This data collection of the 3D Elevation Program (3DEP) consists of Lidar Point Cloud (LPC) projects as provided to the USGS. These point cloud files contain all the original lidar points collected, with the original spatial reference and units preserved. These data may have been used as the source of updates to the 1/3-arcsecond, 1-arcsecond, and 2-arcsecond seamless 3DEP Digital Elevation Models (DEMs). 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. Lidar (Light detection and ranging) discrete-return point cloud data are available in LAZ format. The LAZ format is a lossless compressed version of the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. Point Cloud data can be converted from LAZ to LAS or LAS to LAZ without the loss of any information. Either format stores 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of geo-referenced x, y coordinates and z (elevation), as well as other attributes for each point. Additonal information about the las file format can be found here: https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities. All 3DEP products are public domain.

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.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

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 dataset contains linework of lineaments mapped on 4 <1-m-resolution lidar datasets and the 10-m-resolution National Elevation Dataset digital elevation models in the Pit River region of northeastern California. Lineaments are classified by confidence in tectonic origin, map certainty, and the ages of the bedrock and surficial deposits they cross.

Note: The files can be downloaded from the Attachments section below. Please note that the total size is 180GB, so the download may take some time depending on your system’s capabilities and configuration. Topographic and bathymetric LiDAR data was collected for New York City in 2017. Topographic data was collected for the entire city, plus an additional 100 meter buffer, using a Leica ALS80 sensor equipped to capture at least 8 pulse/m2. Dates of capture for topographic data were between 05/03/2017 and 05/17/2017 during 50% leaf-off conditions. Bathymetric data was collected in select areas of the city (where bathymetric data capture was expected) using a Riegl VQ-880-G sensor equipped to capture approximately 15 pulses/m2 (1.5 Secchi depths). Dates of capture for bathymetric were between 07/04/2017 - 07/26/2017. LiDAR data was tidally-coordinated and captured between mean lower low water (+30% of mean tide) ranges. The horizontal datum for all datasets is NAD83, the vertical datum is NAVD88, Geoid 12B, and the data is projected in New York State Plane - Long Island. Units are in US Survey Feet. To learn more about these datasets, visit the interactive “Understanding the 2017 New York City LiDAR Capture” Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LiDAR_Summary.md

Note: The shapefile download may fail when downloading the entire dataset. If this happens, download the file geodatabase instead.Feature class contains 2-foot interval contours of the area surrounding and including the Eugene urban growth boundary. Contours have been derived by LCOG from 2009 LiDAR data. The LiDAR data was prepared by Watershed Science for DOGAMI. General processing includes (1) joining Junction City, Coburg, Eugene East and Eugene West bare-earth quads into one mosaic, (2) smoothing the mosaic with a focal mean (3 x 3 rectangle), (3) contouring the smoothed bare-earth mosaic, and (4) quality-checking along the edges of the quads to insure matching contours. (DISCLAIMER: The maps and data available for access from the City of Eugene are provided "as is" without warranty or any representation of accuracy, timeliness or completeness. The burden for determining accuracy, completeness, timeliness, merchantability and fitness for or the appropriateness for use rests solely on the user accessing this information. The City of Eugene makes no warranties, expressed or implied, as to the use of the maps and data available for access at this website. There are no implied warranties of merchantability or fitness for a particular purpose. The user acknowledges and accepts all inherent limitations of the maps and data, including the fact that the maps and data are dynamic and in a constant state of maintenance, correction and revision. Any maps and associated data for access do not represent a survey. No liability is assumed for the accuracy of the data delineated on any map, either expressed or implied.)

This is a seamless bare earth digital elevation model (DEM) created from lidar terrain elevation data for the Commonwealth of Massachusetts. It represents the elevation of the surface with vegetation and structures removed. The spatial resolution of the map is 1 meter. The elevation of each 1-meter square cell was linearly interpolated from classified lidar-derived point data.This version of the DEM stores the elevation values as integers. The native VALUE field represents the elevation above/below sea level in meters. MassGIS added a FEET field to the VAT (value attribute table) to store the elevation in feet as calculated by multiplying VALUE x 3.28084.Dates of lidar data used in this DEM range from 2010-2015. The overlapping lidar projects were adjusted to the same projection and datum and then mosaicked, with the most recent data replacing any older data. Several very small gaps between the project areas were patched with older lidar data where necessary or with models from recent aerial photo acquisitions. See https://www.mass.gov/doc/lidar-project-areas-original/download for an index map.This DEM is referenced to the WGS_1984_Web_Mercator_Auxiliary_Sphere spatial reference.See the MassGIS datalayer page to download the data as a file geodatabase raster dataset.View this service in the Massachusetts Elevation Finder.

Lidar point cloud data with classifications – unclassified (1), ground (2), low vegetation (3), medium vegetation (4), high vegetation (5), buildings (6), low point - noise (7), reserved – model keypoint (8), high noise (18).

This record is for Approval for Access product AfA439. A habitat map derived from airborne data, specifically CASI (Compact Airborne Spectrographic Imager) and LIDAR (Light Detection and Ranging) data.

The habitat map is a polygon shapefile showing site relevant habitat classes. Geographical coverage is incomplete because of limits in data available. It includes those areas where the Environment Agency, Natural England and the Regional Coastal Monitoring Programme have carried out sufficient aerial and ground surveys in England.

The habitat map is derived from CASI multispectral data, LIDAR elevation data and other GIS products. The classification uses ground data from sites collected near to the time of CASI capture. We use ground data to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the remaining areas into one of the different habitats.

Habitat maps generated by Geomatics are often derived using multiple data sources (e.g. CASI, LIDAR and OS-base mapping data), which may or may not have been captured coincidentally. In instances where datasets are not coincidentally captured there may be some errors brought about by seasonal, developmental or anthropological change in the habitat.

The collection of ground data used in the classification has some limitations. It has not been collected at the same time as CASI or LIDAR capture; it is normally within a couple of months of CASI capture. Some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped on site there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

No quantitative accuracy assessment has been carried out on the habitat map, although the classification was trained using ground data and the final habitat map has been critically evaluated using Aerial Photography captured simultaneously with the CASI data by the processors and independently by habitat specialists. Please note that this content contains Ordnance Survey data © Crown copyright and database right [2014] and you must ensure that a similar attribution statement is contained in any sub-licences of the Information that you grant, together with a requirement that any further sub-licences do the same.

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

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.

Indiana's Statewide Lidar data is produced at 1.5-meter average post spacing for all 92 Indiana Counties covering more than 36,420 square miles. New Lidar data was captured except where previously captured Lidar data exists, or the participating County bought-up to a higher resolution of 1.0-meter average post spacing Lidar data. Existing Lidar data exists for: Porter, Steuben, Noble, De Kalb, Allen, Madison, Delaware, Hendricks, Marion, Hancock, Morgan, Johnson, Shelby, Monroe, and portions of Vermillion, Parke, Vigo, Clay, Sullivan, Knox, Gibson, and Posey. These existing Lidar datasets were seamlessly integrated into this new statewide dataset. From this seamless Lidar product a statewide 5-foot post spacing hydro-flattened DEM product was created and is also available. See the FGDC Metadata provided for more details. This statewide project is divided into three geographic areas captured over a 3-year period (2011-2013): Area 1 (2011) Indiana central counties: St. Joseph, Elkhart, Starke, Marshall, Kosciusko, Pulaski, Fulton, Cass, Miami, Wabash, Carroll, Howard, Clinton, Tipton, Boone, Hendricks, Marion, Morgan, Johnson, Monroe, Brown, Bartholomew, Lawrence, Jackson, Orange, Washington, Crawford, and Harrison. Area 2 (2012) Indiana eastern counties: LaGrange, Steuben, Noble, DeKalb, Whitley, Allen, Huntington, Wells, Adams, Grant, Blackford, Jay, Hamilton, Madison, Delaware, Randolph, Hancock, Henry, Wayne, Shelby, Rush, Fayette, Union, Decatur, Franklin, Jennings, Ripley, Dearborn, Ohio, Scott, Jefferson, Switzerland, Clark, and Floyd. Area 3 (2013) Indiana western counties: Lake, Porter, LaPorte, Newton, Jasper, Benton, White, Warren, Tippecanoe, Fountain, Montgomery, Vermillion, Parke, Putnam, Vigo, Clay, Owen, Sullivan, Greene, Knox, Daviess, Martin, Gibson, Pike, Dubois, Posey, Vanderburgh, Warrick, Spencer, and Perry. Funders of OpenTopography Hosting of the Indiana Statewide Lidar and DEM data: USDA NRCS, Indiana, ISPLS Foundation, Indiana Geographic Information Office, Indiana Office of Technology, Indiana Geological Survey, Surdex Corporation, Vectren Energy Delivery, Indiana, Woolpert, Inc., and Individual IGIC Member Donations from Jim Stout, Jeff McCann, Cele Morris, Becky McKinley, Phil Worrall, and Andy Nicholson. To explore a web map of topographic differencing for the entire state of Indiana click here

Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download.The DSM data is available in the form of 1-km by 1-km non-overlapping tiles grouped into packages for download.This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters and sensors may vary by project. See the detailed User Guide linked below for additional information. You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Now also available through a web service which exposes the data for visualization, geoprocessing and limited download. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connectionusing the REST endpoint URL. The service draws using the Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer https://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer (Government of Ontario Internal Users)Additional DocumentationOntario DSM (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-2018 - Cochrane - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Peterborough - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Lake Erie - Additional Contractor Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Contractor Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Contractor Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Contractor Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Contractor Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Contractor Metadata (Word)Timmins Lidar 2024 - Additional Contractor Metadata (Word) Ontario DSM (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP)Product PackagesDownload links for the Ontario DSM (Lidar-Derived) (Word)Projects:LEAP 2009GTA 2014-18OMAFRA 2016-18CLOCA 2018South Nation CA 2018-19Muskoka 2018-23York-Lake Simcoe 2019Ottawa River 2019-20Ottawa-Gatineau 2019-20Lake Nipissing 2020Hamilton-Niagara 2021Huron Shores 2021Eastern Ontario 2021-22OMAFRA Lake Huron 2022OMAFRA Lake Simcoe 2022Belleville 2022Digital Elevation Data to Support Flood Mapping 2022-26Huron-Georgian Bay 2022-23Kawartha Lakes 2023Sault Ste Marie 2023-24Sudbury 2023-24Thunder Bay 2023-24Timmins 2024Cataraqui 2024Greater Toronto Area Lidar 2023StatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

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.

The Download Tool is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tool provides easy download access to aerial imagery and lidar elevation collected during multiple flights.

This dataset is a footprint of the current available LiDAR data over for the State of Queensland compiled from numerous LiDAR projects captured on or after the year 2008.

The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study area of this activity is Perry County, Kentucky. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for this study area county. Available mine maps and mine portal locations were obtained from the Kentucky Mine Mapping Information System, Division of Mine Safety, 300 Sower Boulevard, Frankfort, KY 40601 at http://minemaps.ky.gov/Default.aspx?Src=Downloads. These features were used to spatially locate the study areas within Perry County. Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1950-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar high-resolution (1.5 m) DEMs were acquired for Perry County, Kentucky from U.S. Department of Agriculture, Natural Resources Conservation Service Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGOrder.aspx#. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study area. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Perry County, KY on 3/29/12 to 4/6/12. A recent difference raster dataset time span (2008-2012 date range) was analyzed by differencing the Perry County lidar-derived DEM and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the lidar/IFSAR difference dataset. Perry County lidar metadata reported that the acquisition required lidar to be collected with an average of 0.68 m point spacing or better and vertical accuracy of 15 cm root mean square error (RMSE) or better. References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.

This dataset is Lidar data that has been collected by the Scottish public sector and made available under the Open Government Licence. The data are available as point cloud (LAS format or in LAZ compressed format), along with the derived Digital Terrain Model (DTM) and Digital Surface Model (DSM) products as Cloud optimized GeoTIFFs (COG) or standard GeoTIFF. The dataset contains multiple subsets of data which were each commissioned and flown in response to different organisational requirements. The details of each can be found at https://remotesensingdata.gov.scot/data#/list

The Utah Geological Survey (UGS) as part of its mission to provide timely scientific information about Utah's geologic environment, resources, and hazards, acquires Lidar data with its partners in support of various geologic mapping and research projects. In 2011, the UGS and partners acquired approximately 4927 square kilometers of 1 meter Lidar data over the Cedar/Parowan Valley, Great Salt Lake shoreline/wetlands, Hurricane fault zone, Lowry Water, Ogden Valley, and North Ogden areas of Utah. The datasets include raw LAS, LAS, DEM, DSM, and metadata (includes XML metadata, project tile indexes, and area completion reports) files. The datasets acquired by the UGS and its partners are in the public domain and can be freely distributed with proper credit to the UGS and its partners.

These datasets were funded by the Utah Geological Survey, with the exception of the Great Salt Lake area, which was funded by the U.S. Environmental Protection Agency (grant no. CD-96811101-0) and the UGS, and the North Ogden area, which was funded by the Utah Division of Emergency Management, Floodplain Management Program.