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  • The Vegetation Structure classes dataset was derived from Vegetation Height Model (VHM) and Fractional Cover Model (FCM) LiDAR products. The National Vegetation Information System framework was used to classify vegetation height and canopy/cover density into (sub-)stratum, growth forms, and structural formation classes. The classifications contain descriptions and spatial extents of the vegetation types for the East Kimberley LiDAR survey area. The displayed classifications include 19 dominant structural formation classes, and 43 dominant sub-structural formation classes for lower-, mid-, and upper stratum. High resolution LiDAR imagery, including Digital Elevation Model (DEM), Canopy Height Model (CHM), Vegetation Height Model (VHM), Vegetation Cover Model (VCM) and Fractional Cover Model (FCM) surfaces were acquired for the East Kimberley area in June 2017. All the data were released in 2019 (Geoscience Australia, 2019). For the purposes of vegetation structure mapping, the two input datasets were resampled, classified and combined to produce a vegetation structure map for the East Kimberley area. The methods are described by Lawrie et al. (2012), with the following differences: • resampling used Focal Statistic Min in ArcGIS as it more accurately represented vegetation extent • VHM was used instead of CHM as CHM did not include low vegetation (i.e ground cover). • VHM and FCM were classified into height and foliage cover classes using the Australian Vegetation Attribute Manual (NVIS Technical Working Group, 2017). Authors acknowledge the tremendous work of the Geoscience Australia Elevation team who carried out post processing, classification, production, quality assurance and delivery of all released LiDAR data products (see Geoscience Australia, 2019). In particular, the authors thank Graham Hammond, Kevin Kennedy, Jonathan Weales, Grahaem Chiles, Robert Kay, Shane Crossman, and Simon Costelloe. Geoscience Australia, 2019. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985 Lawrie, K.C., Brodie, R.S., Tan, K.P., Gibson, D., Magee, J., Clarke, J.D.A., Halas, L., Gow, L., Somerville, P., Apps, H.E., Christensen, N.B., Brodie, R.C., Abraham, J., Smith, M., Page, D., Dillon, P., Vanderzalm, J., Miotlinski, K., Hostetler, S., Davis, A., Ley-Cooper, A.Y., Schoning, G., Barry, K. and Levett, K. 2012. BHMAR Project: Data Acquisition, processing, analysis and interpretation methods. Geoscience Australia Record 2012/11. 826p. NVIS Technical Working Group. 2017 Chapter 4.0 NVIS attributes listed and described in detail. In: Australian Vegetation Attribute Manual: National. Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., deLacey, C. and Bossard, K.B. (Eds).

  • <p>Six camera tow transects were completed during survey IN2017-V01 on the RV Investigator in January to March 2017. This survey focussed on the upper slope and outer continental shelf on the Sabrina Coast seafloor in East Antarctica. Camera tows targeted seafloor depths between 480 and 1680 m. Imagery was collected using the Marine National Facilities Deep Tow Camera. This system collects oblique downward facing still images with a Canon 1DX and high definition video with a Canon C300. Four DSP&L 3150 High Output SeaLite Sphere lights were used, with MicroSeaLasers providing a 10cm scale guide on the images. Several sensors are also attached to the towed body, including a SBE 37 for CTD measurements, a Kongsberg Mesotech – 1007D altimeter, a Druck PMP 5074 pressure sensor and two Sonardyne USBLs for locational accuracy of the towed body. The USBL failed for tow A008CAM02, with locations recorded as ship’s position instead and camera position based on layback calculations. Images failed to record on A004CAM01, with only 2 images saved. Due to a timing error between the images and the other sensors, there is an offset between the CTD, altimeter and pressure data and the images. This is currently being resolved by the MNF. Tows were designed to run down slope to reduce potential for colliding with the bottom. All tows were run with a ship speed over the ground of approximately 2 kt. All still images were saved in jpeg format with video footage recorded to HD1080i50 format. The video data is unclipped and includes descent and ascent in the water column. Following collection still images were colour corrected in Adobe Photoshop to remove the blue bias. <p>Images are available on the NCI with the following file structure: <p>MARINE IMAGERY COLLECTION <p>└─ PLATFORM_NAME <p> └─ CAMPAIGN_NAME <p> └─ DEPLOYMENT_NAME <p> ├─ images/ <p> └─ metadata.csv <p>Where <p>- PLATFORM_NAME is the name of the platform (e.g. Towed still imagery, Towed video imagery) <p>- CAMPAIGN_NAME is the name of a campaign (e.g. IN2017_V01) <p>- DEPLOYMENT_NAME is the name of the deployment or dive (e.g. IN2017_V01_A04_CAM01 is the voyage name, following by station number, followed by camera tow number) <p>- images/ is a directory containing video and still image files <p>- metadata.csv is a file containing position info and references to image files (e.g. timestamp, image_filename, depth from sensor on camera frame, altitude, CTD temperature, conductivity, pressure, dissolved oxygen, salinity, latitude, longitude and depth from USBL unless otherwise specified, comments) <p>We thank the Marine National Facility, the IN2017-V01 scientific party-led by the Chief Scientists L.K. Armand and P. O’Brien, MNF support staff and ASP crew members led by Capt. M. Watson for their help and support on board the RV Investigator. This dataset is published with the permission of the CEO, Geoscience Australia and according to MNF data policy.