This thesis explores the applicability of Bayesian spatial models for predicting the occurrence of permafrost across Alaska, USA. Exploratory analysis of a large Alaska soil carbon database suggests the impact of some important environmental covariates on permafrost occurrence is non-linear. Also, exploratory analysis using non-spatial regression models shows that substantial spatial autocorrelation among residuals exists even after accounting for available covariates. Spatial regression models specifically designed to accommodate non-linearity between covariates and probability of permafrost are developed and tested. Results show the proposed models provide improved fit and predictive ability over conventional modeling techniques. Considerations for applying the proposed models to large spatial domains for creating high-resolution map permafrost products are also discussed.
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