We describe an approach to estimation of the spatial distribution of reindeer (Rangifer tarandus). Spatial autocorrelation, inherent to the data describing the distribution of wildlife species, contains information that can be utilized to improve the effciency of field inventories. Our data included reindeer fecal pellet counts, satellite imagery and a digital terrain model. We applied ordinary logistic regression, autologistic regression, and the Gibbs sampler to predict spatial distribution of reindeer based on the combined data. A training set was used to compare the outcome for different field sampling designs for each method. Results suggested the possibility to reduce the number of plots by up to 75% with a 15% reduction in prediction accuracy (quality). We also showed that the Gibbs sampler outperformed, in terms of accuracy, the logistic regression. The outcome, however, was dependent on the spectral homogeneity of the area and on the relative position of the sampling design to the elevation curves. Our results justify the incorporation of spatial information when modeling the distribution of reindeer at finer scales (< 1 km).