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390 results for “LiDAR data”
Data from: LiDAR-derived canopy structure supports the more-individuals hypothesis for arthropod diversity in temperate forests
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Data from: Contrasting patterns of population connectivity between regions in a commercially important mollusc Haliotis rubra: integrating population genetics, genomics and marine LiDAR data
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LiDAR-derived tree architecture data of geo-located tropical trees in Luquillo experimental forest, Puerto Rico
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Data from: From lidar waveforms to vegetation products: 7380 km2 of high-resolution airborne and simulated GEDI data over Sierra Nevada, California
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Data from: LiDAR-derived digital elevation model of Whale's Tail Marsh, San Francisco Bay, 2019
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Terrestrial lidar data collected from four large tropical rainforest trees in Floresta Nacional de Caxiuanã
<p>Title<br> -----</p> <p>Terrestrial lidar data collected from four large tropical rainforest trees in Floresta Nacional de Caxiuanã</p> <p>Authors<br> -------</p> <p>A. Burt<br> M. Boni Vicari<br> A. C. L. da Costa<br> I. Coughlin<br> P. Meir<br> L. Rowland<br> M. Disney</p> <p>Contact<br> -------</p> <p>a.burt@ucl.ac.uk</p> <p>License<br> -------</p> <p>These data are distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC BY 4.0) - see the LICENSE file for details.</p> <p>Overview<br> --------</p> <p>Terrestrial lidar data were acquired from four large tropical rainforest trees prior to harvest (diameter range: 0.6-1.2m, height range: 30-46m) in a natural closed forest stand in Floresta Nacional de Caxiuanã, Pará, Brazil (approx. coordinates in the WGS-84 datum: -1.798, -51.435 degrees), during August/October 2018.<br> This dataset includes: i) raw lidar data, ii) tree-level point clouds, and iii) quantitative structural models.<br> A complete description of the four trees, these data, and the companion destructive harvest data can be found in our paper entitled: ‘New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar’.</p> <p>Acquisition<br> -----------</p> <p>Neighbouring vegetation surrounding each tree was removed before data collection.<br> Lidar data were acquired using a RIEGL VZ-400 terrestrial laser scanner.<br> A minimum of 16 scans (upright and tilt) were collected from 8 scan positions around each tree.<br> The angular step between sequentially fired pulses was 0.04 degrees, and the distance between scanner and tree varied.<br> This arrangement provided a 45 degree sampling arc around each tree, and a complete sample of the scene from each position.<br> The laser pulse has a wavelength of 1550nm, a beam divergence of 0.35mrad, and the diameter of the footprint at emission is 7mm.<br> The instrument was in ‘High Speed Mode’ (pulse repetition rate: 300kHZ), ‘Near Range Activation’ was off (minimum measurement range: 1.5m), and waveforms were not stored. </p> <p>Processing<br> ----------</p> <p>i) Individual scans were registered onto a common coordinate system using RIEGL RiSCAN PRO (v2.7.0, http://riegl.com).<br> ii) Tree-level point clouds were extracted from the larger-area point cloud using treeseg (v0.2.0, https://github.com/apburt/treeseg).<br> iii) Points were classified as returns from wood or leaf material using TLSeparation (v1.2.1.5, https://github.com/TLSeparation).<br> iv) Points from buttresses were manually removed using CloudCompare (v2.10.3, https://cloudcompare.org).<br> v) Quantitative structural models were constructed using TreeQSM (v2.3.2, https://github.com/InverseTampere/TreeQSM) via optqsm (v0.1.0, https://github.com/apburt/optqsm). </p> <p>File and directory naming convention<br> ------------------------------------</p> <p>The four trees are identified: CAX-H_T1, CAX-H_T2, CAX-H_T3 and CAX-H_T4.<br> The various files and directories are described as follows: </p> <p>./CAXH-H/<br> ├───CAX-H_T1/ (Directory: tree-level directories)<br> ├───CAX-H_T2/<br> ├───CAX-H_T3/<br> ├───CAX-H_T4/<br> │ ├───2018-10-06.001.riproject/<br> │ │ ├───ScanPos001/ (Directory: individual scan directories containing raw lidar data and other auxiliary files; odd: upright, even: tilt)<br> │ │ ├───ScanPos.../<br> │ │ ├───ScanPos020/<br> │ │ │ ├───181006_194253.rxp (File: measurement data stream)<br> │ │ │ ├───181006_194253.mon.rxp (File: monitoring data stream) <br> │ │ ├───matrix/ (Directory: contains the registration matrices)<br> │ │ │ ├───001.dat<br> │ │ │ ├───....dat<br> │ │ │ ├───020.dat (File: 3x4 matrix used to rotate and translate scan 20 into the coordinate system of scan 1)<br> │ │ ├───clouds/ (Directory: contains tree-level point clouds)<br> │ │ │ ├───CAXH_T4.txt (File: point cloud of CAX-H_T4 as extracted by treeseg)<br> │ │ │ ├───CAXH_T4nb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare)<br> │ │ │ ├───CAXH_T4w.txt (File: CAXH_T4.txt with leafy returns removed using TLSeparation)<br> │ │ │ ├───CAXH_T4l.txt (File: CAXH_T4.txt with woody returns removed using TLSeparation)<br> │ │ │ ├───CAXH_T4wnb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare, and leafy returns removed using TLSeparation)<br> │ │ ├───models/ (Directory: contains quantitative structural models constructed from the tree-level point clouds)<br> │ │ │ ├───CAXH_T4.mat (File: quantitative structural model of CAXH_T4.txt)<br> │ │ │ ├───CAXH_T4nb.mat<br> │ │ │ ├───CAXH_T4w.mat<br> │ │ │ ├───CAXH_T4wnb.mat<br> │ │ │ ├───CAXH_T4.models.dat (File: reports the volume (m3) and standard deviation (m3) of the QSMs)<br> │ │ │ ├───intermediate/ (Directory: contains intermediate QSMs generated by optqsm)<br> │ │ │ │ ├───CAXH_T4/<br> │ │ │ │ ├───CAXH_T4nb/<br> │ │ │ │ ├───CAXH_T4w/<br> │ │ │ │ ├───CAXH_T4wnb/<br> │ │ │ │ │ ├───CAXH_T4wnb-1.mat<br> │ │ │ │ │ ├───CAXH_T4wnb-....mat<br> │ │ │ │ │ ├───CAXH_T4wnb-10.mat</p>
Penzance Harbour, from Airborne LiDAR data
Model of Penzance Harbour for creating the Re-gen Video Source: Objaverse 1.0 / Sketchfab
Observational Data for Lidar and GPS soundings
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Lidar, FPI and ionosonde data during the 05 November 2023 Geomagnetic storm
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Fatiando a Terra Data: British Columbia, Canada - Lidar point cloud
<p>This is a point cloud sliced to the small <a href="https://apps.gov.bc.ca/pub/bcgnws/names/21973.html">Trail Islands</a> to the North of Vancouver to reduce the data size. The islands have some nice looking topography and their isolated nature creates problems for some interpolation methods, like simple nearest neighbours.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Cut out a section of the original data to focus only on these small islands; Change UTM coordinates to geodetic latitude and longitude (WGS84); Keep only the ground reflection picks; Export to a compressed CSV for easier loading with Pandas.</p> <p><strong>Source: </strong><a href="https://www2.gov.bc.ca/gov/content/data/geographic-data-services/lidarbc">LidarBC</a></p> <p><strong>Source license: </strong><a href="https://www2.gov.bc.ca/gov/content/data/open-data/open-government-licence-bc">Open Government Licence - British Columbia</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/british-columbia-lidar">https://github.com/fatiando-data/british-columbia-lidar</a></p>
Lidar data for snowex hackweek
<p>This is a lidar data for 2022 snowex hackweek tutorial</p>
YUTO MMS Dataset, Sequence B (Lidar data, Calibration files, Groundtruth, GPS, IMU, Timestamp)
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Supporting Data for Generating Realistic and Diverse Tests for LiDAR-Based Perception Systems
<p>Supporting data for ICSE 2023 paper "Generating Realistic and Diverse Tests for LiDAR-Based Perception Systems".</p> <p> </p> <p>The "selected_data.tar.xz" file contains a small subsample of the <a href="http://www.semantic-kitti.org">SemanticKITTI data set</a> for use in demonstration of the capabilities of the research prototype tool used in the evaluation. The SemanticKITTI data set is released under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License, which permits redistribution under the same terms.</p> <p>The "study_data_compressed.tar.xz" file contains the intermediate results obtained during the study conducted in the paper. We provide scripts to generate the figures and tables from the paper given this data.</p> <p> </p> <p>The</p>
SJSU Doppler Lidar Data
<p>Continuous scanning mode (CSM) data files from a Halo Photonics Doppler lidar</p>
Data from: Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR
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First ISCCP Regional Experiment (FIRE) Cirrus 2 Volume Imaging Lidar (VIL) Cirrus Scan Data
The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.This data set contains images of cirrus cloud scans of 120 km extent both along the wind and across the wind (at the cirrus clouds heights). These images were collected during FIRE Cirrus 2 in Coffeyville, Kansas.
Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the SAGE III Ozone Loss and Validation Experiment (SOLVE)
LASE_SOLVE is the Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the SAGE III Ozone Loss and Validation Experiment (SOLVE) data product. Data collection for this data set is complete.The LASE SOLVE field experiment was conducted in the Arctic during November 1999 to March 2000 with the scientists based above the Arctic Circle at the airport in Kiruna, Sweden. Measurements of stratospheric composition over the Arctic were made using a large suite of instruments aboard several European aircraft, as well as on NASA's DC-8 and ER-2 aircraft. Additionally, balloons and ground-based instruments also took atmospheric readings and scientists gathered ozone-related data to use in validating measurements by the SAGE III instrument aboard the Russian Meteor-3 satellite. LASE airborne lidar produced measurements of aerosols and water vapor vertical profiles from the aircraft altitude (6-8 km) down to the surface. SOLVE was a measurement campaign designed to examine the processes which control polar to mid-latitude stratospheric ozone levels. The goal of SOLVE was for its results to expand the understanding polar ozone processes to provide greater confidence in ozone monitoring capabilities.
CMS: LiDAR Data for Mangrove Forests in the Zambezi River Delta, Mozambique, 2014
This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.
Wind Profile Data: LIDAR - NOAA (FIFE)
The aim of this wind profile study was to derive wind profiles and momentum fluxes from the National Oceanic and Atmospheric Administration (NOAA)/Wave Propagation Laboratory (WPL) Doppler LIDAR, and compare LIDAR and airborne measurements of mean wind, turbulent structure, momentum flux, and heat flux. Another objective was to compare profiles of mean wind and temperature obtained from aircraft, balloon sondes, and wind LIDAR. These data were collected at one location near the center of the FIFE study area but in the northwest quadrant. Data were acquired for a two week period during June and July 1987. Pulsed Doppler LIDAR measures the radial (along-beam) velocity as a function of range using light-scattering particles in the air as tracers. When the LIDAR beam is directed straight upward and the backscattered return as a function of height is recorded, vertical aerosol profiles may be determined. Various pointing and scanning schemes permit measurement of a variety of mean and turbulent quantities based on assumptions about the flow. The remote-sensing character of LIDAR offers the ability to measure flow parameters simultaneously at all the heights in a profile. The winds were obtained with the VAD (Velocity Azimuth Display) technique. The LIDAR only operates above 500 m, therefore the wind profile begins above the ground surface. Data in the planetary boundary layer are usually continuous, but gaps appear occasionally in profiles extending to several kilometers. Profiles were unsmoothed, and the LIDAR's short pulse made adjacent data points almost independent.
CALIPSO Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard V4-21
CAL_LID_L3_Tropospheric_APro_CloudFree-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Tropospheric Aerosol Profiles, Cloud Free Data, Standard Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is ongoing.The CALIPSO lidar level 3 aerosol data product reports monthly mean profiles of aerosol optical properties on a uniform spatial grid. It is intended to be a tropospheric product, so data are only reported below altitudes of 12 km. All level 3 parameters are derived from the version 4.21 CALIOP level 2 aerosol profile product and have been quality screened before averaging. The primary quantities reported are vertical profiles of the aerosol extinction coefficient at 532nm and its vertical integral, the aerosol optical depth (AOD). Aerosol type and spatial distribution information are also included. Averaged profile data is reported for all aerosols, regardless of type, and for mineral dust aerosols only. Classification of dust is based on the aerosol-type flags in the level 2 profile product. To keep level 3 file sizes manageable, four different types of level 3 files are produced, depending on the sky condition and the temporal coverage of the data prior to averaging.Description of the Four Sky Conditions (Day, Night)1) All Sky: All level 2 columns are averaged, regardless of cloud occurrence2) Cloud-Free: Only cloud-free level 2 columns are averaged3) Cloudy-Sky, Transparent: Only level 2 columns containing transparent clouds are averaged4) Cloud-Sky, Opaque: Only level 2 columns containing opaque clouds are averagedCALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'études spatiales).
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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