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 tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA Sea-Level Rise Viewer DEM to create a new seamless DEM for the San Francisco Bay. Across all GPS points, mean initial lidar error was 22.8 cm (SD=12.0) and root-mean squared error (RMSE) was 25.8 cm. After correction with LEAN, mean error was 0 (SD=0.07) and RMSE was 7.4 cm. 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.

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.

This data was collected by the Geological Survey Ireland, the Department of Culture, Heritage and the Gaeltacht, the Discovery Programme, the Heritage Council, Transport Infrastructure Ireland, New York University, the Office of Public Works and Westmeath County Council. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows: GSI – 1m DCHG/DP/HC - 0.13m, 0.14m, 1m NY – 1m TII – 2m OPW – 2m WMCC - 0.25m Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The grid cell size varies depending on the organisation that collected it. GSI data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared.

Between January and March 2010, lidar data was collected in southeast/coastal Georgia under a multi-agency partnership between the Coastal Georgia Regional Development Center, USGS, FEMA, NOAA and local county governments. Data acquisition is for the full extent of coastal Georgia, approximately 50 miles inland, excluding counties with existing high-resolution lidar derived elevation data. The data capture area consists of an area of approximately 5703 square miles. This project is within the Atlantic Coastal Priority Area as defined by the National Geospatial Program (NGP) and supports homeland security requirements of the National Geospatial-Intelligence Agency (NGA). This project also supports the National Spatial Data Infrastructure (NSDI) and will advance USGS efforts related to The National Map and the National Elevation Dataset. The data were delivered in LAS format version 1.2 in 5000 x 5000 foot tiles. The data are classified according to ASPRS LAS 1.2 classification scheme: Class 1 - Unclassified Class 2 - Bare Earth Class 7 - Low Point (Noise) Class 9 - Water Class 10 - Land below sea level Class 12 - Overlap

This map provides access to vector data layers that incorporate Landsat reflectance data and LiDAR elevation data within the state of New Hampshire. These data were developed by NH GRANIT using data collected between 2011 and 2018. The polygon boundaries represent "image objects" that were derived using eCognition image segmentation software, grouping image pixels into contiguous segments based on spectral reflectance and LiDAR. The attributes for each polygon were then calculated from the LiDAR crown height layer. Attributes include maximum, minimum, and mean elevation above the ground and standard deviation within each polygon. Elevation is in meters. The boundary of each layer is determined by the extent of a LiDAR data collection; Landsat reflectance data were clipped to the extent of the LiDAR. The dates of data collection are listed in the metadata for each data layer.These data were developed by NH GRANIT using funding from the New Hampshire Space Grant Consortium.

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.

As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been developed to pair with field geomorphic assessments for use in the development of a model that can remotely predict streambank erosion potential along streams in the Greater Raleigh, NC Area, however, they have the potential to be used in numerous applications.

These files contain classified topobathy lidar elevations generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. Data are classified as 1 (valid non-ground topographic data), 2 (valid ground topographic data), and 29 (valid bathymetric data). Classes 1 and 2 are defined in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) cla...

The Digital Elevation Model Imagery Catalog layer describes precision elevation datasets acquired from LiDAR and aerial / satellite sensors currently archived in the department. Precision elevation products are defined as Digital Terrain Models (or bare Earth Digital Elevation Models) captured from either LiDAR sources or photogrammetrically derived from aerial photography. LiDAR classified point clouds and derived Digital Terrain Models under a CC-BY license have been uploaded to the ELVIS Elevation and Depth Online Portal (https://elevation.fsdf.org.au/).

Geographic Extent: OLC Baker County 3DEP 2017 defined project area (DPA) extends approximately 224,286 acres; the buffered project area (BPA) covers approximately 229,671 acres. Dataset Description: The OLC Baker County 3DEP 2017 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meter...

The Digital Elevation Model (DEM) 5 Metre Grid of Australia derived from LiDAR model represents a National 5 metre (bare earth) DEM which has been derived from some 236 individual LiDAR surveys between 2001 and 2015 covering an area in excess of 245,000 square kilometres. These surveys cover Australia's populated coastal zone; floodplain surveys within the Murray Darling Basin, and individual surveys of major and minor population centres. All available 1 metre resolution LiDAR-derived DEMs have been compiled and resampled to 5 metre resolution datasets for each survey area, and then merged into a single dataset for each State. These State datasets have also been merged into a 1 second resolution national dataset.

The acquisition of the individual LiDAR surveys and derivation of the 5m product has been part of a long-term collaboration between Geoscience Australia, the Cooperative Research Centre for Spatial Information (CRCSI), the Departments of Climate Change and Environment, State and Territory jurisdictions, Local Government and the Murray Darling Basin Authority under the auspices of the National Elevation Data Framework and Coastal and Urban DEM Program, with additional data supplied by the Australian Department of Defence. The source datasets have been captured to standards that are generally consistent with the Australian ICSM LiDAR Acquisition Specifications with require a fundamental vertical accuracy of at least 0.30m (95% confidence) and horizontal accuracy of at least 0.80m (95% confidence).

In this project, we use the Light Detection and Ranging (LiDAR) data to create the Digital Elevation Model (DEM). The LiDAR data can be downloaded through the Data Access Viewer of NOAA ( https://coast.noaa.gov/dataviewer/#/lidar/search/). For Kauai, the majority of the DEM is created using the data of 2013 U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) Topobathy LiDAR – Local Mean Sea Level (LMSL). For some areas not covered by this data set, we use the LiDAR data from 2006 FEMA LiDAR: Hawaiian Islands and 2007 JALBTCX Hawaii LiDAR: North Coasts of Hawaii (Big Island), Kauai, Maui, Molokai, Oahu, which are accessed in the Data Access Viewer of NOAA. Please read “Description of Digital Elevation Model (DEM) for Kauai, Hawaii.docx” for detailed information.

The lidar survey was conducted by vendor Earth Eye LLC, 3680 Avalon Park Blvd. The data were delivered in LAS 1.1 format with information on return number, easting, northing, elevation, scan angle, and intensity for each return. This project is the data acquisition phase of a administrative study being done in collaboration with the Nez Perce National Forest, Grangeville, ID; Forest Service Region 1 Regional Office, Missoula, MT (Forest Inventory and Analysis and Remote Sensing/ Geospatial Team); and Rocky Mountain Research Station - Forest Sciences Lab, Moscow, ID. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 17, 325 hectares in north central Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components. Clear Creek is a tributary of the Middle Fork Clearwater River located east of Kooskia, Idaho. Vegetation is variable, transitioning from low elevation shrubland and mixed conifers to upper elevation spruce-fir. Ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) are the predominant species at lower to mid elevations occupying a fairly xeric setting transitioning to grand fir (Abies grandis) and western red cedar (Thuja plicata) at mid elevations and subalpine fir (Abies lasiocarpa) at the higher elevations.

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.

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 ac...

Digital elevation models (DEMs) were developed from lidar surveys from 2013, 2015, and 2022 for the Greater Raleigh, NC Area, with 1-meter resolution. A DEM of difference raster was also developed to represent change in elevation from 2015 to 2022. The 2015 and 2022 DEMs were selected for differencing because of the superior quality level (QL2) of base lidar data used to develop the DEMs compared with the poorer quality level (QL3) of base lidar data used to develop the 2013 DEM. The DEMs were developed to use as inputs to generate a suite of geomorphic metrics for use in a machine learning model to predict streambank erosion hotspots. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded.

This is collection level metadata for LAS and ASCII data files from the statewide Iowa Lidar Project. The Iowa Light Detection and Ranging (LiDAR) Project collects location and elevation (X, Y, Z) data to a set standard for the entire state of Iowa. LIDAR is defined as an airborne laser system, flown aboard rotary or fixed-wing aircraft, that is used to acquire x, y, and z coordinates of terrain and terrain features that are both manmade and naturally occurring. LIDAR systems consist of a light-emitting scanning laser, an airborne Global Positioning System (GPS) with attendant GPS base station(s), and an Inertial Measuring Unit (IMU). The laser scanning system measures ranges from the scanning laser to terrain surfaces by measuring the time it takes for the emitted light (LIDAR return) to reach the earth's surface and reflect back to the onboard LIDAR detector. The airborne GPS system ascertains the in-flight three-dimensional position of the sensor, and the IMU delivers precise information about the attitude of the sensor. The LIDAR system incorporates data from these three subsystems to produce a large cloud of points on the land surface whose X, Y, and Z coordinates are known within the specified accuracy. This collection consists of ASCII files of bare earth elevations and intensity (x,y,z,i) and, LAS (version 1.0 lidar data interchange standard) binary files that include all 1st and last returns, intensity and bare earth classification.

This polygon dataset is derived from Landsat reflectance and LiDAR elevation data. The polygon boundaries represent "image objects" that were derived using eCognition image segmentation software. The software groups image pixels into contiguous segments based on spectral reflectance. The attributes for each polygon are then calculated from the LiDAR crown height layer. Attributes include maximum, minimum, and mean elevation above the ground within each polygon. Elevation is in meters. This dataset incorporates Landsat data collected in 2015, 2016, 2017, and 2018. LiDAR data were collected in 2015 and 2016.

Geographic Extent: OLC Baker County 3DEP 2017 defined project area (DPA) extends approximately 224,286 acres; the buffered project area (BPA) covers approximately 229,671 acres.

This bare earth hydro flattened digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed and hydro flattening performed within the OLC Baker County 3DEP 2017 de...

Atlantic was contracted to acquire high resolution topographic LiDAR (Light Detection and Ranging) data located in Mobile County, Alabama. The intent was to collect one (1) Area of Interest (AOI) that encompasses Mobile County. The total client defined AOI was 1,402 square miles or 3,361 square kilometers. The data were collected from January 12 - 22, 2014. Classifications of data available fro...