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| DOI | 10.1016/j.srs.2024.100130 |
| Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights | |
| 发表日期 | 2024 |
| ISSN | 2666-0172 |
| 起始页码 | 9 |
| 卷号 | 9 |
| 英文摘要 | 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, cryospheric, 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 ar & eth;arbunga 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 m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 +/- 8) x 10 3 m 3 and a total area of 0.025 km 2 . 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 m 3 and 1,160,000 +/- 85,000 m 3 , 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. |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001217864600001 |
| 来源期刊 | SCIENCE OF REMOTE SENSING
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/306202 |
| 作者单位 | State University System of Florida; University of Florida; University System of Ohio; Ohio State University; Michigan State University; University of Alaska System; University of Alaska Fairbanks; Helmholtz Association; Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research; Humboldt University of Berlin; Wilfrid Laurier University |
| 推荐引用方式 GB/T 7714 | . Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights[J],2024,9. |
| APA | (2024).Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights.SCIENCE OF REMOTE SENSING,9. |
| MLA | "Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights".SCIENCE OF REMOTE SENSING 9(2024). |
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