Articles | Volume 19, issue 9
https://doi.org/10.5194/nhess-19-2053-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-2053-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2019
Research article |
| 26 Sep 2019
| 26 Sep 2019
The Floodwater Depth Estimation Tool (FwDET v2.0) for improved remote sensing analysis of coastal flooding
Department of Geography, University of Alabama, Tuscaloosa, 35487, USA
Austin Raney
Department of Geography, University of Alabama, Tuscaloosa, 35487, USA
Dinuke Munasinghe
Department of Geography, University of Alabama, Tuscaloosa, 35487, USA
J. Derek Loftis
Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, 23062, USA
Andrew Molthan
Earth Science Branch, NASA Marshall Space Flight Center, Huntsville, 35808, USA
Jordan Bell
Earth System Science Center, University of Alabama Huntsville, Huntsville, Alabama, 35808, USA
Laura Rogers
NASA Langley Research Center, Hampton, 23666, USA
John Galantowicz
Atmospheric and Environmental Research, Inc. (AER), Lexington, 02421, USA
G. Robert Brakenridge
Dartmouth Flood Observatory, University of Colorado, Boulder, 80309, USA
Albert J. Kettner
Department of Natural Resources and Environmental Management, University of Hawai'i at Mānoa, Honolulu, 96822, USA
Yu-Fen Huang
Department of Natural Resources and Environmental Management, University of Hawai'i at Mānoa, Honolulu, 96822, USA
Yin-Phan Tsang
Department of Natural Resources and Environmental Management, University of Hawai'i at Mānoa, Honolulu, 96822, USA
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- Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine C. Odiji et al. 10.2166/wcc.2024.166
- Building-scale flood loss estimation through vulnerability pattern characterization: application to an urban flood in Milan, Italy A. Taramelli et al. 10.5194/nhess-22-3543-2022
- Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform V. Vanama et al. 10.1080/22797254.2021.1983471
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
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- Flood Extent and Volume Estimation Using Remote Sensing Data G. Popandopulo et al. 10.3390/rs15184463
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40 citations as recorded by crossref.
- An exploratory study of Sentinel-1 SAR for rapid urban flood mapping on Google Earth Engine M. Tazmul Islam & Q. Meng 10.1016/j.jag.2022.103002
- A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions J. Teng et al. 10.1029/2022WR032031
- Assessment of the impact of the rise in Lake Victoria water levels on urban flooding using a GIS-based spatial flood modelling approach S. Mugume et al. 10.1080/1573062X.2023.2284960
- Hydrologic balance and inundation dynamics of Southeast Asia's largest inland lake altered by hydropower dams in the Mekong River basin H. Dang et al. 10.1016/j.scitotenv.2022.154833
- The floodplain inundation history of the Murray-Darling Basin through two-monthly maximum water depth maps D. Penton et al. 10.1038/s41597-023-02559-4
- Flood depth mapping in street photos with image processing and deep neural networks B. Alizadeh Kharazi & A. Behzadan 10.1016/j.compenvurbsys.2021.101628
- Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections S. Cohen et al. 10.3390/rs14215313
- Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine C. Odiji et al. 10.2166/wcc.2024.166
- Building-scale flood loss estimation through vulnerability pattern characterization: application to an urban flood in Milan, Italy A. Taramelli et al. 10.5194/nhess-22-3543-2022
- Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform V. Vanama et al. 10.1080/22797254.2021.1983471
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
- Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin A. Pandey et al. 10.3390/su14074210
- Combining SAR images with land cover products for rapid urban flood mapping Z. Wang et al. 10.3389/fenvs.2022.973192
- Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis B. Peter et al. 10.1109/LGRS.2020.3031190
- Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan 10.1007/s43762-023-00090-1
- Earth observation technologies, policies and legislation for the coastal flood risk assessment and management: a European perspective A. Velegrakis et al. 10.1007/s44218-024-00037-x
- Compound Effects of Flood Drivers and Wetland Elevation Correction on Coastal Flood Hazard Assessment D. Muñoz et al. 10.1029/2020WR027544
- Flood prediction with time series data mining: Systematic review D. Hakim et al. 10.1016/j.nhres.2023.10.001
- Mapping of 2021–2022 floods in North America from street photos with artificial intelligence B. Alizadeh Kharazi & A. Behzadan 10.1680/jsmic.22.00029
- A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling W. Tan et al. 10.1016/j.rse.2023.113956
- Integrated Flood Impact and Vulnerability Assessment Using a Multi-Sensor Earth Observation Mission with the Perspective of an Operational Service in Lombardy, Italy M. Righini et al. 10.3390/land13020140
- Improved flood depth estimation with SAR image, digital elevation model, and machine learning schemes Y. Liou & D. Hoang 10.1016/j.ejrh.2024.101775
- Estimations of Crop Losses Due to Flood Using Multiple Sources of Information and Models: The Case Study of the Panaro River B. Monteleone et al. 10.3390/w15111980
- Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
- Built environment and agricultural value at risk from Hurricane Irma flooding in Florida (USA) A. Miller et al. 10.1007/s11069-021-04880-w
- Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province) S. Gord et al. 10.1007/s11069-024-06514-3
- Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood W. Li et al. 10.3390/hydrology10010017
- From local to regional compound flood mapping with deep learning and data fusion techniques D. Muñoz et al. 10.1016/j.scitotenv.2021.146927
- Enhanced flood water depth estimation from Sentinel-1A images A. Nguyen et al. 10.1080/01431161.2023.2268819
- Multi-risk assessment in transboundary areas: A framework for harmonized evaluation considering seismic and flood risks M. Polese et al. 10.1016/j.ijdrr.2024.104275
- Stable diffusion-evolution tendencies of the Lower Yellow River corridor during floods in the 21st century X. Song et al. 10.1016/j.jhydrol.2024.131449
- A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case M. Lagasio et al. 10.3390/rs14246348
- PyGEE-SWToolbox: A Python Jupyter Notebook Toolbox for Interactive Surface Water Mapping and Analysis Using Google Earth Engine C. Owusu et al. 10.3390/su14052557
- Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms J. Soria-Ruiz et al. 10.3390/atmos13111852
- Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India) B. Parida et al. 10.1080/10106049.2021.1899298
- Monthly hydraulic geometry relationship quantification with environmental LAI by utilizing sentinel data: A case in Jingjiang reach of the Yangtze River H. Huang et al. 10.1016/j.jhydrol.2024.131322
- Gridded flood depth estimates from satellite-derived inundations S. Bryant et al. 10.5194/nhess-22-1437-2022
- Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries M. Trinh & F. Molkenthin 10.1007/s11069-021-04843-1
- Flood Extent and Volume Estimation Using Remote Sensing Data G. Popandopulo et al. 10.3390/rs15184463
- Move up or move over: mapping opportunities for climate adaptation in Pakistan’s Indus plains R. Schmitt et al. 10.1088/1748-9326/acfc59
Latest update: 26 Jul 2024
Short summary
Flooding is the most destructive natural disaster on Earth. Satellite and airborne imagery are commonly used for flood monitoring and response. While these remote sensing techniques are effective at providing the extent of flooding, they cannot be used to infer the depth of floodwater. This paper describes and analyzes version 2.0 of the Floodwater Depth Estimation Tool (FwDET). FwDET 2.0 offers an enhanced calculation algorithm for coastal regions and much-improved runtime.
Flooding is the most destructive natural disaster on Earth. Satellite and airborne imagery are...
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