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1,049 results for “height”
Average glacier stake height and snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes stake height and snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Glacier stake height measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes stake height measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
SEV-LTER Mean - Variance Experiment Quadrat Plant Species Cover and Height
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at all Mean - Variance experiment sites. Quadrat locations span five important ecosystems of the American southwest: blue grama-dominated Plains grassland (est. fall 2019), black grama-dominated Chihuahuan Desert grassland (est. fall 2020), creosotebush dominated Chihuahuan Desert shrubland (est. fall 2021), juniper savanna (est. fall 2022) and pinon-juniper woodland (est. fall 2023). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This data package includes plant cover and height only -- for species biomass estimates per quad see package knb-lte
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Building height map of Germany
<p>Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment. </p> <p>This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.</p> <p><strong>Temporal extent</strong><br> The acquisition dates of the different data sources vary to some degree:<br> - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.<br> - Dependent variables: the 3D building models are from 2012-2020 depending on data provider.<br> - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.<br> Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015. </p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-height/">here</a>.</p> <p><strong>Publication</strong><br> Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: <a href="https://doi.org/10.1016/j.rse.2020.112128">https://doi.org/10.1016/j.rse.2020.112128</a></p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. The European Settlement Mask was obtained from the <a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b">European Commission</a>. 3D building models were obtained from <a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/">Berlin Partner für Wirtschaft und Technologie GmbH</a>, <a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true">Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung</a>, <a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information">Landeshauptstadt Potsdam</a>, <a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html">Bezirksregierung Köln / Geobasis NRW</a>, and <a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude">Kompetenzzentrum Geodateninfrastruktur Thüringen</a>. This dataset was partly produced on <a href="https://eodc.eu">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p>
L4E - Maximum tree height extracted from LiDAR transects over the Brazilian Amazon
<p>Between 2016 and 2018, the EBA airborne missions (conducted by the Brazilian National Institute for Space Research (INPE) and funded by Amazon Fund) collected airborne lidar transects of 375 ha (12.5 x 0.3 km) each. A majority of the transects were flown over randomly selected locations of old growth and second growth as forests defined by the PRODES and TerraClass databases (PRODES, INPE, 2016; TerraClass, INPE, 2014). PRODES separates forests from non-forest while TerraClass identifies second growth forest and other land covers.</p>
ICESat-2 sea ice ancillary data - Mean Sea Surface Height Grids
<p>File format: NetCDF</p> <p>Mean Sea Surface (MSS) Height data grids used for the production of ICESat-2 sea ice data products (ATL07, ATL10, ATL20, ATL21). Blended data from CryoSat-2 and DTU13.</p>
Reflector heights in the Arctic permafrost areas measured by GNSS interferometric reflectometry
<p>This dataset is about the measurements of reflector height, i.e., the vertical distance between receiver antenna and groud surface, at the GNSS sites in the Arctic permafrost areas. Each data file has four columns. The 1st and 2nd show the time as year and doy, respectively. The 3rd and 4th are the reflector height and its uncertainty, respectively.</p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Height and density data for marine animal forest forming species
<p>Data file (.csv) of values of height and density for marine animal forest (MAF) forming species collected from the literature. Fields are for discrete observations:</p> <table> <tbody> <tr> <td>Species</td> <td>Species name</td> </tr> <tr> <td>Santavy</td> <td> <p>Morphology class using categories in </p> <p><span>Santavy DL, Lee A. Courtney, William S. Fisher, Robert L. Quarles, Stephen J. Jordan (2013) Estimating surface area of sponges and gorgonians as indicators of habitat availability on Caribbean coral reefs. Hydrobiologia 707:1-16. <span>https://doi.org/10.1007/s10750-012-1359-7</span><br></span></p> </td> </tr> <tr> <td>Height</td> <td>Mean colony height (cm)</td> </tr> <tr> <td>Density</td> <td>Mean colony density m-2</td> </tr> <tr> <td>Btemp</td> <td>Average bottom temperature for species based on OBIS records (K)</td> </tr> <tr> <td>Depth</td> <td>Average depth for species based on OBIS records (m)</td> </tr> <tr> <td>Phylum</td> <td>Taxonomy</td> </tr> <tr> <td>Class</td> <td>Taxonomy</td> </tr> <tr> <td>Order</td> <td>Taxonomy</td> </tr> <tr> <td>Family</td> <td>Taxonomy</td> </tr> <tr> <td>Genus</td> <td>Taxonomy</td> </tr> <tr> <td>Source</td> <td>Publication source for data</td> </tr> <tr> <td>Title</td> <td>Publication title</td> </tr> <tr> <td>DOI/link</td> <td>DOI for source data</td> </tr> </tbody> </table>
IODP Expedition 391 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 397T Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 383 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 378 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 367 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations
<p>SD-WACCM data used in "<strong>Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations"</strong></p>
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)
<p>This repository contains:</p> <ul> <li>a folder called "_toReproduceResults" containing data, script and methodology to reproduce most of the work described in the research manuscript,</li> <li>the main output of the research work: 24 folders (each of them corresponding to a French city), containing building geometry footprints and their corresponding building height as well as averaged building height value aggregated at rectangular grid cell (100 m by 100 m). The footprint geometries comes from the OpenStreetMap project and the building height has been estimated using a RandomForest algorithm using as independent variables indicators describing the building size and shape and the building environment. The data has been produced using the GeoClimate Software (version 0.0.1).</li> </ul> <p>A more detailed description of the content can be used in the file "Metadata.csv".</p>
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