Linear dispersion relation and depth sensitivity to swell parameters: application to synthetic aperture radar imaging and bathymetry.
ABSTRACT: Long gravity waves or swell dominating the sea surface is known to be very useful to estimate seabed morphology in coastal areas. The paper reviews the main phenomena related to swell waves propagation that allow seabed morphology to be sensed. The linear dispersion is analysed and an error budget model is developed to assess the achievable depth accuracy when Synthetic Aperture Radar (SAR) data are used. The relevant issues and potentials of swell-based bathymetry by SAR are identified and discussed. This technique is of particular interest for characteristic regions of the Mediterranean Sea, such as in gulfs and relatively close areas, where traditional SAR-based bathymetric techniques, relying on strong tidal currents, are of limited practical utility.
Project description:This paper examines the impact of ocean surface swell waves on near-coastal L-band high-resolution synthetic aperture radar (SAR) data collected using the National Aeronautics and Space Administration's (NASA) Soil Moisture Active/Passive (SMAP) radar at 40° incidence angle. The two-scale model and a more efficient off-nadir approximation of the second-order small-slope-approximation are used for co- and cross-polarized backscatter normalized radar cross-section (NRCS) predictions of the ocean surface, respectively. Backscatter NRCS predictions are modeled using a combined wind and swell model where wind-driven surface roughness is characterized using the Durden-Vesecky directional spectrum, while swell effects are represented through their contribution to the long wave slope variance (mean-square slopes, or MSS). The swell-only MSS is numerically computed based on a model defined using the JONSWAP spectrum with parameters calculated using the National Data Buoy Center and Wave Watch III data. The backscatter NRCS model is further refined to include fetch-limited and low-wind corrections. The results show an improved agreement between modeled and observed HH-polarized backscatter NRCS when swell effects are included and indicate a relatively larger swell impact on L-band compared to higher radar frequencies. Preliminary investigations into the potential swell retrieval capabilities in the form of excess MSS are encouraging, however further refinements are required to make broadly applicable conclusions.
Project description:The black coral Leiopathes glaberrima is an important habitat forming species that supports benthic biodiversity. Due to its high sensitivity to fishing activities, it has been classified as indicator of Vulnerable Marine Ecosystems (VMEs). However, the information on its habitat selection and large-scale spatial distribution in the Mediterranean Sea is poor. In this study a thorough literature review on the occurrence of L. glaberrima across the Mediterranean Sea was undertaken. Predictive modelling was carried out to produce the first continuous map of L. glaberrima suitable habitat in the central sector of the Mediterranean Sea. MaxEnt modeling was used to predict L. glaberrima probability of presence as a function of seven environmental predictors (bathymetry, slope, aspect North-South and East-West, kinetic energy due to currents at the seabed, seabed habitat types and sea bottom temperature). Our results show that bathymetry, slope and aspect are the most important factors driving L. glaberrima spatial distribution, while in less extent the other environmental variables. This study adds relevant information on the spatial distribution of vulnerable deep water corals in relation to the environmental factors in the Mediterranean Sea. It provides an important background for marine spatial planning especially for prioritizing areas for the conservation of VMEs.
Project description:Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.
Project description:Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30 m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used - Pleiades (2 m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.
Project description:Sustained, quantitative observations of nearshore waves and sand levels are essential for testing beach evolution models, but comprehensive datasets are relatively rare. We document beach profiles and concurrent waves monitored at three southern California beaches during 2001-2016. The beaches include offshore reefs, lagoon mouths, hard substrates, and cobble and sandy (medium-grained) sediments. The data span two energetic El Niño winters and four beach nourishments. Quarterly surveys of 165 total cross-shore transects (all sites) at 100?m alongshore spacing were made from the backbeach to 8?m depth. Monthly surveys of the subaerial beach were obtained at alongshore-oriented transects. The resulting dataset consists of (1) raw sand elevation data, (2) gridded elevations, (3) interpolated elevation maps with error estimates, (4) beach widths, subaerial and total sand volumes, (5) locations of hard substrate and beach nourishments, (6) water levels from a NOAA tide gauge (7) wave conditions from a buoy-driven regional wave model, and (8) time periods and reaches with alongshore uniform bathymetry, suitable for testing 1-dimensional beach profile change models.
Project description:Bottom trawlers land around 19 million tons of fish and invertebrates annually, almost one-quarter of wild marine landings. The extent of bottom trawling footprint (seabed area trawled at least once in a specified region and time period) is often contested but poorly described. We quantify footprints using high-resolution satellite vessel monitoring system (VMS) and logbook data on 24 continental shelves and slopes to 1,000-m depth over at least 2 years. Trawling footprint varied markedly among regions: from <10% of seabed area in Australian and New Zealand waters, the Aleutian Islands, East Bering Sea, South Chile, and Gulf of Alaska to >50% in some European seas. Overall, 14% of the 7.8 million-km<sup>2</sup> study area was trawled, and 86% was not trawled. Trawling activity was aggregated; the most intensively trawled areas accounting for 90% of activity comprised 77% of footprint on average. Regional swept area ratio (SAR; ratio of total swept area trawled annually to total area of region, a metric of trawling intensity) and footprint area were related, providing an approach to estimate regional trawling footprints when high-resolution spatial data are unavailable. If SAR was ?0.1, as in 8 of 24 regions, there was >95% probability that >90% of seabed was not trawled. If SAR was 7.9, equal to the highest SAR recorded, there was >95% probability that >70% of seabed was trawled. Footprints were smaller and SAR was ?0.25 in regions where fishing rates consistently met international sustainability benchmarks for fish stocks, implying collateral environmental benefits from sustainable fishing.
Project description:Theoretical ecology predicts that heterogeneous habitats allow more species to co-exist in a given area. In the deep sea, biodiversity is positively linked with ecosystem functioning, suggesting that deep-seabed heterogeneity could influence ecosystem functions and the relationships between biodiversity and ecosystem functioning (BEF). To shed light on the BEF relationships in a heterogeneous deep seabed, we investigated variations in meiofaunal biodiversity, biomass and ecosystem efficiency within and among different seabed morphologies (e.g., furrows, erosional troughs, sediment waves and other depositional structures, landslide scars and deposits) in a narrow geo-morphologically articulated sector of the Adriatic Sea. We show that distinct seafloor morphologies are characterized by highly diverse nematode assemblages, whereas areas sharing similar seabed morphologies host similar nematode assemblages. BEF relationships are consistently positive across the entire region, but different seabed morphologies are characterised by different slope coefficients of the relationship. Our results suggest that seafloor heterogeneity, allowing diversified assemblages across different habitats, increases diversity and influence ecosystem processes at the regional scale, and BEF relationships at smaller spatial scales. We conclude that high-resolution seabed mapping and a detailed analysis of the species distribution at the habitat scale are crucial for improving management of goods and services delivered by deep-sea ecosystems.
Project description:Volcanic ocean islands generally form on swells-seafloor that is shallower than expected for its age over areas hundreds to more than a thousand kilometers wide-and ultimately subside to form atolls and guyots (flat-topped seamounts). The mechanisms of island drowning remain enigmatic, however, and the subaerial lifespan of volcanic islands varies widely. We examine swell bathymetry and island drowning at 14 hotspots and find a correspondence between island lifespan and residence time atop swell bathymetry, implying that islands drown as tectonic plate motion transports them past mantle sources of swell uplift. This correspondence argues strongly for dynamic uplift of the lithosphere at ocean hotspots. Our results also explain global variations in island lifespan, which influence island topography, biodiversity, and climate.
Project description:Greenland's bed topography is a primary control on ice flow, grounding line migration, calving dynamics, and subglacial drainage. Moreover, fjord bathymetry regulates the penetration of warm Atlantic water (AW) that rapidly melts and undercuts Greenland's marine-terminating glaciers. Here we present a new compilation of Greenland bed topography that assimilates seafloor bathymetry and ice thickness data through a mass conservation approach. A new 150?m horizontal resolution bed topography/bathymetric map of Greenland is constructed with seamless transitions at the ice/ocean interface, yielding major improvements over previous data sets, particularly in the marine-terminating sectors of northwest and southeast Greenland. Our map reveals that the total sea level potential of the Greenland ice sheet is 7.42 ± 0.05 m, which is 7?cm greater than previous estimates. Furthermore, it explains recent calving front response of numerous outlet glaciers and reveals new pathways by which AW can access glaciers with marine-based basins, thereby highlighting sectors of Greenland that are most vulnerable to future oceanic forcing.
Project description:Bathymetry retrievals from 2D, multispectral imagery, referred to as Satellite-Derived Bathymetry (SDB), afford the potential to obtain global, nearshore bathymetric data in optically clear waters. However, accurate SDB depth retrievals are limited in the absence of "seed depths." The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) space-based altimeter has proven capable of accurate bathymetry, but methods of employing ICESat-2 bathymetry for SDB retrievals over broad spatial extents are immature. This research aims to establish and test a baseline methodology for generating bathymetric surface models using SDB with ICESat-2. The workflow is operationally efficient (17-37 min processing time) and capable of producing bathymetry of sufficient vertical accuracy for many coastal science applications, with RMSEs of 0.96 and 1.54 m when using Sentinel-2 and Landsat 8, respectively. The highest priorities for further automation have also been identified, supporting the long-range goal of global coral reef habitat change analysis using ICESat-2-aided SDB.