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Modelling Spatial C...
Modelling Spatial Compositional Data : Reconstructions of past land cover and uncertainties
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- Pirzamanbein, Behnaz (författare)
- Lund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Centre for Environmental and Climate Science (CEC),Faculty of Science,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Technical University of Denmark
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- Lindström, Johan (författare)
- Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Centre for Environmental and Climate Science (CEC),Faculty of Science,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Poska, Anneli (författare)
- Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science,Tallinn University of Technology
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- Gaillard, Marie-José, 1953- (författare)
- Linnaeus University,Linnéuniversitetet,Institutionen för biologi och miljö (BOM)
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(creator_code:org_t)
- Elsevier, 2018
- 2018
- Engelska.
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Ingår i: Spatial Statistics. - : Elsevier. - 2211-6753. ; 24, s. 14-31
- Relaterad länk:
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http://arxiv.org/pdf...
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http://dx.doi.org/10...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- 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. (C) 2018 Elsevier B.V. All rights reserved.
Ämnesord
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Gaussian Markov Random Field
- Dinchlet observation
- Adaptive Metropolis adjusted Langevin
- Pollen records
- Confidence regions
- Paleoecology
- Paleoekologi
- Adaptive Metropolis adjusted Langevin
- Confidence regions
- Dirichlet observation
- Gaussian Markov Random Field
- Pollen records
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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