Research

Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields


Reference:

Bolin, D., Lindström, J., Lindgren, F. and Eklundh, L., 2009. Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields. Computational Statistics & Data Analysis, 53 (8), pp. 2885-2896.

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    Official URL:

    http://dx.doi.org/10.1016/j.csda.2008.09.017

    Abstract

    There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the Expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares regressions for the individual pixels in the data set. Use of the EM algorithm also gives a substantial performance gain over Markov Chain Monte Carlo-based estimation approaches.

    Details

    Item Type Articles
    CreatorsBolin, D., Lindström, J., Lindgren, F. and Eklundh, L.
    DOI10.1016/j.csda.2008.09.017
    DepartmentsFaculty of Science > Mathematical Sciences
    Publisher StatementBolin_et_al_2009.pdf: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, vol 53, issue 8, 2009, DOI 10.1016/j.csda.2008.09.017
    RefereedYes
    StatusPublished
    ID Code32279

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