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Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryo- spheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM’s applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 B´arðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM’s potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of 0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.
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Characterization of decade-time scale change in Retrogressive Thaw Slumps across Northern Alaska, 2021-2023
Delineations of Retrogressive Thaw Slump (RTS) expansion and light detection and ranging (LiDAR) datasets (LAS files) of RTS sites were used to model how rates of RTS change are influenced by topographic and climatic characteristics across northern Alaska. LiDAR data were collected at ten sites, where five were collected from an uncrewed aerial system (UAS) and five were collected from a terrestrial LiDAR systems (TLS). LiDAR datasets were used to bias correct the open-source ArcticDEM (2 meter-resolution) for calculating annual rates of RTS volumetric losses across all sites. RTS Delineations were used to calculate annual rates of RTS areal expansion and summarize topographic characteristics calculated from the corrected ArcticDEM. Two shapefiles were created from historic satellite and aerial imagery (1949-2021) to summarize RTS areal change across 44 slumps: AK_RTS_ExansionDelineations.shp summarizes the area of RTS expansion between two time periods and AK_RTS_Delineations.shp summarizes the total RTS outline in each year where RTS expansion occurs. LiDAR UAS and TLS data are provided as LAS files from 12 slumps (five sites) near Toolik Lake and 9 slumps (5 sites) within the Noatak National Preserve.
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- Award ID(s):
- 1928048
- PAR ID:
- 10578422
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Arctic Tundra UAS LiDAR Remote Sensing Thermokarst Permafrost Disturbance
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
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