High resolution acoustic data of reef habitat - Tasmanian East Coast

Brief description

The principle aim of this project was to map the fine-scale spatial distribution of key abalone habitat impacted by urchins in < 25 m water depth using multibeam acoustic imagery. Detailed substrate type (Pavement Reef, Megaclast Reef, Mixed Consolidated Sediment/Reef and Sand), and kelp coverage maps have been produced for the east coast of Tasmania. Large urchin barrens have been predicted and the minimum quantifiable unit of which small incipient barrens can be detected has been identified using this acoustic water column technique. This data provides a snapshot of the 2021 distribution of seafloor habitats and associated vegetation distribution, and will assist in the facilitation of strategic decision making for urchin control and abalone management. All spatial datasets and derivatives of the have been uploaded onto the Seamap Australia data portal for visualisation as a resource for both managers and scientists for further analysis and study. Data packages have been split by fishing block (22-24, 27-30) and are available to download from each of the 'child' records linked to this record (below).

Lineage

Maintenance and Update Frequency: none-planned

Statement: A full survey was conducted between the 12th and 23rd of April 2021. A Kongsberg Maritime EM2040 multibeam sonar (contracted from CSIRO) was pole mounted on the RV Abyss (Marine Solutions) vessel. CARIS software was used to process the bathymetric data into a gridded depth surface at 50 cm resolution. File-scale benthic substratum classification was derived (using the Seamap Australia Benthic Habitat Classification Scheme) - see associated report for full methodology. The bathymetric surface was analysed in ArcGIS to produce spatial derivatives of (1) seabed slope; (2) curvature (planar and profile); (3) rugosity; and (4) associated contour line information. These spatial derivatives are available for download as associated data with this project. The water column data from the multibeam data record (.all and .wcd format) were read into Matlab using the open-source CoFFee toolbox (https://github.com/alexschimel/CoFFee). The water column samples were filtered using subsequent custom Matlab scripts to eliminate most of the unwanted noise while retaining targets likely to be signal from vegetation. The remaining signals were gridding by calculating the average signal within 50cm grid cells and exporting as point features at 50cm spacing. In ArcGIS, these points were re-gridded to 1m resolution using the mean signal within 1m blocks. The Block Statistic tool was run using a 3x3 neighbourhood and calculating the mean value to create a surface representing 9m2 which would be relevant to the scale that managers can relate to in the natural environment. The block statistic raster was then reclassified into three classes to represent bare (no signal in the water column), patchy (medium level signal) and dense signal (lots of vegetation). The thresholds for the three classes were -64 (minimum) to -60db as bare, -60 to -50 as patchy, and >-50 as dense. The derived water column products are (1) 1m mean signal; (2) raw block statistic (9m2); and (3) vegetation likelihood classified ‘traffic light’ layer. Products (2) and (3) are available as layers for visualisation on the Seamap Australia data portal. Full details of data processing methods for both the seafloor and water column acoustics can be found in the associated report with this data record. Web Mapping Services of bathymetry, habitat, and water column data are available using the following connection parameters: Server URL: https://geoserver.imas.utas.edu.au/geoserver/wms Layer names: • bathymetry = imas:AbHab_bathy_50cm • hillshade = imas:AbHab_hillshade_50cm • substrate type (Seamap Aus classified) = seamap:AbHab_seamap_habitat • water column 9m2 raw block statistic = imas:AbHab_WCD_9m2_means • water column vegetation likelihood classification = imas:AbHab_WCD_classified

Notes

Credit
Abalone Industry Re-investment Fund (AIRF)

Credit
Tasmanian Climate Change Office

Credit
CSIRO Geophysical Mapping and Survey (GSM)

Credit
Sustainable Marine Research Collaboration Agreement (SMRCA)