Abstract
This paper presents a Bayesian mixture model approach for detecting areas of habitat that are suitable for S. invicta infestation, aiding the ongoing surveillance for early detection of these exotic pests. We show that Landsat imagery is an affordable and valuable tool to assist in determining an informed surveillance strategy. In this paper, we use Landsat band 3 (visible red), Landsat band 6 (mid infrared) and a soil brightness index, in various combinations, to assess the probability that the area associated with each pixel is habitable terrain, either in a multivariate analysis, or as a univariate spatial temporal model. The multivariate analysis allows researchers to create meaningful clusters that reflect the sometimes complex combinations of conditions of conditions that form suitable habitat, rather then relying on single derived indices.
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