Synthetic aperture radar and optical remote sensing image fusion for flood monitoring in the Vietnam lower Mekong basin: a prototype application for the Vietnam Open Data Cube

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.

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

“…Image fusion is considered to improve the quality of fused images [11,74] and allows the use of different sources of data for specific applications, particularly in the context of increasing remote sensing availability. Fusing optical and SAR remote scenes is commonly undertaken to enhance cartographic object extraction and improve spatial resolution [14] as well as reducing the effects of clouds in optical images [10,15,75]. It is nevertheless difficult to say whether fused images are always better for a particular use or not.…”

“…We selected Gram-Schmidt (GS) [61] and principal component analysis (PCA) [62] among many other available image fusion methods to generate higher quality (spectral and spatial resolution) MS images. These two methods presented better results compared to the modified intensity-hue-saturation (IHS) and Brovey transformation (BT) methods in a study by Quang et al (2019) [15]. In the GS fusion technique, suitable weights assigned to the high-resolution panchromatic (PAN) layers are simulated from lower spatial multispectral bands [61,63].…”

“…Several methods of image fusion have been developed, hence the decision about which technique is the most suitable is driven by the study goals [14] and expected accuracy requirement. Here, we selected Gram-Schmidt (GS), and principal component analysis (PCA), a method which reduces the dimensionality of the information present in the original multi-band dataset, as they have been reported to be the most accurate methods [15] when fusing SPOT-7 and Sentinel-1 images, with the expectation of achieving improved mangrove species classification [16].…”

Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam

“…The final point to discuss is the water extraction thresholding method, which is a simple but robust method particularly when automatic flood services are established (big data or data cube for example) in order to provide timely flood extent information for a quick flood response (Martinis et al, 2015). However, a thresholding method normally combines all pixels values under the threshold into a target group, so results contain errors even with an optimally determined threshold (Quang et al, 2019a); in many cases, we still need more accurate flood information generated from an integration of multi-temporal remote sensing data sources (Tong et al, 2018).…”

Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

“…Two or more various images were combined to produce a hybrid image by using an algorithm [40] to enhance the spatial resolution and to integrate the different types of data [41][42][43][44]. ALOS/PALSAR and Landsat OLI data were fused together by using Gram-Schmidt's method [45].…”