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390 results for “LiDAR data”
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
OU/NSSL CLAMPS Doppler Lidar Data from LAPSE-RATE
<p>Doppler lidars transmit pulses of 1.5 um wavelength laser energy into the atmosphere, which scatters off aerosol particles and hydrometeors. The lidar measures the intensity of this return, as well as its radial velocity. The lidar has a scanner which allows the system to scan anywhere in the hemisphere, and typically a fixed scan strategy is used. The Doppler lidar data are provided in three different netCDF files: one containing the stare data (DLFP), one containing the PPI data (DLPPI), and the last containing the processed VAD data (DLVAD). These files are provided in netCDF format.</p> <p>For LAPSE-RATE, the OU DL scan strategy consisted of a 24-point plan position indicator (PPI) scan at 70 degree elevation angle, a 6-point PPI at 45 degrees, and a vertical stare. The sequence ran every 5 minutes with the stare filling in the remaining time after the two PPI scans.</p>
LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.
<p><strong>INTRO</strong><br> This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (http://fi.okfn.org/projects/biodiversity-map/)</p> <p><strong>LICENCE</strong><br> The files listed below are all released to the public domain under a CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/)</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em><strong>FILE 1:</strong></em> background.zip<br> Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland.</p> <p><br> <em><strong>FILE 2:</strong></em> conservation.lambdas<br> This file contains fitted parameters for the maxent model. For more information, check maxent documentation at https://www.cs.princeton.edu/~schapire/maxent/</p> <p><strong><em>FILE 3:</em></strong> conserved_swd.csv<br> Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv</p> <p><em><strong>FILE 4:</strong></em> grass_create_forest_rasters_from_las.sh<br> A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7.</p> <p><em><strong>FILE 5:</strong></em> lidar_coverage.png<br> A map showing the extent of LiDAR data available for Central Finland when we did the analyses.</p> <p><em><strong>FILE 6:</strong></em> maxent_model_run_product.sh<br> A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland.</p> <p><em><strong>FILE 7:</strong></em> projection_product.csv<br> The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area".</p> <p><em><strong>FILE 8:</strong></em> README<br> A file with a description of the dataset in human-readable form.</p> <p><strong>VALIDATION FILES</strong><br> The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints.</p> <p>The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste).</p> <p><em><strong>FILE 9:</strong></em> validation_felled.csv<br> A comma separated list of those points that were not measured because they were felled.</p> <p><em><strong>FILE 10:</strong></em> validation_gps_results_2016-09-07.csv<br> A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above.</p> <p><em><strong>FILE 11:</strong></em> validation_lying_deadwood_transects_2016-08-30.csv<br> A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk.<br> </p> <p><em><strong>FILE 12:</strong></em> validation_relascope_2016-08-30.csv<br> Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately.<br> </p> <p><strong>MORE INFORMATION</strong></p> <p>For more in-depth descritions of the files, read the file named README.<br> For some auxilliary files and information, check our old hackathon repository on github: https://github.com/Koalha/bdm_hackathon</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
Forest gap dynamics with repeat lidar in Berchtesgaden National Park - Data and analysis scripts
<p>This repository holds data and code for the paper: Krüger, K., Senf, C., Jucker, T., Pflugmacher, D., Seidl, R. (2024). Gap expansion is the dominant driver of canopy openings in a temperate mountain forest landscape. Journal of Ecology. <span><a href="http://doi.org/10.1111/1365-2745.14320">http://doi.org/10.1111/1365-2745.14320</a> </span></p> <p><strong>NOTE:</strong> This is a static repository, but the project might evolve. See the connected GitHub repository for latest updates!</p> <p>All data to reproduce the results are available, all other layers can be generated with the code provided in this repository. The Canopy Height Models underlying the analysis and respective processing scripts for the lidar data, are available from the corresponding author upon reasonable request. Gap layers derived from the Canopy Height Models are available in this repository.</p> <p>Empty folders are set up to follow the directory structure of the scripts. </p>
LiDAR metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands
<p>This data repository contains the LiDAR metrics generated from country-wide Airborne Laser Scanning (ALS) data from the Netherlands. The LiDAR metrics (10-meter resolution) are derived from AHN3 using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow. Raw point cloud data can be downloaded <a href="https://app.pdok.nl/ahn3-downloadpage/">here</a>. </p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Lidar Scans of the White River near Worthington, Indiana, U.S.A.: Supporting data for Martin et al. (2024)
<p>Supporting data for the manuscript "Four years of meander-bend evolution captured by drone-based lidar reveals lack of width maintenance on the White River, Indiana, USA" by Harrison K Martin, Douglas A Edmonds, and Quinn W Lewis. As of June 2024, the manuscript has been published in the <em>Journal of Geophysical Research: Earth Surface</em> and is available here: <a href="https://doi.org/10.1029/2023JF007574">https://doi.org/10.1029/2023JF007574</a>. You can find additional details in the Supplemental Information for that paper.</p> <p>Also of interest may be another recently published manuscript (in <em>Earth Surface Processes and Landforms</em>) on a pair of failed dams from central Michigan where we quantified topographic change using lidar change detection. On that study, we compared an airborne pre-flood survey to three post-flood drone-based lidar surveys we collected. The methods employed were not identical to this study (namely, there we used a point cloud to point cloud differencing method rather than the differences of DEMs used here), but there could be some helpful information in that manuscript's Supporting Info. It's available here: <a href="https://doi.org/10.1002/esp.5855">https://doi.org/10.1002/esp.5855</a>.</p> <p> </p> <p>In this repository you will find 22 bare-earth Digital Elevation Models (DEMs) of a single river bend on the meandering White River near Worthington, IN. The scans were collected over a period of ~4.5 years between April 2018 and November 2022 -- not coincidentally, nearly the same span of time as my PhD. Each DEM attempts to present the bare earth as if the vegetation were not present; the algorithms and trimming do a better job on tall, forested canopies (such as the northeastern-most part of the point bar) than on short, dense, shrubby grasses (such as certain parts of the cutbank or where crops were grown). The vegetation noise and artifacts will be the greatest in the summer months and the least in the winter. The point bar surface was always well-resolved. The actual river/water surface itself was masked out manually for each of the 22 scans, with null values defined for these and other no-data areas. The filename of each scan describes the date of collection. The cell size for each raster is 25 cm and was created by exporting a triangular lattice constructed from a ground-classified point cloud with a maximum length of 10 meters. Because of this, areas with very low point density (such as the outer boundaries of each scan, outside of the areas where we wanted to measure geomorphic changes) appear to be made of large triangles, and should not be trusted. The CRS for each is NAD83 / UTM zone 16N [https://epsg.io/26916].</p> <p> </p> <p>Please do not hesitate to reach out with any questions, requests, etc! I'm pretty responsive by email (hkm@caltech.edu) and website form (https://harrison.studies.rocks). If you have any questions about the methods, setting up your own drone-based lidar program, or are struggling with some of the arcane software and quirks of this sort of workflow... there is a chance that I've struggled through it before and am happy to share whatever I have learned!</p> <p> </p> <p>Thanks for stopping by!</p> <p> </p> <p>Acknowledgements:</p> <p>A big thanks is owed to Steve Scott of Indiana University, our stalwart drone pilot without whom none of this would have been possible. HKM was supported by National Aeronautics and Space Administration (NASA) Future Investigators in NASA Earth and Space Science and Technology (FINESST) grant 80NSSC21K1598 and a California Institute of Technology Geological and Planetary Sciences Geology Option Postdoctoral position. DAE was supported by National Sciences Foundation grant EAR-2321056. QWL was supported by a University of Waterloo New Faculty Starter Grant. All authors were supported by the Environmental Resilience Institute, funded by Indiana University’s Prepared for Environmental Change Grand Challenge initiative.</p> <p> </p> <p>UPDATES: <br>- 2024-04-29: Added Supporting Tables S1-S6.<br>- 2024-05-04: Updated some column headers in Supporting Tables S1-S6.<br>- 2024-05-08: Made public, updated the first paragraph (including changing manuscript status to accepted), and added contact information for further inquiries.<br>- 2024-06-20: Added DOI link to published manuscript in JGR:ES. Added a reference to our ESPL paper for those interested in more methodology details. Expanded the description of how the data were collected and processed, as well as my contact information, to make the repository a bit more user-friendly.</p>
Testing Data: LIDAR Driven Stemflow Mapping
<p>Data in this upload was and continues to be used for the validation of the model discussed in the paper with the following doi: <a href="http://dx.doi.org/10.2139/ssrn.4600550" target="_blank" rel="noopener">10.2139/ssrn.4600550</a>. These files are sub sections of QSMs generated using SimpleTree and constitue use cases that 1. cover then necessary functionality of the model and 2. assist in the discovery and identification of errors when the model is changed.<br><br>The git repository at the below link leverages GitHub Actions CI/CD to run a suite of 65 tests whenever a pull request is made to the main branch. Model outputs (given these files as inputs) were hand validated and are used to ensure consistent/expected model results. These outputs are stored in the below repository under 'canopyHydrodynamics/test/expected_results'.</p> <p>Repository: https://github.com/wischmcj/canopyHydrodynamics</p>
Extracting Ridge and Valley Lines in Mountainous Areas from Airborne Lidar Data by Utilizing Line Feature Strength
<p><strong><span>Background</span></strong><strong><span>:</span></strong><span> </span><span>DEMs (digital elevation models) are very important in many fields, such as in Geomatics and in water conservation of mountainous areas etc. Geomorphic feature lines are necessary data for the topography interpolation and computation from DEMs.</span></p> <p><strong><span>Methods</span></strong><strong><span>:</span></strong><span> </span><span>Instead of the parameter space, we propose a novel automatic extraction of Geomorphic feature lines in the feature space from discrete airborne LiDAR (Light detection and ranging) data by TVM (tensor voting method) developed originally for image data in this article. A tensor field for discrete airborne LiDAR points is first established and then utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line growing method based on the local maximum line feature strength is proposed in the article.</span></p> <p><strong><span>Results</span></strong><strong><span>:</span></strong><span> </span><span>Compared with the general line growing that is based on a certain threshold, our line growing method is quite effective, in particular for the extraction of primary and minor ridge and valley lines in mountainous areas.</span></p> <p><strong><span>Conclusions</span><span>:</span></strong><span> </span><span>The method presented in this paper is fast and automated and can furnish operators with a wealth of detailed information about minor line features. This will enable the extraction of ridge and valley lines tailored to specific requirements. It is no doubt that the method developed here can be generalized to a large amount of Lidar data.</span></p>
Data supporting the conclusions of Atmospheric boundary layer classification with Doppler lidar
<p>This is data set includes Doppler wind lidar quantities which were calculated from Halo Photonics Streamline measurements between 2 September 2015 and 16 November 2016 at Hyytiälä, Finland and between 1 January 2015 and 31 December 2016 at Jũlich, Germany. The data set also includes the respective boundary layer classification results generated from the calculated lidar quantities from both of the sites.</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Field data validation dataset
<p>Set of 10 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of single trees scanned around the globe, from different biomes (both forest and urban areas), using the Riegl VZ-400 terrestrial laser scanner.</p>
Terrestrial Lidar Point Cloud Data for: Evaporation and condensation dynamics within saturated epiphyte communities in a Quercus virginiana forest
<p><span>Terrestrial lidar scans were captured using a BLK360 scanner (Leica Geosystems, Norcross, GA, USA) which has a range of 0.5 – 45 m and measurement rate up to 680,000 points s<sup>−1</sup> at the high-resolution setting. A georeferenced, 3-D point cloud of the study site was generated from 12 scans, approximately 50 m apart in both horizontal directions. Scans were performed in orientations intended to maximize branch exposure to the scanner and to scan during optimal weather conditions to minimize occlusion of features due to noise or movement generated by wind. Scan co-registration was done in Leica Geosystem’s Cyclone Register 360 software using its Visual Simultaneous Localization and Mapping algorithm (Visual SLAM) and resulted in relatively low overall co-registration error ranging from 0.005-0.009 m. From this study site point cloud, manual straight-line measurements from the ground to the sensors were made using Leica’s Cyclone Register 360 software.</span></p>
Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology
<p>This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern.</p> <p>The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.</p>
Terretrial LiDAR data collected from St Pancras Old Church, Camden, UK
<p>Terrestrial LiDAR data collected by the team at University College London.</p><p>This is Version 2 containing data processed into 10 m x 10 m tiles, this has also been filtered to remove high "deviation" points.</p><p>Data is in .ply format containing xyz fields as well as reflectance, deviation, range, return number and scan position<\p></p><p><b>UCL project name</b>: 2017-07-18.001.riproject</p><p><b>Plot ID</b>: STP</p><p><b>State or region</b>: Camden</p><p><b>Date project started</b>: 7/18/2017</p><p><b>Area scanned</b>: 25,392 m2</p><p><b>Instrument</b>: UCL RIEGL VZ-400</p><p><b>Scan pattern</b>: 19 positions</p><p><b>Angular resolution</b>: 0.04</p><p><b>Images captured</b>: No</p><p><b>Links to media</b>: </p><p><b>Number of scans</b>: 38</p><p><b>Google Maps URL</b>: https://www.google.com/maps/place/The+Hardy+Tree/@51.5348275,-0.1302261,19.07z/data=!4m5!3m4!1s0x48761b22ae4a50ff:0x5ee5e6d9819cb888!8m2!3d51.5351276!4d-0.1297699</p><p><b>Publications</b>: https://doi.org/10.1186/s13021-018-0098-0, https://doi.org/10.1016/j.rse.2020.112102</p><p>For more information on the methods used to capture TLS data please refer to <a href="https://doi.org/10.1016/j.rse.2017.04.030">Wilkes et al. 2017</a></p><p>Please acknowldege the producers of this data set if using this data for publication.</p>
LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species: Code and LiDAR data
<ol> <li>Visibility (viewshed) plays a significant and diverse role in animals' behavior and fitness. Understanding how visibility influences animal behavior requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g., forests). As a result, our ability to model the influence of fine-scale visibility on animal behavior has been impeded or limited.</li> <li>In this study, we demonstrate the application of the concept of 3D cumulative viewshed in the study of animal spatial behavior at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne LiDAR to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS-collared female red deer <em>Cervus</em> <em>elaphus</em> in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer.</li> <li>We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight.</li> <li>For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine-scale visibility by a forest-dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behavior.</li> </ol>
LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species: Deer locational data
<ol> <li>Visibility (viewshed) plays a significant and diverse role in animals' behavior and fitness. Understanding how visibility influences animal behavior requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g., forests). As a result, our ability to model the influence of fine-scale visibility on animal behavior has been impeded or limited.</li> <li>In this study, we demonstrate the application of the concept of 3D cumulative viewshed in the study of animal spatial behavior at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne LiDAR to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS-collared female red deer <em>Cervus</em> <em>elaphus</em> in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer.</li> <li>We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight.</li> <li>For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine-scale visibility by a forest-dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behavior.</li> </ol>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
<p>The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. <br>To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. <br>Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies.<br>We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.</p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
ScienceDex guides
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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.