An evaluation of compiled single-beam bathymetry data as a basis for regional sediment and biotope mapping

Abstract

Maps of surficial sediment distribution and benthic habitats or biotopes provide invaluable information for ocean management and are at the core of many seabed mapping initiatives, including Norway's national offshore mapping programme MAREANO (www.mareano.no). Access to high-quality multibeam echosounder data (bathymetry and backscatter) has been central to many of MAREANO's mapping activities, but in order to maximize the cost-effectiveness of future mapping and ensure timely delivery of scientific information, seabed mappers worldwide may increasingly need to look to existing bathymetry data as a basis for thematic maps. This study examines the potential of compiled single-beam bathymetry data for sediment and biotope mapping. We simulate a mapping scenario where full coverage multibeam data are not available, but where existing bathymetry datasets are supplemented by limited multibeam data to provide the basis for thematic map interpretation and modelling. Encouraging results of sediment interpretation from the compiled bathymetry dataset suggest that production of sediment grain size distribution maps is feasible at a 1:250 000 scale or coarser, depending on the quality of available data. Biotope modelling made use of full-coverage predictor variables based on (i) multibeam data, and (ii) compiled single-beam data supplemented by limited multibeam data. Using the same response variable (biotope point observations obtained from video data), the performance of the respective models could be assessed. Biotope distribution maps based on the two datasets are visually similar, and performance statistics also indicate there is little difference between the models, providing a comparable level of information for regional management purposes. However, whilst our results suggest that using compiled bathymetry data with limited multibeam is viable as a basis for regional sediment and biotope mapping, it is not a substitute. Backscatter data and the better feature resolution provided by multibeam data remain of great value for these and other purposes.

Movement patterns of a commercially important, free-ranging marine invertebrate in the vicinity of a bait source

“…These data were subsequently classified as, 'Soft' , such as mud < 60, 'Mixed' , such as mud and rock > 60 and < 80, and 'Hard' , such as continuous rock > 80. It is not possible to infer habitat type using this method as substrate roughness is not measured [41]; however, these data are a broad indicator of substrate type and have been previously validated using drop-down cameras in the vicinity of the study site [42]. Trap locations within each substrate were chosen randomly, although an effort was made to avoid areas close to hydrophone moorings and ground lines to avoid entanglement and displacement of hydrophones.…”

A review of marine geomorphometry, the quantitative study of the seafloor

“…Although they remain standard for ships navigation, SBESs are less and less used for mapping purposes since MBESs became more affordable. However, recent applications can still be found, particularly where compiled SBES data are available; a SBES bathymetric data set of the English Channel was used by Coggan and Diesing (2012) for the broadscale analysis of an exposed rock ridge system, by Elvenes et al (2014) for surficial sediment and habitat mapping, and by James et al (2012) to identify geomorphic features in a palaeo-valley.…”

“…Davies et al, 2008) or biotope distributions (e.g. Elvenes et al, 2014) and others which are primarily based on physical attributes deemed relevant for the distribution of benthic fauna (e.g. Micallef et al, 2012a;Ismail et al, 2015).…”

Fine-scale movement, activity patterns and home-ranges of European lobster Homarus gammarus

“…This translates linearly into a scale from 1 (low reflection) to 100 (0 dB energy lost). However, Olex cannot assess bottom roughness and only provides a proxy for substrate hardness, precluding discrimination between habitat types (Conner & Plowman 2001, Elvenes et al 2014. Olex provides broad substrate classification, has been previously verified by drop-down camera work within the region and was the best available at the time of writing.…”