India's elevation data as a single TIFF file. See https://github.com/dilawar/map-india-center for more details.MD5 checksum: 97dcbee8b20f3b4de3036cfb9701a5e7 india.clipped.tif# CreditsFile india-composite.geojson is from datameet repository https://github.com/datameet/maps/tree/master/Country (Release under http://creativecommons.org/licenses/by-sa/2.5/in/ )

This data set was assembled as part of the LIS Mapping Program conducted in 2012-2013, Investigator(s): Dr. Timothy Kenna, Dr. Cecilia McHugh, and Dr. Frank Nitsche). These data files are of GeoTIFF (Raster) format and include Geologic Interpretations derived from sediment sample analyses.

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 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) 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 reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using r.resamp.stats -w 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:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
3 arc seconds (approx. 90 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.resamp.stats -w; 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/)

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)

A terrain surface dataset that represents the height value of all natural and built features of the surface of the city. Each pixel within the image contains an elevation value in accordance with …Show full descriptionA terrain surface dataset that represents the height value of all natural and built features of the surface of the city. Each pixel within the image contains an elevation value in accordance with the Australian Height Datum (AHD). The data has been captured in May 2018 as GeoTiff files, and covers the entire municipality. A KML tile index file can be found in the attachments to indicate the location of each tile, along with a sample image. Capture Information: Capture Pixel Resolution: 0.1 metres Limitations: Whilst every effort is made to provide the data as accurate as possible, the content may not be free from errors, omissions or defects.Preview: Download:A zip file containing all relevant files representing the Digital Surface ModelDownload Digital Surface Model data (12.0GB)

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 Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in ongoing studies. This dataset is the same as the Sentinel-2 dataset, except the JP2K files were converted into Cloud-Optimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata has were in a JSON file alongside the data, and a STAC API called Earth-search is freely available to search the archive. This dataset contains all of the scenes in the original Sentinel-2 Public Dataset and will grow as that does. L2A data are available from April 2017 over wider Europe region and globally since December 2018.

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.

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

The data source file is a 10-layered GeoTIFF file, derived from the raster data source GRTSmaster_habitats (link). Both GeoTIFFs (GRTSmaster_habitats, GRTSmh_brick) use the INT4S datatype. The GRTSmh_brick data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from GRTSmaster_habitats (with GRTS cell addresses at the resolution level).

See R-code in the GitHub repository 'n2khab-preprocessing' at commit ecadaf5 for its creation from the GRTSmaster_habitats data source.

A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector 32 * 2^(0:9) (minimum: 32 meters, maximum: 16384 meters).

Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).

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).

Sediment grab samples were taken in summer of 2017 and 2018 using a modified van Veen grab sampler. A sub-sample of the top two centimeters was stored in a jar. Dried and homogenized splits of the samples were sent to the Cornell Isotope Laboratory (COIL) to be analyzed for carbon and nitrogen concentrations as well as stable isotopic compositions. Total carbon (TC), total nitrogen (TN), and δ15N were determined on an aliquot of untreated, dried homogenized sediments. In order to accurately determine total organic carbon (TOC) and δ13C, removal of carbonate is necessary. This was achieved through sequential treatments of the sediments with dilute hydrochloric acid until no bubbling was observed. The acidified sediments were then rinsed in deionized water and re-dried. In the Cornell Lab the samples were analyzed using a NC2500 elemental analyzer interfaced with a Thermo Delta V isotope ratio mass spectrometer (IRMS). ArcGIS Pro empirical kriging has been used to interpolate values for organic carbon, total carbon, nitrogen, d13C and d15N isotope content onto a 50 m raster. The interpolated raster has been clipped to fit the extent of the phase 2 survey area. The final raster data files are in GeoTIFF grid format with UTM-18 N projection.Funding was provided by the Long Island Sound Mapping Fund administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).

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 GRTSmh_diffres data source file is a file collection, composed of nine monolayered GeoTIFF files of the INT4S datatype plus a GeoPackage with six polygon layers:

• The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the GRTSmh_brick data source (link). This means that they provide the decimal (i.e. base 10) integer values of these higher hierarchical levels of the GRTS cell addresses of the raw data source GRTSmaster_habitats (link). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-NA cells of the original GRTSmaster_habitats raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the source code to do such things.

• The GeoTIFF files provide the respective levels 1 to 9 of the GRTSmh_brick data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of NA cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-NA cells along the border.

The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.

See R-code in the GitHub repository 'n2khab-preprocessing' at commit ecadaf5 for the creation from the GRTSmh_brick data source.

A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).

The State of Alaska Division of Geological & Geophysical Surveys (DGGS) produced a digital surface model (DSM) and an orthorectified aerial image (orthoimagery) over Yukon River Crossing in support of landslide hazard mapping. Aerial photographs and Global Navigation Satellite System (GNSS) data were collected on June 30, 2016, and were processed using Structure-from-Motion (SfM) photogrammetric techniques to create the DSM and orthoimagery. 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/10.14509/30183). DSMs represent surface elevations of all surfaces, including vegetation, vegetation-free land, bridges, buildings, etc. The Yukon River (not including tributaries and lakes within the study area) was hydro flattened using standardized hydro flattening workflow in ArcMap (McLean, 2018) with a resulting elevation change from 85.1 m in the east to 81.1 m in the west within the DSM boundary (east-west streamflow direction). The DSM is a single-band, 32-bit float GeoTIFF file, with a ground sample distance (GSD) of 0.47 m. No Data value is set to -3.40282306074e+038.

For the automated workflows, we create Jupyter notebooks for each state. In these workflows, GIS processing to merge, extract and project GeoTIFF data was the most important process. For this process, we used ArcPy which is a python package to perform geographic data analysis, data conversion, and data management in ArcGIS (Toms, 2015). After creating state-scale LSS datasets in GeoTIFF format, we convert GeoTIFF to NetCDF using xarray and rioxarray Python packages. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. We used xarray to manipulate data type and add metadata in NetCDF file and rioxarray to save GeoTIFF to NetCDF format. Through these procedures, we created three composite HyddroShare resources to share state-scale LSS datasets. Due to the limitation of ArcGIS Pro license which is a commercial GIS software, we developed this Jupyter notebook on Windows OS.

An elevation model of Bryce Canyon, USA

Landform features: narrow rock formations known as hoodoos

Resolution: 1 meter, 4,000 x 3,800 height samples

File format: GeoTIFF

This is one model of a set of elevation models: https://doi.org/10.5281/zenodo.3938020. Please cite the entire set of models.

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

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.

Sediment grab samples were taken in summer of 2017 and 2018 using a modified van Veen grab sampler. A sub-sample of the top two centimeters was taken for further lab analysis. Dried and homogenized splits of the samples were analyzed for chemical composition using an Innov-X Alpha series 4000 XRF (Innov-X Systems, Woburn, MA). The results of the measurements are presented as ppm. The XRF analytical protocol included the following elements: P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb, I, Ba, Hg, Pb, Bi, Th, and U. However, only Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Zn, As, Br, Rb, Sr, Zr and Pb were consistently present at levels above background detection in surficial sediments collected in the LIS Phase II area. ArcGIS Pro empirical kriging has been used to interpolate values for selected elements onto a 50 m raster. The interpolated raster has been clipped to fit the extent of the phase 2 survey area. The final raster data files are in GeoTIFF grid format with UTM-18 N projection.Funding was provided by the Long Island Sound Mapping Fund administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).