Documents in the NTIS Technical Reports collection are the results of federally funded research. They are directly submitted to or collected by NTIS from Federal agencies for permanent accessibility to industry, academia and the public.  Before purchasing from NTIS, you may want to check for free access from (1) the issuing organization's website; (2) the U.S. Government Printing Office's Federal Digital System website http://www.gpo.gov/fdsys; (3) the federal government Internet portal USA.gov; or (4) a web search conducted using a commercial search engine such as http://www.google.com.
Accession Number DE2013-1014247
Title Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets.
Publication Date 2011
Media Count 10p
Personal Author F. M. Hoffman J. Kumar R. T. Mills W. W. Hargrove
Abstract Identification of geographic ecoregions has long been of interest to environmental scientists and ecologists for identifying regions of similar ecological and environmental conditions. Such classifications are important for predicting suitable species ranges, for stratification of ecological samples, and to help prioritize habitat preservation and remediation efforts. Hargrove and Hoffman have developed geographical spatio-temporal clustering algorithms and codes and have successfully applied them to a variety of environmental science domains, including ecological regionalization; environmental monitoring network design; analysis of satellite-, airborne-, and ground-based remote sensing, and climate model-model and model-measurement intercomparison. With the advances in state-of-the-art satellite remote sensing and climate models, observations and model outputs are available at increasingly high spatial and temporal resolutions. Long time series of these high resolution datasets are extremely large in size and growing. Analysis and knowledge extraction from these large datasets are not just algorithmic and ecological problems, but also pose a complex computational problem. This paper focuses on the development of a massively parallel multivariate geographical spatio-temporal clustering code for analysis of very large datasets using tens of thousands processors on one of the fastest supercomputers in the world.
Keywords Algorithms
Climates
Design
Ecology
Environmental monitoring
Gegraphical areas
Habitat
K-means clustering
Preservation
Remote sensing
Resolution
Satellites
Stratification
Supercomputers

 
Source Agency Technical Information Center Oak Ridge Tennessee
NTIS Subject Category 62 - Computers, Control & Information Theory
48B - Natural Resource Management
57H - Ecology
68 - Environmental Pollution & Control
Corporate Author Oak Ridge National Lab., TN.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1323
Contract Number DE-AC05-00OR22725

Science and Technology Highlights

See a sampling of the latest scientific, technical and engineering information from NTIS in the NTIS Technical Reports Newsletter

Acrobat Reader Mobile    Acrobat Reader