Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub

    Description

    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.

    Publication Notes

    • You may send email to pubrequest@fs.fed.us to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    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.

    Keywords

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

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/44286