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11 results for “densification”
Forest cover, age and densification rate for Southern China at 30 m resolution
<p>The dataset includes Landsat based forest cover maps from 1986-2020 that were used to calculate forest age and densification rate.</p> <p>Forest probability as the output from a Random Forest model was used to reflect forest cover, and shows how likely an area resembles a dense forest. We set a threshold of 50% probability to define an area as dense forest, and the number of years from the year the threshold is crossed until 2018 as the forest age. Note that the area must remain above 50% until 2018 to qualify as forest. The years before the threshold is crossed are used to calculate the densification rate. It is defined as the mean probability change per year in the period where an area is between a probability of 20% and 50%. If the area falls below 20% the count is reset.</p>
NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: Jan 1980 - Jul 2024
<p><strong>Overview</strong></p> <p>The NASA GSFC-FDM v1.2.1 provides the evolution of firn air content (FAC), surface mass balance (SMB) (and its individual components), and total firn height change over the Greenland and Antarctic Ice Sheets from January 1, 1980 to July 30, 2024 at 5-day temporal resolution. The model uses atmospheric forcing from NASA GMAO's Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global atmospheric reanalysis, combined with a higher resolution replay (see Medley et al., 2022) as input into the Community Firn Model (CFMv1.1.6) to simulate the evolution of firn properties across the ice sheets. The GSFC-FDMv1.2.1 is provided on a 12.5 km x 12.5 km North/South Polar Stereographic Grid, depending on the ice sheet.</p> <p>For a thorough description of how the GSFC-FDMv1.2.1 was generated see Medley et al. (2022) in <em>The Cryosphere</em>. Release 2 contains model output up through June 30, 2022, whereas the initial release only extended through September 30, 2021. Release 3 contains model output up through July 31, 2024 and uses CFMv2.3.1. The model set up is identical between releases.</p> <h3>*** The spatial grids are incorrect in this version, so we have restricted access to these files. Please use Version 4. ***</h3>
Granular piston-probing in microgravity: powder compression, from densification to jamming
<p>The datasets represents all data used in the article "Granular piston-probing in microgravity: powder compression, from densification to jamming", by Olfa D'Angelo, Anabelle Horb, Aidan Cowley, Matthias Sperl, and W. Till Kranz, published in npj Microgravity (2022).</p>
Impact of Seriphium plumosum densification on Mesic Highveld Grassland biodiversity in South Africa
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Data from: Spatial dynamics of alpine treelines under global warming: what explains the mismatch between tree densification and elevational upward shifts at the treeline ecotone?
<p><b>Aim</b></p> <p>Most studies focusing on the alpine treeline responses to climate warming have used either the tree densification within the ecotone or its elevational upshift as indicators. However, it is acknowledged that the relation between densification and upshift is spatially heterogeneous, making inferences and comparability among studies tricky. The lack of consistent empirical evidence on this potential mismatch and its drivers leads us to focus on this issue in this study. The aim was twofold: (i) to quantify the mismatch between the two processes at a regional scale, and (ii) to identify its site-specific determinants.</p> <p><b>Location</b></p> <p>French eastern Pyrenees</p> <p><b>Time period</b></p> <p>1953-2015</p> <p><b>Major taxa studied </b></p> <p><i>Pinus</i><i> uncinata </i>(Ramond ex DC)</p> <p><b>Methods</b></p> <p>An object-oriented supervised classification procedure was performed on historical (1953) and current (2015) air-photos. Based on the resulting rasters, densification of the treeline ecotone and upward shift of the treeline were estimated at the two dates in 191 sites, then standardized, before finally being compared. Three site clusters were derived (no mismatch, densification prevalence, upshift prevalence). After having characterized their spatial patterns through join count statistics, a multinomial logistic regression model was computed to identify the correlates of these clusters among a list of site variables.</p> <p><b>Results</b></p> <p>No spatial pattern among the categories of responses emerges at a local scale, but buffers with no mismatch tend to aggregate at a larger scale. Changes in minimum air temperatures, site elevation, mean slope, slope morphometry and lithology appear as significant drivers of the observed mismatch, implying that the relation between densification and elevational upshift is context-specific.</p> <p><b>Main conclusions</b></p> <p><a name="_Hlk17817327">Our findings suggest that both </a><a name="_Hlk17816517">densification and upshift </a>should be considered in quantitative analyses of treeline spatial dynamics, since these two ecological processes are not controlled by the same drivers.</p>
NMR Data - Densification of Sodium and Magnesium Aluminosilicate Glasses at Ambient Temperature
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Enhanced firn densification in high-accumulation shear margins of the NE Greenland Ice Stream
<p>Seismic data in support of the publication "Enhanced firn densification in high-accumulation shear margins of the NE Greenland Ice Stream" in the Journal of Geophysical Research. Details in README.txt</p>
Data from: Spatial dynamics of alpine treelines under global warming: what explains the mismatch between tree densification and elevational upward shifts at the treeline ecotone?
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Metamorphic densification can account for the missing felsic crust of the Greater Indian continent
<p>Metamorphic densification can account for the missing felsic crust of the Greater Indian continent</p>
Enhancing Regional Quasi-Geoid Refinement Precision: An Analytical Approach Employing ADS80 Tri-linear Array Stereoscopic Imagery for Aerial Triangulation Densification and GNSS Gravity-Potential Leveling
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Trial Exploring Feasibility of Densification and Optimal Sequencing of Postoperative Adjuvant Fluorouracil, Epirubicin Plus Cyclophosphamide (FEC) and Docetaxel Chemotherapy in Patients With High Risk
ClinicalTrials.gov study NCT00256360. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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