Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood

Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information...

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Vydáno v:Hydrology Ročník 10; číslo 1; s. 17
Hlavní autoři: Li, Wenzhao, Li, Dongfeng, Fang, Zheng N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2023
Témata:
Algorithms
Automation
Calibration
Case studies
Cloud computing
Developing countries
Digital Elevation Models
Elevation
Emergency management
Emergency preparedness
European Union
Flood damage
Flood management
Flood mapping
floodplains
Floods
Floodwater
HYDRAFloods
Hydrology
Intercomparison
Internet
Landsat
Landsat satellites
Landsat-9
LDCs
Machine learning
Madagascar
Mapping
Mitigation
mNDWI
near-real-time flood mapping
Otsu thresholding algorithms
Precipitation
radar
Remote sensing
SAR (radar)
Satellite imagery
Satellites
Seasons
Sentinel
Storm damage
Surface water
Synthetic aperture radar
Water depth
Weather
Antananarivo Madagascar
Madagascar
ISSN:2306-5338, 2306-5338
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Shrnutí:Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents.
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ISSN:2306-5338
2306-5338
DOI:10.3390/hydrology10010017