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/ )

Download and use the 250m True Marble global dataset for free! This is a low resolution version of our full 15m product, but it is quite useful. Download to use on your web page or preview a purchase. We only ask that you display our copyright and reference this page when using it. Two types of files are available for download: GeoTIFF and PNG. The GeoTIFF files are better suited for GIS programs, but are generally a larger file size. The PNG files are for general image processing programs, but are not georeferenced. Most of these files are much too large for your web browser to display, so be sure to save the file directly to disk. ![]() ![]() ![]() ![]() ![](

Radar data at 250m. All tiles required for a representation of Europe.Property of NASA, downloaded from https://srtm.csi.cgiar.org.Re-projected at the Berlin University of Applied Sciences according to EPSG 102014 (in metres, centred on 10° East).We also offer the data as a tiled imagery layer. Radar-Daten in 250m. Alle Kacheln, die für eine Darstellung von Europa benötigt werden.Eigentum der NASA, runtergeladen von https://srtm.csi.cgiar.org.An der Berliner Hochschule für Technik umprojiziert nach EPSG 102014 (in Meter, zentriert auf 10° Ost).Weiterhin bieten wir die Daten als tiled imagery layer an.

This data shows areas where merged survey bathymetry and backscatter data exists and allows you to download the data. The data was collected between 2001 and 2021.Bathymetry is the measurement of how deep is the sea. Bathymetry is the study of the shape and features of the seabed. The name comes from Greek words meaning "deep" and “measure". Bathymetry is collected on board boats working at sea and airplanes over land and coastline. The boats use special equipment called a multibeam echosounder. A multibeam echosounder is a type of sonar that is used to map the seabed. Sound waves are emitted in a fan shape beneath the boat. The amount of time it takes for the sound waves to bounce off the bottom of the sea and return to a receiver is used to determine water depth. The strength of the sound wave is used to determine how hard the bottom of the sea is. In other words, backscatter is the measure of sound that is reflected by the seafloor and received by the sonar. A strong sound wave indicates a hard surface (rocks, gravel), and a weak return signal indicates a soft surface (silt, mud).LiDAR is another way to map the seabed, using airplanes. Two laser light beams are emitted from a sensor on-board an airplane. The red beam reaches the water surface and bounces back; while the green beam penetrates the water hits the seabed and bounces back. The difference in time between the two beams returning allows the water depth to be calculated. LiDAR is only suitable for shallow waters (up to 30m depth).This data shows areas which have data available for download in Irish waters. These are areas where several surveys have been merged together.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).This data is shown as polygons. Each polygon holds information on the data type (bathymetry or backscatter), format of data available for download (GEOTIFF, ESRI GRID), its resolution, projection, last update and provides links to download the data.The data available for download are raster datasets. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns.This data was collected using a boat or plane. Data is output in xyz format. X and Y are the location and Z is the depth or backscatter value. A software package converts it into gridded data. The grid cell size varies. Most of this data is available at 10m resolution. Each grid cell size is 10 meter by 10 meter. This means that each cell (pixel) represents an area of 10 meter squared.ESRI GRID datasets contain the depth value. This means you can click on a location and get its depth.GEOTIFFS are images of the data and only record colour values. We use software to create a 3D effect of what the seabed looks like. By using vertical exaggeration, artificial sun-shading (mostly as if there is a light source in the northwest) and colouring the depths using colour maps, it is possible to highlight the subtle relief of the seabed. The darker shading represents a deeper depths and lighter shading represents shallower depths.This data shows areas that have been surveyed. There are plans to fill in the missing areas between 2020 and 2026. The deeper offshore waters were mapped as part of the Irish National Seabed Survey (INSS) between 1999 and 2005. INtegrated Mapping FOr the Sustainable Development of Ireland's MArine Resource (INFOMAR) is mapping the inshore areas. (2006 - 2026).

2021 Orthophoto - 3 inch resolution: This document describes the processes used to create the orthoimagery data produced for the District of Columbia from 2021 digital aerial photography. It was flown on March 11, 2021. The aerial imagery acquisition was flown to support the creation of 4-band digital orthophotography with a 3 inch/0.08 meter pixel resolution over the full project area covering the District of Columbia which is approximately 69 square miles. The contractor received waivers to fly in the Flight Restricted Zone (FRZ) and P-56 areas. The ortho imagery was submitted to DC OCTO in GeoTiff/TFW format tiles following the tile scheme provided by OCTO. MrSID and JPEG2000 compressed mosaics were delivered as well using a 50:1 compression ratio. This dataset provided as an ArcGIS Image service. Please note, the download feature for this image service in Open Data DC provides a compressed PNG, JPEG or TIFF. The compressed MrSID and JPEG2000 mosaic raster datasets are available under additional options when viewing downloads. Requests for the individual GeoTIFF set of images should be sent to open.data@dc.gov.

This feature service is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tile grid service is as an index for accessing aerial imagery and lidar elevation data files for Connecticut and is used in the Download Tool.

This directory includes GeoTIFF grids and shapefiles of magnetic data that cover the countries of the US and Canada. GeoTIFF grids of national-scale magnetic anomaly data for the conterminous United States (Ravat and others, 2009), Alaska (Division of Geological and Geophysical Surveys, 2016) and Canada (Miles and Oneschuk, 2016) were merged to create a composite residual magnetic anomaly grid of the United States and Canada. Several derivative products were calculated from the residual magnetic anomaly grid and are provided in this directory. Derivative grids include a reduced-to-pole (RTP) magnetic anomaly grid, the 1st vertical derivative of the RTP, the horizontal gradient magnitude pseudo-gravity calculated from the RTP grid, the long-wavelength RTP magnetic anomaly, and the horizontal gradient magnitude of the long wavelength pseudo gravity calculated from the long wavelength RTP. The directory also includes shapefiles of locations that trace the maxima of the horizontal gradient magnitude of the pseudo-gravity and of the maxima of the horizontal gradient magnitude of the long wavelength RTP transformed to pseudo-gravity. Otherwise known as “worms”, the points tracking the maxima mark the edges of shallow magnetic sources (in the case of the RTP) and deeper magnetic sources (calculated from the long-wavelength RTP grid). The shapefile of worms also includes attribute fields related to the steepness of the gradient and to the trend or strike of the gradient. These products were used in combination with other geophysical and geological data layers as input into a mineral prospectivity model for basin-hosted Pb-Zn mineralization. The reader is encouraged to read the metadata specific to each data layer for details related to the calculation and derivation of each magnetic anomaly GeoTIFF grid or shapefile. References Division of Geological and Geophysical Surveys, 2016, Alaska merged geophysical data grids: Alaska Division of Geological and Geophysical Surveys Digital Data Series 12, https://doi.org/10.14509/29555. Miles, W., and Oneschuk, G., 2016, Magnetic anomaly map, Canada / Carte des anomalies magnetiques, Canada: Geological Survey of Canada Open File 7799, https://doi.org/10.4095/297337. Ravat, D., Finn, C., Hill, P., Kucks, R., Phillips, J., Blakely, R., Bouligand, C., Sabaka, T., Elshayat, A., Aref, A., and Elawadi, E., 2009, A preliminary, full spectrum, magnetic anomaly grid of the United States with improved long wavelengths for studying continental dynamics: A website for distribution of data: U.S. Geological Survey Open-File Report 2009-12258, https://doi.org/10.3133/ofr20091258. [Also available at https://pubs.usgs.gov/of/2009/1258/.]

This dataset is a collection of spatial climatologies derived from the eReefs hydrodynamic and biogeochemical models for the Great Barrier Reef (GBR) region. It contains GeoTiff raster files representing long-term average conditions for numerous environmental variables across different depths (3m, 9m, 18m, and 39m below surface) and models (GBR4 BGC v3.1 2011 - 2018, GBR4 Hydro v2.0 2011 - 2023, and GBR1 Hydro v2.0 2015 - 2023).

Each GeoTiff represents the average conditions over the set of whole years in the model time series. Only whole years were used to ensure that the averages were not influenced by seasonal differences from partial years. Variables include temperature, salinity, nutrients, currents, chlorophyll, pH, and numerous other water quality parameters, all presented in their original units as continuous spatial fields. The outputs of this processing are saved as a series of GeoTiff images to make subsequent processing and use of the data easy.

Method:

The climatologies were generated by calculating the temporal mean of annual aggregates from the eReefs models available through the AIMS THREDDS server. Data was accessed via OPeNDAP, which allowed for efficient subsetting and processing of specific variables and depths. For each model (GBR4 BGC v3.1, GBR4 Hydro v2.0, and GBR1 Hydro v2.0), only complete years were included in the averaging process to ensure seasonal signals were not biased by partial years. The processing methodology carefully preserved the spatial structure and metadata from the original models.

For variables with a depth dimension, the nearest available model depth layer was selected for each target depth (3m, 9m, 18m, and 39m). For variables without a depth dimension (such as surface elevation), processing occurred once with results stored in a 'surface' directory. The temporal averaging was performed on a cell-by-cell basis, with proper handling of missing values to ensure unbiased results. The GeoTiff outputs maintain the original coordinate reference system (WGS84) and spatial resolution of the source models, with appropriate adjustments to ensure correct geographic registration for use in GIS applications.


Processing Code Availability:

The processing code used to generate this dataset is available in both R and Python, providing examples of how to work with eReefs data services. Both implementations perform identical processing steps and produce the same analysis products, offering users flexibility in their preferred programming language. The code demonstrates techniques for accessing data via OPeNDAP, processing NetCDF files, handling oceanographic data with proper depth selection, and exporting results to GeoTiff format with appropriate metadata. These scripts are available on GitHub as part of the GBR_AIMS_eReefs-climatology-geotiffs repository and can be adapted for other eReefs-related processing tasks. The dual-language implementation serves as a valuable resource for researchers seeking to understand and extend the processing methodology or apply similar techniques to other eReefs datasets.


Data Format:

The dataset is provided as a collection of GeoTiff (.tif) files, a widely supported raster format for geospatial data that can be used directly in GIS software, spatial analysis packages, and modeling frameworks. Each GeoTiff file:

- Contains a single variable at a specific depth from a specific model
- Uses the WGS84 (EPSG:4326) coordinate reference system
- Each GeoTiff uses the regridded version of the eReefs model model data (see technical report on regridding). The regridded grid is a slightly higher resolution than the original eReefs curvilinear grid, with inverse distance weighting interpolation. The higher resolution is to ensure that very little information is lost in the regridding process. This grid also includes approximately a one pixel extrapolation to help ensure that it doesn't have gaps near the coast or around islands.
- The files follow a consistent naming convention facilitating programmatic access and organisation.


Limitations:

This dataset has several important limitations that users should consider:

Temporal Coverage: The climatologies cover different time periods depending on the model: GBR4 BGC (2011-2018), GBR4 Hydro (2011-2023), and GBR1 Hydro (2015-2023). These periods may not capture longer-term climate variability or recent changes.

Model Versions: This dataset is based on eReefs BGC v3.1 and Hydro v2.0, which will be deprecated in July 2025 by the new model versions (v4.0). This dataset will be extended when the new version is available. Older version will be remain available.

Model Accuracy: The GBR4 Hydro v2.0 model is based on a near-real-time configuration rather than a hindcast, which may reduce its accuracy compared to the forthcoming v4.0 hindcast model that uses more consistent forcing data.

Spatial Biases: The eReefs models have known spatial biases, particularly in areas with complex bathymetry or near coastal boundaries. The GBR4 model (4km resolution) may not accurately represent fine-scale coastal processes, while the GBR1 model (1km resolution) provides improved coastal representation but with a shorter time series.

Averaging Limitations: The temporal averaging process smooths out extremes and temporal variability that may be ecologically significant. Species distributions and ecological processes are often influenced by extreme events or seasonal patterns that are obscured in these climatologies. Two locations with identical mean conditions may experience very different variability regimes.

Vector Averaging: For directional variables (u, v, wspeed_u, wspeed_v), the climatologies represent vector averages, which remove fluctuations from tides and capture only net movement. This means that areas with strong but reversing currents may show low average values despite experiencing significant water movement. Complementary variables 'mean_cur' and 'mean_wspeed' represent the average magnitude of currents and wind speed, indicating average strength regardless of direction.

Depth Approximation: The target depths (3m, 9m, 18m, 39m) are approximated to the nearest available depth in the model's vertical grid, which may vary slightly from the target values.

Ecological Interpretation: These climatologies do not capture the temporal dynamics, extremes, or variance that may be crucial for understanding species responses to their environment. They provide a simplified representation of average conditions that may not reflect the actual environmental drivers of ecological patterns.


Data Dictionary:
All variables are available at multiple depths, except for those indicated as surface variables.
GBR4 BGC v3.1 (2011-2018):
TN - Total Nitrogen (mg N m-3)
TP - Total Phosphorus (mg P m-3)
DIN - Dissolved Inorganic Nitrogen (mg N m-3)
DIP - Dissolved Inorganic Phosphorus (mg P m-3)
Chl_a_sum - Chlorophyll a concentration, sum across all phytoplankton groups (mg Chl m-3)
NO3 - Nitrate concentration (mg N m-3)
NH4 - Ammonium concentration (mg N m-3)
DOR_N - Dissolved Organic Nitrogen (mg N m-3)
DOR_P - Dissolved Organic Phosphorus (mg P m-3)
Secchi - Secchi depth (m) - surface variable
PH - pH (log(mM))
omega_ar - Aragonite saturation state (dimensionless)
EFI - Ecology Fine Inorganics (Total suspended solids) (kg m-3)
Oxy_sat = Oxygen saturation percent (%)
Oxygen = Dissolved oxygen (mg O m-3)

GBR4 Hydro v2.0 (2011-2023) and GBR1 Hydro v2.0 (2015-2023):
eta - Sea surface elevation (m) - surface variable
temp - Water temperature (°C)
salt - Salinity (PSU)
u - Eastward water current component (m/s)
v - Northward water current component (m/s)
wspeed_u - Eastward wind speed component (m/s) - surface variable
wspeed_v - Northward wind speed component (m/s) - surface variable
mean_cur - Mean current magnitude (m/s)
mean_wspeed - Mean wind speed magnitude (m/s) - surface variable

Available Depths:
Surface (for variables without depth dimension)
3m (closest available model depth)
9m (closest available model depth)
18m (closest available model depth)
39m (closest available model depth)
Each variable is provided as a separate GeoTiff file, organized by model and depth, in the format: geotiff_name =

GIS data for the global map of Anthropogenic Biomes (version 1) originally published in Ellis and Ramankutty (2008). The ZIP file contains a GeoTiff and associated files for use in GIS software. This dataset is a 5 arc minute thematic grid, prepared as described in WebPanel 1 of Ellis and Ramankutty (2008). An image (.png) file describing the map legend/symbology is: anthromes_v1_legend.png To view these data in ArcGIS, simply unzip to an accessible directory. Please Cite: Ellis, E. C., and N. Ramankutty. 2008. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6:439-447. Download publication here: http://www.ecotope.org/people/ellis/papers/ellis_2008.pdf More information here: http://ecotope.org/anthromes/v1/

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.

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. In this dataset, the following UAS data and metadata of the project site ‘Niederwerth’ (center coordinates [WGS84]: 50.386326°N, 7.613847°E; area: 27 ha) at the Rhine River in Germany is available for download: • Multispectral orthophotos (GeoTiff; 6 bands: B, G, R, Red-Edge, NIR, Flag; camera: Micasense; resolution: 25 cm; abbreviation: MS_RAW) • RGB-orthophotos (GeoTiff; 3 bands: R, G, B; camera: Phantom; resolution: 25 cm; abbreviation: PH_ORTHO) • Digital Surface Models (GeoTiff; 1 band; camera: Phantom; resolution: ca. 5 cm; abbreviation : PH_DEM) • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface model) The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_2019_01_Winter.zip = projectname_projectsite_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are additionally provided: • Overview table and metadata of the above-mentioned data (xlsx) • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface models) Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail. This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Gilles Rock

These data represent detailed land cover in Washington, DC. The data were derived using remote sensing technologies on satellite imagery from the Pleiades satellite, flown in 2020 and 2020 DC LiDAR. This dataset provided as an ArcGIS Image service. Please note, the download feature for this image service in Open Data DC provides a compressed PNG, JPEG or TIFF. The full raster GeoTIFF dataset is available under additional options when viewing downloads.

Abstract: This map layer portrays general forest cover types for the United States. Data were derived from Advanced Very High Resolution Radiometer (AVHRR) composite images recorded during the 1991 growing season, with the exception of Puerto Rico, for which Landsat Thematic Mapper (TM) data were used. A total of 25 classes of forest cover types were interpreted from the AVHRR and TM imagery, aided by field observations and refined with ancillary data from digital elevation models. The data available through the National Atlas of the United States are in GeoTIFF format. This is a revised version of the May 2002 map layer, with a corrected shoreline for Greenland. Purpose: The forest cover types map layer was developed to portray broad distribution patterns of forest cover in the United States. The data should be displayed at scales appropriate for 1:7,500,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. Follow this link to download the GeoTIFF and metada. foresti020l_nt00113.tar.gz

A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.]

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface.

Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.

The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE.

Digitale Geländemodelle (DGM) sind digitale, numerische, auf ein regelmäßiges Raster reduzierte Modelle der Geländehöhen und –formen der Erdoberfläche. Sie beinhalten keine Information über Bauwerke (z. B. Brücken) und Vegetation. Die Bezirksregierung Köln, Geobasis NRW, stellt im Rahmen ihres gesetzlichen Auftrags das DGM mit einer Rasterweite von einem Meter bereit.

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. In this dataset, the following UAS data and metadata of the project site ‘Emmericher Ward’ (center coordinates [WGS84]: 50.385264°N, 6.198692°E; area: 900 ha) at the Rhine River in Germany is available for download: • Multispectral orthophotos (GeoTiff; 5 bands: B, G, R, NIR, Flag; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm/ca. 16 cm; abbreviation: PanX2_ORTHO/PanX3_ORTHO) • Digital Surface Models (GeoTiff; 1 band; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm; abbreviation: PanX_DEM) • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos + digital surface model) The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_GYRO_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: • Overview table and metadata of the above-mentioned data (xlsx) • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos + digital surface models) Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail. This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Jens Bongartz

The LIDAR Composite First Return DSM (Digital Surface Model) is a raster elevation model covering ~99% of England at 2m spatial resolution. The first return DSM is produced from the first or only laser pulse returned to the sensor and includes heights of objects, such as vehicles, buildings and vegetation, as well as the terrain surface where the first or only return was the ground.

Produced by the Environment Agency in 2022, the first return DSM is derived from data captured as part of our national LIDAR programme between 11 November 2016 and 5th May 2022. This programme divided England into ~300 blocks for survey over continuous winters from 2016 onwards. These surveys are merged together to create the first return LIDAR composite using a feathering technique along the overlaps to remove any small differences in elevation between surveys. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.

The first return DSM will not match in coverage or extent of the LIDAR composite last return digital surface model (LZ_DSM) as the last return DSM composite is produced from both the national LIDAR programme and Timeseries surveys.

The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE. Attribution statement: © Environment Agency copyright and/or database right 2022. All rights reserved.

The NSW SPOT6/7 imagery product is a state-wide satellite imagery product provided by Geoimage Pty Ltd for NSW Government. The images were captured September 2022 through to March 2023. The imagery scenes used to create the NSW mosaic includes Lord Howe Island. This imagery data set has been acquired through GeoImages Pty Ltd and Airbus Defence and Space.

SPOT imagery products offer high resolution over broad areas using the SPOT 6/7 satellites. A SPOT satellite acquisition covers large areas in a single pass at resolutions up to 1.5m. Such precise coverage is ideal for applications at national and regional scales from 1:250,000 to 1:15,000. SPOT 6/7 also includes the benefits of the near-infrared (NIR) which enables applications for detection of features not visible to the human eye, such as detecting and monitoring vegetation health.

Data products supplied for all of NSW are:

1. State-wide mosaic

2. 100k Mapsheet tiles (GDA94 and GDA2020)

3. Multi spectral scenes (GDA94 and GDA2020)

4. Pan sharpened scenes (GDA94 and GDA2020)

5. Panchromatic scenes (GDA94 and GDA2020)

6. Shapefile cutlines of statewide mosaic

The statewide mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG2000 file (456gb in size). Individual 100k mapsheet mosaics contain BGR+NIR band combination; unenhanced 16-bit GeoTIFF format tile.

The NSW mosaic is available from internal DPE APOLLO Image Webserver for DCCEEW employees.

The 4band 100k mapsheet tiles are available for download from JDAP(pending). The rectified multispectral, pan sharpened and panchromatic scenes are available for download from JDAP (pending)

Acknowledgement when referencing: includes material © CNES_ (year of production), Distribution Airbus Services/SPOT Image, S.A, France, all rights reserved

Contact spatial.imagery@environment.nsw.gov.au for further information or to request access to JDAP

These image products are only available to other NSW Government agencies upon request.

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.