Remote Sensing for Flood Mapping and Monitoring

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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References

• Ahmad, D., & Afzal, M. (2019). Household vulnerability and resilience in flood hazards from disaster-prone areas of Punjab, Pakistan. Natural Hazards, 99(1), 337–354. ArticleGoogle Scholar
• Akiva, P., et al. (2021). H2O-Net: Self-supervised flood segmentation via adversarial domain adaptation and label refinement. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 111–122). Google Scholar
• Anonymous, A. (2007). Handbook on good practices for flood mapping in Europe. In: CD of project.Google Scholar
• Bai, Y., et al. (2021). Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets. Remote Sensing, 13(11), 2220. ArticleGoogle Scholar
• Banerjee, S., & Pandey, A. C. (2019). Crop insurance model to consolidate academia-industry cooperation: A case study over Assam, India. Spatial Information Research, 27(6), 719–731. ArticleGoogle Scholar
• Bangira, T., et al. (2017). A spectral unmixing method with ensemble estimation of endmembers: Application to flood mapping in the Caprivi floodplain. Remote Sensing, 9(10), 1013. ArticleGoogle Scholar
• Bonafilia, D., et al. (2020). Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 210–211). Google Scholar
• Borah, S. B., et al. (2018). Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data. Environmental Monitoring and Assessment, 190(9), 1–11. ArticleGoogle Scholar
• Brema, J. (2020). Flood modelling and mapping: Case study on Adyar River Basin, Chennai, India. In: Decision Support Methods for Assessing flood risk and Vulnerability (pp. 104–139). IGI Global. Google Scholar
• Bresciani, M., et al. (2011). Assessing remotely sensed chlorophyll-a for the implementation of the water framework directive in European perialpine lakes. Science of the Total Environment, 409(17), 3083–3091. ArticleGoogle Scholar
• Chini, M., et al. (2019). Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and Hurricane Harvey as a test case. Remote Sensing, 11(2), 107. ArticleGoogle Scholar
• D’Addabbo A., et al. (2016a). “SAR/optical data fusion for flood detection”. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE (pp. 7631–7634). Google Scholar
• D’Addabbo, A., et al. (2016b). A Bayesian network for flood detection combining SAR imagery and ancillary data. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3612–3625. ArticleGoogle Scholar
• Das, S. (2018). Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences, 11(19), 1–13. ArticleGoogle Scholar
• Elkhrachy, I., et al. (2021). Sentinel-1 remote sensing data and Hydrologic Engineering Centres River Analysis System two-dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. Journal of Flood Risk Management, 14(2), e12692. ArticleGoogle Scholar
• Etci 2021 competition on Flood detection. https://nasa-impact.github.io/etci2021/.
• Ety, N. J., Chu, Z., & Masum, S. M. (2021). Monitoring of flood water propagation based on microwave and optical imagery. Quaternary International, 574, 137–145. ArticleGoogle Scholar
• Feng, Q., et al. (2015). Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier – The case of Yuyao, China. Remote Sensing, 7(9), 12539–12562. ArticleGoogle Scholar
• Gao, B.-C. (1996). NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. ArticleGoogle Scholar
• Gašparović, M., & Klobučar, D. (2021). Mapping floods in lowland forest using Sentinel-1 and Sentinel-2 data and an object-based approach. Forests, 12(5), 553. ArticleGoogle Scholar
• Giustarini, L., et al. (2012). A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2417–2430. ArticleGoogle Scholar
• Gupta, R., & Shah, M. (2021). Rescuenet: Joint building segmentation and damage assessment from satellite imagery. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE (pp. 4405–4411). Google Scholar
• Hanqiu, X. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. ArticleGoogle Scholar
• Haq, M., et al. (2012). Techniques of remote sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 15(2), 135–141. ArticleGoogle Scholar
• Hlaváčová, I., et al. (2021). Automatic open water flood detection from sentinel-1 multi-temporal imagery. Water, 13(23), 3392. ArticleGoogle Scholar
• Iqbal, U., et al. (2021). How computer vision can facilitate flood management: A systematic review. International Journal of Disaster Risk Reduction, 53, 102030. ArticleGoogle Scholar
• Jain, P., Schoen-Phelan, B., & Ross, R. (2019). MediaEval2019: Flood detection in time sequence satellite images. MediaEval.Google Scholar
• Jiang, X., et al. (2021). Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 36–50. ArticleGoogle Scholar
• Li, J., et al. (2021). Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change. Journal of Environmental Management, 278, 111559. ArticleGoogle Scholar
• Li, Y., et al. (2019). Urban flood mapping using SAR intensity and interferometric coherence via Bayesian network fusion. Remote Sensing, 11(19), 2231. ArticleGoogle Scholar
• Lin, Y. N., et al. (2019). Urban flood detection with Sentinel-1 multi-temporal synthetic aperture radar (SAR) observations in a Bayesian framework: A case study for Hurricane Matthew. Remote Sensing, 11(15), 1778. ArticleGoogle Scholar
• Martinis, S., Kersten, J., & Twele, A. (2015). A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 203–212. ArticleGoogle Scholar
• Mason, D. C., Dance, S. L., & Cloke, H. L. (2021a). Floodwater detection in urban areas using Sentinel-1 and WorldDEM data. Journal of Applied Remote Sensing, 15(3), 032003. ArticleGoogle Scholar
• Mason, D. C., et al. (2021b). Improving urban flood mapping by merging synthetic aperture radar-derived flood footprints with flood hazard maps. Water, 13(11), 1577. ArticleGoogle Scholar
• Mateo-Garcia, G., et al. (2021). Towards global flood mapping onboard low cost satellites with machine learning. Scientific Reports, 11(1), 1–12. ArticleGoogle Scholar
• Matgen, P., et al. (2011). Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies. Physics and Chemistry of the Earth, Parts A/B/C, 36(7–8), 241–252. ArticleGoogle Scholar
• Munasinghe, D., et al. (2018). Intercomparison of satellite remote sensing-based flood inundation mapping techniques. JAWRA Journal of the American Water Resources Association, 54(4), 834–846. ArticleGoogle Scholar
• Munawar, H. S., Hammad, A. W. A., & Travis Waller, S. (2021). A review on flood management technologies related to image processing and machine learning. Automation in Construction, 132, 103916. ArticleGoogle Scholar
• Myneni, R. B., et al. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481–486. ArticleGoogle Scholar
• Notti, D., et al. (2018). Potential and limitations of open satellite data for flood mapping. Remote Sensing, 10(11), 1673. ArticleGoogle Scholar
• O’Hara, R., Green, S., & McCarthy, T. (2019). The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery. Irish Journal of Agricultural and Food Research, 58, 44–65. ArticleGoogle Scholar
• OECD. (2016). Financial management of flood risk. Google Scholar
• Ohki, M., et al. (2020). Automated processing for flood area detection using ALOS-2 and hydrodynamic simulation data. Remote Sensing, 12(17), 2709. ArticleGoogle Scholar
• Paul, S., & Ganju, S. (2021). Flood segmentation on sentinel-1 SAR imagery with semi-supervised learning. In: arXiv preprint arXiv:2107.08369.Google Scholar
• Peng, B., et al. (2019). Patch similarity convolutional neural network for urban flood extent mapping using bi-temporal satellite multispectral imagery. Remote Sensing, 11(21), 2492. ArticleGoogle Scholar
• Peng, B., et al. (2020). Urban flood mapping with bitemporal multispectral imagery via a self-supervised learning framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2001–2016. ArticleGoogle Scholar
• Psomiadis, E., et al. (2019). Synergistic approach of remote sensing and gis techniques for flash-flood monitoring and damage assessment in Thessaly plain area, Greece. Water, 11(3), 448. ArticleGoogle Scholar
• Pulvirenti, L., et al. (2011a). Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sensing of Environment, 115(4), 990–1002. ArticleGoogle Scholar
• Pulvirenti, L., et al. (2011b). An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Natural Hazards and Earth System Sciences, 11(2), 529–540. ArticleGoogle Scholar
• Rahman, M., Di, L., et al. (2017). The state of the art of spaceborne remote sensing in flood management. Natural Hazards, 85(2), 1223–1248. ArticleGoogle Scholar
• Rahman, M. S., & Di, L. (2020). A systematic review on case studies of remote-sensing-based flood crop loss assessment. Agriculture, 10(4), 131. ArticleGoogle Scholar
• Rahnemoonfar, M., et al. (2021). Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, 89644–89654. ArticleGoogle Scholar
• Refice, A., et al. (2020). Integrating C-and L-band SAR imagery for detailed flood monitoring of remote vegetated areas. Water, 12(10), 2745. ArticleGoogle Scholar
• Renschler, C. S., & Wang, Z. (2017). Multi-source data fusion and modeling to assess and communicate complex flood dynamics to support decision-making for downstream areas of dams: The 2011 hurricane irene and schoharie creek floods, NY. International Journal of Applied Earth Observation and Geoinformation, 62, 157–173. ArticleGoogle Scholar
• Robert Brakenridge, G. (2010). Global active archive of large flood events. In: Dartmouth Flood Observatory, University of Colorado. Google Scholar
• Sadiq, R., et al. (2022). Integrating remote sensing and social sensing for flood mapping. Remote Sensing Applications: Society and Environment, 25, 100697. ArticleGoogle Scholar
• Sajjad, A., et al. (2020). Operational monitoring and damage assessment of riverine flood-2014 in the lower Chenab plain, Punjab, Pakistan, using remote sensing and GIS techniques. Remote Sensing, 12(4), 714. ArticleGoogle Scholar
• Schmitt, M., Hughes, L. H., & Zhu, X. X. (2018). The SEN1–2 dataset for deep learning in SAR-optical data fusion”. In: arXiv preprint arXiv:1807.01569.Google Scholar
• Scotti, V., Giannini, M., & Cioffi, F. (2020). Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: A case study of Hurricane Harvey (Houston, TX). Journal of Flood Risk Management, 13(4), e12647. ArticleGoogle Scholar
• Sharma, T. P. P., et al. (2019). Review of flood disaster studies in Nepal: A remote sensing perspective. International Journal of Disaster Risk Reduction, 34, 18–27. ArticleGoogle Scholar
• Shen, X., et al. (2019). Inundation extent mapping by synthetic aperture radar: A review. Remote Sensing, 11(7), 879. ArticleGoogle Scholar
• Shivaprasad Sharma, S. V., & Roy, P. S. (2017). Extraction of detailed level flood hazard zones using multi-temporal historical satellite data-sets–a case study of Kopili River Basin, Assam, India. Geomatics, Natural Hazards and Risk, 8(2), 792–802. ArticleGoogle Scholar
• Solovey, T. (2019). An analysis of flooding coverage using remote sensing within the context of risk assessment. Geologos, 25(3), 241–248. ArticleGoogle Scholar
• Solovey, T. (2020). Flooded wetlands mapping from Sentinel-2 imagery with spectral water index: A case study of Kampinos National Park in Central Poland. Geological Quarterly, 64(2), 492–505. ArticleGoogle Scholar
• Syifa, M., et al. (2019). Flood mapping using remote sensing imagery and artificial intelligence techniques: A case study in Brumadinho, Brazil. Journal of Coastal Research, 90.SI, 197–204. ArticleGoogle Scholar
• Tamkuan, N., Nagai, M., et al. (2021). ALOS-2 and sentinel-1 backscattering coefficients for water and flood detection in Nakhon Phanom Province, Northeastern Thailand. International Journal of Geoinformatics, 17, 3. Google Scholar
• Teng, J., et al. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. ArticleGoogle Scholar
• Tong, X., et al. (2018). An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 144–153. ArticleGoogle Scholar
• Twele, A., et al. (2016). Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. ArticleGoogle Scholar
• Ulloa, N. I., et al. (2022). Sentinel-1 spatiotemporal simulation using convolutional LSTM for flood mapping. Remote Sensing, 14(2), 246. ArticleGoogle Scholar
• UN Office for Disaster Risk Reduction. (2020). The human cost of disasters – An overview of the last 20 years 2000–2019. Google Scholar
• Watik, N., & Jaelani, L. M. (2019). Flood evacuation routes mapping based on derived-flood impact analysis from landsat 8 imagery using network analyst method. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42, 455–460. ArticleGoogle Scholar
• Yang, Q., et al. (2021). A high-resolution flood inundation archive (2016–the present) from Sentinel-1 SAR imagery over CONUS. Bulletin of the American Meteorological Society, 102(5), E1064–E1079. ArticleGoogle Scholar
• Yovan Felix, A., & Sasipraba, T. (2021). Spatial and temporal analysis of flood hazard assessment of Cuddalore District, Tamil Nadu, India. Using geospatial techniques. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2573–2584. ArticleGoogle Scholar
• Zhang, L., & Xia, J. (2021). Flood detection using multiple Chinese satellite datasets during 2020 China summer floods. Remote Sensing, 14(1), 51. ArticleGoogle Scholar
• Zhang, Q. Y., et al. (2016). Risk assessment of flood based on dynamic simulation in downstream of reservoirs in coastal area of Southeast China. Journal of Lake Science, 28(04), 868–874. ArticleGoogle Scholar
• Zhang, Q., Zhang, P., & Xudong, H. (2021). Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries. International Journal of Digital Earth, 14(11), 1561–1581. ArticleGoogle Scholar

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, Hamad Bin Khalifa University, Doha, Qatar Rizwan Sadiq, Muhammad Imran & Ferda Ofli

1. Rizwan Sadiq