A review of remote sensing in flood assessment

Lin, Li; Di, Liping; Yu, Genong; Kang, Lingjun; Shrestha, Ranjay; Rahman, Md. Shahinoor; Tang, Jinshan; Deng, Minghua; Sun, Ziheng; Zhang, Chen; Hu, Lei

doi:10.1109/agro-geoinformatics.2016.7577655

Cited by 55 publications

(33 citation statements)

References 21 publications

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“…SAR systems have been used widely in disaster situations such as earthquakes, volcanic eruptions, tsunamis, typhoons and floods (Weissel et al, 2004;Feng et al, 2013;Liu et al, 2013;Dumitru et al, 2015;Yulianto et al, 2015). Klemas (2015) and Lin et al (2016) summarized recent research on flood assessments using optical and SAR sensors. Because microwaves exhibit specular reflections against a smooth water surface, water regions in a SAR image show low backscattering intensity.…”

Section: Introductionmentioning

confidence: 99%

Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

Yamazaki

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015. Due to the record-breaking amount of rainfall, several riverbanks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor on board the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in the city of Joso, Ibaraki Prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10 m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal synthetic aperture radar (SAR) images. The extracted results were modified by considering the land cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and more than 85 % of the maximum inundation areas were extracted successfully.

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Section: Introductionmentioning

confidence: 99%

Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

Yamazaki

2018

Nat. Hazards Earth Syst. Sci.

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“…Synthetic aperture radar (SAR) sensors offer less sensitivity to cloud cover, allowing for a better frequency of images [9]. Hence, SAR is widely used for mapping water and flooded areas [8,[10][11][12][13][14]. New satellites such as the Sentinel constellation and particularly Sentinel-1 A and B (launched in 2014 and 2016 respectively) provide interesting repetitiveness to measure the impact of natural disasters and study the resilience of environments through time series.…”

Section: Introductionmentioning

confidence: 99%

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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“…Remote-sensing data, in conjunction with other geospatial information, has been recognized as an effective means to provide a spatially distributed flood area in the timely monitoring of flood events [6][7][8][9][10][11][12]. Early applications focused on the flooded area delineation using optical remote sensing such as SPOT XS [13], the Landsat Thermatic Mapper (TM)/Multispectral Scanner (MSS) [14,15], and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) [16].…”

Section: Introductionmentioning

confidence: 99%

Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Lin

Tang

et al. 2019

Remote Sensing

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The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), to provide an estimation of crop loss from floods. However, due to the spectral similarity between water and shadow, a noticeable amount of false classification of shadow can be found in the DFO flood products. Traditional methods can be utilized to remove cloud shadow and part of mountain shadow. This paper aims to develop an algorithm to filter out noise from permanent mountain shadow in the flood layer. The result indicates that mountain shadow was significantly removed by using the proposed approach. In addition, the gold standard test indicated a small number of actual water surfaces were misidentified by the proposed algorithm. Furthermore, experiments also suggest that increasing the spatial resolution of the slope helped reduce more noise in mountains. The proposed algorithm achieved acceptable overall accuracy (>80%) in all different filters and higher overall accuracies were observed when using lower slope filters. This research is one of the very first discussions on identifying false flood classification from terrain shadow by using the highly efficient method.

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A review of remote sensing in flood assessment

Cited by 55 publications

References 21 publications

Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

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