Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland

This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island. <ul> <li>Acquisition date: 07.08.2019 – 10.08.2019</li> <li>Location: Illukunnguaq, Disko Island, Greenland</li> <li>UAV: SenseFly eBee Plus</li> <li>Flight altitude above ground level: >100m</li> <li>Image Overlap forward/side: various</li> <li>Camera: Parrot Sequoia multispectral</li> <li>EPSG: 32622</li> <li>Center coordinates: 69.885277°N, -52.577724°E</li> <li>Flight mode: automatic flight plan</li> </ul> Data products:  <ul> <li>Orthomosaic colour-infrared, 10-16 cm pixel resolution</li> <li>Colour-infrared spectral bands: 790nm, 660nm, 550nm</li> <li>DEM, 20-30cm pixel resolution</li> <li>Data coverage: approx. 5500 x 2500 m</li> <li>Elevation profile: 20-680m </li> <li>Processing in Agisoft Metashape</li> </ul> Additional data supplement for article: Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland.  URN: urn:nbn:se:uu:diva-418858 MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.

opencc-by-4.0May 2024View details →

Ice sheet surface elevation change from ablation stake measurements on bare ice in the western Greenland ablation zone during July 2016

Measurements of ice surface elevation change from a network of twelve bamboo ablation stakes installed in the western Greenland ice sheet ablation zone (67.0496o N, 49.0201o W, 1215 m a.s.l.). Stakes were installed by drilling 3 m deep holes into the ice, inserting the bamboo stakes, and allowing them to freeze into the ice for 24 hours. Following the 24 hour freeze-in period, measurements of the distance from the top of the stake to its base were recorded at nominal 3 hour intervals continuously from 12:00 local time (UTC-2) on 6 July 2016 to 23:00 local time on 12 July 2016. Prior to each measurement, a 24×24 cm square wooden ablation board was placed at the base of the stake and oriented to true north. This board operated as a datum from which the stake height above the ice surface was measured.

opencc-by-4.0Sep 2021View details →

Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019

An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170. Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.

opencc-by-4.0Sep 2021View details →

Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow

An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 . Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with: * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a) * base - basal topography altitude (m) * lithk - ice thickness (m) * orog - surface altitude (m) * strbasemag - magnitude of basal friction tb (MPa) * xvelbase, yvelbase, zvelbase - 3D basal velocity  (m/yr) * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr) * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr) * n - effective pressure (MPa) The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the As Weertman sliding coeffitient. The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).

opencc-by-4.0Mar 2023View details →

MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)

This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS). Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year. Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1. Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.

opencc-by-4.0Apr 2020View details →

Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"

This dataset is used to reproduce the results presented in the following publication: Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.

opencc-by-4.0Mar 2024View details →

UAV-based orthomosaic and digital elevation model of a basalt outcrop on Disko Island, West Greenland

This data set contains an RGB survey conducted with an unoccupied aerial vehicle (UAV) over a flat basaltic outcrop (intrusive and flood volcanics), surrounded by boreal vegetation (Salix species). <ul> <li>Acquisition date: 13.08.2019</li> <li>Location: Qullissat (Qutdlikssat), Disko Island, Greenland</li> <li>UAV: DJI Mavic 1 Pro</li> <li>Flight altitude above ground level: 75 m</li> <li>Image Overlap forward/side: 70 % / 70 %</li> <li>Camera: RGB</li> <li>EPSG: 32622</li> <li>Center coordinates: 70.05330°N, -52.97780°E</li> <li>Flight mode: manual image acquisition</li> </ul> Data products:  <ul> <li>Orthomosaic RGB 2.3 cm pixel resolution</li> <li>DEM 5cm pixel resolution</li> <li>Processing in Agisoft Metashape</li> <li>Data coverage: approx. 250 x 360 m</li> </ul> Acknowledgements MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.

opencc-by-4.0Mar 2024View details →

Supporting Data - Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet

This dataset contains supporting data accompanying Culberg, R., Michaelides, R. J., and Miller, J. Z.: Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2023-2652">https://doi.org/10.5194/egusphere-2023-2652</a>, 2023. The final accepted manuscript will be linked via the same preprint server at the time of publication. The dataset contains the following files: <ul> <li>Sentinel-1 HV and HV/HH backscatter mosaics of the Greenland Ice Sheet formed using data from 1 Oct 2016 - 30 April 2017.</li> <li>Estimated average annual summer melt extent between 1 Nov 2014 and 31 Aug 2020, detected using seasonal variations in Sentinel-1 HH backscatter.</li> <li>The firn aquifer extent over Greenland derived from Sentinel-1 in Brangers et al. (2020), reprojected to EPSG:3413.</li> <li>The ice mask used in the study, derived from the BedMachine Greenland ice mask.</li> <li>The training and validation datasets derived from the Jullien et al. (2023) ice slabs detections from ice penetrating radar data that were used to optimize ice slab detection thresholds for the Sentinel-1 backscatter mosaics. </li> </ul>

opencc-by-4.0Aug 2020View details →

3D model of a cairn grave near the Bear Trap in Northwest Greenland

This dataset consists of a 3D dense point cloud and a textured mesh model (see the README file) of a cairn grave that is positioned near ‘The Bear Trap’. The Bear Trap is a Norse ruin at the western end of the Nuussuaq Peninsula. The 3D model was created from 639 digital photographs that were processed using Agisoft Metashape Pro v1.7; Linux Ubuntu). A 24 megapixel Sony a6000 APS-C mirrorless camera fitted with a 17 mm lens was used to acquire ground-level imagery of the structure. The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts.  Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. Antiquity 94:e6 doi:10.15184/aqy.2019.230 Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to Arctic Anthropology.

opencc-by-4.0Jun 2022View details →

Greenland Ice Sheet crevasse map from ArcticDEM

This dataset is produced using the ArcticDEM v3 mosaic. Since release, the new ArcticDEM v4.1 mosaic has been released, and I have developed newer and open-source methods for extracting crevasse geometries from ArcticDEM and REMA strips. I am happy to help direct you towards the solution or dataset most appropriate for your needs - please contact me at thomas.r.chudley@durham.ac.uk. Data Description Title: Greenland Ice Sheet crevasse map from ArcticDEM Version: 1.00 Format: GeoTiff Projection: WGS84 / NSIDC Sea Ice Polar Stereographic North (EPSG: 3413) Resolution: 2 m (binary) and 200 m (fraction) Size: 3.5 GB (total binary) and 22 MB (total fraction) Citation: Chudley et al. (2021) <a href="https://doi.org/10.1029/2021JF006287" target="_blank" rel="noopener">https://doi.org/10.1029/2021JF006287</a> Contact: Tom Chudley Email: thomas.r.chudley@durham.ac.uk Products This dataset contains crevasse locations identified from the ArcticDEM v3 mosaic. There are two primary products: a 2 m binary crevasse map, and a 200 m crevasse fraction map. It is divided into the six IMBIE 'Rignot' drainage basins: central west (CW), southwest (SW), southeast (SE), northeast (NE), north (NO), northwest (NW). This dataset is for scientific purposes only. The method is not able to detect metre-scale and snow-covered crevasses, and should not be used for field safety purposes. 2 m crevasse binary Byte GeoTiff product indicating derived crevasses at 2 m resolution. File naming convention is crevasse_binary_XX_2m.tif, where XX is the IMBIE basin code. Values have the following meaning:  <ul> <li>0: No data</li> <li>1: No crevasses identified</li> <li>2: Crevasses identified</li> </ul> 200 m crevasse fraction Float32 GeoTiff product indicating fraction of 200 m grid cell identified as crevasses in the 200 m product. File naming convention is crevasse_fraction_XX_2m.tif, where XX is the IMBIE basin code. Values have the following meaning:  <ul> <li>0 - 1: Fraction of grid cell identified as crevasse in 2 m dataset</li> <li>-9999: No data</li> </ul> Method The full processing chain for data derivation is described in Chudley et al. (2021). A binary crevasse mask of the Greenland Ice Sheet is generated using ArcticDEM v3 mosaic data at 2 m resolution (Porter et al., 2018), with data processed in Google Earth Engine (Gorelick et al., 2017). The ArcticDEM is cropped to the GIMP ice mask (Howat et al., 2014), before a smoothed elevation model is generated by performing an image convolution with a circular kernel of 50 m radius. Residuals greater than 1 m between the smoothed and raw elevation values were identified as crevasses. To compare with public velocity datasets (and derived strain rates, stress, etc.), the 2 m dataset was aggregated (using GDAL) into grid cells to match the resolution (200 m) of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) ice sheet surface velocity grid (Joughin, 2010; 2021). Aggregated values represent the fraction of grid cell area classified as crevasses. Caveats <ul> <li>The method, including kernel size was tuned manually based on the region of interest of the original Chudley et al. (2021) paper. As such, it may not be optimal for other regions of interest on the ice sheet, in particular in the east, where medial moraines and marginal valleys are more prevalent (see caveat #4).</li> <li>The ArcticDEM is derived from optical MAXAR imagery. As such, snow-filled crevasses will not be identified here, which will be problematic above the ablation zone.</li> <li>Following comparison with Uncrewed Aerial Vehicle (UAV) data in Chudley et al. (2021), the approximate lower bound of crevasse width identified is ~10 m. These are large crevasses, far greater than are commonly encountered in safe fieldwork environments.</li> <li>This method is relatively crude: at its core, it is effectively a high-pass filter applied to the ArcticDEM mosaic. As such, there are false positives that occur around other supraglacial features (rivers, moraines, etc.) as well as marginal features (proglacial geomorphology, fjord sikkusak, etc.) that are captured in regions where the GIMP ice mask does not accurately capture the terrestrial ice extent at the time of ArcticDEM data capture. Users are encouraged to critically evaluate data in their areas of interest using the 2 m binary map and external data, even when intending to use only the 200 m fraction dataset.</li> </ul> Citation When using this data, please cite the method as being from: Chudley, T. R., Christoffersen, P., Doyle, S. H., Dowling, T. P. F., Law, R., Schoonman, C. M., Bougamont, M., & Hubbard, B. (2021). Controls on water storage and drainage in crevasses on the Greenland Ice Sheet. Journal of Geophysical Research: Earth Surface, 126, e2021JF006287. <a href="https://doi.org/10.1029/2021JF006287">https://doi.org/10.1029/2021JF006287</a>. Acknowledgements Initial processing chain created whilst Chudley was supported by a Natural Environment Research Council Doctoral Training Partnership Studentship (Grant No. NE/L002507/1). ArcticDEM v3 mosaic is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736.

opencc-by-4.0Jun 2022View details →

Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'

Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>.  Data supporting the paper: <blockquote> Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. The Cryosphere. 16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022 </blockquote> Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley et al. (2022).   NetCDF files are created by, and tested to be readable by, Python's xarray package. The dimensions of the netCDF file are as follows: <ul> <li>X - x coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>Y - y coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>time - temporal midpoint of velocity field.</li> </ul> The variables of the netCDF file are as follows: <ul> <li>dmag - the absolute magnitude of the velocity, in metres per day.</li> <li>dx - the velocity in the x direction, in metres per day.</li> <li>dy - the velocity in the y direction, in metres per day.</li> <li>date1 - the date and time of the first scene acquisition.</li> <li>date2 - the date and time of the second scene acquisition.</li> <li>baseline - the temporal baseline, in days, between scene acquisitions.</li> <li>orbit_pair - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li>mag_rmse - the root mean square error of the absolute velocity of the off-ice area. </li> <li>dx_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dx_sd - the standard deviation of the velocity of the off-ice area in the x direction.</li> <li>dy_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dy_sd - the standard deviation of the velocity of the off-ice area in the y direction.</li> </ul>

opencc-by-4.0Oct 2021View details →

Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland

Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland. <a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a> Data set contains 3D model in dxf file, additional images, selected handheld spectra. Publication summary: We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.

opencc-by-4.0Nov 2022View details →

Particulate organic carbon (POC) concentration in meltwater runoff of Leverett Glacier, Russell Glacier, and Isunnguata Sermia, southwest Greenland (2009-2018)

This dataset describes particulate organic carbon (POC) and particulate carbon (PC) concentrations of suspended sediments in the proglacial rivers of 3 land-terminating glaciers in the Kangerlussuaq area, Southwest Greenland: Leverett Glacier (LG), Leverett River; Russell Glacier (RG), Akuliarusiarsuup Kuua; and Isunnguata Sermia (IS), Isortoq River. Both the Leverett River and Akuliarusiarsuup Kuua are tributaries of the Qinnguata Kuussua (also known as Watson River). The data have already been part of 3 different publications (Lawson et al. 2014, Kohler et al. 2017, and Vrbická et al. 2022) but are archived here for the first time. POC data was collected for LG during the 2009 and 2010 melt seasons (Lawson et al. 2014) as well as 2015 (Kohler et al. 2017). For the 2018 melt season, only total carbon concentrations of suspended sediments (PC) is archived as opposed to POC (see Vrbická et al. 2022).

opencc-by-4.0Jan 2023View details →

Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.

An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018).  The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature.  The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here: <product_type>_<mission>_<mode>_<product>_<polarisation>_<starttime>_<endtime>_<orbitnumber>_<dataID>_<image>.fileextension For example: extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif

opencc-by-4.0Sep 2020View details →

CTD Profiles from the R/V Sanna cruise to Northwest Greenland fjords, August 11-31, 2016

Greenland fjords are the gateway connecting the Greenland Ice Sheet to the coastal ocean. The rapidly increasing discharge of meltwater and ice from the Greenland Ice Sheet has complex hydrodynamic and biogeochemically impacts on the coastal marine ecosystem around Greenland, although data is still limited for most fjord systems. Therefore, six different fjord systems in Northwest Greenland were investigated in August 2016 during an interdisciplinary research cruise aboard the R/V Sanna that On aimed to describe the physical, chemical and biological variability from glaciers to the shelf. This data set consists of 55 CTD profiles that were obtained with a Seabird 19plusV2 conductivity, temperature, depth CTD) instrument. In addition to measuring pressure, conductivity, and temperature, the CTD recorded chlorophyll-a fluorescence, photosynthetically available radiation (PAR), and dissolved oxygen. The instrument was factory calibrated before the cruise. The CTD recorded all variables at 4 Hz and the raw data were processed using Seabird standard workflow to produce 0.5 m binned profiles using the downcast data only. This dataset compiles all CTD variables measured from all profiles into a single netCDF file.  We would like to thank the captain and crew of R/V Sanna for excellent collaboration. The cruise was funded by the Danish Center for Marine Research and by the EU Horizon2020 funded project INTAROS.

opencc-by-4.0Oct 2020View details →

Centennial clonal stability of asexual Daphnia in Greenland lakes despite climate variability

Daphnia_microsatellite_data_Dane_etal.2020.csv: Microsatellite genotypes from three study lakes (SS4, SS1381, and SS1590) in the Kangerlussuaq area, West Greenland. Microsatellite loci were amplified in single, 12.5 µl multiplex reactions (Type-it PCR kit, Qiagen Inc, Valencia, CA, USA), using an Eppendorf Nexus Thermal Cycler with thermal cycle conditions recommended in the Type-it PCR kit manual. Ten microsatellite primers representing genome-wide loci were used for genotyping; details in (Colbourne et al. 2004; Frisch et al., 2014). Two primers (Dp90, Dp377) failed to amplify in a consistent manner and were therefore excluded from further analysis. Amplified microsatellites were genotyped on an Applied Biosystems 3730 genetic analyser. We used the microsatellite plugin for Geneious 7.0.6 (https://www.geneious.com) for peak calling and binning. Called peaks were visually inspected and manually adjusted when necessary.  SS4_sediment.core_data_Fig2_Dane_et_al2020.xlsx:   Information on various parameters of sediment cores collected in Lake SS4, Kangerlussuq area, West Greenland. Data used in Dane et al. 2020, Figure 2 (panels B and C) are derived from two sediment cores: one for fluorescence (section at 0.5 cm intervals, Depth) and one for Daphnia ephippia analyses (1-cm intervals). Percentage organic matter content (loss-on-ignition at 550 °C,OM%) was used to correlate the two cores to each other and to a previously-dated sediment core (see Dane et al. 2020, Methods). The fluorescence derived parameter Parafac component C2 was used as an indicator of the abundance of purple sulphur bacteria. The organic carbon burial rate (OC AR, g C m–2 yr–1) was also calculated for this core (see Anderson et al. 2019). The Daphnia core was used for the microsatellite analyses and the accumulation rate of ephippia (ephippia AR) at the core site was estimated. For further details please see associated publication in Ecology and Evolution.

opencc-by-4.0Dec 2020View details →

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-4.0Dec 2020View details →

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-4.0Jun 2017View details →

Landslides from Space - Nuugaatsiaq: landslide-induced tsunami, Greenland (17th June 2017)

On 17th June 2017 the village of Nuugaatsiaq was struck by a large, isolated tsunami. This Tsunami was caused by a landslide on a nearby cliff. The pre-event acquisition is from 2nd June 2017 (Sentinel-2) and the post-event acquisition is from 19th June 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image. Contains modified Copernicus Sentinel data (2017)