TY - JOUR
T1 - Modelling Spatial Compositional Data
T2 - Reconstructions of past land cover and uncertainties
AU - Pirzamanbein, Behnaz
AU - Lindström, Johan
AU - Poska, Anneli
AU - Gaillard, Marie José
PY - 2018/4/1
Y1 - 2018/4/1
N2 - In this paper we construct a hierarchical model for spatial compositional data which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past 6000 years over Europe. The model consists of a Gaussian Markov Random Field (GMRF) with Dirichlet observations. A block updated Markov chain Monte Carlo (MCMC), including an adaptive Metropolis adjusted Langevin step, is used to estimate model parameters. The sparse precision matrix in the GMRF provides computational advantages leading to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map. The evaluation results are promising, and the model is able to capture known structures in past land-cover compositions.
AB - In this paper we construct a hierarchical model for spatial compositional data which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past 6000 years over Europe. The model consists of a Gaussian Markov Random Field (GMRF) with Dirichlet observations. A block updated Markov chain Monte Carlo (MCMC), including an adaptive Metropolis adjusted Langevin step, is used to estimate model parameters. The sparse precision matrix in the GMRF provides computational advantages leading to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map. The evaluation results are promising, and the model is able to capture known structures in past land-cover compositions.
KW - Adaptive Metropolis adjusted Langevin
KW - Confidence regions
KW - Dirichlet observation
KW - Gaussian Markov Random Field
KW - Pollen records
U2 - 10.1016/j.spasta.2018.03.005
DO - 10.1016/j.spasta.2018.03.005
M3 - Article
AN - SCOPUS:85044647476
VL - 24
SP - 14
EP - 31
JO - Spatial Statistics
JF - Spatial Statistics
SN - 2211-6753
ER -