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774 results for “glacier”
High spatial resolution satellite images for glacier outlines
<p>Two RapidEye satellite images acquired on Sep 13, 2013 and Sep 17, 2018. The two satellite images have a high spatial resolution of 5 m × 5 m and were used to derive the outlines of the Parlung No. 94 Glacier in years 2013 and 2018, respectively. The GaoFen-7 (GF-7) satellite image with a high spatial resolution of 3 m × 3 m acquired on Feb 26, 2021 was used to derive the outlines of the Dongkemadi Glacier in 2021</p>
Data from GB-SAR measurements on Lazaun rock glacier (Val Senales, South Tyrol, northern Italy)
<p>The *.csv files report the kinematic data extracted from the displacement maps obtained from the processing of ground-based SAR measurements of Lazaun rock glacier (Senales Valley, Northern Italy) and presented in the paper <strong><em>“Unprecedented observation of hourly rock glacier velocity with Ground-Based SAR</em></strong><strong><em>”</em></strong> (Bertone et al., 2023, submitted). The data were used to display the time series reported in the paper. The data refer to two field campaigns named <em>survey1</em> (<em>from 09/08/2018 to 18/08/2018</em>), and <em>survey2 </em>(<em>from 13/09/2018 to 03/10/2018</em>).</p> <ul> <li><em>TS_August_moving.cs</em>v: includes the survey 1 displacement data of moving points on the rock glacier.</li> <li><em>TS_August_stable.csv</em>: includes the survey 1 displacement data of stable points outside the rock glacier.</li> <li><em>TS_September_moving.csv</em>: includes the survey 2 displacement data of moving points on the rock glacier.</li> <li><em>TS_ September_stable.csv</em>: includes the survey 2 displacement data on stable points outside the rock glacier.</li> </ul> <p>Each *.csv file contains the following fields:</p> <ul> <li>“<em>displacement</em>”: cumulative displacement (mm);</li> <li>“<em>pointName</em>”: name of point where the time series are computed, as displayed in figure 3;</li> <li>“<em>time</em>”: time of acquisition, in <em>day</em>/<em>month</em>-<em>hour</em>:<em>minute </em>format;</li> <li>“<em>velocity</em>”: velocity (mm/hour);</li> <li>“<em>X</em>” and “<em>Y</em>”: coordinates of points (EPGS 32632) where the time series were extracted.</li> </ul> <p>The polygon representing the area of the Lazaun rock glacier is provided by the shapefile <em>Polygon_RG_Lazaun.shp.</em></p> <p>The velocity distribution on the Lazaun rock glacier as obtained in suvey1 and survey2 is provided by raster files. The velocities were obtained along the LOS and are in mm/day. The two raster files, named <em>raster_survey_1.tif</em> and <em>raster_survey_2.tif</em>, are completed by two files of the same name with the colored overlay (<em>raster_survey_1.lyr</em> and <em>raster_survey_2.lyr</em>).</p> <p>The two velocity maps obtained from survey1 and survey2 are also provided as complete georeferenced figures in geotiff format and named <em>figure_survey_1.tif</em> and<em> figure_survey_2.tif.</em></p> <p>All the geospatial data are provided in the EPSG 32632 spatial reference.</p> <p> </p> <p><strong>AIR TEMPERATURE</strong></p> <p>The LAZ_AIRT_survey1.csv and LAZ_AIRT_survey2.csv files contain the air temperature measured about 250 m North from the Lazaun rock glacier by a Gemini Tinytag TGP 4020 data logger connected to an external probe installed in a passive radiation shield. The data cover the two GB-SAR field campaigns (survey 1 from 09/08/2018 to 18/08/2018, and survey 2 from 13/09/2018 to 03/10/2018).</p> <p>The csv files contain the following fields:</p> <ul> <li>Date: time of acquisition, in <em>day/month/year</em> <em>hour:minute</em> format;</li> <li>Mean: the mean air temperature recorded by the datalogger during the corresponding hour;</li> <li>Maximum: the maximum air temperature recorded by the datalogger during the corresponding hour;</li> <li>Minimum: the minimum air temperature recorded by the datalogger during the corresponding hour;</li> </ul>
The First Rock Glacier inventory for the Greater Caucasus
<p>In this study we produce the first inventory of rock glaciers from the entire Greater Caucasus region—Russia, Georgia, and Azerbaijan. A remote sensing survey was conducted using Geo-Information System (GIS) and Google Earth Pro software based on high-resolution satellite imagery—SPOT, Worldview, QuickBird, and IKONOS, based on data obtained during the period 2004–2021. Sentinel-2 imagery from the year 2020 was also used as a supplementary source. The ASTER GDEM (2011) was used to determine location, elevation, and slope for all rock glaciers. </p> <p>This inventory provides a database for understanding the extent of permafrost in the Greater Caucasus and is an important basis for further research of geomorphology and palaeoglaciology in this region.</p>
Cone Glacier velocity and climate data from November 2017 to January 2022
<p>Glacier velocity (m/yr) data obtained using Sentinel-2 near-infrared band 8 satellite images and the Glacier Image Velocimetry (GIV) open-source toolkit (Van Wyk de Vries & Wickert, 2021; https://doi.org/10.5194/tc-15-2115-2021). Precipitation (mm) and temperature (degrees centigrade) data obtained from gridded ERA5 reanalysis data (Hersbach et al., 2020; https://doi.org/10.1002/qj.3803) for the 0.25° x 0.25° grid square within which Cone Glacier sits. (2023-05-09)</p>
Dataset for "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans et al.
<p>Code and data products associated with "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans V., Robel A., Thompson A. F., and Seroussi H.</p> <p>Please see readme file for all the information.</p> <p>Contact: vverjans3@gatech.edu</p> <p>Author: Vincent Verjans</p>
Data of "Inferring the Basal Friction Law from long term observations of Glacier Length, Thickness and Velocity changes on an Alpine Glacier"
<p>This dataset contains the surface velocity and elevation of Argentière Glacier in 2003 and 2018 used in:</p> <p>Gilbert, A., Gimbert, F., Gagliardini, O., & Vincent, C. (2023). Inferring the Basal Friction Law From Long Term Changes of Glacier Length, Thickness and Velocity on an Alpine Glacier. <em>Geophysical Research Letters</em>, <em>50</em>(16), e2023GL104503. <a href="https://doi.org/10.1029/2023GL104503">https://doi.org/10.1029/2023GL104503</a></p> <p>Surface DEM files contain elevation Z on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p> <p>Horizontal Velocity files contain measured horizontal velocities Vh in m/yr on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p>
Toy glacier elevation change dataset
<p>Dataset for conference paper: 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' at the ICML Workshop tructured Probabilistic Inference and Generative Modelling, 2023 (Honolulu, Hawaii, USA).</p> <p><a href="https://arxiv.org/abs/2307.03093">https://arxiv.org/abs/2307.03093</a></p>
Remote Sensing and Geomorphological Covariates and Labels of main Peruvian Glaciers
<p>Abstract: The Peruvian Glacier Classification Dataset is a comprehensive collection designed to facilitate research and analysis in the field of glaciology and remote sensing. Comprising a total of 9 rasterStacks, each representing distinct glacier regions within Peru, the dataset offers a rich set of 25 morphological and Landsat 8 derived covariates for in-depth study. These covariates have been meticulously selected to capture a wide range of features and characteristics associated with glaciers and their surrounding environments. Moreover, the dataset includes true labels indicating the classification of each pixel into Glacier and Non-Glacier classes, as determined by the National Inventory of 2017.</p> <p>Description: The Peruvian Glacier Classification Dataset presents a valuable resource for researchers, scientists, and practitioners interested in glacial dynamics, environmental monitoring, and geospatial analysis. Comprising 9 main glacier regions within Peru (Blanca, Central, Huallanca, Huayhuasha, Huaytapallana, Raura,Urubamba, Vilcabamba,Vilcanota), the dataset provides an extensive collection of covariates derived from both morphological features and Landsat 8 satellite imagery. These covariates have been processed and curated to offer comprehensive insights into the intricate nature of glaciers and their surroundings.</p> <p>Key Features:</p> <ol> <li> <p>Morphological Covariates: The dataset encompasses a diverse array of morphological features extracted from high-resolution elevation data calculated with SAGA GIS. </p> </li> <li> <p>Landsat 8 Derived Covariates: Landsat Covariates corresponding to the period 2017–2018 obtained from Landsat Collection 2 Level 2 and Tier 1 surface reflectance (SR) products (Vermote et al., 2016) available online: https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance (accessed on 1 July 2023). procesing with GoogleEarth Engine study. https://code.earthengine.google.com/fea31b7f2a3fdbc1065644616819134a. This includes the following covariates: BLUE, GREEN, RED, NIR, SWIR1, SWIR2, NDFI, ndsi, ndvi, NDWI , NDWIns, NDSInw, nbr2, VNSIR, NDMI, TCG .</p> </li> <li> <p>True Labels: Ground truth information is provided through accurate classification labels for each pixel, classifying it as either belonging to the Glacier or Non-Glacier class. These labels are based on the authoritative National Inventory of 2017, enhancing the reliability and usability of the dataset.</p> </li> </ol>
Unveiling Transformation: Glacier Area Changes in Changra-Bhaga and Suru-Zanskar Basins (2000-2022), Western Himalaya
<p>The dataset encompasses the temporal variations in glacier extents within the Suru-Zanskar Basin and the Chandra-Bhaga Basin, both integral parts of the Western Himalayan region. Within this dataset, a total of 45 representative glaciers were manually digitized for the years 2000, 2007, 2016, and 2022, using satellite imagery for the Suru-Zanskar Basin. Similarly, temporal glacier areas were calculated for 51 selected glaciers spanning the years 2000, 2009, 2016, and 2022 within the Chandra-Bhaga Basin. For the year 2000, the dataset comprises a recorded area of 913.77 km² for the Suru-Zanskar Basin and 608.95 km² for the Chandra-Bhaga Basin, respectively. This dataset forms an integral component of the manuscript titled "Divergent Temporal Glacier Responses in the Chandra-Bhaga and Suru-Zanskar Basins of the Indian Western Himalaya."</p>
Glacier catalogue for IGM physics-informed deep-learning emulator pretraining
<p>This dataset was created with the iceflow glacier model CfsFlow to generate glacier extent and retreat in the Alps and New zealand with the goal to generate realistic and diverse glacier states for pretraining the physics-informed deep-learning emulator of IGM (https://github.com/jouvetg/igm).</p> <p>The data consists of distributed surface topography (usurf) and ice thickness (thk) of 8 snapshots of 37 glaciers in different stages (advance and retreat). The data is organized glacier-wise: each folder corresponds to one glacier, which contains a unique NetCDF file with 2D distributed raster data of surface elevation and ice thickness.</p>
Taylor Glacier Tunnel: Measurements of the composition, deformation and strength of basal ice made in a tunnel in Taylor Glacier, Antarctica
<p>Data set from Taylor Glacier tunnel</p> <p>Photographs of tunnel excavation</p> <p>Video of tunnel excavation</p>
First rock glacier inventory of the Peruvian Andes: distribution, classification and climatic characterization
<p>The contours of the Rock glaciers (RGs) were manually digitized, identifying a total of 2271 (967 active, 507 inactive, 311 intact and 486 relict) covering 109.1±1.5 km2 and distributed in 15 of the 20 cordilleras of Peru. The Minimum Altitude Front (MAF) of RGs is 4339 m a.s.l., while most of them are between 4800 to 5000 m a.s.l. For the Southwest zone (Z-IV) where 94% of the RGs are located, the Binary Logistic Regression Model (BLRM) shows that MAAT and slope are the variables that have the greatest influence on the presence of 76.5% RGs evaluated. While in the North (Z-I), Central (Z-II) and Southeastern (Z-III) zones, the topoclimatic variables analyzed did not show statistical significance. This first inventory represents an important input for modeling the spatial distribution of permafrost in the Peruvian Tropical Andes.</p>
Data products associated with "Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016 - 2023"
<p>Data for Petersen, E., R. Hock, M. Loso, W. Guo, C. Markovsky, R. Yang, H. Han, D. Shangguan, and S. Kang, “Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016-2023,” Geoscience Data Journal, Submitted January 2025.</p>
Grounding line of Denman Glacier, East Antarctica from satellite radar interferometry
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Data for: Subglacial freshwater driven speedup of East Antarctic outlet glacier retreat
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Widespread seawater intrusions beneath the grounded ice of Thwaites Glacier, West Antarctica
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Data For: Grounding zone of Helheim Glacier, Greenland, from terrestrial radar interferometry
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Data from: Moss community dynamics and canopy-mediated environmental influences during primary succession in a glacier retreat zone
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Similar vegetation-geomorphic disturbance feedbacks shape unstable glacier forelands across mountain regions
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Responses of Pine Island and Thwaites glaciers to melt and sliding parameterizations
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Allen Brain Atlas
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OpenNeuro
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