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390 results for “LiDAR data”
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value. </p>
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7 UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals. Errors of these values are also included. -999 represents missing values. </p>
PS116-Lidar_Data_Profiles
<p>The lidar profiles are retrieved with Klett or Raman method. Averaged periods are determined with taking into account of the continous cloud-free profiles.</p> <p>Each retrieving result consists of one *.txt and one corresponding *-info.txt file. The filename is structured as {instrument}_{date}_{starttime}-{endtime}-{smooth window}. (UTC is used as the time standard for all the analysis.)</p> <p>*.txt contains the backscatter (extinction) coefficient and some other related results. The *-info.txt contains the retrieving configuations, like retrieving method, reference height and so on.</p>
LIDAR data of the Duplin River and Blackbeard creek salt marshes
LIDAR (light detection and ranging) data were acquired on March 9-10, 2009 by the National Center for Airborne Laser Mapping (NCALM) for the Duplin River (35 km2) and Blackbeard Creek (19 km2) salt marshes. The deliverables provided by NCALM included the raw LAS point clouds and acii text files.
LIDAR data of the Duplin River and Blackbeard creek salt marshes
LIDAR (light detection and ranging) data were acquired on March 9-10, 2009 by the National Center for Airborne Laser Mapping (NCALM) for the Duplin River (35 km2) and Blackbeard Creek (19 km2) salt marshes. The deliverables provided by NCALM included the raw LAS point clouds and 1 m spatial resolution gridded digital elevation models (DEMs).
Gridded 1-hectare estimates of shrub community structure at the Jornada Basin LTER site derived from NAIP (2011) and LiDAR (2019) data
This dataset contains four raster maps of shrub community structure at the Jornada Basin LTER site in southern New Mexico U.S.A. These shrub structure estimates were created by combining an existing categorical shrub map (Ji et al. 2019) with USGS LiDAR shrub height estimates from 2019. The resulting raster dataset includes four bands of spatially aligned shrub volume, cover, height, and density estimates at one hectare resolution. Data are also included in tabular format, extracted from the 1 hectare grid upon which estimates were created. These shrub structure estimates are intended to facilitate analyses of habitat structure and community dynamics within the northern Chihuahuan Desert.
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"
<p><strong>Supplementary Material</strong></p> <p>This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>".</p> <p>The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM).</p> <p>Please cite the related article if using the data or the script.</p> <p>Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Data files for A "Boreing" Night of Observations of the Upper Mesosphere and Lower Thermosphere Over the Andes Lidar Observatory
<p>The files in this set are data obtained from the ANI2 airglow imager located at the Andes Lidar Observatory.in Chile (30.23S, 70.73W, 2530 m). The files are named for a JGR paper by J. Hecht et al. entitled A "Boreing" Night of Observations of the UpperMesosphere and Lower Thermosphere Over the Andes Lidar Observatory. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Data from: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark
<p>Environmental and biodiversity data associated with the article: Davison et al. (2023) <strong>Vegetation structure from LiDAR explains the local richness of birds across Denmark</strong>, <em>Journal of Animal Ecology</em>.</p> <p>Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below).</p> <table> <tbody> <tr> <td>Data source</td> <td>Reference</td> </tr> <tr> <td>Danish Common Bird Monitoring programme</td> <td>Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</td> </tr> <tr> <td>EcoDes-DK15 LiDAR data set of Denmark</td> <td>Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022</td> </tr> <tr> <td>Pan-European land cover map of the year 2015 </td> <td>Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001</td> </tr> <tr> <td>AVONET bird traits data</td> <td>Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</td> </tr> <tr> <td>Birds of the Palearctic - original source of trait data </td> <td>Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.</td> </tr> <tr> <td>Life-history characteristics of European birds - trait database</td> <td>Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709</td> </tr> </tbody> </table>
TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)
<p><strong>ABSTRACT</strong></p> <p>The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>'s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Spatial coverage and locations</strong></p> <p>Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are:</p> <ul> <li>SL88: 47.264083°N / 11.384986°E / 575 m MSL</li> <li>SLXR142: 47.26431°N / 11.38529°E / 613 m MSL</li> </ul> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode.</p> <p><strong>3. Instrument details</strong></p> <p><em><strong>General</strong></em></p> <p>Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70°. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired.</p> <p><em><strong>Data correction</strong></em></p> <p>No corrections were applied to the data (level0 data).</p> <p><strong>4. Data file structure</strong></p> <p><em><strong>File format</strong></em></p> <p>Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files.</p> <p><em><strong>Zip files</strong></em></p> <p>SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd).</p> <p>SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm).</p> <p><em><strong>NetCDF files for uncorrected SL88 data</strong></em></p> <p>Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data).</p> <p><em><strong>NetCDF files for SLXR142 data products</strong></em></p> <p>yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70° within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. <em>Bulletin of the American Meteorological Society,</em> <strong>101,</strong> E488–E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment.</em> Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society,</em> <strong>103,</strong> E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
LiDAR data (August 2008) for the Andrews Experimental Forest and Willamette National Forest study areas
Watershed Sciences, Inc. collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (WNF) on August 10-11, 2008. Total area of the study are is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres. This data set includes the base products delivered by Watershed Sciences, and derived products (hill shades, slope and aspect grids, and contours). The base products include the point cloud data (LAS format), and the derived bare-earth and highest-hits digital elevation models (DEM). The DEMs are at 1 meter cell size resolution. The bare-earth DEM is a representation of the topography of the area, with all the vegetation removed. The highest-hit DEM is a representation of the first object the LiDAR system struck during data capture. This includes the bare-earth topography with vegetation and structures. The vegetation DEM is the result of subtracting the bare-earth DEM from the highest-hit DEM. The elevations are the heights of the vegetation. The final products are in ESRI GRID digital format, with a 1 meter cell size resolution. Each cell in the GRID has a value that represents the modeled elevation (either total elevation or vegetation height) at that location. The resulting DEM's were used to create slope, aspect, hill shade, and contour data for the area.
LiDAR data (October 2011) for the Blue River Watershed, Willamette National Forest
Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data on October 27th, 28th and November 1st, 2011 for the United States Forest Service and Environmental Protection Agency. This study includes bare-earth, highest hit, vegetation height, and intensity grids. The raw point cloud data is available on request, due to massive size of the data. Derived entities include aspect, percent slope, hill shades (bare-earth and high hits), flow accumulation grid, and contours.
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.