Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data

Anusha, N.; Bharathi, B.

doi:10.1016/j.ejrs.2019.01.001

Cited by 103 publications

(61 citation statements)

References 13 publications

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“…C-band (5.6 cm) radar data acquired by the Sentinel-1 sensor with 12 days intervals were used for flood mapping during the major rainy season in the San Pedro river basin. Indeed, one of the advantages of using satellite data to map the extent of floods is the possibility of identifying large-scale rainfall floods far from the flood plains [11]. Sentinel-1 data is generally available a few hours after the satellite has passed, allowing near-real-time analysis of the flooding stretching.…”

Section: Flood Mappingmentioning

confidence: 99%

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Kouassı

N'go

Anoh

et al. 2020

AJGR

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Floods result from the overflow of water which submerges the surrounding land. They are frequent on the coast of Côte d'Ivoire during the rainy season and have more or less serious consequences on the populations, property and the environment. The study site is the San Pedro river basin. It is a coastal catchment area characterized by an average annual rainfall of up to 2000 mm and subject to recurrent flooding. The objective of this study is to assess the risk of flooding during the great rainy season of 2017. The study aims to study flood hazard, assess vulnerability and map flood risk areas. The methodological approach is based on the use of C-band (5.6 cm) radar remote sensing Kouassi et al.; AJGR, 3(2): 1-8, 2020; Article no.AJGR.55959 2 data acquired by the Sentinel-1 sensor at 12-day intervals. These data are in GRD (Ground Range Detected) level 1 format and were used to calculate the radar backscatter coefficient. The results obtained allowed to map the extent of the flooded areas and showed that more than 6,000 ha of land is flooded for more than 3 days. Sentinel-1 has enormous potential to identify flooding risky areas and to continuously monitor them. Original Research Article

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Section: Flood Mappingmentioning

confidence: 99%

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Kouassı

N'go

Anoh

et al. 2020

AJGR

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“…After manual user interaction with data processing programs, flood extent maps were derived from synthetic aperture radar (SAR) data, which can be used to provide valuable information for the successful management of flash flood disasters. Many researchers suggest the application of SAR images to detect floods prone areas using remote sensing images (Anusha & Bharathi, 2020; Cao, Zhang, Wang, & Zhang, 2019; Elkhrachy, 2018; García‐Pintado et al, 2015; Giustarini et al, 2011; Hong Quang et al, 2020; Li, Martinis, Plank, & Ludwig, 2018; Schlaffer, Matgen, Hollaus, & Wagner, 2015; Schumann, di Baldassarre, & Bates, 2009; Shahabi et al, 2020; Twele, Cao, Plank, & Martinis, 2016). In the case that there are no data to calibrate, the simulated HEC‐RAS Sentinel‐1 images were used to assess the performance of a hydraulic model.…”

Section: Introductionmentioning

confidence: 99%

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

Elkhrachy

Pham

Costache

et al. 2021

J Flood Risk Management

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Digital surface models, land use and rainfall data were used to simulate water areas using Hydrologic Engineering Centres River Analysis System (HEC‐RAS) software. Multi‐temporal synthetic aperture radar (SAR) was used for the detection of flood prone area to calibrate HEC‐RAS, due to the lack of data validation in the New Cairo City, Egypt. The thresholding water detection method was applied to two Sentinel‐1 images, one pre‐ and one post‐flash flood event from April 24 to 27, 2018. The threshold method was used to detect water areas from SAR Sentinel‐1 images. Feature statistical agreement F1 and F2 values ranged from 73.4 to 77.7% between water areas extracted based on backscattering values between 19.97 and 16.53 in decibels (dB) and reference water areas obtained using an optical image of the Sentinel‐2 satellite. The similarity between simulated HEC‐RAS two‐dimensional (2D) of water areas and reference water areas based on SAR data ranged between 74.2 and 89.7% using feature statistical agreement values F1 and F2. It provides a clear suggestion that, in the absence of field observations, SAR data can be used to calibrate the model. Two flood hazard maps created based on water velocity and depth were obtained from HEC‐RAS 2D simulation. The obtained maps indicated that 11% of the roads and 50% of the buildings in New Cairo City are exposed to high hazard areas. Furthermore, 28% of the bare land is situated in a very high vulnerability area. We recommend the use of obtained hazard map in supporting emergency response, and designing effective emergency plans.

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“…However, information extraction from SAR data is typically considered to be more difficult than multispectral sensors (Amitrano et al, 2018). Due to various advantages of SAR and optical data, many studies in the literature have employed these two data types in a complementary fashion via data fusion methods (Notti et al, 2018;Vanama et al, 2021;Anusha and Bharathi, 2020;Tavus et al, 2019;Tavus et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Flooded Areas Caused by a Dam Break (Sardoba Dam, Uzbekistan)

Tavus

Kocaman

Gökçeoğlu

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Although dams are very useful engineering structures, they can have extremely harmful consequences if they fail. One example of these failures occurred in Sardoba Reservoir (Uzbekistan). On May 1, 2020, a part of an earthfill dam failed along the Sardoba Reservoir, and a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. Accurate mapping and monitoring of the flooded areas are crucial for the damage assessment and the mitigation efforts. Satellite Earth Observation datasets can serve for these purposes due to their greater availability with high spatial and temporal resolutions. However, the optical sensors have limitations for data acquisition due to the atmospheric conditions, particularly the cloud cover, which often severely affects the image usability when floods occur. The synthetic aperture radar sensors provide valuable information under all weather conditions, but their interpretation is relatively difficult. Therefore, a data fusion methodology is proposed here for the integrated use of Sentinel-1 and Sentinel-2 datasets using a set of features obtained from both. Four different feature combinations were evaluated using the random forest classifier. The pre-processing steps for the feature extraction are explained in detail and the results are discussed here. The proposed algorithm exhibits very high classification accuracy for the flooded areas and flooded vegetation classes. The method can be employed for the flash flood mapping at regional scale. In addition, the damage assessment especially for agricultural areas in the region is very important for accounting the economic losses and the resilience purposes.

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Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data

Cited by 103 publications

References 13 publications

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

Assessment of Flooded Areas Caused by a Dam Break (Sardoba Dam, Uzbekistan)

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