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    We investigated the use of principal components analysis (PCA) to visualize dominant patterns and identify anomalies in a multi-year land surface phenology data set (231 m × 231 m normalized difference vegetation index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)) used for detecting threats to forest health in the conterminous United States (CONUS). Our goal is to find ways that PCA can be used with this massive data set to automate the process of detecting forest disturbance and attributing it to particular agents. We briefly describe the parallel computational approaches we used to make PCA feasible, and present some examples in which we have used it to visualize the seasonal vegetation phenology for the CONUS and to detect areas where anomalous NDVI traces suggest potential threats to forest health.

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    Mills, Richard Tran; Kumar, Jitendra; Hoffman, Forrest M.; Hargrove, William W.; Spruce, Joseph P.; Norman, Steven P. 2013. Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis. Procedia Computer Science 18:2396–2405.


    phenology, MODIS, NDVI, remote sensing, principal components analysis, singular value decomposition, data mining, anomaly detection, high performance computing, parallel computing

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