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We employed an integrated approach that combined remote sensing techniques with field measurements to predict the presence/absence of near-surface permafrost in a section of the Alaska Highway corridor. We investigated the correlative relationships among vegetation type, topography, moss thickness, tussock condition and near-surface permafrost in the study area. Analysis of moss thickness and active-layer depth in low-lying plains (slope <8?) showed an inverse relationship in different vegetation classes. The maximum likelihood classification of remotely sensed data mapped 80% of the study area as covered with vegetation. We developed an empirical-statistical (Binary Logistic Regression) model to establish the statistical...
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We used the CRU (1950-1959 and 2000-2009) and projected 5-GCM composite (2001-2010, 2051-2060, and 2091-2100) decadal climate forcing, ecotype (Jorgenson et al. 2008), soil landscape (Jorgenson et al. 2008), and snow (unpublished) maps of WRST to model the presence or absence of near-surface permafrost, temperature at the bottom of seasonal freeze-thaw layer and thickness of seasonal freeze-thaw layer within WRST. We produced permafrost temperature and active-layer and seasonally-frozen-layer thickness distribution maps through this modeling effort at a pixel spacing of 28.5 m. This is an immense improvement over the spatial resolution of existing permafrost maps on any part of Alaska, whether produced through the...
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An up-to-date permafrost distribution map is critical for making engineering decisions during the planning and design of any engineering project in Interior Alaska. I used a combination of empirical-statistical and remote sensing techniques to generate a high-resolution spatially continuous near-surface (< 1.6 m) permafrost map by exploiting the correlative relationships between permafrost and biophysical terrain parameters. A Binary Logistic Regression (BLR) model was used to establish the relationship between vegetation type, aspect-slope and permafrost presence. The logistic coefficients for each variable class obtained from the BLR model were supplied to respective variable classes mapped from remotely sensed...
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A combination of binary logistic regression (BLR) and remote sensing techniques was used to generate a high-resolution spatially continuous near-surface (< 1.6 m) permafrost map. The BLR model was used to establish the relationship between vegetation type, aspect-slope, and permafrost presence; it predicted permafrost presence with an accuracy of 88%. Near-surface permafrost occupies 45% of the total vegetated area and 37% of the total study area. As less than 50% of the study area is underlain by near-surface permafrost, this distribution is characterized as "sporadic" for the study area.; A combination of binary logistic regression (BLR) and remote sensing techniques was used to generate a high-resolution spatially...
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We used the CRU (1950-1959 and 2000-2009) and projected 5-GCM composite (2001-2010, 2051-2060, and 2091-2100) decadal climate forcing, ecotype (Stevens 2001), soil landscape (Clark and Duffy 2006), and snow (unpublished) maps of DENA to model the presence or absence of near-surface permafrost, temperature at the bottom of seasonal freeze-thaw layer and its thickness within DENA. We produced permafrost temperature, and active-layer and seasonally-frozen-layer thickness distribution maps through this modeling effort at a pixel spacing of 28 m. This is an immense improvement over the spatial resolution of existing permafrost maps on any part of Alaska, whether produced through the spatially explicit thermal modeling...
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