Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model

Zotou, Ioanna; Bellos, Vasilis; Gkouma, Angeliki; Karathanassi, Vassilia; Tsihrintzis, Vassiliοs A.

doi:10.1007/s11269-020-02592-7

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…The algorithm was initially implemented for mapping the Pinios flood. Pinios catastrophic flood event occurred in spring of 2018 and was caused by a series of storm events from 21 to 26 February 2018 [35,36]. In the particular flood event, Pinios River and all its tributaries have overflowed since 24 February 2018 and hundreds of acres of rural and urban areas have been affected by flooding [37].…”

Section: Area Of Interestmentioning

confidence: 99%

FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series

Karamvasis

Karathanassi

2021

Water

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A new automatic, free and open-source python toolbox for the mapping of floodwater is presented. The output of the toolbox is a binary mask of floodwater at a user-specified time point within geographical boundaries. It exploits the high spatial (10m) and temporal (6 days per orbit over Europe) resolution of Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to a flood event is calculated. Finally, in the fourth step, a classification procedure based on the t-score map is performed to extract the final flood map. A thorough analysis based on several flood events is presented to demonstrate the main benefits, limitations and the potential of the proposed methodology. The validation was performed using Copernicus Emergency Management Service (EMS) products. In all case studies, overall accuracies were higher than 0.95 with Kappa scores higher than 0.76. We believe that the end-user community can benefit by exploiting the flood maps of the proposed methodological pipeline by using the provided open-source toolbox.

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Section: Area Of Interestmentioning

confidence: 99%

FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series

Karamvasis

Karathanassi

2021

Water

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“…This task is very dangerous because collecting hydrological information in the field, such as river velocity, discharge, and maximum depth, requires technical teams at inaccessible locations (Alves, et al, 2020;Moya et al, 2020) and many urban systems may be inaccessible due to the damage in telecommunication and transportation facilities (Agnihotri et al, 2019). In this condition, the use of images acquired by remote sensing becomes an important tool for retrieving information, that is difficult to access in the field, in order to calibrate or validate flood models without direct contact with the flooded area (Ouled Sghaier et al, 2018;Zotou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Liz

Ribas

2022

Rev. Bras. Geog. Fis.

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Delineating the flood extent using SAR data has been an unexceptional activity since the launch of the first sensors. The methodologies applied to image classification vary in complexity and accuracy and, for Sentinel-1, the technical procedures must be adapted considering their polarization, band, temporal resolution, and other factors. Considering these aspects, this article aimed to realize a systematic literature review discussing the most frequent methodologies applied to flood mapping classification employing Sentinel-1 data; describe the preferred methods by areas of expertise; and, inform about their accuracy level. Results indicate that thresholding is the most common methodologies used to flood delineation whatsoever is the researcher area of work. Excellent accuracy levels (reaching 99%) can be obtained using thresholding as the major method, with refinement provided by additional procedures to correct limitations and misclassification. The Sentinel-1data is a good source of information to realize classification in flooded areas but has its limitations in small areas. The choice of methodology for processing SAR data is subjective and requires specific knowledge in the remote sensing field from who executes a hydrological study.

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“…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. (Zotou et al, 2020) reported that, the hydraulic model performance ranged between 61.04 and 65.49%. High‐resolution (5 m) Radarsat‐2 SAR images and time series were used to calculate water level to validate the 1D HEC‐RAS model, 0.28 m was the best root mean square error between SAR and simulated water level (Desrochers, Trudel, Peters, Siles, & Leconte, 2020).…”

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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Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model

Cited by 17 publications

References 33 publications

FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series

FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series

Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

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

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