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

Pirzamanbein, Behnaz LU (2016)
Abstract (Swedish)
Spatial distribution of land cover plays an important role in climate system and
global carbon cycle. Research shows that changes in land cover are associated with large climatic effects. These changes are either due to climate change or human activities. Human can influence and change the abundance of land cover through deforestation, urbanization and agriculture. Studies show that replacing forests with agricultural land decreases the temperature while urbanization causes local increases in temperature. Comparing the historical temperature records with past natural and human induced land cover might give a better understanding of the interactions among climate, land cover and human effects.
The problem is the existence of... (More)
Spatial distribution of land cover plays an important role in climate system and
global carbon cycle. Research shows that changes in land cover are associated with large climatic effects. These changes are either due to climate change or human activities. Human can influence and change the abundance of land cover through deforestation, urbanization and agriculture. Studies show that replacing forests with agricultural land decreases the temperature while urbanization causes local increases in temperature. Comparing the historical temperature records with past natural and human induced land cover might give a better understanding of the interactions among climate, land cover and human effects.
The problem is the existence of considerably different descriptions of past
land cover and human land use. Existing land cover descriptions are based on
natural land cover combined with human land use. Past human land use maps
are mainly based on simulations of human population density and the amount
of agricultural land needed to feed the given population. Furthermore, natural
land cover maps are simulations based on past climate including temperature,
precipitation and soil type; they represent the natural vegetation that can grow in certain climate conditions without considering human activity. The differences in these available maps are caused by differences in the model assumptions, as well as the simulations of climate variables and population density.
On the other hand, fossil pollen counts can be used to estimate past land
cover based on local observations over the past 10 000 years. The only problem is that the information on pollen counts, extracted from lakes and bogs, are limited in reproducing the land cover for the area surrounding these lakes and bogs.
This thesis aims to develop statistical models that can create continuous maps
of past land cover and human land use based on pollen observations.
Since the spread of pollen as well as certain climate conditions lead to the
growth of similar types of vegetation within a spatial range, one can expect to
observe similar vegetation types in areas closer to each other than farther apart.
Because of this fact, spatial statistics is used as a main tool to identify and model
this space dependency in the pollen observations. (Less)
Abstract
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... (More)
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 transformed
pollen based land cover compositions (PbLCC) with spatial dependency modelled
using 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 Nested
Laplace 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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Senior Lecturer, Dr. Illian, Janine, University of St Andrews, United Kingdom
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Spatial Statistics, Adaptive Markov Chain Monte Carlo, Dirichlet Observation, Confidence Region, Palaeoecology, Past Human Land Use, Stochastic Partial Differential Equation
pages
196 pages
publisher
Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research
defense location
Annexet, lecture hall MA:04, Sölvegatan 20, Lund
defense date
2016-12-19 09:15
ISBN
978-91-7753-077-0
978-91-7753-076-3
language
English
LU publication?
yes
id
c2980af3-a480-45be-a346-80a33a8dd315
alternative location
https://phd.behnaz.pirzamanbin.name/presentation/
date added to LUP
2016-11-18 14:32:20
date last changed
2016-11-24 17:45:21
@phdthesis{c2980af3-a480-45be-a346-80a33a8dd315,
  abstract     = {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.<br/><br/>In this thesis, first, a statistical model is developed to interpolate transformed<br/>pollen based land cover compositions (PbLCC) with spatial dependency modelled<br/>using 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 Nested<br/>Laplace 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.},
  author       = {Pirzamanbein, Behnaz},
  isbn         = {978-91-7753-077-0},
  keyword      = {Spatial Statistics,Adaptive Markov Chain Monte Carlo,Dirichlet Observation,Confidence Region,Palaeoecology,Past Human Land Use,Stochastic Partial Differential Equation},
  language     = {eng},
  pages        = {196},
  publisher    = {Lund University, Faculty of Science, Centre for Mathematical Sciences, Centre for Environmental and Climate Research},
  school       = {Lund University},
  title        = {Reconstruction of Past European Land Cover Based on Fossil Pollen Data : Gaussian Markov Random Field Models for Compositional Data},
  year         = {2016},
}