During 2001 the NOAA Ship RUDE completed charting survey H11044 that covered a roughly 293 km2 area of the sea floor in north-central Long Island Sound, off Milford Connecticut. Although 100 percent coverage was achieved with sidescan sonar for charting purposes, only reconnaissance (spaced line) bathymetry was acquired with shallow-water multibeam and single-beam systems. Therefore, further processing was conducted at the USGS's Woods Hole Science Center to provide bathymetric datasets with more continuous coverage. This project produced grids and GeoTIFF imagery of the combined and interpolated shallow-water multibeam and single-beam bathymetry generated from the northern part of this data set. Anthropogenic wastes, toxic chemicals, and changes in land-use patterns resulting from residential, commercial, and recreational development have stressed the environment of the Sound, causing degradation and potential loss of benthic habitats. Detailed maps of the sea floor are needed to help evaluate the extent of adverse impacts and to help manage resources wisely in the future. Therefore, in a continuing effort to better understand Long Island Sound, we have interpolated and gridded shallow-water multibeam and single-beam bathymetric data within specific areas of special interest.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Landsat TM, and ETM+ data are provided in GeoTIFF for Level 1T (terrain corrected) products, or for either Level 1Gt (systematic terrain corrected) or Level 1G (systematic corrected) products, if Level 1T processing is not available. GeoTIFF defines a set of publicly available TIFF tags that describe cartographic and geodetic information associated with TIFF images. GeoTIFF is a format that enables referencing a raster image to a known geodetic model or map projection.

The initial tags are followed by image data that, in turn, may be interrupted by more descriptive tags. By using the GeoTIFF format, both metadata and image data can be encoded into the same file. The Landsat 7 ETM+ GeoTIFF file format is described in detail in the"Landsat 7 ETM+ Level 1 Product Data Format Control Book (DFCB), LSDS-272": http://landsat.usgs.gov/documents/LSDS-272.pdf. The Landsat 4-5 TM GeoTIFF file format is described in detail in the "Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control Book (DFCB), LS-DFCB-20": http://landsat.usgs.gov/documents/LS-DFCB-20.pdf.

For more information on GeoTIFF visit: http://trac.osgeo.org/geotiff

Dataset History

ORGANIZATION

Each band of Landsat data in the GeoTIFF format is delivered as a grayscale, uncompressed, 8-bit string of unsigned integers. A metadata (MTL) file is included with data processed through the Level-1 Product Generation System (LPGS). A file containing the ground control points (GCP) used during image processing is also included. A processing history (WO) file is included with data processed through the National Landsat Archive Production System (NLAPS). Landsat 7 ETM+ SLC-off products processed after December 11, 2008, will include an additional directory (gap_mask) that contains a set of flat binary scan gap mask files (one per band). (Please note that the processing date and acquisition date are not necessarily the same.)

\* DATA FILE NAMES

The file naming convention for Landsat LPGS-processed GeoTIFF data

is as follows:

LMSppprrrYYYYDOYGSIVV_BN.TIF where:

 L      = Landsat 

 M      = Mission (E for ETM+ data; T for TM data; M for MSS)

 S      = Satellite (7 = Landsat 7, 5 = Landsat 5, 4 = Landsat 4)

 ppp     = starting path of the product

 rrr     = starting and ending rows of the product

 YYYY    = acquisition year

 DOY     = Julian date

 GSI     = Ground Station Identifier 

 VV     = 2 digit version number

 BN     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band 5

   B6_VCID_1 = band 6L (low gain) (ETM+)

   B6_VCID_2 = band 6H (high gain) (ETM+)

   B6     = band 6 (TM and MSS)

   B7     = band 7 

   B8     = band 8 (ETM+)

   MTL    = Level-1 metadata

   GCP    = ground control points

 TIF     = GeoTIFF file extension

The file naming convention for Landsat NLAPS-processed GeoTIFF data

is as follows:

LLNppprrrOOYYDDDMM_AA.TIF where:

 LL     = Landsat sensor (LT for TM data)

 N      = satellite number

 ppp     = starting path of the product

 rrr     = starting row of the product

 OO     = WRS row offset (set to 00)

 YY     = last two digits of the year of 

        acquisition

 DDD     = Julian date of acquisition

 MM     = instrument mode (10 for MSS; 50 for TM)

 AA     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band5

   B6     = band 6

   B7     = band 7

   WO     = processing history file 

 TIF     = GeoTIFF file extension

\* GAP MASKS

All Landsat 7 ETM+ SLC-off imagery processed on or after December 11, 2008, will include gap mask files. (Please note the difference between acquisition date and processing date, files dates are not necessarily the same.) The gap mask files are bit mask files showing the locations of the image gaps (areas that fall between ETM+ scans). One tarred and gzip-compressed gap mask file is provided for each band in GeoTIFF format. The file naming convention for gap mask files is identical to that described above for LPGS-processed GeoTIFF data, with "_GM" inserted before file type.

If gap mask files are not included with the data, a tutorial for creating them can be found at: http://landsat.usgs.gov/gap_mask_files_are_not_provided_can_I_create_my_own.php

\* README

The README_GTF.TXT (or README.GTF) is an ASCII text file and is this file.

\* READING DATA

Delivered via file transfer protocol (FTP): data files are tarred and g-zip compressed and will need to be unzipped and untarred before the data files can be used. UNIX systems should have the "gunzip" and "tar"

commands available for uncompressing and accessing the data. For PC users, free software can be downloaded from an online source. Otherwise, check your PC, as you may already have appropriate software available.

No software is included on this product for viewing Landsat data.

GENERAL INFORMATION and DOCUMENTATION

Landsat Project Information:

http://landsat.usgs.gov

Landsat data access:

\* USGS Global Visualization Viewer (GloVis): http://glovis.usgs.gov

\* USGS EarthExplorer: http://earthexplorer.usgs.gov

\* USGS LandsatLook Viewer: http://landsatlook.usgs.gov

\* Landsat International Ground Station (IGS) network:

         http://landsat.usgs.gov/about_ground_stations.php

FGDC metadata:

http://www.fgdc.gov/metadata

Data restrictions and citation:

https://lta.cr.usgs.gov/citation

\* National Snow and Ice Data Center (NSIDC)

Radarsat Antarctic Mapping Project (RAMP) elevation data citation:

Liu, H., K. Jezek, B. Li, and Z. Zhao. 2001.

Radarsat Antarctic Mapping Project digital elevation model version 2.

Boulder, CO: National Snow and Ice Data Center. Digital media.

For information on the data, please refer to the data set documentation

available at the following web site:

http://nsidc.org/data/nsidc-0082.html

PRODUCT SUPPORT

For further information on this product, contact USGS

EROS Customer Services:

Customer Services (ATTN: Landsat)

U.S. Geological Survey

Earth Resources Observation and Science (EROS) Center

47914 252nd Street

Sioux Falls, SD 57198-0001

Tel: 800-252-4547

Tel: 605-594-6151

Email: custserv@usgs.gov

For information on other products from USGS EROS:

http://eros.usgs.gov/ or https://lta.cr.usgs.gov/

For information on other USGS products:

http://ask.usgs.gov/

or call 1-888-ASK-USGS (275-8747)

DISCLAIMER

Any use of trade, product, or firm names is for descriptive

purposes only and does not imply endorsement by the U.S.

Government.

Publication Date: July 2014

Dataset Citation

U.S. Geological Survey (2014) SYD Landsat raw data v01. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/fe7aa98d-ea2a-48fc-bc09-1d5ce3a50246.

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 30 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 https://gdal.org/drivers/raster/vrt.html 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, 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. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs.

In the Summer of 2023, Franklin County obtained new orthoimagery covering the entire county (+/- 544 sq. mi). The aerial imagery was collected during leaf-on conditions with a 12-inch pixel resolution. Imagery was collected with the Leica ADS100 Airborne Digital Sensor. Along the perimeter of the project area, ortho-imagery is buffered at 100-feet. The orthos are delivered as a countywide dataset, consisting of 5,000' x 5,000' uncompressed 8-bit, 4-band color GeoTIFF files. The file naming convention is as follows: sxxxxyyy (Ohio South Zone); Please note that xxxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Each GeoTIFF ortho file is approximately 100 megabytes in size. Additional deliverables include countywide color and color infrared MrSID (Multi resolution Seamless Image Database) images (20x and 100x compressions), and tile index provided in ESRI shapefile format. Ownership of the data products resides with Franklin County and the state of Ohio. Orthoimagery and ancillary data products produced through this contract are public domain data.

Product: Gross Primary Productivity (GPP)

Year: 2019

Region: Amazon

Temporal Scale: 8 Days

Spatial Resolution: 500 meters

Method: Light-use-efficiency (LUE) approach.

Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149

https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261

This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo.

NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.

This dataset is about: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, digital elevation model source: near-global 90 m SRTM, links to files in GeoTIFF format. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.867115 for more information.

This dataset corresponds to a reformatting of the SRTM30_PLUS digital elevation dataset from 33 NetCDF files into a single GeoTiff for use in GIS applications. No other modifications to the data …Show full descriptionThis dataset corresponds to a reformatting of the SRTM30_PLUS digital elevation dataset from 33 NetCDF files into a single GeoTiff for use in GIS applications. No other modifications to the data were done. The rest of this metadata describes the original SRTM30_PLUS dataset itself. This dataset is a 30-arc second resolution global topography/bathymetry grid (SRTM30_PLUS) developed from a wide variety of data sources. Land and ice topography comes from the SRTM30 and ICESat topography, respectively. Ocean bathymetry is based on a new satellite-gravity model where the gravity-to-topography ratio is calibrated using 298 million edited soundings. The main contribution of this dataset is the compilation and editing of the raw soundings, which come from NOAA, individual scientists, SIO, NGA, JAMSTEC, IFREMER, GEBCO, and NAVOCEANO. The SRTM30_PLUS dataset developed by Scripps Institute Of Oceanography, University of California San Diego (UCSD). Land data are based on the 1-km averages of topography derived from the USGS SRTM30 grided DEM data product created with data from the NASA Shuttle Radar Topography Mission. GTOPO30 data are used for high latitudes where SRTM data are not available. Ocean data are based on the Smith and Sandwell global 1-minute grid between latitudes +/- 81 degrees. Higher resolution grids have been added from the LDEO Ridge Multibeam Synthesis Project, the JAMSTEC Data Site for Research Cruises, and the NGDC Coastal Relief Model. Arctic bathymetry is from the International Bathymetric Chart of the Oceans (IBCAO) [Jakobsson et al., 2003]. This data consists of 33 files of global topography in the same format as the SRTM30 products distributed by the USGS EROS data center. The grid resolution is 30 second which is roughly one kilometer. In addition the global data are also available in a single large file ready for GMT and as 33 NetCDF files. The e-Atlas has also merged and formatted the data as a single GeoTiff file with overviews (1.6 GB). The pixel-registered data are stored in 33 files with names corresponding to the upper left corner of the array shown below. The data are also merged into a single large (1.9 Gbyte, 2-byte integer) file as well as smaller 1-minute and 2-minute netcdf versions. Matching files of source identification number are available for determining the data source for every pixel. This new version (v8.0) includes all of the multibeam bathymetry data collected by U.S. research vessels over the past three decades including 287 Scripps expeditions from research vessels Washington, Melville and Revelle. UCSD undergraduate student Alexis Shakas processed all the U.S. multibeam data and then worked with Google researchers on the global integration. The data is available from UCSD FTP server as 33 NetCDF files and from the e-Atlas as a merged GeoTiff. If you are after high resolution bathymetry/elevation data for regional areas please check the related links. Reference, sounding data: Becker, J. J., D. T. Sandwell, W. H. F. Smith, J. Braud, B. Binder, J. Depner, D. Fabre, J. Factor, S. Ingalls, S-H. Kim, R. Ladner, K. Marks, S. Nelson, A. Pharaoh, R. Trimmer, J. Von Rosenberg, G. Wallace, P. Weatherall., Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS, Marine Geodesy, 32:4, 355-371, 2009. http://topex.ucsd.edu/sandwell/publications/124_MG_Becker.pdf Reference, gravity data: Sandwell, D. T., and W. H. F. Smith, Global marine gravity from retracked Geosat and ERS-1 altimetry: Ridge Segmentation versus spreading rate, J. Geophys. Res., 114, B01411, doi:10.1029/2008JB006008, 2009. http://dx.doi.org/10.1029/2008JB006008 eAtlas Processing: A set of Batch scripts were developed to perform the conversion of the data from NetCDF to GeoTiff and the generation of the hillshading. This processing was based on the GDAL command line tools. Full details of the processing can be found in the downloadable Scripts associated with this dataset. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\13.1_eAtlas\World_UCSD_SRTM30-plus

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).

This dataset consists reprocessing and reformatting the SRTM30 PLUS v8.0 Digital Elevation Model (DEM) dataset developed by Scripps Institute Of Oceanography, University of California San Diego (UCSD) to produce a single raster covering the globe in GeoTiff format and create a full and low resolution hillshading from this DEM. The aim of this derived dataset is to reformat the data to allow easy use with GIS applications.

Full resolution hillshading:

The hillshading was produced by combining the 33 source DEMs using gdal_translate then processing using gdaldem with a z-factor of 0.0001. This output was then formatted as a JPEG compressed GeoTiff file with internal overviews (World_e-Atlas-UCSD_SRTM30-plus_v8_Hillshading.tif).

Low resolution smoothed hillshading:

A lower resolution of the hillshading (World_e-Atlas-UCSD_SRTM30-plus_v8_Hillshading-lr.tif) was also produced for for use when displaying zoomed out global maps. By making the hillshading smoother the bulk features (mountain ranges, etc) are easier to see.

This was generated by subsampling the DEM by two times (down to 21600x10800 pixels) then smoothing it with a pixel Gaussian filter. This was achieved using gdalwarp to subsample the data. Gdalbuildvrt was then used to create a virtual dataset that included a 4 pixel Gaussian filter kernel. The hillshading was then applied to this filtered data source using gdaldem with a z-factor of 0.0003, which 3 times stronger than the high resolution version of this dataset.

These GeoTIFF rasters have the color ramps used in Figure 2 of LeSaout et al. (2020), plus a color ramp that spans the analyzed area. The images are all slope-shaded, hence "slope" in the file names. The depth range, in meters, of the Haxby color ramp used in each of the images is the final part of the filename. These GeoTIFF images were made from a grid of 1-m lateral resolution processed swath bathymetry compiled from MBARI Mapping AUV data acquired on the north rift zone of Axial Seamount with a Reson SeaBat 7125 multibeam sonar system in the summer of 2016 during R/V Rachel Carson cruise RCSN392. The data files are in GeoTIFF grid image format and were generated with several color ramps as indicated by the depth ranges in the file names. The files are in WGS-1984 geographic coordinates, as square cells for use with GIS. Navigation is DVL/USBL, adjusted with MBSystem in post-processing. The AUV navigation adjustment model is consistent with the shapefile of 2015 lava flow boundaries of DOI 10.1594/IEDA/324417 (which is also used in the Le Saout et al. 2020 paper). The files are a subset of the data collected as part of the 2016 Northern Expedition to Axial Seamount. Funding for the work was provided with a grant to MBARI from the David and Lucile Packard Foundation.

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. 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 30 m 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 https://gdal.org/drivers/raster/vrt.html 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 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.

This data release reformats the STATSGO soil thickness (THICK) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. The soil thickness values have units of inches. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the rasterize_statsgo.py Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

The main result of the SAR interferometric analysis is a temporal series of displacement maps on coherent targets in the satellite line-of-sight direction. Local InSAR Digital Elevation models are provided for two regions, Alaska Northslope (US) and Mackenzie River Delta (Canada): ASAR InSAR DEM, North Slope (Alaska); Terra SAR-X InSAR DEM North Slope (Alaska); Terra SAR-X InSAR DEM, Mackenzie River Delta (Canada). […]

Local scale photogrammetric processing of ALOS-Prism Triplets (ground sampling distance 2.5 m) has been carried out within three service case regions. The analysis has been made for central Yakutia (Russia), Polar Bear Pass (Canada) and a site at the Yamal peninsula (Russia, Ob service case region). […]

The Northern Circumpolar Soil Carbon Database version 2 (NCSCDv2) is a geospatial database created for the purpose of quantifying storage of organic carbon in soils of the northern circumpolar permafrost region down to a depth of 300 cm. The NCSCDv2 is based on polygons from different regional soils maps homogenized to the U.S. Soil Taxonomy. The NCSCDv2 contains information on fractions of coverage of different soil types (following U.S. Soil Taxonomy nomenclature) as well as estimated storage of soil organic carbon (kg/m2) between 0-30 cm, 0-100 cm, 100-200 cm and 200-300 cm depth. The database was compiled by combining and homogenizing several regional/national soil maps. To calculate storage of soil organic carbon, these soil maps have been linked to field-data on soil organic carbon storage from sites with circumpolar coverage.

More information on database processing and properties can be found in the product guide.

Citation

In order to use these data, you must cite this data set with the following citation:

Hugelius G, Bockheim JG, Camill, P, Elberling B, Grosse G, Harden JW, Johnson K, Jorgenson T, Koven C, Kuhry P, Michaelson G, Mishra U, Palmtag J, Ping C-L, O’Donnell J, Schirrmeister L, Schuur EAG, Sheng Y, Smith LC, Strauss J, Yu Z. (2013) A new dataset for estimating organic carbon storage to 3m depth in soils of the northern circumpolar permafrost region. Earth System Science Data, 5, 393–402, doi:10.5194/essd-5-393-2013.

This data release reformats the STATSGO KF-factor (KFFACT) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction. Units are inches per hour. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the rasterize_statsgo.py Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

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.

OMEinfo V1 datasetAboutThis dataset represents the version 1 data packet utilised by OMEinfo for the annotation of geographical metadata in a consistent manner. The cloud-optimised GeoTIFF files in the dataset are collated from a variety of sources (see references below) For details on the process for the creation of the OMEinfo data source, see the explanation hereData ReferencesData Source: Fossil Fuel CO2 emissions dataCitation: Tomohiro Oda, Shamil Maksyutov (2015), ODIAC Fossil Fuel CO2 Emissions Dataset (Version name: ODIAC2020b), Center for Global Environmental Research, National Institute for Environmental Studies DOI: https://doi.org/10.17595/20170411.001Data Source: Köppen-Geiger Climate ClassificationCitation: Beck, H., Zimmermann, N., McVicar, T. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018)DOI: https://doi.org/10.1038/sdata.2018.214Data Source: Population DensityCitation: Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC)DOI: https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)Data Source: RuralityCitation: Pesaresi, Martino; Florczyk, Aneta; Schiavina, Marcello; Melchiorri, Michele; Maffenini, Luca (2019): GHS settlement grid, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015), R2019A. European Commission, Joint Research Centre (JRC)DOI: https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)Data Source: Tropospheric NO2 Emissions dataCitation: Romahn, Pedergnana, Loyola, Apituley, Sneep and Veefkind (2022): Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual: Cloud Properties Reference: https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Cloud

This set of GeoTIFF and EPPL7 files represents the Minnesota Department of Transportation's County Highway Map Series in georeferenced image formats. These images of the standard Mn/DOT County Highway Map product can be used in GIS systems and overlayed with other GIS information. The origin of this data is Mn/DOT's Microstation CAD system, where all linework, feature type coding, and symbolization is stored and updated. To produce these data sets, Mn/DOT exported the data from Microstation into postscript files. LMIC then imported the data into GIS systems for georeferencing and further processing. The GeoTIFF data are distributed in both County Highway Map map sheet and full county extents; EPPL7 data sets are distributed only as full county files. Map collars have been removed. This data set represents the Mn/DOT County Highway Map as of January 1, 2002.

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/. 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.