An official website of the United States government
Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging‐derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub‐watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5‐m stream sub‐reach across 426 sub‐watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub‐watersheds that contained training data and decreased to 0.67 for sub‐watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.
more »
« less
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 NHD features are often digitized representations of historic topographic map blue lines and may be outdated.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.
more »
« less
- 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
More Like this
-
-
This data set covers the younger outer coastal plain north of Teshekpuk Lake, North Slope, Alaska. In this region, drained lake basins are abundant features, covering large parts of the landscape. This data set is based on Landsat Thematic Mapper (TM) imagery acquired in August 2010, and a 5 meter (m) resolution Interferometric Synthetic Aperture Radar (IfSAR)-derived digital terrain model. Drained lake basins were manually delineated in a geographic information system (GIS). The data set includes Lake 195, which drained in this area in 2014. For further details please see Jones et al. (2015): Jones, BM, and Arp, CD (2015), Observing a Catastrophic Thermokarst Lake Drainage in Northern Alaska. Permafrost and Periglac. Process., 26, 119– 128. doi: 10.1002/ppp.1842.more » « less
-
This data set covers the younger outer coastal plain north of Teshekpuk Lake, North Slope, Alaska. In this region, drained lake basins are abundant features, covering large parts of the landscape. This data set is based on Landsat Thematic Mapper (TM) imagery acquired in August 2010, and a 5 meter (m) resolution Interferometric Synthetic Aperture Radar (IfSAR)-derived digital terrain model. Drained lake basins were manually delineated in a geographic information system (GIS). The data set includes Lake 195, which drained in this area in 2014. For further details please see Jones et al. (2015): Jones, BM, and Arp, CD (2015), Observing a Catastrophic Thermokarst Lake Drainage in Northern Alaska. Permafrost and Periglac. Process., 26, 119– 128. doi: 10.1002/ppp.1842.more » « less
-
The Native Village of Point Lay (Kali) on the North Slope of Alaska has been identified as the second-most permafrost thaw-affected community in the state of Alaska (Denali Commission, 2019). The village has 82 residential units, housing a population of approximately 330. There are several North Slope Borough municipal structures and the Kali School that serve the community. Most of the residential buildings in the village are built on an elevated surface underlain by ice-rich permafrost that is susceptible to thaw and terrain subsidence. This dataset consists of an orthomosaic and digital surface model (DSM) derived from drone surveys on 26 June 2022 in Point Lay, Alaska. 990 digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 130 hectares (ha). The drone system was flown at 120 meters (m) above ground level (agl) and flight speeds varied from 7–8 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80 percent (%) and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.7.5) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 5 North in Ellipsoid Heights (meters). Elevation information derived over waterbodies is noisy and does not represent the surface elevation of the feature.more » « less
-
### 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.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs13122368
