TY - JOUR

T1 - Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields

AU - Bolin, D.

AU - Lindström, Johan

AU - Lindgren, F.

AU - Eklundh, L.

PY - 2009/6/15

Y1 - 2009/6/15

N2 - 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.

AB - 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.

U2 - 10.1016/j.csda.2008.09.017

DO - 10.1016/j.csda.2008.09.017

M3 - Article

VL - 53

SP - 2885

EP - 2896

JO - Computational statistics & data analysis

JF - Computational statistics & data analysis

SN - 0167-9473

IS - 8

ER -