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As both a central task in Remote Sensing and a common problem in many othersituations involving time series data, change point detection boasts a thoroughand well-documented history of study. However, the treatment of missing dataand proper exploitation of the structure in multivariate time series duringchange point detection remains lacking. Multispectral, high temporal resolutiontime series data from NASA's Moderate Resolution Imaging Spectroradiometer(MODIS) instruments provide an attractive and challenging context to contributeto the change point detection literature. In an effort to better monitor changein land cover using MODIS data, we present a novel approach to identifyingperiods of time in which regions experience some conversion-type of land coverchange. That is, we propose a method for parameter estimation and change pointdetection in the presence of missing data which capitalizes on the highdimensionality of MODIS data. We test the quality of our method in a simulationstudy alongside a contemporary change point method and apply it in a case studyat the Xingu River Basin in the Amazon. Not only does our method maintain ahigh accuracy, but can provide insight into the types of changes occurring vialand cover conversion probabilities. In this way we can better characterize theamount and types of forest disturbance in our study area in comparison totraditional change point methods.

A Bayesian Change Point Model for Detecting Land Cover Changes in MODIS Time Series