AndreaBozzo/ceres-open-data-index huggingface dataset:
Final surface model (SRF) for basin A2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091caebd34e791692e1696f, vegetation
Organization: Department of the Interior
Vegetation classification model (Veg) for basin A2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cafcd34e791692e16971, vegetation
Organization: Department of the Interior
Measured and Estimated Streamflow and Estimated Spring-Flow Data in Harney Basin, Southeastern Oregon, 1982-2016

The supplemental data presented here contain tabular data (in .csv format) including measured and estimated daily and water-year (1982–2016) streamflow for selected watersheds and estimated springflow at Page Springs in Harney Basin. Daily streamflow data are a composite of measured streamflow and extended streamflow records from short-term streamgages in gaged watersheds. Short-term or discontinuous records in gaged watersheds were extended to the period 1982–2016 using the Kendal-Thiel Robust Line (KTRL) method (Helsel and Hirsch, 2020) and ordinary-least squares (OLS) linear regression. Springflow estimates were provided by the U.S. Fish and Wildlife Service.

Tags: farming, groundwater, harney-basin, harney-county, hydrology, oregon, usgs-6092e2dcd34e791692e4113e, water-resources
Organization: Department of the Interior
Orthoimagery for basin B1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cb27d34e791692e16975, vegetation
Organization: Department of the Interior
Digital Elevation Model (DEM) for basin B1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cb3fd34e791692e16978, vegetation
Organization: Department of the Interior
Digital Terrain Model (DTM) for basin B1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cb56d34e791692e1697a, vegetation
Organization: Department of the Interior
Final surface model (SRF) for basin B1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cb8bd34e791692e1697d, vegetation
Organization: Department of the Interior
Digital Terrain Model (DTM) for basin B2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cc12d34e791692e1698a, vegetation
Organization: Department of the Interior
Vegetation classification model (Veg) for basin B1

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cb9fd34e791692e1697f, vegetation
Organization: Department of the Interior
Orthoimagery for basin B2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cbcfd34e791692e16983, vegetation
Organization: Department of the Interior
Digital Elevation Model (DEM) for basin B2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cbe0d34e791692e16986, vegetation
Organization: Department of the Interior
Final surface model (SRF) for basin B2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cbf8d34e791692e16988, vegetation
Organization: Department of the Interior
Evapotranspiration Units Delineated by Region in the Harney Basin Groundwater Evapotranspiration Area and Evapotranspiration-Unit Observations, Southeastern Oregon

The supplemental data presented here contains three raster datasets representing the evapotranspiration (ET) units for northern, southern, and western regions of Harney Basin (raster datasets in .tif format) and one vector dataset of ET-unit observations used to delineate ET units (vector dataset in .shp format). Eleven ET units were identified from ET-unit observations of land cover and include bare soil or playa (1), marsh (2), dry meadow (3), wet meadow (4), open water (5), riparian (6), mixed shrubland (7), phreatophyte shrubland (8), xerophyte shrubland (9), sagebrush shrubland (10), and xerophyte grassland (11). Irrigated areas are excluded from ET units. Unpublished land-cover datasets collected by the U.S. Bureau of Land Management (BLM) and U.S. Fish and Wildlife Service (USFWS) were used, in part, to generate ET-unit observations and are provided (vector datasets in .shp and .csv format) in the sub-child item, Harney Basin Land-Cover Observations.

Tags: farming, groundwater, harney-basin, harney-county, hydrology, oregon, usgs-6092e276d34e791692e41106, water-resources
Organization: Department of the Interior
Vegetation classification model (Veg) for basin B2

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not.  Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin.  A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points.  Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.

Tags: biota, colorado, digital-elevation-models, elevation, remote-sensing, rio-blanco, uas, unmanned-aerial-systems, usgs-6091cc2ad34e791692e1698c, vegetation
Organization: Department of the Interior
Streamflow Statistic Trends at USGS Streamgages within the Chesapeake Bay Watershed (1940-2018)

This Child Item contains (1) a table of trends in eight annual streamflow statistics for the time periods 1940-1969, 1970-1999, and 2000-2018, and
(2) a .zip file of plots of the eight statistical trends for the three time periods.
The annual streamflow statistics and their trends were computed by Climate Year (CY) for 1-,7- and 30-day minima and by Water Year (WY) for 1-,7- and 30-day maxima, median and mean.
Streamgages from the original dataset (n = 409) were excluded from one or more time periods of analysis because of extensive data gaps that would interrupt trend lines;
therefore, streamgages were indexed into the earliest possible time period relative to their installation date 
(for example, a streamgage with an operating start year of 1958 would be included in the analysis for the time periods 1970-1999 and 2000-2018), which resulted in different sample sizes for each period:
1940-1969 (n = 90), 1970-1999 (n = 167), and 2000-2018 (n = 243).
Similarly, multiple streamgages were wholly excluded because of frequent discontinuities in the daily mean streamflow used to derive the annual mean streamflow through all three time periods.

Tags: chesapeake-bay-watershed, delaware, egret, flow, maryland, new-york, pennsylvania, stream-flow-trends, time-series, united-states, usgs-6092890cd34e791692e3e913, virginia, washington-dc, west-virginia
Organization: Department of the Interior
Ecological Flow Statistics at USGS Streamgages within the Chesapeake Bay Watershed (1940-2018)

Ecological flow (EFlow) statistics have been designated to characterize the magnitude, frequency, and duration of extreme high- and low-flows, the timing of seasonal flows, and the consistency of the historic regime. This Child Item contains a table of 178 EFlows for the time periods 1940-1969, 1970-1999, and 2000-2018, with absolute and percent change between periods, where applicable. Statistics were computed by Water Year (WY) for all 178 metrics and absolute and percent change were calculated by comparing metrics between combinations of two of the three time periods (1940-1969 and 1970-1999; 1940-1969 and 2000-2018; 1970-1999 and 2000-2018). Streamgages from the original dataset (n = 409) were excluded from one or more time periods of analysis because of extensive data gaps that would yield incomplete EFlows; therefore, stations were indexed into the earliest possible time period relative to their installation date (for example, a streamgage with an operating start year of 1958 would be included in the analysis for the time periods 1970-1999 and 2000-2018), which resulted in different sample sizes for each period: 1940-1969 (n = 90), 1970-1999 (n = 167), and 2000-2018 (n = 243). Similarly, multiple stations were wholly excluded because of frequent discontinuities in the daily mean streamflow through all three time periods. Finally, a streamgage must have fallen within at least two time periods to have a change value. As such, not all stations are represented in the change analysis (change between 1940-1969 and 1970-1999 [n = 90]; change between 1940-1969 and 2000-2018 [n = 90]; change between 1970-1999 and 2000-2018 [n = 167]).

Tags: chesapeake-bay-watershed, delaware, ecological-flows, eflow, eflow-statistics, egret, maryland, new-york, pennsylvania, streamflow, trends, united-states, usgs-60928931d34e791692e3e92a, virginia, washington-dc, west-virginia
Organization: Department of the Interior