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.DEFRA Data Services Platform Metadata URLDefra Network WMS server provided by the Environment Agency

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.

Map displays LiDAR grids in region to assist with ordering LiDAR data. Background imagery used in this map is Esri's World Imagery service and is not indicative of LiDAR products for sale by H-GAC. To order individual tiles or entire sets of LiDAR data from H-GAC, please visit the H-GAC LiDAR Imagery website.

Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.

Light Detection and Ranging (lidar) is a technology used to create high-resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches).  rnrnFEMA collects lidar elevation data to support flood mapping. USGS is the primary Federal steward of lidar data. FEMA archives lidar data for FEMA projects where USGS does not manage the Lidar data collection. rnrnDatapoints include ground elevation models and vertical metrics for ground elevation.

Market Analysis for Point Cloud LiDAR Data Processing Software The global point cloud LiDAR data processing software market is projected to reach USD 1,666.7 million by 2033, exhibiting a CAGR of 15.9% from 2025 to 2033. The increasing adoption of LiDAR technology in architecture, land surveying, and other industries, coupled with advancements in artificial intelligence (AI) and machine learning (ML) algorithms for data processing, are driving the market growth. The need for accurate and detailed 3D representations of physical spaces and the benefits of LiDAR in generating point clouds for precise modeling and visualization are further fueling market expansion. Key market trends include the adoption of cloud-based solutions, which offer flexibility, scalability, and reduced infrastructure costs. The integration of AI and ML in data processing is automating tasks, enhancing accuracy, and improving efficiency. Moreover, the growing demand for smart city initiatives and infrastructure development is creating new opportunities for the use of point cloud LiDAR data processing software in urban planning, asset management, and transportation systems. Companies in the market include Trimble, Bentley Systems, Leica Geosystems AG, Autodesk, and FARO, who are investing in research and development to provide innovative solutions that meet the evolving needs of their customers.

Airborne LiDAR Market size was valued at USD 1.35 Billion in 2023 and is projected to reach USD 4.05 Billion by 2030, growing at a CAGR of 17% during the forecast period 2024-2030.

Global Airborne LiDAR Market Drivers

The market drivers for the Airborne LiDAR Market can be influenced by various factors. These may include:

Infrastructure Development and Urban Planning: Projects involving infrastructure development, urban planning campaigns, and the requirement for precise topographic data are the main drivers of the need for precise and comprehensive geospatial data. For such initiatives, the collection of high-resolution data is greatly aided by the use of airborne LiDAR. Increased Use in Agriculture and Forestry: Airborne LiDAR is used for agricultural landscape assessment, vegetation health monitoring, and forestry management. The system facilitates decision-making by offering comprehensive three-dimensional information on crop health, tree canopy structure, and terrain. Effective Disaster Response and Management: To swiftly evaluate and model impacted areas, airborne LiDAR is utilized in disaster response and management. Accurate mapping of changing topography, hazard detection, and emergency response activity planning are made possible by it. Applications for Surveying and Mapping: Airborne LiDAR is extensively used in surveying and mapping because it provides quick and accurate data collection for cadastral mapping, terrain modelling, and other geospatial applications. The accuracy and efficiency of surveying procedures are improved by technology. Developments in LiDAR Sensor Technology: The market for aerial LiDAR is expanding as a result of continuous developments in LiDAR sensor technology, which include the creation of lightweight, high-resolution sensors. More precise and comprehensive data collection is made possible by enhanced sensor capabilities. Demand for 3D Modelling and Visualization: The adoption of aerial LiDAR is propelled by the need for 3D modelling and visualization across a range of industries, such as environmental monitoring, urban planning, and geographic mapping. It makes it possible to create intricate 3D models of buildings and landscapes. Crop monitoring and Precision Agriculture: Airborne LiDAR technology is beneficial to agriculture in the context of precision agriculture applications. LiDAR data provides comprehensive information on terrain, vegetation, and soil conditions, which helps with crop monitoring, yield estimation, and farming practice optimization. Environmental Monitoring and Conservation: Efforts to monitor and conserve the environment make use of aerial LiDAR. By giving precise spatial data, it supports conservation efforts, helps evaluate changes in ecosystems, and monitors wildlife habitats. Demand for LiDAR in driverless cars: The advancement of advanced driver-assistance systems (ADAS) and driverless cars is one factor driving the need for LiDAR technology. For navigation and obstacle detection, precise and current maps can be made using airborne LiDAR data. Natural Resource Management: By offering comprehensive data on terrain, waterways, and forests, airborne LiDAR aids in the management of natural resources. Planning sustainably, analyzing wildlife habitats, and keeping an eye on the health of ecosystems can all benefit from this data. Government rules and laws: The use of aerial LiDAR technology is influenced by government rules and laws pertaining to mapping standards, geospatial data, and mapping. Adherence to guidelines established by governmental entities promotes the utilization of LiDAR in diverse contexts. Growing Need for LiDAR in Construction: Topographic surveys, terrain modelling, and construction site planning are all done by the construction sector using aerial LiDAR. LiDAR's precise and up-to-date data improves construction procedures and reduces problems.

This web map allows for the download of KyFromAbove LiDAR data by 5k tile in LAZ format. This point cloud data was acquired during the typical leaf-off acquisition period (winter-spring) over a period of several years and may be provided as LAS version 1.1, 1.2, or 1.4 depending upon the acquisition period. Users will need to download the LAZIP.exe in order to decompress each tile. LiDAR data specifications adopted by the KyFromAbove Technical Advisory Committee can be found here. This is the source data used to create the Commonwealth's 5 foot digital elevation model (DEM) and its associated derivatives. More information regarding this data resource can be found on the KyGeoPortal.

Summary of the surface elevation data collection project in Putnam County, NY (NYSDEC) 2008. Products generated include LiDAR point data in LAS Binary format v1.1. In the spring of 2008, The Sanborn Map Company, Inc. (Sanborn) acquired 111 square miles of terrestrial LiDAR data in Putnam County, NY. An Optech ALTM 2050 Airborne LiDAR sensor was used for the collection. The LiDAR data associat...

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 point cloud is structured into non-overlapping 1 km by 1 km tiles in LAZ format. The following classification codes are applied to the data: * unclassified * ground * water * high noise * low noise This dataset is a compilation of lidar data from multiple acquisition projects, so specifications, parameters, accuracy and sensors may vary by project. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis. Related data: 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).

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

Mapping Lidar Laser Market Outlook

The global market size for Mapping Lidar Laser in 2023 is estimated to be around USD 2.3 billion, and it is projected to reach approximately USD 7.1 billion by 2032, growing at a CAGR of 13.2% during the forecast period. This growth trajectory is driven by the expanding adoption of Lidar technology in various industries such as construction, transportation, and environmental monitoring, as well as technological advancements and the increasing need for precise geospatial measurements.

One of the primary growth factors in the Mapping Lidar Laser market is the rise in infrastructure development activities globally. Governments and private sectors are heavily investing in smart city projects, which require advanced mapping technologies for urban planning and development. Lidar technology, with its high accuracy and rapid data collection capabilities, is becoming indispensable for creating detailed 3D maps and models. Additionally, the increasing demand for autonomous vehicles, which rely heavily on Lidar systems for navigation and safety, is further propelling the market growth.

Furthermore, the need for efficient corridor mapping and aerial surveying has been driving the market. Lidar technology offers precise topographical data, which is crucial for planning transportation routes, such as highways and railway lines. This technology is also being extensively adopted in the forestry and agriculture sectors for vegetation analysis and land use planning. The ability of Lidar to penetrate through foliage and provide detailed ground surface models makes it a valuable tool in these industries.

Technological advancements in Lidar systems are also contributing significantly to market growth. The development of compact, lightweight, and cost-effective Lidar sensors has made the technology more accessible to a broader range of applications. Innovations such as solid-state Lidar and advancements in data processing algorithms have improved the performance and reduced the costs of Lidar systems, making them an attractive option for various industries. This continuous evolution in technology is expected to sustain the market's growth momentum over the forecast period.

Light Detection and Ranging Devices, commonly known as Lidar, have revolutionized the way we perceive and interact with our environment. These devices utilize laser pulses to measure distances with high precision, creating detailed three-dimensional maps of the surroundings. The ability of Lidar to provide accurate and real-time data has made it an essential tool in various industries, from urban planning to autonomous vehicles. As the technology continues to advance, the integration of Lidar into everyday applications is becoming more seamless, enhancing our ability to monitor and manage complex systems. The growing demand for such devices underscores their critical role in driving innovation and efficiency across multiple sectors.

Regionally, North America is expected to dominate the Mapping Lidar Laser market due to the early adoption of advanced technologies and significant investments in infrastructure projects. The presence of major Lidar system manufacturers and the increasing use of Lidar in autonomous vehicles and environmental monitoring are driving the market in this region. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate due to rapid urbanization, infrastructure development, and the adoption of smart city initiatives by countries such as China and India.

Component Analysis

The Mapping Lidar Laser market by component is segmented into hardware, software, and services. The hardware segment includes Lidar sensors, GPS systems, and IMUs (Inertial Measurement Units). This segment currently holds the largest market share due to the essential role of hardware components in Lidar systems. Continuous innovations in sensor technology, such as the development of solid-state Lidar, are enhancing the performance and reducing the costs of these systems, thereby driving market growth.

Software components are also crucial for the efficient processing and analysis of Lidar data. This segment is expected to grow significantly due to the increasing need for sophisticated data processing algorithms and visualization tools. Software advancements are enabling more accurate and faster data interpretation, which is essential for applications like urban planning and environme

MD/PA Sandy Supplemental Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G14PD00397 Woolpert Order No. 74333 CONTRACTOR: Woolpert, Inc. This task is for a high resolution data set of lidar covering approximately 1,845 square miles. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, one (1) meter pixel raster DEMs of the bare-earth surface in ERDAS IMG Format, and 8-bit intensity images. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format, and LAS swath data. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. Coastal tiles 18SVH065720 and 8SVH095690 contain no lidar points as they exist completely in water. A DEM IMG was generated for these two tiles as the digitized hydro breakline assumed the data extent in the area. As such only 2568 LAS and Intensity files will be delivered along with 2570 DEM IMG's.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Talbot/MD_talbot_aspect_m/ImageServer

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.

Raw lidar data consist of positions (x, y) and intensity values. They must undergo a classification process before individual points can be identified as belonging to ground, building, vegetation, etc., features. By completing this tutorial, you will become comfortable with the following skills:Converting .zlas files to .las for editing,Reassigning LAS class codes,Using automated lidar classification tools, andUsing 2D and 3D features to classify lidar data.Software Used: ArcGIS Pro 3.3Time to Complete: 60 - 90 minutesFile Size: 57mbDate Created: September 25, 2020Last Updated: September 27, 2024

LiDAR_Point_Clouds, Classified. AHD have been preocessed to conform to the Australian Height Datum and converted from files collected as swaths in to tiles of data. The file formats is LAS.

LAS is an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data. Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information. Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard: Class code Classification type 0 Never classified 1 Unassigned 2 Ground 3 Low vegetation 4 Medium vegetation 5 High vegetation 6 Building 7 Noise 8 Model key 9 Water

Lineage: Fugro Spatial Solutions (FSS) were awarded a contract by Geoscience Australia to carry out an Aerial LiDAR Survey over the Kakadu National Park. The data will be used to examine the potential impacts of climate change and sea level rise on the West Alligator, South Alligator, East Alligator River systems and other minor areas. The project area was flight planned using parameters as specified. A FSS aircraft and aircrew were mobilised to site and the project area was captured using a Leica ALS60 system positioned using a DGPS base-station at Darwin airport. The Darwin base-station was positioned by DGPS observations from local control stations. A ground control survey was carried out by FSS surveyors to determine ground positions and heights for control and check points throughout the area. All data was returned to FSS office in Perth and processed. The deliverable datasets were generated and supplied to Geoscience Australia with this metadata information.

NEDF Metadata Acquisition Start Date: Saturday, 22 October 2011 Acquisition End Date: Wednesday, 16 November 2011 Sensor: LiDAR Device Name: Leica ALS60 (S/N: 6145) Flying Height (AGL): 1409 INS/IMU Used: uIRS-56024477 Number of Runs: 468 Number of Cross Runs: 28 Swath Width: 997 Flight Direction: Non-Cardinal Swath (side) Overlap: 20 Horizontal Datum: GDA94 Vertical Datum: AHD71 Map Projection: MGA53 Description of Aerotriangulation Process Used: Not Applicable Description of Rectification Process Used: Not Applicable Spatial Accuracy Horizontal: 0.8 Spatial Accuracy Vertical: 0.3 Average Point Spacing (per/sqm): 2 Laser Return Types: 4 pulses (1st 2nd 3rd 4th and intensity) Data Thinning: None Laser Footprint Size: 0.32 Calibration certification (Manufacturer/Cert. Company): Leica Limitations of the Data: To project specification Surface Type: Various Product Type: Other Classification Type: C0 Grid Resolution: 2 Distribution Format: Other Processing/Derivation Lineage: Capture, Geodetic Validation WMS: Not Applicable?

TASK NAME:(NRCS) Saginaw Bay, MI LiDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G11PD01254 Woolpert Order No. 071804 CONTRACTOR: Woolpert, Inc. LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The LiDAR systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. The final products include first, last, and at least one intermediate return LAS, full classified LAS and one (1) meter pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. The LiDAR data was acquired on November 18, 2011, November 21, 2011, November 22, 2011, November 23, 2011, December 26, 2011, April 4, 2012, April 5, 2012, and April 6, 2012.

The Environment Agency National LIDAR Programme provides accurate elevation data at 1m spatial resolution for all of England.

In 2017 we divided the country into 302 survey blocks covering all of England which were subsequently captured during the winter months (approximately November to April each year) between January 2017 and February 2023. These are known as our 'Phase 1' national lidar programme surveys.

Subsequently we have undertaken repeat surveys of specific blocks based on the on-going requirements for upto date elevation data. Each repeat survey block is given a new incrementing phase number, for example the second time we capture a block this is that blocks 'phase 2' whilst the 3rd time will be 'phase 3'. There is not curretly a plan to capture all the origianl phase 1 survey blocks over a rolling programme with repeat surveys be based on the requirements for upto date elevation data for an area.

All data is published through the DEFRA Data Services survey portal on a quartely on-going bases and a number of different products area available for each survey block. All products are available in 5km tiles aligned to the ordnance survey grid. The tiles are named by the unique survey id, OS grid reference and the first and last survey date of the survey id (P_XXXXX_OSOSOS_SDFLOWN_EDFLOWN.*). The surface models are available in GeoTiff raster format whilst the point cloud is available in *.laz. An index catalogue is also available with provides survey specific information about each tile.

Outlined below is a description of each product that is available for each survey block:

LIDAR Point Cloud: is the discrete LIDAR returns that are used in the creation of the surface models. Supplied in *.laz format they the discrete LIDAR returns have been classified into ground, low, medium and high vegetation classes using an automated classification process.

Digital Surface Model(s) (DSM) are created from the last or only LIDAR pulse returned to the sensor and contains all ground and surface objects.

Digital Terrain Model(s) (DTM) is created from the last return LIDAR pulse classified as ground, filtering out surface objects. Manual filtering is undertaken on the DTM to improve the automated classification routines to produce a most likely ground surface model. Areas of no data, such as water bodies, are also filled to ensure there are no gaps in the model.

First Return Digital Surface Model(s) (FZ DSM) is created from the either the first or only LIDAR pulse returned to the sensor and contains all ground and surface objects. It is more likely to return elevations from the top or near top of trees and the edges of buildings. It can often be used in canopy height modelling and production of building outlines.

Intensity Surface Model(s) (Int DSM) is a measure of the amount of laser light from each laser pulse reflecting from an object. This reflectivity is a function of the near infrared wavelength used and varies with the composition of the surface object reflecting the return and angle of incidence.The intensity surface model produces a grayscale image where darker surfaces such as roads reflect less light than other surfaces such as vegetation.

The LIDAR Composite DSM (Digital Surface Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DSM (Digital Surface Model) is produced from the last or only laser pulse returned to the sensor and includes heights of objects, such as vehicles, buildings and vegetation, as well as the terrain surface

Produced by the Environment Agency in 2022, the DSM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalogue which show for any location which survey was used in the production of the LIDAR composite.

The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE.

Metadata information

Full Title

Data Fusion from Airborne Hyperspectral Data, Airborne LiDAR Data and Aerial photographs at Aramo, Spain

Fusion of different airborne remote sensed and already processed data gathered from color aerial photography, LiDAR and hyperspectral data acquisition over the Aramo site in Spain.

Abstract

This dataset comprises results from the S34I Project, derived from processing of airborne hyperspectral data, airborne LiDAR data and color aerial imagery acquired at the Aramo pilot site in Spain. This document describes processing of color imagery, production of color orthophoto, processing of LiDAR data, and fusion of these data with processed and classified thematic hyperspectral data.

Eurosense conducted complex airborne data acquisition in two consecutive days 30.09.2023 and 01.10.2023 using Riegl LM7800-9184 LiDAR sensor and IGI Digicam H4D-50 medium format RGB camera. 1,645 high resolution RGB images were collected over 24 flight lines. Eurosense produced LiDAR point cloud and color orthophoto mosaic.LiDAR data processing:

Description of the software’s used

AeroOffice and GrafNav – software used for direct georeferencing of mobile and aerial mapping sensors using GNSS and inertial technology.

SDCimport applies the so-called ONLINE Full Waveform Analysis to the digitized echo signals provided by the laser scanner and additionally transforms the geometry data (i.e., range and scan angle) into Cartesian coordinates. The output is a point cloud in the well-defined Scanner's Own Coordinate System (SOCS) with additional descriptors for every point, e.g., a precise time stamp, the echo signal intensity, the echo pulse width, a classification according to first, second, up to last target.

RiWorld transforms the scan data into the coordinate system of the position and orientation data set, usually ETRS89 of WGS84 geocentric. It thus provides the acquired laser data of the object's surfaces within a geocentric coordinate system for further processing. In that case the final coordinate system was WGS84 UTM30N – GRS80.

TerraMatch fixes systematic orientation errors in airborne laser data. It measures the differences between laser surfaces from overlapping flight lines or differences between laser surfaces and known points. These observed differences are translated into correction values for the system orientation - easting, northing, elevation, heading, roll and/or pitch.

TerraScan is the main application in the Terrasolid Software family for managing and processing all types of point clouds. It offers import and project structuring tools for handling the massive number of points of a laser scanning campaign as well as the corresponding trajectory information. Various classification routines enable the automatic filtering of the point cloud.

Geometric corrections

Its content mainly concerns the geometry of the point cloud and quality control.

Initial setting

At the start of treatment, data was calculated by applying the sensor alignment settings corresponding to the last scanner calibration (boresight angles).

Roll: -0.22300

Pitch: -0.04320

Yaw: 0.00170

Determination of connecting lines

The first operation is the extraction of the tie lines used for the adjustment. They are determined by automatic analysis of the data of the different bands, classified as ground (2) and building (6).

They are extracted after the expedited automatic classification described in the previous paragraph.

Absolute control of altimetry

Absolute control of the altimetry is carried out using field measurements of the reference and control fields.

Elevation reference fields

A set of 6 altimetric reference fields were measured in the field by a surveyor.

Result of the absolute adjustment.

Average dz -0.001

Minimum dz: -0.091

Maximum dz: 0.089

Average magnitude: 0.026

Root mean square: 0.034

Std deviation: 0.034

Classification

The delivered classification contains class “Ground” (2), “Vegetation” (4), “Building” (6), “Water” (9) and class 1 “Unclassified”, based on the ASPRS standard.

Evaluation of LiDAR processing results

Absolute height

Both the connection fields and the independent control fields fit within the height tolerances. Global average difference on control fields it is less than -0.001 cm.

Point density and data coverage.

The covered area meets the point density requirement of 10 pts/sqrm.

All checks show that the data meets the accuracy specifications of an accurate LiDAR project.

Orthoprocessing:Triangulation is needed for precise positioning of aerial photographs. The full camera calibration performed because the practice shows that it is necessary for medium format cameras. The control points were collected from point cloud on such objects which were well recognizable in point cloud and also on aerial photographs. For the full area 43 control points are defined and measured in both datasets. The control points coordinate mean residuals are the following in the result of aerial triangulation adjustment: rmsx =0.18 m; rmsy =0.17 m; rmsz =0.26 m.Because of double flights (opposite directions on same flight lines) gave the possibility to produce dsm based ortho-mosaic in 25cm ground resolution.

Data fusion of different sensors data (Postprocessing)The generated raster data are delivered as georeferenced TIFF files. These raster data are covering 116 km² from LiDAR data and 114.6 km² from aerial photographs with a spatial resolution of 1.2 m per pixel. The no-data value is set to -9999, representing areas which are outside of photo and LiDAR coverage. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.

Generated LiDAR raster data and aerial ortho-mosaic image down-sampled to hyperspectral band ratio mosaics resolution (which has the following pixel size x: ~1.2m y: ~1.09m).Generated raster from point cloud are the following: Intensity, Digital Terrain Model, Digital Surface Model.Intensity band had been interpolated with average method while DTM (from class 2) and DSM (from class 2,4,6,9) with IDW methods. RGB true color composite ortho-mosaic resampled to 1.2m. The ortho-mosaic R, G, B bands are separated to 3 single bands and reformatted to float pixel type and no-data value set to -9999

All bands of three sensors, merged into one composite image with following bands and with the following short names:BRn Band1 – 9 Band ratio of hyperspectral data according to former document (https://zenodo.org/uploads/14193286) BR1 - BR9

LDint Band10 LiDAR intensity raster

LDdtm Band11 DTM layer generated from LiDAR data class 2

LDdsm Band12 DSM layer generated from LiDAR data class 2,4,6,9

OmosR, OmosG, OmosB Band13,14,15 are R G B channels of true color ortho-mosaic of aerial images

Keywords

Earth Observation, Remote Sensing, Hyperspectral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials

Pilot area

Aramo

Language

English

URL Zenodo

https://zenodo.org/uploads/xxxxxxxxx

Temporal reference

Acquisition date (dd.mm.yyyy)

30.09.2023; 01.10.2023

Upload date (dd.mm.yyyy)

04.02.2025

Quality and validity

Format

GeoTiff

Spatial resolution

1.2m

Positional accuracy

0.5m

Coordinate system

EPGS 4326

Access and use constrains

Use limitation

None

Access constraint

None

Public/Private

Public

Responsible organisation

Responsible Party

EUROSENSE - Esri Belux

Responsible Contact

Victoria Jadot

Metadata on metadata

Contact

victoria.jadot@eurosense.com

Metadata language

English