The dataset is captured over Samford Ecological Research Facility (SERF), which is located within the Samford valley in south east Queensland, Australia. The central point of the dataset is located at coordinates: 27.38572oS, 152.877098oE. The Vegetation Management Act 1999 protects the vegetation on this property as it provides a refuge to native flora and fauna that are under increasing pressure caused by urbanization.The hyperspectral image was acquired by the SPECIM AsiaEAGLE II sensor on the second of February, 2013. This sensor captures 252 spectral channels ranging from 400.7nm to 999.2nm. The last five channels, i.e., channels 248 to 252, are corrupted and can be excluded. The spatial resolution of the hyperspectral data was set to 1m.The airborne light detection and ranging (LiDAR) data were captured by the ALTM Leica ALS50-II sensor in 2009 composing of a total of 3716157 points in the study area: 2133050 for the first return points, 1213712 for the second return points, 345.736 for the third return points, and 23659 for the fourth return points.The average flight height was 1700 meters and the average point density is two points per square meter. The laser pulse wavelength is 1064nm with a repetition rate of 126 kHz, an average sample spacing of 0.8m and a footprint of 0.34m. The data were collected up to four returns per pulse and the intensity records were supplied on all pulse returns.The nominal vertical accuracy was ±0.15m at 1 sigma and the measured vertical accuracy was ±0.05m at 1 sigma. These values have been determined from check points contrived on an open clear ground. The measured horizontal accuracy was ± 0.31m at 1 sigma.The obtained ground LiDAR returns were interpolated and rasterized into a 1m×1m digital elevation model (DEM) provided by the LiDAR contractor, which was produced from the LiDAR ground points and interpolated coastal boundaries.The first returns of the airborne LiDAR sensor were utilized to produce the normalized digital surface model (nDSM) at 1m spatial resolution using Las2dem.The 1m spatial resolution intensity image was also produced using Las2dem. This software interpolated the points using triangulated irregular networks (TIN). Then, the TINs were rasterized into the nDSM and the intensity image with a pixel size of 1m. The intensity image with 1m spatial resolution was also produced using Las2dem.The LiDAR data were classified into ground" andnon-ground" by the data contractor using algorithms tailored especially for the project area. For the areas covered by dense vegetation, less laser pulse reaches the ground. Consequently, fewer ground points were available for DEM and nDSM surfaces interpolation in those areas. Therefore, the DEM and the nDSM tend to be less accurate in these areas.In order to use the datasets, please fulfill the following three requirements:

1) Giving an acknowledgement as follows:

The authors gratefully acknowledge TERN AusCover and Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, QLD for providing the hyperspectral and LiDAR data, respectively. Airborne lidar are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+LidarAirborne hyperspectral are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+Hyperspectral

2) Using the following license for LiDAR and hyperspectral data:

http://creativecommons.org/licenses/by/3.0/3) This dataset was made public by Dr. Pedram Ghamisi from German Aerospace Center (DLR) and Prof. Stuart Phinn from the University of Queensland. Please cite: In WORD:Pedram Ghamisi and Stuart Phinn, Fusion of LiDAR and Hyperspectral Data, Figshare, December 2015, https://dx.doi.org/10.6084/m9.figshare.2007723.v3In LaTex:@article{Ghamisi2015,author = "Pedram Ghamisi and Stuart Phinn",title = "{Fusion of LiDAR and Hyperspectral Data}",journal={Figshare},year = {2015},month = {12},url = "10.6084/m9.figshare.2007723.v3",

}

The models come from the cloud of points obtained by LiDAR technology in 2014 (active system of lasers that emits a beam of light on the earth's surface (pulses) and then collect their reflections). The data collected by the Lidar has been corrected manually. (This correction has focused on the classification of buildings). An interpolation has been applied to generate the digital models with a horizontal resolution of 2 meters. From the point model the terrain model was obtained: - DMT: digital terrain model - DMS: digital model of surfaces - Digital slopes model (in percenetage) - Digital aspect model - Relief image (shaded)

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 10m 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. The digital terrain model has been resampled to 10 metres from the LIDAR Composite DTM 2m dataset using a bilinear interpolation technique. 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 a single GeoTiff raster aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE. Attribution statement: © Environment Agency copyright and/or database right 2022. All rights reserved.

Dataset includes LIDAR-derived digital elevation models (DEMs), LIDAR-derived highest hit interpolation surfaces, and archival DEMs. Data are provided as-is. For infromation related to specific data products, refer to the metadata associated with the individual data layer.

The LIDAR Composite DSM (Digital Surface Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DSM (Digital Surface Model) is produced from the last or only laser pulse returned to the sensor and includes heights of objects, such as vehicles, buildings and vegetation, as well as the terrain surface Produced by the Environment Agency in 2022, the DSM 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 catalogue 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. Attribution statement: © Environment Agency copyright and/or database right 2022. All rights reserved.

This data set is comprised of lidar point cloud data. This project required lidar data to be acquired over Horry County, South Carolina. The total area of the Horry County Elevation Data and Imagery AOI is approximately 1092 square miles. Lidar data was collected and processed to meet the requirements of the project task order. The lidar collection was a collaborative effort between two data acquisition firms. While Woolpert was responsible for collection of the majority of the county, the coastal portion of the data was collected by Quantum Geospatial and is detailed in the processing steps of the metadata. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, four (4) foot pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Ground conditions: Water at normal levels; no unusual inundation; no snow. The bare earth DEMs along the coast may have a variance in the water heights due to temporal differences during the lidar data acquisition and will be represented in DEM as a seam-like anomaly. One coastal elevation was applied to entire project area. Due to differing acquisition dates and thus differing tide levels there will be areas in the DEM exhibiting what appears to be "digging" water features. Sometimes as much as approximately 2.5 feet. This was done to ensure that no coastal hydro feature was "floating" above ground surface. This coastal elevation will also affect connected river features wherein a sudden increase in flow will be observed in the DEM to accommodate the coastal elevation value. During Hydrologic breakline collection, Woolpert excluded obvious above-water piers or pier-like structures from the breakline placement. Some features extend beyond the apparent coastline and are constructed in a manner that can be considered an extension of the ground. These features were treated as ground during classification and subsequent hydrologic delineation. In all cases, professional practice was applied to delineate what appeared to be the coast based on data from multiple sources; Due to the many substructures and the complexity of the urban environment, interpolation and apparent "divots" (caused by tinning) may be evident in the surface of the bare earth DEM. In all cases, professional practice was applied to best represent the topography. The data received by the NOAA OCM are topographic data in LAS 1.2 format, classified as unclassified (1), ground (2), all noise (7), water (9), ignored ground (10), overlap unclassified (17), and overlap ground (18). Digital Elevation Models (DEMs) and breakline data are also available. The DEM data are available at: ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid18/data/4814/DEMs/ The breakline data are available at: ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid18/data/4814/breaklines Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office of Coastal Management (OCM)or its partners. Original contact information: Contact Org: Woolpert Phone: (937) 461-5660

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.

PLEASE NOTE: This dataset has been retired. A new version of the data is available here: https://environment.data.gov.uk/dataset/09ea3b37-df3a-4e8b-ac69-fb0842227b04

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering >93% of England at 2m spatial resolution.

Produced by the Environment Agency in 2020, this dataset is derived from a combination of our Time Stamped archive and National LIDAR Programme, which has 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 2020 LIDAR Composite contains surveys undertaken between 6th June 2000 and 1st September 2020. Please refer to the survey index files which shows, for any location, what Time Stamped survey or National LIDAR Programme block went into the production of the LIDAR composite for a specific location.

The DTM (Digital Terrain Model) is produced from the last return LIDAR signal. 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. Available to download as GeoTiff files in 5km grids, data is presented in metres, referenced to Ordinance Survey Newlyn, using the OSTN’15 transformation. All LIDAR data has a vertical accuracy of +/-15cm RMSE.

Light Detection and Ranging (LIDAR) is an airborne mapping technique, which uses a laser to measure the distance between the aircraft and the ground. Up to 500,000 measurements per second are made of the ground, allowing highly detailed terrain models to be generated at spatial resolutions of between 25cm and 2 metres. The Environment Agency’s open data LIDAR archives includes the Point Cloud data, and derived raster surface models of survey specific areas dating back to 1998 and composites of the best data available in any location.

This metadata record is for Approval for Access product AfA458.

Attribution statement: (c) Environment Agency copyright and/or database right 2021. All rights reserved. Attribution Statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 2m 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.

The level digital terrain model (LiDAR 2014) is the mapping of the earth’s surface without buildings and vegetation. For the calculation of the DTM with a resolution of 50 cm, the interpolation method “Triangle meshing” was used. The data are based on airborne laser measurements with an average point density of 4 points per m² and a height accuracy of 15 cm (1 Sigma=68 %). The flight took place on 14.03.14/17.03.14/3.-7.04.14

The dataset includes the ArcticDTMs estimated from a high-resolution digital elevation model (DEM) product, namely ArcticDEM, at three study sites in Livengood, Alaska. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of digital surface model (DSM) over non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is essential in studies requiring bare ground elevation such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. The proposed ArcticDTM generation method includes two steps: 1) identifying ground pixels from WorldView-2 imagery using Gaussian mixture model (GMM) with local refinement by morphological operation, and 2) generating continuous DTM surface using ArcticDEMs at ground locations and natural neighbor (NN) interpolation. In addition to the estimated ArcticDTM, the original ArcticDEM products including its source WorldView-2 multispectral imagery, and the reference lidar-derived digital terrain model (DTM) used for ArcticDTM evaluation are also attached. This dataset can be used as a benchmark dataset for examining the efficiency of optical stereoscopy-based DTM generation approaches. Paper citation: Zhang, T.; Liu, D. Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska. Remote Sens. 2023, 15, 2061. https://doi.org/10.3390/rs15082061

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

Produced by the Environment Agency in 2022, the DSM 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.

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The polygonal layer of lidar slope classes expresses the slope of the terrain. The slopes are generated from a digital terrain model (DTM) with a resolution of 10 meters. The latter is the result of an aggregation by bilinear interpolation of lidar NCDs at 1 m. The minimum area of the resulting polygons is 0.2 hectares. Lidar digital slopes are divided into 7 classes. + A - Null from [0 to 3]% + B - Low from] 3 to 8]% + C - Soft from] 8 to 15]% + D - Moderate from] 15 to 30]% +% +% + E - Strong from] 30 to 40]% +% + B - Low from] 3 to 8]% + C - Soft from] 8 to 15]% + D - Moderate from] 15 to 30%]% +% + D - Moderate from] 15 to 30]%% +% +% + D - Moderate from] 15 to 30%]% +% +% + E - Strong from] 30 to 40]% + F - Steep from] 40 to ∞ [% + S — Summit entirely surrounded by slopes F This map covers the entire territory of the Southern Quebec Ecoforest Inventory (IEQM) and was developed in order to provide stakeholders with the tools they need when applying for financial assistance from the Forest Management Investment Program (PIAF). _We do not recommend using the information in this layer for detailed analysis. _**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This work integrated multiple topographic and bathymetric data sources to generate a merged topobathymetric map of western Prince William Sound. We converted all data sources to NAD 83 UTM Zone 6 N and mean higher high water (MHHW) before compiling. In Barry Arm, north of Port Wells, we used a digital terrain model (DTM) derived from subaerial light detection and ranging (lidar) data collected on June 26, 2020, (Daanen and others, 2021) and submarine multibeam sonar bathymetric data collected between August 12 and 23, 2020 (NOAA, 2020). In College Fiord, adjacent to Barry Arm to the east, we used multibeam sonar bathymetric data collected between March 25 and August 26, 2021 (NOAA, 2021). These data were combined at 5 m horizontal resolution. For the subaerial portions of the computational domain outside of Barry Arm, we used a 5 m interferometric synthetic aperture radar (IFSAR)-derived DTM for Alaska (U.S. Geological Survey, 2018, accessed through Alaska Division of Geological and Geophysical Surveys, 2013). Below the MHHW waterline and outside of Barry Arm and College Fiord, we used one of two existing topobathymetric sources. In Passage Canal, we used an 8/15 arc-second dataset (~12 m grid cells) for Whittier and Passage Canal (NOAA, 2009b). Elsewhere, we used an 8/3 arc-second dataset (~59 m grid cells) for Prince William Sound (NOAA, 2009a). These two topobathymetric datasets were themselves derived from multiple data sources, including, but not limited to: National Ocean Service hydrographic surveys, National Elevation Dataset topography, and digital coastlines datasets. The source data for both topobathymetric datasets were sparse in both deep water and near shore (up to 1.5 km spacing between observations), which necessitated interpolating over those areas. This process, which is detailed by Caldwell and others (2011), gave substantial weight to the shoreline topography in the assignment of interpolated depths in the nearshore zone. Because our results use the more recent and higher resolution IFSAR-derived topography, which has a different shoreline, we re-interpolated a narrow band of nearshore grid cells using a similar methodology. We defined the nearshore re-interpolation zone based on a constant horizontal distance from the edge of valid IFSAR observation. We used a distance of 83 m because it results in the re-interpolation of at least one but no more than two of the 8/3 arc-second topobathymetric grid cells. We first removed any grid cell of either the Prince William Sound topobathymetric dataset (NOAA, 2009a) or the Whittier and Passage Canal topobathymetric dataset (NOAA, 2009b) at its original resolution that overlapped the near-shore re-interpolation zone. After removing the grid cells in the nearshore re-interpolation zone from these two topobathymetric datasets, we bilinearly interpolated and resampled both datasets from their original resolution to match the 5 m resolution of the IFSAR DTM. We then merged the two topobathymetric datasets with the IFSAR DTM. This yielded a 5 m dataset with missing values only in the nearshore re-interpolation zone. We then added the higher resolution and more recent Barry Arm and College Fiord multibeam sonar bathymetric data, as well as the Barry Arm lidar topographic data, directly to the regional dataset in their original footprints. These data were collected closer to shore, well within the re-interpolation zone that we defined for the lower resolution topobathymetric data and had no previously interpolated zones, thus requiring no additional clipping. Accordingly, we allowed the gap between the edge of the multibeam bathymetric data footprints and the lidar- or IFSAR-defined shoreline to represent the interpolation zone for these data. Finally, we interpolated across all the missing nearshore values using bilinear interpolation, thereby generating a single, continuous 5 m topobathymetric raster. References Cited Alaska Division of Geological & Geophysical Surveys [DGGS], 2013, Elevation Datasets of Alaska: Alaska Division of Geological & Geophysical Surveys Digital Data Series 4, https://elevation.alaska.gov/, accessed May 6, 2022, at https://doi.org/10.14509/25239. Caldwell, R. J., Eakins, B. W., and Lim, E., 2011, Digital Elevation Models of Prince William Sound, Alaska: Procedures, Data Sources, and Analysis: NOAA Technical Memorandum NESDIS NGDC-40, accessed June 16, 2022, at https://www.ngdc.noaa.gov/mgg/dat/dems/regional_tr/prince_william_sound_83_mhhw_2009.pdf Daanen, R.P., Wolken, G.J., Wikstrom Jones, K., and Herbst, A.M., 2021, High resolution lidar-derived elevation data for Barry Arm landslide, southcentral Alaska, June 26, 2020: Alaska Division of Geological & Geophysical Surveys Raw Data File 2021–3, 9 p., accessed June 17, 2021, at https://doi.org/10.14509/30593. National Oceanic and Atmospheric Administration [NOAA], 1995, Report for H10655: National Oceanic and Atmospheric Administration [NOAA] web page, accessed July 22, 2021, at https://www.ngdc.noaa.gov/nos/H10001-H12000/H10655.html. National Oceanic and Atmospheric Administration [NOAA], 2020, Report for H13396: National Oceanic and Atmospheric Administration [NOAA] web page, accessed April 5, 2021, at https://www.ngdc.noaa.gov/nos/H12001-H14000/H13396.html National Oceanic and Atmospheric Administration [NOAA], 2021, Report for H13420: National Oceanic and Atmospheric Administration [NOAA] web page, accessed March 15, 2023, at https://www.ngdc.noaa.gov/nos/H12001-H14000/H13420.html. National Oceanic and Atmospheric Administration [NOAA] National Geophysical Data Center, 2009a, Prince William Sound, Alaska 8/3 arc-second MHHW coastal digital elevation model: National Oceanic and Atmospheric Administration [NOAA], National Centers for Environmental Information web page, accessed June 16, 2022, at https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.dem:735 National Oceanic and Atmospheric Administration [NOAA] National Geophysical Data Center, 2009b, Whittier, Alaska 8/15 arc-second MHHW coastal digital elevation model: National Oceanic and Atmospheric Administration [NOAA], National Centers for Environmental Information web page, accessed April 5, 2021, at https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.dem:530. U.S. Geological Survey, 2018, USGS EROS Archive – Digital Elevation – Interferometric Synthetic Aperture Radar (IFSAR) – Alaska, Accessed May 6, 2022, at https://doi.org/10.5066/P9C064CO.

Basin-wide Digital Elevation Models (DEMs) with embedded bathymetry for selected main rivers Murray-Darling Basin at 5 metre (GDA2020 Lambert Conformal Conic) and 1 second (WGS 1984) resolution.

This collection is an enhancement of the Seamless Composite High Resolution Digital Elevation Model (DEM) for the Murray Darling Basin, whereby available channel bathymetry has been incised into the input DEM.

This collection was produced for use in basin- wide flood extent and depth modelling which requires an accurate representation of channel bathymetry in the MDB's trunk rivers. Lineage: The base DEM is the High resolution Digital Elevation Model (DEM) for the Murray Darling Basin (https://data.csiro.au/collection/csiro:64134)

BATHYMETRY Bathymetry point data were received for large sections of the Murray River (Hume to Wellington), sections of the Darling Anabranches, Edward River, and for waterholes on the Darling and Barwon river. Additionally, bathymetry embedded LIDAR for the Murrumbidgee and Darling River LIDAR flown in 2015, when the riverbed was largely exposed due to dry conditions, was also utilised. See Collaborating Organisations and metadata document for complete list of bathymetry data sources

Some small sections were clearly derived from gridded bathymetry having dense regular spacing of 10 to 15m. Most of the points were a continuous line of points following either a zig-zag or square wave track, with the remainder being transects perpendicular to the riverbanks at regular intervals ranging from a few hundred metres to many kilometres apart. Coverage for the Murray downstream of Lake Hume was continuous in some form (grid, track or transect) save for gaps between Narrung and Mildura. Various techniques were developed to process these data to form a consistent bathymetry ready to be embedded into the DEM.

Grid derived points were interpolated directly to a 5m grid conforming to the DEM using Triangular Irregular Network (TIN) in ArcGIS. The remaining point configurations were unsuitable for direct interpolation to a DEM conforming raster in their received form, and so underwent a data point densification process to make them suitable.

A method was devised to form gridded bathymetry from observed data points through data point densification. This was achieved by first creating a dense regular array of points consisting of 30 to 40 files of closely spaced (~10- ~20m) points across the width of the channel and following the course of the channel.

Input bathymetry point data was transferred to its nearest (within 5m to 20m search radius) array point. Intervening array points within a given file that had not inherited a close neighbouring bathymetry datapoint value, then had a value linearly interpolated from its next upstream and downstream value.

Bathymetry data points confer their bathymetry value to nearby array points. Remaining array points then have an interpolated value calculated based on the next upstream and downstream conferred data in their file. This dense array of data was then interpolated to the DEM conforming 5m raster using TIN. The rasterised bathymetry data were then inserted into the DEM replacing the non-ground channel values with interpolated bathymetry values.

For the Murrumbidgee and Darling Rivers, existing LIDAR already had a good representation of river bathymetry, due to bathymetry embedding having already been implemented by a third party or the bed being exposed when LIDAR was flown. In these cases the LIDAR DEM values occurring within the channel were clipped out, resampled and reprojected to match the base DEM and then inserted into the base DEM.

For both the rasterised interpolated point data and the clipped LIDAR inserts, bathymetry was embedded into the base DEM by taking the minimum elevation occurring in the overlying cells.

This dataset was resampled to ≈ 30 m and used as an input to create the two-monthly maximum water depth spatial timeseries for the MDB version 2024 (https://doi.org/10.25919/1t0t-y110) and monthly maximum water depth spatial timeseries for the MDB (https://doi.org/10.25919/zffy-a921).

The first four columns are the F1 scores for different classes, and the last two columns are the AvgF1 and compute time.

To advance the U.S. Geological Survey 3D National Topography Model (3DNTM) including the next generation of the 3D Elevation Program (3DEP) and the 3D Hydrography Program (3DHP), the USGS researched and created a Seamless 1-meter resolution (S1M) Digital Elevation Model (DEM) for the conterminous United States (CONUS). This dataset is a result of a joint project between the National Geospatial Technical Operations Center (NGTOC) and the Earth Resources Observation and Science Center (EROS) of the USGS National Geospatial Directorate (NGD). Scientists and resource managers can use the S1M data for global change research, hydrologic modeling, resource monitoring, mapping, visualization, and many other applications. A S1M DEM requires merging multiple lidar projects in which the lidar sensor, bare-earth DEM generation methodology, source resolution, datums/projection, unit of measure, and geoid (mean sea level model) can vary between projects. This tile of the Seamless 1-m DEM was created from the best available 3DEP Original Product Resolution source DEMs from one or several intersecting 3DEP data collection projects. Spatially referenced metadata are contained within an open-source GeoPackage that stores footprints for each of the input source DEMs along with source data characteristics. The source DEMs were processed to align vertically to North American Vertical Datum of 1988 (EPSG: 5703) updated to the current GEOID18 model and projected horizontally to North American Datum of 1983 (2011) USA Contiguous Albers Equal Area Conic projection (EPSG: 6350). Horizontal units and elevation values are in meters. Large data voids wider than 10 meters in the tile were backfilled with 1/9 arc-second or 1/3 arc-second DEMs in the 3DEP data repository while small data voids were interpolated across using bilinear interpolation. For tiles containing more than one 3DEP project or with large data voids, up to three blending routines were used: a simple blend, narrow blend, or a backfill blend. The spatial metadata GeoPackage contains information on where backfilling, void interpolation, and blending occurs within the tile. The tile spatial extent is 10 km x 10 km. The S1M DEM is available in a Cloud Optimized Georeferenced Tagged Image File Format (GeoTIFF). The S1M DEM has floating point numeric values and a spatial resolution of one meter. NoData values (areas where data is incomplete due to lack of full data coverage) are represented with the numeric value of -999999. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include project-based one-meter DEMs in CONUS, five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

This paper presents a method of filtering point clouds generated by laser scanning, to obtain a Digital Terrain Model (DTM). The filtering process is performed based on an approximated surface obtained from urban road points. These points are sampled in straight lines detected by Steger in the intensity image of the laser pulse. The main assumption of the method is that the ground has smooth behavior inside the block, so the sample laser points collected along the urban roads allow, using the kriging interpolation method, a suitable representation of the land inside the block, that is, relatively close to the ground point laser in these regions. Thus, filtering is performed by proximity of the original cloud laser points with approximate surface. For thus a DTM is obtained from the new sample by kriging interpolation method, increasing the description of the surface. From the experiments it was possible to verify the feasibility of the proposed method, with results of good visual consistency and satisfactory numerical indicators.

This metadata file and associated files contain metadata for the Geoscience Australia Kakadu LiDAR project carried out in 2011. This metadata is for the Digital Elevation Model created as 1m grid generated from the LiDAR point data classified as ground. The grid was generated employing a point to TIN and TIN to grid point process with nearest neighbour interpolation. The DEM was generated in ESRI ArcGIS V10 format.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in March 2015. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.