The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Download In State Plane Projection Here The 2017 Digital Terrain Model (DTM) is a 2 foot pixel resolution raster in Erdas IMG format. This was created using the ground (class = 2) lidar points and incorporating the breaklines. The DTMs were developed using LiDAR data. LiDAR is an acronym for LIght Detection And Ranging. Light detection and ranging is the science of using a laser to measure distances to specific points. A specially equipped airplane with positioning tools and LiDAR technology was used to measure the distance to the surface of the earth to determine ground elevation. The classified points were developed using data collected in April to May 2017. The LiDAR points, specialized software, and technology provide the ability to create a high precision three-dimensional digital elevation and/or terrain models (DEM/DTM). The use of LiDAR significantly reduces the cost for developing this information. The DTMs are intended to correspond to the orthometric heights of the bare surface of the county (no buildings or vegetation cover). DTM data is used by county agencies to study drainage issues such as flooding and erosion; contour generation; slope and aspect; and hill shade images. This dataset was compiled to meet the American Society for Photogrammetry and Remote Sensing (ASPRS) Accuracy Standards for Large-Scale Maps, CLASS 1 map accuracy. The U.S. Army Corps of Engineers Engineering and Design Manual for Photogrammetric Production recommends that data intended for this usage scale be used for any of the following purposes: route location, preliminary alignment and design, preliminary project planning, hydraulic sections, rough earthwork estimates, or high-gradient terrain / low unit cost earthwork excavation estimates. The manual does not recommend that these data be used for final design, excavation and grading plans, earthwork computations for bid estimates or contract measurement and payment. This dataset does not take the place of an on-site survey for design, construction or regulatory purposes.

Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML). EML was trainined using GEDI level 2B points (Level 2A; "elev_lowestmode") and ICESat-2 (ATL08; "h_te_mean"): about 9 million points were overlaid vs MERITDEM, AW3D30, GLO-30, EU DEM, GLAD canopy height, tree cover and surface water cover maps, then an ensemble prediction model (mlr package in R) was fitted using random forest, Cubist and GLM, and used to predict most probable terrain height (bare earth). Input layers used to train the EML include:

• "lcv_bare.earth_glcf.landsat": UMD GLAD bare earth estimate for year 2010 based on Landsat time series,
• "dtm_elev.dsm_alos.aw3d": Digital Surface Model based on ALOS AW3D,
• "dtm_canopy.height_glad.umd": UMD GLAD canopy height for 2019 based on GEDI data,
• "dtm_elev.dsm_eudem.eea": Copernicus EU DEM based on the SRTM and ASTER DEMs,
• "hyd_surface.water_jrc.gswe": JRC Global Surface Water Explorer surface water probability based on the Landsat time-series,
• "lcv_landcover.12_pflugmacher2019": land cover map of Europe at 30 based on Pflugmacher et al. (2019),
• "lcv_tree.cover_umd.landsat_2000": forest tree cover for year 2000 based on the Global Forest Change data,
• "lcv_tree.cover_umd.landsat_2010": forest tree cover for year 2010 based on the Global Forest Change data,

Detailed processing steps can be found here. Read more about the processing steps here.

Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". The initial linear model fitted using the four independent Digital Surface / Digital Terrain models shows:

Residuals:
 Min    1Q  Median    3Q   Max 
-124.627  -1.097  0.973  2.544  59.324 
 
Coefficients:
 Estimate Std. Error t value Pr(>|t|)  
(Intercept)     -1.6220640 0.0032415 -500.4  <2e-16 ***
 eu_dem25m_     -0.1092988 0.0005531 -197.6  <2e-16 ***
 eu_AW3Dv2012_30m_  0.0933774 0.0005957  156.7  <2e-16 ***
 eu_GLO30_30m_    0.2637153 0.0006062  435.1  <2e-16 ***
 eu_MERITv1.0.1_30m_ 0.7496494 0.0005009 1496.6  <2e-16 ***
 ---
 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.059 on 9588230 degrees of freedom
(2046196 observations deleted due to missingness)
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 5.343e+09 on 4 and 9588230 DF, p-value: < 2.2e-16

Which show that MERIT DEM (Yamazaki et al., 2019) is the most correlated DEM with GEDI and ICESat-2, most likely because it has been systematically post-processed and majority of canopy problems have been removed. Summary results of the model training (mlr::makeStackedLearner) using all covariates (including canopy height, tree cover, bare earth cover) shows:

Variable: elev_lowestmode.f 
R-square: 1 
Fitted values sd: 333 
RMSE: 6.54 

Ensemble model:
Call:
stats::lm(formula = f, data = d)

Residuals:
   Min    1Q  Median    3Q   Max 
-118.788  -0.871  0.569  1.956 165.119 

Coefficients:
       Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.198402  0.003045 -65.15  <2e-16 ***
regr.ranger 0.452543  0.001117 405.04  <2e-16 ***
regr.cubist 0.527011  0.001516 347.61  <2e-16 ***
regr.glm   0.020033  0.001217  16.47  <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.544 on 9588231 degrees of freedom
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 8.29e+09 on 3 and 9588231 DF, p-value: < 2.2e-16

Which indicates that the elevation errors are in average (2/3rd of pixels) between +1-2 m. The variable importance based on Random Forest package ranger shows:

Variable importance:
        variable  importance
4  eu_MERITv1.0.1_30m_ 430641370770
1   eu_AW3Dv2012_30m_ 291483345389
2     eu_GLO30_30m_ 201517488587
3      eu_dem25m_ 132742500162
9 eu_canopy_height_30m_  5148617173
7       bare2010_  2087304901
8    treecover2000_  1761597272
6    treecover2010_  141670217

The output predicted terrain model includes the following two layers:

• "dtm_elev.lowestmode_gedi.eml_mf": mean estimate of the terrain elevation in dm (decimeters) filtered using Gaussian filter and 2x pixel window in SAGA GIS,
• "dtm_elev.lowestmode_gedi.eml_md": standard deviation of the independently fitted stacked predictors quantifying the prediction uncertainty in dm (decimeters),

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. Before modeling, we have corrected the reference elevations to the Earth Gravitational Model 2008 (EGM2008) by using the 5-arcdegree resolution correction surface (Pavlis et al, 2012).

All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL.

Disclaimer: The output DTM still shows forest canopy (overestimation of the terrain elevation) and has not been hydrologically corrected for spurious sinks and similar. This data set is continuously updated. To report a bug or suggest an improvement, please visit here. To access DTM derivatives at 30-m, 100-m and 250-m please visit here. To register for updates please subscribe to: https://twitter.com/HarmonizerGeo.

description: Digital Surface and Terrain Models (DSM,DTM) dataset current as of 2009. This bare earth DEM dataset was created from LiDAR supporting the generation of 2 foot contours..; abstract: Digital Surface and Terrain Models (DSM,DTM) dataset current as of 2009. This bare earth DEM dataset was created from LiDAR supporting the generation of 2 foot contours..

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.

Bare earth elevation surface (DTM) and actual surface (DSM) given in meters in the NAVD88 (Geoid12A realization) vertical reference frame. Horizontal coordinates referenced to appropriate UTM zone. Bare earth is created by classifying and removing vegetation and man-made structures from lidar point cloud prior to surface generation. Both the DSM and DTM are mosaicked onto a spatially uniform grid at 1 m spatial resolution in 1 km by 1 km tiles provided in a geotiff format.

The dataset is a 10 m-resolution DEM in grid format covering the whole Italian territory. The DEM is encoded as “ESRI ASCII Raster” obtained by interpolating the original DEM in Triangular Irregular Network (TIN) format. The TIN version benefited from the systematic application of the DEST algorithm. The projection is UTM, the World Geodetic System 1984 (WGS 84). To provide the dataset as a single seamless DEM, the sole zone 32 N was selected, although about half of Italy belongs to zone 33 N. The database is arranged in 193 square tiles having 50 km side. Data e Risorse Questo dataset non ha dati ambiente terremoti vulcani

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 ...

DTM and DSM from LiDAR data

Digital Terrain Model of the Gold Coast LGA, including bathymetry in certain areas. This dataset has been created from multiple data sources and surveys, details of which can be found in the associated metadata dataset - DTM Metadata

Disclaimer: The Digital Elevation Model (DEM) is a digital topographical representation of the City of Gold Coast. It was developed using Airborne Laser and bathymetric surveys of various accuracies and at different times. Thus the DEM accuracy can differ by at least 15cm to actual ground survey information which means that it will not be fit for all purposes.

The Digital Elevation Model (DEM) market, encompassing Digital Surface Models (DSM) and Digital Terrain Models (DTM), is experiencing robust growth, projected to reach $669.8 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.4% from 2025 to 2033. This expansion is fueled by increasing demand across diverse sectors. The Planning & Construction industry leverages DEMs for accurate site analysis, infrastructure planning, and risk assessment, driving significant market share. Furthermore, advancements in air traffic management and navigation systems rely heavily on precise DEM data for safer and more efficient flight operations. The burgeoning fields of weather forecasting and geological exploration also contribute significantly to market growth, as DEMs are crucial for accurate weather modeling and resource exploration. Technological advancements, including improved sensor technologies (LiDAR, photogrammetry) and enhanced data processing capabilities, are further accelerating market expansion. While data acquisition costs can be a restraint, particularly in challenging terrains, the overall benefits of DEM data in various applications outweigh this factor, ensuring continued market growth. The market's geographical distribution shows a strong presence in North America and Europe, driven by established infrastructure and technological advancements. However, the Asia-Pacific region is poised for significant growth due to rapid urbanization, infrastructure development, and increasing government investments in mapping and surveying initiatives. Competition in the DEM market is characterized by a mix of established players like Harris MapMart and Intermap Technologies, along with emerging companies offering innovative solutions and data processing techniques. The market is expected to witness strategic partnerships, mergers, and acquisitions as companies aim to expand their market reach and service offerings, further consolidating the market landscape. The continued demand for high-resolution, accurate DEM data across diverse applications points towards a sustained period of growth for this crucial geospatial data segment.

The Medium Resolution Digital Elevation Model (MRDEM) product is a multi-source product that integrates elevation data from the Copernicus DEM acquired during the TanDEM-X Mission, and the High Resolution Digital Elevation Model data derived from airborne lidar. This product provides a complete, 30 meters resolution, nationwide coverage for Canada. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived products. The spatial coverage extends into the USA, where needed, to provide coverage for cross-border watersheds in support of hydrological studies and applications.

The MRDEM DSM dataset is based on the GLO-30 version of the Copernicus DEM. The process to generate the MRDEM DTM dataset is more complex and involves different sources. Where available, the HRDEM Mosaic derived from lidar was used since it already provides reliable terrain elevation values. The HRDEM Mosaic data used was resampled from 1 meter to 30 meters. Elsewhere, the processing workflow combines a forest removal model and a settlement removal model that is applied to the GLO-30 values in order to estimate the terrain elevation values.

In the scope of the International Civil Aviation Organization (ICAO) requiring countries and airports to provide electronic Terrain and Obstacle Data (eTOD), the Administration de la navigation aérienne has been tasked by the Government to take the steps necessary to comply with this requirement. This Digital Terrain Model (DTM) is the result of a first LIDAR survey flight that has been done in October 2017 and is of a higher resolution than required by ICAO, thus for general purpose. For this reason this DTM also uses the national reference systems LUREF and NGL. The data itself is split up in 4 different areas, which are specified as follows: Area 1: The entire territory of Luxembourg; Area 2: Terminal Control Area (this area is larger than the territory of Luxembourg); Area 3: Aerodrome movement area; Area 4: Category II or III operations (Runway 24). The different areas come with different numerical requirements, such as data accuracy and resolution. Follow the links in the description or consult metadata for further Information.

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.

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products. Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope. The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada. Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners. Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Digital Elevation Model (DEM) market, encompassing Digital Surface Models (DSM) and Digital Terrain Models (DTM), is experiencing robust growth, driven by increasing demand across diverse sectors. The market, currently valued at $1034.9 million in 2025, is projected to expand significantly over the forecast period (2025-2033). Key application areas like planning and construction, where DEMs are crucial for site analysis, infrastructure development, and 3D modeling, are fueling this expansion. Furthermore, the aviation industry relies heavily on DEMs for air traffic route planning and navigation, ensuring safe and efficient flight operations. Meteorological services utilize DEMs for accurate weather forecasting and modeling, while geological exploration leverages them for subsurface analysis and resource mapping. Technological advancements, including improved sensor technologies (LiDAR, photogrammetry) and the increasing availability of high-resolution satellite imagery, are further contributing to market growth. While data acquisition costs and processing complexities can pose challenges, the overall market outlook remains positive, supported by the continuous demand for precise and detailed elevation data across a broad range of applications. The geographical distribution of the DEM market is widespread, with North America and Europe currently holding significant market share. However, rapid economic development and infrastructure projects in Asia-Pacific regions, particularly China and India, are expected to drive substantial growth in these markets. Government initiatives promoting digital mapping and spatial data infrastructure are also contributing to market expansion globally. Competition in the DEM market is intense, with a mix of established players and emerging technology providers. The market is characterized by ongoing innovation in data acquisition techniques, processing algorithms, and data delivery platforms. Companies are focusing on developing integrated solutions that combine DEM data with other geospatial information, providing comprehensive analysis and visualization tools for end-users. The future of the DEM market is expected to be shaped by advancements in artificial intelligence (AI) and machine learning (ML), enabling automated data processing, improved accuracy, and enhanced analytical capabilities.

Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:

• 30-m EDTM

https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif

• Standard deviation

https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif

Derived using ALOS AW3D, GLO-30, MERITDEM, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used canopy height (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written here in Python.

To access and visualize maps use: OpenLandMap.org

If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues
• General questions and comments: https://disqus.com/home/forums/landgis/

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:

• dtm.bareearth = variable: digital terrain model (m), bare earth
• ensemble = determination method: ensemble of mutli-source dsm and dtm
• p10/std = aggregation/statistics method: 10th percentile / standard deviation
• 250m = spatial resolution / block support: 250 m,
• s = vertical reference: at surface,
• go = bounding box: global land without Antarctica
• epsg.4326 = ESPG code: epsg.4326
• v20230221 = version code: creation date 20230221

Landgate Digital Elevation Models (DEMs) are of various postings around mainland Western Australia - excluding Cocos/Keeling Islands and Christmas Island. A DEM is a generic term for both a Digital Surface Model (DSM) or a Digital Terrain Model (DTM). In the main, Landgate DEMs are edited DSMs that remove the majority of buildings and trees to create pseudo-DTMs. Additional information, including a coverage map, is available on the Landgate website. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

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 ...

Unmanned Aerial System (UAS) flights were conducted over four stream catchments in Rio Blanco County, Colorado, during the summer of 2016. Two sties had active oil and gas operations within the basin whereas the other two sites did not. Structure from motion (SfM) was used to align raw images and create a dense point cloud, georectified orthoimage, and Digital Elevation Model (DEM) for each basin. A Digital Terrain Model (DTM), or bare earth model, for each basin was created by reclassifying the dense point cloud as either bare ground or other (vegetation, oil and gas infrastructure, etc.) and interpolating the land surface between bare ground points. Ideally, the DTM would always be equal or lower than the DEM; however, the interpolated surface can sometimes be higher than the DEM if bare ground points surround depressions with vegetation or in thick vegetation strands with an undulating surface. Therefore, a final surface model, created by merging the DTM with the DEM for all areas where the DTM was greater than the DEM, was produced for each basin. Lastly, a random forest classification approach was used to classify the orthoimagery on a pixel level into five vegetation/land cover classifications - bare ground, grass, litter, shrub/woody vegetation, and shadow.