First Return Light Detection and Ranging (LIDAR) Data - 1m resolution. The dataset contains locations and attributes of first return elevations in meters. LIDAR data provided by the Joint Precision Strike Demonstration Project Office of the US Army contained bare earth rasters. This raster contains elevations of all structures, tree canopies, and bare earth. The first Lidar return reflects the highest elevations of ground features such as tree canopy, buildings, power lines, etc.

Unclassified three-dimensional point cloud by flightline and classified point cloud by 1 km tile, provided in LAZ format. Classifications follow standard ASPRS definitions. All point coordinates are provided in meters. Horizontal coordinates are referenced in the appropriate UTM zone and the ITRF00 datum. Elevations are referenced to Geoid12A.

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?

2008 Last Return Light Detection and Ranging (LIDAR) Data for Washington, DC at 1 meter resolution. The last Lidar return reflects the ground level, from which a bald-earth dataset can be extracted.

The dataset is captured over Samford Ecological Research Facility (SERF), which is located within the Samford valley in south east Queensland, Australia. The central point of the dataset is located at coordinates: 27.38572oS, 152.877098oE. The Vegetation Management Act 1999 protects the vegetation on this property as it provides a refuge to native flora and fauna that are under increasing pressure caused by urbanization.The hyperspectral image was acquired by the SPECIM AsiaEAGLE II sensor on the second of February, 2013. This sensor captures 252 spectral channels ranging from 400.7nm to 999.2nm. The last five channels, i.e., channels 248 to 252, are corrupted and can be excluded. The spatial resolution of the hyperspectral data was set to 1m.The airborne light detection and ranging (LiDAR) data were captured by the ALTM Leica ALS50-II sensor in 2009 composing of a total of 3716157 points in the study area: 2133050 for the first return points, 1213712 for the second return points, 345.736 for the third return points, and 23659 for the fourth return points.The average flight height was 1700 meters and the average point density is two points per square meter. The laser pulse wavelength is 1064nm with a repetition rate of 126 kHz, an average sample spacing of 0.8m and a footprint of 0.34m. The data were collected up to four returns per pulse and the intensity records were supplied on all pulse returns.The nominal vertical accuracy was ±0.15m at 1 sigma and the measured vertical accuracy was ±0.05m at 1 sigma. These values have been determined from check points contrived on an open clear ground. The measured horizontal accuracy was ± 0.31m at 1 sigma.The obtained ground LiDAR returns were interpolated and rasterized into a 1m×1m digital elevation model (DEM) provided by the LiDAR contractor, which was produced from the LiDAR ground points and interpolated coastal boundaries.The first returns of the airborne LiDAR sensor were utilized to produce the normalized digital surface model (nDSM) at 1m spatial resolution using Las2dem.The 1m spatial resolution intensity image was also produced using Las2dem. This software interpolated the points using triangulated irregular networks (TIN). Then, the TINs were rasterized into the nDSM and the intensity image with a pixel size of 1m. The intensity image with 1m spatial resolution was also produced using Las2dem.The LiDAR data were classified into ground" andnon-ground" by the data contractor using algorithms tailored especially for the project area. For the areas covered by dense vegetation, less laser pulse reaches the ground. Consequently, fewer ground points were available for DEM and nDSM surfaces interpolation in those areas. Therefore, the DEM and the nDSM tend to be less accurate in these areas.In order to use the datasets, please fulfill the following three requirements:

1) Giving an acknowledgement as follows:

The authors gratefully acknowledge TERN AusCover and Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, QLD for providing the hyperspectral and LiDAR data, respectively. Airborne lidar are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+LidarAirborne hyperspectral are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+Hyperspectral

2) Using the following license for LiDAR and hyperspectral data:

http://creativecommons.org/licenses/by/3.0/3) This dataset was made public by Dr. Pedram Ghamisi from German Aerospace Center (DLR) and Prof. Stuart Phinn from the University of Queensland. Please cite: In WORD:Pedram Ghamisi and Stuart Phinn, Fusion of LiDAR and Hyperspectral Data, Figshare, December 2015, https://dx.doi.org/10.6084/m9.figshare.2007723.v3In LaTex:@article{Ghamisi2015,author = "Pedram Ghamisi and Stuart Phinn",title = "{Fusion of LiDAR and Hyperspectral Data}",journal={Figshare},year = {2015},month = {12},url = "10.6084/m9.figshare.2007723.v3",

}

The LIDAR Composite First Return DSM (Digital Surface Model) is a raster elevation model covering ~99% of England at 2m spatial resolution. The first return DSM is produced from the first 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 where the first or only return was the ground.

Produced by the Environment Agency in 2022, the first return DSM is derived from data captured as part of our national LIDAR programme between 11 November 2016 and 5th May 2022. This programme divided England into ~300 blocks for survey over continuous winters from 2016 onwards. These surveys are merged together to create the first return LIDAR composite using a feathering technique along the overlaps to remove any small differences in elevation between surveys. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.

The first return DSM will not match in coverage or extent of the LIDAR composite last return digital surface model (LZ_DSM) as the last return DSM composite is produced from both the national LIDAR programme and Timeseries surveys.

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.

LiDAR_Point_Clouds, UNClassified. ELL Files swaths of flight collected data. Points are located by Elevation, Latitude and Longitude. 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: LIDAR Survey of the floodplains within Kakadu National Park conducted by Fugro Spatial Solutions for Geoscience Australia Fugro Spatial Solutions were awarded a contract by Geoscience Australia to carry out an Aerial LiDAR Survey over the Kakadu. 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?

LiDAR_Point_Clouds, Classified. ELL have been preocessed to from swaths in to tiles of data. Points are located by Elevation, Latitude and Longitude. 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: LIDAR Survey of the floodplains within Kakadu National Park conducted by Fugro Spatial Solutions for Geoscience Australia Fugro Spatial Solutions were awarded a contract by Geoscience Australia to carry out an Aerial LiDAR Survey over the Kakadu. 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?

LiDAR features for the Calhoun CZO forest plots Dataset includes LiDAR features for 35 forest plots located at the Calhoun CZO. Both LiDAR data acquisition and field measurements using LAI-2000 (LI-COR, 1992) were performed during summer 2014. LAI-2000 is an optical device used to estimate Leaf Area Index (LAI, hemisurface area of foliage per unit horizontal ground surface area) and canopy gap fraction at five zenith angles. The LiDAR data were processed to match with the LAI-2000 measurements, and thus LiDAR returns arriving at angles more than 15 degrees were excluded before any calculations were performed. Processing chain of LiDAR data included: 1) merging .las files which have forest plots near the edges, 2) calculating the ground surface, 3) removing noise, 4) removing duplicates, 5) extracting returns for the forest plots using a 15-m circle, 6) excluding returns arriving at an angle bigger than 15 degrees, 7) calculation of return heights, 8) classifying returns to ground and vegetation based on return heights (limit set to 1.37-m), 9) calculation of LiDAR features for all forest plots. The LiDAR features provided in this dataset are updated version of those presented earlier by Majasalmi et al. (2015). References: Majasalmi, T., Palmroth, S., Cook, W., Brecheisen, Z., Richter, D. (2015): Estimation of LAI, fPAR and AGB based on data from Landsat 8 and LiDAR at the Calhoun CZO. Calhoun CZO 2015 Summer Science Meeting. http://criticalzone.org/calhoun/publications/pub/majasalmi-et-al-2015-estimation-of-lai-fpar-and-agb-based-on-data-from-land/ LI-COR, 1992. LAI-2000 Plant Canopy Analyzer, Instruction manual. ftp://ftp.licor.com/perm/env/LAI-2000/Manual/LAI-2000_Manual.pdf COMMENTS: Definitions: cov = The canopy cover is computed as the number of first returns above the height cutoff divided by the number of all first returns and output as a percentage. dns = canopy density is computed as the number of points above the height cutoff divided by the number of all returns. For more details see Korhonen and Morsdorf, 2013: FCI = First echo cover index: FCI=((??Single?_Canopy+??First?_Canopy))/((??Single?_All+??First?_All)) VCC1 and VCC2 = Vertical canopy cover indices: VCC1=FCI-0.6233*?_Scan VCC2=FCI-0.0253*?_Scan*F_Max SCI = near-vertical canopy closure or Solberg's cover index: SCI=??Single?_Canopy+0.5 ((??First?_Canopy+??Last?_Canopy ))/(??Single?_All )+0.5(??First?_All+??Last?_All ) ACI = All echo cover index, similar to FCI, but take into account all echo types above the height threshold: ACI=(??All?_Canopy)/(?All) Korhonen, L., Morsdorf, F. 2013. Estimation of canopy cover, gap fraction and leaf area index with airborne laser scanning, chapter 20 in Forestry applications of airborne laser scanning concepts and case studies, pp.397-413, Springer. http://link.springer.com/chapter/10.1007/978-94-017-8663-8_20

On 19 August 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the Lidar point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

This data set represents a 2-meter resolution LiDAR first return surface or Digital Surface Model (DSM) for New Hampshire. It was derived from a statewide LAS Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The LAS Dataset was used as input to the ArcGIS "LAS Dataset to Raster" geoprocessing tool which converted the LAS first return values to raster values in the output data set. In some areas, users may notice unusual linear edges which appear unlikely or anomalous. The LiDAR vendor explained that these anomalies may be the result of changes in the degrees of tree canopy closure that occurred between the times adjacent flight lines were completed. Although leaf-off conditions were specified for data collection, strict adherence to the project specifications was not possible in all locations and exceptions occurred in order to complete data acquisition in a timely manner. As a result, abrupt discontinuities may be noticeable where data were collected on different dates. Eamples of these anomalies can be found in the areas of Cave Mountain in Bartlett and to the west of Woodstock.

With increasing wildfire frequency and severity, understanding ladder fuels—vegetation structures facilitating fire propagation from forest floors to canopies—is crucial for effective wildfire management. Active remote sensors, such as light detection and ranging (LiDAR), are able to penetrate the forest canopy and allow for the characterization of understory vegetation structures. This study investigates the use of airborne LiDAR technology for estimating ladder fuels in the Resort Municipality of Whistler, British Columbia, Canada. Normalized relative density (NRD) provides an understory LiDAR density metric by dividing the number of returns in a stratum of interest by all returns within and below the stratum of interest. NRD of returns in the 1 – 4 m height stratum was compared between treated (NRD = 0.06 ± 0.09) and control (NRD = 0.31 ± 0.27) plots and was shown to be statistically different between treatments (p = 0.0002). A model was developed using LiDAR-derived metrics—NRD and percentage of returns below the 30th height percentile (ipcumzq30)—to estimate understory stems per hectare (stems/ha) as a proxy for ladder fuel density (Adjusted R2 = 0.37). Despite the model’s relatively low explanatory power, p-values for both input variables were < 0.05, indicating significant contribution of the predictors in explaining variability in understory stem density. Estimating understory stems per hectare allows classifying ladder fuels into categories as described by the BC Wildfire Threat Assessment Guide, facilitating the identification and prioritization of areas for ladder fuel reduction treatments.

On 19 July 2012 (UTC+8), Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. The relative flight altitude is 1500 m (the elevation of 2700 m). Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm with the point cloud density 4 points per square meter. Based on the original Airborne LiDAR-DEM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.

On 25 August 2012, Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5200 m with the point cloud density 1 point per square meter. Airborne LiDAR-DEM and DSM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.

In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.

These LAS and associated files cover areas of the Payette and Boise National Forests, Idaho, and were collected for and processed by the USDA Forest Service, Moscow, ID in cooperation with Watershed Sciences Inc. of Corvallis Oregon, as a part of a project to use Lidar to study wildfire effects on forest canopy structure and hillslope erosion patterns. Data included with this set include the original .las data tiles, a shapefile delimiting the extent and placement of all of these tiles, another shapefile delimiting the total extent (boundary) of the data , and a vendor's report in PDF format detailing the data collection, processing, and error-checking steps. The data are in LAS 1.0 format with information on return number, easting, northing, elevation, scan angle, number of returns of given pulse, intensity, user data, point source ID, and GPS time.

On 25 July 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Aerial LiDAR- DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.

LiDAR (Light Detection And Ranging) is a modern survey method that produces three-dimensional spatial information in the form of a data point cloud. LiDAR is an active remote sensing system; it produces its own energy to acquire information, versus passive systems, like cameras, that only receive energy. LiDAR systems are made up of a scanner, which is a laser transmitter and receiver; a GNSS (GPS) receiver; and an inertial navigation system (INS). These instruments are mounted to an aircraft. The laser scanner transmits near-infrared light to the ground. The light reflects off the ground and returns to the scanner. The scanner measures the time interval and intensity of the reflected signals. This information is integrated with the positional information provided by the GNSS and INS to create a three-dimensional point cloud representing the surface. A LiDAR system can record millions of points per second, resulting in high spatial resolution, which allows for differentiation of many fine terrain features. Point clouds collected with LiDAR can be used to create three-dimensional representations of the Earth’s surface, such as Digital Elevation Models (DEMs) and Digital Surface Models (DSMs). DEMs model the elevation of the ground without objects on the surface, and DSMs model ground elevations as well as surface objects such as trees and buildings. LidarBC's Open LiDAR Data Portal (see link under Resources) is an initiative to provide open public access to LiDAR and associated datasets collected by the Government of British Columbia. The data in the portal is released as Open Data under the Open Government Licence – British Columbia (OGL-BC). Four Government of British Columbia business areas and one department of the Government of Canada make LiDAR data available through the portal: * GeoBC * Emergency Management and Climate Readiness (EMCR) * BC Timber Sales (BCTS) * Forest Analysis and Inventory Branch (FAIB) * Natural Resources Canada (NRCan) GeoBC is the provincial branch that oversees and manages LidarBC’s Open LiDAR Data Portal, including storage, distribution, maintenance, and updates. Please direct questions to LiDAR@gov.bc.ca.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface.

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

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

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