Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Thaler, Evan; Uhlemann, Sebastian; Rowland, Joel; Dafflon, Baptiste; Schwenk, Jon; Bennett, Katrina; Thomas, Lauren

doi:10.5440/1970774

Title: Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

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Abstract

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is inmore »« less

Authors:

;

Los Alamos National Laboratory

Publication Date:: Mon May 08 00:00:00 EDT 2023

Other Number(s):: https://doi.org/10.5440/1970774; NGA318

DOE Contract Number:: AC05-00OR22725

Research Org.:: Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Collaborations:: ORNL

Subject:: 54 ENVIRONMENTAL SCIENCES; EARTH SCIENCE > LAND SURFACE > SOILS; EARTH SCIENCE > LAND SURFACE > SOILS > PERMAFROST; ESS-DIVE CSV File Formatting Guidelines Reporting Format; ESS-DIVE File Level Metadata Reporting Format; Kougarok mm 64; Seward Peninsula, Alaska; Teller mm 27; Teller mm 47; permafrost presence

OSTI Identifier:: 1970774

DOI:: https://doi.org/10.5440/1970774

Citation Formats


                    Thaler, Evan, Uhlemann, Sebastian, Rowland, Joel, Dafflon, Baptiste, Schwenk, Jon, Bennett, Katrina, and Thomas, Lauren. Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data.  United States: N. p., 2023. 
        Web.  doi:10.5440/1970774.

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                    Thaler, Evan, Uhlemann, Sebastian, Rowland, Joel, Dafflon, Baptiste, Schwenk, Jon, Bennett, Katrina, & Thomas, Lauren. Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data.  United States.  doi:https://doi.org/10.5440/1970774

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                    Thaler, Evan, Uhlemann, Sebastian, Rowland, Joel, Dafflon, Baptiste, Schwenk, Jon, Bennett, Katrina, and Thomas, Lauren. 2023.  
"Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data".  United States.  doi:https://doi.org/10.5440/1970774.  https://www.osti.gov/servlets/purl/1970774. Pub date:Mon May 08 00:00:00 EDT 2023

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@article{osti_1970774,

  title        = {Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data},

  author       = {Thaler, Evan and Uhlemann, Sebastian and Rowland, Joel and Dafflon, Baptiste and Schwenk, Jon and Bennett, Katrina and Thomas, Lauren},

  abstractNote = {Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), 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          = {10.5440/1970774},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon May 08 00:00:00 EDT 2023},

  month        = {Mon May 08 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.5440/1970774

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