This collection is a legacy product that is no longer supported. It may not meet current government standards. The Canadian Digital Elevation Model (CDEM) is part of Natural Resources Canada's altimetry system designed to better meet the users' needs for elevation data and products. The CDEM stems from the existing Canadian Digital Elevation Data (CDED). In these data, elevations can be either ground or reflective surface elevations. A CDEM mosaic can be obtained for a pre-defined or user-defined extent. The coverage and resolution of a mosaic varies according to latitude and to the extent of the requested area. Derived products such as slope, shaded relief and colour shaded relief maps can also be generated on demand by using the Geospatial-Data Extraction tool. Data can then be saved in many formats. The pre-packaged GeoTiff datasets are based on the National Topographic System of Canada (NTS) at the 1:250 000 scale; the NTS index file is available in the Resources section in many formats.

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (AST14DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an ASTER Level 1A image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The band 3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a base-to-height ratio of 0.6 and an intersection angle of 27.7 degrees. There is a time lag of approximately one minute between the acquisition of the nadir and backward images.For a better understanding, refer to the ASTER Along Track Imaging Geometry diagram depicting the along-track imaging geometry of the ASTER VNIR nadir and backward-viewing sensors.The accuracy of the new LP DAAC produced DEMs will meet or exceed accuracy specifications set for the ASTER relative DEMs by the Algorithm Theoretical Basis Document (ATBD). Users likely will find that the DEMs produced by the new LP DAAC system have accuracies approaching those specified in the ATBD for absolute DEMs. Validation testing has shown that DEMs produced by the new system frequently are more accurate than 25 meters root mean square error (RMSE) in xyz dimensions.The ASTER Digital Elevation Model data product is only available through NASA's Earthdata Search. The ASTER Order Instructions provide step-by-step directions for ordering this product.Known Issues Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.Improvements/Changes from Previous Versions As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website.* Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.

Error statistics of the accuracy assessment vs. roadway monuments.

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model and Orthorectified Registered Radiance at the Sensor (AST14DMO) product form a multi-file product. The product contains both a Digital Elevation Model (DEM) and up to 15 orthorectified images representing Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) data layers, if acquired.

For more information, see the links below:
AST14DEM
AST14OTH

The ASTER Digital Elevation Model and Orthorectified Registered Radiance at the Sensor data product is only available through NASA's Earthdata Search. The ASTER Order Instructions provide step-by-step directions for ordering this product.

Known Issues * Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.

Improvements/Changes from Previous Version * As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website. * Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.

Babineaux, C. & Cartwright, J. H. (2025). GEO Tutorial: Deriving a Vector Shoreline from a Digital Elevation Model. Mississippi State University: Geosystems Research Institute. [View Document] GEO TutorialNumber of Pages: 4Publication Date: 03/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Error statistics of the accuracy assessment vs. GPS benchmarks.

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional continental-scale land cover mapping, extraction of drainage features for hydrologic modeling, and geometric and radiometric correction of medium and coarse resolution satellite image data. The global aggregated vertical accuracy of GMTED2010 can be summarized in terms of the resolution and RMSE of the products with respect to a global set of control points (estimated global accuracy of 6 m RMSE) provided by the National Geospatial-Intelligence Agency (NGA). At 30 arc-seconds, the GMTED2010 RMSE range is between 25 and 42 meters; at 15 arc-seconds, the RMSE range is between 29 and 32 meters; and at 7.5 arc-seconds, the RMSE range is between 26 and 30 meters. GMTED2010 is a major improvement in consistency and vertical accuracy over GTOPO30, which has a 66 m RMSE globally compared to the same NGA control points. In areas where new sources of higher resolution data were available, the GMTED2010 products are substantially better than the aggregated global statistics; however, large areas still exist, particularly above 60 degrees North latitude, that lack good elevation data. As new data become available, especially in areas that have poor coverage in the current model, it is hoped that new versions of GMTED2010 might be generated and thus gradually improve the global model.

ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:

* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;
* land-use/land-cover data extracted from ESA Worldcover;
* climate zone information extracted from Beck et al. 2018;
* environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;
* a seasonal encoding.

This archive contains the digital elevation model (DEM) data of ben-ge, which were extracted from the Copernicus Digital Elevation Model (GLO-30).

Data

Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground.

Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.

Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:
* patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;
* patch_id_s1: the Sentinel-1 patch id for this specific patch;
* timestamp_s2: the timestamp for the Sentinel-2 observation;
* timestamp_s1: the timestamp for the Sentinel-1 observation;
* season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;
* season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;
* lon: longitude (WGS-84) of the center of the patch [degrees];
* lat: latitude (WGS-84) of the center of the patch [degrees];
* climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).

File and directory structure

This archive contains the following directory and file structure:

|
|--- README (this file)
|--- ben-ge_meta.csv (ben-ge meta data)
|--- dem/ (digital elevation model data)
|--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
...

To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/DEM requires 17.2 GB of space.

Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows:

|
|--- ben-ge_meta.csv (ben-ge meta data)
|--- ben-ge_era-5.csv (ben-ge environmental data)
|--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)
|--- dem/ (digital elevation model data)
| |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
| ...
|--- esaworldcover/ (land-use/land-cover data)
| |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif
| ...
|--- sentinel-1/ (Sentinel-1 SAR data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)
| ...
|--- sentinel-2/ (Sentinel-2 multispectral data)
| |--- S2B_MSIL2A_20170818T112109_31_83/
| |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)
| |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)
...

More Information

For more information, please refer to https://github.com/HSG-AIML/ben-ge.

Citing ben-ge
If you use data contained in this archive, please cite the following paper:

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

Download .zipThis grid dataset is a digital-elevation model (DEM) for Ohio and portions of Pennsylvania, West Virginia, Kentucky, Indiana, and Michigan. The grid dataset was initially extracted from the United States Geological Survey (USGS) National Elevation Dataset (NED), which has a grid cell size of 30 meters.

Even though the NED dataset was produced to provide a seamless and consistent DEM data across the United States, there were still visible errors associated with USGS Level 1 DEM's. These errors were removed and replaced with new grids derived from the USGS Digital Line Graph (DLG) hypsography. The resulting DEM will be used in the analysis of geological features with respect to the earth's surface, and will be one component of cartographic basemaps.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesOffice of Information TechnologyGIS Records2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

(Link to Metadata) The U.S. Geological Survey has developed a National Elevation Database (NED). VCGI has extracted a portion of the NED for Vermont and re-projected the file into Vermont State Plane Meters NAD83 (vertical units in feet). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives.

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model and Orthorectified Registered Radiance at the Sensor (AST14DMO) product form a multi-file product. The product contains both a Digital Elevation Model (DEM) and up to 15 orthorectified images representing Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) data layers, if acquired.For more information, see the links below: AST14DEM AST14OTHThe ASTER Digital Elevation Model and Orthorectified Registered Radiance at the Sensor data product is only available through NASA's Earthdata Search. The ASTER Order Instructions provide step-by-step directions for ordering this product.Known Issues Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.Improvements/Changes from Previous Version As of January 2021, the LP DAAC has implemented version 3.0 of the Sensor Information Laboratory Corporation ASTER DEM/Ortho (SILCAST) software, which is used to generate the Level 2 on-demand ASTER Orthorectified and Digital Elevation Model (DEM) products (AST14). The updated software provides digital elevation extraction and orthorectification from ASTER L1A input data without needing to enter ground control points or depending on external global DEMs at 30-arc-second resolution (GTOPO30). It utilizes the ephemeris and attitude data derived from both the ASTER instrument and the Terra spacecraft platform. The outputs are geoid height-corrected and waterbodies are automatically detected in this version. Users will notice differences between AST14DEM, AST14DMO, and AST14OTH products ordered before January 2021 (generated with SILCAST V1) and those generated with the updated version of the production software (version 3.0). Differences may include slight elevation changes over different surface types, including waterbodies. Differences have also been observed over cloudy portions of ASTER scenes. Additional information on SILCAST version 3.0 can be found on the SILCAST website. Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This data is an extract from the 1 second hydrologically enforced SRTM, clipped to a rectangle to take in the extent of the BA_SYD bioregion and its contributing subcatchments (BA_SYD_DEMh1sec).

The data is extracted from

Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

GUID: 9a9284b6-eb45-4a13-97d0-91bf25f1187b

Three additional layers are derived from this data for hydrological analysis and catchment delineation. They are:

BA_SYD_DEMh1sec_fill: as above but with sinks filled

BA_SYD_FDIR: Flow direction

BA_SYD_FACC: Flow accumulation.

Purpose

Hydrological analysis and contribution catchment delineation

Dataset History

This data is an extract from the 1 second hydrologically enforced SRTM, clipped to a rectangle to take in the extent of the BA_SYD bioregion and its contributing subcatchments (BA_SYD_DEMh1sec).

The data is extracted from Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) using the Spatial Analyst "Extract by Rectangle" tool snapped to the source raster.

GUID: 9a9284b6-eb45-4a13-97d0-91bf25f1187b

Three additional layers are derived from the extracted DEM using the respective Tools in the ArcGIS Spatial Analyst-> Hydrology toolset. They are:

BA_SYD_DEMh1sec_fill: as above but with sinks Filled

BA_SYD_FDIR: Flow Direction

BA_SYD_FACC: Flow Accumulation.

Dataset Citation

Bioregional Assessment Programme (2014) BA SYD 1 sec SRTM (h) DEM and hydrological derivatives. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/90f5ef3c-2141-4450-b44c-1afbd843236f.

Dataset Ancestors

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

Adopt aster with 30 meter resolution provided by Heihe project data management center GDEM data and 90 meter resolution SRTM data are two sets of grid data, as well as multi-source point data. These point data include radar point cloud elevation data in the middle and upper reaches; elevation data extracted from soil sample points and vegetation sample in the data management center of Heihe plan; elevation data extracted from climate and hydrological stations; and elevation sample data measured by the research group. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high-precision DEM data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final DEM surface. The spatial resolution is 500 meters.

The USGS and the NGA have collaborated on the development of a notably enhanced global elevation model named the GMTED2010 that replaces GTOPO30 as the elevation dataset of choice for global and continental scale applications. The new model has been generated at three separate resolutions (horizontal post spacing) of 30 arc-seconds (about 1 kilometer), 15 arc-seconds (about 500 meters), and 7.5 arc-seconds (about 250 meters). This new product suite provides global coverage of all land areas from latitude 84 degrees N to 56 degrees S for most products, and coverage from 84 degrees N to 90 degrees S for several products. Some areas, namely Greenland and Antarctica, do not have data available at the 15- and 7.5-arc-second resolutions because the input source data do not support that level of detail. An additional advantage of the new multi-resolution global model over GTOPO30 is that seven new raster elevation products are available at each resolution. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. The systematic subsample product is defined using a nearest neighbor resampling function, whereby an actual elevation value is extracted from the input source at the center of a processing window. Most vertical heights in GMTED2010 are referenced to the Earth Gravitational Model 1996 (EGM 96) geoid (NGA, 2010). In addition to the elevation products, detailed spatially referenced metadata containing attribute fields such as coordinates, projection information, and raw source elevation statistics have been generated on a tile-by-tile basis for all the input datasets that constitute the global elevation model. GMTED2010 is based on data derived from 11 raster-based elevation sources.

The Polar Geospatial Center’s ArcticDEM elevation products are the result of an institutional collaboration between the U.S. National Geospatial-Intelligence Agency (NGA) and the National Science Foundation (NSF). The objective of these efforts are to automatically produce high-resolution, high-quality digital surface models (DSM) of polar regions using optical imagery, high-performance computing, and open source photogrammetry software. The result is a collection of time-dependent DSM strips and seamless terrain mosaics. ArcticDEM can be used and distributed without restriction.This Image Service drives the ArcticDEM Explorer application developed in partnership with ESRI. See the ArcticDEM v4.1 Storymap for visual examples illustrating how we built the newest mosaic layers.Key PropertiesGeographic Coverage: Arctic - land and offshore areas north of 60°N plus all of Greenland, Alaska (before June 2022), and the Kamchatka PeninsulaTemporal Coverage: 2007 – 2024Refresh Rate: YearlyProduct Type: Digital surface model, which includes surface features such as man-made structures and vegetationProduct Values: Elevation in meters above the WG84 ellipsoidSpatial Resolution: 2-meters per pixelCoordinate System: NSIDC Sea Ice Polar Stereographic North WGS84 (EPSG:3413)Data ProductsElevation products are built using the Surface Extraction from Tin-based Search-space Minimization (SETSM) and median mosaic algorithms developed by Myong-Jong Noh and Ian Howat at the Byrd Polar Research and Climate Center. Individual DEM strips are extracted from pairs of Maxar images. DEM strip dimensions will vary according to the sensor, off-nadir angle of collection, and the corresponding stereo-pair overlap. Most strips are between 16 km and 18 km in width, and 110 km and 120 km in length.DEM Tiles are compiled from multiple strips that have been co-registered, blended, and feathered to reduce edge-matching artifacts. Tile sizes are standardized at 50 km x 50 km.ApplicationsThe time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topographic data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over larger areas while also providing a per-pixel minimum and maximum date range to enable change detection.ArcticDEM products can be used for a variety of applications:Terrain correction for radar and optical remote sensingFusion with lidar-based systemGlacier monitoring and change measurementVegetation characterization and analysisHydrologic modellingFine-scale mapping Dynamic Renderings The default rendering is Hillshade Elevation TintedVarious pre-defined on-the-fly Raster Functions can be selected:

Rendering
Description

Height EllisoidalElevation values in meters above the ellipsoid (WGS84) Height OrthometricElevation values in meters above the geoid (EGM08) Hillshade GrayHillshade image with a solar azimuth of 315 degrees and solar altitude of 45 degrees. Z-factor defaults to 4, but can be specified via the the REST API Hillshade Elevation TintedHillshade image with color tints indicating elevation values (default) Hillshade MultidirectionalHillshade image derived weighted contributions of six different directions Contour 2525-meter contour lines Contour Smoothed 2525-meter smoothed contour lines Aspect DegreesAspect in degrees Aspect MapColor representation of aspect values Slope DegreesSlope in degrees Slope MapColor representation of slope values

Additional Usage NotesWithout ground control points absolute accuracy is approximately 4 meters in horizontal and vertical planes. Uniform ground control must be applied to achieve higher accuracy.The data has not been edited to remove processing anomalies. Pits, spikes, false landforms, and other DEM anomalies may exist in this dataset. Hydrographic features have not been flattened in the DEM Strips.Optically-derived DEMs are subject to clouds, fog, shadows, and other atmospheric obstructions obscuring the ground and resulting in data gaps.The data spans multiple years and seasons.Mosaic tiles are displayed by default. Strips can be selected and displayed via image filtering.For more information on the source data and project, see the ArcticDEM website and the ArcticDEM Explorer app.

The Canadian Digital Elevation Model (CDEM) is part of Natural Resources Canada's altimetry system designed to better meet the users' needs for elevation data and products.

The CDEM stems from the existing Canadian Digital Elevation Data (CDED). In these data, elevations can be either ground or reflective surface elevations.

A CDEM mosaic can be obtained for a pre-defined or user-defined extent. The coverage and resolution of a mosaic varies according to latitude and to the extent of the requested area.

Derived products such as slope, shaded relief and colour shaded relief maps can also be generated on demand by using the Geospatial-Data Extraction tool. Data can then be saved in many formats.

The pre-packaged GeoTif datasets are based on the National Topographic System of Canada (NTS) at the 1:250 000 scale; the NTS index file is available in the Resources section in many formats.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

A clipped version of the Australia wide 1 second -S DEM, version 1, which limits the size to the rectangular extent of the Galilee Basin Subregion, enhancing speed and efficiency for visualisation and processing.

The metadata for the Geoscience Australia 1 sec SRTM is below:

The 1 second DSM, DEM, DEM-S and DEM-H are national elevation data products derived from the Shuttle Radar Topography Mission (SRTM) data. The SRTM data is not suitable for routine application due to various artefacts and noise.

The data has been treated with several processes to produce more usable products:

* A cleaned digital surface model (DSM)

* regular grid representing ground surface topography as well as other features including vegetation and man-made structures

* A bare-earth digital elevation model (DEM)

* regular grid representing ground surface topography, and where possible, excluding other features such as vegetation and man-made structures.

* A smoothed digital elevation model (DEM-S)

* A smoothed DEM based on the bare-earth DEM that has been adaptively smoothed to reduce random noise typically associated with the SRTM data in low relief areas.

* A hydrologically enforced digital elevation model (DEM-H)

* A hydrologically enforced DEM is based on DEM-S that has had drainage lines imposed and been further smoothed using the ANUDEM interpolation software.

The last product, a hydrologically enforced DEM, is most similar to the DEMs commonly in use around Australia, such as the GEODATA 9 Second DEM and the 25 m resolution DEMs produced by State and Territory agencies from digitised topographic maps.

For any analysis where surface shape is important, one of the smoothed DEMs (DEM-S or DEM-H) should be used. DEM-S is preferred for shape and vertical accuracy and DEM-H for hydrological connectivity. The DSM is suitable if you want to see the vegetation as well as the land surface height. There are few cases where DEM is the best data source, unless access to a less processed product is necessary.

The 1 second DEM (in its various incarnations) has quite different characteristics to DEMs derived by interpolation from topographic data. Those DEMs are typically quite smooth and are based on fairly accurate but sparse source data, usually contours and spot heights supplemented by drainage lines. The SRTM data is derived from radar measurements that are dense (there is essentially a measurement at almost every grid cell) but noisy.

Version 1.0 of the DSM was released in early 2009 and version 1.0 of the DEM was released in late 2009. Version 1.0 of the DEM-S was released in July 2010 and version 1.0 of the hydrologically enforced DEM-H was released in October 2011. These products provide substantial improvements in the quality and consistency of the data relative to the original SRTM data, but are not free from artefacts. Improved products will be released over time.

The 3 second products were derived from the 1 second data and version 1.0 was released in August 2010. Future releases of these products will occur when the 1 second products have been improved. At this stage there is no 3 second DEM-H product, which requires re-interpolation with drainage enforcement at that resolution.

Purpose

To enhance the speed and efficiency for visualisation and processing of the smoothed 1 second DEM data within the Galilee Basin Subregion

Dataset History

The original, Australia wide, 1 second smoothed DEM was clipped to rectangular extents of the Galilee subregion using the Spatial Analyst 'Extract By Rectangle' tool in ESRI ArcCatalog v10.0 with the following parameters:

Input raster: source 1 second SRTM

Extent: Galilee Basin subregion polygon

Extraction Area: INSIDE

'no data' values are created outside the clip extent therefore the extent of the dataset may still reflect the national DEM extent in ArcCatalog. Check the tool details for more info.

The lineage of the source 1 second SRTM is below:

The following datasets were used to derive this version of the 1 second DEM products:

Source data

1. SRTM 1 second Version 2 data (Slater et al., 2006), supplied by Defence Imagery and Geospatial Organisation (DIGO) as 813 1 x 1 degree tiles. Data were produced by NASA from radar data collected by the Shuttle Radar Topography Mission in February 2000.

2. GEODATA 9 second DEM Version 3 (Geoscience Australia, 2008) used to fill voids.

3. SRTM Water Body Data (SWBD) shapefile accompanying the SRTM data (Slater et al., 2006). This defines the coastline and larger inland waterbodies for the DEM and DSM.

4. Vegetation masks and water masks applied to the DEM to remove vegetation.

Full metadata, methodologies and lineage descriptions can be found in the PDF userguide within this dataset.

Dataset Citation

Bioregional Assessment Programme (2014) Smoothed Digital Elevation Model (DEM) - 1 arc second resolution - Clipped to Galilee Subregion extent. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/0fe257aa-8845-4183-9d05-5b48edd98f34.

Dataset Ancestors

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

Digital Elevation Model Market size was valued at USD 72 Billion in 2023 and is projected to reach USD 156.2 Billion by 2031, growing at a CAGR of 16.2% during the forecast period 2024-2031.

Global Digital Elevation Model Market Drivers

The market drivers for the Digital Elevation Model Market can be influenced by various factors. These may include:

Increased Demand for Geospatial Analysis: The growing demand for geospatial analysis across various industries such as agriculture, urban planning, forestry, and disaster management is a significant driver for the Digital Elevation Model (DEM) market. Organizations are increasingly leveraging DEMs to analyze terrain, assess land use, and develop infrastructure projects. The ability to visualize topography, identify potential hazards, and optimize land use promotes efficient decision-making, aiding in sustainability efforts. This shift towards data-driven insights enhances the demand for high-resolution, accurate DEMs, encouraging advancements in remote sensing technologies and GIS software, ultimately boosting market growth. Advancements in Remote Sensing Technology: Technological advancements in remote sensing have greatly contributed to the Digital Elevation Model Market. Innovations such as LIDAR (Light Detection and Ranging), satellite imaging, and drone-based surveys have enhanced the accuracy and resolution of DEMs. These technologies allow for rapid data collection over vast areas, making it easier to create high-quality elevation datasets. The integration of artificial intelligence and machine learning techniques into processing algorithms further improves the extraction of terrain features and reduces processing time. This evolution in data acquisition methods is fueling the demand for DEMs across multiple sectors.

Global Digital Elevation Model Market Restraints

Several factors can act as restraints or challenges for the Digital Elevation Model Market. These may include:

High Initial Investment Costs: The digital elevation model (DEM) market faces significant restraints due to high initial investment costs associated with advanced technologies and data acquisition processes. Organizations are required to invest heavily in specialized equipment, software, and skilled personnel to create and manage high-quality DEMs. These initial expenditures can be a barrier, particularly for small and medium-sized enterprises (SMEs) lacking the necessary capital. As a result, the high cost of entry limits market participation and the ability to scale offerings. Moreover, ongoing maintenance and operational costs can further strain budgets, discouraging potential users from adopting DEM technologies, thus stunting market growth. Data Accuracy and Integrity Issues: Another considerable restraint in the Digital Elevation Model Market is the challenge of data accuracy and integrity. With varying methods of data collection—such as LiDAR, photogrammetry, and satellite remote sensing—consistency and reliability can differ significantly. Poor-quality data can lead to inaccuracies in elevation modeling, negatively impacting critical applications such as urban planning, environmental monitoring, and disaster management. These discrepancies can undermine the credibility of DEM products, resulting in skepticism from potential clients. In sectors where precision is paramount, maintaining high standards while incorporating diverse data sources presents an ongoing challenge hindering wider market adoption.

A Digital Elevation Model (DEM) is a digital data file containing an array of elevation information over a portion of the earth's surface. This array is developed using information extracted from digitized elevation contours from Primary Base Series (PBS) maps. FSTopo or PBS are 1:24,000 scale topographic maps. This dataset is a digital elevation model grid at a resolution of 10 meters by 10 meters. The data was originated from 1:24,000 scale topographic maps (primarily contours). The base data is in the form of an esri lattice file. Derived datasets include generated contours at 10, 25, and 50 meter intervals, degree slope, aspects, and a hillshade for topographic visualization.