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61 results for “topographic data”
Scarp height data and topographic profiles from the Zomba Graben, southern Malawi
<p>Measurements of fault scarp height from five faults in the Zomba Graben, southern Malawi (Table S1-S6). Topographic profiles used to measure the height of the scarp are in Tables S7-S11.</p> <p>For more details of this dataset please refer to Wedmore, L. N. J., Biggs, J., Williams, J. N., Fagereng, Å. Dulanya, Z., Mphepo, F., & Mdala, H. (2020). Active fault scarps in southern Malawi and their implications for the distribution of strain in incipient continental rifts. <em>Tectonics</em>, 39(3), <a href="https://doi.org/10.1029/2019TC005834">https://doi.org/10.1029/2019TC005834</a></p> <p> </p> <p>Please contact the author, Luke Wedmore (luke.wedmore@bristol.ac.uk) for more details.</p>
Topographic data and orthomosaics of the Noordwest Natuurkern project
<p>This data set contains digital elevation models and orthomosaics of the Noordwest Natuurkern region near Bloemendaal aan Zee, the Netherlands. High-frequency wind and rain data are also provided. The data descriptor paper can be found <a href="https://doi.org/10.3390/data9020037" target="_blank" rel="noopener">here</a>, and provides full details of data collection and processing, file format, file naming, and so on. The point clouds from which the elevation models were computed, are not available in this data base but can be obtained from the data creator upon reasonable request.</p> <p>v2.1.0: 41 digital elevation models (20080510 - 20241112; 1x1 m) and 27 orthomosaics (20130501 - 20241112; 1x1 and 0.05x0.05 m) - meta data file - wind and rain data - updated from v2.0.0 on January 8, 2025 (see ChangeLog.txt)</p> <p> </p>
Topography extraction and topographic change measurements using PlanetScope data: Shisper Glacier (Pakistan)
<p>The study area is located in the north flank of Hunza Valley in the Central Karakoram. Shisper glacier covers ~53.7 km2 at an elevation range of 2567-611 m a.s.l. It is a surge-type glacier, which has recently gained the attention of the scientific community and disaster response agencies during its surge. In 2018, the glacier surged beyond the confluence with the outlet stream of Mochwar glacier. The blockage resulted in the creation of a lake, which then drained causing a GLOF (Glacial Lake Outbreak Flood) and has recently begun reforming. The melt water from the two glaciers feeds hydropower plants in the Hunza valley and is a major source of fresh water for agriculture. Glacier-related hazards threaten both the town of Hassanabad and the Karakoram Highway, the only paved road through the mountain range. Here we show the potential of CubSat data to monitor such glaciers, which are not easily accessible to field observation and their potential impact on power generation, water resources and infrastructure.</p> <p>We use multi-date L1B DOVE-C PlanetScope data to extract two DEMs in 2017 and 2019 over the study area in order to compute the elevation difference caused by the glacier surge.</p> <p>Supplementary material for our paper: Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery.</p>
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>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
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>
Dataset - Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Topographic Influences
<p>This dataset provides three products:</p> <p><strong>1. The topographic data. </strong></p> <p>These data are provided as GeoTIFF files for Europe with 1km x 1km spatial resolution. These maps include:</p> <ul> <li>Digital Elevation Model (DEM) map: Europe_DEM.tif</li> <li>Slope map: Europe_slope.tif</li> <li>Aspect map: Europe_aspect.tif</li> <li>Topographic Position Index (TPI) with a 5 km radius map: Europe_TPI_5.tif</li> <li>Topographic Position Index (TPI) with a 75 km radius map: Europe_TPI_75.tif</li> <li>Terrain Diversity Index (TDI) map: Europe_TDI.tif</li> </ul> <p>These data can be used as input maps for the preprocessing step. In addition, the two TPI maps can also be used in the regression process.</p> <p><strong>2. The resulting map of the preprocessing step.</strong> </p> <p>This map offers predictions on the quality of ERA5 data across Europe and is also provided as a GeoTIFF file with 1km x 1km spatial resolution under the name:</p> <ul> <li> Europe_classification.tif</li> </ul> <p>In this map, Class1 represents a good ERA5 quality with an RMSE of less than 1.5 m/s, Class2 represents a moderate ERA5 quality with an RMSE bigger than 1.5 m/s but less than 3 m/s, while Class 3 indicates a poor ERA5 quality with an RMSE greater than 3 m/s.</p> <p><strong>3. The downscaled wind speed time series data. </strong></p> <p>Europe has been divided into 64 equal area blocks to accommodate the large data size. Each downscaled dataset is provided as a NetCDF file, offering hourly wind speed time series for a year (8760 hours) at approximately 1km x 1km spatial resolution. Each NetCDF file has three dimensions: 'lon' representing longitude, 'lat' representing latitude, and 'time' representing the hour. The variable name for wind speed in the NetCDF file is 'WindSpeed'. The 'WindSpeed' variable is stored as an Int32 data type in the NetCDF file, with values multiplied by 10000 in order to significantly reduce the data size. To utilize this variable, please divide it by 10000.</p> <p>For regions identified as Class1 and Class2, the downscaled wind speed is obtained through a simple nearest neighbour spatial interpolation of ERA5 due to the good quality of ERA5 in these regions. However, for the regions identified as Class3, the downscaled wind speed is derived using the machine learning-based regression approach described in the relevant publication. The geographic extent and the visual representation for each block are provided in 'Readme.pdf' document.</p> <p> </p> <p>To cite this dataset, please cite our published paper in Environmental Research Letters (<strong>DOI:</strong> 10.1088/1748-9326/aceb0a)</p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.
Data from : Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics
<p>These datasets have been used in the following paper:</p> <p>Ibanez, T., Bauman, B., Aiba, S.-i., Arsouze, T. Bellingham, P.J., Birkinshaw, C., Birnbaum, P., Curran, T.J., DeWalt, S.J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T.S., Menkes, C. Metcalfe, D.J., Murphy, H., Muscarella, R., Plunkett, G.M., Sam, C., Tanner, E., Taylor, B.N., Thompson, J., Ticktin, T., Tuiwawa, M.V., Uriarte, U., Webb, E.L., Zimmerman, J.K., Keppel, G. Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. Accepted for publication in <em>Global Change Biology</em>.</p> <p>Data users are invited to cite this paper and the original paper(s) corresponding to the data they use (see "Reference" column in each dataset). We also encourage potential users to contact the data owners for collaboration.</p> <p>These datasets are compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide. Damage are given at the tree (whether or not each tree has been uprooted or snapped) and at the plot level (number of uprooted or snapped trees in each plot).</p> <p>MSW: Maximum sustained wind speed (m.s-1)</p> <p>EXP: Topographical exposure to wind</p> <p>DBH: Diameter at breast height (cm)</p> <p>WD: Wood density (g.cm-3)</p>
Supplementary Material 7 including Salamandra salamandra occurrence data, topographic, geological and land cover data and node-based resistances
<p>Supplementary material for the article "Habitat connectivity supports the local abundance of fire salamanders (Salamandra salamandra) but also the spread of Batrachochytrium salamandrivorans" by Bolte <em>et al</em>. (2023) published in Landscape Ecology (DOI: 10.1007/s10980-023-01636-8)</p> <p>This folder comprises a .shp file with fire salamander occurrences, topographic and land cover data (GeoTiff) from the northern Eifel region as well as the R Code used for the statistical analysis of salamander habitat suitability and connectivity.</p>
Supporting data for: Topographic information improves simulated patterns of post-fire conifer regeneration in the Southwest U.S.
<p>The western U.S. is projected to experience more frequent and severe wildfires in the future due to drier and hotter climate conditions, exacerbating destructive wildfire impacts on forest ecosystems such as tree mortality and unsuccessful post-fire regeneration. While empirical studies have revealed strong relationships between topographical information and plant regeneration, ecological processes in ecosystem models have either not fully addressed topography-mediated effects on the probability of plant regeneration, or the probability is only controlled by climate-related factors, e.g., water and light stresses. In this study, we incorporated seedling survival data based on a planting experiment in the footprint of the 2011 Las Conchas Fire into the PnET extension of the LANDIS-II model by adding topographic and an additional climatic variable to the probability of regeneration. The modified algorithm included topographic parameters such as heat load index (HLI) and ground slope and spring precipitation. We ran simulations on the Las Conchas Fire landscape for 2012–2099 using observed and projected climate data (i.e., RCP 4.5 and 8.5). Our modification significantly reduced the number of regeneration events of three common southwestern conifer tree species (piñon, ponderosa pine and Douglas-fir), leading to decreases in aboveground biomass, regardless of climate scenario. The modified algorithm decreased regeneration at higher elevations and increased regeneration at lower elevations relative to the original algorithm. Regenerations of three species also decreased on eastern aspects. Our findings suggest that ecosystem models may overestimate post-fire regeneration events in the southwest U.S. To better represent regeneration processes following wildfire, ecosystem models need refinement to better account for the range of factors that influence tree seedling establishment. This will improve model utility for projecting the combined effects of climate and wildfire on tree species distributions.</p>
Physiology data for: Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg
<p>Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Because they are located within complex and dynamic peripheral tissues, the underlying mechanisms of proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing, we found that proprioceptor subtypes in the <em>Drosophila</em> leg express similar complements of mechanosensory and other ion channels. However, anatomical reconstruction of the proprioceptive organ and connected tendons revealed major biomechanical differences between proprioceptor subtypes. We constructed a computational model that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a goniotopic map of joint angle among position-tuned proprioceptors, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in <em>Drosophila</em>. The discovery of proprioceptive maps in the fly leg reveals common organizational principles between proprioception and other topographically organized sensory systems.</p>
NASA's Airborne Topographic Mapper (ATM) airborne waveform and ground calibration data for the Arctic Spring campaign 2016
<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>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 airborne waveform data from the T2 lidar, as well as the ground test data and true ranges for the Arctic Spring campaign 2016.</p> <p>A collection of MATLAB® functions to read ground test waveform and airborne waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p> <p><strong><strong>See also:</strong> </strong></p> <ul> <li>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></li> <li>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></li> <li>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></li> <li>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></li> <li>All ATM data products are freely available at the National Snow and Ice Data Center (NSIDC) at <a href="https://nsidc.org/data/icebridge">https://nsidc.org/data/icebridge</a> and can also be downloaded from the NASA Earthdata portal at <a href="https://earthdata.nasa.gov/">https://earthdata.nasa.gov/</a></li> <li>The ILATMW1B airborne waveform data is available at NSIDC: <a href="https://nsidc.org/data/ILNSAW1B/versions/1">https://nsidc.org/data/ILNSAW1B/versions/1</a> (narrow swath) <a href="https://nsidc.org/data/ILATMW1B/versions/1">https://nsidc.org/data/ILATMW1B/versions/1</a> (wide swath)</li> </ul>
Supporting data for: Topographic information improves simulated patterns of post-fire conifer regeneration in the Southwest U.S.
Open the record for dataset details and reuse information.
Physiology data for: Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg
Open the record for dataset details and reuse information.
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Data from: Influence of topographic features and vegetation on climate refugia in the boreal plains
<ol> <li>Climate-change refugia can be defined by the strength of influence of physiographic and biotic properties on local atmospheric conditions relative to regional averages. In flatter landscapes, thermal heterogeneity and decoupling may be limited. We sought to quantify the magnitude of climate decoupling produced by topographic variation and vegetation in North America's boreal-parkland-grassland transition zone.</li> <li>We deployed iButton data loggers at four hill and 12 river valley systems along a latitudinal gradient in Alberta, Canada (N = 240) between 2014 and 2018. We modelled monthly temperature metrics by season as a function of topography and percent vegetation cover using general linear mixed effect models.</li> <li>Summer T<sub>max</sub> varied ~6 <sup>o</sup>C across the entire elevation gradient sampled, and ~3.5 <sup>o</sup>C within the range of topographic conditions sampled, after accounting for latitude. We found that canopy cover type had a negative effect on most temperature metrics, with coniferous forests having the strongest effect on T<sub>min </sub>and T<sub>ave</sub>. Spatial predictions derived from models based on iButton sensors resulted in similar regional patterns compared to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover and terrain roughness.</li> <li>We observed that local T<sub>max</sub> on steep, north-facing slopes with relatively low levels of potential solar radiation were 0.5 – 1.5 <sup>o</sup>C cooler than the surrounding landscape on average, while terrain roughness may further contribute to 0.5 – 0.7 <sup>o</sup>C. When combined, these local landscape characteristics may compensate for expected warming by the end of the century. Topographic and vegetation characteristics that are related to colder local climates should be considered in the evaluation of future climate-change impacts and to identify potential refugia from climate-change.</li> </ol>
Raw data for the manuscript entitled "Forest age and topographic position jointly shape the species richness and composition of vascular plants in karstic habitats"
<p>Doline surveys from the Mecsek Mountains, Hungary. Transects were established with north to south orientation across each doline, traversing their deepest point. Transects began and ended on doline rims, and consisted of 1 m  × 1 m plots spaced at 2 m intervals (94, 89, 90 and 99 plots in the different forest age classes, respectively; 372 plots in total). We recorded the presence/absence data of shrubs and herbs in each plot. Fieldwork was carried out between 2007 and 2019 from June to August, at the peak of the growing season.</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.