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Remote Sensing for Flood Map** and Monitoring

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International Handbook of Disaster Research

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Abstract

Working on countermeasures to reduce floods and respond quickly is vital for ensuring fatalities are reduced to a minimum. Remote sensing can provide an adequate amount of information for flood management systems. Techniques from several disciplines, considering image processing, remote sensing, machine learning, and data analysis, have been investigated in the literature to manage various flood management duties. Despite the growing number of research articles outlining the application of numerous computer vision techniques in the field of remote sensing applications, there exists a dire need for a complete analysis of these technologies from the standpoint of flood management. This chapter aims to fulfill this need by providing a comprehensive review of the literature covering all aspects of remote sensing applications for flood management including flood detection, flood delineation of affected areas, and damage assessment. The review is organized to cover both traditional and deep learning methods as well as the open-source datasets used in the relevant studies. In addition, the chapter investigates existing products and services that currently provide usable insights about past or ongoing flood disasters to emergency response operations. Finally, the chapter highlights the challenges and future areas of research in flood management.

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Acknowledgments

This chapter was made possible by BFC grant #BFC03-0630-190011 from the Qatar National Research Fund (a member of the Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors. The authors would like to gratefully acknowledge the financial support of the project Re-Energize Governance of Disaster Risk Reduction and Resilience for Sustainable Development (Re-Energize DR3) provided by the Belmont Forum’s first disaster-focused funding call DR3 CRA Joint Research, which was supported by the Ministry of Science and Technology (MOST) of Chinese Taipei in partnership with funders from Brazil (FAPESP), Japan (JST), Qatar (QNRF), the UK (UKRI), and the USA (NSF).

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Authors and Affiliations

  1. Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar

    Rizwan Sadiq, Muhammad Imran & Ferda Ofli

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  1. Rizwan Sadiq
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  2. Muhammad Imran
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  3. Ferda Ofli
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Correspondence to Rizwan Sadiq .

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Editors and Affiliations

  1. NDRG (Asia Pacific Disaster Research Group), New Delhi, India

    Amita Singh

Section Editor information

  1. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Ferda Ofli

  2. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Muhammad Imran

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Sadiq, R., Imran, M., Ofli, F. (2023). Remote Sensing for Flood Map** and Monitoring. In: Singh, A. (eds) International Handbook of Disaster Research. Springer, Singapore. https://doi.org/10.1007/978-981-19-8388-7_178

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