ĭeVries B, Huang C, Armston J, Huang W, Jones JW, Lang MW (2020) Rapid and robust monitoring of flood events using Sentinel-1 and landsat data on the google earth engine. Accessed on 12 December 2020.Ĭlement MA, Kilsby CG, Moore P (2018) Multi-temporal synthetic aperture radar flood mapping using change detection. RADARSAT-1: Components and specifications. Springer, Berlin, Heidelberg, pp 25–36īui DT, Hoang ND, Martínez-Álvarez F, Ngo PTT, Hoa PV, Pham TD, Costache R (2020) A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. In: Nayak Shailesh, Zlatanova Sisi (eds) Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters. īrecht H (2008) The Application of Geo-Technologies after Hurricane Katrina. The GFM shall enable first responders and practitioners across the globe to overcome technical barriers and lack of computational resources to map the extent of inundation during and after floods.Īrora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Bhardwaj A (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain India. The GFM can be used to map and download the extent of multiple flood events of an area as vector data (.kml format), which can be a critical input for flood modeling and risk and impact assessments. By comparing our results with Sentinel-2 MSI derived flood maps and field photographs, we show that the GFM can generate flood maps with precision. Second, it advances an existing flood mapping method to (a) Map the peak of the floods by combining ascending and descending scenes when necessary, and (b) Check for false positives in hilly terrains by adding slope and elevation mask parameters. To derive the flood extent from Sentinel-1 satellite data, the pre-flood collection is considered as base and anomaly cells in the during-flood image(s) are identified using Z-Score values. First, the paper presents a new web application, the Global Flood Mapper (GFM) that allows the user to generate flood maps quickly and without getting into technical intricacies. To bridge this gap, this paper makes two contributions. Despite the advances in computational resources and in the field of remote sensing, there is a clear gap that restricts the disaster community from leveraging the technological resources in real time, which impedes on-ground response efforts. Room exists to improve this product with the addition of other remotely sensed datasets.Timely and accurate information about the extent of floodwater is critical for emergency planning and disaster management efforts. The FMA can be used to create historical flood inundation maps and potential flood risk maps. There are some limitations where the terrain influences the accuracy of the outputs, but these are easily characterized and can be further calibrated and accounted for with more event inputs. FMA can also be used in an exploratory capacity prior to flood mapping, as it is significantly lower in cost, only requiring access to the internet and using open-source EO data. The study found that FMA can be an effective supplement to current inundation and flood risk maps, especially in the Global South, where data and technological gaps are common. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. This study evaluated whether the Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems.
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