Probabilistic estimates of the distribution of near-surface (within 1m) permafrost in Alaska.

This medium resolution (i.e. 30-m pixels) spatial dataset provides an estimate of the occurrence of near-surface (within 1 m of the ground surface) permafrost in Alaska. The dataset was developed using a decision-tree model that statistically and spatially extended late-season field observations (n ~ 17,000) using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. To circumvent the use of seasonal frost observations, only thaw-depth measurements taken during late-season months (late July to mid-September) or measurements designated to have no near-surface (within 1m) permafrost were used for model calibration and validation. Individual pixel values represent the probability of encountering near-surface permafrost (i.e. 0 to 100 %), as derived from a tree ensemble model, and pixel values greater than 100 correspond to masked land cover types (i.e. 101 = open water; 102 = perennial ice/snow; 103 = developed; 104 = barren, and; 105 = cultivated areas) as defined by the 2001 National Land Cover Database. Decision tree models and resultant maps were tested using independent field observations and f-fold cross validations, which indicated that the map product has an overall accuracy of approximately 85 % when using a probability threshold of 50 % (i.e. less than 50 % = near-surface permafrost absent; greater than or equal to 50 % = near-surface permafrost present).

                                            [
  "010:12"
]
                                        

                                            {
  "fn": "Neal J Pastick",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:njpastick@usgs.gov"
}
                                        

                                            [
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    "accessURL": "http://dx.doi.org/10.5066/F7C53HX6",
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                                            [
  "Pedometrics",
  "Permafrost",
  "Remote Sensing",
  "Spatial Modeling",
  "USGS:5602ab5ae4b03bc34f5448b4"
]
                                        

                                            {
  "name": "U.S. Geological Survey",
  "@type": "org:Organization"
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                                            [
  "Geospatial"
]