 2019. Development of a spatially complete floodplain map of the conterminous United States using random forest. Science of the Total Environment, [online] 647, pp.942–953. Available at: <https://doi.org/10.1016/j.scitotenv.2018.07.353>.



DOI: https://doi.org/10.22146/jcef.64497

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