U.S. Department of EnergyOffice of Scientific and Technical Information
Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery
Abstract
This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two fieldmore »
- Authors:
-
;
;
;
- University of Alaska Fairbanks; University of Alaska Fairbanks
- University of Alaska Fairbanks
- Oak Ridge National Laboratory
- U.S. Fish and Wildlife Service
- Publication Date:
- Other Number(s):
- NGA559
- DOE Contract Number:
- AC02-05CH11231
- Research Org.:
- Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic
- Sponsoring Org.:
- U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Subject:
- 54 ENVIRONMENTAL SCIENCES; EARTH SCIENCE > CLIMATE INDICATORS > CRYOSPHERIC INDICATORS > ICE EXTENT; EARTH SCIENCE > CRYOSPHERE; EARTH SCIENCE > CRYOSPHERE > SNOW/ICE; EARTH SCIENCE > CRYOSPHERE > SNOW/ICE > ICE EXTENT; EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE/LAND COVER CLASSIFICATION; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE
- OSTI Identifier:
- 2519690
- DOI:
- https://doi.org/10.15485/2519690
Citation Formats
Dann, Julian, Zwieback, Simon, Bolton, Bob, and Leonard, Paul. Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery. United States: N. p., 2025.
Web. doi:10.15485/2519690.
Dann, Julian, Zwieback, Simon, Bolton, Bob, & Leonard, Paul. Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery. United States. doi:https://doi.org/10.15485/2519690
Dann, Julian, Zwieback, Simon, Bolton, Bob, and Leonard, Paul. 2025.
"Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery". United States. doi:https://doi.org/10.15485/2519690. https://www.osti.gov/servlets/purl/2519690. Pub date:Wed Jan 01 04:00:00 UTC 2025
@article{osti_2519690,
title = {Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery},
author = {Dann, Julian and Zwieback, Simon and Bolton, Bob and Leonard, Paul},
abstractNote = {This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},
doi = {10.15485/2519690},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 04:00:00 UTC 2025},
month = {Wed Jan 01 04:00:00 UTC 2025}
}