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

D. Bolin, J. Lindström, F. Lindgren, L. Eklundh

Research output: Contribution to journalArticle

15 Citations (Scopus)
128 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)2885-2896
Number of pages12
JournalComputational Statistics & Data Analysis
Volume53
Issue number8
DOIs
Publication statusPublished - 15 Jun 2009

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