Aerial photographs were collected from a small, fixed-wing aircraft over the coast of Barter Island, Alaska on September 07 2014. Precise aircraft position information and structure-from-motion photogrammetric methods were combined to derive a high-resolution orthophotomosaic. This orthophotomosaic contain 3-band, 8-bit, unsigned raster data (red/green/blue; file format-GeoTIFF) with a ground sample distance (GSD) resolution of 11 cm. The file employs Lempel-Ziv-Welch (LZW) compression. This orthophotomosaic was shifted (registered) to coincide with surveyed ground control points relative to the WGS84 datum.

description: In 2008, the U.S. Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC), in cooperation with the U.S. Army Corps of Engineers conducted a geophysical and sampling survey of the riverbed of the Upper St. Clair River between Port Huron, MI, and Sarnia, Ontario, Canada. The objectives were to define the Quaternary geologic framework of the St. Clair River to evaluate the relationship between morphologic change of the riverbed and underlying stratigraphy. This report presents the geophysical and sample data collected from the St. Clair River, May 29-June 6, 2008 as part of the International Upper Great Lakes Study, a 5-year project funded by the International Joint Commission of the United States and Canada to examine whether physical changes in the St. Clair River are affecting water levels within the upper Great Lakes, to assess regulation plans for outflows from Lake Superior, and to examine the potential effect of climate change on the Great Lakes water levels ( http://www.iugls.org). This document makes available the data that were used in a separate report, U.S. Geological Survey Open-File Report 2009-1137, which detailed the interpretations of the Quaternary geologic framework of the region. This report includes a description of the suite of high-resolution acoustic and sediment-sampling systems that were used to map the morphology, surficial sediment distribution, and underlying geology of the Upper St. Clair River during USGS field activity 2008-016-FA . Video and photographs of the riverbed were also collected and are included in this data release. Future analyses will be focused on substrate erosion and its effects on river-channel morphology and geometry. Ultimately, the International Upper Great Lakes Study will attempt to determine where physical changes in the St. Clair River affect water flow and, subsequently, water levels in the Upper Great Lakes.; abstract: In 2008, the U.S. Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC), in cooperation with the U.S. Army Corps of Engineers conducted a geophysical and sampling survey of the riverbed of the Upper St. Clair River between Port Huron, MI, and Sarnia, Ontario, Canada. The objectives were to define the Quaternary geologic framework of the St. Clair River to evaluate the relationship between morphologic change of the riverbed and underlying stratigraphy. This report presents the geophysical and sample data collected from the St. Clair River, May 29-June 6, 2008 as part of the International Upper Great Lakes Study, a 5-year project funded by the International Joint Commission of the United States and Canada to examine whether physical changes in the St. Clair River are affecting water levels within the upper Great Lakes, to assess regulation plans for outflows from Lake Superior, and to examine the potential effect of climate change on the Great Lakes water levels ( http://www.iugls.org). This document makes available the data that were used in a separate report, U.S. Geological Survey Open-File Report 2009-1137, which detailed the interpretations of the Quaternary geologic framework of the region. This report includes a description of the suite of high-resolution acoustic and sediment-sampling systems that were used to map the morphology, surficial sediment distribution, and underlying geology of the Upper St. Clair River during USGS field activity 2008-016-FA . Video and photographs of the riverbed were also collected and are included in this data release. Future analyses will be focused on substrate erosion and its effects on river-channel morphology and geometry. Ultimately, the International Upper Great Lakes Study will attempt to determine where physical changes in the St. Clair River affect water flow and, subsequently, water levels in the Upper Great Lakes.

The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

This publication contains spatial data, tabular data and scripts used to analyze the spatial patterns of refugia and associated plant communities following each of several fires in northern New Mexico. Four of the geotiff files were derived during the project (*Kernel.tif) using dNBR (delta Normalized Burn Ratio) or dNDVI (delta Normalized Difference Vegetation Index). The kernel raster data represent density of unburned/low severity grid cells in approximately 10-hectare neighborhoods following the Cerro Grande, Dome, La Mesa, and Las Conchas fire events in 2000, 1996, 1977, and 2011, respectively. The data were produced using a kernel smooth process, with output values range from 0 to 1, representing a gradient in neighborhood density of refugia. In addition, geotiff files of the dNBR for Las Conchas (this version is not available at mtbs.gov, but was provided for the study by S. Howard, USGS-EROS), the dNDVI for La Mesa and the La Mesa footprint (both developed for the Fire atlas for the Gila and Aldo Leopold Wilderness Areas project; https://doi.org/10.2737/RDS-2015-0023) are also included. Finally, the archive contains a digital elevation model (developed by USGS-EROS), cropped to the study area; the DEM was used to derive terrain metrics describing topographic heterogeneity at local and catchment scales. The text files contain R scripts and associated tabular data that can be used to repeat the analysis presented in the publication by performing the following functions: 1) generate the kernel rasters (kernel geotiffs described, above); 2) generate terrain metrics from DEM (geotiff included), 3) sample the kernel rasters, terrain metric outputs and 1 kilometer resolution bioclimatic data (downloaded from https://adaptwest.databasin.org/pages/adaptwest-climatena); 4) develop environmental models from the raster sample data (text file included); and 5) conduct a multivariate analysis of species and communities using species data recorded in the field (text file included).

Multiscale elevation models centered on Churfirsten, Switzerland

Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each

File format: GeoTIFF

When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:

Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: 10.1080/15230406.2020.1830856

The USGS, in cooperation with NOAA, is producing detailed maps of the seafloor off southern New England. The current phase of this cooperative research program is directed toward analyzing how bathymetric relief relates to the distribution of sedimentary environments and benthic communities. As part of this program, digital terrain models (DTMs) from bathymetry collected as part of NOAA's hydrographic charting activities are converted into ESRI raster grids and imagery, verified with bottom sampling and photography, and used to produce interpretations of seabed geology and hydrodynamic processes. Although each of the 7 continuous-coverage, completed surveys individually provides important benthic environmental information, many applications require a geographically broader perspective. For example, the usefulness of individual surveys is limited for the planning and construction of cross-Sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated the 7 contiguous multibeam bathymetric DTMs into one dataset that covers much of Block Island Sound. The new dataset is adjusted to mean lower low water, is provided in UTM Zone 19 NAD83 and geographic WGS84 projections, and is gridded to 4-m resolution. This resolution is adequate for seafloor-feature and process interpretation, but small enough to be queried and manipulated with standard GIS programs and to allow for future growth. Natural features visible in the grid include boulder lag deposits of submerged moraines, sand-wave fields, and scour depressions that reflect the strength of the oscillating tidal currents. Bedform asymmetry allows interpretations of net sediment transport. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities off this portion of the Rhode Island coast.

description: These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30 m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (http://woodshole.er.usgs.gov/project-pages/coastal_mass/). This is the spatial dataset for the Red Brook Harbor survey area within Buzzards Bay, Massachusetts. These data are the results of a high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and ground validation (sediment samples and bottom photographs) survey, conducted in 2009. In addition to inclusion within the USGS-CZM geologic mapping effort, these Red Brook Harbor data will be used to assess the shallow-water mapping capability of the geophysical systems deployed for this project, with an emphasis on identifying resolution benchmarks for the interferometric sonar system. (http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2009-018-FA); abstract: These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30 m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (http://woodshole.er.usgs.gov/project-pages/coastal_mass/). This is the spatial dataset for the Red Brook Harbor survey area within Buzzards Bay, Massachusetts. These data are the results of a high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and ground validation (sediment samples and bottom photographs) survey, conducted in 2009. In addition to inclusion within the USGS-CZM geologic mapping effort, these Red Brook Harbor data will be used to assess the shallow-water mapping capability of the geophysical systems deployed for this project, with an emphasis on identifying resolution benchmarks for the interferometric sonar system. (http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2009-018-FA)

The USGS, in cooperation with NOAA and the Connecticut DEP, is producing detailed maps of the seafloor in Long Island Sound. The current phase of this cooperative research program is directed toward analyzing how bathymetric relief relates to the distribution of sedimentary environments and benthic communities. As part of this program, digital terrain models (DTMs) from bathymetry collected as part of NOAA's hydrographic charting activities are converted into ESRI raster grids and imagery (primarily of 2-m resolution), verified with bottom sampling and photography, and used to produce interpretations of seabed geology and hydrodynamic processes. Although each of the 18 completed surveys, ranging in area from 12 to 293 square kilometers, individually provides important benthic environmental information, many applications require a geographically broader perspective. For example, the usefulness of individual surveys is limited for the planning and construction of cross-Sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated 12 multibeam and 2 LIDAR (Light Detection and Ranging) contiguous bathymetric DTMs into one dataset that covers much of eastern Long Island Sound. The new dataset is adjusted to mean lower low water, is provided in UTM Zone 18 NAD83 and geographic WGS84 projections, and is gridded to 4-m resolution. This resolution is adequate for seafloor-feature and process interpretation, but small enough to be queried and manipulated with standard GIS programs and to allow for future growth. Natural features visible in the grid include exposed bedrock outcrops, boulder lag deposits of submerged moraines, sand-wave fields, and scour depressions that reflect the strength of the oscillating tidal currents. Bedform asymmetry allows interpretations of net sediment transport. Anthropogenic artifacts visible in the bathymetric data include a dredged channel, shipwrecks, dredge spoils, mooring anchors, prop-scour depressions, buried cables, and bridge footings. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities in this major east-coast estuary.

description: These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Science Center. Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine reserves, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reportshttp://woodshole.er.usgs.gov/project-pages/coastal_mass/html/current_map.html. This spatial dataset is from the Cape Ann and Salisbury Beach Massachusetts project area. They were collected in two separate surveys in 2004 and 2005 and cover approximately 325 square kilometers of the inner continental shelf. High resolution bathymetry and backscatter intensity were collected in 2004 and 2005. Seismic profile data, sediment samples and bottom photography were also collected in 2005.; abstract: These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Science Center. Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine reserves, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reportshttp://woodshole.er.usgs.gov/project-pages/coastal_mass/html/current_map.html. This spatial dataset is from the Cape Ann and Salisbury Beach Massachusetts project area. They were collected in two separate surveys in 2004 and 2005 and cover approximately 325 square kilometers of the inner continental shelf. High resolution bathymetry and backscatter intensity were collected in 2004 and 2005. Seismic profile data, sediment samples and bottom photography were also collected in 2005.

Overview:
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps:
The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in VRT format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:

gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using r.proj and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Projection + EPSG code:
ETRS89-extended / LAEA Europe (EPSG: 3035)

Spatial extent:
north: 6874000
south: -485000
west: 869000
east: 8712000

Spatial resolution:
1000 m

Pixel values:
meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)

Original dataset license:
https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Dataset for: Bedding scale correlation on Mars in western Arabia Terra

A.M. Annex et al.

Data Product Overview

This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.

HiRISE Digital Elevation Models (DEMs).

HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.

HiRISE map-projected imagery (DRGs).

Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.

3D Topography files (.ply).

Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.

3D Geological Model outputs (.vtk)

VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.

Geological Model geologic maps (geologic_map.tif).

Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.

Beds Geopackage File (.gpkg).

Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.

    Column
    Type
    Description




    uuid
    String
    unique identifier


    stratum_order
    Real
    0-indexed bed order


    section
    Real
    section number


    layer_id
    Real
    bed number/index


    layer_id_bk
    Real
    unused backup bed number/index


    source_raster
    String
    dem file path used


    raster
    String
    dem file name


    gsd
    Real
    ground sampling distant for dem


    wkn
    String
    well known name for dem


    rtype
    String
    raster type


    minx
    Real
    minimum x position of trace in dem crs


    miny
    Real
    minimum y position of trace in dem crs


    maxx
    Real
    maximum x position of trace in dem crs


    maxy
    Real
    maximum y position of trace in dem crs


    method
    String
    internal interpolation method


    sl
    Real
    slope in degrees


    az
    Real
    azimuth in degrees


    error
    Real
    maximum error ellipse angle


    stdr
    Real
    standard deviation of the residuals


    semr
    Real
    standard error of the residuals


    X
    Real
    mean x position in CRS


    Y
    Real
    mean y position in CRS


    Z
    Real
    mean z position in CRS


    b1
    Real
    plane coefficient 1


    b2
    Real
    plane coefficient 2


    b3
    Real
    plane coefficient 3


    b1_se
    Real
    standard error plane coefficient 1


    b2_se
    Real
    standard error plane coefficient 2


    b3_se
    Real
    standard error plane coefficient 3


    b1_ci_low
    Real
    plane coefficient 1 95% confidence interval low


    b1_ci_high
    Real
    plane coefficient 1 95% confidence interval high


    b2_ci_low
    Real
    plane coefficient 2 95% confidence interval low


    b2_ci_high
    Real
    plane coefficient 2 95% confidence interval high


    b3_ci_low
    Real
    plane coefficient 3 95% confidence interval low


    b3_ci_high
    Real
    plane coefficient 3 95% confidence interval high


    pca_ev_1
    Real
    pca explained variance ratio pc 1


    pca_ev_2
    Real
    pca explained variance ratio pc 2


    pca_ev_3
    Real
    pca explained variance ratio pc 3


    condition_number
    Real
    condition number for regression


    n
    Integer64
    number of data points used in regression


    rls
    Integer(Boolean)
    unused flag


    demeaned_regressions
    Integer(Boolean)
    centering indicator


    meansl
    Real
    mean section slope


    meanaz
    Real
    mean section azimuth


    angular_error
    Real
    angular error for section


    mB_1
    Real
    mean plane coefficient 1 for section


    mB_2
    Real
    mean plane coefficient 2 for section


    mB_3
    Real
    mean plane coefficient 3 for section


    R
    Real
    mean plane normal orientation vector magnitude


    num_valid
    Integer64
    number of valid planes in section


    meanc
    Real
    mean stratigraphic position


    medianc
    Real
    median stratigraphic position


    stdc
    Real
    standard deviation of stratigraphic index


    stec
    Real
    standard error of stratigraphic index


    was_monotonic_increasing_layer_id
    Integer(Boolean)
    monotonic layer_id after projection to stratigraphic index


    was_monotonic_increasing_meanc
    Integer(Boolean)
    monotonic meanc after projection to stratigraphic index


    was_monotonic_increasing_z
    Integer(Boolean)
    monotonic z increasing after projection to stratigraphic index


    meanc_l3sigma_std
    Real
    lower 3-sigma meanc standard deviation


    meanc_u3sigma_std
    Real
    upper 3-sigma meanc standard deviation


    meanc_l2sigma_sem
    Real
    lower 3-sigma meanc standard error


    meanc_u2sigma_sem
    Real
    upper 3-sigma meanc standard error


    thickness
    Real
    difference in meanc


    thickness_fromz
    Real
    difference in Z value


    dip_cor
    Real
    dip correction


    dc_thick
    Real
    thickness after dip correction


    dc_thick_fromz
    Real
    z thickness after dip correction


    dc_thick_dev
    Integer(Boolean)
    dc_thick <= total mean dc_thick


    dc_thick_fromz_dev
    Integer(Boolean)
    dc_thick <= total mean dc_thick_fromz


    thickness_fromz_dev
    Integer(Boolean)
    dc_thick <= total mean thickness_fromz


    dc_thick_dev_bg
    Integer(Boolean)
    dc_thick <= section mean dc_thick


    dc_thick_fromz_dev_bg
    Integer(Boolean)
    dc_thick <= section mean dc_thick_fromz


    thickness_fromz_dev_bg
    Integer(Boolean)
    dc_thick <= section mean thickness_fromz


    slr
    Real
    slope in radians


    azr
    Real
    azimuth in radians


    meanslr
    Real
    mean slope in radians


    meanazr
    Real
    mean azimuth in radians


    angular_error_r
    Real
    angular error of section in radians


    pca_ev_1_ok
    Integer(Boolean)
    pca_ev_1 < 99.5%


    pca_ev_2_3_ratio
    Real
    pca_ev_2/pca_ev_3


    pca_ev_2_3_ratio_ok
    Integer(Boolean)
    pca_ev_2_3_ratio > 15


    xyz_wkb_hex
    String
    hex encoded wkb geometry for all points used in regression

Geological Model input files (.gpkg).

Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy).

    Column
    Type
    Description




    azimuth_mean
    String
    Mean section dip azimuth 


    azimuth_indi
    Real
    Individual bed azimuth


    azimuth
    Real
    Azimuth of trace used by the geological model


    dip
    Real
    Dip for the trace used by the geological mode


    polarity
    Real
    Polarity of the dip vector normal vector 


    formation
    String
    String representation of layer_id required for GemPy models


    X
    Real
    X position in the CRS of the sampled point on the trace


    Y
    Real
    Y position in the CRS of the sampled point on the trace


    Z
    Real
    Z position in the CRS of the sampled point on the trace

Stratigraphic Column Files (.gpkg).

Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes _sc, rbsc, and rbssc. File names include the wkn site name.

sc (_sc.gpkg).

Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions

    Column
    Type
    Description




    X
    Real
    X position of thickness measurement


    Y
    Real
    Y position of thickness measurement


    Z
    Real
    Z position of thickness measurement


    formation
    String
    Model required string representation of bed index


    bed thickness (m)
    Real
    difference of bed elevations


    azimuths
    Real
    azimuth as measured from model in degrees


    dip_degrees
    Real
    dip as measured from model in

Multiscale elevation models centered on Gore Range, Colorado, USA

Resolutions: 1, 5, 15, 30, 90, 250, 500, 1,000, 2,000, 2,500, and 5,000 meters, 1500 x 1,500 height samples each

File format: GeoTIFF

When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:

Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: 10.1080/15230406.2020.1830856

Description

The Interpolated Strontium Values dataset Ver. 3.1 presents the interpolated data of strontium isotopes for the southern Trans-Urals, based on the data gathered in 2020-2022. The current dataset consists of five sets of files for five various interpolations: based on grass, mollusks, soil, and water samples, as well as the average of three (excluding the mollusk dataset). Each of the five sets consists of a CSV file and a KML file where the interpolated values are presented to use with a GIS software (ordinary kriging, 5000 m x 5000 m grid). In addition, two GeoTIFF files are provided for each set for a visual reference.

Average 5000 m interpolated points.kml / csv: these files contain averaged values of all three sample types.

Grass 5000 m interpolated points.kml / csv: these files contain data interpolated from the grass sample dataset.

Mollusks 5000 m interpolated points.kml / csv: these files contain data interpolated from the mollusk sample dataset.

Soil 5000 m interpolated points.kml / csv: these files contain data interpolated from the soil sample dataset.

Water 5000 m interpolated points.kml / csv: these files contain data interpolated from the water sample dataset.

The current version is also supplemented with GeoTiff raster files where the same interpolated values are color-coded. These files can be added to Google Earth or any GIS software together with KML files for better interpretation and comparison.

Averaged 5000 m interpolation raster.tif: this file contains a raster representing the averaged values of all three sample types.

Grass 5000 m interpolation raster.tif: this file contains a raster representing the data interpolated from the grass sample dataset.

Mollusks 5000 m interpolation raster.tif: this file contains a raster representing the data interpolated from the mollusk sample dataset.

Soil 5000 m interpolation raster.tif: this file contains a raster representing the data interpolated from the soil sample dataset.

Water 5000 m interpolation raster.tif: this file contains a raster representing the data interpolated from the water sample dataset

In addition, the cross-validation rasters created during the interpolation process are also provided. They can be used as a visual reference of the interpolation reliability. The grey areas on the raster represent the areas where expected values do not differ from interpolated values for more than 0.001. The red areas represent the areas where the error exceeded 0.001 and, thus, the interpolation is not reliable.

How to use it?

The data provided can be used to access interpolated background values of bioavailable strontium in the area of interest. Note that a single value is not a good enough predictor and should never be used as a proxy. Always calculate a mean of 4-6 (or more) nearby values to achieve the best guess possible. Never calculate averages from a single dataset, always rely on cross-validation by comparing data from all five datasets. Check the cross-validation rasters to make sure that the interpolation is reliable for the area of interest.

References

The interpolated datasets are based upon the actual measured values published as follows:

Epimakhov, Andrey; Kisileva, Daria; Chechushkov, Igor; Ankushev, Maksim; Ankusheva, Polina (2022): Strontium isotope ratios (87Sr/86Sr) analysis from various sources the southern Trans-Urals. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.950380

Description of the original dataset of measured strontium isotopic values

The present dataset contains measurements of bioavailable strontium isotopes (87Sr/86Sr) gathered in the southern Trans-Urals. There are four sample types, such as wormwood (n = 103), leached soil (n = 103), water (n = 101), and freshwater mollusks (n = 80), collected to measure bioavailable strontium isotopes. The analysis of Sr isotopic composition was carried out in the cleanrooms (6 and 7 ISO classes) of the Geoanalitik shared research facilities of the Institute of Geology and Geochemistry, the Ural Branch of the Russian Academy of Sciences (Ekaterinburg). Mollusk shell samples preliminarily cleaned with acetic acid, as well as vegetation samples rinsed with deionized water and ashed, were dissolved by open digestion in concentrated HNO 3 with the addition of H 2 O 2 on a hotplate at 150°C. Water samples were acidified with concentrated nitric acid and filtered. To obtain aqueous leachates, pre-ground soil samples weighing 1 g were taken into polypropylene containers, 10 ml of ultrapure water was added and shaken in for 1 hour, after which they were filtered through membrane cellulose acetate filters with a pore diameter of 0.2 μm. In all samples, the strontium content was determined by ICP-MS (NexION 300S). Then the sample volume corresponding to the Sr content of 600 ng was evaporated on a hotplate at 120°C, and the precipitate was dissolved in 7M HNO 3. Sample solutions were centrifuged at 6000 rpm, and strontium was chromatographically isolated using SR resin (Triskem). The strontium isotopic composition was measured on a Neptune Plus multicollector mass spectrometer with inductively coupled plasma (MC-ICP-MS). To correct mass bias, a combination of bracketing and internal normalization according to the exponential law 88 Sr/ 86 Sr = 8.375209 was used. The results were additionally bracketed using the NIST SRM 987 strontium carbonate reference material using an average deviation from the reference value of 0.710245 for every two samples bracketed between NIST SRM 987 measurements. The long-term reproducibility of the strontium isotopic analysis was evaluated using repeated measurements of NIST SRM 987 during 2020-2022 and yielded 87 Sr/ 86 Sr = 0.71025, 2SD = 0.00012 (104 measurements in two replicates). The within-laboratory standard uncertainty (2σ) obtained for SRM-987 was ± 0.003 %.

The State of Alaska Division of Geological & Geophysical Surveys (DGGS) produced a digital surface model (DSM) and an orthorectified aerial optical image mosaic (orthoimagery) of the Resurrection Bay watershed, surrounding the city of Seward in south-central Alaska. Aerial photographs and Global Navigation Satellite System (GNSS) data were collected on August 13, 2015, and were processed using Structure-from-Motion (SfM) photogrammetric techniques to create the DSM and orthoimagery. All data are projected in UTM Zone 6 North (meters) using the NAD83 (2011; EPSG 26906) horizontal datum and NAVD88 (Geoid12A; EPOCH 2010.00) vertical datum. The project was part of an ongoing investigation of the impact of flooding, slope instability, and cryosphere hazards on infrastructure and public safety. For the purpose of enabling open access to geospatial datasets in Alaska, this collection is being released as a Raw Data File with an open end-user license. All files can be downloaded free of charge from the DGGS website (http://doi.org/10.14509/29824). DSMs represent surface elevations of all surfaces, including vegetation, vegetation-free land, bridges, buildings, etc. The DSM is a single-band, 32-bit float GeoTIFF files using Lempel-Ziv-Welch (LZW) compression, with a ground sample distance (GSD) of 0.41 m. The No Data value is set to -32767.

This data set provides three related land cover products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include (1) orthorectified JERS-1 and RadarSat images, (2) land cover classifications derived from the SAR data, and (3) biomass estimates in tons per hectare based on the land cover classification. There are 12 image files (.tif) with this data set.Orthorectified JERS-1 and RadarSat images are provided as GeoTIFF images - one file for each study area.For the Manaus and Tapajos sites: The images are orthorectified at 12.5-meter resolution and then re-sampled at 25-meter resolution.For the Rondonia and Rio Branco sites: The images from 1978 are orthorectified at 25-meter resolution and then re-sampled at 90-meter resolution. Each GeoTIFF file contains 3 image channels: - 2 L-band JERS-1 data in Fall and Spring seasons and - 1 C-band RadarSat data.Land cover classifications are based on two JERS-1 images and one RadarSat image and provided as GeoTIFFs - one file for each study area. Four major land cover classes are distinguished: (1) Flat surface; (2) Regrowth area; (3) Short vegetation; and (4) Tall vegetation. The biomass estimates in tons per hectare are based on the land cover classification results and are reported in one GeoTIFF file for each study area.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.KNOWN PROBLEMS: The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data.Any use of the derived data is not recommended because the results have not been validated. However, the DEM and vectors (related data set), and orthorectified SAR data can be used if the user understands how these were produced and accepts the limitations.

These data were collected under a cooperative agreement between the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center (WHSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of seafloor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters of coastal Massachusetts, primarily in water depths of 5 to 30 meters (m) deep. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (http://woodshole.er.usgs.gov/project-pages/coastal_mass/). The data collected in the study area in Buzzards Bay, Massachusetts, include high-resolution geophysics (bathymetry, backscatter intensity, and seismic reflection) and ground validation (sediment samples, video tracklines, and bottom photographs). The geophysical data are released in USGS Open-File Report 2012-1002, High-Resolution Geophysical Data from the Inner Continental Shelf: Buzzards Bay, Massachusetts (http://pubs.usgs.gov/of/2012/1002/). The sampling data have not been prepared for publication yet. The geophysical data were collected during four separate surveys conducted between 2004 and 2011 (National Oceanic and Atmospheric Administration (NOAA) survey H11319 (in 2004; bathymetry only) and USGS surveys 2009-002-FA, 2010-004-FA, and 2011-004-FA)) and cover 410 square kilometers of the inner continental shelf. More information about the individual USGS surveys conducted as part of the Buzzards Bay project can be found on WHCS Field Activity Web pages: 2009-002-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2009-002-FA 2010-004-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2010-004-FA 2011-004-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2011-004-FA Information about the NOAA survey can be found at: H11319: http://surveys.ngdc.noaa.gov/mgg/NOS/coast/H10001-H12000/H11319/DR/

Detailed bathymetric maps of the sea floor in Block Island and Rhode Island Sounds are of great interest to the New York, Rhode Island, and Massachusetts research and management communities because of this area's ecological, recreational, and commercial importance. Geologically interpreted digital terrain models (DTMs) from individual surveys provide important benthic environmental information, yet many applications require a geographically broader perspective. For example, individual surveys are of limited use for the planning and construction of cross-sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated 14 contiguous multibeam bathymetric DTMs, produced by the National Oceanic and Atmospheric Administration during charting operations, into one dataset that covers much of Block Island Sound and extends eastward across Rhode Island Sound. The new dataset, which covers over 1255 square kilometers, is adjusted to mean lower low water, is gridded to 4-meter resolution, and is provided in UTM Zone 19 NAD 83 and geographic WGS 84 projections. This resolution is adequate for sea-floor feature and process interpretation but is small enough to be queried and manipulated with standard Geographic Information System programs and to allow for future growth. Natural features visible in the grid include boulder lag deposits of winnowed Pleistocene strata, sand-wave fields, and scour depressions that reflect the strength of the oscillating and asymmetric tidal currents and scour by storm-induced waves. Bedform asymmetry allows interpretations of net sediment transport. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities off the Rhode Island coast.

In 2008, the U.S. Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC), in cooperation with the U.S. Army Corps of Engineers conducted a geophysical and sampling survey of the riverbed of the Upper St. Clair River between Port Huron, MI, and Sarnia, Ontario, Canada. The objectives were to define the Quaternary geologic framework of the St. Clair River to evaluate the relationship between morphologic change of the riverbed and underlying stratigraphy. This report presents the geophysical and sample data collected from the St. Clair River, May 29-June 6, 2008 as part of the International Upper Great Lakes Study, a 5-year project funded by the International Joint Commission of the United States and Canada to examine whether physical changes in the St. Clair River are affecting water levels within the upper Great Lakes, to assess regulation plans for outflows from Lake Superior, and to examine the potential effect of climate change on the Great Lakes water levels ( http://www.iugls.org). This document makes available the data that were used in a separate report, U.S. Geological Survey Open-File Report 2009-1137, which detailed the interpretations of the Quaternary geologic framework of the region. This report includes a description of the suite of high-resolution acoustic and sediment-sampling systems that were used to map the morphology, surficial sediment distribution, and underlying geology of the Upper St. Clair River during USGS field activity 2008-016-FA . Video and photographs of the riverbed were also collected and are included in this data release. Future analyses will be focused on substrate erosion and its effects on river-channel morphology and geometry. Ultimately, the International Upper Great Lakes Study will attempt to determine where physical changes in the St. Clair River affect water flow and, subsequently, water levels in the Upper Great Lakes.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains terrain elevation above sea level (ELE) in [m a.s.l.] covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km).

The data is hyperlinked under 'resources' with the following characeristics:
ELE - GISdata (GeoTIFF)
Data format: GEOTIFF
File size : 826.8 MB

There are two temporal representation of solar resource and PVOUT data available:
• Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals)
• Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals)

Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations:
• LTAy_YearlyTotals = LTAy_DailyTotals * 365.25
• LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month

*For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest)
*For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world.

For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Land cover is the physical evidence on the surface of the Earth. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale are rare. With the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first record of 34-year long annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global LC products, GLASS-GLC is characterized by high consistency, more detailed, and longer temporal coverage. The average overall accuracy for the 34 years each with 7 classes, including cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431 test sample units. We implemented a systematic uncertainty analysis and carried out a comprehensive spatiotemporal pattern analysis. Significant changes at various scales were found, including barren land loss and cropland gain in the tropics, forest gain in northern hemisphere and grassland loss in Asia, etc. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable vegetation gain, especially for forest. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling to facilitate research on global carbon and water cycling, vegetation dynamics, and climate change. This GLASS-GLC data set is related to the paper at doi:10.5194/essd-2019-23. It consists of one readme file and 34 GeoTIFF files of annual 5 km global maps from 1982 to 2015 in a WGS 84 projection.