Efficient flood monitoring based on RADARSAT-1 images data and information fusion with object-oriented technology

Kuehn, Seppe; Benz, U.; Hurley, J.

doi:10.1109/igarss.2002.1026803

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…Kuehn et al [21] employed three Radarsat-1 data sets as well as optical Aster data for flood mapping in the Ganges Delta in Bangladesh. Hoque et al [6] utilized multi-temporal optical and SAR data for flood monitoring in Bangladesh, Henry et al [12] analysed one multi-polarized ASAR data set, and Schumann et al [22] focus on future flood modelling using SAR data for surface roughness derivation.…”

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confidence: 99%

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

et al. 2013

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Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007-2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people's rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood

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Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

et al. 2013

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“…Specifically, for those flood events with few or even no cloud-free remote-sensing images, the blending result could be very poor. Thanks to their independence of weather and daylight, SAR images provide the potential for more accurate flood monitoring and assessment (Kuehn, Benz, and Hurley 2002). Recently, various methods have been developed to fuse SAR and optical images to take full advantage of SAR images in bad weather while improving its interpretation in heterogeneous areas (Xie and Keller 2006;Hong, Zhang, and Mercer 2009;Zhang 2009).…”

Section: Discussionmentioning

confidence: 99%

Blending MODIS and Landsat images for urban flood mapping

Zhang

Zhu

Liu³

2014

International Journal of Remote Sensing

100

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Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.

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“…Additionally, the flexible fuzzy rule base for data and information fusion helps to build up an efficient strategy to derive synergy from SAR and optical data. The hierarchical network provides object features, such as neighborhood relations, together with geometric features and can distinguish between small rivers and flooded areas (Kuehn et al, 2002). In order to correctly highlight the flooded areas by mean values of optical imagery, the best combination of features includes the coherence difference between the acquisitions before and during the flooding, the backscatter intensity in the reference period and the coherence computed during the flood.…”

Section: Introductionmentioning

confidence: 99%

Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment

Auynirundronkool

Chen

Caihua

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

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Efficient flood monitoring based on RADARSAT-1 images data and information fusion with object-oriented technology

Cited by 16 publications

References 2 publications

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

Blending MODIS and Landsat images for urban flood mapping

Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment

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