Coastal morphology from space: A showcase of monitoring the topography-bathymetry continuum

Bergsma, Erwin W. J.; Almar, Rafaël; Rolland, A.; Binet, Renaud; Brodie, Katherine L.; Bak, Spicer

doi:10.1016/j.rse.2021.112469

Cited by 45 publications

(36 citation statements)

References 35 publications

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“…The method employed in this work is amply documented [33,[39][40][41]. As the water depth decreases toward the shore, the waves increasingly "feel" the bottom by increasing bottom friction until the waves eventually break near the shore.…”

Section: Wave-based Bathymetry Inversionmentioning

confidence: 99%

“…where c is wave phase celerity, k the wavenumber, and h the water depth and g is the gravitational acceleration (9.8 m/s 2 here). To solve Equation (1), we look for the pair of c and k. Here the wave phase shift (leading to celerity c) per k is computed from different detector bands using a discrete Fourier slicing technique based on a polar Radon transform [33,40,42]. The intensity signal I(x, y) is first transformed into polar space R I (θ, ρ) with the Radon transform (Equation ( 2)):…”

Section: Wave-based Bathymetry Inversionmentioning

confidence: 99%

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Global Satellite-Based Coastal Bathymetry from Waves

et al. 2021

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The seafloor—or bathymetry—of the world’s coastal waters remains largely unknown despite its primary importance to human activities and ecosystems. Here we present S2Shores (Satellite to Shores), the first sub-kilometer global atlas of coastal bathymetry based on depth inversion from wave kinematics captured by the Sentinel-2 constellation. The methodology reveals coastal seafloors up to a hundred meters in depth which allows covering most continental shelves and represents 4.9 million km2 along the world coastline. Although the vertical accuracy (RMSE 6–9 m) is currently coarser than that of traditional surveying techniques, S2Shores is of particular interest to countries that do not have the means to carry out in situ surveys and to unexplored regions such as polar areas. S2Shores is a major step forward in mitigating the effects of global changes on coastal communities and ecosystems by providing scientists, engineers, and policy makers with new science-based decision tools.

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Section: Wave-based Bathymetry Inversionmentioning

confidence: 99%

Section: Wave-based Bathymetry Inversionmentioning

confidence: 99%

Global Satellite-Based Coastal Bathymetry from Waves

et al. 2021

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“…For example, airborne LiDAR technology can be quite accurate, O (cm), and can cover large zones, but remains relatively costly so is limited in temporal update and by turbid coastal waters [9,10]. Satellite-derived coastal bathymetry observations [11][12][13][14][15][16][17] or even sampling of the topo-bathy continuum [18] can be carried out globally but with limits on repeat times due to orbital constraints, issues of atmospheric dilution of quality, and overall lower accuracy, O (m), than in situ methods. However, these global methods are entering a new era of increasing accuracy and public availability [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…However, these global methods are entering a new era of increasing accuracy and public availability [19,20]. By comparison, local bathymetry measurements using video cameras that are either land-based or mounted on inexpensive drones or using land-based radar observations can provide low cost, complementary, near-continuous, near real-time, bathymetric observations, O (dm), enabling the monitoring of high-frequency morphological changes such as storm impacts and providing information that is useful to coastal zone managers [4,17,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Around the early 2000s, bathymetry estimation using land-based video cameras [28], airborne deployments [29,30], and X-Band radar [31] started to develop. Since the early 2000s, methods have improved significantly: estimations became more accurate, consistent, and more versatile, hence, accessible, enabling for example drone applications [17,[32][33][34]. In general, there are three distinctive approaches, ones that use numerical models to match wave breaking patterns [35][36][37], others that use time-varying signatures and stay in the spatio-temporal domain [34,38] and still others that exploit the time signal but with analysis in the spectral domain [34,38].…”

Section: Introductionmentioning

confidence: 99%

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Updates to and Performance of the cBathy Algorithm for Estimating Nearshore Bathymetry from Remote Sensing Imagery

Holman

Bergsma

2021

Remote Sensing

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This manuscript describes and tests a set of improvements to the cBathy algorithm, published in 2013 by Holman et al. [hereafter HPH13], for the estimation of bathymetry based on optical observations of propagating nearshore waves. Three versions are considered, the original HPH13 algorithm (now labeled V1.0), an intermediate version that has seen moderate use but limited testing (V1.2), and a substantially updated version (V2.0). Important improvements from V1.0 include a new deep-water weighting scheme, removal of a spurious variable in the nonlinear fitting, an adaptive scheme for determining the optimum tile size based on the approximate wavelength, and a much-improved search seed algorithm. While V1.2 was tested and results listed, the primary interest is in comparing V1.0, the original code, with the new version V2.0. The three versions were tested against an updated dataset of 39 ground-truth surveys collected from 2015 to 2019 at the Field Research Facility in Duck, NC. In all, 624 cBathy collections were processed spanning a four-day period up to and including each survey date. Both the unfiltered phase 2 and the Kalman-filtered phase 3 bathymetry estimates were tested. For the Kalman-filtered estimates, only the estimate from mid-afternoon on the survey date was used for statistical measures. Of those 39 Kalman products, the bias, rms error, and 95% exceedance for V1.0 were 0.15, 0.47, and 0.96 m, respectively, while for V2.0, they were 0.08, 0.38, and 0.78 m. The mean observed coverage, the percentage of successful estimate locations in the map, were 99.1% for V1.0 and 99.9% for V2.0. Phase 2 (unfiltered) bathymetry estimates were also compared to ground truth for the 624 available data runs. The mean bias, rms error, and 95% exceedance statistics for V1.0 were 0.19, 0.64, and 1.27 m, respectively, and for V2.0 were 0.16, 0.56, and 1.19 m, an improvement in all cases. The coverage also increased from 78.8% for V1.0 to 84.7% for V2.0, about a 27% reduction in the number of failed estimates. The largest errors were associated with both large waves and poor imaging conditions such as fog, rain, or darkness that greatly reduced the percentage of successful coverage. As a practical mitigation of large errors, data runs for which the significant wave height was greater than 1.2 m or the coverage was less than 50% were omitted from the analysis, reducing the number of runs from 624 to 563. For this reduced dataset, the bias, rms error, and 95% exceedance errors for V1.0 were 0.15, 0.58, and 1.16 m and for V2.0 were 0.09, 0.41, and 0.85 m, respectively. Successful coverage for V1.0 was 82.8%, while for V2.0, it was 90.0%, a roughly 42% reduction in the number of failed estimates. Performance for V2.0 individual (non-filtered) estimates is slightly better than the Kalman results in the original HPH13 paper, and it is recommended that version 2.0 becomes the new standard algorithm.

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New Applications of Spaceborne Optical Image Cross‐Correlation: Digital Elevation Models of Volcanic Clouds and Shallow Bathymetry from Space

Michele¹,

Raucoules²

2022

Surface Displacement Measurement From Remote Sensing Images

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Coastal morphology from space: A showcase of monitoring the topography-bathymetry continuum

Cited by 45 publications

References 35 publications

Global Satellite-Based Coastal Bathymetry from Waves

Global Satellite-Based Coastal Bathymetry from Waves

Updates to and Performance of the cBathy Algorithm for Estimating Nearshore Bathymetry from Remote Sensing Imagery

New Applications of Spaceborne Optical Image Cross‐Correlation: Digital Elevation Models of Volcanic Clouds and Shallow Bathymetry from Space

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