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Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats.

, , , and . ICCS, volume 4 of Procedia Computer Science, page 1612-1621. Elsevier, (2011)

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Multivariate Geographic Cluster Using a Beowulf-style Parallel Computer., and . PDPTA, page 1292-1298. CSREA Press, (1999)Geospatiotemporal data mining in an early warning system for forest threats in the United States., , , , and . IGARSS, page 170-173. IEEE, (2010)Characterization and Classification of Vegetation Canopy Structure and Distribution within the Great Smoky Mountains National Park Using LiDAR., , , , , and . ICDM Workshops, page 1478-1485. IEEE Computer Society, (2015)Using multivariate clustering to characterize ecoregion borders., and . Computing in Science and Engineering, 1 (4): 18-25 (1999)Monitoring of Land Use/Land-Cover Changes in the Arid Transboundary Middle Rio Grande Basin Using Remote Sensing., , , , and . Remote Sensing, 10 (12): 2005 (2018)Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers., , , , , and . CLUSTER, page 267-277. IEEE Computer Society, (2017)Using dendronal signatures for feature extraction and retrieval., , and . Int. J. Imaging Systems and Technology, 11 (4): 243-253 (2000)Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA., , and . Remote Sensing, 9 (5): 407 (2017)Identification and Visualization of Dominant Patterns and Anomalies in Remotely Sensed Vegetation Phenology Using a Parallel Tool for Principal Components Analysis., , , , , and . ICCS, volume 18 of Procedia Computer Science, page 2396-2405. Elsevier, (2013)Data Mining in Earth System Science (DMESS 2011)., , , , , , , , and . ICCS, volume 4 of Procedia Computer Science, page 1450-1455. Elsevier, (2011)