Reconstruction of Past European Land Cover Based on Fossil Pollen Data: Gaussian Markov Random Field Models for Compositional Data

Forskningsoutput: AvhandlingDoktorsavhandling (sammanläggning)

Standard

Reconstruction of Past European Land Cover Based on Fossil Pollen Data : Gaussian Markov Random Field Models for Compositional Data. / Pirzamanbein, Behnaz.

Lund, Sweden : Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research, 2016. 196 s.

Forskningsoutput: AvhandlingDoktorsavhandling (sammanläggning)

Harvard

APA

Pirzamanbein, B. (2016). Reconstruction of Past European Land Cover Based on Fossil Pollen Data: Gaussian Markov Random Field Models for Compositional Data. Lund, Sweden: Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research.

CBE

Pirzamanbein B. 2016. Reconstruction of Past European Land Cover Based on Fossil Pollen Data: Gaussian Markov Random Field Models for Compositional Data. Lund, Sweden: Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research. 196 s.

MLA

Pirzamanbein, Behnaz Reconstruction of Past European Land Cover Based on Fossil Pollen Data: Gaussian Markov Random Field Models for Compositional Data Lund, Sweden: Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research. 2016.

Vancouver

Pirzamanbein B. Reconstruction of Past European Land Cover Based on Fossil Pollen Data: Gaussian Markov Random Field Models for Compositional Data. Lund, Sweden: Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research, 2016. 196 s.

Author

Pirzamanbein, Behnaz. / Reconstruction of Past European Land Cover Based on Fossil Pollen Data : Gaussian Markov Random Field Models for Compositional Data. Lund, Sweden : Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research, 2016. 196 s.

RIS

TY - THES

T1 - Reconstruction of Past European Land Cover Based on Fossil Pollen Data

T2 - Gaussian Markov Random Field Models for Compositional Data

AU - Pirzamanbein, Behnaz

N1 - Defence details Date: 2016-12-19 Time: 09:15 Place: Annexet, lecture hall MA:04, Sölvegatan 20, Lund External reviewer(s) Name: Illian, Janine Title: Senior Lecturer, Dr. Affiliation: University of St Andrews, United Kingdom ---

PY - 2016/11

Y1 - 2016/11

N2 - The aim of this thesis is to develop statistical models to reconstruct past land cover composition and human land use based on fossil pollen records over Europe for different time periods over the past 6000 years. Accurate maps of past land cover and human land use are needed when studying the interaction between climate and land surface, and the effects of human land use on past climate. Existing land cover maps are mainly simulations from dynamic vegetation models and anthropogenic land cover change scenarios. Pollen records is an alternative to existing land cover estimates that might give better insight into past land cover. The pollen counts are extracted from lake and bog sediments and used to estimate the three land cover compositions; coniferous forest, broadleaved forest, and unforested land for grid cells surrounding the lakes and bogs.In this thesis, first, a statistical model is developed to interpolate transformedpollen based land cover compositions (PbLCC) with spatial dependency modelledusing a Gaussian Markov random Field (GMRF). The mean structure is modelled using a regression on different sets of covariates including elevation and model based vegetation estimates. The model is fitted using Integrated NestedLaplace Approximation. The results indicated the existence of spatial dependence structure in the PbLCC and the possibility of reconstructing past land cover from PbLCC. If the compositional data is over-dispersed, the transformed Gaussian model might underestimate the uncertainties. To capture the variation in the composition correctly, a Bayesian hierarchical model (BHM) for Dirichlet observations of a GMRF is developed. The model is estimated using MCMC with sparse precision matrix of the GMRF being used for computational efficiency. Comparison between the Dirichlet and Gaussian models showed the advantages of the Dirichlet in describing the PbLCC. The large discrepancies in the model based estimates used as covariates could affect the Dirichlet models ability to reconstruct past land cover. To assess this concern a sensitivity study was performed, showing that the results are robust to the choice of covariates. Finally, the BHM is extended to reconstruct past human land use by combing the PbLCC with anthropogenic land cover change estimates. This extension aims at decomposing the PbLCC into past natural land cover and human land use.

AB - The aim of this thesis is to develop statistical models to reconstruct past land cover composition and human land use based on fossil pollen records over Europe for different time periods over the past 6000 years. Accurate maps of past land cover and human land use are needed when studying the interaction between climate and land surface, and the effects of human land use on past climate. Existing land cover maps are mainly simulations from dynamic vegetation models and anthropogenic land cover change scenarios. Pollen records is an alternative to existing land cover estimates that might give better insight into past land cover. The pollen counts are extracted from lake and bog sediments and used to estimate the three land cover compositions; coniferous forest, broadleaved forest, and unforested land for grid cells surrounding the lakes and bogs.In this thesis, first, a statistical model is developed to interpolate transformedpollen based land cover compositions (PbLCC) with spatial dependency modelledusing a Gaussian Markov random Field (GMRF). The mean structure is modelled using a regression on different sets of covariates including elevation and model based vegetation estimates. The model is fitted using Integrated NestedLaplace Approximation. The results indicated the existence of spatial dependence structure in the PbLCC and the possibility of reconstructing past land cover from PbLCC. If the compositional data is over-dispersed, the transformed Gaussian model might underestimate the uncertainties. To capture the variation in the composition correctly, a Bayesian hierarchical model (BHM) for Dirichlet observations of a GMRF is developed. The model is estimated using MCMC with sparse precision matrix of the GMRF being used for computational efficiency. Comparison between the Dirichlet and Gaussian models showed the advantages of the Dirichlet in describing the PbLCC. The large discrepancies in the model based estimates used as covariates could affect the Dirichlet models ability to reconstruct past land cover. To assess this concern a sensitivity study was performed, showing that the results are robust to the choice of covariates. Finally, the BHM is extended to reconstruct past human land use by combing the PbLCC with anthropogenic land cover change estimates. This extension aims at decomposing the PbLCC into past natural land cover and human land use.

KW - Spatial Statistics

KW - Adaptive Markov Chain Monte Carlo

KW - Dirichlet Observation

KW - Confidence Region

KW - Palaeoecology

KW - Past Human Land Use

KW - Stochastic Partial Differential Equation

UR - https://phd.behnaz.pirzamanbin.name/presentation

M3 - Doctoral Thesis (compilation)

SN - 978-91-7753-076-3

PB - Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research

CY - Lund, Sweden

ER -