Estimating floodwater depths from flood inundation maps and topography

Cohen, Sagy; Raney, Austin; Munasinghe, Dinuke; Galantowicz, J.F.; Brakenridge, G. Robert

doi:10.1117/12.2324982

Cited by 19 publications

(38 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cohen et al. () developed a floodwater depth estimation tool, based on the extraction of elevation vales for each boundary pixel of flood inundation domain. This allows for local estimation of floodwater elevation.…”

Section: Resultsmentioning

confidence: 99%

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

Munasinghe

Cohen

Huang

et al. 2018

J American Water Resour Assoc

Self Cite

View full text Add to dashboard Cite

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.

show abstract

Section: Resultsmentioning

confidence: 99%

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

Munasinghe

Cohen

Huang

et al. 2018

J American Water Resour Assoc

Self Cite

View full text Add to dashboard Cite

show abstract

“…Space-filling method approximately simulates the ultimate equilibrium of inundation by viewing the water surface as a plane. Based on the overland runoff model, this method estimates inundation extent as well as inundation depth [12], such as the automatic integrated estimation approach by Noman et al [13]. Since space-filling method is implemented by simple mathematical calculations with large errors, the research mainly focuses on improving the estimation accuracy.…”

Section: Space-filling Methodmentioning

confidence: 99%

Estimation Method of Inundation Extent Based on Mathematical Morphology

Lai

Safonov

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Estimation of inundation extent is critical for the flood control system. It helps decision-maker determine the scope of damage to save residents' lives and property. In this paper, we analyzed the drawbacks of two kinds of methods of inundation estimation, hydrodynamic method and space-filling method, and divided inundation extent into water-body extent and water-invasion extent for the purpose of enhancing estimation accuracy. Based on Mathematical Morphology, an inundation estimation method suitable for flood mapping around natural and artificial lakes was put forward by integrating remote sensing (RS) data and digital elevation model (DEM) data. is method consists of three aspects, including the subdivision of water-body extent, the deduction of water-invasion extent which is viewed as the expansion of water-body extent, and the smoothing the boundaries of waterinvasion extent. With subdividing DEM data horizontally into cross sections to build equiangular subsets, the expansion operation was implemented by structuring elements which were established based on set operations and scalar flexibilities. us, water-invasion extent was easily described by the structuring elements under given water volume, so that inundation extent was conveniently estimated by spatial data. e experiment results show that the method has the advantages of high accuracy and expressiveness and low time consumption. It had been used to analyze the inundation caused by the water conservancy project named Hanjiang-to-Weihe River Water Transfer Project in China. e method served as decision support for building an efficient emergency response mechanism and helped implement flood disaster forecast and simulation.

show abstract

“…Cohen et al. () developed a novel floodwater depth detection algorithm based on remotely sensed flood‐inundation extents and topography from digital elevation models (DEMs). Their algorithm identifies boundary grid cells for the inundated area, extracts their elevations from the DEM, and calculates the flood depth by subtracting the elevation of each inundated grid cell from its nearest boundary cell.…”

Section: Overviewmentioning

confidence: 99%