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Data mining for visual exploration and detection of ecosystem disturbances

This paper presents a case study on the application of data mining to the problem of detecting ecosystem disturbances from vegetation cover data obtained from satellite observations. We describe two anomaly detection approaches-moving average and random walk-for detecting such events. We also illustrate how clustering can be used to locate similar incidents of disturbance events. Finally, we present a clustering-based framework to aid the visual exploration of ecosystem disturbances from high resolution data.