Skip to main content
Advanced Search

Filters: Tags: image analysis (X)

106 results (96ms)   

View Results as: JSON ATOM CSV
thumbnail
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.
Tags: Bathymetry and Elevation, Big Sur, CMHRP, California, Cape San Martin, All tags...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
thumbnail
Fine-grained sediments, or “fines,” are nearly ubiquitous in natural sediments, even in the predominantly coarse-grained sediments that host gas hydrates. Fines within these sandy sediments can be mobilized and subsequently clog flow pathways while methane is being extracted from gas hydrate as an energy resource. Using two-dimensional (2D) micromodels to test the conditions in which clogging occurs provides insights for choosing production operation parameters that optimize methane recovery in the field. During methane extraction, several processes can alter the mobility and clogging potential of fines: (1) fluid flow as the formation is depressurized to release methane from gas hydrate, (2) shifting pore-fluid...
thumbnail
This part of the Data Release contains the raster representation of the water-level altitude and water-level change maps developed every 5 years from 1980-2015 for the upper Rio Grande Focus Area Study. The input point data used to generate the water-level altitude maps can be found in the "Groundwater level measurement data used to develop water-level altitude maps in the upper Rio Grande Alluvial Basins" child item of this data release. These digital data accompany Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2020, Hydrogeologic framework, groundwater-level altitudes, groundwater-level changes, and groundwater-storage changes in selected alluvial basins of the upper Rio Grande...
Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Abiquiu Reservoir, Ahumada, Alamosa, Alamosa County, Alamosa Creek, All tags...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
thumbnail
These data are supplementary to the journal article Bassiouni, M., Scholl, M.A., Torres-Sanchez, A.J., Murphy, S.F., 2017, A Method for Quantifying Cloud Immersion in a Tropical Mountain Forest Using Time-Lapse Photography, Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2017.04.010. The data set includes cloud immersion frequency, mean temperature, relative humidity and dew point depression values for five sites, representing Figures 7a and 7b in the article, and values used to calculate the averages shown in Table 2. The results cover the time period from March 2014 to May 2016. A list of validation image filenames with their classifications and the set of 7360 validation images for...


map background search result map search result map Supplementary Data for Method for Quantifying Cloud Immersion in a Tropical Mountain Forest Using Time-Lapse Photography 2D micromodel studies of pore-throat clogging by pure fine-grained sediments and natural sediments from NGHP-02, offshore India Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 Groundwater-level altitude and groundwater-level change maps developed for the groundwater component of the upper Rio Grande Focus Area Study SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 Supplementary Data for Method for Quantifying Cloud Immersion in a Tropical Mountain Forest Using Time-Lapse Photography 2D micromodel studies of pore-throat clogging by pure fine-grained sediments and natural sediments from NGHP-02, offshore India Groundwater-level altitude and groundwater-level change maps developed for the groundwater component of the upper Rio Grande Focus Area Study