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Multi-Source and Multi-Temporal Google Earth Engine App for Emergency Flood Mapping |
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13 February 2024 |
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Recent studies suggest that global warming will lead to a greater chance of extreme flooding in the coming decades, doubling every five years in the near future. Coastal regions, being densely populated areas, are highly vulnerable to flooding due to various drivers such as extreme rainfall and tropical cyclones. Therefore, identifying efficient and accurate models for mapping floods is key in flood risk assessment. While remotely sensed observations have provided researchers with decades of continuous and reliable data for extracting flood information, it is important to note that relying solely on single-source remote sensing data may not provide a comprehensive solution for urgent flood monitoring. Satellite revisit cycles, spatial resolution, weather conditions, and solar reflectance dependency, as well as sensor defects, are a number of remote sensing limitations that can hinder flood mapping progress. Multi-source methods can address part of the limitations inherent in single-source methods. These methods can cope with the drawbacks of satellite data while leveraging their advantages. For example, using multi-source optical/SAR imagery can provide abundant spectral information and highly accurate water extraction from the optical imagery, thereby benefiting the SAR images all-weather and day-night operation capabilities for flood mapping. In this study, we build on our previous research and introduce an App that leverages state-of-the-art remote sensing resources and the capability of the Google Earth Engine (GEE) platform to produce a rapid estimation of floods using an advanced multi-source remote sensing approach that is geographically generalizable. Then, this GEE App extracts multidisciplinary information from the final flood map for responsible responders to adopt flexible measures based on the types of land-use and land-cover affected. This tool will be among the first user-friendly GEE Apps that produce flood extent maps at a scale that will help emergency responders as well as the scientific community with rapid and reliable flood inundation information and improves the current methods of flood mapping.
Floods, common natural disasters worldwide, resulted in approximately $40 billion in losses between 2011 and 2015, as reported by the Financial Management of Flood Risk [@OECD:2016]. Science and technology advancements provide alternatives for managing natural disasters such as floods. Satellite-based flood inundation mapping is crucial for emergency responders, aiding crisis managers in monitoring and managing floods. It enhances situational awareness, identifying areas requiring immediate action across large geographical areas, expediting relief response activities. Remote sensing is widely employed not only for flood inundation mapping but also for assessing flood impact, offering a comprehensive review to address diverse stakeholder needs [@Sadiq:2023]. These stakeholders have specific requirements aligned with particular objectives, serving various purposes. • Flood monitoring • Strategies for preventing and mitigating flood risks • Emergency response planning • Prioritizing preventive actions through classifying flooded areas • Estimating the population at risk • Land-use planning and management • Public awareness campaigns • Providing information for the insurance sector Due to the unpredictable nature of flooding and the slow retrieval rate of satellite images, local implementation faces limitations. Relying solely on remotely sensed images poses challenges in fully capturing the scope of flood monitoring. This has prompted research into integrating multiple sources for improved flood mapping [@Hamidi:2023]. This research study introduces an App that uses multi-source and multi-temporal remote sensing data for fast flood monitoring. This App provides tools to facilitate flood monitoring tasks by defining a flood period and drawing a boundary to observe the flood extent conditions in their area of interest. Through this App, our aim is to empower decision-makers, first responders, scientists, and other stakeholders with clear, useful information, enabling them to better understand, prepare for, and respond to treacherous flooding hazards. This information is crucial, considering the populations, infrastructure facilities, urban area, and cropland at risk.
@author:2016-> "OECD (2016)"[@Sadiq:2023]-> "(Sadiq et al., 2023)"[@Hamidi:2023]-> "(Hamidi et al., 2023)"
App created by Ebrahim Hamidi (University of Alabama) and reviewed by Brad G. Peter (University of Arkansas), funded by the National Science Foundation INFEWS Program and the U.S. Army Corps of Engineers. Also, partial support for development of this App is awarded through CUAHSI’s 2023 Hydroinformatics Innovation Fellowship (HIF).
- OECD, Financial Management of Flood Risk. OECD, 2016. doi: 10.1787/9789264257689-en.
- R. Sadiq, M. Imran, and F. Ofli, “Remote Sensing for Flood Mapping and Monitoring,” in International Handbook of Disaster Research, A. Singh, Ed., Singapore: Springer Nature Singapore, 2023, pp. 679–697. doi: 10.1007/978-981-19-8388-7_178.
- E. Hamidi, B. G. Peter, D. F. Munoz, H. Moftakhari, and H. Moradkhani, “Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine,” IEEE Trans. Geosci. Remote Sensing, pp. 1–1, 2023, doi: 10.1109/TGRS.2023.3240097.