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Rao, Y. S.
- Application of DInSAR Technique for Post-Earthquake Land Deformation Mapping of Eastern Nepal
Abstract Views :294 |
PDF Views:101
Authors
Affiliations
1 Space Applications Centre (ISRO), Ahmedabad 380 058, IN
2 Indian Institute of Technology Bombay, Mumbai 400 076, IN
1 Space Applications Centre (ISRO), Ahmedabad 380 058, IN
2 Indian Institute of Technology Bombay, Mumbai 400 076, IN
Source
Current Science, Vol 110, No 3 (2016), Pagination: 302-304Abstract
No Abstract.Full Text
- Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images
Abstract Views :158 |
PDF Views:92
Authors
Affiliations
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
Source
Current Science, Vol 120, No 5 (2021), Pagination: 915-925Abstract
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.Full Text
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