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Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin

Author

Listed:
  • Arvind Chandra Pandey

    (Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India)

  • Kavita Kaushik

    (Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India)

  • Bikash Ranjan Parida

    (Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India)

Abstract

The Ganga-Brahmaputra basin is highly sensitive to the impacts of climate change and experiences recurrent flooding, which affects large agricultural areas and poses a high risk to the population. The present study is focused on the recent flood disaster in the Ganga-Brahmaputra basin, which mainly affected the regions of Bihar, West Bengal, and Assam in India and neighboring Bangladesh during July, August, and September 2020. Using the Sentinel-1A Synthetic Aperture Radar (SAR) data, the flood extent was derived in the Google Earth Engine (GEE) platform. The composite area under flood inundation for July–September was estimated to be 25,889.1 km 2 for Bangladesh, followed by Bihar (20,837 km 2 ), West Bengal (17,307.1 km 2 ), and Assam (13,460.1 km 2 ). The Copernicus Global Land Cover dataset was used to extract the affected agricultural area and flood-affected settlement. Floods have caused adverse impacts on agricultural lands and settlements, affecting 23.68–28.47% and 5.66–9.15% of these areas, respectively. The Gridded Population of the World (GPW) population density and Global Human Settlement Layer (GHSL) population dataset were also employed to evaluate flood impacts, which revealed that 23.29 million of the population was affected by floods in the Ganga-Brahmaputra basin. The highest impacts of floods can be seen from the Bihar state, as people reside in the lower valley and near to the riverbank due to their dependency on river water. Similarly, the highest impact was from Bangladesh because of the high population density as well as the settlement density. The study provided a holistic spatial assessment of flood inundation in the region due to the combined impact of the Ganga-Brahmaputra River basin. The identification of highly flood-prone areas with an estimated impact on cropland and build-up will provide necessary information to decision-makers for flood risk reduction, mitigation activities, and management.

Suggested Citation

  • Arvind Chandra Pandey & Kavita Kaushik & Bikash Ranjan Parida, 2022. "Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin," Sustainability, MDPI, vol. 14(7), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4210-:d:785312
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    References listed on IDEAS

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    2. Bester Tawona Mudereri & Elfatih M. Abdel-Rahman & Shepard Ndlela & Louisa Delfin Mutsa Makumbe & Christabel Chiedza Nyanga & Henri E. Z. Tonnang & Samira A. Mohamed, 2022. "Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango ( Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    3. Pratyush Tripathy & Teja Malladi, 2022. "Global Flood Mapper: a novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1341-1363, November.

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