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Classification of large pollen datasets using neural networks with application to mapping and modelling pollen data

Holmqvist, Björn LU (2005) In Lundqua Report 39.
Abstract
This thesis concerns the usage of large pollen databases and their application to mapping and modelling past vegetation. Maps of past taxon distributions are generated and classification techniques are used to compile maps of past woodland types. These visualisations of pollen data have applications in forest ecology and in modelling the

impacts of climate change. Maps of the distribution limits of Picea abies in southern Scandinavia are compared with output from a bioclimatic model to explore distribution-climate relationships during the last 1500 years. Further a classification technique is used to map distributions of Danish forest types over the last 3000 years. Classification is done by assigning a sample to a group or a... (More)
This thesis concerns the usage of large pollen databases and their application to mapping and modelling past vegetation. Maps of past taxon distributions are generated and classification techniques are used to compile maps of past woodland types. These visualisations of pollen data have applications in forest ecology and in modelling the

impacts of climate change. Maps of the distribution limits of Picea abies in southern Scandinavia are compared with output from a bioclimatic model to explore distribution-climate relationships during the last 1500 years. Further a classification technique is used to map distributions of Danish forest types over the last 3000 years. Classification is done by assigning a sample to a group or a category of similar properties. The categories in this case are woodland types. The classification model is an artificial neural network as trained on an entire database of actual pollen assemblages, resulting in a classification model able to classify pollen samples to a woodland type. This classification model is then used on the grid of interpolated fossil pollen assemblages to produce woodland history maps. Classification methods group the most similar samples, but somewhere a decision has to be made on how many classes or groups to use. I have developed a method for choosing the number of classes that have the highest reproducibility . This is an objective, repeatable method for assessing the optimal number of clusters in a multivariate dataset. (Less)
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author
supervisor
opponent
  • Prof. Björck, Svante, Centrum för GeoBiosfärvetenskap, Kvartärgeologi, Lunds universitet.
  • Prof. Odgaard, Bent, Geologisk Institut, Aarhus universitet, Århus, Danmnark.
  • Doc. Smith, Ben, Centrum för GeoBiosfärvetenskap, Naturgeografi och Ekosystemanalys, Lunds universitet.
organization
publishing date
type
Thesis
publication status
published
subject
in
Lundqua Report
volume
39
pages
9 pages
publisher
Department of Geology, Lund University
defense location
Geocentrum, Sölvegatan 12, rum 237 (Baltica)
defense date
2005-04-08 10:15
external identifiers
  • other:SE-LUNDBDS/NBGK-05/39+9
ISSN
0281-3076
language
English
LU publication?
yes
id
41a9bd87-ee51-4c67-8877-bc68d150367f (old id 3163799)
date added to LUP
2012-11-05 13:05:08
date last changed
2016-09-19 08:44:55
@phdthesis{41a9bd87-ee51-4c67-8877-bc68d150367f,
  abstract     = {This thesis concerns the usage of large pollen databases and their application to mapping and modelling past vegetation. Maps of past taxon distributions are generated and classification techniques are used to compile maps of past woodland types. These visualisations of pollen data have applications in forest ecology and in modelling the<br/><br>
impacts of climate change. Maps of the distribution limits of Picea abies in southern Scandinavia are compared with output from a bioclimatic model to explore distribution-climate relationships during the last 1500 years. Further a classification technique is used to map distributions of Danish forest types over the last 3000 years. Classification is done by assigning a sample to a group or a category of similar properties. The categories in this case are woodland types. The classification model is an artificial neural network as trained on an entire database of actual pollen assemblages, resulting in a classification model able to classify pollen samples to a woodland type. This classification model is then used on the grid of interpolated fossil pollen assemblages to produce woodland history maps. Classification methods group the most similar samples, but somewhere a decision has to be made on how many classes or groups to use. I have developed a method for choosing the number of classes that have the highest reproducibility . This is an objective, repeatable method for assessing the optimal number of clusters in a multivariate dataset.},
  author       = {Holmqvist, Björn},
  issn         = {0281-3076},
  language     = {eng},
  pages        = {9},
  publisher    = {Department of Geology, Lund University},
  school       = {Lund University},
  series       = {Lundqua Report},
  title        = {Classification of large pollen datasets using neural networks with application to mapping and modelling pollen data},
  volume       = {39},
  year         = {2005},
}