A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X

Giustarini, Laura; Hostache, Renaud; Matgen, Patrick; Schumann, Guy; Bates, Paul; Mason, David C.

doi:10.1109/tgrs.2012.2210901

Cited by 352 publications

(247 citation statements)

References 24 publications

(27 reference statements)

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“…Yet, such exercise is prone to uncertainty (Matgen et al, 2011;Schumann et al, 2014;Giustarini 40 et al, 2015). Some good examples of delineation algorithms can be found in the studies of Horritt (1999), Mason et al (2007), Schumann et al (2009a) and Giustarini et al (2013). Schumann et al (2014) employed a slightly different approach by avoiding the need for an a priori classification of the SAR image by calibrating the roughness parameters on an ensemble of SAR-extracted binary 45 …”

Section: Introductionmentioning

confidence: 99%

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Gobeyn

Wesemael

Neal

et al. 2017

Advances in Water Resources

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Section: Introductionmentioning

confidence: 99%

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Gobeyn

Wesemael

Neal

et al. 2017

Advances in Water Resources

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“…If data are available during the maximum flooding phase, it is possible to accurately map the affected area using high-resolution SAR images, such as those acquired by the TerraSAR-X (Giustarini et al, 2013) and COSMO-SkyMed (CSKM) (Refice et al, 2014) satellites. In particular, the identification of the flooded area is performed by analysing the SAR backscattering, which shows low values in water-covered areas.…”

Section: Sar Datamentioning

confidence: 99%

Low cost, multiscale and multi-sensor application for flooded area mapping

Giordan

Notti

Villa³

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed.In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multisensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy) flood. We first mapped the flooded areas at the basin scale using free satellite data from low-to mediumhigh-resolution from both the SAR (Sentinel-1, COSMOSkymed) and multispectral sensors (MODIS, Sentinel-2). Using very-and ultra-high-resolution images from the lowcost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and nonurbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very-and ultra-high-resolution images were processed with structure from motion (SfM) for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.

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“…O'Grady et al (2011) conclude that misclassification due to low backscatter values from non-flooded areas can be reduced via image differencing approaches. Matgen et al (2011) and Giustarini et al (2012) present a method relying on the calibration of a statistical distribution of "open water" backscatter values inferred from SAR images of floods. Given the many circumstances that can affect classification results, it is difficult to derive a consistent classification technique that, ideally, also includes an error or accuracy assessment, and for all incidence angles.…”

Section: Relative Advantages Of Sar and Optical Imagingmentioning

confidence: 99%

Automated global water mapping based on wide-swath orbital synthetic-aperture radar

Westerhoff

Kleuskens

Winsemius

et al. 2013

Hydrol. Earth Syst. Sci.

146

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Abstract. This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1 • tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter -incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.

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A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X

Cited by 352 publications

References 24 publications

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Low cost, multiscale and multi-sensor application for flooded area mapping

Automated global water mapping based on wide-swath orbital synthetic-aperture radar

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