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Modelling Spatial Compositional Data : Reconstructions of past land cover and uncertainties

Pirzamanbein, Behnaz LU ; Lindström, Johan LU ; Poska, Anneli LU and Gaillard, Marie José (2018) In Spatial Statistics 24. p.14-31
Abstract

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... (More)

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.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Adaptive Metropolis adjusted Langevin, Confidence regions, Dirichlet observation, Gaussian Markov Random Field, Pollen records
in
Spatial Statistics
volume
24
pages
18 pages
publisher
Elsevier
external identifiers
  • scopus:85044647476
ISSN
2211-6753
DOI
10.1016/j.spasta.2018.03.005
language
English
LU publication?
yes
id
cfad0987-2f00-41db-b584-a60ff46146d4
date added to LUP
2018-04-10 12:26:43
date last changed
2020-10-07 05:49:28
@article{cfad0987-2f00-41db-b584-a60ff46146d4,
  abstract     = {<p>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.</p>},
  author       = {Pirzamanbein, Behnaz and Lindström, Johan and Poska, Anneli and Gaillard, Marie José},
  issn         = {2211-6753},
  language     = {eng},
  month        = {04},
  pages        = {14--31},
  publisher    = {Elsevier},
  series       = {Spatial Statistics},
  title        = {Modelling Spatial Compositional Data : Reconstructions of past land cover and uncertainties},
  url          = {http://dx.doi.org/10.1016/j.spasta.2018.03.005},
  doi          = {10.1016/j.spasta.2018.03.005},
  volume       = {24},
  year         = {2018},
}