Advertisement

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India

  • Samvedya Surampudi
  • Kiran YarrakulaEmail author
Research Article

Abstract

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as − 18 to − 30 dB and the range is identified as − 19 to − 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km2 on peak flood date for the chosen study area.

Keywords

Assam 2017 floods Visual interpretation map Colour segmentation Statistical distribution Histogram analysis 

Notes

Acknowledgements

The authors would like to thank the Indian Space Research Organization (ISRO) for funding this project under grant NDM-01. The authors are also thankful to Vellore Institute of Technology (VIT, Vellore) for providing necessary facilities to carry out the research work. They would like to thank Space Application Centre, Ahmedabad, for providing access to IMD AWS rainfall data. Finally, the authors would also like to thank European Space Agency (ESA) for providing the Sentinel-1A data.

References

  1. ASDMA report, Assam State Disaster Management authority, 2017a,Google Scholar
  2. ASDMA report, Assam State Disaster Management authority, 2017b,Google Scholar
  3. ASDMA report, Assam State Disaster Management authority, 2017c.Google Scholar
  4. Bazi Y, Bruzzone L, Melgani F (2005) An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43:874–886.  https://doi.org/10.1109/TGRS.2004.842441 CrossRefGoogle Scholar
  5. Bhattachaiyya NN, Bora AK (1997) Floods of the Brahmaputra river in India. Water Int. 22:222–229.  https://doi.org/10.1080/02508069708686709 CrossRefGoogle Scholar
  6. Dewan M, Ashraf, Kankam-Yeboah K, Nishigaki M (2006) Using Synthetic Aperture Radar (SAR) data for mapping river water flooding in an urban landscape: a case study of Greater Dhaka. Bangladesh Ashraf. J. Japan Soc. Hydrol. Water Resour. 19:44–54CrossRefGoogle Scholar
  7. Di Baldassarre, G., Schumann, G., Brandimarte, L., Bates, P., 2011. Timely Low Resolution SAR Imagery To Support Floodplain Modelling: A Case Study Review. Surv. Geophys. 32, 255–269.  https://doi.org/10.1007/s10712-011-9111-9 CrossRefGoogle Scholar
  8. Elsafi SH (2014) Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile. Sudan. Alexandria Eng. J. 53:655–662.  https://doi.org/10.1016/j.aej.2014.06.010 CrossRefGoogle Scholar
  9. Giustarini L, Hostache R, Matgen P, Schumann GJ, Bates PD, Mason DC (2013) A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sens. 51:2417–2430.  https://doi.org/10.1109/TGRS.2012.2210901 CrossRefGoogle Scholar
  10. Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21:2141–2151.  https://doi.org/10.1109/TIP.2011.2170702 CrossRefGoogle Scholar
  11. Goswami D.C., 1985. BrahmaPutra River, India’ Physiography, Basin 21, 959–978.Google Scholar
  12. Henry JB, Chastanet P, Fellah K, Desnos YL (2006) Envisat multi-polarized ASAR data for flood mapping. Int. J. Remote Sens. 27:1921–1929.  https://doi.org/10.1080/01431160500486724 CrossRefGoogle Scholar
  13. Heremans R, Wiilekens A, Borghys D, Verbeeck B, Valckenborgh J, Acheroy M, Perneel C (2003) Automatic detection of flooded areas on ENVISAT/ASAR images using an object-oriented classification technique and an active contour algorithm. RAST 2003 - Proc. Int. Conf. Recent Adv. Sp. Technol:311–316.  https://doi.org/10.1109/RAST.2003.1303926
  14. Hong S, Jang H, Kim N, Sohn HG (2015) Water area extraction using RADARSAT SAR imagery combined with landsat imagery and terrain information. Sensors (Switzerland) 15:6652–6667.  https://doi.org/10.3390/s150306652 CrossRefGoogle Scholar
  15. Horritt, M.S., Mason, D.C., Luckman, A.J., 2001. Flood boundary delineation from synthetic aperture radar imagery using a statistical active contour model. Int. J. Remote Sens. 22, 2489–2507.  https://doi.org/10.1080/01431160116902 CrossRefGoogle Scholar
  16. Huang S, Cai X, Chen S, Liu D (2011) Change detection method based on fractal model and wavelet transform for multitemporal SAR images. Int. J. Appl. Earth Obs. Geoinf. 13:863–872.  https://doi.org/10.1016/j.jag.2011.05.018 CrossRefGoogle Scholar
  17. Imhoff ML, Vermillion C, Story MH, Choudhury AM, Gafoor A, Polcyn F (1987) Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat Data. Photogramm. Eng. Remote Sensing 53:405–413Google Scholar
  18. Lee JS, Jurkevich I, Dewaele P, Wambacq P, Oosterlinck A (1994) Speckle filtering of synthetic aperture radar images: a review. Remote Sens. Rev. 8:313–340.  https://doi.org/10.1080/02757259409532206 CrossRefGoogle Scholar
  19. Lee JS, Wen JH, Ainsworth TL, Chen KS, Chen AJ (2009) Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans. Geosci. Remote Sens. 47:202–213.  https://doi.org/10.1109/TGRS.2008.2002881 CrossRefGoogle Scholar
  20. Long S, Fatoyinbo TE, Policelli F (2014) Flood extent mapping for Namibia using change detection and thresholding with SAR. Environ. Res. Lett. 9.  https://doi.org/10.1088/1748-9326/9/3/035002 CrossRefGoogle Scholar
  21. Martinis S, Twele A, Voigt S (2009) Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci. 9:303–314.  https://doi.org/10.5194/nhess-9-303-2009 CrossRefGoogle Scholar
  22. Mason DC, Davenport IJ, Neal JC, Schumann GJ-P, Bates PD (2012) Near real-time flood detection in urban and rural areas using high-resolution synthetic aperture radar images. IEEE Trans. Geosci. Remote Sens. 50:3041–3052.  https://doi.org/10.1109/TGRS.2011.2178030 CrossRefGoogle Scholar
  23. Matgen P, Hostache R, Schumann G, Pfister L, Hoffmann L, Savenije HHG (2011) Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies. Phys. Chem. Earth 36:241–252.  https://doi.org/10.1016/j.pce.2010.12.009 CrossRefGoogle Scholar
  24. Parthasarathy B, Sontakke NA, Monot AA, Kothawale DR (1987) Droughts/floods in the summer monsoon season over different meteorological subdivisions of India for the period 1871–1984. J. Climatol. 7:57–70.  https://doi.org/10.1002/joc.3370070106 CrossRefGoogle Scholar
  25. Rahman, M.R., Thakur, P.K., 2018. Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. Egypt. J. Remote Sens. Sp. Sci. 21, S37–S41.  https://doi.org/10.1016/j.ejrs.2017.10.002 CrossRefGoogle Scholar
  26. Schlaffer S, Matgen P, Hollaus M, Wagner W (2015) Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int. J. Appl. Earth Obs. Geoinf. 38:15–24.  https://doi.org/10.1016/j.jag.2014.12.001 CrossRefGoogle Scholar
  27. Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009) Progress in intergration of remote sensing derived flood extent and stage data and hydraulic models. Rev. Geophys. 47:1–20.  https://doi.org/10.1029/2008RG000274.1.INTRODUCTION CrossRefGoogle Scholar
  28. Xie H, Pierce LE, Ulaby FT (2002) SAR speckle reduction using wavelet denoising and Markov random field modeling. IEEE Trans. Geosci. Remote Sens. 40:2196–2212.  https://doi.org/10.1109/TGRS.2002.802473 CrossRefGoogle Scholar
  29. Yarrakula K, Deb D, Samanta B (2010) Hydrodynamic modeling of Subernarekha River and its floodplain using remote sensing and GIS techniques. J. Sci. Ind. Res. 69:529–536Google Scholar
  30. Zito RR, 1988. Pn. Comput. vision,graphics image Process. 43, 281–293.Google Scholar
  31. Zlatanova, S., 2013. Flood and flood risk: mapping, monitoring and damage assessment. In: Altan, O., Backhaus, R., Boccardo, P., Tonolo, F.G., Trinder, J., van Manen, N., Zlatanova, S. (Eds.), The Value of Geoinformation for Disaster and Risk Man- agement (VALID) – benefit analysis and stakeholder assessment. Joint Board of Geospatial Information Societies, Copenhagen, pp. 33–43, Ch.4.1Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre for Disaster Management and MitigationVellore Institute of TechnologyVelloreIndia

Personalised recommendations