SAR image analysis techniques for flood area mapping - literature survey

Manavalan, R.

doi:10.1007/s12145-016-0274-2

Cited by 73 publications

(36 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they are severely limited by meteorological conditions and cannot work at night due to their passive imaging characteristics. Unlike optical sensors, Synthetic Aperture Radar (SAR) sensors are active sensors and capable of working in all weather conditions and at all times of the day [ 1 ], which make emergency disaster monitoring through rain and clouds possible. Due to the coherent imaging mechanism and long revisit interval, SAR images are limited by speckle noise and data volume.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Kang

Xiang

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in this paper. Considering the requirement of flood detection, we fine-tune the model to get higher accuracy results with shorter training time and fewer training samples. Compared with state-of-the-art methods, our proposed algorithm not only gives robust and accurate detection results but also significantly reduces the detection time.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In the past decades, flood detection methods based on SAR images have been thoroughly studied. Some papers have made detailed summaries of these methods [ 1 , 2 ]. Flood detection methods can be mainly divided into four classes: thresholding, region growing, active counter, and hydrologic method.…”

Section: Introductionmentioning

confidence: 99%

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Kang

Xiang

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Optical data can map the presence of surface water at relatively high spatial resolution and accuracy, but it is limited by the presence of clouds . On the other hand, datasets collected in the microwave region, such as those collected by synthetic-aperture radar (SAR), are not limited by the presence of clouds Manavalan, 2017;Huang et al, 2018). The recent launch of Sentinel-1 ESA sensors in September 2014 (Sentinel-1A) and April 2016 (Sentinel-1B; https:// sentinel.esa.int/web/sentinel/missions/sentinel-1, last access: October 2019) allows mapping of flood extent at unprecedented temporal and spatial resolutions.…”

Section: Sentinel-1 Radar Data and Identification Of Inundated Areasmentioning

confidence: 99%

Exposure of real estate properties to the 2018 Hurricane Florence flooding

Tedesco

McAlpine²,

Porter

2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Quantifying the potential exposure of property to damages associated with storm surges, extreme weather and hurricanes is fundamental to developing frameworks that can be used to conceive and implement mitigation plans as well as support urban development that accounts for such events. In this study, we aim at quantifying the total value and area of properties exposed to the flooding associated with Hurricane Florence that occurred in September 2018. To this aim, we implement an approach for the identification of affected areas by generating a map of the maximum flood extent obtained from a combination of the flood extent produced by the Federal Emergency Management Agency's (FEMA's) water marks with those obtained from spaceborne radar remote-sensing data. The use of radar in the creation of the flood extent allows for those properties commonly missed by FEMA's interpolation methods, especially from pluvial or non-fluvial sources, and can be used in more accurately estimating the exposure and market value of properties to event-specific flooding. Lastly, we study and quantify how the urban development over the past decades in the regions flooded by Hurricane Florence might have impacted the exposure of properties to present-day storms and floods. This approach is conceptually similar to what experts are addressing as the “expanding bull's eye effect”, in which “targets” of geophysical hazards, such as people and their built environments, enlarge as populations grow and spread. Our results indicate that the total value of property exposed to flooding during Hurricane Florence was USD 52 billion (in 2018 USD), with this value increasing from USD ∼10 billion at the beginning of the past century to the final amount based on the expansion of the number of properties exposed. We also found that, despite the decrease in the number of properties built during the decade before Florence, much of the new construction was in proximity to permanent water bodies, hence increasing exposure to flooding. Ultimately, the results of this paper provide a new tool for shedding light on the relationships between urban development in coastal areas and the flooding of those areas, which is estimated to increase in view of projected increasing sea level rise, storm surges and the strength of storms.

show abstract

“…Various studies have been performed to increase the accuracy of flood detection [16][17][18][19]. Multiple sensors combine to provide external data [20][21][22][23], the stacking of pre-flood observations [24,25], and applying machine learning, which are the typical methods used for increasing flood detection accuracies [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

Natsuaki

Nagai

2020

Remote Sensing

View full text Add to dashboard Cite

Flood detection using a spaceborne synthetic aperture radar (SAR) has become a powerful tool for organizing disaster responses. The detection accuracy is increased by accumulating pre-event observations, whereas applying multiple observation modes results in an inadequate number of observations with the same mode from the same orbit. Recent flood detection studies take advantage of the large number of pre-event observations taken from an identical orbit and observation mode. On the other hand, those studies do not take account of the use of multiple orbits and modes. In this study, we examined how the analysis results suffered when pre-event observations were only taken from a different orbit or mode to that of the post-event observation. Experimental results showed that inundation areas were overlooked under such non-ideal conditions. On the other hand, the detection accuracy could be recovered by combining analysis results from possible alternate datasets and became compatible with ideal cases.

show abstract

SAR image analysis techniques for flood area mapping - literature survey

Cited by 73 publications

References 58 publications

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Exposure of real estate properties to the 2018 Hurricane Florence flooding

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

Contact Info

Product

Resources

About