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419 results for “airborn”
NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>This particular data set was used in a publication by Studinger <em>et al.</em>, 2022 (<a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>) that developed methods for estimating water depths of supraglacial lakes on the Greenland Ice Sheet.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM waveform data from the T6 and T7 lidars, as well as the true ranges and a MATLAB® function to read ground test waveform data.</p> <p><strong><strong>See also:</strong> </strong></p> <p>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Airborne EM data (Belgium) from flight line 306025
<p>This dataset contains Airborne EM data from a SkyTEM instrument from the Flanders region, Belgium. </p> <p>Details about the instrument set-up can be found in the data report.</p> <p>Details about the region, geology, saltwater intrusion context can be found in </p> <p>Delsman, J., van Baaren, E., Vermaas, T., Karaoulis, M., Bootsma, H., de Louw, P. G. B., ... & Thofte, S. (2019). TOPSOIL Airborne EM kartering van zoet en zout grondwater in Vlaanderen (FRESHEM Vlaanderen: Deelopdrachten 1 tot en met 3.</p> <p><strong>When using this dataset, always cite the above reference. </strong></p> <p>The actual measured data is in "dat_skytem_306025_flightline.csv". Columns refer to either Low or High moment and time of measurement. Corresponding estimated relative errors can be found in rel_err_skytem_306025_flightline. Distances between soundings/measurment locations and their hieghts above the surface is found in "distances_between_soundings" and "altitudes_per_sounding" respectively.</p> <p>To use this data for the appraisal method, see https://github.com/WouterDls/AEM_appraisal. For more information, write to wouter.deleersnyder@kuleuven.be </p>
Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia
<p>Plot-level mapping data derived from the Global Airborne Observatory mission in Sabah (Borneo), Malaysia in 2016. Use of these data requires citation of this publication as well as this dataset as follows:</p> <p>Ordway E.M., G.P. Asner, D. Burslem, S. Lewis, R. Martin, R. Nilus, M.J. O’Brien, O. Phillips, L. Qie, N.R. Vaughn, and P.R. Moorcroft. 2022. Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits. <em>Communications Earth & Environment.</em></p> <p>Asner, G.P., E. Ordway, J. Heckler, and N.R. Vaughn. 2022. Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia (1.0) [Data set]. <em>Zenodo</em>. https://doi.org/10.5281/zenodo.7051897</p> <p>Data details:</p> <p>(1) Data cover the Danum and Sepilok sites as described in Ordway et al. (2022) <em>Communication Earth and Environment. </em>All data layers are presented in GeoTIFF format.</p> <p>(2) ACD = Aboveground carbon density in units of Mg C per hectare at 30 meter spatial resolution, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p>(3) LAD = Leaf area density in units of m2 per m3 at 50 meter spatial resolution.</p> <p>(4) chems = Leaf chemical traits at 4 meter spatial resolution, as described in <em>https://www.mdpi.com/2072-4292/10/2/199</em></p> <p>(5) TCH = top-of-canopy height in units of meters, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p> </p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>The purpose of this data set is to enable users working with NASA’s ATM airborne lidar waveform data to estimate their own range calibration if using range tracking methods different from the centroid estimate included in the ATM data files (Studinger <em>et al.</em>, 2022; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>). The airborne data are freely available from the National Snow and Ice Data Center (<a href="https://nsidc.org/home">NSIDC</a>) at the links listed in the table below. </p> <p>In pressurized aircraft the transmitted laser pulse travels through the aircraft’s optical window close to the scan mirror. Backscatter from both the scan mirror and the aircraft’s optical window in the fuselage are close in time to the transmitted laser pulse and partially overlap with the transmit waveform recorded by the ATM’s optical receiver. To record a “clean” transmit waveform the transmit pulse is sampled from behind a translucent beam splitter and subsequently injected into a multimode fiber-optic cable to provide a fixed optical delay that results in temporal separation between the recorded transmit pulse and contamination from backscattered photons from the scan mirror and the aircraft’s optical window. The delay due to the optical fiber and other system components introduce a laser time-of-flight range bias. The signal strength can also affect the calculated range in a way that depends on the waveform tracking algorithm. This variable influence, known as range walk, and the bias are determined from ground calibration measurements in which ATM data is collected from a stationary target at known range (true range) while varying the return intensity from signal extinction to detector saturation. The resulting signal-dependent deviation of measured range from the true range combine the system delay and range walk correction and are subtracted from the uncalibrated ranges to yield the calibrated range estimates "/laser/calrng" in the airborne waveform files (Studinger <em>et al.</em>, 2022; Appendix B3; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>).</p> <p>This data set includes ATM ground test waveform data from the wide and narrow scanner, the true ranges, as well as the range calibration tables (caltables) used for processing the airborne data products. A MATLAB® function to read the ground test waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6248436">https://doi.org/10.5281/zenodo.6248436</a></p> <p> </p> <table> <tbody> <tr> <td> <p><strong><strong>ATM Data Product ID at NSIDC</strong></strong></p> </td> <td> <p><strong><strong>Temporal Coverage</strong></strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilatmw1b">https://nsidc.org/data/ilatmw1b</a></strong></p> </td> <td> <p><strong>17 July 2017 - 20 November 2019</strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilnsaw1b">https://nsidc.org/data/ilnsaw1b</a></strong></p> </td> <td> <p><strong>29 October 2017 - 20 November 2019</strong></p> </td> </tr> </tbody> </table> <p>The file name of the groundtest calibration table that was used to process the airborne data is stored in the field "/ancillary_data/documentation/header_text" of each airborne data file. The corresponding groundtest waveform file has the same time tag and data set identifier as the calibration table file. E.g., the corresponding ground test waveform file for the calibration table “caltable_20170628_193814.atm6AT5.binned_data.txt” is “ILATMW1B_20170628_193814.atm6AT5.h5”. The table below lists the ground test waveform files that should be used for each campaign and instrument:</p> <table> <tbody> <tr> <td> <p><strong><strong>Year</strong></strong></p> </td> <td> <p><strong><strong>Campaign</strong></strong></p> </td> <td> <p><strong><strong>Data Set</strong></strong></p> </td> <td> <p><strong><strong>Groundtest Waveform Data</strong></strong></p> </td> </tr> <tr> <td> <p><strong><strong>2017</strong></strong></p> </td> <td> <p><strong>17-JUL-2017 25-JUL-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20170628_193814.atm6AT5.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20171017_141954.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20171208_122808.atm6BT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2018</strong></strong></p> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20180309_110944.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20181002_151220.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20180301_115659.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20181002_160127.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2019</strong></strong></p> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190321_102239.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190817_132201.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20191017_121209.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190327_105730.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190817_131026.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20191017_122059.atm6DT7.h5</strong></p> </td> </tr> </tbody> </table> <p> </p> <p><strong><strong>See also:</strong> </strong></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Global Airborne Observatory: Submarine Groundwater Discharge on West Hawaii Island
<p>Mapped submarine groundwater discharge (SGD) for the west coast of Hawaii Island. A full description of the data source and methodology is available at:</p> <p>Asner, G.P., N.R. Vaughn, and J. Heckler. 2024. Operational mapping of submarine groundwater discharge into coral reefs: Application to West Hawaii Island. Oceans 5, 547-559. https://doi.org/10.3390/oceans5030031</p> <p>There are two data layers:</p> <ol> <li>Estimated point sources of SGD</li> <li>Estimated dischage areas of SGD </li> </ol> <p>SGD discharge point sources as a point layer and SGD discharge areas are provided as polygon layers, both in GeoJSON format. The point source layer includes a Field LocCertainty, describing the certainty in percent that the discharge location is correctly identified. The discharge area layer includes fields InsideC and OutsideC, which average thermal sensor temperature in Celsius inside and outside, respectively, as well as fields for the temperature difference (dTemp) and the size of the dicharge area in hectares (ha). Both layers use the WGS84 coordinate system with spatial coordinates giving positions as degrees longitiude and latitude.</p>
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
Dataset of "Exposure to airborne SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures."
<p>The authors highly appreciate being contacted if the data is to be used for any purpose.</p> <p>The following data set was used in the study entitled "<strong>Exposure to airborne </strong><strong>SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures.</strong>" and published in <em>Heliyon</em> Journal.</p> <p>This study characterized the transmission dynamics of airborne SARS-CoV-2 in normal and intensive care units. The data were collected over the period of 2020. In total, 165 and 62 air and environmental samples, respectively, were collected in four COVID-19 wards and ICUs in Cyprus and analyzed by RT-PCR. The comparison between RT-PCR and an alternative method for SARS-CoV-2 detection in air that provides comparable results but is less cumbersome and time demanding, is also given in the tab "Comparison with BELD".</p> <p>The data from sampling airborne SARS-CoV-2 using a MOUDI impactor are not included in this document but can be found in the supplement of the relevant publication.</p> <p>Please refer to the manuscript and its supplementary material for more information about how the data was collected. </p> <p> </p>
Global Airborne Observatory: Mapped Infrastructure on Hawaii Island
<p>There are three mapping datasets showing infrastructure on Hawaii Island (Hawaii County) in the State of Hawaii. The datasets were created using a combination of a convolutional neural network (CNN) and gradient boosted trees (GBT) run on Global Airborne Observatory laser scanning and imaging spectroscopy data.. Full methodology is available in the following reference:</p> <p>Mason, R.E.; Vaughn, N.R.; Asner, G.P. Mapping Buildings across Heterogeneous Landscapes: Machine Learning and Deep Learning Applied to Multi-Modal Remote Sensing Data. <em>Remote Sensing </em><strong>2023</strong>, <em>15</em>, x. https://doi.org/10.3390/xxxxx</p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Airborne Infrasound Data from The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct
<p>Airborne infrasound data including waveform recordings from two payloads attached to a single 6 m heliotrope that was launched at dawn (~0700 local) out of Belen Regional Airport, NM, USA. Balloon trajectory is also included. This data accompanies the publication titled, "The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct" submitted to Earth & Space Science.</p>
Peat depth and occurrence in areas covered by airborne radiometric surveys, southeastern and western Norway, 1983-2023.
This data package contains point measurements of peat depth and occurrence from two study sites in Norway: Skrimfjella, located in eastern Norway, and Ørskogfjellet, located in western Norway. These sites represent contrasting physical geographies, with diverse bedrock and landscape types, including inland hills and mountains, coastal plains, and inland valleys. The data were collected primarily during field campaigns in August 2020 (Skrimfjella) and August 2023 (Ørskogfjellet). The data package also contains peat depth measurements from beyond these primary field campaigns. These ancillary data were provided by the Norwegian Public Roads Administration and the Norwegian Institute of Bioeconomy Research. Peat depths were measured using manual probing, boreholes, and ground-penetrating radar (GPR). The data package is intended for use in digital soil mapping (DSM) applications at the landscape-regional scale, particularly for predicting peat depth and assessing relationships between peat depth and environmental variables. It supports research on peatland carbon stocks, land use planning, and climate change mitigation. The peat depth measurements are intended to be representative of sampled locations within the study areas, with a focus on peatland environments. The data package does not include comprehensive peatland extent mapping but provides insights into the spatial variability of peat depths in Norwegian landscapes.
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>
TVC Experiment 2018/19: UMass Airborne Ku-Band SAR data
<p>This dataset contains the processed, backscatter data from the airborne UMass Ku-Band SAR data, as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These measurements were collected to evaluate coincident ground snow data, airborne and satellite radar measurements to better understand snow-radar interactions in a tundra environment. The measurements were acquired around the Trail Valley Creek research station (https://www.trailvalleycreek.ca/) in Northwest Territories, Canada. Three periods of ground measurement, coincident with these airborne measurements, took place in November 2018, January 2019, and March 2019. The dataset consists of processed imagery with 100m averaged backscatter data and incidence angle data around all surveyed sites. Flight information and coincident field measurement metadata are also included.</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</p>
Fatiando a Terra Data: Britain - Airborne total-field magnetic anomaly
<p>This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean 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>Datum was changed to WGS8; Year was separated from the survey name; Some fields were dropped; Exported to compressed CSV format.</p> <p><strong>Source: </strong><a href="https://www.bgs.ac.uk/datasets/gb-aeromagnetic-survey/">British Geological Survey</a></p> <p><strong>Source license: </strong><a href="https://www.bgs.ac.uk/bgs-intellectual-property-rights/open-government-licence/">Open Government Licence</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/britain-magnetic">https://github.com/fatiando-data/britain-magnetic</a></p> <p>Contains British Geological Survey materials © UKRI 2021.</p>
Fatiando a Terra Data: Osborne Mine, Australia - Airborne total-field magnetic anomaly
<p>This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.</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>Change the horizontal datum from GDA94 to WGS84. Convert terrain clearance to flight height using an SRTM grid. Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID. Cut to a smaller region containing only the 2 anomalies of interest.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/osborne-magnetic">https://github.com/fatiando-data/osborne-magnetic</a></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>
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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.