The ability to monitor forest related change events like forest res, deforestation for agriculture intensification, and logging is critical for effective forest management. Time series remote sensing data sets such as MODIS Enhanced Vegetation Index (EVI) can be used to identify these changes. Most existing approaches work on small data sets spanning over a specific geographic region of a homogeneous vegetation type. Also, most of these need training samples or require setting of parameters for each geographic region individually. These limitations make the algorithms unscalable and restrict their global applicability. In this paper, we present a scalable time series based change detection framework that overcomes these limitations of the existing methods. We introduce the concept of natural variation in EVI for a given of location and incorporate it into the change detection paradigm. We evaluate the change events identified by our approach using forest re validation data in California and Canada. The results of this study demonstrate that the inclusion of a measure of natural variability improves detection accuracy, and makes the paradigm more robust across vegetation types and regions.