Probabilistic Flood Mapping Using Synthetic Aperture Radar Data

Giustarini, Laura; Hostache, Renaud; Kavetski, Dmitri; Chini, Marco; Corato, Giovanni; Schlaffer, Stefan; Matgen, Patrick

doi:10.1109/tgrs.2016.2592951

Cited by 115 publications

(118 citation statements)

References 50 publications

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“…A comparison to the work of Giustarini et al (2016), who produced probabilistic flood maps from synthetic aperture radar (SAR) data and used the same validation technique, indicates that results are similar. It illustrates that probabilistic flood maps from SAR data provide a degree of accuracy comparable to the ones in our study, with probability-error values up 0.38.…”

Section: Potentialmentioning

confidence: 85%

Probabilistic flood extent estimates from social media flood observations

Brouwer¹,

Eilander²,

Loenen³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F (2) = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.

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Section: Potentialmentioning

confidence: 85%

Probabilistic flood extent estimates from social media flood observations

Brouwer¹,

Eilander²,

Loenen³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…Its subscript indicates VV or VH polarization. Directly, bitemporal SAR information with single polarization, denoted as VV or VH, is often used in the related studies (Chini et al, ; Giustarini et al, ; Kang et al, ). In this study, for the input information design, based on the radar RS physics, we have two considerations: (1) the VV and VH polarization information should be fused, for they can compensate each other; and (2)

σ_{VV}^{post} - σ_{VV}^{pre}

and

σ_{VH}^{post} - σ_{VH}^{pre}

; that is, the difference images for the VV and VH polarizations, respectively, should be added.…”

Section: Methodsmentioning

confidence: 99%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

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This study develops an effective and robust method to mine bitemporal and dual-polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network-based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics-aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel-1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land-cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km 2 are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost-sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane-induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications. Plain Language SummaryCoastal flooding caused by hurricanes has a huge impact on the safety and properties of people in coastal areas. Coastal flooding mapping using synthetic aperture radar (SAR) data is a low-cost and wide-coverage means; moreover, SAR has day-and-night, all-weather observation abilities. High-accuracy and robust coastal flooding mapping from SAR imagery information will help the management to formulate more targeted responses and the researchers to better understand coastal flooding mechanisms and develop more precise forecasting models. Based on deep convolutional neural networks, a specially tailored SAR coastal flooding mapping network method is developed to mine bitemporal and dual-polarization SAR imagery information for coastal flooding mapping. The performance of the proposed SAR coastal flooding mapping network is demonstrated by mapping the coastal flooding near Houston, Texas, USA, after 2017 Hurricane Harvey. The study verifies that the mapping accuracy and robustness of the proposed method are better than those of the classic and widely applied deep convolutional neural network method and also shows the mapping products contribute to a better understanding of the geospatial and temporal characteristics of coastal flooding.

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“…When attempting to assimilate flood extent maps into a model, it is necessary to estimate the uncertainty associated with the flood extent observations (Giustarini et al, ). These uncertainty estimates are obtained using the method of Giustarini et al (), which is a further development of previous studies (Giustarini et al, ; Hostache et al, ; Matgen et al, ).…”

Section: Flood Mapping and Flood Forecasting Systemmentioning

confidence: 99%

“…In equation , p ( σ 0 ) is the marginal probability of recording the backscatter σ 0 for any pixel, p ( σ 0 | w ) is the conditional probability of recording the backscatter σ 0 if the pixel is water, p ( σ 0 | n w ) is the conditional probability of recording the backscatter σ 0 if the pixel is nonwater, p ( w ) and p (nw) are the prior probabilities of a pixel being water and nonwater, respectively. As no a priori information is available, we set p ( w ) = p (nw) = 0.5 as suggested in Giustarini et al (). The unknown quantities p ( σ 0 | w ) and p ( σ 0 | n w ) are estimated from the empirical distribution of backscatter values derived from the SAR image.…”

Section: Flood Mapping and Flood Forecasting Systemmentioning

confidence: 99%

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

Hostache

Chini

Giustarini

et al. 2018

Water Resources Research

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109

108

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Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.

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Probabilistic Flood Mapping Using Synthetic Aperture Radar Data

Cited by 115 publications

References 50 publications

Probabilistic flood extent estimates from social media flood observations

Probabilistic flood extent estimates from social media flood observations

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

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