Abstract
ABSTRACT Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system.
Highlights
Flood monitoring has been crucial for management and mitigation of impacts in flood prone regions in terms of providing updates and sufficient information (Merkuryeva et al, 2015)
The program searches for image pairs from the Vietnam Open Data Cube (VODC) database that meet three conditions: the acquisition dates of the pairs must not be more than 2 weeks apart; tile overlay must be greater than one third of the smaller image; and cloud percentage of the overlay area must be less than 30%
Visual comparison of the images produced from the four fusion techniques (ISH, Brovey, Principal Component Analysis (PCA) and GS) revealed differences
Summary
Flood monitoring has been crucial for management and mitigation of impacts in flood prone regions in terms of providing updates and sufficient information (Merkuryeva et al, 2015). Synthetic aperture radar images (SAR) have been widely applied to flood studies because smooth surface flood water has a dark appearance that can be clearly distinguished from other objects (Gan, Zunic, Kuo, & Strobl, 2012; Horritt, 2003), regardless of weather-related obstacles such as clouds (Javelle et al, 2002; Schlaffer, Matgen, Hollaus, & Wagner, 2015). Optical RS data provides a wide range of spectral bands that produce finer spatial resolution, but it is often affected by cloud and low light levels. For these reasons, and to overcome limitations, SAR and optical RS imagery have been fused in some flood studies (Dey, Jia, & Fraser, 2008; Kyriou & Nikolakopoulos, 2017)
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