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Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest ThreatsAuthor(s): Richard Trans Mills; Forrest M Hoffman; Jitendra Kumar; William W. Hargrove
Source: Procedia Computer Science 4:1612-1621
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
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DescriptionWe investigate methods for geospatiotemporal data mining of multi-year land surface phenology data (250 m2 Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectrometer (MODIS) in this study) for the conterminous United States (CONUS) as part of an early warning system for detecting threats to forest ecosystems. The approaches explored here are based on k-means cluster analysis of this massive data set, which provides a basis for defining the bounds of the expected or “normal” phenological patterns that indicate healthy vegetation at a given geographic location. We briefly describe the computational approaches we have used to make cluster analysis of such massive data sets feasible, describe approaches we have explored for distinguishing between normal and abnormal phenology, and present some examples in which we have applied these approaches to identify various forest disturbances in the CONUS.
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CitationMills, Richard Trans.; Hoffman, Forrest M.; Kumar, Jitendra; Hargrove, William W. 2011. Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats. Procedia Computer Science 4:1612-1621.
Keywordsphenology, MODIS, NDVI, remote sensing, k-means clustering, data mining, anomaly detection, high performance computing
- Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis
- Geospatiotemporal data mining in an early warning system for forest threats in the United States
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