On the Horizon: Better Bottom Detection for Areas of Sub-Aquatic Vegetation

Bottom detection methods in single beam echo sounding (SBES) are often less robust in areas with subaquatic vegetation. Due to current mapping efforts emphasizing near shore coverage for safety of navigation and the mission for alternative uses of hydrographic quality data with the Integrated Ocean and Coastal Mapping (IOCM) Center, there is a requirement for both robust bottom detection in areas with complex vegetation and delineation of the vegetated areas themselves. Vegetation can often be found growing in close proximity to rocks and other features of navigational significance and would provide valuable information to fisheries if prime fish habitats like eelgrass could also be mapped with the navigational hazards. A bottom detection algorithm implemented in the program TracEd is being evaluated for handling bottom detections on eelgrass in the water column. This algorithm allows for detections of multiple returns in a full waveform trace for each ping. Each of these returns is then tagged as being associated to seafloor or water column features. Should this algorithm prove to be more robust in recognizing returns from vegetation and identifying the underlying bottom, a systematic approach for NOAA to more accurately determine depth in areas of sub-aquatic vegetation might be possible. A full waveform SBES dataset collected in New Hampshire’s Great Bay Estuary is under analysis to determine whether bare earth can be distinguished from the eelgrass canopy in this area where eelgrass is common and well studied. Additionally, characteristics of the waveform necessary for bottom detection are also being evaluated for eelgrass mapping

Oceanographic Weather Maps: Using Oceanographic Models to Improve Seabed Mapping Planning and Acquisition

In a world of high precision sensors, one of the few remaining challenges in multibeam echosounding is that of refraction based uncertainty. A poor understanding of oceanographic variability can lead to inadequate sampling of the water mass and the uncertainties that result from this can dominate the uncertainty budget of even state-of-the-art echosounding systems. Though dramatic improvements have been made in sensor accuracies over the past few decades, survey accuracy and efficiency is still potentially limited by a poor understanding of the “underwater weather”. Advances in the sophistication of numerical oceanographic forecast modeling, combined with ever increasing computing power, allow for the timely operation and dissemination of oceanographic nowcast and forecast model systems on regional and global scales. These sources of information, when examined using sound speed uncertainty analysis techniques, have the potential to change the way hydrographers work by increasing our understanding of what to expect from the ocean and when to expect it. Sound speed analyses derived from ocean modeling system’s three-dimensional predictions could provide guidance for hydrographers during survey planning, acquisition and post-processing of hydrographic data. In this work, we examine techniques for processing and visualizing of predictions from global and regional operational oceanographic forecast models and climatological analyses from an ocean atlas to better understand how these data could best be put to use to in the field of hydrograph