222 research outputs found
Glacier catchments/basins for the Greenland Ice Sheet
No full textWe divide Greenland, including its peripheral glaciers and ice caps, into 260 basins grouped in seven regions: southwest (SW), central west (CW), (iii) northwest (NW), north (NO), northeast (NE), central east (CE), and southeast (SE). These regions are selected based on ice flow regimes, climate, and the need to partition the ice sheet into zones comparable in size (200,000 km2 to 400,000 km2) and ice production (50 Gt/y to 100 Gt/y, or billion tons per year). Out of the 260 surveyed glaciers, 217 are marine-terminating, i.e., calving into icebergs and melting in contact with ocean waters, and 43 are land-terminating.The actual number of land-terminating glaciers is far larger than 43, but we lump them into larger units for simplification.
Each glacier catchment is defined using a combination of ice flow direction and surface slope. In areas of fast flow (> 100 m), we use a composite velocity mosaic (Mouginot et al. 2017). In slowmoving areas, we use surface slope using the GIMP DEM (https://nsidc.org/data/nsidc- 0715/versions/1) (Howat et al. 2014) smoothed over 10 ice thicknesses to remove shortwavelength undulations.
References:
Mouginot J, Rignot E, Scheuchl B, Millan R (2017) Comprehensive annual ice sheet velocity mapping using landsat-8, sentinel-1, and radarsat-2 data. Remote Sensing 9(4).
Howat IM, Negrete A, Smith BE (2014) The greenland ice mapping project (gimp) land classification and surface elevation data sets. The Cryosphere 8(4):1509–1518
Annual Ice Velocity of the Greenland Ice Sheet (2010-2017)
No full text<p>We derive surface ice velocity using data from 16 satellite sensors deployed by 6 different space agencies. The list of sensors is given in the Table S1. The SAR data are processed from raw to single look complex using the GAMMA processor (www.gamma-rs.ch). All measurements rely on consecutive images where the ice displacement is estimated from tracking or interferometry (Joughin et al. 1998, Michel and Rignot 1999, Mouginot et al. 2012). Surface ice motion is detected using a speckle tracking algorithm for SAR instruments and feature tracking for Landsat. The cross-correlation program for both SAR and optical images is ampcor from the JPL/Caltech repeat orbit interferometry package (ROI_PAC). We assemble a composite ice velocity mosaic at 150 m posting using our entire speed database as described in Mouginot et al. 2017 (Fig. 1A). The ice velocity maps are also mosaicked in annual maps at 150 m posting, covering July, 1st to June, 30th of the following year, i.e. centered on January, 1st (12) because a majority of historic data were acquired in winter season, hence spanning two calendar years.<br>
We use Landsat-1&2/MSS images between 1972 and 1976 and combine image pairs up to 2 years apart to measure the displacement of surface features between images as described in Dehecq et al., 2015 or Mouginot et al. 2017. We use the 1978 2-m orthorectified aerial images to correct the geolocation of Landsat-1 and -2 images (Korsgaard et al., 2016). Between 1984 and 1991, we process Landsat-4&5/TM image pairs acquired up to 1-year apart. Only few Landsat-4 and -5 images (~3%) needed geocoding refinement using the same 1978 reference as used previously. Between 1991 and 1998, we process radar images from the European ERS-1/2, with a repeat cycle varying from 3 to 36 days depending on the mission phase. Between 1999 and 2013, we used Landsat-7, ASTER, RADARSAT-1/2, ALOS/PALSAR, ENVISAT/ASAR to determine surface velocity (Joughin et al., 2010; Howat, I. 2017; Rignot and Mouginot, 2012). After 2013, we use Landsat-8, Sentinel-1a/b and RADARSAT-2 (Mouginot et al., 2017). All synthetic aperture radar (SAR) datasets are processed assuming surface parallel flow using the digital elevation model (DEM) from the Greenland Mapping Project (GIMP; Howat et al., 2014) and calibrated as described in Mouginot et al., 2012, 2017. Data were provided by the European Space Agency (ESA), the EU Copernicus program (through ESA), the Canadian Space Agency (CSA), the Japan Aerospace Exploration Agency (JAXA), the Agenzia Spaziale Italiana (ASI), the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and the National Aeronautics and Space Administration (NASA). SAR data acquisitions were coordinated by the Polar Space Task Group (PSTG). Errors are estimated based on sensor resolution and time lapse between consecutive images as described in Mouginot et al. 2017. </p>
Annual Ice Velocity of the Greenland Ice Sheet (2001-2010)
No full textWe derive surface ice velocity using data from 16 satellite sensors deployed by 6 different space agencies. The list of sensors and the year that they were used are listed in the following (Table S1). The SAR data are processed from raw to single look complex using the GAMMA processor (www.gamma-rs.ch). All measurements rely on consecutive images where the ice displacement is estimated from tracking or interferometry (Joughin et al. 1998, Michel and Rignot 1999, Mouginot et al. 2012). Surface ice motion is detected using a speckle tracking algorithm for SAR instruments and feature tracking for Landsat. The cross-correlation program for both SAR and optical images is ampcor from the JPL/Caltech repeat orbit interferometry package (ROI_PAC). We assembled a composite ice velocity mosaic at 150 m posting using our entire speed database as described in Mouginot et al. 2017 (Fig. 1A). The ice velocity maps are also mosaicked in annual maps at 150 m posting, covering July, 1st to June, 30th of the following year, i.e. centered on January, 1st (12) because a majority of historic data were acquired in winter season, hence spanning two calendar years.
We use Landsat-1&2/MSS images between 1972 and 1976 and combine image pairs up to 1 year apart to measure the displacement of surface features between images as described in Dehecq et al., 2015 or Mouginot et al. 2017. We use the 1978 2-m orthorectified aerial images to correct the geolocation of Landsat-1 and -2 images (Korsgaard et al., 2016). Between 1984 and 1991, we processed Landsat-4&5/TM image pairs acquired up to 1-year apart. Only few Landsat-4 and -5 images (~3%) needed geocoding refinement using the same 1978 reference as used previously. Between 1991 and 1998, we process radar images from the European ERS-1/2, with a repeat cycle varying from 3 to 36 days depending on the mission phase. Between 1999 and 2013, we use Landsat-7, ASTER, RADARSAT-1/2, ALOS/PALSAR, ENVISAT/ASAR to determine surface velocity (Joughin et al., 2010; Howat, I. 2017; Rignot & Mouginot, 2012). After 2013, we use Landsat-8, Sentinel-1a/b and RADARSAT-2 (Mouginot et al., 2017). All synthetic aperture radar (SAR) datasets are processed assuming surface parallel flow using the digital elevation model (DEM) from the Greenland Mapping Project (GIMP; Howat et al., 2014) and calibrated as described in Mouginot et al., 2012, 2017.
Data were provided by the European Space Agency (ESA) the EU Copernicus program (through ESA), the Canadian Space Agency (CSA), the Japan Aerospace Exploration Agency (JAXA), the Agenzia Spaziale Italiana (ASI), the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and the National Aeronautics and Space Administration (NASA). SAR data acquisition were coordinated by the Polar Space Task Group (PSTG).
References:
Dehecq, A, Gourmelen, N, Trouve, E (2015). Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir-Karakoram-Himalaya. Remote Sensing of Environment, 162, 55–66.
Howat IM, Negrete A, Smith BE (2014) The greenland ice mapping project (gimp) land classification and surface elevation data sets. The Cryosphere 8(4):1509–1518.
Howat, I (2017). MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from Optical Images, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.
Joughin, I., B. Smith, I. Howat, T. Scambos, and T. Moon. (2010). Greenland Flow Variability from Ice-Sheet-Wide Velocity Mapping, J. of Glac.. 56. 415-430.
Joughin IR, Kwok R, Fahnestock MA (1998) Interferometric estimation of three dimensional ice-flow using ascending and descending passes. IEEE Trans. Geosci. Remote Sens. 36(1):25–37.
Joughin, I, Smith S, Howat I, and Scambos T (2015). MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.
Michel R, Rignot E (1999) Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle Imaging Radar images: comparison of the phase correlation method with radar interferometry. J. Glaciol. 45(149):93–100.
Mouginot J, Scheuchl B, Rignot E (2012) Mapping of ice motion in Antarctica using synthetic-aperture radar data. Remote Sens. 4(12):2753–2767.
Mouginot J, Rignot E, Scheuchl B, Millan R (2017) Comprehensive annual ice sheet velocity mapping using landsat-8, sentinel-1, and radarsat-2 data. Remote Sensing 9(4).
Rignot E, Mouginot J (2012) Ice flow in Greenland for the International Polar Year 2008-2009. Geophys. Res. Lett. 39, L11501:1–7
Annual Ice Velocity of the Greenland Ice Sheet (1972-1990)
No full text<p>We derive surface ice velocity using data from 16 satellite sensors deployed by 6 different space agencies. The list of sensors and the year that they were used are listed in the following (Table S1). The SAR data are processed from raw to single look complex using the GAMMA processor (www.gamma-rs.ch). All measurements rely on consecutive images where the ice displacement is estimated from tracking or interferometry (Joughin et al. 1998, Michel and Rignot 1999, Mouginot et al. 2012). Surface ice motion is detected using a speckle tracking algorithm for SAR instruments and feature tracking for Landsat. The cross-correlation program for both SAR and optical images is ampcor from the JPL/Caltech repeat orbit interferometry package (ROI_PAC). We assembled a composite ice velocity mosaic at 150 m posting using our entire speed database as described in Mouginot et al. 2017 (Fig. 1A). The ice velocity maps are also mosaicked in annual maps at 150 m posting, covering July, 1st to June, 30th of the following year, i.e. centered on January, 1st (12) because a majority of historic data were acquired in winter season, hence spanning two calendar years.</p>
<p>We use Landsat-1&2/MSS images between 1972 and 1976 and combine image pairs up to 1 years apart to measure the displacement of surface features between images as described in Dehecq et al., 2015 or Mouginot et al. 2017. We use the 1978 2-m orthorectified aerial images to correct the geolocation of Landsat-1 and -2 images (Korsgaard et al., 2016). Between 1984 and 1991, we processed Landsat-4&5/TM image pairs acquired up to 1-year apart. Only few Landsat-4 and -5 images (~3%) needed geocoding refinement using the same 1978 reference as used previously. Between 1991 and 1998, we process radar images from the European ERS-1/2, with a repeat cycle varying from 3 to 36 days depending on the mission phase. Between 1999 and 2013, we use Landsat-7, ASTER, RADARSAT-1/2, ALOS/PALSAR, ENVISAT/ASAR to determine surface velocity (Joughin et al., 2010; <span>Howat, I.</span> 2017; Rignot & Mouginot, 2012). After 2013, we use Landsat-8, Sentinel-1a/b and RADARSAT-2 (Mouginot et al., 2017). All synthetic aperture radar (SAR) datasets are processed assuming surface parallel flow using the digital elevation model (DEM) from the Greenland Mapping Project (GIMP; Howat et al., 2014) and calibrated as described in Mouginot et al., 2012, 2017.</p>
<p>Data were provided by the European Space Agency (ESA) the EU Copernicus program (through ESA), the Canadian Space Agency (CSA), the Japan Aerospace Exploration Agency (JAXA), the Agenzia Spaziale Italiana (ASI), the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS). SAR data acquisition were coordinated by the Polar Space Task Group (PSTG).</p>
<p> </p>
<p class="SMcaption">References:</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Dehecq, A, Gourmelen, N, Trouve, E (2015). Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir-Karakoram-Himalaya. Remote Sensing of Environment, 162, 55–66.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Howat IM, Negrete A, Smith BE (2014) The greenland ice mapping project (gimp) land</p>
<p class="SMcaption">classification and surface elevation data sets. The Cryosphere 8(4):1509–1518.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Howat, I (2017). MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from Optical Images, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.</p>
<p class="SMcaption"> </p>
<p>Joughin, I., B. Smith, I. Howat, T. Scambos, and T. Moon. (2010). Greenland Flow Variability from Ice-Sheet-Wide Velocity Mapping, J. of Glac.. 56. 415-430.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Joughin IR, Kwok R, Fahnestock MA (1998) Interferometric estimation of three dimensional ice-flow using ascending and descending passes. IEEE Trans. Geosci. Remote Sens. 36(1):25–37.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Joughin, I, Smith S, Howat I, and Scambos T (2015). MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Michel R, Rignot E (1999) Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle</p>
<p class="SMcaption">Imaging Radar images: comparison of the phase correlation method with radar interferometry. J. Glaciol. 45(149):93–100.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Mouginot J, Scheuchl B, Rignot E (2012) Mapping of ice motion in Antarctica using</p>
<p class="SMcaption">synthetic-aperture radar data. Remote Sens. 4(12):2753–2767.</p>
<p class="SMcaption"> </p>
<p class="SMcaption">Mouginot J, Rignot E, Scheuchl B, Millan R (2017) Comprehensive annual ice sheet velocity mapping using landsat-8, sentinel-1, and radarsat-2 data. Remote Sensing 9(4).</p>
<p class="SMcaption"> </p>
<p>Rignot E, Mouginot J (2012) Ice flow in Greenland for the International Polar Year 2008-<br>
2009. Geophys. Res. Lett. 39, L11501:1–7.</p>
<p class="SMcaption"> </p>
<p class="SMcaption"> </p>
North polar deposits of Mars: Extreme purity of the water ice
No full textThe polar layered deposits are the largest reservoir of water on the surface of Mars. The physical properties of the ice and their spatial distribution are largely unknown. 140,000 data points from the sounding radar SHARAD on the Mars Reconnaissance Orbiter were analyzed over the Gemina Lingula region, one-fourth of the north polar layered deposits area. Maps of the dielectric properties of the bulk ice were drawn up. There is no basal melting signature. A drop of the dielectric constant in north-west of Gemina Lingula could be explained by an abrupt 250-meter uplift of the base. The bulk ice of the studied region has an average dielectric constant of 3.10 (s = 0.12) and a loss tangent <0.0026 (s = 0.0005). Analytic interpretations shown the volume of ice is pure at 95%. The impurities have a radial distribution, with higher concentrations at margins
Indigenous Peoples and Litigation:Strategies for Legal Empowerment
No full textAcross the globe indigenous peoples are increasingly using litigation to seek remedies for violation of their fundamental human rights. The rise of litigation is to be placed in the larger issue of increased land grabbing, natural resources exploitation and the general lack of recognition of their rights at the national level. This lack of legal rights is usually coupled with a lack of political will to address the issues faced by indigenous peoples, often leading to serious human rights violations, leaving indigenous advocates with few options but to turn to courts as a last resort to seek remedies. This article examines some of the issues faced by indigenous peoples and their advocates when engaging in human rights litigation. The goal is to offer a practice-based reflection on the encounter between courts and indigenous peoples with a specific focus on analysing strategies to ensure their legal empowerment. This is particularly important knowing the technicality, externalities and complexities of the process of litigation, and the fact that many decisions do not get implemented. In this context this article explores how the process of litigation in itself can support legal empowerment and the wider fight for justice. © 2020, The Author(s). The attached document (embargoed until 10/10/2022) is an author produced version of a paper published in JOURNAL OF HUMAN RIGHTS PRACTICE uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it.<br/
Storstrømmen, Northeast Greenland - speed - grounding lines - ice fronts
No full textThe datasets describe the ice speed, grounding line positions and ice fronts of Storstrømmen and L. Bistrup Bræ located in Northeast Greenland. This data set consists of GeoTIFF (.tif) for the speed maps and ESRI shapefiles (.shp, .shx, .dbf, .prj) for the grounding lines and ice fronts.
Surface ice velocity derived from feature or speckle tracking using declassified CORONA images from August 1967 and March 1968, Landsat-1&2/MSS images between 1973 and 1976, orthorectified American HEXAGON spy image acquired in1978 and 1982, Landsat-4&5/TM image pairs between 1984 and 1991, ERS-1/2 in 1993. GeoTIFF speed data are provided on polar stereographic projection. The file naming convention is first acquisition date (date1) followed by the second acquisition date (date2) and velocity component along x or y axis. (vx or v). Example : 19931231-19940118_vy.tif where date1=19931231, date2=19940118 and vy is the displacement along the y-axis.
Grounding Lines position using InSAR data from the European Earth Remote Sensing (ERS-1/2) radar satellite collected in 1992 (3-day repeat cycle), in 1996 (1-day apart) and Sentinel-1a/b in Nov. 2015 and Apr. 2017. To digitize the grounding lines, we pick the inward limit of detection of vertical motion, where the glacier becomes afloat with a precision of about 50 m, as in Rignot et al., 2011.
Ice fronts position between 1907 and 2015 from historical maps, aerial and satellite imagery
Dielectric map of the Martian northern hemisphere and the nature of plain filling materials
No full text[1] A number of observations suggest that an extended ocean once covered a significant part of the Martian northern hemisphere. By probing the physical properties of the subsurface to unprecedented depth, the MARSIS/Mars Express provides new geophysical evidences for the former existence of a Late Hesperian ocean. The Vastitas Borealis formation, located inside a putative shoreline of the ancient ocean, has a low dielectric constant compared with that of typical volcanic materials. We show that the measured value is only consistent with low-density sedimentary deposits, massive deposits of ground-ice, or a combination of the two. In contrast, radar observations indicate a distribution of shallow ground ice in equilibrium with the atmosphere in the south polar region. We conclude that the northern plains are filled with remnants of a late Hesperian ocean, fed by water and sediments from the outflow channels about 3 Gy ago
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
No full text3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
M. Jessell 1,2, J. Giraud 1,2, M. Lindsay 1,2
1 Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
2 Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia
Contact author: Jeremie Giraud ([email protected])
Companion dataset to the paper:
Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.
This dataset contains models and data shown in the paper, in both 2D and 3D:
1. Geological model
Reference lithology voxet:
The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.
Probability voxet
The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.
2. True and inverted models for density and magnetic susceptibility
Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.
3. Bouguer and total magnetic field anomaly
Calculation is detailed in the paper.
The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.
It is a companion dataset to:
Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.445262
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