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

Abstract: Spatial distribution of land cover plays an important role in climate system andglobal 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 pastland cover and human land use. Existing land cover descriptions are based onnatural land cover combined with human land use. Past human land use mapsare mainly based on simulations of human population density and the amountof agricultural land needed to feed the given population. Furthermore, naturalland 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 landcover 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 mapsof past land cover and human land use based on pollen observations.Since the spread of pollen as well as certain climate conditions lead to thegrowth of similar types of vegetation within a spatial range, one can expect toobserve 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 modelthis space dependency in the pollen observations.