### Access
Photos of ~50 permaforst boreholes and associated cores can be accessed and downloaded from the 'AR\_Fire\_Core_Photos' directory via: [https://arcticdata.io/data/10.18739/A2251FM9P/](https://arcticdata.io/data/10.18739/A2251FM9P/)
### Overview
The Anaktuvuk River tundra fire burned more than 1,000 square kilometers of permafrost-affected arctic tundra in northern Alaska in 2007. The fire is the largest historical recorded tundra fire on the North Slope of Alaska. Fifty percent of the burn area is underlain by Yedoma permafrost that is characterized by extremely high ground-ice content of organic-rich, silty buried soils and the occurrence of large, syngenetic polygonal ice wedges. Given the high ground-ice content of this terrain, Yedoma is thought to be among the most vulnerable to fire-induced thermokarst in the Arctic. With this dataset, we update observations on near-surface permafrost in the Anaktuvuk River tundra fire burn area from 2009 to 2023 using repeat airborne LiDAR-derived elevation data, ground temperature measurements, and cryostratigraphic studies.
We have provided all of the data that has gone into an analysis and resulting paper that has been submitted for peer review at the journal Scientific Reports. The datasets include:
- 1 m spatial resolution airborne LiDAR-derived digital terrain models from the summers of 2009, 2014, and 2021.
- The area in which thaw subsidence was detected in the multi-temporal LiDAR data using the Geomorphic Change Detection software.
- A terrain unit map developed for the 50 square kilometer study area.
- Ground temperature time series measurements for a logger located in the burned area and a logger located in an unburned area. The ground temperature data consist of daily mean measurements at a depth of 0.15 m (active layer) and 1.00 m (permafrost) from July 2009 to August 2023.
- Photos ~50 permafrost boreholes and the associated cores collected there.
- A borehole log and notes pdf also accompanies our studies on the cryostratigraphy of permafrost post-fire and our observations on the recovery of permafrost.
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Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling
Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.
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- NSF-PAR ID:
- 10283520
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 12
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2368
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs13122368
