Observations and Machine-Learned Models of Near-Surface Permafrost along the Koyukuk River, Alaska, USA ...

• Main Authors: Schwenk, Jon, Piliouras, Anastasia, Rowland, Joel
• Format: Dataset
• Language: English
• Published: Environmental System Science Data Infrastructure for a Virtual Ecosystem 2023
• Subjects: 54 ENVIRONMENTAL SCIENCES; permafrost; floodplains; riverbank erosion; machine learning; training data; permafrost presence; multispectral image; surface water; EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE/LAND COVER CLASSIFICATION > VEGETATION INDEX > NORMALIZED DIFFERENCE VEGETATION INDEX NDVI; Huslia; Rowland; Ice; Tundra; wedge*; Alaska
• Online Access: https://dx.doi.org/10.15485/1922517; https://www.osti.gov/servlets/purl/1922517/

This dataset contains GeoTIFs (raster) and GeoPackages (vector) that map observations of near-surface permafrost and not-permafrost from a field campaign conducted near the village of Huslia, AK along the Koyukuk River and its floodplain in July 2018. These data were collected as part of a campaign to understand if and how permafrost impacts riverbank erosion. This problem cannot be assessed without knowing where permafrost exists. Permafrost was observed via frost probing (to a maximum depth of one meter), coring (to a maximum depth of two meters) and bank/bar excavations. An additional boat survey was performed wherein expert (Joel Rowland) judgment assessed the presence or absence of distinctive permafrost features (e.g., overhanging tundra mats, thermoerosional niching, ice wedges, active drainage of ice melt from soils). This dataset also contains the input features and results of two machine learning models (random forest and convolutional neural network) that extrapolate the observations to the full ...