The recognition of underwater dunes has a central role to ensure safe navigation. Indeed, the presence of these dynamic landforms on the seafloor represents a hazard for navigation, especially in navigation channels, and should be at least highlighted to avoid collision with vessels. This paper proposes a novel method dedicated to the segmentation of these landforms in the fluvio-marine context. Its originality relies on the use of a conceptual model in which dunes are characterized by three salient features, namely the crest line, the stoss trough, and the lee trough. The proposed segmentation implements the conceptual model by considering the DBM (digital bathymetric model) as the seafloor surface from which the dunes shall be segmented. A geomorphometric analysis of the seabed is conducted to identify the salient features of the dunes. It is followed by an OBIA (object-based image analysis) approach aiming to eliminate the pixel-based analysis of the seabed surface, forming objects to better describe the dunes present in the seafloor. To validate the segmentation method, more than 850 dunes were segmented in the fluvio-marine context of the Northern Traverse of the Saint-Lawrence river. A performance rate of nearly 92% of well segmented dunes (i.e., true positive) was achieved.
The identification of underwater landforms represents an important role in the study of the seafloor morphology. In this context, the segmentation and characterization of underwater dunes allow a better understanding of the dynamism of the seafloor, since the formation of these structures is directly related to environmental conditions, such as current, tide, grain size, etc. In addition, it helps to ensure safe navigation, especially in the context of navigation channels requiring periodic maintenance. This paper proposes a novel method to automatically characterize the underwater dunes. Its originality relies on the extraction of morphological descriptors not only related to the dune itself, but also to the fields where the dunes are located. Furthermore, the proposed approach involves the entire surface of the dunes, rather than profiles or group of pixels as generally found in previous works. Considering the surface modelled by a digital bathymetric model (DBM), the salient features of the dunes (i.e., crest line, stoss trough, and lee trough) are first identified using a geomorphometric analysis of the DBM. The individual dunes are built by matching the crest lines with their respective troughs according to an object-oriented approach. Then, a series of morphological descriptors, selected through a literature review, are computed by taking advantage of the dune salient features, surface representation, and spatial distribution in the fields where they are located. The validation of the proposed method has been conducted using more than 1200 dunes in the fluvio-marine context of the Northern Traverse of the Saint Lawrence River.
The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, MultiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g., Tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of 4 days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.
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