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.

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

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

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).

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 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 survey covers portions of Hawaii Volcano National Park, Upper Waiakea Forest Reserve, and Mauna Loa Forest Reserve on the Big Island of Hawaii. The survey area covers 299 square kilometers. These data were collected by the National Center for Airborne Laser Mapping (NCALM) on behalf of Steve Martel (University of Hawaii), Scott Rowland (University of Hawaii), Adam Soule (Woods Hole Oceanographic Institution) and Kathy Cashman (U. Oregon / Bristol U.).

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.

This dataset is no longer available on the Data Services Platform. New version of the LIDAR Composite DSM data is available here: https://environment.data.gov.uk/searchresults;query=lidar%20composite%202020;searchtype=All;page=1;pagesize=20;orderby=Relevancy The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering areas of England at 25cm spatial resolution. Produced by the Environment Agency in 2017, this dataset is derived from a combination of our full time stamped archive, 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. The composite is updated on an annual basis to include the latest surveys. 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 ASCII 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. A tinted shaded relief, which is an image showing what LIDAR looks like when loaded into specialist software, is also available as a WMS feed. You can also download survey index files which shows, for any location, what Time Stamped survey went into the production of the LIDAR composite. 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 and composites of the best data available in any location. To find out more about LIDAR and the various surface models we produce please read our story map This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reserved. Attribution Statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

Lidar (light detection and ranging) imagery provides valuable information in the field of remote sensing, allowing users to determine elevation, vegetation structure, and terrain with remarkable levels of detail. This manual will lead ArcGIS Pro users through the tools and methods needed to access, process, and analyze lidar data through a series of step-by-step tutorials. By completing this series of tutorials, you will be able to: •Manipulate data to create maps and map templates in ArcGIS Pro •Obtain and display lidar imagery •Use ArcGIS Pro tools to process and analyze lidar data •Classify lidar points using different classification methods • Process lidar point clouds to create digital elevation models

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.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr. This metadata record describes the Florida Keys digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Miami-Dade and Monroe Counties. The DEM was produced from the following lidar data sets: 1. 2015 Miami-Dade County, Florida Lidar 2. 2015 NOAA NGS Topobathy Lidar: Dry Tortugas 3. 2018 - 2019 NOAA NGS Topobathy Lidar Hurricane Irma: Miami to Marquesas Key, FL The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

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.

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link. See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index in shapefile format. Data sizes by project area are listed below. The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format. This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors vary by project. Some projects have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders. Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived). You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Additional Documentation Ontario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Metadata (Word)Timmins Lidar 2024 - Additional Metadata (Word) OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP)CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP)South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP)York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP)Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP)Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP)DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP)DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP)DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP)GTA 2023 - Lift Metadata (SHP) Ontario Classified Point Cloud (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP)Data Package SizesLEAP 2009 - 22.9 GBOMAFRA Lidar 2016-18 - Cochrane - 442 GBOMAFRA Lidar 2016-18 - Lake Erie - 1.22 TBOMAFRA Lidar 2016-18 - Peterborough - 443 GBGTA 2014 - 57.6 GBGTA 2015 - 63.4 GBBrampton 2015 - 5.9 GBPeel 2016 - 49.2 GBMilton 2017 - 15.3 GBHalton 2018 - 73 GBCLOCA 2018 - 36.2 GBSouth Nation 2018-19 - 72.4 GBYork Region-Lake Simcoe Watershed 2019 - 75 GBOttawa River 2019-20 - 836 GBLake Nipissing 2020 - 700 GBOttawa-Gatineau 2019-20 - 551 GBHamilton-Niagara 2021 - 660 GBOMAFRA Lidar 2022 - Lake Huron - 204 GBOMAFRA Lidar 2022 - Lake Simcoe - 154 GBBelleville 2022 - 1.09 TBEastern Ontario 2021-22 - 1.5 TBHuron Shores 2021 - 35.5 GBMuskoka 2018 - 72.1 GBMuskoka 2021 - 74.2 GBMuskoka 2023 - 532 GBDigital Elevation Data to Support Flood Mapping 2022-26:Huron-Georgian Bay 2022 - 1.37 TBHuron-Georgian Bay 2023 - 257 GBHuron-Georgian Bay 2023 Bruce - 95.2 GBKawartha Lakes 2023 - 385 GBSault Ste Marie 2023-24 - 1.15 TBSudbury 2023-24 - 741 GBThunder Bay 2023-24 - 654 GBTimmins 2024 - 318 GBCataraqui 2024 - 50.5 GBChapleau 2024GTA 2023 - 985 GBStatusOn 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

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.

This dataset is a lidar survey by the Middle Usumacinta Archaeological Project. It examines the distribution of archaeological sites in the Middle Usumacinta region in eastern Tabasco, Mexico. Data was collected for Dr. Takeshi Inomata at the University of Arizona.

This data set is tiled lidar point cloud LAS files v1.4, for the 2016 Alabama 25 County lidar area of interest (AOI).

USGS NGTOC task order G17PD00243 required Spring 2017 LiDAR surveys to be collected over 18,845 square miles covering part or all of 25 counties in Alabama. These counties are Autauga, Baldwin, Barbour, Bullock, Butler, Chambers, Cherokee, Clarke, Conecuh, Covington, Cre...

This Data Series Report contains lidar elevation data collected September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5-10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. Lidar data exchange format (LAS) 1.2 formatted point data files were generated based on these data. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are organized into only four classes: 1-unclassified, 2-ground, 7-noise and 9-water. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were collected at a nominal pulse spacing (NPS) of 1.0 meter (m). The horizontal projection and datum of the data are Universe Transverse Mercator, zones 15N and 16N, North American Datum 1983 (UTM Zone 15N or 16N NAD83), meters. The vertical datum is North American Vertical Datum 1988, Geoid 2012 (NAVD88, GEOID12), meters. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

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.