A sea bottom classification of the Robredo area in the Northern San Jorge Gulf (Argentina)

Sánchez-Carnero, Noela; Rodríguez‐Pérez, Daniel

doi:10.1007/s00367-020-00682-4

Cited by 2 publications

(3 citation statements)

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“…It can be seen as a particular approach to geostatistical simulation, that attempts to include complex fine-scale features into (or onto) coarse resolution DEMs, taking into account larger scale spatial height distribution to estimate smaller ones [48]; our estimation method is parametric as it assumes a fractal model. Keeping surface roughness, even if it is simulated, helps to perform terrain classification and regionalization based on geomorphological features computed usually as focal statistics of elevation distribution [7,9], and then terrain classification based on feature distribution across the study area [39,77]; smooth interpolated areas would appear as unreal separate classes, otherwise. From the most basic interest in DEM assessment, fractal extrapolation provides a more realistic estimation of error: in areas where interpolated DEM is totally determined by distant measurements, error can be underestimated based on error propagation (assuming or not a underlying convex formula and gaussian process), or on cross-validation.…”

Section: Assessment Of the Methodsmentioning

confidence: 99%

“…For example, a continental DEM or an ocean-wide bathymetry do not require resolving details smaller than several kilometers. On the other hand, the study of coastal tidal dynamics or coastal geomorphometry, or lake or water dam bathymetry may require resolving details of tens of meters or even meters [4,[39][40][41][42]. When dealing with large areas involving continental scale features data size grows rapidly making it almost impossible to efficiently estimate elevation at points where data are not available, hence techniques are required that are able to efficiently handle large data sets.…”

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confidence: 99%

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Multigrid/Multiresolution Interpolation: Reducing Oversmoothing and Other Sampling Effects

Rodríguez‐Pérez

Sánchez-Carnero

2022

Geomatics

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Traditional interpolation methods, such as IDW, kriging, radial basis functions, and regularized splines, are commonly used to generate digital elevation models (DEM). All of these methods have strong statistical and analytical foundations (such as the assumption of randomly distributed data points from a gaussian correlated stochastic surface); however, when data are acquired non-homogeneously (e.g., along transects) all of them show over/under-smoothing of the interpolated surface depending on local point density. As a result, actual information is lost in high point density areas (caused by over-smoothing) or artifacts appear around uneven density areas (“pimple” or “transect” effects). In this paper, we introduce a simple but robust multigrid/multiresolution interpolation (MMI) method which adapts to the spatial resolution available, being an exact interpolator where data exist and a smoothing generalizer where data are missing, but always fulfilling the statistical requirement that surface height mathematical expectation at the proper working resolution equals the mean height of the data at that same scale. The MMI is efficient enough to use K-fold cross-validation to estimate local errors. We also introduce a fractal extrapolation that simulates the elevation in data-depleted areas (rendering a visually realistic surface and also realistic error estimations). In this work, MMI is applied to reconstruct a real DEM, thus testing its accuracy and local error estimation capabilities under different sampling strategies (random points and transects). It is also applied to compute the bathymetry of Gulf of San Jorge (Argentina) from multisource data of different origins and sampling qualities. The results show visually realistic surfaces with estimated local validation errors that are within the bounds of direct DEM comparison, in the case of the simulation, and within the 10% of the bathymetric surface typical deviation in the real calculation.

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Section: Assessment Of the Methodsmentioning

confidence: 99%

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confidence: 99%

Multigrid/Multiresolution Interpolation: Reducing Oversmoothing and Other Sampling Effects

Rodríguez‐Pérez

Sánchez-Carnero

2022

Geomatics

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“…Because of its ecological relevance and high dynamism, this area has been previously studied from different perspectives: seabirds and marine mammals communities [37,[39][40][41], bottom characterization (Sánchez-Carnero et al, 2020 [42]), biological and chemical oceanography [43][44][45][46][47][48], physical oceanography [45,[49][50][51], even submesoscale processes characterization [32,36]. However, due to its large size, no systematic study of the submesoscale processes of the entire protected area of the northern San Jorge Gulf has been carried out so far.…”

Section: Introductionmentioning

confidence: 99%

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

et al. 2023

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Coastal oceanographic processes, like up- and downwelling, topographic fronts, etc., have consequences in biological communities. In some cases, these processes show up as surface structures observable in satellite imagery at different scales. In this work, we focus on the persistent structures observed in the coastal marine protected area of Parque Interjurisdiccional Marino-Costero Patagonia Austral (PIMCPA), one of the most relevant areas of the Argentine coast in terms of biodiversity and productivity. Using 80 Landsat-8 30 m-resolution images from the years 2017–2021, more than 20 structures were identified in the PIMCPA that appear consistently across seasons in approximately the same areas. We focus on four of them, those that are persistent and of medium scale, whose dimensions do not extend in most cases more than 10 km from their region of generation, and describe their location and shape in detail, and analyze their dependence on forcing variables such as tides, wind, bathymetry, and seasonality. Tidal currents prove to be the most significant variable in the formation and evolution of the structures described, above wind or seasonal stratification, which play only a secondary role.

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A sea bottom classification of the Robredo area in the Northern San Jorge Gulf (Argentina)

Cited by 2 publications

References 68 publications

Multigrid/Multiresolution Interpolation: Reducing Oversmoothing and Other Sampling Effects

Multigrid/Multiresolution Interpolation: Reducing Oversmoothing and Other Sampling Effects

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

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