Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera)

Abstract

Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (Macrocystis pyrifera) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.

Laboratory Measurements to Image Endobenthos and Bioturbation with a High-Frequency 3D Seismic Lander

“…Backscatter maps mostly correlate to abiotic seafloor properties such as sediment grain size [19]. However, recent developments aim to derive biotic parameters from acoustic data [20][21][22][23][24][25]. The backscatter level depends, besides the physical seafloor properties itself, on the incidence angle and frequency of the acoustic wave.…”

Estimates of Methane Release From Gas Seeps at the Southern Hikurangi Margin, New Zealand

“…The use of the multibeam data was twofold: 1) to accurately locate gas seeps on the seafloor and 2) to calculate the area of seepage at the seafloor. For the former objective, the data were processed using the National Institute of Water and Atmospheric Research (NIWA) custom-built software Espresso with the following steps: seafloor detection filtering, removal of the outermost noisy beams (>45 °), removal of bad pings, filtering side lobe artefacts and muting the first 5 m of data above the automatically picked seafloor, to avoid misinterpreting the smearing of the beams at the seafloor as gas bubbles (Schimel et al, 2020). The correct pinpoint (as well as could be determined) of seepage at the seafloor was facilitated by the fan visualization of MBES data (Figure 3).…”

Testing Side-Scan Sonar and Multibeam Echosounder to Study Black Coral Gardens: A Case Study from Macaronesia

“…As displayed in Figures 8 and 9, based on the available ground-truthing, the water column backscatter BS wc is sensitive to the occurrence of black corals, with the coral skeletons increasing scatter in the water column. Comparable approaches were recently utilized to detect giant kelp forests in MBES-derived water column data [73]. The water column scatter at 0.75 m to 1.0 m above the seafloor is in principle not affected by the geological seafloor composition (rock/sand), as clearly demonstrated by the low correlation between the water column signal and the backscatter around the bottom detection point.…”