Remote sensing data from Earth observation satellites offer unprecedented opportunity for predicting and understanding the behavior of the Earth's ecosystem. However, because of their massive volume, extracting interesting patterns such as ecosystem disturbances from the data is a challenging task. In this paper, we present a case study on the application of data mining to the disturbance event detection problem. We describe two approaches-moving average and random walk-for detecting ecosystem disturbances. We then illustrate how clustering can be used to identify locations with similar incidents of ecosystem disturbance events. Finally, we develop a clustering-based framework to aid the visual exploration and detection of ecosystem disturbances from high resolution vegetation cover data.