Challenges in Building Coastal Digital Elevation Models

Eakins, Barry W.; Grothe, Pamela R.

doi:10.2112/jcoastres-d-13-00192.1

Cited by 31 publications

(36 citation statements)

References 14 publications

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“…Clearly, the ongoing sea level rise primarily impacts the intertidal zone by permanently flooding and changing the hydrodynamics [2]. Our understanding and modeling of the relevant hydrodynamic processes in the intertidal region require detailed knowledge of its topography, which remains very poor worldwide due to very high costs associated with necessary field campaigns and instrumentation [3].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

et al. 2019

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The intertidal zones are well recognized for their dynamic nature and role in near-shore hydrodynamics. The intertidal topography is poorly mapped worldwide due to the high cost of associated field campaigns. Here we present a combination of remote-sensing and hydrodynamic modeling to overcome the lack of in situ measurements. We derive a digital elevation model (DEM) by linking the corresponding water level to a sample of shorelines at various stages of the tide. Our shoreline detection method is fully automatic and capable of processing high-resolution imagery from state-of-the-art satellite missions, e.g., Sentinel-2. We demonstrate the use of a tidal model to infer the corresponding water level in each shoreline pixel at the sampled timestamp. As a test case, this methodology is applied to the vast coastal region of the Bengal delta and an intertidal DEM at 10 m resolution covering an area of 1134 km 2 is developed from Sentinel-2 imagery. We assessed the quality of the DEM with two independent in situ datasets and conclude that the accuracy of our DEM amounts to about 1.5 m, which is commensurate with the typical error bar of the validation datasets. This DEM can be useful for high-resolution hydrodynamic and wave modeling of the near-shore area. Additionally, being automatic and numerically effective, our methodology is compliant with near-real-time monitoring constraints.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

et al. 2019

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“…Integrating several bathymetric and topographic data sets to create a coastal DEM and estimating its vertical uncertainty at the individual cell-level provide numerous challenges (Eakins and Grothe, 2014). The diverse data sets are typically collected with a wide range of technology, at different time periods, and referenced to different vertical datums.…”

Section: Discussionmentioning

confidence: 99%

“…Current state-of-the-art linear-mode LIDAR sensors have a data density of approximately 2-4 elevation returns/m 2 , and emerging single-photon LIDAR and Geiger-mode LIDAR have a data density of approximately 23 and 25 elevation returns/m 2 for open terrain, respectively (Stoker et al, 2016). NOAA NCEI coastal DEMs have spatial resolutions that usually range from approximately 3 to 10 m (Amante and Eakins, 2016;Eakins and Grothe, 2014), resulting in multiple measurements per DEM grid cell where there is LIDAR coverage. DEM values typically represent a distance-weighted mean of all measurements located within an individual DEM grid cell when using an exact interpolation technique (Amante and Eakins, 2016;Caress and Chayes, 1996).…”

Section: Dem Spatial Resolution and Cell-level Measurement Uncertaintymentioning

confidence: 99%

“…Integrated bathymetric-topographic digital elevation models (DEMs) are representations of Earth's solid surface that extend across the coastal land-water interface by seamlessly merging subaerial topography with adjacent bathymetry Eakins and Grothe, 2014;Gesch and Wilson, 2001;Thatcher et al, 2016). The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) develops DEMs for United States' coastal communities to support numerous coastal modeling efforts, including the modeling of tsunami propagation and coastal inundation (Eakins and Taylor, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Coastal Digital Elevation Model Uncertainty

Amante

2018

Journal of Coastal Research

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“…Local but high-accuracy multibeam survey data are preferably incorporated into the fundamental ETOPO or GEBCO datasets (e.g., Satake et al 2013;Eakins and Grothe 2014;Weatherall et al 2015;Calisto et al 2015). In the present study, we used the framework of a global tsunami simulation Saito 2013, 2016), but the simulations will be suitably carried out in the regional domain in the vicinity of the SWIFT seismic networks in Indonesia/the Philippines, and Chile, in order to efficiently reduce the computation time with the high grid resolution.…”

Section: Discussion For Future Perspectivesmentioning

confidence: 99%

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

et al. 2016

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We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis (M w 7.5-8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).

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Challenges in Building Coastal Digital Elevation Models

Cited by 31 publications

References 14 publications

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

Estimating Coastal Digital Elevation Model Uncertainty

Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

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