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Fast estimation of spatially dependent temporal trends using Gaussian Markov Random fields

Author:
  • David Bolin
  • Johan Lindström
  • Lars Eklundh
  • Finn Lindgren
Publishing year: 2009
Language: English
Pages: 2885-2896
Publication/Series: Computational Statistics & Data Analysis
Volume: 53
Issue: 8
Document type: Journal article
Publisher: Elsevier

Abstract english

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.

Keywords

  • Probability Theory and Statistics
  • Physical Geography

Other

Published
  • ISSN: 0167-9473
E-mail: lars [dot] eklundh [at] nateko [dot] lu [dot] se

Professor

Dept of Physical Geography and Ecosystem Science

+46 46 222 96 55

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Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden

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