Bare earth data were derived from discrete return LiDAR data collected near the Toolik Field Station (AK) as part of the NASA funded Terrestrial Ecology Project NNX12AK83G, PIs: Lee A. Vierling (University of Idaho, Moscow, Idaho, USA), Jan U.H. Eitel (University of Idaho, Moscow, Idaho, USA), Natalie T. Boelman (Columbia University, New York City, New York, USA), and Kevin L. Griffin (Columbia University, New York City, New York, USA). The bare earth data were derived using the software package TerraScan. The spatial extend of the dataset is: -149.675381 (West bounding long); -148.853695 (East bounding long); 68.372569 (South bounding lat); and 68.756794 (North bounding lat). The dataset is discontinuous within this spatial extend covering the following five areas of Interest (AOIs)with approximate mile point locations on the Dalton Highway in parentheses: Roche Moutonnee Creek (263.9 miles), an unnamed site (288.8 miles), Toolik Field Station (284.3 miles), Imnavait (290.6 miles), and Sagavanirktok Department of Transportation (DOT) (305.6 miles). Data were collected August 1, 2013, with an approximate point density of 30 points/m2.

Use of this dataset should be cited as: Vierling, L.A., Eitel, J.U.H., Boelman, N.T., Griffin, K.L., Greaves, H., Magney, T.S., Prager, C., Ajayi, M., and Gibson, R. 2013. Bare earth LiDAR dataset for Toolik Field Station, AK, and nearby field sites along Dalton Highway. doi:10.7923/G4057CV5

Bare Earth Light Detection and Ranging (LIDAR) Data - 2 foot resolution. The dataset contains locations and attributes of bare earth elevations in meters. Bare earth model is created by identifying those returns that fall on the ground surface and interpolating a surface between these points. In this manner buildings and vegetation are removed from Bare Earth Model. This data set does not include bridges and overpasses in the Bare Earth model as the delineation point for these structures is not reliably discernible in the LiDAR data. This metadata record describes the data products derived from the LiDAR data for the DC OCTO 2015 LiDAR project covering approximately 80 square miles, in which its extents cover Arlington County in Washington DC. This project consists of deliverables in accordance with USGS v1.2 specifications and meets or exceeds the level of quality for QL1 (8 points per meter).

This deposit includes Bare-earth airborne lidar data collected in 2015, from the south flank of the Santa Ynez Range, north of Montecito, CA, USA. The raw data, collected in 2015 using a Geiger-mode LiDAR scanner, was purchased from Harris Corporation Archive with the Montecito Partners for Resilient Communities fund. Data were subsequently processed using LasTool software. This software was used to clip to the six mountainous watersheds that experienced debris flows during January 2018 and to isolate only classified ground points. The data files series of .laz files encompass an area on the south flank of Santa Ynez Mountains north of Montecito, CA, USA, near 34.4206, -119.6363 in degrees longitude and latitude.

This lidar data set includes unclassified swath LAS 1.4 files, classified LAS 1.4 files, breaklines, digital elevation models (DEMs), first return digital surface models (DSMs), and intensity imagery. Geographic Extent: Fourteen partial counties in Utah, covering approximately 7,005 total square kilometers; partial coverage of three counties covering approximately 182 square kilometers in the Minidoka QL1 AOI. This area is part of the Bear Lake / Cache Valley QL1 AOI. Dataset Description: The Utah 2016 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 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), UTM Zone 12, meters and vertical datum of NAVD88 (GEOID12B), meters. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files formatted to 215 individual 1,000 meter x 1,000 meter tiles; as tiled intensity imagery, as tiled bare earth DEMs, and as tiled first return DSMs all tiled a 2,000 meter x 2,000 meter schema (82 tiles). Continuous breaklines were produced in Esri shapefile format. Ground Conditions: Lidar was partially collected in fall of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. established a total of 28 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 5 independent accuracy checkpoints, 5 in Bare Earth and Urban landcovers (5 NVA points), 6 in the Shrubs and Tall Grass category (6 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

A laser based terminal descent sensor is proposed that will provide real-time ground-relative altitude, attitude, and vertical velocity at high data rates to a navigation computer of a vehicle during landing on a near earth object or planetary body. The operational range of the sensor in Mars, for example, can exceed ten kilometers through touchdown, and may conceivably be a low mass, volume, and cost replacement for the Terminal Descent Sensor (TDS) on missions like the Mars Science Laboratory (MSL). The sensor is compact, rugged, and can be easily integrated with other NASA smart sensor systems coming of age, such as the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project or JPL's Lander Vision System (LVS). During Phase I we propose to detail the complete system design, model the transmitter laser, and test key components that will benchmark our model in preparation of a full system development in Phase II.

OWLETS1_SurfaceLidar_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) lidar data collected at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete.

Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.

Sanborn was required to perform quality assurance checks on the Pierce County LiDAR dataset collected late December, 2010 through early February, 2011 by Watershed Sciences, Inc. A Leica ALS50 II was used for collection and flown 900-1300 meters above ground level using pulse rates of 83-105.9 kHz. This collection was designed to achieve a pulse density ≥8 points per meter over terrestrial surfaces in western Washington.https://www.co.pierce.wa.us/DocumentCenter/View/78139/Pierce-County-Lidar-Report-Final

This metadata record describes the ortho & LIDAR mapping of Citrus County, FL. The mapping consists of LIDAR data collection, contour generation, and production of natural color orthophotography with a 1ft pixel using imagery collected with a Wild RC-30 Aerial Camera.

Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. This LiDAR dataset is a survey of Coastal California. The project area consists of approximately 2616 square miles. The project design of the LiDAR data acquisition was developed to support a nominal post spacing of 1 meter. Fugro EarthData, Inc. acquired 1546 flight lines in 108 lifts between October 2009 and August 2011. LiDAR data collection was performed with two Piper Navajo twin engine aircrafts, utilizing a Leica ALS60 MPiA sensor; collecting multiple return x, y, and z as well as intensity data. The bare-earth lidar data was used to create hydro-flattened DEMs (Digital Elevation Models) available for download from the NOAA OCM Digital Coast.

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.

Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, water supply, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data was combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table. Collection of bathymetric data in October 2018 at Beaver Lake near Rogers, Arkansas, used a marine-based mobile mapping unit that operates with several components: a multibeam echosounder (MBES) unit, an inertial navigation system (INS), and a data acquisition computer. Bathymetric data were collected using the MBES unit in longitudinal transects to provide complete coverage of the lake. The MBES was tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than 2.5 meters deep (the practical limit of reasonable and safe data collection with the MBES). Two bathymetric datasets collected during the October 2018 survey include the gridded bathymetric point data (BeaverLake2018_bathy.zip) computed on a 3.28-foot (1-meter) grid using the Combined Uncertainty and Bathymetry Estimator (CUBE) method, and the bathymetric quality-assurance dataset (BeaverLake2018_QA.zip). The gridded point data used to create the bathymetric surface (BeaverLake2018_bathy.zip) was quality-assured with data from 9 selected resurvey areas (BeaverLake2018_QA.zip) to test the accuracy of the gridded bathymetric point data. The data are provided as comma delimited text files that have been compressed into zip archives. The shoreline was created from bare-earth lidar resampled to a 3.28-foot (1-meter) grid spacing. A contour line representing the flood pool elevation of 1,135 feet was generated from the gridded data. The data are provided in the Environmental Systems Research Institute shapefile format and have the common root name of BeaverLake2018_1135-ft. All files in the shapefile group must be retrieved to be useable.

This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2017 SnowEx campaign. Data were collected in the fall (September and October) and winter (February) seasons. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available. This is unprocessed data which has not been classified by land use (e.g. bare earth, low vegetation, trees).

Lidar bare earth point density rasters were developed from lidar surveys from 2013, 2015, and 2022 for the Greater Raleigh, NC Area, with 1 meter resolution. These rasters were developed to assess the spatial accuracy of other lidar-derived metrics within this data release based on density and location of lidar points. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded.

A Digital Elevation Model (DEM) and Digital Surface Model (DSM) were derived from airborne Light Detection and Ranging (LiDAR) data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. Following pre-processing in Routescene LidarViewer Pro software, the LiDAR point clouds were cleaned and processed using CloudCompare software to separate ground and off-ground points. A high resolution DEM and DSM were then created using ArcGIS Pro software. This data package contains fully cleaned point clouds of ground and off-ground points (.las), a 25 cm DEM (.tif), and a 25 cm DSM (.tif) for the Teller 27 field site. Ancillary aircraft data, flight mission parameters, weather conditions, and raw lidar data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018). Minimally processed point clouds and auxiliary files can be found in the L1 dataset: NGA304 (2017 and 2018). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30° cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.

All LiDAR overflights by drone were performed in June 2022. The LiDAR sensor used was a Yellowscan Surveyor and was embarked on a DJI Matrice 600 Pro UAV. The YellowScan Surveyor includes: - GNSS-inertial station : Applanix APX-15 UAV - LiDAR : Velodyne VLP16 (also known as Puck) : Wavelength: 905 nm / 300,000 pulses per second (300 kHz) / 2 echoes per pulse / Viewing angle: 360° - Accuracy: 4 cm The design of a flight path consists of a double grid with interline distances of 40 m. The drone flew at 50 m height constrained by a 5 m resolution DTM from the BD ALTI® by IGN. The flights were made at a speed of 5 m per second. All trajectories were planned with the UGCS-4.0.134 software. Then, the LiDAR dataset was post-processed in several steps by using different softwares: - Applanix POSPac UAV 8.4: post-processing (PPK) of trajectories based on the UAV's GNSS-Inertial Measurement System data and using a reference GNSS base station. The correction solution for each trajectory is exported in an ASCII SBET (Smoothed Best Estimated Trajectory) file. - Yellowscan CloudStation V2106.0.0: The SBET file is integrated in the software to generate point clouds in .las format projected in RGF93/Lambert 93. - LidR (R package): reading of las files, redrawing into tiles in compressed format .laz file corresponding to the output format. Telepilots from the TETIS resarch unit, Montpellier : Samuel Alleaume and Florian de Boissieu. This dataset includes raw data and derived raster products. The data is provided in a cloud-optimized format (COPC).

(Link to Metadata) This metadata applies to contours derived from the various LiDAR project areas with different resolutions (RESCLASS), i.e., 0p6m, 0p7m, 1m, 1p4m, 1p6m, 2p4m & 3m. For an overview of the "Vermont LiDAR Initiative" please see "VCGI.VERMONT.GOV/LIDAR". For specific details on each project extent's "point cloud", e.g., flight dates, nominal pulse spacing and RMSEz etc., see the VCGI LiDAR data product page for links to each extents vendor based metadata - http://vcgi.vermont.gov/warehouse/products/ALL-LDR_MIX_LIDAR_STATE_ALL.

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.

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 GBGTA 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

Advanced Scientific Concepts, Inc. (ASC) is a small business, which has developed a compact, eye-safe 3D Flash LIDARTM Camera (FLC) well suited for real-time spacecraft trajectory, speed, orientation measurements relative to the planet's surfaces and evaluating potential hazards during the critical landing sequence. Data collected using ASC's FLC at JPL's Mars Yard and in NASA ALHAT flight tests demonstrated that ASC Flash LIDAR system can meet the requirements for Entry Descent and Landing (EDL). Aboard the Space Shuttle Endeavour (STS-127), SpaceX and ASC demonstrated the DragonEye Autonomous Rendezvous and Docking (AR&D) Flash LIDAR solution in low earth orbit, the first Flash LIDAR in space. ASC has developed the core technology for Flash LIDAR with its 3D-FPA hybrid and would like to work with NASA to further enhance the functionality of the 3D sensor by adding embedded image enhancement and classification algorithms. For this SBIR solicitation, ASC is proposing embedded processing, for image enhancement and hazard identification, of 3D Flash LIDAR point clouds.