Stage-Based Flood Inundation Mapping

Mapping Intimacies ◽

10.21203/rs.3.rs-841313/v1 ◽

2021 ◽

Author(s):

Robert E Criss ◽

David L. Nelson

Keyword(s):

Cross Sections ◽

Lidar Data ◽

Flood Inundation ◽

Hydraulic Analysis ◽

Direct Computation ◽

New Methods ◽

Inundation Maps ◽

Flood Inundation Mapping ◽

Channel Bottom ◽

Upstream Catchment

Abstract New methods allow the direct computation of flood inundation maps from lidar data, independently of discharge estimates, hydraulic analysis, or defined cross sections. One method projects the interpolated profile of measured flood levels onto surrounding topography, creating a smooth inundation surface that is entirely based on data and geometrical relationships. A second method computes inundation maps for any simple function that relates the water surface to the elevation of the channel bottom, exploiting their known, sub-parallel character. A final method theoretically combines the elevation of the channel bottom and the upstream catchment area for points along the thalweg, all defined by lidar data. The conceptual simplicity and realism of these maps facilitate data-based planning.

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Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

10.5194/egusphere-egu21-10445 ◽

2021 ◽

Author(s):

Goutam Konapala ◽

Sujay Kumar

Keyword(s):

Flood Mapping ◽

Attractive Alternative ◽

Flood Inundation ◽

Positive Role ◽

Inundation Maps ◽

Flood Inundation Mapping ◽

Sar Imagery ◽

Operational Errors ◽

Water Indices ◽

Sentinel 2

<p>Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. But MS sensors may not penetrate cloud cover, whereas SAR is plagued by operational errors such as noise-like speckle challenging their viability to global flood mapping applications. An attractive alternative is to effectively combine MS data and SAR, i.e., two aspects that can be considered complementary with respect to flood mapping tasks. Therefore, in this study, we explore the diverse bands of Sentinel 2 (S2) derived water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to access their capability in generating accurate flood inundation maps. For this purpose, a fully connected deep convolutional neural network known as U-Net is applied to combinations of S1 and S2 bands to 446 (training: 313, validating: 44, testing: 89) hand labeled flood inundation extents derived from Sen1Floods11 dataset spanning across 11 flood events. The trained U-net was able to achieve a median F1 score of 0.74 when using DEM and S1 bands as input in comparison to 0.63 when using only S1 bands highlighting the active positive role of DEM in mapping floods. Among the, S2 bands, HSV (Hue, Saturation, Value) transformation of Sentinel 2 data has achieved a median F1 score of 0.94 outperforming the commonly used water spectral indices owing to HSV&#8217;s transformation&#8217;s superior contrast distinguishing abilities. Also, when combined with Sentinel 1 SAR imagery too, HSV achieves a median F1 score 0.95 outperforming all the well-established water indices in detecting floods in majority of test images.</p>

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Development of Flood Warning System and Flood Inundation Mapping Using Field Survey and LiDAR Data for the Grand River near the City of Painesville, Ohio

Hydrology ◽

10.3390/hydrology4020024 ◽

2017 ◽

Vol 4 (2) ◽

pp. 24 ◽

Cited By ~ 10

Author(s):

Niraj Lamichhane ◽

Suresh Sharma

Keyword(s):

Field Survey ◽

Warning System ◽

Lidar Data ◽

Flood Inundation ◽

Flood Warning ◽

Inundation Mapping ◽

Flood Inundation Mapping ◽

Grand River ◽

Flood Warning System ◽

The City

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Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

Remote Sensing ◽

10.3390/rs11131581 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1581 ◽

Cited By ~ 34

Author(s):

Uddin ◽

Matin ◽

Meyer

Keyword(s):

Land Cover ◽

Flood Frequency ◽

Landsat 8 ◽

Flood Inundation ◽

Inundation Maps ◽

Flood Inundation Mapping ◽

Multi Temporal ◽

Land Cover Map ◽

Hazard Response ◽

Land Cover Maps

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

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Comparison between Topographic and Bathymetric LiDAR Terrain Models in Flood Inundation Estimations

Remote Sensing ◽

10.3390/rs14010227 ◽

2022 ◽

Vol 14 (1) ◽

pp. 227

Author(s):

Mahmoud Omer Mahmoud Awadallah ◽

Ana Juárez ◽

Knut Alfredsen

Keyword(s):

General Trend ◽

Lidar Data ◽

Flood Inundation ◽

Inundation Maps ◽

Terrain Models ◽

Improve Accuracy ◽

Geomorphic Features ◽

Map Generation ◽

Data Source ◽

The Difference

Remotely sensed LiDAR data has allowed for more accurate flood map generation through hydraulic simulations. Topographic and bathymetric LiDARs are the two types of LiDAR used, of which the former cannot penetrate water bodies while the latter can. Usually, the topographic LiDAR is more available than bathymetric LiDAR, and it is, therefore, a very interesting data source for flood mapping. In this study, we made comparisons between flood inundation maps from several flood scenarios generated by the HEC-RAS 2D model for 11 sites in Norway using both bathymetric and topographic terrain models. The main objective is to investigate the accuracy of the flood inundations generated from the plain topographic LiDAR, the links of the inaccuracies with geomorphic features, and the potential of using corrections for missing underwater geometry in the topographic LiDAR data to improve accuracy. The results show that the difference in inundation between topographic and bathymetric LiDAR models decreases with increasing the flood size, and this trend was found to be correlated with the amount of protection embankments in the reach. In reaches where considerable embankments are constructed, the difference between the inundations increases until the embankments are overtopped and then returns to the general trend. In addition, the magnitude of the inundation error was found to correlate positively with the sinuosity and embankment coverage and negatively with the angle of the bank. Corrections were conducted by modifying the flood discharge based on the flight discharge of the topographic LiDAR or by correcting the topographic LiDAR terrain based on the volume of the flight discharge, where the latter method generally gave better improvements.

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