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Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images


Affiliations
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
 

In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.

Keywords

Disaster Management, Floods, Inundation Mapping, Remote Sensing, Rule-based Classification.
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  • Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images

Abstract Views: 343  |  PDF Views: 150

Authors

V. S. K. Vanama
Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
Mohamed Musthafa
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
Unmesh Khati
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
R. Gowtham
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
Gulab Singh
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
Y. S. Rao
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India

Abstract


In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.

Keywords


Disaster Management, Floods, Inundation Mapping, Remote Sensing, Rule-based Classification.

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DOI: https://doi.org/10.18520/cs%2Fv120%2Fi5%2F915-925