Digitised habitat layers for the New South Wales continental shelf predominantly to 3NM. The shape file contains polygons of areas of 1) reef and 2) unconsolidated seafloor types as interpreted from a number of remote sensing methods predominantly mulitbeam, LIDAR (LADS) and Aerial Imagery obtained in surveys across 2005-2013.

A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.

A supervised classification was applied to a Landsat TM5 image. This image was acquired on the 8th August 2004, 15 minutes after low tide. The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 2800 survey sites by UQ, 18 Seagrass-Watch sites and 60 Port of Brisbane Corporation survey sites. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.

This study presents a method for rapidly collecting, processing, and interrogating real-time abiotic and biotic seabed data to determine seabed habitat classifications. This is done from data collected over a large area of an acoustically derived seabed map, along multidirectional transects, using a towed small camera-sled. The seabed, within the newly designated Point Harris Marine Reserve on the northern coast of San Miguel Island, California, was acoustically imaged using sidescan sonar, then ground-truthed using a towed small camera-sled. Seabed characterizations were made from video observations, and were logged to a laptop computer (PC) in real time. To ground-truth the acoustic mosaic, and to characterize abiotic and biotic aspects of the seabed, a three-tiered characterization scheme was employed that described the substratum type, physical structure (i.e., bedform or vertical relief), and the occurrence of benthic macrofauna and flora. A crucial advantage of the method described here, is that preliminary seabed characterisations can be interrogated and mapped over the sidescan mosaic and other seabed information within hours of data collection. This ability to rapidly process seabed data is invaluable to scientists and managers, particularly in modifying concurrent or planning subsequent surveys.

This study presents a method for rapidly collecting, processing, and interrogating real-time abiotic and biotic seabed data to determine seabed habitat classifications. This is done from data collected over a large area of an acoustically derived seabed map, along multidirectional transects, using a towed small camera-sled. The seabed, within the newly designated Point Harris Marine Reserve on the northern coast of San Miguel Island, California, was acoustically imaged using sidescan sonar, then ground-truthed using a towed small camera-sled. Seabed characterizations were made from video observations, and were logged to a laptop computer (PC) in real time. To ground-truth the acoustic mosaic, and to characterize abiotic and biotic aspects of the seabed, a three-tiered characterization scheme was employed that described the substratum type, physical structure (i.e., bedform or vertical relief), and the occurrence of benthic macrofauna and flora. A crucial advantage of the method described here, is that preliminary seabed characterisations can be interrogated and mapped over the sidescan mosaic and other seabed information within hours of data collection. This ability to rapidly process seabed data is invaluable to scientists and managers, particularly in modifying concurrent or planning subsequent surveys.

This study presents a method for rapidly collecting, processing, and interrogating real-time abiotic and biotic seabed data to determine seabed habitat classifications. This is done from data collected over a large area of an acoustically derived seabed map, along multidirectional transects, using a towed small camera-sled. The seabed, within the newly designated Point Harris Marine Reserve on the northern coast of San Miguel Island, California, was acoustically imaged using sidescan sonar, then ground-truthed using a towed small camera-sled. Seabed characterizations were made from video observations, and were logged to a laptop computer (PC) in real time. To ground-truth the acoustic mosaic, and to characterize abiotic and biotic aspects of the seabed, a three-tiered characterization scheme was employed that described the substratum type, physical structure (i.e., bedform or vertical relief), and the occurrence of benthic macrofauna and flora. A crucial advantage of the method described here, is that preliminary seabed characterisations can be interrogated and mapped over the sidescan mosaic and other seabed information within hours of data collection. This ability to rapidly process seabed data is invaluable to scientists and managers, particularly in modifying concurrent or planning subsequent surveys.

This study presents a method for rapidly collecting, processing, and interrogating real-time abiotic and biotic seabed data to determine seabed habitat classifications. This is done from data collected over a large area of an acoustically derived seabed map, along multidirectional transects, using a towed small camera-sled. The seabed, within the newly designated Point Harris Marine Reserve on the northern coast of San Miguel Island, California, was acoustically imaged using sidescan sonar, then ground-truthed using a towed small camera-sled. Seabed characterizations were made from video observations, and were logged to a laptop computer (PC) in real time. To ground-truth the acoustic mosaic, and to characterize abiotic and biotic aspects of the seabed, a three-tiered characterization scheme was employed that described the substratum type, physical structure (i.e., bedform or vertical relief), and the occurrence of benthic macrofauna and flora. A crucial advantage of the method described here, is that preliminary seabed characterisations can be interrogated and mapped over the sidescan mosaic and other seabed information within hours of data collection. This ability to rapidly process seabed data is invaluable to scientists and managers, particularly in modifying concurrent or planning subsequent surveys.

This part of DS 781 presents the seafloor-character map Offshore of Point Reyes, California (raster data file is included in "SFC_PointReyes.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePointReyes/data_catalog_PointReyes.html). These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Point Reyes, California: U.S. Geological Survey Open-File Report 2015–1114, pamphlet 39 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151114. This raster-format seafloor-character map shows four substrate classes offshore of Point Reyes, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zone 4 (100 to 200 m), Depth Zone 5 (greater than 200 m), and Slope Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). References Cited: California Department of Fish and Game, 2008, California Marine Life Protection Act master plan for marine protected areas; Revised draft: California Department of Fish and Game, accessed April 5 2011, at http://www.dfg.ca.gov/mlpa/masterplan.asp. Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape ruggedness for animal habitat analysis--A case study using bighorn sheep in the Mojave Desert: Journal of Wildlife Management, v. 71, p. 1419-1426.