This part of DS 781 presents benthic biological observations of the California coast in support of the California Seafloor Mapping Project. A shapefile and corresponding comma-delimited text file are included in "Benthic_Biological_Interpretation.zip," which is accessible from https://pubs.usgs.gov/ds/781/video_observations/data_catalog_video_observations.html.

This part of DS 781 presents benthic biological observations of the California coast in support of the California Seafloor Mapping Project. A shapefile and corresponding comma-delimited text file are included in "Benthic_Biological_Interpretation.zip," which is accessible from https://pubs.usgs.gov/ds/781/video_observations/data_catalog_video_observations.html.

Seafloor character was derived from interpretations of lidar data available for the mainland coast within the study area from the California State Waters Mapping Program (Johnson and others, 2012; Johnson and others, 2013a; Johnson and others, 2013b; Johnson and others, 2013c). The number of substrate classes was reduced because rugosity could not be derived for all areas. References Cited: Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.E., Gutierrez, C.I., Wong, F.L., Yoklavich, M.M., Draut, A.E., Hart, P.E., and Conrad, J.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013a, California State Waters Map Series—Offshore of Santa Barbara, California: U.S. Geological Survey Scientific Investigations Map 3281, 45 p., 11 sheets, scale 1:24,000, https://doi.org/10.3133/sim3281. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013b, California State Waters Map Series—Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3261/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M.D., Wong, F.L., Yoklavich, M.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013c, California State Waters Map Series—Offshore of Ventura, California: U.S. Geological Survey Scientific Investigations Map 3254, pamphlet 42 p., 11 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3254/.

Seafloor character was derived from interpretations of lidar data available for the mainland coast within the study area from the California State Waters Mapping Program (Johnson and others, 2012; Johnson and others, 2013a; Johnson and others, 2013b; Johnson and others, 2013c). The number of substrate classes was reduced because rugosity could not be derived for all areas. References Cited: Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.E., Gutierrez, C.I., Wong, F.L., Yoklavich, M.M., Draut, A.E., Hart, P.E., and Conrad, J.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013a, California State Waters Map Series—Offshore of Santa Barbara, California: U.S. Geological Survey Scientific Investigations Map 3281, 45 p., 11 sheets, scale 1:24,000, https://doi.org/10.3133/sim3281. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013b, California State Waters Map Series—Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3261/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M.D., Wong, F.L., Yoklavich, M.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013c, California State Waters Map Series—Offshore of Ventura, California: U.S. Geological Survey Scientific Investigations Map 3254, pamphlet 42 p., 11 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3254/.

Standard deviation of depth was calculated from the bathymetry surface for each cell using the ArcGIS Spatial Analyst Focal Statistics "STD" parameter. Standard deviation of depth represents the dispersion of depth values (in meters) around the mean depth within a square 3x3 cell window. The 2x2 meter resolution standard deviation of depth GeoTIFF was exported and added as a new map layer to aid in benthic habitat classification. Acoustic imagery was acquired for the VICRNM on two separate missions onboard the NOAA ship, Nancy Foster. The first mission took place from 2/18/04 to 3/5/04. The second mission took place from 2/1/05 to 2/12/05. On both missions, seafloor depths between 14 to 55 m were mapped using a RESON SeaBat 8101 ER (240 kHz) MBES sensor. This pole-mounted system measured water depths across a 150 degree swath consisting of 101 individual 1.5 degree x 1.5 degree beams. The beams to the port and starboard of nadir (i.e., directly underneath the ship) overlapped adjacent survey lines by approximately 10 m. The vessel survey speed was between 5 and 8 kn. In 2004, the ship's location was determined by a Trimble DSM 132 DGPS system, which provided a RTCM differential data stream from the U.S. Coast Guard Continually Operating Reference Station (CORS) at Port Isabel, Puerto Rico. Gyro, heave, pitch and roll correctors were acquired using an Ixsea Octans gyrocompass. In 2005, the ship's positioning and orientation were determined by the Applanix POS/MV 320 V4, which is a GPS aided Inertial Motion Unit (IMU) providing measurements of roll, pitch and heading. The POS/MV obtained its positions from two dual frequency Trimble Zephyr GPS antennae. An auxiliary Trimble DSM 132 DGPS system provided a RTCM differential data stream from the U.S. Coast Guard CORS at Port Isabel, Puerto Rico. For both years, CTD (conductivity, temperature and depth) measurements were taken approximately every 4 hours using a Seabird Electronics SBE-19 to correct for the changing sound velocities in the water column. In 2004, raw data were logged in .xtf (extended triton format) using Triton ISIS software 6.2. In 2005, raw data were logged in .gsf (generic sensor format) using SAIC ISS 2000 software. Data from 2004 were referenced to the WGS84 UTM 20 N horizontal coordinate system, and data from 2005 were referenced to the NAD83 UTM 20 N horizontal coordinate system. Data from both projects were referenced to the Mean Lower Low Water (MLLW) vertical tidal coordinate system. The 2004 and 2005 MBES bathymetric data were both corrected for sensor offsets, latency, roll, pitch, yaw, static draft, the changing speed of sound in the water column and the influence of tides in CARIS Hips & Sips 5.3 and 5.4, respectively. The 2004 data was then binned to create a 1 x 1 m raster surface, and the 2005 data was binned to a create 2 x 2 m raster surface. After these final surfaces were created, the datum for the 2004 bathymetric surfaces was transformed from WGS84 to NAD83 using the "Project Raster" function in ArcGIS 9.1. The 2004 surface was transformed so that it would have the same datum as the 2005 surface. The 2004 bathymetric surface was then down sampled from 1 x 1 to 2 x 2 m using the "Resample" function in ArcGIS 9.1. The 2004 surface was resampled so it would have the same spatial resolution as the 2005 surface. Having the same coordinate systems and spatial resolutions, the final 2004 and 2005 bathymetry rasters were then merged using the Raster Calculator function "Merge" in ArcGIS's Spatial Analyst Extension to create a seamless bathymetry surface for the entire VICRNM area south of St. John. For a complete description of the data acquisition and processing parameters, please see the data acquisition and processing reports (DAPRs) for projects: NF-04-06-VI and NF-05-05-VI (Monaco & Rooney, 2004; Battista & Lazar, 2005).

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat on German Bank (off South West Nova Scotia, Canada). This method involves separating environmental variables derived from multibeam bathymetry (Slope, Curvature) and backscatter (principal components: Q1, Q2, and Q3) into spatial units (i.e. pixels) and classifying the acoustically separated units into 5 habitat classes (Reef, Glacial Till, Silt, Silt with Bedforms, and Sand with Bedforms) using in situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically distinguishable. Unsupervised classifications (acoustic classifications) optimized at 15 classes using Idrisi CLUSTER method (pixel based) Number representing the benthoscape classes (CLASS) derived from in situ imagery and video (See Brown et al., 2012, Figure 3, Table 1). Benthoscape classes (See Brown et al., 2012, Figure 3). Reference: Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009 Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Benthoscape Map of German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7f81d4a-2cb6-4393-b35b-e536ec63e834

Supervised classification utilized training texels of 30 x 30 to 90 x 90 pixels cut from GeoTiff orthotiles centered on the coordinates of the grab sample stations. Each texel was assigned to a cluster training set based on that sample’s classification in the original (latent) cluster analysis calculated on similarity of sediment characteristics. However, none of the potential 2470 combinations of backscatter signal characters and their treatments were able to discriminate significantly among these 5 classes, meaning that variation among samples of at least 2 classes overlapped considerably. Recombination into 4 classes (combining Classes 3 and 4) yielded significant discrimination. Mapping of the results showed that one of these classes was likely to be legitimate when applied to the bayside, but additionally was duplicated as an artifact of edge between orthotiles on the oceanside because of fading at the swath margins. This means that backscatter was characteristic of the larger habitat distinctions shown in the latent dendrogram with confidence, and of lesser branches with less confidence. Therefore, the entire oceanside was characterized as one habitat, and classification of the bayside was attempted again in isolation. Recombination into 3 classes (“mud”, “sand”, “gravelly sand”) was able to resolve 3 classes significantly (score = 0.33548) using input factors Contrast, Gray Mean, and Directionality with 30 x 30 pixel (15 x 15 m) texels. Despite good separation in the training texels, with some slight overlap at the 5% confidence ellipsoid for mud and gravel, most areas known to be muddy were classified as being gravelly sand in the resulting classification map. This is likely a function of reflective shell hash in acoustically dark mud having similar contrast to reflective gravel with acoustically dark shadows created by high relief. A test of natural separation (Davies-Bouldin Index) indicated four modes using these characters, so the same factors were used in an unsupervised classification allowing four latent classes. The four latent classes mapped very similar to the previous supervised classification but broke up the latent analog to the “gravelly sand” class. Class error was low at 0.1110. The newly resolved class was clearly mud with shell, based on video ground truthing. This class was combined with the mud class in compiling the final habitat classification map.

Supervised classification utilized training texels of 30 x 30 to 90 x 90 pixels cut from GeoTiff orthotiles centered on the coordinates of the grab sample stations. Each texel was assigned to a cluster training set based on that sample’s classification in the original (latent) cluster analysis calculated on similarity of sediment characteristics. However, none of the potential 2470 combinations of backscatter signal characters and their treatments were able to discriminate significantly among these 5 classes, meaning that variation among samples of at least 2 classes overlapped considerably. Recombination into 4 classes (combining Classes 3 and 4) yielded significant discrimination. Mapping of the results showed that one of these classes was likely to be legitimate when applied to the bayside, but additionally was duplicated as an artifact of edge between orthotiles on the oceanside because of fading at the swath margins. This means that backscatter was characteristic of the larger habitat distinctions shown in the latent dendrogram with confidence, and of lesser branches with less confidence. Therefore, the entire oceanside was characterized as one habitat, and classification of the bayside was attempted again in isolation. Recombination into 3 classes (“mud”, “sand”, “gravelly sand”) was able to resolve 3 classes significantly (score = 0.33548) using input factors Contrast, Gray Mean, and Directionality with 30 x 30 pixel (15 x 15 m) texels. Despite good separation in the training texels, with some slight overlap at the 5% confidence ellipsoid for mud and gravel, most areas known to be muddy were classified as being gravelly sand in the resulting classification map. This is likely a function of reflective shell hash in acoustically dark mud having similar contrast to reflective gravel with acoustically dark shadows created by high relief. A test of natural separation (Davies-Bouldin Index) indicated four modes using these characters, so the same factors were used in an unsupervised classification allowing four latent classes. The four latent classes mapped very similar to the previous supervised classification but broke up the latent analog to the “gravelly sand” class. Class error was low at 0.1110. The newly resolved class was clearly mud with shell, based on video ground truthing. This class was combined with the mud class in compiling the final habitat classification map.

Supervised classification utilized training texels of 30 x 30 to 90 x 90 pixels cut from GeoTiff orthotiles centered on the coordinates of the grab sample stations. Each texel was assigned to a cluster training set based on that sample’s classification in the original (latent) cluster analysis calculated on similarity of sediment characteristics. However, none of the potential 2470 combinations of backscatter signal characters and their treatments were able to discriminate significantly among these 5 classes, meaning that variation among samples of at least 2 classes overlapped considerably. Recombination into 4 classes (combining Classes 3 and 4) yielded significant discrimination. Mapping of the results showed that one of these classes was likely to be legitimate when applied to the bayside, but additionally was duplicated as an artifact of edge between orthotiles on the oceanside because of fading at the swath margins. This means that backscatter was characteristic of the larger habitat distinctions shown in the latent dendrogram with confidence, and of lesser branches with less confidence. Therefore, the entire oceanside was characterized as one habitat, and classification of the bayside was attempted again in isolation. Recombination into 3 classes (“mud”, “sand”, “gravelly sand”) was able to resolve 3 classes significantly (score = 0.33548) using input factors Contrast, Gray Mean, and Directionality with 30 x 30 pixel (15 x 15 m) texels. Despite good separation in the training texels, with some slight overlap at the 5% confidence ellipsoid for mud and gravel, most areas known to be muddy were classified as being gravelly sand in the resulting classification map. This is likely a function of reflective shell hash in acoustically dark mud having similar contrast to reflective gravel with acoustically dark shadows created by high relief. A test of natural separation (Davies-Bouldin Index) indicated four modes using these characters, so the same factors were used in an unsupervised classification allowing four latent classes. The four latent classes mapped very similar to the previous supervised classification but broke up the latent analog to the “gravelly sand” class. Class error was low at 0.1110. The newly resolved class was clearly mud with shell, based on video ground truthing. This class was combined with the mud class in compiling the final habitat classification map.

A coastal surficial substrate layer for the coastal Scotian Shelf and Bay of Fundy. To create the layer, previous geological characterizations from NRCan were translated into consistent substrate and habitat characterizations; including surficial grain size and primary habitat type. In areas where no geological description was available, data including digital elevation models and substrate samples from NRCan, CHS and DFO Science were interpreted to produce a regional scale substrate and habitat characterization. Each characterization in the layer was given a ranking of confidence and original data resolution to ensure that decision makers are informed of the quality and scale of data that went into each interpretation. Cite this data as: Greenlaw, M., Harvey, C. Data of: A substrate classification for the Inshore Scotian Shelf and Bay of Fundy, Maritimes Region. Published: March 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/f2c493e4-ceaa-11eb-be59-1860247f53e3