  As both a central task in Remote Sensing and a common problem in many other
situations involving time series data, change point detection boasts a thorough
and well-documented history of study. However, the treatment of missing data
and proper exploitation of the structure in multivariate time series during
change point detection remains lacking. Multispectral, high temporal resolution
time series data from NASA's Moderate Resolution Imaging Spectroradiometer
(MODIS) instruments provide an attractive and challenging context to contribute
to the change point detection literature. In an effort to better monitor change
in land cover using MODIS data, we present a novel approach to identifying
periods of time in which regions experience some conversion-type of land cover
change. That is, we propose a method for parameter estimation and change point
detection in the presence of missing data which capitalizes on the high
dimensionality of MODIS data. We test the quality of our method in a simulation
study alongside a contemporary change point method and apply it in a case study
at the Xingu River Basin in the Amazon. Not only does our method maintain a
high accuracy, but can provide insight into the types of changes occurring via
land cover conversion probabilities. In this way we can better characterize the
amount and types of forest disturbance in our study area in comparison to
traditional change point methods.
