A change detection approach to flood inundation mapping using multi-temporal Sentinel-1 SAR images, the Brahmaputra River, Assam (India): 2015–2020

• Published in: Journal of Earth System Science 2022-12, Vol.132 (1), p.3, Article 3
• Main Authors: Vekaria, Darshil, Chander, Shard, Singh, R P, Dixit, Sudhanshu
• Format: Article
• Language: English
• Subjects: Agricultural land; Change detection; Damage assessment; Datasets; Detection; Disasters; Earth and Environmental Science; Earth Sciences; Flood mapping; Flooded areas; Flooding; Floods; Forecasting; Hydrologic models; Mapping; Precipitation; Precipitation variations; Probability theory; Rainfall; Rainfall forecasting; Rainfall variations; Remote sensing; River discharge; River flow; Rivers; Satellites; Sensors; Shadows; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Synthetic aperture radar; Vegetation; Water discharge
• ISSN: 0973-774X; 0253-4126; 0973-774X
• DOI: 10.1007/s12040-022-02020-x

Flood is one of the major disasters occurring worldwide, which occurs frequently and affects many lives and property. In the present study, an automatic flood inundation mapping approach based on Sentinel-1 SAR datasets was used for the assessment of monsoonal flood events of the Brahmaputra River from 2015 to 2020. Google Earth Engine was used to prepare potential flood inundation maps using a change detection technique by processing SAR images. In total, around 144 SAR images were analysed for the study period, and it was found that the flood inundation extent was more in 2015 and 2016 at about 6 lakh hectares; thereby, it decreased in 2017 and 2018 near to 3.5 lakh hectares. Again, an increase in inundation extent was observed of about 6 lakh hectares in 2019 and 2020, respectively. The results were evaluated by applying binarisation thresholding, removing permanent water bodies and shadows from SAR images to delineate the actual flooded areas. It shows an encouraging overall validation accuracy of 93.6% and 95.15% for the pre-flood events of 2019 and 2020, respectively. There was a change in the trend of inundation extent observed in 2015, and it was confirmed with the Integrated Multi-Satellite Retrievals for GPM (IMERG) precipitation dataset. The results were further analysed for damage assessment, and it was figured out that the flood event in July 2020 resulted in the highest crop area affected. The present study shows the technological advancements over the traditional approach of flood mapping and focuses on rapid flood assessment. The generated flood extent database can be used further by the hydrologist to generate the inundation probability maps based on the forecasting rainfall and hydrological model generated river discharge measurements. Research highlights The present study shows the technological advancements over the traditional approach of flood mapping and focuses on rapid flood assessment. The generated flood extent database can be used further by the hydrologist to generate the inundation probability maps based on the forecasting rainfall and hydrological model generated river discharge measurements. Detailed study has been carried out to show the broader aspect of flood assessment work (including inundation change detection, rainfall variation, impact on agricultural land and population) for the Brahmaputra River in Assam using Sentinel-1 SAR images that too for the longer duration of six years. Variation in precipitation was one of