Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
- Univ. of Texas, Austin, TX (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
We introduce a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network utilized to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times (<5 min) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiagvik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robus tperformance in diverse tundra settings, with manual validations demonstrating 70–96% accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC). Biological and Environmental Research (BER) (SC-23)
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1544726
- Report Number(s):
- LA-UR--18-29238
- Journal Information:
- The Cryosphere (Online), Journal Name: The Cryosphere (Online) Journal Issue: 1 Vol. 13; ISSN 1994-0424
- Publisher:
- European Geosciences UnionCopyright Statement
- Country of Publication:
- United States
- Language:
- English
High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska
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journal | March 2020 |
High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska
|
journal | March 2020 |
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