Reconstruction of past human land use from pollen data and anthropogenic land cover changes
Pirzamanbein, Behnaz LU
and Lindström, Johan
LU
(2022)
In Environmetrics
33(6).
- Abstract
Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land-cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human-induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen-based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land-use estimates to decompose the unforested land into natural vegetation... (More)
Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land-cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human-induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen-based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land-use estimates to decompose the unforested land into natural vegetation and human deforestation. This results in maps of both natural and human-induced vegetation, which can be used by climate modelers to quantify the influence of deforestation on the past climate. The model was applied to five time periods from 1900 CE to 4000 BCE over Europe. The model uses a latent Gaussian Markov random field (GMRF) for the interpolation and Markov chain Monte Carlo for the estimation. The sparse precision matrix of the GMRF, together with an adaptive Metropolis-adjusted Langevin step, allows for rapid inference.
(Less)
- author
- Pirzamanbein, Behnaz
LU
and Lindström, Johan
LU
- organization
- publishing date
- 2022-06-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- compositional data, dirichlet and beta observations, fossil pollen record, Gaussian Markov random field, Markov chain Monte Carlo, spatial statistics
- in
- Environmetrics
- volume
- 33
- issue
- 6
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85131718699
- ISSN
- 1180-4009
- DOI
- 10.1002/env.2743
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022 The Authors. Environmetrics published by John Wiley & Sons Ltd.
- id
- 71c29539-3746-4eaa-a856-09386797d4bc
- date added to LUP
- 2022-07-12 09:46:44
- date last changed
- 2023-05-11 07:47:06
@article{71c29539-3746-4eaa-a856-09386797d4bc,
abstract = {{<p>Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land-cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human-induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen-based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land-use estimates to decompose the unforested land into natural vegetation and human deforestation. This results in maps of both natural and human-induced vegetation, which can be used by climate modelers to quantify the influence of deforestation on the past climate. The model was applied to five time periods from 1900 CE to 4000 BCE over Europe. The model uses a latent Gaussian Markov random field (GMRF) for the interpolation and Markov chain Monte Carlo for the estimation. The sparse precision matrix of the GMRF, together with an adaptive Metropolis-adjusted Langevin step, allows for rapid inference.</p>}},
author = {{Pirzamanbein, Behnaz and Lindström, Johan}},
issn = {{1180-4009}},
keywords = {{compositional data; dirichlet and beta observations; fossil pollen record; Gaussian Markov random field; Markov chain Monte Carlo; spatial statistics}},
language = {{eng}},
month = {{06}},
number = {{6}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Environmetrics}},
title = {{Reconstruction of past human land use from pollen data and anthropogenic land cover changes}},
url = {{http://dx.doi.org/10.1002/env.2743}},
doi = {{10.1002/env.2743}},
volume = {{33}},
year = {{2022}},
}