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


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.


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



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.


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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

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