This dataset contains surface sediment grain size distributions derived from automated image processing of in situ seafloor images obtained with an underwater camera system at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The surface sediment grain size distribution data are provided in comma-separated text (.csv) format for each site and for data used in calibration and validation of the automated image processing technique.

This dataset contains surface sediment grain size distributions derived from automated image processing of in situ seafloor images obtained with an underwater camera system at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The surface sediment grain size distribution data are provided in comma-separated text (.csv) format for each site and for data used in calibration and validation of the automated image processing technique.

This dataset contains surface sediment grain size distributions derived from manual point counts of in situ seafloor images obtained with an underwater camera system in the lower Columbia River, Washington and Oregon, in 2021. The distributions derived from manual point counts were compared with results from an automated image processing technique to calibrate and validate the automated method used to quantify surface sediment grain size distributions in objective images. The surface sediment grain size distribution data derived from manual point counts are provided in comma-separated text format (point_count_distributions.csv) and should be compared with grain size distributions for the same set of images (dgs_calibration_validation.csv) derived from automated image processing that can be found in the "Surface sediment grain size distributions derived from automated image processing of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021" portion of the overall data release.

This dataset contains surface sediment grain size distributions derived from manual point counts of in situ seafloor images obtained with an underwater camera system in the lower Columbia River, Washington and Oregon, in 2021. The distributions derived from manual point counts were compared with results from an automated image processing technique to calibrate and validate the automated method used to quantify surface sediment grain size distributions in objective images. The surface sediment grain size distribution data derived from manual point counts are provided in comma-separated text format (point_count_distributions.csv) and should be compared with grain size distributions for the same set of images (dgs_calibration_validation.csv) derived from automated image processing that can be found in the "Surface sediment grain size distributions derived from automated image processing of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021" portion of the overall data release.

This dataset contains surface sediment grain size distributions derived from manual point counts of in situ seafloor images obtained with an underwater camera system in the lower Columbia River, Washington and Oregon, in 2021. The distributions derived from manual point counts were compared with results from an automated image processing technique to calibrate and validate the automated method used to quantify surface sediment grain size distributions in objective images. The surface sediment grain size distribution data derived from manual point counts are provided in comma-separated text format (point_count_distributions.csv) and should be compared with grain size distributions for the same set of images (dgs_calibration_validation.csv) derived from automated image processing that can be found in the "Surface sediment grain size distributions derived from automated image processing of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021" portion of the overall data release.

This dataset contains surface sediment grain diameter measurements from in situ seafloor images collected in the lower Columbia River, Washington and Oregon, in 2021. Surface sediment grain diameters were derived from manual measurements (or "point counts") in a subset of images used to calibrate and validate an automated image processing algorithm to determine surface sediment grain size distributions. For each calibration and validation image that was selected, the long and short axis of 100 grains were manually measured. The grain diameter measurements are provided in comma-separated (.csv) format and compressed into separate .zip archives for images used for calibration and validation of the automated image processing technique.

This dataset contains surface sediment grain diameter measurements from in situ seafloor images collected in the lower Columbia River, Washington and Oregon, in 2021. Surface sediment grain diameters were derived from manual measurements (or "point counts") in a subset of images used to calibrate and validate an automated image processing algorithm to determine surface sediment grain size distributions. For each calibration and validation image that was selected, the long and short axis of 100 grains were manually measured. The grain diameter measurements are provided in comma-separated (.csv) format and compressed into separate .zip archives for images used for calibration and validation of the automated image processing technique.

This dataset contains surface sediment grain diameter measurements from in situ seafloor images collected in the lower Columbia River, Washington and Oregon, in 2021. Surface sediment grain diameters were derived from manual measurements (or "point counts") in a subset of images used to calibrate and validate an automated image processing algorithm to determine surface sediment grain size distributions. For each calibration and validation image that was selected, the long and short axis of 100 grains were manually measured. The grain diameter measurements are provided in comma-separated (.csv) format and compressed into separate .zip archives for images used for calibration and validation of the automated image processing technique.

This section of the data release contains grain-size analysis of twenty-six seafloor surface grab samples that were collected aboard the R/V Snavely in 2017 on U.S. Geological Survey cruise 2017-686-FA offshore Oceanside, southern California. The samples were collected at strategic locations along the same transects as seismic-reflection lines oriented to assess sand and gravel resources in Federal and State waters for potential use in future beach nourishment projects along stretches of the coast where critical erosion hotspots have been identified. Samples were collected at 23 locations using a Van Veen sediment sampler along 4 shore-perpendicular transects spaced 5 to 9 km apart, aligned with previously collected seismic profile lines, at depths ranging from about 20 to 80 m. One sample on the outer shelf (BSS-06) was collected to evaluate an area of suspected sand occurrence. Two duplicate samples were collected at one location (BSS-08) in order to evaluate local sample variability.

This section of the data release contains grain-size analysis of twenty-six seafloor surface grab samples that were collected aboard the R/V Snavely in 2017 on U.S. Geological Survey cruise 2017-686-FA offshore Oceanside, southern California. The samples were collected at strategic locations along the same transects as seismic-reflection lines oriented to assess sand and gravel resources in Federal and State waters for potential use in future beach nourishment projects along stretches of the coast where critical erosion hotspots have been identified. Samples were collected at 23 locations using a Van Veen sediment sampler along 4 shore-perpendicular transects spaced 5 to 9 km apart, aligned with previously collected seismic profile lines, at depths ranging from about 20 to 80 m. One sample on the outer shelf (BSS-06) was collected to evaluate an area of suspected sand occurrence. Two duplicate samples were collected at one location (BSS-08) in order to evaluate local sample variability.