Modeling and mapping of coastal processes (e.g. tsunamis, hurricane storm-surge, and sea-level rise) requires digital representations of Earth's solid surface, referred to as digital elevation models (DEMs). Some modeling utilizes structured, square-cell DEMs, while others utilize unstructured grids that have no regular cell size or pattern. Usually, these different DEM types are developed independently, even though they are built from the same source bathymetric and topographic datasets. The National Geophysical Data Center (NGDC), an office of the National Oceanic and Atmospheric Administration (NOAA), has developed two bathymetric-topographic square-celled DEMs and one bathymetric-topographic unstructured DEM of southern Louisiana. The DEMs were developed for the Hurricane Forecast Improvement Project (HFIP), with the purpose of developing a new methodology for unstructured grid production from structured square-celled grids. The 1/3 arc-second DEM referenced to North American Vertical Datum of 1988 (NAVD 88) was carefully developed and evaluated. A NAVD 88 to mean high water (MHW) 1/3 arc-second conversion grid derived from VDatum project areas was created to model the relationship between NAVD 88 and MHW in the southern Louisiana region. NGDC combined the NAVD 88 DEM and the conversion grid to develop a 1/3 arc-second MHW DEM. The NAVD 88 DEM was generated from diverse digital datasets in the region. The DEMs were developed to be used for storm surge inundation and sea level rise modeling. The source bathymetric and topographic datasets used in the development of the NAVD 88 DEM were utilized along with the NAVD 88 DEM and derivative grids to develop the NAVD 88 unstructured 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.

Peel's Digital Elevation Model (DEM) provides a generalized representation of both surface and ground features at a 1 metre resolution. The data is created using breaklines and a 10-metre grid of masspoints, both of which are photogrammatically created.

Available products

Peel Digital Elevation Model in TIFF format - 1.5 Gigabytes

Specifications

Capture year: Spring 2022 Spatial resolution: 1-metre File format: GeoTIFF, losslessly compressed Pixel type and depth: 32-bit float Horizontal projection: NAD 1983 UTM Zone 17N (EPSG: 26917) Vertical projection: CGVD 1928 (EPSG: 5713) Horizontal accuracy: ±50 centimetres Vertical accuracy: ±50 centimetres Method of creation: photogrammetric

Other data (Lidar) The Region of Peel doesn't have Lidar data in-house. The Province of Ontario through Land Information Ontario provides the following Lidar and Lidar-based datasets through their open data program:

Lidar-derived Digital Terrain Model (DTM) Lidar-derived Digital Surface Model (DSM) Lidar-derived classified point cloud - by request

Digital elevation model (DEM) data are arrays of regularly spaced elevation values referenced horizontally either to a Universal Transverse Mercator (UTM) projection or to a geographic coordinate system. The grid cells are spaced at regular intervals along south to north profiles that are ordered from west to east. The U.S. Geological Survey (USGS) produces five primary types of elevation data: 7.5-minute DEM, 30-minute DEM, 1-degree DEM.These datasets were derived from USGS 7.5' DEM Quads for the main 8 Hawaiian Islands. Individual DEM quads were converted to a common datum, and vertical unit, and subsequently mosaicked in ArcGIS 9.x. The DEM for Hawaii (Big Island) has a coordinate system of NAD83 UTM5N. The DEM for the remaining 7 islands (Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau) have a coordinate system of NAD83 UTM4N. All rasters have a spatial resolution of 10 meters and are in the ESRI grid format. On this metadata sheet, the bounding coordinates and row and column counts are for a hypothetical 10m grid that would contain the 8 main Hawaiian Islands. For bounding coordinates and the number of rows and columns for each actual, individual DEM, users should consult their respective layer properties.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

These data depict the elevation features of Konza Prairie. Record type 1 is a 2 meter resolution digital elevation model (DEM) of Konza Prairie, generated from 2006 LiDAR DEM data collected to standard USGS specifications (GIS200). Record type 3 is a 2010 10 meter (1/3 arc second) resolution National Elevation Dataset (NED) DEM of Konza Prairie (GIS202). Record type 4 is a 10 meter resolution NED DEM of Konza Prairie with a modified 3 kilometer buffer (GIS203). Record type 5 is a USGS topographic map of Konza Prairie (GIS204). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

A gap-free, region-wide combined topographic/bathymetric grid at a fixed resolution is useful for describing the topography of the seafloor and for a wide variety of oceanographic studies. Generating a bathymetric grid of this type consists of (1) locating and retrieving digital datasets from a variety of sources, (2) correcting errors and determining the dataset that best represents the topography in specific regions, (3) converting the depth data to common horizontal and vertical datums, and (4) selecting and applying a gridding algorithm to create the final seamless grid.

A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.

Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for Chesapeake Bay using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (3699 points, collected across four tidal marsh sites (Eastern Neck, Bishops Head, Martin, and Blackwater) in 2010 and 2017, Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (2013), a 1 m lidar DEM and a 1 m canopy surface model were used to generate models of predicted bias across the study domain. The modeled predicted bias for each cover type was then subtracted from the original lidar DEM to generate a new DEM. Across all GPS points, mean initial lidar error was -1.0 centimeters (SD=12.8) and root-mean squared error (RMSE) was 12.8 centimeters. After correction with LEAN, mean error was 0 cm (SD=6.4) and RMSE was 6.4 cm, a 50 percent improvement in accuracy. References: Buffington, K.J., Dugger, B.D., Thorne, K.M. and Takekawa, J.Y., 2016. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sensing of Environment, 186, pp.616-625.

Spatial coverage index compiled by East View Geospatial of set "USGS National Elevation Dataset (NED) 30M DEM ". Source data from USGS (publisher). Type: Elevation Database. Scale: 1-arcsec. Region: North America.

The Digital Elevation Model (DEM) dataset consists of tiled lidar DEM Images. Each file contains a raster image of the DEM. The Geographical Extent of this dataset extends to the entirety of the Southwest FL Lidar boundary delivery, approximately 2,347 square miles of the AOIs.

DEM stands for Digital Elevation Model. RASTER type data (ESRI GRID format), made up of square cells with sides of 10m. Each cell contains the Height information relating to the center of the cell itself. This version of the grid is almost identical to the basic one (dem10), but constitutes its indispensable correction to overcome the problems that any type of hydrological calculation would encounter when the flow would face a closed depression. For this reason there are various algorithms which, executed before the "launch" of the hydrological calculations, fill the closed depressions in various ways. The unit of measure is [m a.s.l.]

This is a tiled collection of the 3D Elevation Program (3DEP) and is 1/3 arc-second (approximately 10 m) resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. The seamless 1/3 arc-second DEM layers are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 (NAVD88). The seamless ...

(Link to Metadata) VTHYDRODEM was created to produce a "hydrologically correct" DEM, compliant with the Vermont Hydrography Dataset (VHD) in support of the "flow regime" project whose goal it is to derive stream perenniality for the VHD through application of logistic regression techniques. Some very important notes about the data: 1)Produced specifically for hydrologic modeling purposes and elevation surface has been altered and should not be used for analyses requiring unmodified elevation values; 2) ELEVATION VALUES, i.e., "Z units", are in CENTIMETERS (details below); and 3) Source data spans a five year period where varying techniques were used. This may explain observed inconsistencies both between and within tiles (detailed in the Attribute Accuracy Report below). This dataset has elevation values present in the surface that accurately reflect the down gradient nature and location of surface water features, i.e. the VHD. This process is also known as "hydro-enforcement" or "drainage enforcement". It is largely unknown that the 1:24k scale National Elevation Dataset (NED) is not "hydrologically correct" in relation to the National Hydrography Dataset (NHD) vector data of the same scale, e.g., the flow paths in the NED surface are not perfectly coincident to those in the scale NHD surface water features. This fact precluded the use of the NED data for hydrologic modeling efforts and reaffirmed the need to create a new "hydrologically correct" DEM. All processing was done using ARCINFO workstation (v.8.3) commands. The ARCINFO "TOPOGRID" command was used to create VTHYDRODEM as it was specifically designed to create "hydrologically correct" digital elevation models (DEM's) from elevation, stream and lake data sets. Single line "1D" streams and lake/pond "2D" polygons, from the 1:5k scale VHD, were given priority over input elevation data in the interpolation process to ensure that the resulting data is "hydrologically correct". Both the VHD and VTHYDRODEM share a common base of the state digital orthophotos, ensuring their interoperability. The Triangulated Irregular Networks (TIN) method was not considered but interested readers should review West Virginia's approach http://www.wvgis.wvu.edu/stateactivities/wvsamb/elevation/topogrid_vs_tin.pdf. This report notes the advantages and disadvantages of each approach. It should be noted that the WV effort included more recent imagery, and a much tighter sampling interval of source data. Nonetheless, it makes a strong case for the TIN approach that should be considered in any subsequent DEM development efforts. The density of input points used to create VTHYDRODEM was lower than the 1:24k NED but the vertical accuracy of those points tested at a higher accuracy and these points were generated with less variability in technique than that of the NED (see http://gisdata.usgs.net/website/USGS_GN_NED_DSI/viewer.htm and check "production methods" under "Layers" for NED data sources and methods). Vertical accuracy was derived using the FGDC National Standards for Spatial Data Accuracy (NSSDA) standards. For the sake of comparison, VTHYDRODEM tested at 6.05 meters, vertical accuracy at the 95% confidence level, whereas, the 1:24k National Elevation Dataset (DEM_24) tested at 21.3 meters. VTHYDRODEM was created for a specific, in-house project to support hydrologic modeling activities using the 1:5k scale VHD. It was interpolated from: 1) the Vermont Mapping Program (VMP) "x, y, z" data known as the "DEM points" (originally used to georectify the state digital orthophotos); and 2) VHD surface water features. A 10-meter cell resolution was chosen for VTHYDRODEM as a balance between input data accuracy and practical considerations and does not necessarily reflect the accuracy of the input data. The 10-meter resolution of this dataset was chosen arbitrarily for reasons noted below and should not be confused with an accuracy of 10 meters. This data should not be confused with the "1/3 arc second" 10m NED data. The lower 10m cell resolution has the following advantages when compared to the existing 30m 1:24k NED: 1) Stream confluences (junctions) can be defined with a greater degree of precision; 2) Confluences in close proximity can be represented individually; 3) Smaller landscape features can be represented and larger ones in greater detail; 4) Exponential improvement in volumetric measurement and tripling of precision in linear measurement of derived vector features, e.g., a watershed boundary is composed of aggregated 10m, i.e., 3 cells equals 30m vs. 30m resolution where 3 cells equals 90m. Similarly the concept applies to volumetric measurements); and 5) Improved cartographic accuracy for derived vector features. NOTE! Elevation units, e.g., "Z units" are in CENTIMETERS. This seeming arbitrary decision has a number of advantages worth considering. The output grid can now be stored as an "integer" type grid while simultaneously preserving the precision of the input data to the nearest centimeter. Integer type grids require one-tenth the storage space and are consequently much faster to process, e.g., deriving watershed boundaries. While it is unlikely that the input data is accurate to the nearest centimeter, this approach allows for greater precision storage, improves the overall appearance of the DEM and precludes problems with the model's depiction of over land flow in hydrologic related analyses when compared to coarser vertical resolutions. This approach mirrors a trend among the USGS and its contractors, who are now producing DEM's with a vertical resolution of decimeters (0.1 meter) for the benefits outlined above.

The present dataset is part of the Alaiz Experiment-2017 (ALEX17). The information is divided into two groups based on their source. 1)Two raster-tpye geotif files containing the Digital Elevation and Digital Surface Models (DEM and DSM) data of the ALEX17 domain. The models were built by TRACASA ( https://tracasa.es/all-about-us/) which is a company part of the Navarra Government. The original dataset is cropped to fit the ALEX17 experimental domain with the following spatial coverage: 607700, 4720300 628010, 4738800 The datasets are generated through lidar airborne scans taken during years 2011 and 2012 and updated by photogrammetry with orthophotos of year 2014. The original lidar scans (2011-2012) have a density of 1pnt/m^2 . The raw data are then processed and converted to orthometric heights (from the original ellipsoidal heights ) and later projected into a 2x2m grid with spatial reference EPSG:25830. The conversion from ellipsoidal to orthometric height is carried out with the EGM2008_REDNAP model, generated by the Spanish Geographic National Institute available in: ftp://ftp.geodesia.ign.es/geoide/ 2)The second dataset is also a raster-type file which contains the approximate annual mean of aerodynamic roughness length in meters. The maps was created with two data sources: Visual estimation of the roughness length values & zones. The Corine Land Cover (CLC) 2006 data. 2.1) The visual estimations of roughness values w carried out with the use of both, orthophotos gathered from the National Geographic Institute of Spain (IGN) as well as site visits. These values were assigned to the Alaiz mountain region while the 2.2) CLC-derived roughness was set to the rest of the domain area. The orthophotos are obtained from the National Plan for Aerial Orthophotogrpy (PNOA) program (available at http://www.ign.es/ign/layoutIn/faimgsataerea.do ). These photos have a pixel size of 50cm and were taken in summer 2014. On the other hand, the Corine Land Cover (CLC) 2006 raster dataset have a 100 m grid size. These data are available at http://www.eea.europa.eu/data-and-maps/data/corine- land-cover-2006-raster-3 (g100_06.zip file). The roughness values were derived from the Land Cover data mostly based on the relation between CLC and the aerodynamic roughness length applied by the Finnish wind atlas (http://www.tuuliatlas.fi/modelling/mallinnus_3.html ). The final composed roughness raster map was built by interpolation (nearest-neighbor) of the two data sources onto a 10x10 meters grid . The map is also projected with the same spatial reference as the DEM/DSM data described above.

Digital Terrain Model - 1m resolution. The dataset contains the 1m Digital Terrain Model for the District of Columbia. Some areas have limited data. The lidar dataset redaction was conducted under the guidance of the United States Secret Service. All data returns were removed from the dataset within the United States Secret Service redaction boundary except for classified ground points and clas...

NASADEM is a modernization of the Digital Elevation Model (DEM) and associated products generated from the Shuttle Radar Topography Mission (SRTM) data. Interferometric SAR data from SRTM were reprocessed with an optimized hybrid processing technique in producing the data products. The data rely on multiple radar images to create interferograms with 2-dimensional phase arrays that result in greater elevation accuracy. Because of inherent characteristics of interferometric data, it needs to be wrapped and unwrapped so the data are quantifiable. NASADEM relied on the latest unwrapping techniques and auxiliary data that were not available during the original processing of SRTM data. The optimized technique minimized data voids and extended spatial coverage of the SRTM. Additional voids were filled with a variety of sources including ASTER GDEM, Advanced Land Observing Satellite (ALOS) Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM), USGS National Elevation Dataset (NED), and Canada and Alaska DEMs Global DEM Specifications. Vertical and tilt adjustments were applied based on ground control points and laser profiles from the Ice, Cloud and Land Elevation Satellite (ICESat) mission. This application improved the vertical accuracy, swath consistency, and uniformity within the swath mosaic. The NASADEM products are freely available through the Land Processes Distributed Active Archive Center (LP DAAC) at one arcsecond spacing.

For more information about this dataset, visit the Land Processes Distributed Active Archive Center (LP DAAC)

The SurfZone Digital Elevation Model (DEM) was produced in 2019. Combining LIDAR and near-shore multibeam SONAR Bathymetry elevation data, it is the best currently available Digital Elevation Model (DEM) covering the inter-tidal zone produced by the Environment Agency.

The EA SurfZone DEM 2019 is supplied as a tiled raster dataset in GeoTiff format. Each tile is 5km * 5km and aligned to the Ordinance Survey National Grid. Each pixel represents 2 metres spatial resolution on the ground and elevations are presented in metres to Ordinance Survey Great Britain using the OSGM'15 and OSTM'15 transformation models. Elevations are referenced to Newlyn except for the Isles of Scilly which is referenced to St Marys.

The SurfZone DEM was produced by using a bespoke feathering technique to smooth the overlaps between LIDAR and Bathymetric surveys to produce a merged surface. Where small gaps existed between the LIDAR and Bathymetric surveys these were interpolated using a bilinear interpolation technique.

Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the SurfZone DEM. The Metadata Index Catalogue provides information about the source of the survey data used, either LIDAR or Bathymetry for any area as well as the surface type, coastal monitoring region, geoidal model and transformation models used.

All LIDAR data used in the production of the SurfZone DEM was surveyed by the Environment Agency. Bathymetry data was surveyed by the Environment Agency or sourced from the National Network of Regional Coastal Monitoring Programmes of England from the Channel Coastal Observatory (www.channelcoast.org) website. The National Network of Regional Coastal Monitoring Programmes of England comprises of 6 Regional Programmes. When re-using these data, you must use the copyright statements in the licence to acknowledge the individual regions when reusing this dataset.

Rapid development and growing availability of Unmanned Aerial Vehicles (UAV) translates into their more wide-spread application in monitoring of the natural environment. Moreover, advances in computer analysis techniques allow the imaging performed with UAVs to be used in creating Digital Elevation Models (DEM) and Digital Surface Models (DSM). DEMs are often employed in studies on geology, environment, engineering, and architecture. The presented paper discusses the procedures enabling the making of a precise DEM, discusses the aerial imaging data processing technique as well as determines the accuracy of obtained products in comparison with an existing Digital Elevation Model. Based on available literature the author indicates four sets of input parameters applicable in UAV imaging. Data collection missions were performed on two separate days in the area of a small peatland located in the Tuchola Pinewood, Poland. The study aims to address two research issues. Firstly, the author investigates the possibility of creating a DSM based on UAV imaging performed under unfavorable conditions and indicates whether results obtained via this method display sufficient quality to be seen as an alternative to the traditional surveying techniques (LiDAR). Secondly, the article determines the input parameters for a photogrammetric flight that ensure the highest accuracy of a resulting DSM. The analyses show a strong positive correlation between the DSMs prepared based on UAV imaging with data obtained by means of traditional methods (LiDAR). Mean correlation coefficient ranged from 0.45 to 0.75 depending on the type of land use and input parameters selected for a given flight. Furthermore, the analysis revealed that DSMs prepared based on UAV imaging—provided the most suitable input parameters are selected—can be a viable alternative to standard measurements, with the added benefit of low cost and the capacity for repeatable data collection in time. Admittedly, the method in question cannot be utilized in relation to peatlands overgrown with high vegetation (trees, shrubs) as it effectively diminishes the accuracy of obtained DSMs.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied.

The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global GLO-90 Defence Gridded Elevation Data (DGED) 2023_1 version of the data hosted by ESA via the https PRISM service. Details on the Copernicus DSM can be found on this ESA site.

• Previous to July 23rd 2024, OpenTopography was providing access to the Copernicus data through the public AWS S3 bucket established by Sinergise. As of July 23rd 2024, Opentopography is providing the DGED 2023_1 version of GLO-90 as downloaded directly from ESA (direct download instructions can be found here)
• The original gridded data from ESA is in geographic coordinates where the longitudinal cell spacing increases as a function of latitude for regions north of 50N and south of 50S. For more details see the Grid Spacing section of the Copernicus DEM handbook. In order to keep the pixel dimensions uniform, OpenTopography resamples data north of 50 degrees latitude and south of -50 degrees latitude in order to output a consistent 3 Arc-second product for data accessed through the web-interface or API. Users who need data north of 50N or south of 50S, and prefer to use the original, longitude-varying grid spacing can download cloud optimized geotiff (COG) versions of the tiles from our bulk download interface, or download the original data directly from ESA.
• The GLO-90 datasets are available on a free basis for the general public under the terms and conditions of the Copernicus license found here.